Journal articles: 'Radio Resources Allocation'

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Ma, Lu, Xiangming Wen, Luhan Wang, Zhaoming Lu, Raymond Knopp, and Irfan Ghauri. "A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet." Wireless Communications and Mobile Computing 2018 (September 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/1745904.

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Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic-aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resource management framework is proposed, where the radio resources of different physical networks are virtualized into a virtual resource pool and mobile virtual network operators (MVNOs) compete for virtual resources from the pool to provide service to users. A virtual radio resource allocation algorithm based on biological model is developed, considering system utility, fairness, and dynamics. Simulation results are provided to verify that the proposed virtual resource allocation algorithm not only converges within a few iterations, but also achieves a better trade-off between total utility and fairness than existing algorithm. Besides, it can also be utilized to analyze the population dynamics of system.

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.., Ishwarlal, and Ankit Saxena. "Design, Simulation and Analysis of Multi-Dimensional Multiple Access (MDMA) Schemes Using MATLAB for Quality of Service (QoS) Enhancement." Journal of Intelligent Systems and Internet of Things 11, no. 2 (2024): 111–28. http://dx.doi.org/10.54216/jisiot.110210.

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To provide better Quality of Service (QoS), which is expected in contemporary 6G wireless networks. We project a MDMA scheme to fulfill UE-specific QoS needs with the aid of multi-dimensional radio resource cost. This method can be successfully called Multi-Dimensional Radio Resource Allocation (MDRA). Specifically, the planned scheme incorporates two novel aspects: for each UE, the choice of user-specific non-orthogonal multiple approach mode whose cost is determined by UE-specific non-orthogonal interference cancellation; and allocating multiple dimensional radio resources for co-existing UEs in dynamic network environment. To reduce the costs of using UE-specific resources, the BS mounts UEs with diverse multi-domain resources. Specific to each UE coalition by taking into consideration restrictions such as the availability of resources, the perceived quality of those resources, and the possibility for use. Every UE that is a part of the coalition has access to the radio resources that it needs, which helps to lower the costs of use while preventing resource-sharing disputes with the other nodes in the coalition. Furthermore, the allocation of multi-dimensional radio resources among co-existing user equipment makes it possible to solve the issue of maximizing the sum of cost-conscious utility. This is done to fulfil UE-specific quality of service needs as well as varied resource circumstances on the user equipment side. The gradient convexity with low complexity approximation and the Lagrange double decomposition approach are used in the development of the solution to this NP-hard issue. The efficacy of the system that we have presented is shown via the use of numerical simulations and a comparison of its performance with that of other methods.

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R G, Umesh, Sushil Kumar G N, Santhosh K, Suraksha M S, and Dr Praveen Kumar K V. "Radio Resource Allocation for 5G Network Using Deep Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 677–83. http://dx.doi.org/10.22214/ijraset.2023.49468.

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Abstract: Resource allocation is a critical task in 5Gnetworks that determines how network resources are assigned to different devices and services. Traditional methods rely on predefined rules or heuristics, which may not always be optimal. Deep reinforcement learning (DRL)is a promising approach for radio resource allocation in 5Gnetworks as it can learn to optimize resource allocation based on feedback from the network. In DRL, an agent learns to make decisions based on rewards and penalties received from the environment. In radio resource allocation, the agent would learn to allocate resources, such as frequency bands and power levels, to different devices and services to maximize some performance metric, such asthroughput or energy efficiency. The main challenge in applying DRL to radio resource allocation is designing an appropriate reward function that incentivizes the agent to improve the performance metric while avoiding undesirable behavior. Additionally, the radio resource allocation problem is complex, requiring the agent to consider many variables and constraints, such as channel conditions, interference, and QoS requirements. To address this, researchers have proposed various techniques such as hierarchical RL, multi-agent RL, and curriculum learning. Despite the challenges, DRL has shown promising results inradio resource allocation for 5G networks. It has outperformed traditional methods in some scenarios, especially when network conditions are dynamic and unpredictable. However, further research is necessary to explore the scalability and robustness of DRL-based approaches in practical 5G networks. In this method we suggest an algorithm for voice and data carriers in sub-6 GHz and millimeter wave (mmWave) frequencies respectively. The mmWave ranges between 30GHz to 300GHz

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Benmammar, Badr. "Recent Advances on Artificial Intelligence in Cognitive Radio Networks." International Journal of Wireless Networks and Broadband Technologies 9, no. 1 (January 2020): 27–42. http://dx.doi.org/10.4018/ijwnbt.2020010102.

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Cognitive radio is a form of wireless communication that makes decisions about allocating and managing radio resources after detecting its environment and analyzing the parameters of its radio frequency environment. Decision making in cognitive radio can be based on optimization techniques. In this context, machine learning and artificial intelligence are to be used in cognitive radio networks in order to reduce complexity, obtain resource allocation in a reasonable time and improve the user's quality of service. This article presents recent advances on artificial intelligence in cognitive radio networks. The article also categorizes the techniques presented according to the type of learning—supervised or unsupervised—and presents their applications and challenges according to the tasks of the cognitive radio.

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Mathew, Alex. "SliceOptiAI: Smart Resource Allocation for Seamless Network Slicing." International Journal of Computer Science and Mobile Computing 13, no. 1 (January 30, 2024): 82–87. http://dx.doi.org/10.47760/ijcsmc.2024.v13i01.006.

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An AI-driven model for resource allocation in network slicing is examined in this research paper. The model’s algorithm comprises three stages, each with its specific algorithm. Resource allocation begins with reservation, where the controller reserves minimum resources for each slice. The second stage is autonomous radio resource management, mainly focusing on AI model training and decision engines. The last stage is physical resource allocation, where resources are distributed to the slices and users. Simulations on MATLAB software indicated this model to be effective in enhancing resource allocation.

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Wulandari, Astri, Nachwan Mufti Adriansyah, and Vinsensius Sigit Widhi Prabowo. "Greedy Based Radio Resource Allocation Algorithm with SARSA Power Control Scheme in D2D Underlaying Communication." Journal of Measurements, Electronics, Communications, and Systems 7, no. 1 (December 30, 2020): 6. http://dx.doi.org/10.25124/jmecs.v7i1.3472.

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Device-to-Device (D2D) underlaying communication system is a solution in reducing the workload of eNodeB and increasing the system data rate. This communication system consists of two users, namely Cellular User Equipment (CUE) and D2D pair, where CUE will share its resources with the D2D pair. This sharing resources also causes interference and should be managed using the resource allocation algorithm. In this work, the resource allocation scheme occurs in a single cell with an uplink communication direction. The resource allocation process uses greedy and joint greedy algorithms. After CUE allocates all of its resources, SARSA algorithm performs the power allocation process. The resource allocation process involves the scheduled CUE and D2D pair. After all the resource and power are allocated, parameter performance of the system is calculated. Based on the work results, joint greedy algorithm with power allocation using SARSA algorithm have performance results 1.375 × 107 bps/Watt in energy efficiency, 43.105 bps/Hz in spectral efficiency, and 0.993 in D2D fairness index.

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Yadav, Savita, Pradeep Kumar Shah, Sowmiya Kumar, and Anjali Singh. "Enhancing resource allocation for power sharing in cognitive radio communication networks using ensemble moth-flame optimized dynamic recurrent neural networks." Multidisciplinary Science Journal 6 (July 12, 2024): 2024ss0307. http://dx.doi.org/10.31893/multiscience.2024ss0307.

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Wireless data transmission networks are radio communication networks (RCN). These networks allow radios as well as phones to communicate, exchange data and calls across radio waves. Broadcasting, emergency services and mobile telecommunications are among the numerous industries that use RCN's flexibility and ease. Our proposed method, Ensemble Moth-Flame Optimized Dynamic Recurrent Neural Network (EMFO-DRNN), addresses the challenges inherent in DNN-based systems, maximizing power distribution, minimizing interference and optimizing spectrum utilization. This solution enables adaptive channel selection in real-time, catering to dynamic conditions and user demands in cognitive radio communication networks. We use the radio wave dataset in our research. We preprocessed using z-score normalization and employed the Fast Fourier Transform (FFT) for feature extraction to develop the EMFO-DRNN approach for improving the allocation of resources for power sharing in Cognitive Radio Communication Networks (CRCN). The EMFO-DRNN method has better performance metrics, such as high accuracy (92%), precision (95%), f1-score (96%) and recall (94%) in enhancing resource allocation for power sharing in CRCN. The proposed approach outperforms traditional methods in resource allocation due to its ensemble nature, which improves stability along with generalization and its Moth-Flame optimization aids in finding optimal solutions promptly.

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Mathonsi, Topside E., Tshimangadzo Mavin Tshilongamulenzhe, and Bongisizwe Erasmus Buthelezi. "Enhanced Resource Allocation Algorithm for Heterogeneous Wireless Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 6 (November 20, 2020): 763–73. http://dx.doi.org/10.20965/jaciii.2020.p0763.

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In heterogeneous wireless networks, service providers typically employ multiple radio access technologies to satisfy the requirements of quality of service (QoS) and improve the system performance. However, many challenges remain when using modern cellular mobile communications radio access technologies (e.g., wireless local area network, long-term evolution, and fifth generation), such as inefficient allocation and management of wireless network resources in heterogeneous wireless networks (HWNs). This problem is caused by the sharing of available resources by several users, random distribution of wireless channels, scarcity of wireless spectral resources, and dynamic behavior of generated traffic. Previously, resource allocation schemes have been proposed for HWNs. However, these schemes focus on resource allocation and management, whereas traffic class is not considered. Hence, these existing schemes significantly increase the end-to-end delay and packet loss, resulting in poor user QoS and network throughput in HWNs. Therefore, this study attempts to solve the identified problem by designing an enhanced resource allocation (ERA) algorithm to address the inefficient allocation of available resources vs. QoS challenges. Computer simulation was performed to evaluate the performance of the proposed ERA algorithm by comparing it with a joint power bandwidth allocation algorithm and a dynamic bandwidth allocation algorithm. On average, the proposed ERA algorithm demonstrates a 98.2% bandwidth allocation, 0.75 s end-to-end delay, 1.1% packet loss, and 98.9% improved throughput performance at a time interval of 100 s.

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Razmi, Shirin, and Naser Parhizgar. "Adaptive resources assignment in OFDM-based cognitive radio systems." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1935. http://dx.doi.org/10.11591/ijece.v9i3.pp1935-1943.

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Spectrum efficiency of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems can be improved by adaptive resources allocation. In resources allocation, transmission resources such as modulation level and transmission power are adaptively assigned based on channel variations. The goal of this paper is maximize the total transmission rate of secondary user (SU). Hence, we investigate adaptive power and modulation allocation to achieve this purpose. For power allocation, we investigate optimal and conventional methods and then introduce a novel suboptimal algorithm to calculate the transmission power of each subcarrier. In addition, for adaptive modulation, we consider two kinds of modulations including multi-quadrature amplitude modulation (MQAM) and multi-phase-shift keying (MPSK). Also, simulation results are indicated the performance of our algorithm.

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Mach, Pavel, and Robert Bestak. "Radio resources allocation for decentrally controlled relay stations." Wireless Networks 17, no. 1 (July 30, 2010): 133–48. http://dx.doi.org/10.1007/s11276-010-0269-8.

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Amirsaidov, Ulugbek, and Azamat Qodirov. "Cross-Layer Model of Dynamic Distribution of Radio Resources and Data Flow Service in LTE Networks." International journal of electrical and computer engineering systems 14, no. 1 (January 26, 2023): 13–19. http://dx.doi.org/10.32985/ijeces.14.1.2.

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In this article, the results of the development of a mathematical model for the time-frequency resource allocation of the uplink channel and flow service in LTE (Long Term Evolution) networks are given. The proposed model is aimed at ensuring the maximum performance of the radio channel and the guaranteed quality of service for data flows of wireless network users. A comparative analysis of the proposed model with the existing methods of the time-frequency resource allocation of the LTE technology is carried out in terms of ensuring the overall performance of the uplink and allocating the required transmission rate to user stations while maintaining the quality of service. It is shown that the proposed model of dynamic distribution of radio resources and servicing of data streams increases the overall performance of the uplink compared to the Round Robin and Proportional Fair methods, by 1.42 and 1.23 times, respectively.

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Bouleanu, Iulian, Dorin Alexandrescu, and Mircea Bora. "Radio Frequency Co-Site Management." International conference KNOWLEDGE-BASED ORGANIZATION 21, no. 3 (June 1, 2015): 660–65. http://dx.doi.org/10.1515/kbo-2015-0112.

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Abstract The radio spectrum is a limited national resource, essential for some governmental applications and increasingly important for a series of non-governmental applications. The allocation of radio resources is done in a centralized manner, designating frequency managers of the defense system structures as local administrators of the resources allotted to the supported echelon. They have a limited number of frequencies they can assign to the emission sources in their area of responsibility. The article addresses the issue of radio spectrum management in the frequency allocation plans when using a large number of emission and reception sources for means of communication and non-communication in a small area. Locating several emission sources in the same site leads to different types of disturbing signals: emissions outside the bandwidth, harmonics and intermodulation. The article categorizes and describes these sources, presents the results of measurements distinguishing them, as well as the results of implementing some protective measures. Finally, the authors suggest a software solution for the local distribution of frequency resources.

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Shelikhova, T. S., and V. G. Drozdova. "The Analysis of the Usage Efficiency of the Time-frequency Resources for Different 5G NR CORESET Configuration Settings." Herald of the Siberian State University of Telecommunications and Information Science 17, no. 4 (October 1, 2023): 97–108. http://dx.doi.org/10.55648/1998-6920-2023-17-4-97-108.

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The 5G mobile networks is a New Radio for finding wireless access solutions for Internet access of users with the most demanding requirements for quality service. To implement it radio interface resource allocation functions implemented by hardware and software vendors at base stations must notify subscribers about their decisions with so-called control channels the functions of which are distributed in the CORESET configuration area. The settings of the CORESET parameters affect the efficiency of using radio channel resources. This article discusses CORESET issues and their impact on the efficiency of using radio resources.

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Zafar, Ammar, Mohamed-Slim Alouini, Yunfei Chen, and Redha M. Radaydeh. "Optimizing Cooperative Cognitive Radio Networks with Opportunistic Access." Journal of Computer Networks and Communications 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/294581.

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Optimal resource allocation for cooperative cognitive radio networks with opportunistic access to the licensed spectrum is studied. Resource allocation is based on minimizing the symbol error rate at the receiver. Both the cases of all-participate relaying and selective relaying are considered. The objective function is derived and the constraints are detailed for both scenarios. It is then shown that the objective functions and the constraints are nonlinear and nonconvex functions of the parameters of interest, that is, source and relay powers, symbol time, and sensing time. Therefore, it is difficult to obtain closed-form solutions for the optimal resource allocation. The optimization problem is then solved using numerical techniques. Numerical results show that the all-participate system provides better performance than its selection counterpart, at the cost of greater resources.

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Yin, Han, and Duo Zhang. "Radio Resource Allocation Algorithm Based on Bargaining Game Theory for LTE System." Applied Mechanics and Materials 644-650 (September 2014): 1527–30. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1527.

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With the rapid development of wireless communication technologies, users could get many kinds of services and applications now. And as the number of users and the amount of traffic are growing, the contradiction between the infinite demand of users and the finite radio resources is getting increasingly apparent. According to this situation, this paper propose a radio resource allocation algorithm based on bargaining game theory for fourth generation long term evolution (LTE) system, with which the network could balance the situations of users in different classes and enhance the utility of users. The simulation results show that the proposed algorithm could allocate the radio resources efficiently and provide users with higher utility.

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Bendaoud, Fayssal, Marwen Abdennebi, and Fedoua Didi. "Survey on Scheduling and Radio Resources Allocation in LTE." International Journal of Next-Generation Networks 6, no. 1 (March 31, 2014): 17–29. http://dx.doi.org/10.5121/ijngn.2014.6102.

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Jararweh, Yaser, Mahmoud Al-Ayyoub, Ahmad Doulat, Ahmad Al Abed Al Aziz, Haythem A. Bany Salameh, and Abdallah A. Khreishah. "Software Defined Cognitive Radio Network Framework." International Journal of Grid and High Performance Computing 7, no. 1 (January 2015): 15–31. http://dx.doi.org/10.4018/ijghpc.2015010102.

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Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.

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Sabeeh , Saif, and Krzysztof Wesołowski . "On Adaptation of Resources in New Radio Vehicle-to-Everything Mode 1 Dynamic Resource Allocation." Electronics 14, no. 1 (December 27, 2024): 77. https://doi.org/10.3390/electronics14010077.

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Vehicle-to-Everything (V2X) communication is one of the essential technologies in 5G systems and will certainly play an important role in incoming 6G communications. Two modes of 5G New Radio V2X communication (NR-V2X) have been defined to standardize the direct exchange of messages between vehicles. This paper concentrates on Mode 1, in which message exchange takes place with the support of the cellular infrastructure. In this mode, each vehicle uses a fixed number of subchannels with pre-configured subchannel sizes to transmit packet messages. However, if the packet sizes vary in each transmission, some resource blocks (RBs) assigned to V2X links are wasted. This paper presents the results of investigations on more efficient use of resource blocks, intending to minimize their waste and limit the delay in resource selection. In this paper, two new algorithms for radio resource block assignment are proposed and analyzed. The algorithms are characterized by a lower waste of RBs and a shorter delay in resource assignment compared to current solutions. The first algorithm uses adjacent RBs, whereas the second one can assign non-adjacent RBs, resulting in an even lower waste of radio resources and a shorter delay in their assignment. The simulation results presented confirm the quality of the proposed algorithms.

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Mishra, Mangala Prasad, Sunil Kumar Singh, and Deo Prakash Vidyarthi. "Opportunistic Channel Allocation Model in Collocated Primary Cognitive Network." International Journal of Mathematical, Engineering and Management Sciences 5, no. 5 (October 1, 2020): 995–1012. http://dx.doi.org/10.33889/ijmems.2020.5.5.076.

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The growing demand of radio spectrum to facilitate the primary/secondary users in a cellular network is a challenging task. Many channel allocation models, applying cognition, have been proposed to increase the radio spectrum utilization. The proposed model peruses three types of users: primary users (PUs), opportunistic primary users (OPUs), and secondary users (SUs) that use the radio resources in collocated primary base stations. Out of these users, the opportunistic primary users and secondary users may request for handover as per their requirements. The objective of the model is to enhance the radio spectrum utilization by the opportunistic utilization of radio resources by OPUs and by enabling cognitive radio base stations to collect free channel information dynamically. The cognitive radio base station maintains the centralized free channel at collocated primary base stations to facilitate the SUs opportunistically. The proposed channel allocation technique maintains the Quality of Experience (QoE) of the users as well. The performance analysis of the model is done by simulation which diversifies the importance of the proposed model in the view of minimum blocked services.

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Yasin Ramadhan, Mohamad, Vinsensius Sigit, and Arfianto Fahmi. "Radio Resource Allocation For Device to Device Network Using Auction Algorithm." Jurnal TIARSIE 16, no. 2 (July 16, 2019): 53. http://dx.doi.org/10.32816/tiarsie.v16i2.52.

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One application of communication from the 5G network in the future is to implement Device to Device (D2D) into heterogeneous multi-tiered communication networks consisting of small cell communications between eNB, cellular and D2D. The application of D2D is useful for the future even though it has several problems with one of them being interference with the frequency of other devices in the same cell. This can affect Quality of Service (QoS) in D2D communication so that it requires the application of a resource allocation distribution that can increase data rate and reduce interference. One of the algorithms used for the distribution of resource allocation in communication network systems is the Auction allocation algorithm. The auction allocation algorithm introduced in this journal provides a solution to divide resources fairly for all D2D pairs. The data rate increases by increasing the number of resource blocks and decreasing the cell radius.

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Liu, Xingguang, Li Zhou, Xiaoying Zhang, Xiang Tan, and Jibo Wei. "Joint Radio Map Construction and Dissemination in MEC Networks: A Deep Reinforcement Learning Approach." Wireless Communications and Mobile Computing 2022 (July 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/4621440.

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With the development of 6G, the rapidly increasing number of smart devices deployed in the Industrial Internet of Things (IIoT) environment has been witnessed. The radio environment is showing a trend of complexity, and spectrum conflicts are becoming increasingly acute. User equipment (UE) can accurately sense and utilize spectrum resources through radio map (RM). However, the construction and dissemination of RM incur a heavy computational burden and large dissemination delay, which limit the real-time sensing of spatial spectrum situations. In this paper, we propose an RM construction and dissemination method based on deep reinforcement learning (DRL) in the context of mobile edge computing (MEC) networks. We formulate the dissemination modes selection and resource allocation problems during RM construction and dissemination as a mixed-integer nonlinear programming problem. Then, we propose an actor-critic-based joint offloading and resource allocation (ACJORA) algorithm for intelligent scheduling of computational offloading and resource allocation. We design a novel weighted loss function for the actor network, which combines the discrete actions for offloading decisions and the continuous actions for resource allocation. And the simulation results show that the proposed algorithm can reduce the cost of dissemination by optimizing the offloading strategies and resources, which is more applicable for real-time RM applications in MEC networks.

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Lan, Hai Yan, Hong Tao Song, Yun Long Zhao, and Guo Yin Zhang. "A Resource Allocation Algorithm in RFID System." Advanced Materials Research 694-697 (May 2013): 2462–65. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2462.

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For problem of limited resources in the RFID (Radio frequency identification) system, a power resource allocation scheme is proposed. The method aims to maximize the system throughput, using cultural algorithm (CA) to search for the optimal power allocation scheme. By dynamically adjusting the signal transmission power of the reader, the overlap area between the reader can be reduced so that the maximum reading range can be obtained. Simulation results show that the algorithm has better performance in the system throughput and energy consumption, reducing the impact of interference between the readers and efficiently using the resources in RFID system.

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Zou, Hong, Min Zhou, Yaping Cui, Peng He, Hong Zhang, and Ruyan Wang. "Service Provisioning in Sliced Cloud Radio Access Networks." Wireless Communications and Mobile Computing 2022 (February 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/7326172.

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Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy.

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AlQahtani, Salman Ali. "Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities." Future Internet 15, no. 10 (October 19, 2023): 343. http://dx.doi.org/10.3390/fi15100343.

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With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

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Masmoudi, Ahlem, Kais Mnif, and Faouzi Zarai. "A Survey on Radio Resource Allocation for V2X Communication." Wireless Communications and Mobile Computing 2019 (October 24, 2019): 1–12. http://dx.doi.org/10.1155/2019/2430656.

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Thanks to the deployment of new techniques to support high data rate, high reliability, and QoS provision, Long-Term Evolution (LTE) can be applied for diverse applications. Vehicle-to-everything (V2X) is one of the evolving applications for LTE technology to improve traffic safety, to minimize congestion, and to ensure comfortable driving which requires stringent reliability and latency requirements. As mentioned in the 3rd Generation Partnership Project (3GPP), LTE-based Device-to-Device (D2D) communication is an enabler for V2X services to meet these requirements. Therefore, radio resource management (RRM) is important to efficiently allocate resources to V2X communications. In this paper, we present the V2X communications, their requirements and services, the V2X-based LTE-D2D communication modes, and the existing resource allocation algorithms for V2X communications. Moreover, we classify the existing resource allocation algorithms proposed in the literature and we compare them according to selected criteria.

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Mattoo, Mohd Mueen Ul Islam, and Huda Adibah Mohd Ramli. "A study of packet scheduling algorithms in long term evolution-advanced." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 516. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp516-524.

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<span lang="EN-GB">The allocation of radio resources is one of the most critical functions performed by the Radio Resource Management (RRM) mechanisms in the downlink Long Term Evolution – Advanced (LTE-Advanced). Packet scheduling concerns itself with allocation of these radio resources in an intelligent manner such that system throughput/capacity can be maximized whilst the required multimedia Quality of Service (QoS) is met. Majority of the previous studies of packet scheduling algorithms for LTE-Advanced did not take the effect of channel impairments into account. However, in real world the channel impairments cannot be obliterated completely and have a direct impact on the packet scheduling performance. As such, this work studies the impact of channel impairments on packet scheduling performance in a practical downlink LTE-Advanced. The simulation results obtained demonstrate the efficacy of RM2 scheduling algorithm over other scheduling algorithms in maximizing the system capacity and is more robust on the effect of the cellular channel impairments. </span>

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Pei-Pei, Chen, Zhang Qin-yu, Wang Ye, and Meng Jing. "Multi-Objective Resources Allocation for OFDM-Based Cognitive Radio Systems." Information Technology Journal 9, no. 3 (March 15, 2010): 494–99. http://dx.doi.org/10.3923/itj.2010.494.499.

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Mbainaissem, Teubé Cyrille, Abdulfatai Atte Momoh, Déthié Dione, and Paul Python Ndekou. "OPTIMAL ALLOCATION OF RADIO RESOURCES IN A HETEROGENEOUS NETWORK SYSTEM." Advances and Applications in Discrete Mathematics 41, no. 3 (March 4, 2024): 261–80. http://dx.doi.org/10.17654/0974165824019.

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Sawant, Rupali, and Shikha Nema. "Outage Analysis in Underlay OFDMA Based Cooperative Cognitive Radio Networks." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (December 18, 2020): 625–33. http://dx.doi.org/10.2174/2210327910666191218125527.

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Background: Efficient resource allocation in Cooperative Cognitive Radio Network (CCRN) is necessary in order to meet the challenges in future wireless networks. With proper resource allocation, the Quality of Service (QoS) comprising of outage probability and data rate are evaluated in this paper and sufficiently improved with proper subcarrier allocation. Objective: Another important parameter is Signal to Interference Ratio (SIR) which should be above a threshold called minimum protection ratio to maintain the required QoS. Results: The network considered is Orthogonal Frequency Division Multiple Access (OFDMA) based Hybrid Cooperative Cognitive Radio Network (HCCRN) in downlink in which licensed as well as unlicensed resources are used by cognitive user depending on it’s availability keeping the interference constraint in limit. The number of subcarriers required is different for every user depending upon its distance from the base station to satisfy the requirement of data rate which depends on the experienced SIR. To avoid outage of users at the boundary of a cell, it is necessary to allocate more number of subcarriers. Conclusion: It is observed that for a given user position and outage probability, as the number of subcarrier allocation in a subchannel increases high data rates can be achieved. This analysis can be useful in allocation of subcarriers to users depending upon their position.

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Orike, Sunny, Winner Minah-Eeba, and Nkechinyere Eyidia. "Harvesting cognitive radio networks using artificial intelligence." BOHR Journal of Computational Intelligence and Communication Network 2, no. 1 (2024): 11–17. http://dx.doi.org/10.54646/bjcicn.2024.13.

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The utilization of Artificial Intelligence (AI) in leveraging Cognitive Radio Networks (CRNs) represents an emerging field of study. This surge is primarily driven by operational expenses, concerns over traditional power sources, and limitations inherent in current CRN technologies. Furthermore, integrating AI into CRN operations significantly enhances efficiency and maximizes the application of the electromagnetic spectrum. To enable real-time processing, Cognitive Radio (CR) is paired with AI methodologies, fostering adaptive and intelligent resource allocation. This research paper outlines CRNs: their objectives, available resources, and constraints. It subsequently introduces AI techniques, emphasizing the profound influence of learning within CR contexts. The application of model methods such as Markov Model, fuzzy logic, and Neural Network is explored. AI technology is employed in critical CR tasks like spectrum sharing, spectrum sensing, resource allocation, optimization of spectrum mobility, decision-making processes, and more. The overarching goal is to showcase how AI can assist researchers in harnessing and implementing diverse CR designs effectively.

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Awoyemi, B. S., B. T. Maharaj, and A. S. Alfa. "Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7385627.

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Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA’s productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.

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Yang, Guang Long, Xiao Wang, and Xue Zhi Tan. "Power Control Optimization Algorithm in Cognitive Radio Network." Advanced Materials Research 926-930 (May 2014): 3669–72. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3669.

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For cognitive radio environment needs of different users, A space-time diversity multi-carrier code division multiple access (OFDM-CDMA) technology architecture of the cognitive radio (CR) system is used, a novel non-cooperative power control algorithm and the price game (NPGP), in order to protect the economic interests of the spectrum of network providers, to achieve a fair and efficient allocation of spectrum resources have cognitive and improve spectrum efficiency. Simulation results show that the algorithm under the protection of the economic spectrum premise network provider benefits, both to ensure the fair and efficient allocation of spectrum resources, and achieve effective control of power users, system performance improved significantly.

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Munir, Rizwan, Yifei Wei, Chao Ma, and Bizhu Yang. "Dynamically Resource Allocation in Beyond 5G (B5G) Network RAN Slicing Using Deep Deterministic Policy Gradient." Wireless Communications and Mobile Computing 2022 (December 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/9958786.

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Network slicing makes it possible for future applications with a variety of adaptability requirements and performance requirements by spliting the physical network into several logical networks. Radio access network (RAN) slicing’s main goal is to assign physical resource blocks (RBs) to mMTC, eMBB, and uRLLC services while ensuring the Quality of service (QoS). Consequently, it is challenging to determine the optimal strategies for 5G radio access network (5G-RAN) slicing because of dynamically changes in slice needs and environmental data, and conventional approaches have difficulty addressing resource allocation issues. In this paper, we present an energy-efficient deep deterministic policy gradient resource allocation (EE-DDPG-RA) method for RAN slicing in 5G networks to choose the resource allocation policy that increases long-term throughput while satisfying the requirements of B5G systems for quality of service. This method’s main goal is to remove unnecessary actions in order to lower the amount of available action space. The numerical outcomes demonstrate that the proposed approach outperforms boundaries by enhancing deep-rooted throughput and effectively managing resources.

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Yang, Peng, Liao Chen, Hong Zhang, Jing Yang, Ruyan Wang, and Zhidu Li. "Joint Optical and Wireless Resource Allocation for Cooperative Transmission in C-RAN." Sensors 21, no. 1 (December 31, 2020): 217. http://dx.doi.org/10.3390/s21010217.

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Cooperative multipoint transmission (CoMP) is one of the most promising paradigms for mitigating interference in cloud radio access networks (C-RAN). It allows multiple remote radio units (RRUs) to transmit the same data flow to a user to further improve the signal quality. However, CoMP may incur redundant data transmission over fronthaul network in the C-RAN. In a C-RAN employing CoMP, a key problem is how to coordinate heterogeneous resource allocation to maximize the cooperation gain while reducing the fronthaul load. In this paper, the cooperation transmission based on a multi-dimensional resource schedule (MRSCT) scheme, jointly considering user association, spectrum resource allocation, and wavelength resource allocation, is firstly envisioned in the underlying C-RAN integrating time and wavelength division multiplexing passive optical network (TWDM-PON) to maximize fronthaul efficiency. Then a two-timescale resource allocation framework including two sub-approaches is established. More specially, the first sub-approach mainly focuses on exploiting reinforcement learning to obtain a wavelength resource allocation strategy to relieve fronthaul traffic load. Moreover, the second sub-approach adopts the overlapping coalition formation game to establish a user-centric cooperative set, where spectrum resources are dynamically allocated to further alleviate the interference issue. The theoretical analysis and simulation results validate the performance of MRSCT scheme on the fronthaul efficiency, user experience, and system service capability.

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Kułacz, Łukasz, and Adrian Kliks. "Dynamic Spectrum Allocation Using Multi-Source Context Information in OpenRAN Networks." Sensors 22, no. 9 (May 5, 2022): 3515. http://dx.doi.org/10.3390/s22093515.

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Bearing in mind the stringent problem of limited and inefficiently used radio resources, a multi-source mechanism for the dynamic adjustment of occupied frequency bands is proposed. Instead of relying only on radio-related information, the system that collects data from various sources is discussed. Mainly, using the ubiquitous sources of information about the presence of users (such as city monitoring), it is possible to identify areas that have high or low expected traffic with high probabilities. Consequently, in low-traffic areas, it is not necessary to allocate all available spectrum resources while maintaining the quality of service. This leads to the improved spectral efficiency of the network. As the level of trust in certain information sources may differ among various operators, we propose to implement such functionality in the form of an application. Our contribution is a proposal for an algorithm that limits the use of radio resources through fuzzy and soft connections of multiple sources of contextual information. The simulation results presented in this paper show that it is possible to reduce the spectrum used with a slight and simultaneous reduction in user bitrate, which increases the spectral efficiency of the entire system. Hence, following the concept of an open radio access network, various policies for information merging may be specified.

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Wang, Min, Shu Guang Zhang, Qiao Yun Sun, and Yu Zhang. "Resources Allocation Scheme Based on Mode Switch for Multicast Services in MBSFN." Applied Mechanics and Materials 543-547 (March 2014): 3044–48. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3044.

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The multimedia broadcast and multicast services (MBMS) in 3GPP LTE is characterized by multicast broadcast single frequency network (MBSFN) operation. The multicast services are transmitted by single frequency network (SFN) mode, and the unicast services are delivered with point-to-point (PTP) mode. To avoid the network congestion of the multicast services in MBSFN, a novel resources allocation scheme (RAS) based on mode switch for the multicast services is proposed. The RAS takes advantages of the mode switch between SFN and PTP for the multicast services and minimizes the demanded radio resources of the maximum load cell. The simulation results show that the proposed RAS needs less demanded radio resources of the maximum load cell than SFN mode for all the multicast services.

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Gharbi, Atef, Abdulsamad Ebrahim Yahya, and Mohamed Ayari. "Comparative Study of Radio Resource Distribution Algorithms." Engineering, Technology & Applied Science Research 14, no. 1 (February 8, 2024): 13006–11. http://dx.doi.org/10.48084/etasr.6805.

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The equitable distribution of radio resources among different users in wireless networks is a difficult problem and has attracted the interest of many studies. This study presents the Proportional Fair Q-Learning Algorithm (PFLA) to enable the equitable distribution of radio resources among diverse users through the integration of Q-learning and proportional fairness principles. The PFLA, Round Robin (RR), and Max Throughput (MaxTP) algorithms were compared to evaluate their effectiveness in resource allocation. Performance was measured in terms of sum-rate throughputs and fairness index. The comparison results showed an improvement in the fairness index metrics for PFLA compared to the other algorithms. PFLA showed gains of 11.62 and 43% in the fairness index compared to RR and MaxTP, respectively. These results show that PFLA is more efficient in utilizing available resources, leading to higher overall system throughput and demonstrating its ability to balance performance metrics between users, especially when the number of users increases.

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Ferreira, Lúcio Studer, and Luís M. Correia. "An Efficient and Fair Strategy for Radio Resources Allocation in Multi-radio Wireless Mesh Networks." Wireless Personal Communications 75, no. 2 (October 13, 2013): 1463–87. http://dx.doi.org/10.1007/s11277-013-1433-0.

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Syifana, Alfiya, Linda Meylani, and Vinsensius Sigit Widhi Prabowo. "Radio Resource Allocation in D2D Underlay Communication Using Two Phased Auction Based Fair and Interference Resource Allocation." JMECS 8, no. 2 (October 7, 2021): 1. http://dx.doi.org/10.25124/jmecs.v8i2.3972.

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The high demands of the mobile user will affect the workload of eNodeB, which results in the decreasingperformance system of eNodeB. Device-to-Device (D2D) underlaying communication system is a solution inreducing the workload of eNodeB and increasing the system data rate. This communication system consistsof two users, namely Cellular User Equipment (CUE) and D2D pair, where CUE shares its resources withthe D2D pair. This sharing of resources also causes interference and should be managed using the resourceallocation algorithm. This research used the TAFIRA D2D algorithm and compared it with the greedyalgorithm and the TAFIRA CUE algorithm. The research calculates parameter performance of the system,such as spectral efficiency, power efficiency, and fairness among D2D pairs. The simulation results showthat Greedy algorithm has a better performance compared with TAFIRA algorithm. TAFIRA D2D onlycan achieve 19.94 bps/Hz in spectral efficiency, 23.88 Kbps/watt in power efficiency, and 89% fairnessamong D2D pairs.

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Deng, Hongyu, Cheng Wu, and Yiming Wang. "A cognitive gateway-based spectrum sharing method in downlink round robin scheduling of LTE system." Modern Physics Letters B 31, no. 19-21 (July 27, 2017): 1740070. http://dx.doi.org/10.1142/s021798491740070x.

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A key technique of LTE is how to allocate efficiently the resource of radio spectrum. Traditional Round Robin (RR) scheduling scheme may lead to too many resource residues when allocating resources. When the number of users in the current transmission time interval (TTI) is not the greatest common divisor of resource block groups (RBGs), and such a phenomenon lasts for a long time, the spectrum utilization would be greatly decreased. In this paper, a novel spectrum allocation scheme of cognitive gateway (CG) was proposed, in which the LTE spectrum utilization and CG’s throughput were greatly increased by allocating idle resource blocks in the shared TTI in LTE system to CG. Our simulation results show that the spectrum resource sharing method can improve LTE spectral utilization and increase the CG’s throughput as well as network use time.

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Feng, Lei, Wenjing Li, Peng Yu, and Xuesong Qiu. "An Enhanced OFDM Resource Allocation Algorithm in C-RAN Based 5G Public Safety Network." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9586287.

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Public Safety Network (PSN) is the network for critical communication when disaster occurs. As a key technology in 5G, Cloud-Radio Access Network (C-RAN) can play an important role in PSN instead of LTE-based RAN. This paper firstly introduces C-RAN based PSN architecture and models the OFDM resource allocation problem in C-RAN based PSN as an integer quadratic programming, which allows the trade-off between expected bitrates and allocating fairness of PSN Service User (PSU). However, C-RAN based PSN needs to improve the efficiency of allocating algorithm because of a mass of PSU-RRH associations when disaster occurs. To deal with it, the resources allocating problem with integer variables is relaxed into one with continuous variables in the first step and an algorithm based on Generalized Bender’s Decomposition (GBD) is proposed to solve it. Then we use Feasible Pump (FP) method to get a feasible integer solution on the original OFDM resources allocation problem. The final experiments show the total throughput achieved by C-RAN based PSN is at most higher by 19.17% than the LTE-based one. And the average computational time of the proposed GBD and FP algorithm is at most lower than Barrier by 51.5% and GBD with no relaxation by 30.1%, respectively.

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G, Manjula, Pratibha Deshmukh, Udaya Kumar N. L., Víctor Daniel Jiménez Macedo, Vikhyath K B, Achyutha Prasad N, and Amit Kumar Tiwari. "Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (March 11, 2023): 118–26. http://dx.doi.org/10.17762/ijritcc.v11i3s.6172.

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In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design.

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Xue, Tianyu, Kamran Ali Memon, and Chunguo Li. "Joint Resource Allocation in TWDM-PON-Enabled Cell-Free mMIMO System." Photonics 10, no. 11 (October 24, 2023): 1180. http://dx.doi.org/10.3390/photonics10111180.

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Cell-free massive multiple input multiple outputs (CF-mMIMO) is considered a promising technology for sixth-generation (6G) telecommunication systems. In the CF-mMIMO system, an extensive array of distributed small base stations (BSs) is deployed across the network, which enables us to facilitate seamless collaboration among BSs. To achieve this goal, the baseband signal from these BSs needs to be transmitted to a central server via fronthaul networks. Due to the large number of BSs, the data that needs to be transmitted is usually huge, which brings severe requirements on fronthaul networks. Time and wavelength division multiplexed passive optical networks (TWDM-PON) can be a potential solution for CF-mMIMO fronthaul due to their large capacity and high flexibility. However, how to efficiently allocate both optical and wireless resources in a TWDM-PON-enabled CF-mMIMO system is still a problem to be addressed. This paper proposes a joint scheduling method of wavelength, antenna, radio unit (RU), and radio resource block (RB) resources in the TWDM-PON-enabled CF-mMIMO system. Furthermore, an integer linear programming (ILP) model for joint resource allocation is proposed to minimize the fronthaul resource occupancy, thereby increasing network scalability. Considering the complexity of the ILP model, two heuristic algorithms are also presented to solve this model. We compare the ILP with heuristic algorithms under different scenarios. Simulation results show that the proposed algorithm can reduce the fronthaul resource occupancy to improve the network scalability of the CF-mMIMO system.

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Weyl, E. Glen, and Anthony Lee Zhang. "Depreciating Licenses." American Economic Journal: Economic Policy 14, no. 3 (August 1, 2022): 422–48. http://dx.doi.org/10.1257/pol.20200426.

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Many governments assign use licenses for natural resources, such as radio spectrum, fishing rights, and mineral extraction rights, through auctions or other market-like mechanisms. License design affects resource users’ investment incentives as well as the efficiency of asset allocation. No existing license design achieves first-best outcomes on both dimensions. Long-term licenses give owners high investment incentives but impede reallocation to high-valued entrants. Short-term licenses improve allocative efficiency but discourage investment. We propose a simple new mechanism, the depreciating license, and we argue that it navigates this trade-off more effectively than existing license designs. (JEL D44, D45, H82, K11, Q28, Q38, Q58)

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Naseer, Sundus, Qurratul-Ain Minhas, Khalid Saleem, Ghazanfar Farooq Siddiqui, Naeem Bhatti, and Hasan Mahmood. "A game theoretic power control and spectrum sharing approach using cost dominance in cognitive radio networks." PeerJ Computer Science 7 (July 15, 2021): e617. http://dx.doi.org/10.7717/peerj-cs.617.

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The wireless networks face challenges in efficient utilization of bandwidth due to paucity of resources and lack of central management, which may result in undesired congestion. The cognitive radio (CR) paradigm can bring efficiency, better utilization of bandwidth, and appropriate management of limited resources. While the CR paradigm is an attractive choice, the CRs selfishly compete to acquire and utilize available bandwidth that may ultimately result in inappropriate power levels, causing degradation in network’s Quality of Service (QoS). A cooperative game theoretic approach can ease the problem of spectrum sharing and power utilization in a hostile and selfish environment. We focus on the challenge of congestion control that results in inadequate and uncontrolled access of channels and utilization of resources. The Nash equilibrium (NE) of a cooperative congestion game is examined by considering the cost basis, which is embedded in the utility function. The proposed algorithm inhibits the utility, which leads to the decrease in aggregate cost and global function maximization. The cost dominance is a pivotal agent for cooperation in CRs that results in efficient power allocation. Simulation results show reduction in power utilization due to improved management in cognitive radio resource allocation.

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Gutierrez, Amado, Victor Rangel, Javier Gomez, Robert M. Edwards, and David H. Covarrubias. "A Joint Modulation-Coding Scheme and Resource Allocation in LTE Uplink." Elektronika ir Elektrotechnika 26, no. 5 (October 27, 2020): 50–58. http://dx.doi.org/10.5755/j01.eie.26.5.22313.

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In Long Term Evolution (LTE) Resource Allocation Algorithms (RAAs) are an area of work where researchers are seeking to optimize the efficient use of scarce radio resources. The selection of an optimal Modulation and Coding Scheme (MCS) that allows LTE to adapt to channel conditions is a second area of ongoing work. In the wireless part of LTE, these two factors, RAA and MCS selection, are the most critical in optimization. In this paper, the performance of three resource allocation schemes is compared, and a new allocation scheme, Average MCS (AMCS) allocation, is proposed. AMCS is seen to outperform both “Minimum MCS (MMCS)” and “Average Signal to Interference and Noise Ratio MCS (SINR AMCS)” in terms of improvements to LTE Uplink (UL) performance. The three algorithms were implemented in the Vienna LTE-A Uplink Simulator v1.5.

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Jasim, Sabbar Insaif, Mustafa Mahmood Akawee, and Raed Abdulkareem Hasan. "A spectrum sensing approaches in cognitive radio network by using cloud computing environment." Bulletin of Electrical Engineering and Informatics 11, no. 2 (April 1, 2022): 750–57. http://dx.doi.org/10.11591/eei.v11i2.3162.

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A spectrum agreement has failed to meet the demands of new applications due to the fixed spectrum allocation (FSA) concept. But current efforts are targeted towards the utilization of cognitive radio as a way of addressing the issue of resources deficiency. The number of radio spectrum users keeps increasing daily owing to the advancement in technology in all aspects of life; even the licensed band users are currently demanding for extension of their radio spectrum and to balance the congestion in radio spectrum, some users may have to be placed on other bands. This article focused on voids detection (via spectrum sensing) in radio spectrum and secondary user assignment in cloud computing. Spectrum sensing was approached in two was in this study-underlay and interweave spectrum allocation. Both approaches are evaluated using certain performance metrics, such as throughput enhancement and queuing time minimization.

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Zhang, Er Qing, Si Xing Yin, Liang Yin, and Shu Fang Li. "Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks with Imperfect Spectrum Sensing." Advanced Materials Research 846-847 (November 2013): 635–42. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.635.

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With the rapid development of wireless network technologies and proliferation of related services such as multimedia applications, demands for wireless spectrum resources keep rising. Cognitive radio (CR) is a novel approach for better utilization of the scarce, already packed but highly underutilized radio spectrum. Meanwhile, exclusive functionalities such as spectrum sensing make energy efficiency (EE) a crucial issue in Cognitive Radios (CRs). In this paper, we focus on the energy-efficient power allocation for OFDM-based CRs with imperfect spectrum sensing. The EE maximization for secondary users (SUs) is formulated as a nonlinear fractional programming problem taking into account imperfect spectrum sensing such as miss detection and false alarm. Then by transforming the original problem into a parameter programming, the optimal power allocation is derived with the bisection search (BS) method and dual decomposition method (DDM). Simulation results illustrate the significant performance improvement of our scheme compared to an existing one with objective of maximizing system throughput rather than EE.

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Qin, Tao, Cyril Leung, Chunyan Miao, and Zhiqi Shen. "Resource Allocation in a Cognitive Radio System with Imperfect Channel State Estimation." Journal of Electrical and Computer Engineering 2010 (2010): 1–5. http://dx.doi.org/10.1155/2010/419430.

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Cognitive radio (CR) is a promising concept for improving the utilization of scarce radio spectrum resources. Orthogonal frequency division multiplexing (OFDM) is regarded as a technology which is well matched for CR systems. It is shown that channel estimation errors can result in a severe performance degradation in a multiuser OFDM CR system. A simple back-off scheme is proposed, and simulation results are provided which show that the proposed scheme is very effective in mitigating the negative impact of channel estimation errors.

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J, Logeshwaran, Kiruthiga T, and Jaime Lloret. "A NOVEL ARCHITECTURE OF INTELLIGENT DECISION MODEL FOR EFFICIENT RESOURCE ALLOCATION IN 5G BROADBAND COMMUNICATION NETWORKS." ICTACT Journal on Soft Computing 13, no. 3 (April 1, 2023): 2986–94. http://dx.doi.org/10.21917/ijsc.2023.0420.

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Intelligent Decision Model for efficient resource allocation in 5G broadband communication networks is essential for ensuring the most efficient use of available resources. This model considers several factors, such as traffic demand, network topology, and radio access technology, to make the most efficient decisions about resource allocation. It is based on intelligent algorithms and advanced analytics, which allow the network to quickly and accurately identify the optimal resource allocation for a given situation. This model can reduce costs, improve network performance, and increase customer satisfaction. In addition, the Intelligent Decision Model can help operators reduce the complexity and cost of managing a 5G network. The intelligent decision model for efficient resource allocation in 5G broadband communication networks is based on a combination of artificial intelligence (AI) and optimization techniques. The proposed decision models can use AI to identify patterns in traffic and user behavior. In contrast, the proposed can use optimization techniques to maximize resource utilization and reduce latency in the network. This model can also leverage predictive analytics and machine learning algorithms to determine the most efficient allocation of resources. Additionally, the proposed model can use AI to detect and mitigate potential security threats and malicious activities in the network. the proposed IDM has reached 91.85% of accuracy, 90.05% of precision, 90.96% of recall and 91.33% of F1-score.

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Journal articles on the topic 'Radio Resources Allocation'

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