Статті в журналах: "Power allocation techniques"

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B., Sruthi. "A Review on Power Allocation Techniques in MIMO-NOMA 5G Systems." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1409–15. http://dx.doi.org/10.5373/jardcs/v12sp4/20201619.

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Feng, Zhao Hong, Tie Jun Jia, Xi Ming Xiao, and Fu Jie Zhang. "Wind Power Allocation Based on Predictive Power Correction." Applied Mechanics and Materials 644-650 (September 2014): 3445–48. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3445.

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Wind power prediction techniques can be used in wind power scheduling control. Aim at the scheduling control deviation caused by the error between predicted power and actual power output, a Wind power scheduling optimization allocation algorithm based on predictive power correction is proposed, which adopts Auxiliary Particle Filter Algorithm to adjust the values of predicted wind power .Then the adjusted values are used in the proportional allocation according to the maximum power strategy ,and the superiority of this method will be verified by MATLAB simulation with the real wind farm operating data .

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Ali, Zuhura J., Nor K. Noordin, Aduwati Sali, Fazirulhisyam Hashim, and Mohammed Balfaqih. "Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems." Applied Sciences 10, no. 17 (August 26, 2020): 5892. http://dx.doi.org/10.3390/app10175892.

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Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush–Kuhn–Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC.

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Narieda, Shusuke, Takeo Fujii, and Kenta Umebayashi. "Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN." Sensors 20, no. 16 (August 7, 2020): 4417. http://dx.doi.org/10.3390/s20164417.

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This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.

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Huzaifa, Muhammad, Arif Hussain, Waseem Haider, Syed Ali Abbas Kazmi, Usman Ahmad, and Habib Ur Rehman. "Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation." Sustainability 15, no. 9 (May 3, 2023): 7499. http://dx.doi.org/10.3390/su15097499.

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With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

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Almashhadani, Yazen S., and Ghassan A. QasMarrogy. "Dynamic Power Allocation for Downlink NOMA." Cihan University-Erbil Scientific Journal 8, no. 1 (June 30, 2024): 85–90. http://dx.doi.org/10.24086/cuesj.v8n1y2024.pp85-90.

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Dynamic Power Allocation for Downlink Non-Orthogonal Multiple Access (NOMA) is a critical research area in wireless communication systems. This study focuses on implementing and evaluating dynamic power allocation strategies in the context of downlink NOMA. We compare the performance of dynamic power allocation with fixed power allocation schemes to assess their impact on system throughput, interference management, and overall efficiency. Through extensive simulations and analysis in this paper using MATLAB, we demonstrate a comparison between dynamic power allocation and fixed power allocation, and the advantages of dynamic power allocation in adapting to changing channel conditions and user requirements, leading to improved system performance. Our results show the data rate and outage probability which provides valuable insights into the benefits of dynamic power allocation in downlink NOMA systems and highlight the importance of adaptive power management techniques in enhancing wireless communication networks.

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Liu, Yongfei, Lu Si, Yuhuan Wang, Bo Zhang, and Weizhang Xu. "Efficient Precoding and Power Allocation Techniques for Maximizing Spectral Efficiency in Beamspace MIMO-NOMA Systems." Sensors 23, no. 18 (September 20, 2023): 7996. http://dx.doi.org/10.3390/s23187996.

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Beamspace MIMO-NOMA is an effective way to improve spectral efficiency. This paper focuses on a downlink non-orthogonal multiple access (NOMA) transmission scheme for a beamspace multiple-input multiple-output (MIMO) system. To increase the sum rate, we jointly optimize precoding and power allocation, which presents a non-convex problem. To solve this difficulty, we employ an alternating algorithm to optimize the precoding and power allocation. Regarding the precoding subproblem, we demonstrate that the original optimization problem can be transformed into an unconstrained optimization problem. Drawing inspiration from fraction programming (FP), we reconstruct the problem and derive a closed-form expression of the optimization variable. In addition, we effectively reduce the complexity of precoding by utilizing Neumann series expansion (NSE). For the power allocation subproblem, we adopt a dynamic power allocation scheme that considers both the intra-beam power optimization and the inter-beam power optimization. Simulation results show that the energy efficiency of the proposed beamspace MIMO-NOMA is significantly better than other conventional schemes.

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Manzoor, Muhammad Faraz, Adnan Abid, Muhammad Shoaib Farooq, Naeem A. Azam, and Uzma Farooq. "Resource Allocation Techniques in Cloud Computing: A Review and Future Directions." Elektronika ir Elektrotechnika 26, no. 6 (December 18, 2020): 40–51. http://dx.doi.org/10.5755/j01.eie.26.6.25865.

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Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of resource allocation in cloud computing, including strategic, target resources, optimization, scheduling and power. More than 50 articles, between year 2007 and 2019, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and they are reviewed under clearly defined objectives. It presents a topical taxonomy of resource allocation dimensions, and articles under each category are discussed and analysed. Lastly, salient future directions in this area are discussed.

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Lal, Mrs Jaya Dipti, and Dr Dolly Thankachan. "Comparison of Various Techniques with PSO based Power Allocation Strategy in Cooperative Wireless Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 469–75. http://dx.doi.org/10.22214/ijraset.2022.40264.

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Abstract: Now a days, wireless communication using co-operative networks have attracted many attentions due to its suitability of working in different environments with acceptable performances. During the transmission, a major requirement of optimal power allocation strategy is observed and this is the major concern of many researchers. This power allocation technique requires a suitable optimization algorithm while selecting the power allocation factor for power assignment. In the paper various optimization strategies are compared with proposed particle swarm optimization technique. A cooperative communication frame work is used with amplify and forward as relaying technique. The proposed algorithm using the tech-nique is simulated and the comparison results are obtained. Simulation results when compared outperformed with other previous works. Keywords: Cooperative networks; optimal power alloca-tion; Amplify and forward relaying; Particle Swarm Opti-mization (PSO).

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10

Mohammed Hassan, Ahmed Hassan, and Ahmed M.Alhassan. "Cooperative Communications Combination Diversity Techniques And Optimal Power Allocation Expression." International Journal of Applied Control, Electrical and Electronics Engineering 3, no. 4 (November 30, 2015): 1–11. http://dx.doi.org/10.5121/ijaceee.2015.3401.

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Shayesteh, E., D. F. Gayme, and M. Amelin. "System reduction techniques for storage allocation in large power systems." International Journal of Electrical Power & Energy Systems 95 (February 2018): 108–17. http://dx.doi.org/10.1016/j.ijepes.2017.08.007.

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Alabed, Samer, Issam Maaz, and Mohammad Al-Rabayah. "Distributed differential beamforming and power allocation for cooperative communication networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 5923. http://dx.doi.org/10.11591/ijece.v10i6.pp5923-5931.

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Many coherent cooperative diversity techniques for wireless relay networks have recently been suggested to improve the overall system performance in terms of the achievable data rate or bit error rate (BER) with low decoding complexity and delay. However, these techniques require channel state information (CSI) at the transmitter side, at the receiver side, or at both sides. Therefore, due to the overhead associated with estimating CSI, distributed differential space-time coding techniques have been suggested to overcome this overhead by detecting the information symbols without requiring any (CSI) at any transmitting or receiving antenna. However, the latter techniques suffer from low performance in terms of BER as well as high latency and decoding complexity. In this paper, a distributed differential beamforming technique with power allocation is proposed to overcome all drawbacks associated with the later techniques without needing CSI at any antenna and to be used for cooperative communication networks. We prove through our analytical and simulation results that the proposed technique outperforms the state-of-the-art techniques in terms of BER with comparably low decoding complexity and latency.

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13

Mancharla, Ravi, and Yaka Bulo. "A Comparative Analysis of the various Power Allocation Algorithm in NOMA-MIMO Network Using DNN and DLS Algorithm." ICST Transactions on Mobile Communications and Applications 7, no. 2 (August 31, 2022): e3. http://dx.doi.org/10.4108/eetmca.v7i2.2651.

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The high data rate, huge spectrum efficiency, successive interference cancellation (SIC), and ultra-reliable low latency (URLL) are of the demand for next-generation technologies. Non-orthogonal multiple access (NOMA) scheme provides multi-user scaling (multiplexing), optimum spectral efficiency (SE), excellent user-pairing improvement, and a single resource block sharing by multiple users because of which it is a more preferable scheme over orthogonal multiple access (OMA) for the next generation technologies. This article investigates comparative analysis of various power allocation algorithms in multiple-input multiple-output-NOMA (MIMO-NOMA) technology and to come up with the best power allocation algorithm which suited best for MIMO-NOMA technology. Firstly, comparison analysis will be carried out considering direct methodologies followed by power allocation algorithm using Deep Neural Network (DNN) along with the Depth limited search (DLS) algorithm. These techniques are tested on two users initially then followed by multi-user communication. Allocating optimal power to the poor signal strength user terminal (user not receiving appropriate signal power) is a difficult task in actual scenario, and moreover, SIC also creates complexity in the proper allocation of Base station (BS) source power. The above problems can be solved with the assistance of the DNN along with the DLS algorithm, where the weaker user receives maximum power and the stronger user receives minimum power. The DNN-MIMO-NOMA technology, which is based on the DLS algorithm, helps user terminals to get their signals free from noise (inter_user_interference) and with greater precision. The DLS process (algorithm) offers higher potential in MIMO-NOMA with DNN technology for successfully applying SIC. Here, MIMO helps to improve the channel gain. A DLS provides an optimum power allocation (OPA) based on the position of user equipment. The simulation results show that the power allocation method using DNN along with DLS algorithm achieves better performance than the traditional multi-user.

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Khan, Baseem, and Ganga Agnihotri. "A Comprehensive Review of Embedded Transmission Pricing Methods Based on Power Flow Tracing Techniques." Chinese Journal of Engineering 2013 (October 9, 2013): 1–13. http://dx.doi.org/10.1155/2013/501587.

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Restructuring of electricity supply industry introduced the concept of deregulation. After deregulation transmission cost allocation is a vital issue. In the available literature, various authors have presented different methods for allocation of transmission cost. This paper presents the review of a variety of methods and algorithm based on the principle of power-flow tracing. For a fair and equitable transmission charge allocation it is necessary to know the quantum of power flowing by each generator and load in line flows. Power flow tracing techniques are mainly based on the concepts of proportional sharing principle, graph theory, circuit theory (Z-bus tracing), optimization approach, relative electrical distance concept, Equilateral bilateral exchange (EBE), and game theory. This paper presents a comprehensive review of all the available literature on the above.

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Barukčić, Marinko, Toni Varga, Vedrana Jerković Štil, and Tin Benšić. "Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques." Electronics 10, no. 14 (July 10, 2021): 1648. http://dx.doi.org/10.3390/electronics10141648.

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The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

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Alhamad, Raed, and Hatem Boujemaa. "Throughput Analysis and Optimization of Relay Selection Techniques for Millimeter Wave Communications." Journal of Computer Networks and Communications 2019 (October 1, 2019): 1–8. http://dx.doi.org/10.1155/2019/7523181.

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In this paper, we optimize the throughput of millimeter wave communications using relay selection techniques. We study opportunistic amplify and forward (OAF), opportunistic decode and forward (ODF), and partial and reactive relay selection (PRS and RRS). Our analysis is valid for interference-limited millimeter wave communications. We suggest a new optimal power allocation (OPA) strategy that offers significant performance enhancement with respect to uniform power allocation (UPA). The proposed OPA offers up to 2 dB gain with respect to UPA. Our analysis is confirmed with extensive simulation results for Nakagami fading channels.

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SK, Khaleelahmed, Venkateswararao N, Varshasree KN, and P. V. Naidu. "Improving MIMO system throughput using power transmission scheduling." International Journal of Engineering & Technology 7, no. 3 (June 23, 2018): 1181. http://dx.doi.org/10.14419/ijet.v7i3.13097.

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The main idea of this paper is to select the best power allocation throughput model by simulating the different power allocation theoretical modules in MIMO arbitrary multipath environment to address the effects of channel parameters on throughput. In general, power allocation techniques are used for minimizing the overall Bit Error Rate (BER). In this process, the channel estimation is usually done at the receiver by accessing the Channel State Information (CSI). The optimized system can be designed with respect to channel parameters so that it can be suitable for transmitter side during power allocation. The simulation analysis is carried out in NI LabVIEW and it is observed from the studies that the throughput results are as a function of received power. Under static system parameters, the relative throughput of the water filling (WF) power allocation model is found to be high efficient 15.74% when it is compared with the open loop zero-forcing (ZF) and it is 4.45 % with respect to inverse singular value decomposition (SVD) equal power allocation models.

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Saeed, Shatha Hellan, and Ibrahim Khalil Sileh. "Performance Analysis of Cooperative NOMA for Different Power Allocation Strategies." Tikrit Journal of Engineering Sciences 30, no. 4 (December 1, 2023): 102–17. http://dx.doi.org/10.25130/tjes.30.4.11.

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Cooperative None Orthogonal Multiple Access (C-NOMA) is a promising approach for 5G and beyond communication networks. Users will benefit from the full bandwidth of the channel without time constraints. NOMA’s features are incomplete without efficient power allocation that ensures power distribution among users fairly. Integrating power management (allocation) with Cooperative-NOMA (C-NOMA) may improve the system metrics. In addition, a physical layer security (PLS) is added to make the process of sending and receiving safe, and the system works in an integrated manner, preventing any interrupting or eavesdropping inside or outside the network. This paper demonstrates the user and channel performance of C-NOMA with Amplify-and-forward (AF) and Decode-and-forward (DF) approaches. The analysis is performed by varying the power allocation techniques to derive the best system configurations. The simulation results confirmed the analytic findings and showed that the proposed system outperforms orthogonal multiple access (OMA), conventional NOMA, and conventional cooperative NOMA, enhancing the performance metrics in terms of throughput, sum rate, and outage probability. The bit-error-rate (BER) of the far user can be identical to that of the near user if power allocation is properly set. All techniques excelled except for the fixed power allocation, which had the same BER. The Channel capacity and outage probability were also considered. A slight variation in the channel capacity in all the experiments for different numbers of users was found. The generalized power allocation for AF and DF models had the optimum channel capacity close to 14 bps/Hz. Moreover, far users always had a higher outage probability than near users and channels, and generalized power allocation was the highest outage probability technique when the transmit power was close to (4 dB).

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Abuajwa, Osama, Mardeni Bin Roslee, and Zubaida Binti Yusoff. "Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks." Applied Sciences 11, no. 10 (May 18, 2021): 4592. http://dx.doi.org/10.3390/app11104592.

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In this work, we investigate resource allocation and user pairing to improve the system’s Throughput for the downlink non-orthogonal multiple access (NOMA)-based 5G networks. The proposed resource allocation involves user pairing, subchannel power allocation, and proportional power allocation among the multiplexed users. The resource allocation is a non-deterministic polynomial (NP-hard) problem that is difficult to tackle throughput maximization. The user pairing and power allocation are coupled to address the substantial requirements of the NOMA system. The NOMA system requires an efficient deployment of resource allocation techniques to enhance the system’s throughput performance. In this work, we propose simulated annealing (SA) to optimize the power allocation and perform user pairing to maximize the throughput for the NOMA system. Also, we provide mathematical proof on the near-optimal solution for subchannel power and mathematical analysis on the optimal value of the power ratio for the multiplexed users in the NOMA system. The SA provides a significant throughput performance that increases by 7% compared to the existing numerical optimization methods. Results obtained show that SA performs with sufficient reliability and low time complexity in terms of Throughput improvement.

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Elmadina, Nahla Nur, Rashid Saeed, Elsadig Saeid, Elmustafa Sayed Ali, Maha Abdelhaq, Raed Alsaqour, and Nawaf Alharbe. "Downlink Power Allocation for CR-NOMA-Based Femtocell D2D Using Greedy Asynchronous Distributed Interference Avoidance Algorithm." Computers 12, no. 8 (August 3, 2023): 158. http://dx.doi.org/10.3390/computers12080158.

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This paper focuses on downlink power allocation for a cognitive radio-based non-orthogonal multiple access (CR-NOMA) system in a femtocell environment involving device-to-device (D2D) communication. The proposed power allocation scheme employs the greedy asynchronous distributed interference avoidance (GADIA) algorithm. This research aims to optimize the power allocation in the downlink transmission, considering the unique characteristics of the CR-NOMA-based femtocell D2D system. The GADIA algorithm is utilized to mitigate interference and effectively optimize power allocation across the network. This research uses a fairness index to present a novel fairness-constrained power allocation algorithm for a downlink non-orthogonal multiple access (NOMA) system. Through extensive simulations, the maximum rate under fairness (MRF) algorithm is shown to optimize system performance while maintaining fairness among users effectively. The fairness index is demonstrated to be adaptable to various user counts, offering a specified range with excellent responsiveness. The implementation of the GADIA algorithm exhibits promising results for sub-optimal frequency band distribution within the network. Mathematical models evaluated in MATLAB further confirm the superiority of CR-NOMA over optimum power allocation NOMA (OPA) and fixed power allocation NOMA (FPA) techniques.

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Eltawil, A. M., and B. Daneshrad. "A low-power DS-CDMA RAKE receiver utilizing resource allocation techniques." IEEE Journal of Solid-State Circuits 39, no. 8 (August 2004): 1321–30. http://dx.doi.org/10.1109/jssc.2004.831466.

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M, Mittu, Sangeeta T.R, and Avinash Manu Aravindan. "Optimal Power Allocation Techniques using SVD for MIMOOFDM Multimedia Mobile Networks." International Journal of Engineering Trends and Technology 26, no. 5 (August 25, 2015): 239–45. http://dx.doi.org/10.14445/22315381/ijett-v26p242.

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Ibrahim, Hesham S., Mohamed Abaza, Ali Mansour, and Ayman Alfalou. "Diversity Combining and Power Allocation Techniques for NOMA-MIMO-VLC Systems." Procedia Computer Science 246 (2024): 4683–91. http://dx.doi.org/10.1016/j.procs.2024.09.333.

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Rathee, Amit, Yogesh Chaba, and Deepak Dembla. "Power Coordination based Efficient Resource Allocation for Device-to-Device Communication in 5G Networks." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 760–64. http://dx.doi.org/10.37391/ijeer.100357.

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Device to device communication for mobile networks establishes connections between parameters of mobile devices. As the number of D2D connections and resources are increasing, optimization of power allocation and spectrum feasibility is required. Most of the proposed algorithm schemes for resource allocation support slow-moving D2D terminals in a cellular network, therefore causing huge amount of signaling loss and reducing the efficiency of the cellular network. In energy and spectrum efficiency for the wireless network to meet the power requirement in D2D communication for better resource allocation in upcoming 5G technology is required. The proposed approach outplays the older power distribution approach using MATLAB simulation. The optimal allocation of resources and spectrum can enhance the system throughput with resource allocation techniques. D2D nodes not only perform better frequency resources but also provide better energy-efficient communication by using power control.

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Mai Elshreay, Eman Serag, Hussien M. Elattar, and Hesham Elbadawy. "D2D Matching Techniques for Resource Allocation Management and Power Control Management in B5G/6G." Journal of Advanced Research in Applied Sciences and Engineering Technology 43, no. 1 (April 9, 2024): 17–32. http://dx.doi.org/10.37934/araset.43.1.1732.

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The upcoming 6G network is expected to have a great integration of Device-to-Device (D2D) technology. One of the key advantages of D2D technology is its potential to minimize the load on cellular base stations, which can help extend the lifespan of cellular network infrastructure. D2D multicast communication can meet the growing demand for multimedia content in local area services by maximizing energy efficiency and device lifespan by reusing the same resources for the cellular network. In this paper, we investigate an efficient resource allocation scheme by using the One-To-Many Gale Shapley pairing algorithm (GSA) for efficient allocation and power optimization. We propose a joint optimization approach for D2MD clusters that considers the Signal-to-Interference Noise ratio and energy levels of devices' batteries. The problem is optimized, so it is divided into two convex sub-problems. In the first sub-problem, power allocation is performed for each candidate cluster-head (CH)and cellular user to maximize energy efficiency using the Dinkelbach matching algorithm. In the second sub-problem, the One-to-Many Gale Shapley matching algorithm is used to optimize resource allocation and cluster-head selection to select the Cluster Nodes (CRns) to form the cluster. Numerous investigations show that the suggested technique maintains QoS and minimal battery power requirements while increasing cluster-head longevity and energy efficiency (EE) in D2D applications.

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Sarkar, Firoz, Nahid Akter, and Md Maruf Hasan. "An Efficient Multiplexing Technique For 5G Wireless Network: Analysis and Selection." Journal of Information Technology 11 (July 31, 2023): 33–41. http://dx.doi.org/10.59185/25fjcv76.

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The 5G cellular communication technology is intended to base on the present Long-TermEvolution (LTE) 4G networks with adopted features. Features of low power consumption and theconstant power level should be the major requirement for an efficient channel allocation method. In thisresearch work, an analysis was performed on multiplexing techniques: Orthogonal Frequency DivisionMultiplexing (OFDM), Generalized Frequency Division Multiplexing (GFDM), Universal FilteredMulticarrier (UFMC) and Filter-Bank Multicarrier (FBMC). Smooth power level had been analyzedaccording to Power Spectral Density (PSD) function vs normalized frequency using AWGN channelmodel. The simulation was done by MATLAB 2019 Simulink software for OFDM and GFDM that werefurther compared to existing results of UFMC and FBMC multiplexing techniques. Calculated power forOFDM and GFDM were 32.1mW and 40.23 µW respectively. Comparative results showed the bettersmooth power spectrum, micro-watt power consumption and better hardware configuration for GFDM.That’s why GFDM channel allocation method selection is one step ahead for the next generationmultiplexing technique

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Abbas, Wurood Fadhil, and Bashar Jabbar Hamza. "Optimization of Interference Management andPower Control in (Noma) With (Hetnets) Through Sectoring Techniques." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (July 18, 2024): 77–86. http://dx.doi.org/10.46649/fjiece.v3.2.7a.16.5.2024.

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The development of heterogeneous networks(HetNets), in the dynamic field of wireless communications still depends critically on the improvement of power control and interference management. An original sectoring technique to address the problems with Non-orthogonal Multiple Access (NOMA) systems such power allocation and interference management is presented in this study. It attempts to greatly lower inter-user and co-tier interference, increasing network efficiency and user fairness, by combining a strategic sectoring approach, round-robin channel allocation, and an inverse route loss power allocation framework. In the present work, the Bit Error Rate (BER) performance in a simulated two-tier NOMA-enabled HetNet is analysed using Monte Carlo simulation. The findings show that, especially at higher signal-to-noise ratios (SNR), sectoring considerably lowers BER by around 50% compared to the non-sectoring situation. The results of this work emphasize the need of sectoring in optimizing the efficiency of future wireless network infrastructures and have wide-ranging implications for the deployment of 5G networks and beyond. The findings back up the idea of combining sector-based designs to increase spectrum and energy efficiency, hence increasing the reliability and resilience of future communication systems

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Garus, Jerzy. "Thrust Allocation in Propulsion System of Underwater Robotic Vehicle via Linear Programming." Solid State Phenomena 210 (October 2013): 326–32. http://dx.doi.org/10.4028/www.scientific.net/ssp.210.326.

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This paper focuses on a problem of power distribution in a multi-thrusters propulsion system of an underwater robot. The presented thrust allocation methods are directed towards minimization of energy expenditures necessary to obtain required control. A power/thrust relation is mapped by a linear function. Such approach allows using linear programming (LP) techniques to finding an optimal thrust allocation for demanded propulsive forces and moments. The LP models of thrust distribution are regarded for unconstrained and constrained allocation control.

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Fukuyama, Yoshikazu. "Comparative Studies of Particle Swarm Optimization Techniques for Reactive Power Allocation Planning in Power Systems." IEEJ Transactions on Power and Energy 124, no. 5 (2004): 690–96. http://dx.doi.org/10.1541/ieejpes.124.690.

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30

Fukuyama, Yoshikazu. "Comparative studies of particle swarm optimization techniques for reactive power allocation planning in power systems." Electrical Engineering in Japan 153, no. 1 (2005): 34–41. http://dx.doi.org/10.1002/eej.20135.

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Nawaz, Sarfaraz, Ajay Kumar Bansal, and Mahaveer Prasad Sharma. "Power Loss Minimization in Radial Distribution Networks Using Reconfiguration and DGs." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (September 1, 2017): 583. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp583-592.

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A novel approach is proposed in this paper to achieve the objective of real power loss minimization and voltage profile enhancement. Network reconfiguration and allocation of various DG units are used to meet the objective. Selective particle swarm Optimization (SPSO) and novel analytical techniques are used to solve the problem of network reconfiguration and allocation of DG units simultaneously. A new constant, Power Voltage Sensitivity Constant (PVSC), has been proposed to solve the allocation problem. The formulated mathematical expression (PVSC) determines site and size of DG units. The level of DG penetration is considered in a range of 0–50% of total system load. A novel index is also proposed which incorporates level of DG penetration and % reduction in real power losses. Standard 69 bus system is used to validate the results obtained by proposed hybrid approaches. To show the efficacy and strength of the proposed hybrid approach, it has been compared with various techniques

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Vishalakshi, Vishalakshi, and Gangadhar Shivsharanappa Biradar. "A taxonomy on power optimization techniques for fifth-generation heterogenous non-orthogonal multiple access networks." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (April 1, 2024): 1616. http://dx.doi.org/10.11591/ijece.v14i2.pp1616-1624.

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Non-orthogonal multiple access (NOMA) is an anticipated technology for fifth-generation networks for increasing mass connectivity, spectrum efficiency, user-fairness, and higher capacity. NOMA allows end-clients to share indistinguishable radio resources such as spreading code, subcarrier, and time slots simultaneously. Thus, the main challenge involved in conceptualizing effective NOMA design is selection of resource allocation (i.e., user clustering, power allocation, and quality-of-service (QoS) assurance) algorithms. NOMA can be easily integrated with current fifth-generation multi-access methodologies. In this survey paper, the NOMA methodologies are discussed, and provide an overview of the methodologies and algorithms designed for optimizing power allocation, interference management, and network selection management in the heterogenous multiple carrier NOMA. The survey highlights the current limitation of the existing resource provisioning framework and presents a solution to overcome the current limitation.

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Hamedoon, Syed Muhammad, Jawwad Nasar Chattha, and Muhammad Bilal. "A novel user clustering and efficient resource allocation in non-orthogonal mutliple access for IoT networks." PLOS ONE 19, no. 9 (September 9, 2024): e0309695. http://dx.doi.org/10.1371/journal.pone.0309695.

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Optimal resource allocation is crucial for 5G and beyond networks, especially when connecting numerous IoT devices. In this paper, user clustering and power allocation challenges in the downlink of a multi-carrier NOMA system are investigated, with sum rate as the optimization objective. The paper presents an iterative optimization process, starting with user clustering followed by power allocation of the users. Although the simultaneous transmission for multiple users achieves high system throughput in NOMA, it leads to more energy consumption, which is limited by the battery capacity of IoT devices. Enhancing energy efficiency by considering the QoS requirement is a primary challenge in NOMA-enabled IoT devices. Currently, fixed user clustering techniques are proposed without considering the diversity and heterogeneity of channels, leading to poor throughput performance. The proposed user clustering technique is based on the partial brute force search (P-BFS) method, which reduces complexity compared to the traditional exhaustive search method. After the user clustering, we performed optimal power allocation using the Lagrangian multiplier method with Karush-Kuhn-Tucker (KKT) optimal conditions for each user assigned to a subchannel in each cluster. Lastly, a deep neural network (DNN) based proposed P-BFS scheme is used to reduce resource allocation’s complexity further. The simulation results show a significant improvement in the sum rate of the network.

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Hota, Ambika Prasad, Sivkumar Mishra, Debani Prasad Mishra, and Surender Reddy Salkuti. "Allocating active power loss with network reconfiguration in electrical power distribution systems." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 1 (March 1, 2021): 130. http://dx.doi.org/10.11591/ijpeds.v12.i1.pp130-138.

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This paper presents a branch exchange (BE) based heuristic network reconfiguration technique where, the proposed bus classification strategy remodels dynamically as per the modified topology in order to provide a reconfigured network with minimum loss. Further, for fair allocation of the active power losses, it develops a new active power loss allocation (APLA) technique which eradicates the influence of cross-term analytically from loss formulation without any assumptions and approximations. The effectiveness of the proposed procedure has been investigated against other established methods using a 69-bus radial distribution network (RDN). The results of APLA achieved for original and reconfigured 69-bus RDN are found to be promising and judicious as regard to their load demands and geographical locations. The implementation of present reconfiguration procedure provides a total loss reduction benefit of 55.73% to the utility which highlights the significance of the developed procedure against other established techniques.

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Lin, Jia-Chin, and Chih-Yen Liu. "Power Allocation Adaptation With Coherence Time for Nonorthogonal Multiple Access on Downlink Transmission." International Journal of Interdisciplinary Telecommunications and Networking 13, no. 3 (July 2021): 54–71. http://dx.doi.org/10.4018/ijitn.2021070104.

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Nonorthogonal multiple access (NOMA) methods are expected to improve spectral efficiency or increase sum rate in a recent mobile communication network. The performance of the power-domain NOMA highly relies on the effectiveness of successive interference cancelation (SIC) and power allocation techniques. This paper proposes an adaptive power allocation method according to analytical coherence time of time-varying channels to improve the SIC effectiveness. Simulations show that the proposed technique can improve SIC, therefore reducing error probability without reduction of the sum rate.

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36

K. Mohammed, Raya, and Nasser N. Khamiss. "Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation." Iraqi Journal of Information and Communication Technology 7, no. 1 (May 3, 2024): 23–34. http://dx.doi.org/10.31987/ijict.7.1.243.

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The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.

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Monisha, M., M. Meena, C. Sharanya, and T. Jaya. "Amplify-forward and decode-forward: a novel power allocation technique for MIMO cognitive radio network." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 670. http://dx.doi.org/10.14419/ijet.v7i2.33.15472.

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Nowadays transmitting data through wireless communication channel requires more power than the wired communication channel. In other words any communication through wireless medium will consume more power. Since power is a limited source, it is hard to conserve it or reduce its usage and this leads to the concept of “Efficient power allocation scheme for MIMO (Multi Input Multi Output) cognitive radio” system. The proposed system uses two algorithms namely On/Off Based Scheduling (OOBS) and Selective Based Scheduling (SBS) in Amplify and Forward (AAF) and Decode and Forward (DAF) techniques. OOBS algorithm is used for efficient allocation of the input power to each user’s Signal to Noise Ratio (SNR) value. The proposed algorithm provides better power utilization with low bit error rate (9.53×10-7).

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38

Saad, Hussein, Amr Mohamed, and Tamer ElBatt. "Cooperative Q-learning techniques for distributed online power allocation in femtocell networks." Wireless Communications and Mobile Computing 15, no. 15 (February 25, 2014): 1929–44. http://dx.doi.org/10.1002/wcm.2470.

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Wang, Hongzhi, Meng Zhu, and Mingyue Zhou. "A Robust Power Allocation Scheme in Ad-Hoc Cognitive Radio Networks." International Journal of Online Engineering (iJOE) 13, no. 08 (August 4, 2017): 45. http://dx.doi.org/10.3991/ijoe.v13i08.6906.

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<p>We investigate the secondary users (SUs) power allocation in ad-hoc cognitive radio networks, considering an underlay paradigm where SUs can access the licensed spectrum owned by the primary users (PUs), if the total power to SUs and the interference power threshold from PUs and the signal-to-interference-noise-ratio (SINR) requirements of SUs are guaranteed. While prevalent works about distributed resources allocation algorithms are mainly focused on power allocation under the consideration of many practical limitations, which may not be the case in practice. We assume that SUs also are needed to satisfy heterogenous constraints with different positions by taking the fairness of SUs into account. Moreover, we develop a weighted robust distributed power allocation scheme to guarantees the fairness of SUs. Techniques from robust optimization theory are used to study an objective formulation with infinite numbers of constraints. The optimal solution is obtained by using Lagrange dual method, and the Lagrange multiplier is solved by the sub-gradient update algorithm. The feasibility and availability of our proposed power allocation scheme is confirmed by numerical results.</p>

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40

T K, Sara Kutty, and Hanumanthappa M. "Optimal Water Allocation Using Data Mining Techniques- A Survey." International Journal of Emerging Research in Management and Technology 6, no. 8 (June 25, 2018): 225. http://dx.doi.org/10.23956/ijermt.v6i8.143.

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Water is one of the most precious resources on earth. All living beings depend on water and it is used for agriculture, environment, household, power generation, industries, navigation, recreation etc. The volume of water resources data in the world is increasing day by day and various studies are carried-out on these data for decision making process. To handle this enormous volume of water data, many methods are available, but the most adequate and suitable method for optimal allocation of water data is data mining. It can be used to predict the results for future action related to weather forecasting, climate change, water management, flood controlling, optimal water allocation etc. This survey paper elaborates the theoretical background of data-mining models and highlights the applications in knowledge data discovery from a water resources database, in particularly on optimal water allocation. Application of data-mining to water management is at a developmental stage and very few research works have been carried out on this domain.

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41

Sakthidasan Sankaran, K., and Xiao-Zhi Gao. "An iterative scheme for optimal and energy effective power allocation using multi-scale resource GOA-EM in distributed antenna system." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 2061–72. http://dx.doi.org/10.3233/jifs-210727.

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Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper Optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority.

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42

Bosupally, Dhanadeepika, Vanithasri Muniyamuthu, and Chakravarthy Muktevi. "Congestion Management of Power Systems by Optimal Allocation of FACTS devices using Hybrid Techniques." International Journal of Electrical and Electronics Research 11, no. 2 (May 18, 2023): 299–307. http://dx.doi.org/10.37391/ijeer.110208.

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For system operators, Congestion management is a difficult task as the market’s security and reliability are protected by this methodology. As the magnitude of an electric transmission system is extremely dynamic, limits must be estimated much beforehand, in order to manage the congestion issues at the right time. Flexible AC transmission systems (FACTS) are used to control voltage fluctuation by adjusting the system's real and reactive power. A combination of Improved Remora Optimization (IRO) and Improved Radial Basis Function (IRBF) is used to allocate positions and sizes of the FACTS devices. In this study, Static Synchronous Compensator (STATCOM), Interlink Power Flow Controllers (IPFC) and Unified Power Flow Controllers (UPFC) are among the FACTS devices used. In the proposed hybrid IRO-IRBF technique, following are the functional aims calculated: build-on-expenditure, Line Loading (LL), Total Voltage Deviation (TVD) and real power loss. Additionally, the hybrid IRO-IRBF technique is used to confirm the proper location using the IEEE 30 bus structure. TVD, power loss, installation costs, and line loading are the measurements used to assess the implementation performance of the hybrid IRO-IRBF approach. From the result analysis, the hybrid IRO-IRBF achieved a real power loss of 0.1591 p.u., and TVD of 0.02 p.u., which is lesser than the existing Whale Optimization Algorithm and Mayfly Optimization Algorithm.

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Lei, Tongfei, Saleem Riaz, Hira Raziq, Munira Batool, Feng Pan, and Jianfeng Wang. "A Comparison of Metaheuristic Techniques for Solving Optimal Sitting and Sizing Problems of Capacitor Banks to Reduce the Power Loss in Radial Distribution System." Complexity 2022 (March 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/4547212.

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The losses in the radial distribution system are inevitable which needs to be minimized for the proper transmission of power to the end customers. This problem can be solved by the allocation of capacitor banks at proper locations with appropriate sizing. These allocations need an efficient approach for the performance enhancement of RDS. In this paper, several metaheuristic techniques such as particle swarm optimization (PSO), Harmony search, Bat, Cuckoo, and Grey-wolf (GW) algorithms are employed to find the size of capacitor banks. Loss sensitivity analysis is considered for the indication of candidate buses where a capacitor has to be installed to reduce the total system losses and ultimately increase efficiency. Cost-effectiveness, power loss minimization, and voltage enhancement can be determined and compared for these 5 techniques and are implemented on the IEEE-34 bus system to illustrate the efficacy of each of them. The results show the advantages and drawbacks of the techniques separately. The simulations are carried out in MATLAB.

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44

Patsias, Vasilios, Petros Amanatidis, Dimitris Karampatzakis, Thomas Lagkas, Kalliopi Michalakopoulou, and Alexandros Nikitas. "Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature." Future Internet 15, no. 8 (July 28, 2023): 254. http://dx.doi.org/10.3390/fi15080254.

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Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

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S. Nithya, Et al. "A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 1809–17. http://dx.doi.org/10.17762/ijritcc.v11i10.8757.

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Each other, leading to issues such as interference, limited bandwidth, and varying channel conditions. These challenges require specialized optimization techniques tailored to the wireless environment. In wireless communication networks is to maximize the overall system throughput while ensuring fairness among users and maintaining quality of service requirements. This objective can be achieved through resource allocation optimization, where the available network resources such as bandwidth, power, and time slots are allocated to users in an optimal manner. Optimization-based approaches in wireless resource allocation typically involve formulating the resource allocation problem as an optimization problem with certain constraints.. These techniques provide practical solutions with reduced computational complexity, although they may not guarantee optimality. In summary, optimization-based approaches have been instrumental in studying resource allocation problems in communication networks, including the wireless domain. While techniques from the Internet setting have influenced the understanding of congestion control and protocol design, specific challenges in wireless networks necessitate tailored optimization techniques that account for interference, limited bandwidth, and varying channel conditions. power allocation problem in wireless ad hoc networks Cross-layer optimization refers to the process of jointly optimizing the allocation of resources across different layers of wireless networks, the interactions between different layers become more complex due to the shared medium and time-varying channel conditions. Nash equilibrium, where no user can unilaterally improve its own performance by changing its strategy. Game theory can capture the distributed nature of wireless networks and provide insights into the behavior of users in resource allocation scenarios Additionally, heuristics and approximation algorithms are often employed in wireless resource allocation due to the complexity of the optimization problems involved. In traditional cellular systems, each user is allocated a fixed time slot for transmission, regardless of their channel conditions. However, in opportunistic scheduling. Alternative parameters for “Data rate Ž kbps, Geographic coverage , Service requirements , cost ” Evaluation parameter for “Circuit-switched cell, CDPD, WLAN, Paging, Satellite.” “the first ranking training is obtained with the lowest quality of compensation.”

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Le, Anh-Tu, Minh-Sang Van Nguyen, and Dinh-Thuan Do. "Benefiting wireless power transfer scheme in power domain based multiple access: ergodic rate performance evaluation." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 785–92. http://dx.doi.org/10.11591/eei.v10i2.2215.

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Power domain based multiple access scheme is introduced in this paper, namely Non-orthogonal multiple-access (NOMA). We deploy a wireless network using NOMA together with a wireless power transfer (WPT) scheme for dedicated user over Nakagami-$m$ fading channel. When combined, these promising techniques (NOMA and WPT) improve the system performance in term of ergodic performance at reasonable coefficient of harvested power. However, fixed power allocation factors for each NOMA user can be adjusted at the base station and it further provide performance improvement. We design a new signal frame to deploy a NOMA scheme in WPT which adopts a linear energy harvesting model. The ergodic capacity in such a NOMA network and power allocation factors can be updated frequently in order to achieve a fair distribution among NOMA users. The exact expressions of ergodic capacity for each user is derived. The simulation results show that an agreement between analytic performance and Monte-Carlo simulation can be achieved.

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47

Ko, Kyeongjun, Ilmu Byun, Jungwoo Lee, and Wonjae Shin. "Joint Power Allocation and Scheduling Techniques for BER Minimization in Multiuser MIMO Systems." IEEE Access 9 (2021): 66675–86. http://dx.doi.org/10.1109/access.2021.3074980.

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Almeida, Thales Schuabb, Lucas Eduardo Silva Braga, Leonardo Willer Oliveira, Edimar Jose Oliveira, and Julio Cesar Stacchini Souza. "A Hybrid Meta-Heuristic Approach for Optimal Meter Allocation in Electric Power Distribution Systems." Learning and Nonlinear Models 21, no. 1 (February 28, 2023): 36–46. http://dx.doi.org/10.21528/lnlm-vol21-no1-art3.

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The number of nodes present in Electric Power Distribution Systems (EPDS) is a complicating factor for carrying out the State Estimation (SE) and the choice of allocation of available meters affects the quality of observability obtained by the SE. Thus, it is necessary to use optimization methods that evaluate the positions of meters in the system that can contribute to an optimal SE. Artificial Neural Networks (ANN) can perform SE, processing the information obtained by the available meters in an agile way. Meta-heuristics techniques apply to the optimal allocation problem but can be slow processing. Thus, the work seeks to evaluate the potential of a hybrid method that associates the meta-heuristic technique, Artificial Immune System (AIS), with ANNs for evaluating several allocation options in an agile way to find an optimal solution for the allocation of meters.

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49

Ahrens, Andreas, and Christoph Lange. "Modulation-mode and power assignment in SVD-equalized MIMO systems." Facta universitatis - series: Electronics and Energetics 21, no. 2 (2008): 167–81. http://dx.doi.org/10.2298/fuee0802167a.

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Existing bit loading and transmit power allocation techniques are often optimized for maintaining both a fixed transmit power and a fixed target bit-error rate while attempting to maximize the overall data-rate. However, delay-critical real-time interactive applications, such as voice or video transmission, may require a fixed data rate. In this contribution the number of activated layers in a multiple input multiple output (MIMO) system and the number of bits per symbol along with the appropriate allocation of the transmit power are jointly optimized under the constraint of a given fixed data throughput. Our results show that in order to achieve the best bit-error rate, not necessarily all MIMO layers have to be activated. .

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Nair, Rohith Sunilkumar. "Dynamic Resource Allocation in Cloud Environments." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 417–22. http://dx.doi.org/10.22214/ijraset.2023.53668.

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Abstract: The provisioning and use of computing resources has been completely transformed by cloud computing, which provides previously unheard-of levels of scalability, flexibility, and cost-effectiveness. In order to maximize resource utilizatio and guarantee peak performance in cloud settings, dynamic resource allocation is essential. In order to provide a thorough grasp of the most recent developments in this subject, this survey paper offers an in-depth review of dynamic resource allocation methods in cloud computing. The survey includes a wide range of dynamic resource allocation techniques, including as cost-optimization methods, resource provisioning and scheduling techniques, and load-balancing algorithms. Each method is examined in terms of its advantages, drawbacks, and applicability to particular cloud deployment scenarios. The research also investigates evaluation approaches and performance indicators for evaluating the efficacy of dynamic resource allocation systems. It emphasizes the need for continued research and innovation to address the evolving demands of cloud computing environments.

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