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Journal articles on the topic 'Throughput allocation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Ismail, M. S., Noorhafiza Muhammad, M. I. Hussain, Zuraidah Mohd Zain, and R. Ahmad. "Buffer Allocation Using 6 Steps Oba Method: A Case Study." Applied Mechanics and Materials 815 (November 2015): 287–92. http://dx.doi.org/10.4028/www.scientific.net/amm.815.287.

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This paper utilized a simulation software to determine an optimal buffer allocation for any unpaced production line when a set of buffer quantity is known. The optimal buffer allocation will give the highest throughput rate (productivity) to the production line. From the simulation result, a trend of relation between mean processing time (μ) trend of the stations in the production line and buffer allocation configuration of the buffer slots were studied. It was concluded that for any unpaced production line, the best throughput rate of the production line will be obtained if the buffer allocation configuration follow the mean processing time (μ) trend. By applying this concept, a mathematical relation was formed results in a development of a technique called 6 Steps OBA (optimal buffer allocation) method . In order to validate the result from this 6 Steps OBA method, a case study was carried out in one of the multinational electronics company in Malaysia (Company S). An unpaced and u-unbalanced production line was selected and this line was producing a product which is known as model A. In this production line, a few possible buffer allocations quantity were allocated on each buffer slots including buffer allocation suggested by 6 Steps OBA method. For each buffer allocation possibilities, the actual production line productivity (throughput rate) was recorded and compared to validate that the best line throughput rate was the buffer allocation suggested by the 6 Steps OBA method. From the overall result, it was proven that the 6 Steps OBA method will give a maximum throughput rate to a production line.

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2

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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3

Bonald, Thomas, Léonce Mekinda, and Luca Muscariello. "Fair throughput allocation in Information-Centric Networks." Computer Networks 125 (October 2017): 122–31. http://dx.doi.org/10.1016/j.comnet.2017.05.019.

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4

Tassiulas, Leandros, and Partha P. Bhattacharya. "Allocation of interdependent resources for maximal throughput." Communications in Statistics. Stochastic Models 16, no. 1 (January 2000): 27–48. http://dx.doi.org/10.1080/15326340008807575.

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5

Gao, Sixiao, Toshimitsu Higashi, Toyokazu Kobayashi, Kosuke Taneda, Jose I. U. Rubrico, and Jun Ota. "Buffer Allocation via Bottleneck-Based Variable Neighborhood Search." Applied Sciences 10, no. 23 (November 30, 2020): 8569. http://dx.doi.org/10.3390/app10238569.

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This study addresses the challenging problem of efficient buffer allocation in production lines. Suitable locations for buffer allocation are determined to satisfy the desired throughput, while a suitable balance between solution quality and computation time is achieved. A throughput calculation approach that yields the state probability of production lines is adopted to evaluate the effectiveness of candidate buffer allocation solutions. To generate candidate buffer allocation solutions, an active probability index based on state probability is proposed to rapidly detect suitable locations of buffer allocations. A variable neighborhood search algorithm is used to maintain acceptable solution quality; an additional neighborhood structure is used in the case where no satisfactory solution is generated in the initial neighborhood structure. Extensive numerical experiments demonstrate the efficacy of the proposed approach. The proposed approach can facilitate agile design of production lines in industry by rapidly estimating production line topologies.

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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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7

Le, Van Hoa, Viet Minh Nhat Vo, and Manh Thanh Le. "Throughput-based fair bandwidth allocation in OBS networks." ETRI Journal 40, no. 5 (September 15, 2018): 624–33. http://dx.doi.org/10.4218/etrij.2017-0253.

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8

Hong, Bo, and Viktor Prasanna. "Adaptive Allocation of Independent Tasks to Maximize Throughput." IEEE Transactions on Parallel and Distributed Systems 18, no. 10 (October 2007): 1420–35. http://dx.doi.org/10.1109/tpds.2007.1042.

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9

Maeng, Juhyun, Mwamba Kasongo Dahouda, and Inwhee Joe. "Optimal Power Allocation with Sectored Cells for Sum-Throughput Maximization in Wireless-Powered Communication Networks Based on Hybrid SDMA/NOMA." Electronics 11, no. 6 (March 8, 2022): 844. http://dx.doi.org/10.3390/electronics11060844.

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Wireless-powered communication networks (WPCNs) consist of wireless devices (WDs) that transmit information to the hybrid access point (HAP). In this situation, there is interference among WDs that is considered to be noise and causes information loss because of adjacent signals. Moreover, power is limited and can be lost if transmission distance is long. This paper studies sum-throughput maximization with sectored cells for WPCN. We designed a downlink (DL) energy beamforming by sector based on the hybrid space division multiple access (SDMA) and nonorthogonal multiple access (NOMA) approach to maximize the sum throughput. First, a cell is divided into several sectors, and signals from each sector are transmitted to each antenna of the HAP, so that the signals are not adjacent. Further, the HAP decodes the overlapping information of each sector. Next, power allocation is optimized by sector. To optimize power allocation, a constrained optimization problem is formulated and then converted into a nonconstraint optimization problem using the interior penalty method. The optimal solution derives the maximal value to the problem. Power for each sector is optimally allocated according to this optimal solution. Under this consideration, sum-throughput maximization is performed by optimally allocating DL energy beamforming by sector. We analyzed sum throughput and fairness, and then compared them according to the number of sectors. Performance results show that the proposed optimal power allocation by sector using hybrid SDMA/NOMA outperforms the existing equal power allocation by sector in terms of the sum throughput while fairness is also maintained. Moreover, the performance difference between the hybrid approach and SDMA, which optimally allocates power by sector, was about 1.4 times that of sum throughput on average, and the hybrid approach was dominant. There was also no difference in fairness performance.

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10

Diamantidis, A. C., and C. T. Papadopoulos. "A dynamic programming algorithm for the buffer allocation problem in homogeneous asymptotically reliable serial production lines." Mathematical Problems in Engineering 2004, no. 3 (2004): 209–23. http://dx.doi.org/10.1155/s1024123x04402014.

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In this study, the buffer allocation problem (BAP) in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990), for the performance evaluation (i.e., estimation of throughput) of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP) algorithm for the BAP. The proposed algorithm is applied to production lines where the number of machines is varying from four up to a hundred machines. The proposed algorithm is fast because it reduces the volume of computations by rejecting allocations that do not lead to maximization of the line's throughput. Numerical results are also given for large production lines.

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11

Spinellis, Diomidis D., and Chrissoleon T. Papadopoulos. "Stochastic algorithms for buffer allocation in reliable production lines." Mathematical Problems in Engineering 5, no. 6 (2000): 441–58. http://dx.doi.org/10.1155/s1024123x99001180.

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The allocation of buffers between workstations is a major optimization problem faced by manufacturing systems designers. It entails the determination of optimal buffer allocation plans in production lines with the objective of maximizing their throughput. We present and compare two stochastic approaches for solving the buffer allocation problem in large reliable production lines. The allocation plan is calculated subject to a given amount of total buffer slots using simulated annealing and genetic algorithms. The throughput is calculated utilizing a decomposition method.

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12

Khan, A., J. Ch Fricke, and P. A. Hoeher. "Throughput improvement by reliability-dependent layer allocation in IDM." Electronics Letters 44, no. 16 (2008): 958. http://dx.doi.org/10.1049/el:20081084.

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13

salya, E. kow, D. Sara vanan, and R. Parthi ban. "Energy Aware Resource Allocation for Throughput Maximized IOT Network." International Journal of Computer Trends and Technology 45, no. 2 (March 25, 2017): 59–61. http://dx.doi.org/10.14445/22312803/ijctt-v45p112.

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14

Zhang, Yao, Zhongliang Deng, and Aihua Hu. "Study on the User Density Identification via Improved Whale Optimization Algorithm in Device-to-Device Communication." Complexity 2019 (November 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/8147289.

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The present study proposes a new algorithm for device-to-device (D2D) user density identification in a 5G network based on resource allocation. The method initially established a multiobjective optimization function that calculates system throughput and quality of service (QoS) of D2D users. The optimal resource allocation result of the multiobjective function is obtained via the improved whale optimization algorithm (IWOA). System throughput after resource allocation exhibits a linear relationship with the number of users. Therefore, the D2D user density areas are accurately identified via the throughput value. The simulation result reveals that the accuracy of D2D user density identification reaches 95%.

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15

Hassan, Hammad, Irfan Ahmed, Rizwan Ahmad, Hedi Khammari, Ghulam Bhatti, Waqas Ahmed, and Muhammad Mahtab Alam. "A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System." Sensors 19, no. 16 (August 8, 2019): 3461. http://dx.doi.org/10.3390/s19163461.

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In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors’ names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users’ association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.

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16

Ploumidis, Manolis, Nikolaos Pappas, and Apostolos Traganitis. "Performance of Flow Allocation with Successive Interference Cancelation for Random Access WMNs." Applied Sciences 12, no. 6 (March 10, 2022): 2866. http://dx.doi.org/10.3390/app12062866.

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In this study, we explore the gain that can be achieved by jointly allocating flow on multiple paths and applying successive interference cancelation (SIC), for random access wireless mesh networks with multi-packet reception capabilities. We explore a distributed flow allocation scheme aimed at maximizing average aggregate flow throughput, while also providing bounded delay when SIC is employed. The aforementioned scheme is evaluated both in terms of delay and throughput, and is also compared with other simple flow allocation schemes. We present simulation results from three illustrative topologies. Our results show that the gain for the scheme with SIC, when compared with a variant that treats interference as noise (IAN), can be up to 15.2%, for an SINR threshold value equal to 0.5. For SINR threshold values as high as 2.0 however, SIC does not always result in higher throughput. In some scenarios, the gain of SIC over IAN is insignificant, while in some others treating interference as noise proves to be better. The reason is that, although SIC improves the throughput on a specific link, it also increases the interference imposed on neighboring receivers. We also show that the gain from applying SIC is more profound in cases of a large degree of asymmetry among interfering links.

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17

Wu, Horng Huei, and Cheng Shin Yeh. "A Study of the Bin Inventory Allocation Model for LED-CM Plants." Applied Mechanics and Materials 543-547 (March 2014): 4440–43. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.4440.

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The LED chip manufacturing (LED-CM) is an important process in the LED supply chain. The specification of an order of LED chips is composed by several feasible bins. A bin is determined by a different lightness grade and wavelength. In order to achieve the optimal throughput or minimum inventory, the inventory of feasible bins must be reasonably allocated to orders. LED-CM plants thus confront the issue of allocating the inventory of different bins to different orders. A bin inventory allocation model for LED-CM plants is thus proposed in this paper. This model can get the optimal throughput and minimum inventory.

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18

Zhong, Kaiqi, and Liqun Fu. "Throughput Maximization for the Full-Duplex Two-Way Relay System with Energy Harvesting." Electronics 12, no. 1 (December 21, 2022): 16. http://dx.doi.org/10.3390/electronics12010016.

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The full-duplex technique can improve the transmission capacity of the communication systems, and energy harvesting (EH) is a promising operation to prolong the lifespan of a wireless node by utilizing the radio-frequency signals. In this paper, the throughput performance of a full-duplex two-way energy EH capable relay system is investigated. In particular, a practical EH protocol, named the time-switching-based relaying (TSR) protocol, is used for EH and the decode-and-forward (DF) policy for information transmission. The outage probability is successfully obtained, and the corresponding system throughput for TSR protocol can be derived by it. The derived throughput is a function of different system parameters, including the time-switching (TS) ratio, power allocation ratio, and the length of the communication time slot. Meanwhile, the throughput is used to characterize a joint time and power allocation scheme for the system, and we aim to find the optimal time and power allocation to achieve the optimal throughput. Due to the existence of three variables and the integral form of throughput expression, an optimization for the throughput is difficult. However, a modified simulated annealing-based search (SABS) algorithm can be used to optimize the throughput. The modified SABS algorithm overcomes being highly impacted by the initial point, and derives the optimal solution fast. Simulation results show that the analytical throughput expression is related with the TS ratio, power allocation ratio, and the length of the communication time slot. The analytical curve of the throughput matches with the simulated one well, which shows that the obtained analytical system throughput for the TSR protocol is valid. Meanwhile, the proposed modified SABS algorithm could be used to derive accurate throughput when SNR is higher than 10 dB.

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19

Sun, Danfeng, Yanlong Xi, Abdullah Yaqot, Horst Hellbrück, and Huifeng Wu. "Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things." Sensors 23, no. 2 (January 13, 2023): 951. http://dx.doi.org/10.3390/s23020951.

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The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).

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20

Bai, Xiangwei, Qing Li, and Yanqun Tang. "A Low-Complexity Resource Allocation Algorithm for Indoor Visible Light Communication Ultra-Dense Networks." Applied Sciences 9, no. 7 (April 2, 2019): 1391. http://dx.doi.org/10.3390/app9071391.

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In this paper, a low-complexity multi-cell resource allocation algorithm with a near-optimal system throughput is proposed to resolve the conflict between the high system throughput and low complexity of indoor visible light communication ultra-dense networks (VLC-UDNs). First, by establishing the optimal model of the resource allocation problem in each cell, we concluded that the problem is a convex optimization problem. After this, the analytic formula of the normalized scaling factor of each terminal for resource allocation is derived after reasonable approximate treatment. The resource allocation algorithm is subsequently proposed. Finally, the complexity analysis shows that the proposed algorithm has polynomial complexity, which is lower than the classical optimal inter-point method. The simulation results show that the proposed method achieves a improvement of 57% in performance in terms of the average system throughput and improvement of 67% in performance in terms of the quality of service (QoS) guarantee against the required data rate proportion allocation (RDR-PA) method.

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21

Kłos, Sławomir, and Peter Trebuna. "Using Computer Simulation Method to Improve Throughput of Production Systems by Buffers and Workers Allocation." Management and Production Engineering Review 6, no. 4 (December 1, 2015): 60–69. http://dx.doi.org/10.1515/mper-2015-0037.

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Abstract This paper proposes the application of computer simulation methods to support decision making regarding intermediate buffer allocations in a series-parallel production line. The simulation model of the production system is based on a real example of a manufacturing company working in the automotive industry. Simulation experiments were conducted for different allocations of buffer capacities and different numbers of employees. The production system consists of three technological operations with intermediate buffers between each operation. The technological operations are carried out using machines and every machine can be operated by one worker. Multi-work in the production system is available (one operator operates several machines). On the basis of the simulation experiments, the relationship between system throughput, buffer allocation and the number of employees is analyzed. Increasing the buffer capacity results in an increase in the average product lifespan. Therefore, in the article a new index is proposed that includes the throughput of the manufacturing system and product life span. Simulation experiments were performed for different configurations of technological operations.

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22

He, Ji-Ai, Lu Jia, Lei Xu, and Wei Chen. "Throughput Maximization for Multiple D2D Group Communications Underlaying Cellular Networks." Wireless Communications and Mobile Computing 2020 (October 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/8833579.

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In the scenario where multiple device-to-device (D2D) users and cellular users coexist, the large number of D2D users not only results in the shortage of spectrum resources but also brings interference to the communication of cellular users. In this paper, we establish a clustering model centered on cellular users and propose a resource allocation algorithm based on a D2D clustering model. On the basis of ensuring the throughput requirements of cellular users, this algorithm reconstructs various matrices in the graph theory through probability models, sets the priority of D2D pairs, and maximizes the number of user accesses under interference tolerance. Besides, on the optimization of the number of users, we adopt the Rubinstein game model to adjust the game order according to the priority and optimize the bandwidth allocation mode, so as to improve the overall throughput of the network. Simulation results show that the proposed algorithm can increase the number of users and the network throughput, while shortening the spectrum allocation time by more than half.

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23

Raman, Y. S. V., Dr S. Sri Gowri, and Dr B. Prabhakara Rao. "Performance Enhancement of Spectral efficiency and throughput with Distributed Dynamic channel allocation using genetic algorithm." International Journal of Electrical and Electronics Research 3, no. 3 (September 30, 2015): 54–57. http://dx.doi.org/10.37391/ijeer.030303.

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The efficiency associated with mobile systems will depend on a good channel allocation method. Some sort of genetic algorithm (GA) dependent on distributed dynamic channel allocation model is offered. Mobile service stations (MSS) help to make every one of the channel allocation decision intended for mobile hosts in that cell based on the local information. Proposed model which reuses available channels more efficiently. A reserved pool associated with channels in each and every cell to make the model fault tolerant which allows any mobile or portable to continue communication with its mobile hosts within the absence of adequate channel within the cell. Therefore, the offered GA intended for distributed dynamic channel allocation to reinforce the spectral efficiency along with throughput of the cell network. Simulation results evaluate the performance of the distributed dynamic channel allocation based on genetic algorithm.

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24

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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Shihabudin Ismail, Mohd, M. I. Hussain, Zuraidah M. Zain, and Noorhafiza Muhd. "A Management Guide for Determination of Optimal Buffer Allocation in Unpaced Production Line Using 6 Steps OBA." Applied Mechanics and Materials 793 (September 2015): 637–41. http://dx.doi.org/10.4028/www.scientific.net/amm.793.637.

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This paper deals with a buffer allocation problem in unpaced μ-unbalanced production line. Unpaced line is referred to a line with workstations act independently and the material is not pulled by demand but in push mode. In this study, the production line is considered unpaced, μ-unbalanced but reliable. Mean processing time (μ) is the most difficult variable to fix constant in an actual production line due to the complexity of assembly process. The unbalance variance will become higher if the assembly process is more complex. The optimal buffer allocation need to be found for this particular type of production line so that it can maximize throughput rate and minimize total number of work-in-progress (WIP) on the line. This paper used a simulation method to simulate the throughput rate by given sets of buffer size. Based on the simulation results, a basic optimal buffer allocation (OBA) concept represented the characteristics of OBA for a production line was summarized. By applying the concept, a development of 6 Steps OBA tool is carried out. This tool could be utilized by a management in allocating the buffer during designing of the line layout.

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26

Armando Giral Ramirez, Diego, Cesar Hernandez, and Fredy Martinez. "Throughput in cooperative wireless networks." Bulletin of Electrical Engineering and Informatics 9, no. 2 (April 1, 2020): 698–706. http://dx.doi.org/10.11591/eei.v9i2.2025.

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Cognitive radio networks emerge as a solution to fixed allocation issues and spectrum scarcity through the dynamic access to spectrum. In cognitive networks, users must make intelligent decisions based on spectrum variation and actions taken by other users. Under this dynamic, cooperative systems can significantly improve quality of service parameters. This article presents the comparative study of the multi-criteria decision-making algorithms SAW and FFAHP through four levels of cooperation (10%, 20%, 50%, 80% y 100%) established between secondary users. The results show the performance evaluation obtained through of simulations and experimental measurements. The analysis is carried out based on throughput, depending on the class of service and the type of traffic.

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27

Chen, Dong, Xiang Li, Li Ping Su, and Jin Liang. "Enhanced Power Allocation and Relay Selection Scheme in Two-Way Relaying Cognitive Radio Networks." Applied Mechanics and Materials 397-400 (September 2013): 1979–83. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1979.

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This paper addresses the enhanced power allocation (PA) and relay selection scheme (RS) in two-way relaying cognitive radio networks consisting of multiple user-pairs and multiple relays. In order to reduce the computational complexity for practical scenario, we propose a branch and bound based (BnB-based) power allocation and relay selection scheme and a greedy power allocation and relay selection scheme to maximize the system throughput. Simulation results show that the proposed BnB-based PA and RS scheme can achieve better tradeoff of system throughput and complexity.

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28

HUO, Zhisheng, Limin XIAO, Zhenxue HE, Xiaoling RONG, and Bing WEI. "Dynamic Throughput Allocation among Multiple Servers for Heterogeneous Storage System." IEICE Transactions on Information and Systems E102.D, no. 9 (September 1, 2019): 1731–39. http://dx.doi.org/10.1587/transinf.2018edp7431.

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29

Jiao, Jian, Shaohua Wu, Yunyu Sun, Ye Wang, and Qinyu Zhang. "Power Allocation Optimization of Multibeam High-Throughput Satellite Communication Systems." Journal of Communications and Information Networks 4, no. 1 (March 2019): 33–41. http://dx.doi.org/10.23919/jcin.2019.8916644.

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30

Rodriguez, Ricardo J., and Javier Campos. "On Throughput Approximation of Resource-Allocation Systems by Bottleneck Regrowing." IEEE Transactions on Control Systems Technology 27, no. 1 (January 2019): 370–77. http://dx.doi.org/10.1109/tcst.2017.2768512.

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31

Roumeliotis, Anargyros J., Charilaos I. Kourogiorgas, and Athanasios D. Panagopoulos. "Optimal Dynamic Capacity Allocation for High Throughput Satellite Communications Systems." IEEE Wireless Communications Letters 8, no. 2 (April 2019): 596–99. http://dx.doi.org/10.1109/lwc.2018.2881693.

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32

YAMASHITA, HIDEAKI, and TAYFUR ALTIOK. "Buffer capacity allocation for a desired throughput in production lines." IIE Transactions 30, no. 10 (October 1998): 883–91. http://dx.doi.org/10.1080/07408179808966542.

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Lau, Mark S. K., Wuyi Yue, and Li Ping. "Equal power allocation of IDMA systems: Feasibility, optimality, and throughput." IEEE Transactions on Wireless Communications 8, no. 2 (February 2009): 746–56. http://dx.doi.org/10.1109/twc.2009.070744.

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Guo, Jia, and Chenyang Yang. "Impact of Prediction Errors on High Throughput Predictive Resource Allocation." IEEE Transactions on Vehicular Technology 69, no. 9 (September 2020): 9984–99. http://dx.doi.org/10.1109/tvt.2020.3004552.

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Ferdosian, Nasim, Mohamed Othman, Borhanuddin Mohd Ali, and Kweh Yeah Lun. "Throughput-aware Resource Allocation for QoS Classes in LTE Networks." Procedia Computer Science 59 (2015): 115–22. http://dx.doi.org/10.1016/j.procs.2015.07.344.

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Di, Sheng, and Cho-Li Wang. "Decentralized proactive resource allocation for maximizing throughput of P2P Grid." Journal of Parallel and Distributed Computing 72, no. 2 (February 2012): 308–21. http://dx.doi.org/10.1016/j.jpdc.2011.10.010.

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Hyang-Won Lee, E. Modiano, and Long Bao Le. "Distributed Throughput Maximization in Wireless Networks via Random Power Allocation." IEEE Transactions on Mobile Computing 11, no. 4 (April 2012): 577–90. http://dx.doi.org/10.1109/tmc.2011.58.

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Banchs, Albert, Pablo Serrano, and Huw Oliver. "Proportional fair throughput allocation in multirate IEEE 802.11e wireless LANs." Wireless Networks 13, no. 5 (June 15, 2006): 649–62. http://dx.doi.org/10.1007/s11276-006-6972-9.

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Ji, Bo, Xiaojun Lin, and Ness B. Shroff. "Advances in Multi-Channel Resource Allocation: Throughput, Delay, and Complexity." Synthesis Lectures on Communication Networks 9, no. 1 (November 9, 2016): 1–130. http://dx.doi.org/10.2200/s00739ed1v01y201610cnt017.

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Li, Zhuo, Song Guo, Deze Zeng, Ahmed Barnawi, and Ivan Stojmenovic. "Joint Resource Allocation for Max-Min Throughput in Multicell Networks." IEEE Transactions on Vehicular Technology 63, no. 9 (November 2014): 4546–59. http://dx.doi.org/10.1109/tvt.2014.2317235.

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Gao, Yayu, and Lin Dai. "Optimal Downlink/Uplink Throughput Allocation for IEEE 802.11 DCF Networks." IEEE Wireless Communications Letters 2, no. 6 (December 2013): 627–30. http://dx.doi.org/10.1109/wcl.2013.082813.130520.

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Accongiagioco, Giovanni, Matteo Andreozzi, Daniele Migliorini, and Giovanni Stea. "Throughput-optimal resource allocation in LTE-Advanced with distributed antennas." Computer Networks 57, no. 18 (December 2013): 3997–4009. http://dx.doi.org/10.1016/j.comnet.2013.10.002.

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Sastry, Anitha S., and Akhila S. "Resource Allocation using Dynamic Fractional Frequency Reuse." International Journal of Wireless Networks and Broadband Technologies 6, no. 1 (January 2017): 34–44. http://dx.doi.org/10.4018/ijwnbt.2017010103.

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This article describes how a multi user cellular system insists on having increase in the spectral efficiency for the number of users and base stations. As far as cellular structures are concerned, the users at the edges experience inter cellular interference (ICI) than the users at the cell center. This is due to lack of resource allocation at cell edges. To improve the throughput at the edges a technique called Fractional Frequency Reuse (FFR) is employed. This article explores the Dynamic FFR(DFFR) in OFDMA system to improve the overall throughput.

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Yang, Jie, Jiajia Zhu, and Ziyu Pan. "A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System." Future Internet 14, no. 9 (August 31, 2022): 261. http://dx.doi.org/10.3390/fi14090261.

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Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power allocation problem in NOMA system is studied; the problem is decoupled into the intra cluster power allocation problem and the inter cluster power allocation problem. The nonconvex optimization problem is solved by the continuous convex approximation (SCA) method, and an intra and inter cluster power iterative allocation algorithm with fairness constrained is proposed to maximize the total throughput. Simulation results show that the proposed algorithm can take into account intra cluster, inter cluster, and system fairness, and maximize the system throughput on the premise of fairness.

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T., Praveena, and Nagaraja G. S. "Social-sine cosine algorithm-based cross layer resource allocation in wireless network." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 458. http://dx.doi.org/10.11591/ijece.v11i1.pp458-470.

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Cross layer resource allocation in the wireless networks is approached traditionally either by communications networks or information theory. The major issue in networking is the allocation of limited resources from the users of network. In traditional layered network, the resource are allocated at medium access control (MAC) and the network layers uses the communication links in bit pipes for delivering the data at fixed rate with the occasional random errors. Hence, this paper presents the cross-layer resource allocation in wireless network based on the proposed social-sine cosine algorithm (SSCA). The proposed SSCA is designed by integrating social ski driver (SSD) and sine cosine algorithm (SCA). Also, for further refining the resource allocation scheme, the proposed SSCA uses the fitness based on energy and fairness in which max-min, hard-fairness, proportional fairness, mixed-bias and the maximum throughput is considered. Based on energy and fairness, the cross-layer optimization entity makes the decision on resource allocation to mitigate the sum rate of network. The performance of resource allocation based on proposed model is evaluated based on energy, throughput, and the fairness. The developed model achieves the maximal energy of 258213, maximal throughput of 3.703, and the maximal fairness of 0.868, respectively.

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Su, Li Ping, Dong Chen, and Wei Hua Huang. "Semi-Distributed Power and Subcarrier Allocation in LTE Hetnet System." Advanced Materials Research 760-762 (September 2013): 141–46. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.141.

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In order to provide the large coverage and high data rate in future system, more and more low power nodes (LPNs) e.g., pico nodes, relay nodes, etc., are introduced into the LTE heterogeneous network (HetNet). The resource allocation and optimization scheme is the key issue in Hetnet system since the interference environment is quite complicated compared with the traditional homogenous networks. In this paper, the semi-distributed power and subcarrier allocation scheme is proposed, which aims to achieve the good performance tradeoff between the macro eNBs and the pico nodes. One novel utility function is investigated taking both macro edge UEs and pico edge UEs into account based on limited average data rate information exchanges between macro eNB and pico eNBs. The interference for the edge UEs can be coordinated efficiently due to the dynamic power and subcarrier allocations. Compared with the proportional fairness (PF) scheduling algorithm, the simulation results show that the proposed semi-distributed scheme can achieve about 8% throughput gain in average cell throughput. Furthermore, the pico edge UEs, and macro edge UEs of the proposed scheme can achieve 17%, 12% capacity gain, respectively.

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Varade, Pradip, Akanksha Wabale, Ravinder Yerram, and Rupesh Jaiswal. "Throughput Maximization of Cognitive Radio Multi Relay Network with Interference Management." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2230. http://dx.doi.org/10.11591/ijece.v8i4.pp2230-2238.

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<span>In this paper, an Orthogonal Frequency Division Multiplexing (OFDM) based cognitive multi relay network is investigated to maximize the transmission rate of the cognitive radio (CR) with enhanced fairness among CR users with interference to the primary users (PUs) being managed below a certain threshold level. In order to improve the transmission rate of the CR, optimization of the subcarrier pairing and power allocation is to be carried out simultaneously. Firstly joint optimization problem is formulated and Composite Genetic and Ordered Subcarrier Pairing (CGOSP) algorithm is proposed to solve the problem. The motivation behind merging genetic and OSP algorithm is to reduce the complexity of Genetic Algorithm (GA). Further, to have a fair allocation of resources among CR users, the Round Robin allocation method is adopted so as to allocate subcarrier pairs to relays efficiently. The degree of fairness of the system is calculated using Jain’s Fairness Index (JFI). Simulation results demonstrate the significant improvement in transmission rate of the CR, low computational complexity and enhanced fairness.</span>

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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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Shah, Bhavin, and Vivek Khanzode. "Designing a lean storage allocation policy for non-uniform unit loads in a forward-reserve model." Journal of Enterprise Information Management 31, no. 1 (February 12, 2018): 112–45. http://dx.doi.org/10.1108/jeim-01-2017-0018.

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Purpose The contemporary e-tailing marketplace insists that distribution centers are playing the roles of both wholesalers and retailers which require different storage-handling load sizes due to different product variants. To fulfill piecewise retail orders, a separate small size-fast pick area is design called “forward buffer” wherein pallets are allocated from reserve area. Due to non-uniform pallets, the static allocation policy diminishes forward space utilization and also, more than practically required buffer size has been identified as wastage. Thus, dynamic storage allocation policy is required to design for reducing storage wastage and improving throughput considering non-uniform unit load sizes. The purpose of this paper is to model such policy and develop an e-decision support system assisting enterprise practitioners with real-time decision making. Design/methodology/approach The research method is developed as a dynamic storage allocation policy and mathematical modeled as knapsack-based heuristics. The execution procedure of policy is explained as an example and tested with case-specific data. The developed model is implemented as a web-based support system and tested with rational data instances, as well as overcoming prejudices against single case findings. Findings The provided model considers variable size storage-handling unit loads and recommends number of pallets allocations in forward area reducing storage wastes. The algorithm searches and suggests the “just-right” amount of allocations for each product balancing existing forward capacity. It also helps to determine “lean buffer” size for forward area ensuring desired throughput. Sensitivity and buffer performance analysis is carried out for Poisson distributed data sets followed by research synthesis. Practical implications Warehouse practitioners can use this model ensuring a desired throughput level with least forward storage wastages. The model driven e-decision support system (DSS) helps for effective real-time decision making under complicated business scenarios wherein products are having different physical dimensions. It assists the researchers who would like to explore the emerging field of “lean” adoption in enterprise information and retail-distribution management. Originality/value The paper provides an inventive approach endorsing lean thinking in storage allocation policy design for a forward-reserve model. Also, the developed methodology incorporating features of e-DSS along with quantitative modeling is an inimitable research contribution justifying rational data support.

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Sparaggis, Panayotis D., and Wei-Bo Gong. "Optimal buffer allocation in a two-stage queueing system." Journal of Applied Probability 30, no. 2 (June 1993): 478–82. http://dx.doi.org/10.2307/3214858.

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We study the buffer allocation problem in a two-stage cyclic queueing system. First, we show that transposing the number of buffers assigned to each queue does not affect the throughput. Second, we prove that the optimal buffer allocation scheme, in the sense of maximizing the system's throughput, is the one for which the absolute difference between the number of buffers, assigned to each queue, is minimized, i.e., it becomes either 0 or 1. This optimal allocation is insensitive to the general-type service-time distributions. These two distributions may be different and service times may even be correlated.

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