scholarly journals NOMA resource allocation method in IoV based on prioritized DQN-DDPG network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mengli He ◽  
Yue Li ◽  
Xiaofei Wang ◽  
Zelong Liu
Keyword(s):  
Resource Allocation ◽  
Computational Complexity ◽  
Power Allocation ◽  
Optimization Algorithm ◽  
Power Optimization ◽  
Search Algorithm ◽  
Optimization Methods ◽  
Exhaustive Search ◽  
User Grouping ◽  
Sum Rate

AbstractTo meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.

scholarly journals NOMA Resource Allocation Method in IoV Based on Prioritized DQN-DDPG Network

2021 ◽  
Author(s):  
Mengli He ◽  
Yue Li ◽  
Xiaofei Wang ◽  
Zelong Liu
Keyword(s):  
Resource Allocation ◽  
Computational Complexity ◽  
Power Allocation ◽  
Optimization Algorithm ◽  
Power Optimization ◽  
Search Algorithm ◽  
Exhaustive Search ◽  
System Capacity ◽  
User Grouping ◽  
Sum Rate

Abstract To meet the demands of massive connections in the Internet-of-vehicle (IoV) communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, power multiplexing and successive interference cancellation techniques are utilized at the transmitter and the receiver respectively to increase system capacity, and user grouping and power allocation are two key issues to ensure the performance enhancement. Various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning (DRL) network is utilized to solve the resource allocation problem. In a DRL network, an experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. Different from conventional methods, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the sum rate of the NOMA system. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed to solve the problem of random sampling, where TD-error is used to represent the priority of sample, and the DQN takes samples according to their priorities. In addition, sum tree is used to store the priority to speed up the searching process. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized to complete power allocation tasks for each user. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. While the computational complexity is reduced by 43% and 64% compared with the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively.

scholarly journals Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems

2020 ◽  
Vol 10 (17) ◽  
pp. 5892 ◽  
Author(s):  
Zuhura J. Ali ◽  
Nor K. Noordin ◽  
Aduwati Sali ◽  
Fazirulhisyam Hashim ◽  
Mohammed Balfaqih
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Power Allocation ◽  
Multiple Access ◽  
High Capacity ◽  
Kkt Conditions ◽  
Optimal Power Allocation ◽  
User Pairing ◽  
Optimal Power ◽  
Sum Rate

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.

Comparison of Optimization Algorithms for a Tubular Aspiration Robot for Maximum Coverage in Intracerebral Hemorrhage Evacuation

2017 ◽  
Vol 02 (01) ◽  
pp. 1750004 ◽  
Author(s):  
Josephine Granna ◽  
Yi Guo ◽  
Kyle D. Weaver ◽  
Jessica Burgner-Kahrs
Keyword(s):  
Intracerebral Hemorrhage ◽  
Optimization Algorithm ◽  
Experimental Evaluation ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Exhaustive Search ◽  
Simulated Patient ◽  
Coordinated Motion ◽  
Maximum Coverage ◽  
The Brain

Intracerebral hemorrhage evacuation (ICH) using a tubular aspiration robot promises benefits over conventional approaches to release the pressure of an hemorrhage within the brain. The blood of the hemorrhage is evacuated through preplanned, coordinated motion of a flexible, curved, concentric tube that aspirates from within the hemorrhage. To achieve maximum decompression, the curvature of the inner aspirator tube has to be selected such that its workspace covers the hemorrhage. As the use of multiple aspiration tubes sequentially is advisable, one can perform an exhaustive search over all possible aspiration tube shapes as has been previously proposed in the literature. In this paper, we introduce a new optimization algorithm which is computationally more efficient and thus allows for quick optimization during surgery. To demonstrate its performance and compare it to the previously proposed exhaustive search algorithm, we present experimental evaluation results on 175 simulated patient trials.

scholarly journals An Efficient Resource Allocation Algorithm for OFDM-Based NOMA in 5G Systems

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1399 ◽  
Author(s):  
Omar A. Saraereh ◽  
Amer Alsaraira ◽  
Imran Khan ◽  
Peerapong Uthansakul
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
Multiple Access ◽  
Low Complexity ◽  
System Capacity ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
User Grouping ◽  
Power Domain ◽  
Efficient Resource

Non-orthogonal multiple access (NOMA) has become the key technology in the future 5G wireless networks. It can achieve multi-user multiplexing in the transmit power domain by allocating different power, which can effectively improve the system capacity and spectral efficiency. Aiming at the problem of high computational complexity and improving system capacity in non-orthogonal multiple access (NOMA) based on orthogonal frequency division multiple access (OFDMA) for 5G wireless cellular networks, this paper proposes an improved low complexity radio resource allocation algorithm for user grouping and power allocation optimization. The optimization model is established with the goal of maximizing system capacity. Through the step-by-step optimization idea, the complex non-convex optimization problem is decomposed into two sub-problems to be solved separately. Firstly, all users are grouped based on the greedy method, and then the power allocation is performed on the sub-carriers of the fixed group. Simulation results show that the proposed algorithm has better system capacity than the existing state-of-the-art algorithms and reduced complexity performance.

scholarly journals Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4592
Author(s):  
Osama Abuajwa ◽  
Mardeni Bin Roslee ◽  
Zubaida Binti Yusoff
Keyword(s):  
Resource Allocation ◽  
Simulated Annealing ◽  
Power Allocation ◽  
Mathematical Proof ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Throughput Maximization ◽  
5G Networks ◽  
Throughput Performance ◽  
User Pairing

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.

scholarly journals QoS and QoE aware multi objective resource allocation algorithm for cloud gaming

2021 ◽  
pp. 1-18
Author(s):  
Koné Kigninman Désiré ◽  
Eya Dhib ◽  
Nabil Tabbane ◽  
Olivier Asseu
Keyword(s):  
Resource Allocation ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Fitness Function ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Potential Game ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Level Of Satisfaction

Cloud gaming is an innovative model that congregates video games. The user may have different Quality-of-Experience (QoE), which is a term used to measure a user’s level of satisfaction and enjoyment for a particular service. To guarantee general satisfaction for all users with limited cloud resources, it becomes a major issue in the cloud. This paper leverages a game theory in the cloud gaming model with resource optimization to discover optimal solutions to resolve resource allocation. The Rider-based harmony search algorithm (Rider-based HSA), which is the combination of Rider optimization algorithm (ROA) and Harmony search algorithm (HSA), is proposed for resource allocation to improve the cloud computing system’s efficiency. The fitness function is newly devised considering certain QoE parameters, which involves fairness index, Quantified experience of players (QE), and Mean Opinion Score (MOS). The proposed Rider-based HSA showed better performance compared to Potential game-based optimization algorithm, Proactive resource allocation algorithm, QoE-aware resource allocation algorithm, Distributed algorithm, and Yusen Li et al., with maximal fairness of 0.999, maximal MOS of 0.873, and maximal QE of 1.

scholarly journals User Selection Approach in Multiantenna Beamforming NOMA Video Communication Systems

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1737
Author(s):  
Shu-Ming Tseng ◽  
Shih-Chun Kao
Keyword(s):  
Computational Complexity ◽  
Communication Systems ◽  
Exhaustive Search ◽  
User Selection ◽  
Power Assignment ◽  
Selection Scheme ◽  
Optimal Scheme ◽  
User Grouping ◽  
Optimal Power ◽  
Testing Stage

For symmetric non-orthogonal multiple access (NOMA)/multiple-input multiple-output (MIMO) systems, radio resource allocation is an important research problem. The optimal solution is of high computational complexity. Thus, one existing solution Kim et al. proposed is a suboptimal user selection and optimal power assignment for total data rate maximization. Another existing solution Tseng et al. proposed is different suboptimal user grouping and optimal power assignment for sum video distortion minimization. However, the performance of sub-optimal schemes by Kim et al. and Tseng et al. is still much lower than the optimal user grouping scheme. To approach the optimal scheme and outperform the existing sub-optimal schemes, a deep neural network (DNN) based approach, using the results from the optimal user selection (exhaustive search) as the training data, and a loss function modification specific for NOMA user selection to meet the constraint that a user cannot be in both the strong and weak set, and avoid the post processing online computational complexity, are proposed. The simulation results show that the theoretical peak signal-to-noise ratio (PSNR) of the proposed scheme is higher than the state-of-the-art suboptimal schemes Kim et al. and Tseng et al. by 0.7~2.3 dB and is only 0.4 dB less than the optimal scheme at lower online computational complexity. The online computational complexity (testing stage) of the proposed DNN user selection scheme is 60 times less than the optimal user selection scheme. The proposed DNN-based scheme outperforms the existing suboptimal solution, and slightly underperforms the optimal scheme (exhaustive search) at a much lower computation complexity.

scholarly journals Economic and Ecological Design of Hybrid Renewable Energy Systems Based on a Developed IWO/BSA Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 687
Author(s):  
Mohammed Kharrich ◽  
Salah Kamel ◽  
Rachid Ellaia ◽  
Mohammed Akherraz ◽  
Ali S. Alghamdi ◽  
...  
Keyword(s):  
Optimal Design ◽  
Wind Turbine ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Invasive Weed ◽  
Battery Capacity ◽  
Battery Systems

In this paper, an optimal design of a microgrid including four houses in Dakhla city (Morocco) is proposed. To make this study comprehensive and applicable to any hybrid system, each house has a different configuration of renewable energies. The configurations of these four houses are PV/wind turbine (WT)/biomass/battery, PV/biomass, PV/diesel/battery, and WT/diesel/battery systems. The comparison factor among these configurations is the cost of energy (COE), comparative index, where the load is different in the four houses. Otherwise, the main objective function is the minimization of the net present cost (NPC), subject to several operating constraints, the power loss, the power generated by the renewable sources (renewable fraction), and the availability. This objective function is achieved using a developed optimization algorithm. The main contribution of this paper is to propose and apply a new optimization technique for the optimal design of a microgrid considering different economic and ecological aspects. The developed optimization algorithm is based on the hybridization of two metaheuristic algorithms, the invasive weed optimization (IWO) and backtracking search algorithm (BSA), with the aim of collecting the advantages of both. The proposed hybrid optimization algorithm (IWO/BSA) is compared with the original two optimization methods (IWO and BSA) as well as other well-known optimization methods. The results indicate that PV/biomass and PV/diesel/battery systems have the best energy cost using the proposed IWO/BSA algorithm with 0.1184 $/kWh and 0.1354 $/kWh, respectively. The best system based on its LCOE factor is the PV/biomass which represents an NPC of 124,689 $, the size of this system is 349.55 m2 of PV area and the capacity of the biomass is 18.99 ton/year. The PV/diesel/battery option has also good results, with a system NPC of 142,233 $, the size of this system is about 391.39 m2 of PV area, rated power of diesel generator about 0.55 kW, and a battery capacity of 12.97 kWh. Otherwise, the proposed IWO/BSA has the best convergence in all cases. It is observed that the wind turbine generates more dumped power, and the PV system is highly suitable for the studied area.

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