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 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 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.

A Novel User Grouping Algorithm for Downlink NOMA

2021 ◽  
Author(s):  
Navideh Ghafouri Jeshvaghani ◽  
Naser Movahhedinia ◽  
Mohammad Reza Khayyambashi
Keyword(s):  
Interference Cancellation ◽  
Multiple Access ◽  
Spectrum Efficiency ◽  
System Capacity ◽  
Substantial Impact ◽  
User Grouping ◽  
Radio Access ◽  
Power Domain ◽  
Rate Maximization ◽  
Sum Rate

Abstract Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for resource allocation improvement in the 5th generation of cellular networks. Compared to orthogonal multiple access techniques (OMA), NOMA offers extra benefits, including greater spectrum efficiency which is provided through multiplexing users in the transmission power domain while using the same spectrum resources non-orthogonally. Even though NOMA uses Successive Interference Cancellation (SIC) to repeal the interference among users, user grouping has shown to have a substantial impact on its performance. This prformance improvement can appear in different parameters such as system capacity, rate, or the power consumption. In this paper, we propose a novel user grouping scheme for sum-rate maximization which increases the sum-rate up to 25 percent in comparison with two authenticated recent works. In addition to being matrix-based and having a polynomial time complexity, the proposed method is also able to cope with users experiencing different channel gains and powers in different sub-bands.

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.

scholarly journals An Efficient Resource Allocation Algorithm for Device-To-Device Communications

2019 ◽  
Vol 9 (18) ◽  
pp. 3816 ◽  
Author(s):  
Saraereh ◽  
Mohammed ◽  
Khan ◽  
Rabie ◽  
Affess
Keyword(s):  
Resource Allocation ◽  
Optimal Algorithm ◽  
Search Algorithm ◽  
Low Complexity ◽  
System Capacity ◽  
Total System ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Device To Device ◽  
Total Capacity

In order to solve the problem of interference and spectrum optimization caused by D2D (device-to-device) communication multiplexing uplink channel of heterogeneous cellular networks, the allocation algorithm based on the many-to-one Gale-Shapley (M21GS) algorithm proposed in this paper can effectively solve the resource allocation problem of D2D users multiplexed cellular user channels in heterogeneous cellular network environments. In order to improve the utilization of the wireless spectrum, the algorithm allows multiple D2D users to share the channel resources of one cellular user and maintains the communication service quality of the cellular users and D2D users by setting the signal to interference and noise ratio (SINR) threshold. A D2D user and channel preference list are established based on the implemented system’s capacity to maximize the system total capacity objective function. Finally, we use the Kuhn–Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. The MATLAB simulation is used to compare and analyze the total system capacity of the proposed algorithm, the resource allocation algorithm based on the delay acceptance algorithm, the random resource allocation algorithm and the optimal exhaustive search algorithm, and the maximum allowable access for D2D users. The simulation results show that the proposed algorithm has fast convergence and low complexity, and the total capacity is close to the optimal algorithm.

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 Two-Stage Precoding Based on Overlapping User Grouping Approach in IoT-Oriented 5G MU-MIMO Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Djordje B. Lukic ◽  
Goran B. Markovic ◽  
Dejan D. Drajic
Keyword(s):  
Computational Complexity ◽  
Mimo Systems ◽  
Mimo System ◽  
Optimal Solution ◽  
System Capacity ◽  
User Selection ◽  
Coverage Problem ◽  
Two Stage ◽  
User Grouping ◽  
And Performance

Downlink transmission techniques for multiuser (MU) multiple-input multiple-output (MIMO) systems have been comprehensively studied during the last two decades. The well-known low complexity linear precoding schemes are currently deployed in long-term evolution (LTE) networks. However, these schemes exhibit serious shortcomings in scenarios when users’ channels are strongly correlated. The nonlinear precoding schemes show better performance, but their complexity is prohibitively high for a real-time implementation. Two-stage precoding schemes, proposed in the standardization process for 5G new radio (5G NR), combine these two approaches and present a reasonable trade-off between computational complexity and performance degradation. Before applying the precoding procedure, users should be properly allocated into beamforming subgroups. Yet, the optimal solution for user selection problem requires an exhaustive search which is infeasible in practical scenarios. Suboptimal user grouping approaches have been mostly focused on capacity maximization through greedy user selection. Recently, overlapping user grouping concept was introduced. It ensures that each user is scheduled in at least one beamforming subgroup. To the best of our knowledge, the existing two-stage precoding schemes proposed in literature have not considered overlapping user grouping strategy that solves user selection, ordering, and coverage problem simultaneously. In this paper, we present a two-stage precoding technique for MU-MIMO based on the overlapping user grouping approach and assess its computational complexity and performance in IoT-oriented 5G environment. The proposed solution deploys two-stage precoding in which linear zero forcing (ZF) precoding suppresses interference between the beamforming subgroups and nonlinear Tomlinson-Harashima precoding (THP) mitigates interuser interference within subgroups. The overlapping user grouping approach enables additional capacity improvement, while ZF-THP precoding attains balance between the capacity gains and suffered computational complexity. The proposed algorithm achieves up to 45% higher MU-MIMO system capacity with lower complexity order in comparison with two-stage precoding schemes based on legacy user grouping strategies.

scholarly journals The research of power allocation algorithm with lower computational complexity for non-orthogonal multiple access

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ying Lin ◽  
Suoping Li ◽  
Kejun Jia ◽  
Kathryn L. Kingsley
Keyword(s):  
Computational Complexity ◽  
Power Allocation ◽  
Multiple Access ◽  
Signal To Noise Ratio ◽  
System Capacity ◽  
High Signal ◽  
Allocation Algorithm ◽  
Low Snr ◽  
High Spectral Efficiency ◽  
Noise Ratio

Abstract Non-orthogonal multiple access (NOMA) has very high spectral efficiency and system capacity. NOMA has become one of the most competitive access solutions in 5G systems. In this article, the principle of NOMA is discussed first. Then, the NOMA system capacity optimisation problems are studied. Signal to interference plus noise ratio (SINR) is an important factor which affects the system capacity. The SINR of current user n is only related to the power allocated to users n+1 to N with high signal-to-noise ratio (SNR) but not interfered by users with low SNR. Therefore, a tree topology power allocation (TTPA) algorithm is introduced. When users are allocated to each layer of the tree structure, the current power allocation of each layer will not be affected by the previous layer. Through theoretical analysis, TTPA can achieve the same performance as the full search power allocation algorithm; however, its computational complexity is reduced from exponential to constant. It can be seen from the numerical simulation results that the proposed algorithm can achieve higher system capacity and has lower computational complexity.

scholarly journals Optimization of Power Allocation for Multiusers in Multi-Spot-Beam Satellite Communication Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Heng Wang ◽  
Aijun Liu ◽  
Xiaofei Pan ◽  
Jianfei Yang
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
Communication Systems ◽  
Satellite Communication ◽  
Utilization Efficiency ◽  
System Capacity ◽  
Total System ◽  
Allocation Algorithm ◽  
Satellite Platform ◽  
Spot Beam

In recent years, multi-spot-beam satellite communication systems have played a key role in global seamless communication. However, satellite power resources are scarce and expensive, due to the limitations of satellite platform. Therefore, this paper proposes optimizing the power allocation of each user in order to improve the power utilization efficiency. Initially the capacity allocated to each user is calculated according to the satellite link budget equations, which can be achieved in the practical satellite communication systems. The problem of power allocation is then formulated as a convex optimization, taking account of a trade-off between the maximization of the total system capacity and the fairness of power allocation amongst the users. Finally, an iterative algorithm based on the duality theory is proposed to obtain the optimal solution to the optimization. Compared with the traditional uniform resource allocation or proportional resource allocation algorithms, the proposed optimal power allocation algorithm improves the fairness of power allocation amongst the users. Moreover, the computational complexity of the proposed algorithm is linear with both the numbers of the spot beams and users. As a result, the proposed power allocation algorithm is easy to be implemented in practice.

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