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.

Relay Selection Scheme for Cooperative Communication Systems in Fixed Decode-and-Forward Mode

2014 ◽  
Vol 6 (2) ◽  
pp. 68-77
Author(s):  
Jyh-Horng Wen ◽  
Jheng-Sian Li ◽  
Hsiang-Shan Hou ◽  
Cheng-Ying Yang
Keyword(s):  
Cooperative Communication ◽  
System Performance ◽  
Communication Systems ◽  
Relay Selection ◽  
Spatial Diversity ◽  
Search Method ◽  
Exhaustive Search ◽  
Decode And Forward ◽  
Selection Scheme ◽  
Relay Stations

Cooperative system is a tendency in the future communications because it provides a spatial diversity to improve the system performance. This work considers the cooperative communication systems in Fixed DF Mode. The scenario includes multiple source stations, multiple relay stations and multiple destination stations. For the whole system, the maximum throughput approaching is the major purpose. Hence, to select the relay stations for signal transmission could be the important scheme to achieve the optimal system performance. With the exhaustive search method, easily to realize, the optimal selection scheme could be found with a highly complicated calculation. In order to reduce the computational complexity, a relay selection scheme is proposed. With different situations of the communication systems, the performances evaluations obtained with both the proposed algorithm and the exhaustive search method are given for comparison. It shows the proposed algorithm could provide a solution approaches to the optimal one. It could apply the proposed scheme to the practical without a delay because of long-time calculation.

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 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 Improved Pilot Selection for Channel Estimation of Polar Codes in Wireless 5G

2019 ◽  
Vol 9 (2) ◽  
pp. 4978-4983
Keyword(s):  
Channel Estimation ◽  
Fading Channels ◽  
Communication Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Poor Performance ◽  
Polar Codes ◽  
Selection Scheme ◽  
Polar Code ◽  
Pilot Selection

Fading channels learning about polar codes is great prominence. Knowledge of polar codes is very important while they are applied to the wireless communication systems. In fading Channels the communication through channel estimation which is an essential step for communication. The structure is constructed by a set of information bits for both systematic polar code and non-systematic polar code and the information set recognized frozen bits. In fading channels uneven pilot selection scheme and even pilot selection scheme are two pilot selection schemes are considered for polar codes. There is an improvement in decoding performance of polar codes using these selection schemes. In this choosing of coded symbols treated as pilots is a replacement of insertion of pilots. Polar codes have poor performance in fixed domain. So the EPS selection scheme can be active for tracing or channel estimation. The structure of polar code encoding is acapable structure and pilot selection is grave since whole selections cannot use the existing structure again. By conjoining the above advantages, pilot signals are selected without any addition from outside and insertion of pilot symbols impartial to estimation of the channel. Leveraging this, the DM-BS scheme is applyto multiple input multiple output (MIMO) system in frequency selective fading channel.

scholarly journals Analysis of Computational Complexity and Processing Time Evaluation of the Protocol Stack in 5G New Radio

2020 ◽  
Vol 23 (3) ◽  
pp. 31-37
Author(s):  
Ya. V. Kryukov ◽  
◽  
D. A. Pokamestov ◽  
E. V. Rogozhnikov ◽  
S. A. Novichkov ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Personal Computer ◽  
Communication Systems ◽  
Network Architectures ◽  
Protocol Stack ◽  
Computing Power ◽  
Radio Access ◽  
New Radio ◽  
The Cost ◽  
Time Evaluation

Currently, an active deployment of radio access networks for mobile communication systems 5G New Radio is being observed. The architecture of networks is developing rapidly, where significant part of the functions is performed in a virtual cloud space of a personal computer. The computing power of a personal computer must be sufficient to execute network protocols in real time. To reduce the cost of deploying 5G NR networks, the configuration of each remote computer must be optimally matched to the scale of a particular network. Therefore, an urgent direction of research is the assessment of the execution time of the 5G NR protocol stack on various configurations of computers and the development of a mathematical model for data analysis, approximation of dependencies and making recommendations. In this paper, the authors provide an overview of the main 5G NR network architectures, as well as a description of the methods and tools that can be used to estimate the computational complexity of the 5G NR protocol stack. The final section provides an analysis of the computational complexity of the protocol stack, obtained during the experiments by colleagues in partner institutions.

scholarly journals A Joint Optimization Scheme of Resource Allocation in Downlink NOMA with Statistical Channel State Information

2020 ◽  
Author(s):  
Lei Xu ◽  
Jing Yi Yao ◽  
Jing Cai ◽  
Yu Hong Fang ◽  
Hui Xiao Li
Keyword(s):  
Power Allocation ◽  
Channel State Information ◽  
Communication Systems ◽  
Multiple Access ◽  
Joint Optimization ◽  
System Throughput ◽  
Optimization Scheme ◽  
Channel State ◽  
State Information ◽  
User Grouping

Abstract In a real communication scenario, it is very difficult to obtain the real-time Channel State Information(CSI) accurately, so the communication systems with statistical CSI have been researched. In order to maximize the throughput of the downlink Non-Orthogonal Multiple Access (NOMA) system with statistical CSI, the formula of system throughput is derived at first. Then, according to the combinatorial characteristics of the original optimization problem, it is divided into two subproblems, that is user grouping and power allocation. At last, a joint optimization scheme is proposed. In which, Genetic algorithm is introduced to solve the subproblem of power allocation, and Hungarian algorithm is introduced to solve the subproblem of user grouping. By comparing the ergodic date rate of NOMA users with statistical CSI and perfect CSI, the effectiveness of the statistical CSI sorting is verified. Compared with the Orthogonal Multiple Access (OMA) scheme, the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme, the proposed scheme can effectively improve the system throughput.

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