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 Coordinated Beamforming with Altruistic Precoding and User Selection for MU-MIMO System

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Gaofeng Cui ◽  
Yanjie Dong ◽  
Weidong Wang ◽  
Yinghai Zhang
Keyword(s):  
Degrees Of Freedom ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
User Selection ◽  
Average Cell ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Local Optimal Solution

Other cell interference (OCI) degrades the achievable capacity of downlink multiuser multiple-input multiple-output (MU-MIMO) systems seriously. Among OCI mitigation schemes, methods that sacrificeξdegrees of freedom to nullify the OCI have been proven to be helpful to improve the cell edge throughput. However, since interference nulling schemes can only improve the signal to interference plus noise ratio (SINR) ofξusers, they are not optimal in terms of average cell throughput, especially for low to medium OCI levels. We explore the question whether it is better to improve the SINR of every user in other cells rather than benefitξusers. An altruistic precoding method to minimize the sum of generated interference for all of the other cell users is proposed withξdegrees of freedom being sacrificed. With the altruistic precoding method, we deduce the lower bound on the capacity and solve the multicell user selection problem with a local optimal solution in which only eigenvalues of interfering channels are needed to be shared. Simulation results demonstrate that the proposed method outperforms the existing algorithms at any OCI level. Furthermore, we also analyze the best choice of degrees of freedom used to mitigate OCI through simulation.

scholarly journals Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xinhe Zhang ◽  
Yuehua Zhang ◽  
Chang Liu ◽  
Hanzhong Jia
Keyword(s):  
Computational Complexity ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Low Complexity ◽  
Search Tree ◽  
Spatial Modulation ◽  
Algorithm Analysis ◽  
Detection Algorithms ◽  
Search Order ◽  
Ber Performance

In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. The MBS detector, which splits the search tree into some subtrees, can reduce the computational complexity by decreasing the nodes retained in each layer. However, the MBS detector does not take into account the effect of subtree search order on computational complexity, and it does not consider the effect of layers search order on the bit-error-rate (BER) performance. The ost-MBS detector starts the search from the subtree where the optimal solution is most likely to be located, which can reduce total searches of nodes in the subsequent subtrees. Thus, it can decrease the computational complexity. When the number of the retained nodes is fixed, which nodes are retained is very important. That is, the different search orders of layers have a direct influence on BER. Based on this, we propose the oy-MBS detector. The ost-oy-MBS detector combines the detection order of ost-MBS and oy-MBS together. The algorithm analysis and experimental results show that the proposed detectors outstrip MBS with respect to the BER performance and the computational complexity.

scholarly journals On the Capacity and Transmission Techniques of Massive MIMO Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mohamed Abdul Haleem
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Transmission Rate ◽  
Mimo System ◽  
Coupling Factor ◽  
Base Station ◽  
Power Sharing ◽  
System Capacity ◽  
Bit Rate ◽  
Channel Coupling

A massive MIMO wireless system is a multiuser MISO system where base stations consist of a large number of antennas with respect to number of user devices, each equipped with a single antenna. Massive MIMO is seen as the way forward in enhancing the transmission rate and user capacity in 5G wireless. The potential of massive MIMO system lies in the ability to almost always realize multiuser channels with near zero mutual coupling. Coupling factor reduces by 1/2 for each doubling of transmit antennas. In a high bit rate massive MIMO system with m base station antennas and n users, downlink capacity increases as log2⁡m bps/Hz, and the capacity per user reduces as log2⁡n bps/Hz. This capacity can be achieved by power sharing and using signal weighting vectors aligned to respective 1×m channels of the users. For low bit rate transmission, time sharing achieves the capacity as much as power sharing does. System capacity reduces as channel coupling factor increases. Interference avoidance or minimization strategies can be used to achieve the available capacity in such scenarios. Probability distribution of channel coupling factor is a convenient tool to predict the number of antennas needed to qualify a system as massive MIMO.

scholarly journals A Fast Adaptive Receive Antenna Selection Method in MIMO System

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Chaowei Wang ◽  
Weidong Wang ◽  
Cheng Wang ◽  
Shuai Wang ◽  
Yang Yu
Keyword(s):  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
System Capacity ◽  
Adaptive Antenna ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Hardware Costs ◽  
Receive Antenna

Antenna selection has been regarded as an effective method to acquire the diversity benefits of multiple antennas while potentially reduce hardware costs. This paper focuses on receive antenna selection. According to the proportion between the numbers of total receive antennas and selected antennas and the influence of each antenna on system capacity, we propose a fast adaptive antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Mathematical analysis and numerical results show that our algorithm significantly reduces the computational complexity and memory requirement and achieves considerable system capacity gain compared with the optimal selection technique in the same time.

scholarly journals Joint Transmit and Receive Antenna Selection in MIMO Systems Based on Swarm Intelligence Algorithm

2021 ◽  
Author(s):  
Zhang Yiwen ◽  
Su Sunqing ◽  
Liao Wenliang ◽  
Lei Guowei ◽  
Yang Guangsong
Keyword(s):  
Mimo Systems ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
System Capacity ◽  
Particle Swarm Algorithm ◽  
Input Multiple Output ◽  
Swarm Algorithm ◽  
Speed Accuracy ◽  
Receive Antenna

Abstract In multiple-input-multiple-output (MIMO) systems, the selection of receive and transmit antennas is not just effective in increasing system capacity, but also in reducing RF link costs and system complexity. The exhaustive algorithm, i.e. the joint transmit and receive antenna selection (JTRAS) with the best accuracy, can search all the subsets of both transmit and receive antennas in order to find the optimal solution. However, with the increase of the number of antennas, the computational complexity is too large and its applicability is limited. In this paper, the antennas are coded by fractional coding with the maximization of channel capacity as the basic criterion, and three intelligent algorithms, namely genetic algorithm, cat swarm algorithm and particle swarm algorithm, are applied for antenna selection. The simulation results demonstrate that all three algorithms can efficiently accomplish the antenna selection. In the end, we compare them in terms of speed, accuracy and complexity of the search in MIMO systems.

scholarly journals Comparison of Semidefinite Relaxation Detectors for High-Order Modulation MIMO Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Z. Y. Shao ◽  
S. W. Cheung ◽  
T. I. Yuk
Keyword(s):  
Computational Complexity ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Semidefinite Relaxation ◽  
Attractive Alternative ◽  
Computationally Efficient ◽  
Multiple Input ◽  
Key Technologies ◽  
Input Multiple Output

Multiple-input multiple-output (MIMO) system is considered to be one of the key technologies of LTE since it achieves requirements of high throughput and spectral efficiency. The semidefinite relaxation (SDR) detection for MIMO systems is an attractive alternative to the optimum maximum likelihood (ML) decoding because it is very computationally efficient. We propose a new SDR detector for 256-QAM MIMO system and compare its performance with two other SDR detectors, namely, BC-SDR detector and VA-SDR detector. The tightness and complexity of these three SDR detectors are analyzed. Both theoretical analysis and simulation results demonstrate that the proposed SDR can provide the best BLER performance among the three detectors, while the BC-SDR detector and the VA-SDR detector provide identical BLER performance. Moreover, the BC-SDR has the lowest computational complexity and the VA-SDR has the highest computational complexity, while the proposed SDR is in between.

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.

A practical approach to improve product of energy efficiency and capacity rate in MU-MIMO systems

2020 ◽  
Vol 16 (2) ◽  
pp. 183-202
Author(s):  
Ashu Taneja ◽  
Nitin Saluja
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Communication Systems ◽  
Mimo Systems ◽  
Mimo System ◽  
Practical Approach ◽  
System Capacity ◽  
Content Type ◽  
Channel Interference ◽  
Sum Rate

Purpose The major challenges in the modern-day wireless communication systems are increased co-channel interference owing to large number of users and the increased energy consumption owing to high circuit and/or hardware power consumption. Hence, the purpose of this paper is to present a practical approach involving linear precoding, channel estimation, user selection (US) and transmit antenna selection (AS) for enhanced reliability in multiuser multiple-input multiple output (MU-MIMO) system. Design/methodology/approach The proposed technique considers systematic and optimum deployment of users and transmits antennas for each selected user which enhances the sum rate or the system capacity. The comparison of algorithms, namely, norm-based and capacity-based US is presented with its implementation with precoding techniques, namely, block-diagonalization (BD) and zero-forcing with combining (ZFC) which is used to minimize co-channel interference. In this paper, a power consumption model is proposed for energy efficiency calculation in MU-MIMO system. Also, post analysis, the variant of US and AS algorithms optimizing the performance of BD and ZFC precoding technique is proposed. Findings It is seen that the proposed MU-MIMO system with norm-based US and norm-based AS improves over existing US-based systems by 43% in terms of sum rate and 19% in terms of energy efficiency for 100 users. Originality/value It is seen that the proposed MU-MIMO system with norm-based US and norm-based AS improves over existing US-based systems by 43% in terms of sum rate and 19% in terms of energy efficiency for 100 users.

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