NOMA with optimal transmit antenna selection scheme with self adaptive metaheuristic model

2021 ◽  
Author(s):  
Mehak Saini ◽  
Surender K. Grewal
Keyword(s):  
Antenna Selection ◽  
Transmit Antenna Selection ◽  
Selection Scheme ◽  
Self Adaptive

A Novel Transmit Diversity Technique for MIMO System

2012 ◽  
Vol 468-471 ◽  
pp. 355-359
Author(s):  
You Yan Zhang ◽  
Shu Yue Hong
Keyword(s):  
Likelihood Ratio ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Signal To Noise Ratio ◽  
Gray Code ◽  
Transmit Antenna Selection ◽  
Selection Scheme ◽  
Log Likelihood ◽  
Log Likelihood Ratio

The antenna diversity based on log-likelihood ratio (LLR) is better than that based on signal-to-noise ratio (SNR) in bit error rate performance for MIMO systems. Thus in this paper, we present a novel transmit antenna selection scheme based on bit log-likelihood ratio when the Alamouti code is employed .Then the BER expressions of application based on Bit-LLR (BLLR) for MPSK and MQAM modulation with Gray code are derived. The simulation results show that the new scheme based on BLLR is superior to SNR. With the increase of the transmit antennas, the performance of system is improved significantly. Furthermore, the diversity order is the same as that of the full complexity systems.

scholarly journals Enhanced Transmit-Antenna Selection Schemes for Multiuser Massive MIMO Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Byung-Jin Lee ◽  
Sang-Lim Ju ◽  
Nam-il Kim ◽  
Kyung-Seok Kim
Keyword(s):  
Computational Complexity ◽  
Radio Frequency ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Base Station ◽  
Base Stations ◽  
Transmit Antenna Selection ◽  
Selection Scheme

Massive multiple-input multiple-output (MIMO) systems are a core technology designed to achieve the performance objectives defined for 5G wireless communications. They achieve high spectral efficiency, reliability, and diversity gain. However, the many radio frequency chains required in base stations equipped with a high number of transmit antennas imply high hardware costs and computational complexity. Therefore, in this paper, we investigate the use of a transmit-antenna selection scheme, with which the number of required radio frequency chains in the base station can be reduced. This paper proposes two efficient transmit-antenna selection (TAS) schemes designed to consider a trade-off between performance and computational complexity in massive MIMO systems. The spectral efficiency and computational complexity of the proposed schemes are analyzed and compared with existing TAS schemes, showing that the proposed algorithms increase the TAS performance and can be used in practical systems. Additionally, the obtained results enable a better understanding of how TAS affects massive MIMO systems.

scholarly journals An Energy Efficient Resource Allocation and Transmit Antenna Selection Scheme in mm-Wave Using Massive MIMO Technology

2021 ◽  
Author(s):  
Charanjeet Singh ◽  
P C Kishoreraja
Keyword(s):  
Resource Allocation ◽  
Wireless Communication ◽  
Optimization Algorithm ◽  
Massive Mimo ◽  
Antenna Selection ◽  
Transmit Antenna Selection ◽  
Millimetre Wave ◽  
Selection Scheme ◽  
Efficient Resource ◽  
Mimo Technology

Abstract The massive Multiple-Input Multiple-Output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and Transmit Antenna Selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA as well as the TAS of wireless communication in millimetre- wave (mm-wave) with massive MIMO technology is considered. Two different solutions are developed for this research such as the Deep Learning method for efficient resource allocation process and optimization algorithm for Transmit Antenna Selection (TAS) process. Here, the RA process is done with the help of Attention Based Capsule Auto-Encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, Battle Royale Optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipments (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances Spectral Efficiency (SE), Energy Efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE and SE.

scholarly journals A Novel Machine Learning Aided Antenna Selection Scheme for MIMO Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2250 ◽  
Author(s):  
Wannian An ◽  
Peichang Zhang ◽  
Jiajun Xu ◽  
Huancong Luo ◽  
Lei Huang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Communication Systems ◽  
Large Scale ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Training Data ◽  
Mimo Channel ◽  
Transmit Antenna Selection ◽  
Selection Scheme ◽  
Channel Conditions

In this article, we propose a multi-label convolution neural network (MLCNN)-aided transmit antenna selection (AS) scheme for end-to-end multiple-input multiple-output (MIMO) Internet of Things (IoT) communication systems in correlated channel conditions. In contrast to the conventional single-label multi-class classification ML schemes, we opt for using the concept of multi-label in the proposed MLCNN-aided transmit AS MIMO IoT system, which may greatly reduce the length of training labels in the case of multi-antenna selection. Additionally, applying multi-label concept may significantly improve the prediction accuracy of the trained MLCNN model under correlated large-scale MIMO channel conditions with less training data. The corresponding simulation results verified that the proposed MLCNN-aided AS scheme may be capable of achieving near-optimal capacity performance in real time, and the performance is relatively insensitive to the effects of imperfect CSI.

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