A transmit antenna selection (TxAS) aided multi-user multiple-input multiple-output (MU-MIMO) system is proposed for operating in the MIMO downlink channel environments, which shows significant improvement in terms of higher data rate when compared to the conventional MU-MIMO systems operating without adopting TxAS, while maintaining low hardware costs. We opt for employing a simple yet efficient zero-forcing beamforming (ZFBF) linear precoding scheme at the transmitter in order to reduce the decoding complexity when considering users’ side. Moreover, considering that users within the same cell may require various qualities of service (QoS), we further propose a novel user-oriented smart TxAS (UOSTxAS) scheme, of which the main idea is to carry out AS based on the QoS requirements of different users. At last, we implement the proposed UOSTxAS scheme in the software defined radio (SDR) MIMO communication hardware platform, which is the first prototype hardware system that runs the UOSTxAS MU-MIMO scheme. Our results show that, by employing TxAS, the proposed UOSTxAS scheme is capable of offering higher data rates for priority users, while reasonably ensuring the performance of the common users requiring lower rates both in simulation and in the implemented SDR MIMO communication platform.

In recent years, antenna selection (AS) has become one of the most popular research topics for massive multiple-input multiple-output (MIMO) system due to its capability of reducing the number of radio frequency chains utilized for MIMO communications, while remaining the MIMO advantages, such as increased bandwidth efficiency and reliability. In this paper, an efficient norm-based AS (NBAS) algorithm is investigated and implemented in the software defined radio (SDR) MIMO communication platform, which consists of field-programmable gate arrays (FPGAs). Owing to the high freedom and fast reconfigurable FPGA hardware, the SDR MIMO communication platform is capable of developing prototype of NBAS-aided MIMO system. More specifically, the implemented NBAS aided SDR MIMO system is capable of achieving uplink communication from users to the base station via time division duplex (TDD). A time-varying fading channel generation model is designed for SDR MIMO platform to enrich our experimental results. Additionally, a novel multiplexer (MUX) circuit module is designed and implemented to enhance the hardware performance of the FPGA in term of delay and resource usage. The results show that by implementing the low-complexity NBAS, the channel capacity performance of the SDR MIMO system can be significantly improved by around 15%. It is also showed that our proposed optimized MUX circuit intellectual property may reduce the critical path delay by about 2.16 ns, and save at lease 3% hardware resources in SDR FPGA. INDEX TERMS Multiple-input multiple-output, antenna selection, software defined radio, FPGA.

In this paper, we propose and implement a novel framework of deep learning based antenna selection (DLBAS)-aided multiple-input–multiple-output (MIMO) software defined radio (SDR) system. The system is constructed with the following three steps: (1) a MIMO SDR communication platform is first constructed, which is capable of achieving uplink communication from users to the base station via time division duplex (TDD); (2) we use the deep neural network (DNN) from our previous work to construct a deep learning decision server to assist the MIMO SDR platform for making intelligent decision for antenna selection, which transforms the optimization-driven decision making method into a data-driven decision making method; and (3) we set up the deep learning decision server as a multithreading server to improve the resource utilization ratio. To evaluate the performance of the DLBAS-aided MIMO SDR system, a norm-based antenna selection (NBAS) scheme is selected for comparison. The results show that the proposed DLBAS scheme performed equally to the NBAS scheme in real-time and out-performed the MIMO system without AS with up to 53% improvement on average channel capacity gain.

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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