Performance Improvement of Multi-User Multiple-Input Multiple-Output Protocol for WLAN

The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

Ergodic secrecy capacity of MRC/SC in single-input multiple-output wiretap systems with imperfect channel state information

This paper investigates the secrecy performance of maximal ratio combining （MRC） and selection combining （SC） with imperfect channel state information （CSI） in the physical layer. In a single-input multiple- output （SIMO） wiretap channel, a source transmits confidential messages to the destination equipped with M antennas using the MRC/SC scheme to process the received multiple signals. An eavesdropper equipped with N antennas also adopts the MRC/SC scheme to promote successful eavesdropping. We derive the exact and asymptotic closed-form expressions for the ergodic secrecy capacity （ESC） in two cases： （1） MRC with weighting errors, and （2） SC with outdated CSI. Moreover, two important indicators, namely high signal-to-noise ratio （SNR） slope and high SNR power offset, which govern ESC at the high SNR region, are derived. Finally, simulations are conducted to validate the accuracy of our proposed analytical models. Results indicate that ESC rises with the increase of the number of antennas and the received SNR at the destination, and fades with the increase of those at the eavesdropper. Another finding is that the high SNR slope is constant, while the high SNR power offset is correlated with the number of antennas at both the destination and the eavesdropper.

Codebook selected beamforming algorithm for multiuser MIMO systems

For reducing the inter-user interference in multi-user multiple-input multiple-output（MU-MIMO） wireless communication systems,e.g.,MIMO-orthogonal frequency division multiplexing（MIMO-OFDM） systems,it is often desirable to the complex preprocessing at the transmitter.This paper proposes a multi-user beamforming algorithm with sub-codebook selection.Based on the minimal leakage criterion,the codebook selection,limited feed-forward and minimum mean square error（MMSE） detection are combined in the proposed algorithm.This avoids the complex channel matrix decomposition and inversion.Consequently,the computational complexity at the transmitter is significantly reduced.Simulation results show that the proposed algorithm performs better than existing beamforming algorithms.

Power duality for multi-antenna OFDM system in broadcast channel with user scheduling

This paper deals with design and analysis of user scheduling and power allocation for multi-antenna OFDM systems with DPC,ZF-DPC,ZF-BF and TDMA transmit strategies.We consider the general multi-user downlink scheduling problem and power minimization with multi-user rate constraints.According to the channel state,it is shown that there is a power optimal policy which selects a subset of users in each scheduling interval.We present user selection algorithms for DPC,ZF-DPC,ZF-BF and TDMA for multi-antenna OFDM system in broadcast channels,and we also present the practical water-filling solution in this paper.By the selected users with the consideration of fairness,we derive the power optimization algorithm with multi-user rate constraints.We also analyze the power duality of uplink-downlink for the transmit strategies of DPC,ZF-DPC and ZF-BF.Simulation results show that the present user-scheduling algorithm and power minimization algorithm can achieve good power performance,and that the scheduling algorithm can guarantee fairness.

Study on Performance of Wireless Multihop Networks Using MIMO MRC and MIMO MRT

This paper presents the concepts of completely connected network,mean path length and cluster for analysis performance of wireless multihop network,where matrix are used to express topology of network and use a new algorithm to compute the number of cluster in the network.Multiple-input/multiple-output(MIMO) communication promises performance enhancement over conventional single-input/single-output(SISO) technology for the same radiated power,if leveraged in multihop network,MIMO may be able to provide significant network performance improvement in network robustness and in power consumption,this paper analyzes three types of multihop networks employing SISO, MIMO with maximum ratio combining(MRC) and MIMO with maximum ratio transmission(MRT) as link model respectively,and get that using MIMO link model can increase robust,decrease mean path length by simulation.

Blind Joint Maximum Likelihood Channel Estimation and Data Detection for SIMO Systems

A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.

Design of Energy Modulation Massive SIMO Transceivers via Machine Learning

This paper considers a massive single-input multiple-output(SIMO)system,where multiple singleantenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas.Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading,we propose a joint transceiver design method based on machine learning,requiring a limited number of channel realizations.In the proposed method,the multiple transmitters,the channel,and the receiver are represented with a deep neural network(NN),and an autoencoder is adopted to minimize the end-to-end transmission error probability.Besides,the relationship between the number of training samples and the transmission error probability is analyzed based on the confidence interval method.Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios,and is more robust against the channel parameters variation compared with the existing methods.

Physical layer security of underlay cognitive radio using maximal ratio combining

We investigate the secrecy outage performance of maximal ratio combining(MRC) in cognitive radio networks over Rayleigh fading channels. In a single-input multiple-output wiretap system, we consider a secondary user(SU-TX) that transmits confidential messages to another secondary user(SU-RX) equipped with M(M ≥ 1)antennas where the MRC technique is adopted to improve its received signal-to-noise ratio. Meanwhile, an eavesdropper equipped with N(N ≥ 1) antennas adopts the MRC scheme to overhear the information between SU-TX and SU-RX. SU-TX adopts the underlay strategy to guarantee the service quality of the primary user without spectrum sensing. We derive the closed-form expressions for an exact and asymptotic secrecy outage probability.