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.

ERGODIC CAPACITY ANALYSIS FOR SIMO COGNITIVE FADING CHANNEL IN SPECTRUM SHARING ENVIRONMENT

In this paper,we consider the spectrum sharing Cognitive Radio(CR) system,wherein Single-Input Multi-Output(SIMO) cognitive fading channel is assumed.Subject to the average in-terference constraint of primary user,the ergodic capacity of cognitive user involving joint beamforming and power control is analyzed.We derive the optimal joint beamforming and power control strategy and deduce the general integral expression for ergodic capacity.Furthermore,under different channel fadings including Raleigh fading,Nakagami-m fading and Lognormal shadowing fading,the specific expressions of ergodic capacity for SIMO cognitive channel are exhibited.Most of the expressions are presented in closed-form or with some special integral functions.Large amount of simulations are conducted to corroborate our theoretical results.

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.

Visible light communication for Vehicle to Everything beyond 1 Gb/s based on an LED car headlight and a 2 × 2 PIN array

Visible light communication(VLC) shows great potential in Internet of Vehicle applications. A single-input multi-output VLC system for Vehicle to Everything is proposed and demonstrated. A commercial car headlight is used as transmitter. With a self-designed 2 × 2 positive-intrinsic-negative(PIN) array, four independent signals are received and equalized by deep-neural-network post-equalizers, respectively. Maximum-ratio combining brings high signal-to-noise ratio and data rate gain. The transmission data rate reaches 1.25 Gb/s at 1 m and exceeds 1 Gb/s at 4 m. To the best of our knowledge, it is the first-time demonstration of beyond 1 Gb/s employing a commercial car headlight.

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.

Developing a Super-lift Luo-converter with Integration of Buck Converters for Electric Vehicle Applications

In this paper,a DC-DC multi-port converter is introduced by integrating a super-lift and a buck converter(SLBC).The proposed single-input dual-output(SIDO)converter has conventional positive output voltage super-lift advantages while simultaneously generating a step・up voltage by Luo・converter and a step-down voltage by the buck converter.In this structure,without utilizing electromagnetic components to generate a dual output,the ripple in output voltages is kept low.Meanwhile,the introduced SLBC has a simple structure and an appropriate control method providing a wide range of output voltages.Besides,to illustrate the advantages of the proposed SIDO converter,a comparison with other similar configurations is carried out.Also,simulation and experiment results indicate a considerable reduction in conduction losses compared to other SIDO converters in the same situations.The operation accuracy of SLBC is validated by performing several simulations in PSCAD/EMTDC software and testing a 150W prototype in the laboratory.

State Observer Based Dynamic Fuzzy Logic System for a Class of SISO Nonlinear Systems

This paper presents an observer based dynamic fuzzy logic system （DFLS） scheme for a class of unknown single-input single-output （SISO） nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.