Performance Analysis And Transmission Optimization Of MIMO Communications With Electromagnetic Reflection

Fifth generation(5G)integrates various key enabling technologies,such as massive multiple-input multiple-output(MIMO),for the significant performance improvements of spectral efficiency,connection,etc.However,system energy consumption and cost increase rapidly.Research on finding innovative tech-nologies for reducing the energy consumption and hardware cost without sacrificing quality of service be-comes one of the important projects of sustainable green 5G design.Ambient backscatter is well acknowl-edged as an energy-efficient and low cost technology for future Internet-of-things(IoT)applications due to no energy-consuming and expensive transmit radio-frequency(RF)chains.In ambient backscatter commu-nication,backscatter transmitter modulates the amplitude and phase of received signals by switching antenna impedances and then reflects the modulated signals to send information to a neighboring receiver.On the other hand,intelligent reflecting surface(IRS)is a planar array consisting of a large number of low-cost re-flecting elements and triggered by an attached controller.It adjusts the amplitude or/and phase of impinging signals and reconfigures the wireless communication environment via easily programmable and intelligent reflection.Traditional communication theories may no longer be applied for the wireless networks with the above two electromagnetic reflection technologies.Therefore,it is necessary to study the transmission op-timization and performance analysis of the electromagnetic reflection MIMO communication.This thesis focuses on signal processing scheme and performance analysis considering systems with ambient backscatter and IRS,respectively.Firstly,the thesis considers the ambient backscatter communication.For downlink MIMO transmission,we derive the respective rates of cellular date link with noncoherent detection and ambient backscatter link with successive interference cancellation(SIC)detection and maximum ratio combining(MRC).Based on the concept of multi-objective optimization,a binary vector optimization problem is formulated to find the optimal signal processing of transmitter and receiver for the cooperative transmission of the both links.We propose an alternating optimizing algorithm for transmit beamforming and receive combining based on linear search to obtain the Pareto optimal points.It is shown in simulations that the proposed optimal cooperative ambient backscatter communication can balance the cellular date rate and the ambient backscatter rate.Secondly,the thesis focuses on the cooperative ambient backscatter MIMO systems where ambient backscatter communication is integrated with conventional wireless communication and cooperative receiver needs to decode both signals directly from primary transmitter and reflectively from ambient backscatter transmitter.A signal processing scheme is provided where the transmit beamforming of ambient RF source matches the cellular channel while the receive combining schemes of primary and secondary links respectively match the cellular channel and the backscatter channel.We derive the ergodic rates of both the primary and secondary links,as well as their upper bounds.For cellular communication with M transmit antennas and N receive antennas,transmit power is reduced by the proportion of 1/((?))2.For ambient backscatter communication with a symbol period which is K times that of the cellular communication,the ergodic rate scales like 1/K log2 KN.Moreover,the impact of ambient backscatter is quantitatively analyzed.Simulation results verify the analysis.It also shows that more receive antennas can compensate for larger symbol period in the ambient backscatter communication.Finally,the thesis considers the IRS-assisted multi-antenna communication with hardware impairments.An extended error vector magnitude(EEVM)model is utilized to characterize the impact of RF impairments at access point(AP)and phase noise is considered for the phase errors at IRS due to hardware limitations and imperfect channel estimation.We analyze the spectral efficiency and find it limited by the hardware impairments even when the numbers of AP antennas and IRS elements grow infinitely large,which is contrast to the conventional case with ideal hardware.Moreover,the performance degradation at high SNR is shown to be mainly affected by AP hardware impairments rather than phase noise of IRS.We further obtain the optimal transmit power in closed form for energy efficiency maximization.The optimal transmit power increases with more severe RF impairments and the corresponding optimal energy efficiency decreases.