Performance Analysis And Optimization Of Distributed Massive MIMO Systems

The explosive demand in high-data-rate services poses a great challenge to wireless communication systems.Considered as a critical technology to handle this challenge,distributed massive multiple-input multiple-output(MIMO)has received increasing research interest.Distributed massive MIMO combines the advantages of massive MIMO and distributed antenna systems,which can improve spectral efficiency,energy efficiency,and system coverage.However,for the reason of hardware cost and power consumption constraints,the inexpensive but hardware-constraint hardware may be deployed in the practical communications systems,which will degrade the system performance.In this paper,phase noise caused by local oscillators and quantization noise caused by low-resolution Analog-to-Digital converters(ADCs)are mainly considered.Under the imperfect hardware,the spectral efficiency and energy efficiency are analyzed and optimized,which offers a guideline for parameter selection and deployment of distributed massive MIMO.In order to further improve the spectral efficiency and handle the increasing traffic asymmetry between downlink transmission and uplink reception,a network-assisted full-duplex system is considered.In network-assisted full-duplex systems,a scheme is proposed to improve the spectral efficiency and the spectral efficiency is analyzed.Firstly,the impacts of phase noise on spectral efficiency in distributed massive MIMO systems are evaluated.Considering large-scale fading,small-scaling fading and phase,an effective channel model is provided,and the estimated channel state information(CSI)is obtained during the pilot training phase.Under the imperfect CSI,the closed-form expressions for achievable rates with maximum ratio transmission(MRT)and zero-forcing(ZF)precoders are derived.Furthermore,the ultimate rates with these two precoders are derived when the number of antennas tends to infinity.Simulation results verify the accuracy of the closed-form expressions.It can be concluded that the phase noise degrades the spectral efficiency,and although ZF precoder can provide higher spectral efficiency,ZF is more sensitive to phase noise.Next,the impacts of quantization noise on spectral and energy efficiencies in distributed massive MIMO systems are evaluated,and an ADCs resolution selection algorithm is designed.Firstly,additive quantization noise model(AQNM)is utilized to explore the impacts of low-resolution ADCs.Next,Under the AQNM and imperfect CSI,the closed-form expression for achievable rate with maximum ratio combining(MRC)receivers is derived.Furthermore,the ultimate rates are also derived when the number of quantization bits,the transmit power per user,and the number of antennas go to infinity,respectively.Finally,under the closed-form expressions,the spectral efficiency and energy efficiency are simultaneously maximized by utilizing the multiobjective optimization(MOOP)framework,which offers a guideline for the resolution selection of ADCs.Simulation results verify the accuracy of the closed-form expressions.It can be seen that the quantization noise degrades the spectral efficiency,while the degradation can be compensated by increasing the number of antennas.As for energy efficiency,low-resolution ADCs can provide higher energy efficiency than perfect ADCs.From the analysis,it can be concluded that low-resolution ADCs are preferable in distributed massive MIMO systems.Finally,considering a network-assisted full-duplex distributed massive MIMO system,a beamforming training scheme is proposed to enable interference cancellation at the base station and coherent decoding at users.Under the beamforming training scheme,the spectral efficiency is analyzed and an effective pilot and data transmitted power allocation scheme is designed.Firstly,the CSI is estimated based on the beamforming training scheme,and the closed-form expressions for achievable rates with different receivers and precoders are derived.Then,under the closed-form expressions which are only dependent on the large-scale fading,the pilot transmitted power and data transmitted power are simultaneously minimized by utilizing the MOOP framework.Simulation results verify the accuracy of the closed-form expressions.The beamforming training based interference cancellation and downlink channel estimation can significantly improve the spectral efficiency.The trade-off region between the two objectives offers a guideline for system power allocation.