Green Wireless Transmission Design For Multiuser MIMO Systems

Due to the sheer number of mobile devices and emerging applications,the demands of wireless data services have been drastically increasing in recent years.Those ubiquitous communication services have numerous exceptionally high requirements,such as ultra-low latencies,excellent spectral efficiency(SE),reliability,wireless charging,and high energy efficiency(EE),and therefore pose new challenges for future wireless transmission.To cope with these challenges,more and more new technologies emerge for nextgeneration wireless communications.Among these advanced approaches,massive multiple-input multipleoutput(MIMO)and reconfigurable intelligent surface(RIS)are two promising multiple antenna technologies,which have quickly gained tremendous research attentions.Thanks to the deployment of large-scale antenna arrays at the base stations(BSs),massive MIMO can significantly improve the EE and SE,and the performance is highly dependant on the degree of the available channel state information(CSI).While most existing works on massive MIMO focused on the case where the instantaneous CSI is available,it is usually not an easy task to obtain precise instantaneous CSI.The emergence of RIS enables us to establish programmable radio wave propagation that caters for wireless communications,via employing low-cost passive reflecting units.Meanwhile,the applications of RIS also introduce a paradigm shift in wireless transmission designs and optimizations.Energy-aware optimization for wireless communications has received tremendous attention in the last few years,owing to both ecological and economical concerns.Conventionally,SE was deemed to be a more important design objective than EE as data rate was a major concern given the limited radio spectrum.However,with the ever rapid growing number of connected user terminals(UTs),the power consumption could be significantly increased and EE becomes an inevitable consideration.Consequently,green communication metrics such as EE have emerged as a vital design criterion for practical cellular networks.In this paper,we investigate energy-efficient wireless transmission in massive MIMO downlink and RIS-assisted multiuser MIMO uplink systems.The main contributions of this paper are summarized as follows.Firstly,we investigate EE optimization for single-cell massive MIMO downlink transmission with only statistical CSI available at the BS.We first show that beam domain transmission is favorable for energy efficiency in the massive MIMO downlink,by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrix.With this conclusion,the EE optimization problem in terms of transmit covariance matrices is reduced to a real-valued power allocation problem,which is much easier to tackle than the original large-dimensional complex matrix-valued precoding design problem.Exploiting the majorizationminimization(MM)algorithm,a sequential algorithm was further proposed to solve such a power allocation problem,together with the reduction of computational complexity using the deterministic equivalent(DE)theory.Furthermore,we proposed a generalized iterative water-filling scheme via separating the constrained EE maximization problem into an unconstrained EE maximization problem and a constrained sum-rate maximization problem.Numerical results demonstrate the EE improvement of our proposed EE optimization method over the sum-rate optimization method,especially in the high power budget regime.Secondly,we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSI.To this end,we aim to optimize the system resource efficiency(RE),which is capable of striking an EE-SE balance.We first figure out a closed-form solution for the optimal transmit subspaces of different UTs,which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink.Based on this insight,the RE optimization precoding design is reduced to a real-valued power allocation problem.Exploiting the techniques of sequential optimization and random matrix theory,we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm.Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI-aided RE optimization approach.Thirdly,we consider the application of RIS to assist multiuser MIMO uplink transmission from several multi-antenna UTs to a multi-antenna BS.For reducing the required signaling overhead and energy consumption,our transmission strategy design is based on the partial CSI,including the statistical CSI between the RIS and UTs and the instantaneous CSI between the RIS and the base station.In particular,an optimization framework is proposed for jointly designing the transmit covariance matrices of the UTs and the RIS phase shift matrix to maximize the system EE with partial CSI.We first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs.Then,to facilitate the design of the transmit power allocation matrices and the RIS phase shifts,we derive an asymptotically deterministic equivalent of the objective function with the aid of random matrix theory.We further propose a suboptimal algorithm to tackle the EE maximization problem with guaranteed convergence,capitalizing on the approaches of alternating optimization,fractional programming,and sequential optimization.Numerical results substantiate the effectiveness of the proposed approach as well as the considerable EE gains provided by the RIS-assisted transmission scheme over the traditional baselines.Lastly,we investigate the non-trivial EE-SE tradeoff in RIS-aided multiuser MIMO uplink communications,under both continuous-and discrete-phase shifts at the RIS.To investigate the EE-SE tradeoff,we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize the system RE.For the design of UT's precoding,it is simplified into the design of UTs' transmit powers with the aid of the closed-form solutions of UTs' optimal transmit directions.To avoid the high complexity in computing the nested integrals involved in the expectations,we utilize an asymptotic deterministic objective expression.For the design of the RIS phases,an iterative mean-square error minimization approach is proposed via capitalizing on the homotopy,accelerated projected gradient,and MM methods.Numerical results illustrate the effectiveness and rapid convergence rate of our proposed optimization framework.