Research On Beamforming Optimization In Multiuser MIMO Systems

Multiple Input Multiple Output(MIMO)technology can make use of spatial degrees of freedom to achieve spatial diversity and multiplexing,and effectively improve the spectrum utilization.Beamforming is a main technology to implement space division multiple access in multiuser MIMO system and can significantly suppress the inter-cell interference and the multi-user interference.Thus,it is becoming one of the research hotspot in the field of wireless communications.This paper focuses on the beamforming technology for multiuser MIMO systems which include multicell multicast systems,cooperative multicast systems and interference channels.The main works of this paper can be summarized into the following aspects:1)The weighted sum rate maximization problem is investigated for multi-cell multicast systems under perfect channel state information(CSI)at the transmitters or imperfect CSI at the transmitters,the corresponding beamforming algorithms are proposed.When perfect CSI is known at the transmitters,the weighted sum rate maximization problem subject to per-BS constraints is Non-deterministic Polynomial-hard(NP-hard),so hard to solve.We propose a low-complexity,provably convergent iterative algorithm,where in each iteration,the original problem can be expressed as a second-order cone programming(SOCP)by using successive convex approximation.Under Gaussian CSI error model,the outage-constrained weighted sum rate maximization problem is non-convex.Such problem is nonconvex due to the outage constraint which has no closed-form,and hard to solve.To tackle this problem,we first obtain a tractable closed-form approximation of the outage constraint using Bernstein-type inequality and semidefinite relaxation(SDR),an approximation problem is then recast.But resulting approximation problem is still nonconvex.We propose an alternating optimization approach to tackle this problem.A convex optimization problem is solved at each iteration.The effectiveness of the two proposed schemes are verified by numerical results.2)The multicast rate maximization is investigated for cooperative multicast sytems,a joint optimization method for beamforming and relay selection is proposed.In order to achieve the higher multicast transmission rate,the joint optimization problem of beamforming,relay selection,time allocation factor and the set of users who can successfully decode the date in the first-phase is studied for two-phase cooperative multicast sytems.This problem is quite complicated and is convex even though the set of successful users in the first-phase and relay have been fixed.Our idea is to first determine the set of successful users in the first-phase,then select the relay,and finally optimize the remaining variables.Simulation results show that,the proposed scheme obtains a lot of performance improvement at a low cost(relay power consumption)compared with the traditional multicast scheme.3)The outage-constrained sum rate maximization problem is studied for interference channels where the transmitters have no knowledge of the exact values of channel coefficients,only the statistical information,a beamforming algorithm on the tradeoff between performance and complexity is proposed.The outage-constrained sum rate maximization problem for K-user multiple-input single-output(MISO)interference channels is nonconvex and very difficult to deal with.We propose a new,provably convergent iterative algorithm where in each iteration,the original problem is approximated as second-order cone programming(SOCP)by introducing slack variables and using convex approximation.Simulation results show that the proposed SOCP algorithm converges very fast,and yields a better performance gain with a lower computational complexity than existing algorithms.