Matrix-Monotonic Optimization for MIMO Systems

For MIMO systems, due to the deployment of multiple antennas at both the transmitter and the receiver, the design variables e.g., precoders, equalizers, training sequences, etc. are usually matrices. It is well known that matrix operations are usually more complicated compared to their vector counterparts. In order to overcome the high complexity resulting from matrix variables, in this paper we investigate a class of elegant multi-objective optimization problems, namely matrix-monotonic optimization problems (MMOPs). In our work, various representative MIMO optimization problems are unified into a framework of matrix-monotonic optimization, which includes linear transceiver design, nonlinear transceiver design, training sequence design, radar waveform optimization, the corresponding robust design and so on as its special cases. Then exploiting the framework of matrix-monotonic optimization the optimal structures of the considered matrix variables can be derived first. Based on the optimal structure, the matrix-variate optimization problems can be greatly simplified into the ones with only vector variables. In particular, the dimension of the new vector variable is equal to the minimum number of columns and rows of the original matrix variable. Finally, we also extend our work to some more general cases with multiple matrix variables.Comment: 37 Pages, 5 figures, IEEE Transactions on Signal Processing, Final Versio

Hybrid Transceiver Optimization for Multi-Hop Communications

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic optimization framework, the optimal structures of the robust hybrid transceivers are derived. By utilizing these optimal structures, the optimizations of analog transceivers and digital transceivers can be separated without loss of optimality. This fact greatly simplifies the joint optimization of analog and digital transceivers. Since the optimization of analog transceivers under unit-modulus constraints is non-convex, a projection type algorithm is proposed for analog transceiver optimization to overcome this difficulty. Based on the derived analog transceivers, the optimal digital transceivers can then be derived using matrix-monotonic optimization. Numeral results obtained demonstrate the performance advantages of the proposed hybrid transceiver designs over other existing solutions.Comment: 32 pages, 6 figures. This manuscript has been submitted to IEEE Journal on Selected Areas in Communications (special issue on Multiple Antenna Technologies for Beyond 5G

Robust transceiver designs for MIMO relay communication systems

The thesis investigates robust linear and non-linear transceiver design problems for wireless MIMO relay communication systems with the assumption that the partial information of the channel is available at the relay node. The joint source and relay optimization problems for MIMO relay systems are highly nonconvex, in general. We transform the problems into suitable forms which can be efficiently solved using standard convex optimization techniques. The proposed design schemes outperform the existing techniques

How to Understand LMMSE Transceiver Design for MIMO Systems From Quadratic Matrix Programming

In this paper, a unified linear minimum mean-square-error (LMMSE) transceiver design framework is investigated, which is suitable for a wide range of wireless systems. The unified design is based on an elegant and powerful mathematical programming technology termed as quadratic matrix programming (QMP). Based on QMP it can be observed that for different wireless systems, there are certain common characteristics which can be exploited to design LMMSE transceivers e.g., the quadratic forms. It is also discovered that evolving from a point-to-point MIMO system to various advanced wireless systems such as multi-cell coordinated systems, multi-user MIMO systems, MIMO cognitive radio systems, amplify-and-forward MIMO relaying systems and so on, the quadratic nature is always kept and the LMMSE transceiver designs can always be carried out via iteratively solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given. The work presented in this paper is likely to be the first shoot for the transceiver design for the future ever-changing wireless systems.Comment: 31 pages, 4 figures, Accepted by IET Communication

Joint Transceiver Design Algorithms for Multiuser MISO Relay Systems with Energy Harvesting

In this paper, we investigate a multiuser relay system with simultaneous wireless information and power transfer. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, this work studies the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix and the power splitting (PS) ratios at the single-antenna receivers. Firstly, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. Secondly, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed which consists of BS beamforming vectors, optimally scaled RS permutation matrix and receiver PS ratios. For the given CSI, the optimal transceiver with the lowest total power consumption is selected for transmission. We propose a concave-convex procedure based and subgradient-type iterative algorithms for the non-robust and robust latent transceiver designs. Simulation results are presented to validate the effectiveness of all the proposed algorithms

Robust Beamforming for Amplify-and-Forward MIMO Relay Systems Based on Quadratic Matrix Programming

In this paper, robust transceiver design based on minimum-mean-square-error (MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is investigated. The channel estimation errors are modeled as Gaussian random variables, and then the effect are incorporated into the robust transceiver based on the Bayesian framework. An iterative algorithm is proposed to jointly design the precoder at the source, the forward matrix at the relay and the equalizer at the destination, and the joint design problem can be efficiently solved by quadratic matrix programming (QMP).Comment: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2010), U.S.