Robust transceiver design for K-pairs quasi-static MIMO interference channels via semi-definite relaxation

Abstract

In this paper, we propose a robust transceiver design for the K-pair quasi-static MIMO interference channel. Each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the k th transmitter sends L k independent data streams to the desired receiver. In the literature, there exist a variety of theoretically promising transceiver designs for the interference channel such as interference alignment-based schemes, which have feasibility and practical limitations. In order to address practical system issues and requirements, we consider a transceiver design that enforces robustness against imperfect channel state information (CSI) as well as fair performance among the users in the interference channel. Specifically, we formulate the transceiver design as an optimization problem to maximize the worst-case signal-to-interference-plus-noise ratio among all users. We devise a low complexity iterative algorithm based on alternative optimization and semi-definite relaxation techniques. Numerical results verify the advantages of incorporating into transceiver design for the interference channel important practical issues such as CSI uncertainty and fairness performance. Index Terms-Interference channel, robust transceiver, imperfect CSI, precoder design, decorrelator design, max-min fair, alternative optimization, semi-definite relaxation. Technology as Professor. His current research interests include robust and delay-sensitive cross-layer scheduling of MIMO/OFDM wireless systems with imperfect channel state information, cooperative and cognitive communications as well as stochastic approximation and Markov Decision Process. Huang Huang received the B.Eng. and M.Eng. (Gold medal) from the Harbin Institute of Technology (HIT) in 2005 and 2007 respectively, all in Electrical Engineering. He is currently a PhD student at the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology. His recent research interests include cross layer design, interference management in interference network, and embedded system design.

Robust transceiver design for reciprocal M × N interference channel based on statistical linearization approximation

“…Monte Carlo simulations demonstrate that the first proposed algorithm, i.e., expectation maximization (EM), achieves higher sum rates compared to the existing algorithms in [21], and [22]. The second proposed algorithm, that is variation minimization (VM), provides a SINR with low variance.…”

“…On the other hand, in [18] the authors consider a space-time coding scheme for the point-to-point channel with imperfect channel knowledge. However, these existing works cannot be extended to robust transceiver design for the MIMO IC [21].…”

“…They improved robustness in presence of the channel uncertainty. Authors in [21] proposed a transceiver design that enforces robustness against imperfect CSI as well as providing a fair performance among the users in the interference channel. They adopted worst-case optimization approach to improve robustness and fairness.…”

“…The receiver j in the reciprocal channel can learn U j ← in a similar manner. For TDD systems, the transmitters can estimate the channels from the sounding signals received in the reverse link ( [21], section II-A). Using MMSE channel prediction, the CSI estimate H kk is obtained, whereas the CSI error E kj is Gaussian distributed and independent from the CSI estimate H kj ( [21], section II-A).…”

Simultaneous Wireless Information and Power Transfer for MIMO Interference Channel Networks Based on Interference Alignment

“…Moreover, for an MISO IC network, Problem (15) can be solved efficiently by using the uplink-downlink duality [20,47,48]. For the MIMO IC network, the feasibility of Problem (15) is an open problem, and it is very difficult to find the solution of Problem (15), since it is a non-convex and NP-hard problem [41,49]. In the following proposition, a sufficient condition for the feasibility of Problem (15) is presented based on IA.…”