Asymptotic mean loss in sum rate compared to the perfect CSI case was derived. In and, the performance of IA under CSI error was quantified. The performance of IA scheme is sensitive to channel state information (CSI) inaccuracies.
K AND N DESIGNS FREE
The other signal subspace is left free of interference for the desirable signal. In this scheme, unwanted signals from other users are fitted into a small part of the signal space observed by each receiver, called interference subspace. A new method, termed interference alignment (IA), leads to the efficient use of communication resources, since it successfully achieves the theoretical bound on the multiplexing gain. Normally, wireless network scenarios, such as interference channel (IC), share the channel among the users, resulting in multi-user interference. Monte Carlo simulations are employed to investigate sum rate performance of the proposed algorithms and the advantage of incorporating variation minimization into the transceiver design. The proposed robust algorithms utilize the reciprocity of wireless networks to optimize the estimated statistical properties in two different working modes. Taylor expansion is exploited to approximate the effect of CSI imperfection on mean and variance. On the other hand, the second algorithm tries to minimize the variances of the SINRs to hedge against the variability due to CSI error. In the first proposed algorithm, each transceiver adjusts its filter to maximize the expected value of signal-to-interference-plus-noise ratio (SINR). Each transmitter and receiver has, respectively, M and N antennas and IC operates in a time division duplex mode. In this paper, two algorithms are proposed to improve the throughput of the multi-input multi-output (MIMO) IC. This paper focuses on robust transceiver design for throughput enhancement on the interference channel (IC), under imperfect channel state information (CSI).