MIMO Interference Alignment Over Correlated Channels With Imperfect CSI

Abstract

Interference alignment (IA), given uncorrelated channel components and perfect channel state information, obtains the maximum degrees of freedom in an interference channel. Little is known, however, about how the sum rate of IA behaves at finite transmit power, with imperfect channel state information, or antenna correlation. This paper provides an approximate closed-form signalto-interference-plus-noise-ratio (SINR) expression for IA over multiple-input-multiple-output (MIMO) channels with imperfect channel state information and transmit antenna correlation. Assuming linear processing at the transmitters and zero-forcing receivers, random matrix theory tools are utilized to derive an approximation for the post-processing SINR distribution of each stream for each user. Perfect channel knowledge and i.i.d. channel coefficients constitute special cases. This SINR distribution not only allows easy calculation of useful performance metrics like sum rate and symbol error rate, but also permits a realistic comparison of IA with other transmission techniques. More specifically, IA is compared with spatial multiplexing and beamforming and it is shown that IA may not be optimal for some performance criteria.The authors are with the

Interference Alignment in Two-Tier Randomly Distributed Heterogeneous Wireless Networks Using Stochastic Geometry Approach

“…Symbols p denote the success probabilities affected by the nearest cross-tier interference, the remaining crosstier interference and the inter-cluster interference, respectively. Note that the success probability affected by the inter-cluster interference has been well-studied in [27]. In the scenario when all the transmitters have the same power constraint and number of data streams, p inter s can be expressed by the following expression:…”

MIMO Interference Alignment in Random Access Networks

“…The linear equalizer presented in [5] and the projection matrix presented in [13, Section III.A] are examples of a possible receive filter in (4). We assume the set of {N, N s , K} constitutes a feasible IA system [14].…”

“…Except for blind IA techniques [2], [3], which usually attain a lower multiplexing gain, IA requires cooperation between the transmitting/receiving nodes using global channel state information (CSI) [4] or some form of channel reciprocity. In practical scenarios, where CSI is estimated and fed back to the other nodes, the accuracy of the available CSI plays a crucial role in determining the performance of IA [5]. Hence IA is not necessarily the optimal transmission technique in practice.…”

“…Most work on performance of interference alignment is confined to single-cluster performance analysis effectively ignoring the impact of the rest of the network on the distribution of the signal and/or interference (see [5], [7], [8] and references This work was supported by the DARPA IT-MANET program, Grant W911NF-07-1-0028 and the Army Research Labs, Grant W911NF-10-1-0420. therein).…”

“…In [10] the spatial distribution of nodes is considered but the accuracy of the acquired CSI is ignored. This effectively favors IA over transmission techniques which either do not require CSI at the transmitters or are less sensitive to CSI imperfections [5,Section V].…”