MIMO Capacity Through Correlated Channels in the Presence of Correlated Interferers and Noise: A (Not So) Large N Analysis

Abstract

The use of multiple-antenna arrays in both transmission and reception promises huge increases in the throughput of wireless communication systems. It is therefore important to analyze the capacities of such systems in realistic situations, which may include spatially correlated channels and correlated noise, as well as correlated interferers with known channel at the receiver. Here, we present an approach that provides analytic expressions for the statistics, i.e., the moments of the distribution, of the mutual information of multiple-antenna systems with arbitrary correlations, interferers, and noise. We assume that the channels of the signal and the interference are Gaussian with arbitrary covariance. Although this method is valid formally for large antenna numbers, it produces extremely accurate results even for arrays with as few as two or three antennas. We also develop a method to analytically optimize over the input signal covariance, which enables us to calculate analytic capacities when the transmitter has knowledge of the statistics of the channel (i.e., the channel covariance). In many cases of interest, this capacity is very close to the full closed-loop capacity, in which the transmitter has instantaneous channel knowledge. We apply this analytic approach to a number of examples and we compare our results with simulations to establish the validity of this approach. This method provides a simple tool to analyze the statistics of throughput for arrays of any size. The emphasis of this paper is on elucidating the novel mathematical methods used.

High-SNR power offset in multi-antenna communication

“…As in other mutual information analyses [24]- [26], we find an excellent agreement between the asymptotic ( ) results and the exact results for small numbers of antennas.…”

An Accurate Closed-Form Approximation of the Energy Efficiency-Spectral Efficiency Trade-Off over the MIMO Rayleigh Fading Channel

“…Thus, it has been pointed out that EE can mainly be improved through receive diversity in the very low-SE regime and that MIMO has a large potential for improving the EE of communication systems, especially in the high-SE regime. In the future, we would like to extend our method to cooperative MIMO communication and derive closed-form approximations of the EE-SE trade-off by using the works in [23] and [24] as a starting point.…”

Optimization of MIMO Systems Capacity Using Large Random Matrix Methods

“…This optimization problem is easier because the approximation can be very often nearly expressed in closed form so that its maximization does not need Monte-Carlo simulations. Large system approximations of average mutual information were derived in the past using the replica method (see e.g., [6] for bi-correlated MIMO Rayleigh channels, [7] for frequency selective Rayleigh channels, [8] for bi-correlated MIMO Ricean channels, [9] for jointly correlated Rician channel) or rigorous random matrix methods ( [10] for bi-correlated MIMO Rayleigh channels, [11] for bi-correlated MIMO Ricean channels, [12] for frequency selective Rayleigh channels, [13] for multiple access Rayleigh MIMO channels). Optimization algorithms of the large system approximation were first proposed in [14] in the context of bi-correlated MIMO Rayleigh channels.…”