Capacity Scaling in MIMO Systems With General Unitarily Invariant Random Matrices

Abstract

We investigate the capacity scaling of MIMO systems with the system dimensions. To that end we quantify how the mutual information varies when the number of antennas (at either the receiver or transmitter side) is altered. For a system comprising R receive and T transmit antennas with R > T , we find the following: By removing as many receive antennas as needed to obtain a square system (provided the channel matrices before and after the removal have full rank) the maximum resulting loss of mutual information over all signal-to-noise ratios (SNRs) depends only on R, T and the matrix of left-singular vectors of the initial channel matrix, but not on its singular values. In particular, if the latter matrix is Haar distributed the ergodic rate loss is given by T t=1 R r=T +1 1 r−t nats. Under the same assumption, if T, R → ∞ with the ratio φ T /R fixed, the rate loss normalized by R converges almost surely to H(φ) bits with H(·) denoting the binary entropy function. We also quantify and study how the mutual information as a function of the system dimensions deviates from the traditionally assumed linear growth in the minimum of the system dimensions at high SNR.

Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

“…Theorem 1 characterizes the rate loss caused by deactivating antennas at the user terminals [59]. In fact, by considering the case with full transmit complexity as the reference, one observes MA-SM reduces the rate loss by H 2 (η), compared to the antenna selection technique.…”