A Vector-Perturbation Technique for Near-Capacity Multiantenna Multiuser Communication—Part I: Channel Inversion and Regularization

TLDR

Theoretical work shows that multi‑antenna multi‑user systems can achieve high sum capacity, but practical schemes lag because the channel matrix’s singular values are widely spread. The authors propose a simple encoding algorithm that achieves near‑capacity sum rates of tens of bits per channel use. The method regularizes channel inversion, employs a sphere encoder to perturb data, and optimizes the inverse to improve the signal‑to‑interference‑plus‑noise ratio. The algorithm achieves near‑capacity rates, and regularization yields linear growth in sum capacity, especially at low SNRs, though an extra step is required for full near‑capacity across all SNRs. This work is presented in two parts.

Abstract

Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the power of the transmitted signal. This work is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signal-to-interference-plus-noise ratio at the receivers. Regularization enables linear growth and works especially well at low signal-to-noise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve near-capacity performance at all SNRs.