Co-Ordinate Interleaved Spatial Multiplexing with Channel Knowledge at Transmitter and Receiver

Abstract

Spatial multiplexing (SM) over multiple-input multiple-output wireless channels provides significant capacity gains. In a SM scheme, the eigenmode having the least signal-to-noise ratio (SNR), degrades the overall error rate performance. In this paper, we propose co-ordinate interleaved spatial multiplexing that maximizes the minimum SNR over all eigenmodes. This linearly decodable SM scheme needs the knowledge of the right singular vectors of the channel at the transmitter, and the singular values and left singular vectors at the receiver. We derive the SNR expressions for the proposed scheme and compare its performance with other closed-loop schemes using computer simulations.

Co-ordinate Interleaving over Parallel Gaussian Channels

“…CI over parallel Gaussian channels: We generalize CI for spatial multiplexing reported in [3] to CI over any set of N parallel Gaussian channels and describe it below.…”

“…This paper studies CI from the mutual information perspective. With the insight gained from MWF and the precoding scheme of [3], we propose employing CI over parallel Gaussian channels for improving sum MI.…”

Signal space diversity for spatial multiplexing

“…To solve the problem, interleaving the co-ordinates of twodimensional QAM symbols across eigenchannels was first proposed in [3]. Building on [3], in the current work, we divide the eigenchannels into sets and in each set we interleave the co-ordinates of multi-dimensional symbols across the different eigenchannels.…”

“…Our system operates under a short term power constraint E[s † s] ≤ P . We generalize the scheme in [3] for significantly improved performance with arbitrary n using multidimensional QAM constellations, which were first proposed in [5] as a method of improving throughput at the expense of decoding complexity. We partition the n eigenchannels into sets of eigenchannels Q 1 , Q 2 , .…”

“…Building on [3], in the current work, we divide the eigenchannels into sets and in each set we interleave the co-ordinates of multi-dimensional symbols across the different eigenchannels. Use of rotated multi-dimensional constellations to exploit signal space diversity was first proposed in [4] and analyzed in [5].…”