2017/02/26 by Ahmed Hindy, Hindy, Ahmed, Aria Nosratinia +1
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Algorithm #Channel (broadcasting) #Channel capacity #Channel state information #Computer science #Control theory (sociology) #Cooperative Communication and Network Coding #Decoding methods #Electronic engineering #Encoder #Engineering #FOS: Computer and information sciences #Fading #Information Theory (cs.IT) #MIMO #Mathematics #Precoding #Rayleigh fading #Telecommunications #Topology (electrical circuits) #Wireless #Wireless Communication Security Techniques #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1702.08099
published in arXiv (Cornell University) (Cornell University) · Accepted at IEEE Transactions on Communications
arxiv created 2017/02/26 · openalex publication_date 2017/02/26 · arxiv updated 2017/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
For ergodic fading, a lattice coding and decoding strategy is proposed and its performance is analyzed for the single-input single-output (SISO) and multiple-input multiple-output (MIMO) point-to-point channel as well as the multiple-access channel (MAC), with channel state information available only at the receiver (CSIR). At the decoder a novel strategy is proposed consisting of a time-varying equalization matrix followed by decision regions that depend only on channel statistics, not individual realizations. Our encoder has a similar structure to that of Erez and Zamir. For the SISO channel, the gap to capacity is bounded by a constant under a wide range of fading distributions. For the MIMO channel under Rayleigh fading, the rate achieved is within a gap to capacity that does not depend on the signal-to-noise ratio (SNR), and diminishes with the number of receive antennas. The analysis is extended to the K-user MAC where similar results hold. Achieving a small gap to capacity while limiting the use of CSIR to the equalizer highlights the scope for efficient decoder implementations, since decision regions are fixed, i.e., independent of channel realizations.