2012/09/29 by Song-Nam Hong, Giuseppe Caire, Hong, Song-Nam +1 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Wireless Communication Security Techniques
paper · pdf · doi:10.48550/arxiv.1210.0160
openalex publication_date 2012/09/29 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We study a distributed antenna system where L antenna terminals (ATs) are connected to a Central Processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). We contribute to the subject in the following ways: 1) for the uplink, we apply the "Compute and Forward" (CoF) approach and examine the corresponding system optimization at finite SNR; 2) For the downlink, we propose a novel precoding scheme nicknamed "Reverse Compute and Forward" (RCoF); 3) In both cases, we present low-complexity versions of CoF and RCoF based on standard scalar quantization at the receivers, that lead to discrete-input discrete-output symmetric memoryless channel models for which near-optimal performance can be achieved by standard single-user linear coding; 4) For the case of large R0, we propose a novel "Integer Forcing Beamforming" (IFB) scheme that generalizes the popular zero-forcing beamforming and achieves sum rate performance close to the optimal Gaussian Dirty-Paper Coding. The proposed uplink and downlink system optimization focuses specifically on the ATs and UTs selection problem. We present low-complexity ATs and UTs selection schemes and demonstrate, through Monte Carlo simulation in a realistic environment with fading and shadowing, that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the non-integer penalty affecting CoF/RCoF at high SNR. Comparison with other state-of-the art information theoretic schemes, such as "Quantize reMap and Forward" for the uplink and "Compressed Dirty Paper Coding" for the downlink, show competitive performance of the proposed approaches with significantly lower complexity.