2017/10/30 by Stelios Stefanatos, Stefanatos, Stelios, Gerhard Wunder +1
Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1710.10796
openalex publication_date 2017/10/30 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Towards reducing the training signaling overhead in large scale and dense\ncloud radio access networks (CRAN), various approaches have been proposed based\non the channel sparsification assumption, namely, only a small subset of the\ndeployed remote radio heads (RRHs) are of significance to any user in the\nsystem. Motivated by the potential of compressive sensing (CS) techniques in\nthis setting, this paper provides a rigorous description of the performance\nlimits of many practical CS algorithms by considering the performance of the,\nso called, oracle estimator, which knows a priori which RRHs are of\nsignificance but not their corresponding channel values. By using tools from\nstochastic geometry, a closed form analytical expression of the oracle\nestimator performance is obtained, averaged over distribution of RRH positions\nand channel statistics. Apart from a bound on practical CS algorithms, the\nanalysis provides important design insights, e.g., on how the training sequence\nlength affects performance, and identifies the operational conditions where the\nchannel sparsification assumption is valid. It is shown that the latter is true\nonly in operational conditions with sufficiently large path loss exponents.\n