2014/03/24 by Wasim Huleihel, Neri Merhav, Huleihel, Wasim +3
Computer Science · Engineering · Mathematics · #Advanced MIMO Systems Optimization #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1403.5874
arxiv created 2014/03/24 · openalex publication_date 2014/03/24 · arxiv updated 2014/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We take an information theoretic perspective on a classical sparse-sampling noisy linear model and present an analytical expression for the mutual information, which plays central role in a variety of communications/processing problems. Such an expression was addressed previously either by bounds, by simulations and by the (non-rigorous) replica method. The expression of the mutual information is based on techniques used in [1], addressing the minimum mean square error (MMSE) analysis. Using these expressions, we study specifically a variety of sparse linear communications models which include coding in different settings, accounting also for multiple access channels and different wiretap problems. For those, we provide single-letter expressions and derive achievable rates, capturing the communications/processing features of these timely models.