Markov Chain Analysis for Compressed Sensing based Random Access in Cellular Systems

Abstract

In large scale cellular systems, it is needed to simultaneously support a massive number of users with low latency. However, in conventional cellular systems, random access performance is severely degraded as the number of users increases due to frequent collisions. Recently, compressed sensing based random access is touted as a good solution to improve random access performance of cellular systems. To investigate latency performance of compressed based random access in cellular systems, we model the random access process by a Markov chain, and derive successful access probability of users and expected access latency based on steady state analysis.