Compressed Sensing Algorithm for Pattern Division Multiple Access (PDMA) in 5G Radio Networks

摘要

In this paper, a 5G technique called Pattern Division Multiple Access (PDMA) using Compressed Sensing Algorithm is put forward for communication systems. A resource group is defined using PDMA patterns with reference to either time or frequency for data transmission. Users sharing the same resources are differentiated by utilizing those set of patterns, and the patterns are designed with different sparsity so as to enhance the overall performance by preserving the estimation complexity to a huge extent. In a field containing many users, the number of people taking part in transfer of messages is quite less. Hence considering those inactive users to be sparse, certain Compressed Sensing (CS) methods are used to analyze an underdetermined PDMA system using Compressive Sampling Matching Pursuit (CoSaMP) and Orthogonal Matching Pursuit (OMP) Algorithm where the active locations are caught for estimating the message signal. Hence, an improved execution in terms of Spectral efficiency, bit error rate and uplink-downlink performance is obtained through CoSaMP algorithm.