Performance comparison of Compressive Spread Spectrum communication system using different reconstruction algorithms

Compressive Sensing is a technique which enables sampling below Nyquist rate, and helps in reconstruction of original signal from sub Nyquist samples. The structure of Spread Spectrum communication system is capable of incorporating Compressive Sensing acquisition system. The effective integration of Spread Spectrum communication system and Compressive Sensing acquisition system results in Compressive Spread Spectrum communication system. In this paper, the effect of different sparsity levels and compression rates on different Compressive Sensing reconstruction algorithms such as Orthogonal Matching Pursuit, Subspace Pursuit, Compressive Sampling Matching Pursuit is studied using Mean Square Error verses Signal to Noise Ratio analysis. The performance of Compressive Spread Spectrum communication system for these reconstruction algorithms is compared using Bit Error Rate verses Signal to Noise Ratio analysis. The analysis of the system for different sparsity levels and compression rates is also done. From the results, it is evident that as compared to other two algorithms, Orthogonal Matching Pursuit shows better performance when used for reconstruction in Compressive Spread Spectrum communication system. Lower sparsity level of signal favours the performance of system. Similarly, performance of the system improves as number of measurements used to represent the signal in compressed domain increases.