In this paper we evaluate the performance of ½ rate convolution coding with different modulation techniques such as Binary phase shift keying (BPSK), Quadrature phase shift keying (QPSK) and Quadrature amplitude modulation (QAM-16) for direct sequence code division multiple access(DS-CDMA) system using maximal ratio combining (MRC) and equal gain combining (EGC) diversity techniques over Rician fading channel. The performance of ½ rate convolution coding with different modulation techniques are analyzed in terms of Bit error rate (BER) and Signal to noise ratio (SNR). Based on simulation results we have concluded that we obtain better gain in SNR performance when ½ rate convolution coding is used with different modulation techniques.

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Compressive sensing (CS) is a novel paradigm for acquiring signals, based on the idea that one can efficiently capture all the information of sparse domain by sampling only a part of signal through sub-Nyquist signal acquisition. As Bayesian inference is substituting conventional CS methods, the work employ Bayesian inference which depict CS matrix by a factor graph to accelerate both encoding and belief propagation (BP) decoding. Two state mixture Gaussian model is used to model prior for sparse signal. To decode a length-N signal having K large coefficients, our BCS-BP decoding algorithm uses O (K log (N)) and O (N log2 (N)) computation. BCS-BP algorithm is easily versatile to various signal models.