Channel estimation with Bayesian framework based on compressed sensing algorithm in multimedia transmission system

With the emergence of Wireless multimedia transmission system, the distribution of multimedia contents has now become a reality. To solve the problem of stability in the process of transmission, this paper proposes an improved channel estimation with Bayesian framework based on compressed sensing algorithm in multimedia transmission system. The algorithm uses the sparse characteristics of the channel and can reduce the pilot sequence length under the same conditions. Due to the high complexity of the support agnostic Bayesian matching pursuit algorithm, our algorithm improves the support set, which proposed Expectation Prune Matching Pursuit algorithm in the paper. At each sparsity level of the channel, an expanded support set is given by adding some positions corresponding to the atoms that have a larger inner product value with the current residual signal. Then the best support set is obtained by removing the wrong positions and adopting the idea of Bayesian estimation algorithm in the expanded support set. The estimated channel and the relative probability of the best support set at each sparse level are calculated. Finally, the expectation of the channel is calculated and regarded as the estimation of the channel. Compared with comparison algorithm in the error and bit error rate under different SNR conditions, our proposed algorithm can reduce the computational complexity while maintaining the estimation accuracy.