We present a computationally efficient blind sequential detection method for data transmitted over a sparse\nintersymbol interference channel. Unlike blind sequential detection methods designed for general channels, the\nproposed method exploits the channel sparsity by using estimated channel sparsity to assist in the detection of the\ntransmitted sequence. A Gaussian mixture model is used to describe sparse channels, and two tree-search strategies\nare applied to estimate the channel sparsity and the transmitted sequence, respectively. To demonstrate the\nperformance improvement achieved by the proposed blind detector, we compare it to conventional joint channel\nand sequence detection methods that use sparse channel estimation techniques. Simulation results show that the\nproposed detector not only reduces computational complexity compared to existing methods but also provides\nsuperior performance, particularly when the signal to noise ratio is low.
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