Abstract
We present a computationally efficient blind sequential detection method for data transmitted over a sparse intersymbol interference channel. Unlike blind sequential detection methods designed for general channels, the proposed method exploits the channel sparsity by using estimated channel sparsity to assist in the detection of the transmitted sequence. A Gaussian mixture model is used to describe sparse channels, and two tree-search strategies are applied to estimate the channel sparsity and the transmitted sequence, respectively. To demonstrate the performance improvement achieved by the proposed blind detector, we compare it to conventional joint channel and sequence detection methods that use sparse channel estimation techniques. Simulation results show that the proposed detector not only reduces computational complexity compared to existing methods but also provides superior performance, particularly when the signal to noise ratio is low.
Highlights
Intersymbol interference (ISI) poses a great challenge for reliable high-speed wireless communications, significantly degrading system performance, and channel equalization is typically employed at the receiver to mitigate its harmful effects
6 Conclusions We have developed and evaluated a tree-based sequential detection method for detecting data transmitted over a sparse ISI channel
The comparison is made for length Lh = 10 sparse channels with La = 3 active taps
Summary
Intersymbol interference (ISI) poses a great challenge for reliable high-speed wireless communications, significantly degrading system performance, and channel equalization is typically employed at the receiver to mitigate its harmful effects. One approach to combating time-varying distortion is via adaptive equalizers [1] that first use training symbols to estimate the channel and perform conventional detection using the channel estimate. When blind equalization approaches that have been designed for general channels are applied to sparse channels, all channel coefficients are treated as significant when estimating the transmitted signal, yielding highcomputational burden for the receiver and decreasing the accuracy of the resulting data detection. We propose a computationally efficient blind sequential detection method for recovering data transmitted over sparse ISI channels.
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