Generalized feedback detection for MIMO systems

In this paper, we present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems. We propose a generalized feedback detector (GFD) by modifying the classical feedback decoding algorithm for convolutional codes. When the three controlling parameters of the GFD vary, the diversity order of the GFD varies between 1 and N and the SNR gain also varies. Many previous MIMO detectors are special cases of our GFD. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed using a tree data structure. The complexity of the GFD varies between those of maximum-likelihood (ML) detection and zero-forcing decision feedback detector (ZF-DFD). Our proposed GFD provides a flexible performance-complexity tradeoff

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