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A family of low-complexity detection schemes based on channel matrixpuncturing targeted for large multiple-input multiple-output (MIMO) systems isproposed. It is well-known that the computational cost of MIMO detection basedon QR decomposition is directly proportional to the number of non-zero entriesinvolved in back-substitution and slicing operations in the triangularizedchannel matrix, which can be too high for low-latency applications involvinglarge MIMO dimensions. By systematically puncturing the channel to have aspecific structure, it is demonstrated that the detection process can beaccelerated by employing standard schemes such as chase detection, listdetection, nulling-and-cancellation detection, and sub-space detection on thetransformed matrix. The performance of these schemes is characterized andanalyzed mathematically, and bounds on the achievable diversity gain andprobability of bit error are derived. Surprisingly, it is shown that puncturingdoes not negatively impact the receive diversity gain in hard-output detectors.The analysis is extended to soft-output detection when computing per-layer bitlog-likelihood ratios; it is shown that significant performance gains areattainable by ordering the layer of interest to be at the root when puncturingthe channel. Simulations of coded and uncoded scenarios certify that theproposed schemes scale up efficiently both in the number of antennas andconstellation size, as well as in the presence of correlated channels. Inparticular, soft-output per-layer sub-space detection is shown to achieve a2.5dB SNR gain at $10^{-4}$ bit error rate in $256$-QAM $16\!\times\!16$ MIMO,while saving $77\%$ of nulling-and-cancellation computations.

Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis