LR-Aided Linear Detection Combined with Adaptive QRD-M in MIMO-OFDM Systems

This paper proposes an LR-aided adaptive QRD-M detector for complexity reduction in MIMO-OFDM systems. The proposed approach integrates lattice-reduction preprocessing with an adaptive candidate selection strategy to control the search width of the QRD-M algorithm. Unlike the conventional QRD-M detector with a fixed candidate size, the proposed method dynamically adjusts the number of surviving paths to limit unnecessary computations while preserving detection reliability. Simulation results demonstrate that the proposed detector achieves near-optimal detection performance in frequency-selective MIMO-OFDM channels, reaching a symbol error rate (SER) of 10⁻⁵ for an 8×8 system using 16-QAM modulation at high SNR. For a 4×4 configuration, the proposed method reduces computational complexity by approximately 26% in terms of average squared Euclidean distance (SED) computations compared with the conventional QRD-M detector. Analytical FLOP evaluation further confirms consistent complexity savings as the antenna dimension increases. These results demonstrate an improved performance–complexity trade-off and strong scalability for practical large-scale MIMO deployments.