Neighborhood Search Aided QRDM with Adaptive Node Selection for MIMO-OFDM Systems
Sandesh Rao Mattu, Bharath Shamasundar, Hari Krishna Boddapati, Ashok Kumar Reddy Chavva · 2024
QR decomposition based M-algorithm (QRDM) is a tree search method that achieves near-optimal signal detection in multiple-input multiple-output (MIMO) systems at a significantly lower complexity compared to maximum-likelihood detection (MLD). However, the complexity of QRDM still remains much higher than linear detectors typically employed in practical systems. While there exist some low-complexity variants of QRDM, they often sacrifice performance for complexity. The present work proposes a robust low-complexity variant of QRDM that leverages an initial linear solution to introduce two fundamental optimizations. First, it computes the costs for only k-bit neighbors of the linear solution in each layer, instead of all symbols of the modulation alphabet. Second, it dynamically changes the number of surviving nodes in each layer based on a threshold on the accumulated cost. These enhancements lead to a significant reduction in complexity while also achieving improved performance compared to conventional QRDM and its existing low-complexity variants. Notably, the proposed detector addresses the problem of premature pruning of reliable solutions in early layers, which is the key cause of sub-optimality in existing QRDM variants.