A simplified hard output sphere decoder for large MIMO systems with the use of efficient search center and reduced domain neighborhood study

Multiple-input multiple-output (MIMO) with a spatial-multiplexing (SM) scheme is a topic of high interest for the next generation of wireless communications systems. At the receiver, neighborhood studies (NS) and lattice reduction (LR)-aided techniques are common solutions in the literature to approach the optimal and computationally complex maximum likelihood (ML) detection. However, the NS and LR solutions might not offer optimal performance for large dimensional systems, such as large number of antennas, and high-order constellations when they are considered separately. In this paper, we propose a novel equivalent metric dealing with the association of these solutions by introducing a reduced domain neighborhood study. We show that the proposed metric presents a relevant complexity reduction while maintaining near-ML performance. Moreover, the corresponding computational complexity is shown to be independent of the constellation size, but it is quadratic in the number of transmit antennas. For instance, for a 4 × 4 MIMO system with 16-QAM modulation on each layer, the proposed solution is simultaneously near-ML with perfect and real channel estimation and ten times less complex than the classical neighborhood-based K-best solution.