Memory-Constrained Tree Search Detection and New Ordering Schemes

Hardware implementations of tree search-based multiple-input multiple-output (MIMO) detection often have limited performance due to large memory requirement or high computational complexity of sophisticated MIMO detection algorithms. In this paper, we propose new tree search-based detection algorithms that achieve maximum-likelihood (ML) performance under any given memory constraints and with reduced computational complexity. To this end, we make two main contributions. First, we propose a memory-constrained tree search (MCTS) algorithm that bridges the gap between the sphere decoding (SD) and stack algorithms. Our MCTS algorithm dynamically adapts to any pre-specified memory constraint and offers a graceful tradeoff between computational complexity and memory requirement while maintaining the ML performance. When the memory size is set as the minimum, our MCTS algorithm is similar to the SD algorithm. As the memory size increases, the average computational complexity of our MCTS algorithm decreases. When the memory size becomes large, our MCTS algorithm is similar to the stack algorithm, having similar average computational complexity but requiring significantly less memory. To further reduce the computational complexity of tree search-based ML detection algorithms, we propose novel ordering schemes that can be easily embedded in the QR decomposition and take into account both the channel matrix and the received signal (noise); simulation results show that our ordering schemes lead to reduced average computational complexity for the SD and MCTS algorithms, and the reduction is significant at low to medium signal-to-noise ratio region.