Stopping Rule-Based Iterative Tree Search for Low-Complexity Detection in MIMO Systems
Breadth first tree search (BFTS) algorithms are known to provide a close to maximum likelihood (quasi-ML) solution at a low-complexity if the received sequence is detected in the right sequence order. However, finding the right sequence order has an exponential overhead. In view of this, we propose to repeatedly apply a BFTS algorithm to all sequence orders. Since it will test all the orders, it is expected to achieve quasi-ML performance. However, this will increase the complexity because of redundant iterations. The complexity can be reduced if we can stop the iterations as soon as a quasi-ML solution is achieved. For this, we propose two stopping rules, one relies on a constellation based heuristic and the other one uses the distribution of ML cost. It is found that their complexity curves have a cross-over point. Thus, a combination of the two rules provides a quasi-ML error performance at a low-complexity for uncoded as well as coded systems. We further show that the proposed stopping rule can reduce the complexity of depth first tree search algorithms also. Last, for large MIMO systems, compared with existing algorithms, it is found to be exceptionally better in terms of both error performance and complexity.