Adaptive Dijkstra’s Search Algorithm for MIMO detection

Employing Maximum Likelihood (ML) algorithm for signal detection in a large-scale Multiple-Input- Multiple-Output (MIMO) system with high modulation order is a computationally expensive approach. In this paper an adaptive best first search detection algorithm is proposed. The proposed Adaptive Dijkstra’s Search (ADS) algorithm exploits the resources available in the search procedure to reduce the required number of nodes to be visited in the tree. A tunable parameter is used to control the number of the best possible candidate nodes required. Unlike the conventional DS, the ADS algorithm results in signal detection with low computation complexity and quasi-optimal performance for systems under low and medium SNR regimes. Simulation results demonstrate a 25% computational complexity reduction, compared to the conventional DS

Low-Complexity Precoding Design for Massive Multiuser MIMO Systems Using Approximate Message Passing

A practical challenge in the precoding design of massive multiuser multiple-input multiple-output (MIMO) systems is to facilitate hardware-friendly implementation. To achieve this, we propose a low peak-to-average power ratio (PAPR) precoding based on an approximate message passing (AMP) algorithm to minimize multiuser interference (MUI) in massive multiuser MIMO systems. The proposed approach exhibits fast convergence and low complexity characteristics. Compared with a conventional constant-envelope precoding and an annulus-constrained precoding, simulation results demonstrate that the proposed AMP precoding is superior both in terms of computational complexity and average running time. In addition, the proposed AMP precoding exhibits a much desirable tradeoff between MUI suppression and PAPR reduction. These findings indicate that the proposed AMP precoding is a suitable candidate for hardware implementation, which is very appealing for massive MIMO systems