An Improved LR-aided K-Best Algorithm for MIMO Detection

TL;DR

This paper introduces a more efficient LR-aided K-best algorithm for MIMO detection that reduces computational complexity using a priority queue, enabling high-performance detection in large MIMO systems.

Contribution

The paper proposes a novel LR-aided K-best algorithm with lower complexity by utilizing a priority queue, making large MIMO detection more feasible.

Findings

Complexity reduced to O(N_t^2 K + N_t K log_2(K))

Enables detection in large MIMO systems like 50x50

Performance approaches AWGN channel as antennas increase

Abstract

Recently, lattice reduction (LR) technique has caught great attention for multi-input multi-output (MIMO) receiver because of its low complexity and high performance. However, when the number of antennas is large, LR-aided linear detectors and successive interference cancellation (SIC) detectors still exhibit considerable performance gap to the optimal maximum likelihood detector (MLD). To enhance the performance of the LR-aided detectors, the LR-aided K-best algorithm was developed at the cost of the extra complexity on the order $\mathcal{O}(N_t^2 K + N_t K^2)$, where $N_t$ is the number of transmit antennas and $K$ is the number of candidates. In this paper, we develop an LR-aided K-best algorithm with lower complexity by exploiting a priority queue. With the aid of the priority queue, our analysis shows that the complexity of the LR-aided K-best algorithm can be further reduced to $\mathcal{O}(N_t^2 K + N_t K {\rm log}_2(K))$. The low complexity of the proposed LR-aided K-best algorithm allows us to perform the algorithm for large MIMO systems (e.g., 50x50 MIMO systems) with large candidate sizes. Simulations show that as the number of antennas increases, the error performance approaches that of AWGN channel.