Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multi-User Large-Scale MIMO Systems (1411.2791v1)

Abstract: For uplink large-scale MIMO systems, minimum mean square error (MMSE) algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose to exploit the Gauss-Seidel (GS) method to iteratively realize the MMSE algorithm without the complicated matrix inversion. To further accelerate the convergence rate and reduce the complexity, we propose a diagonal-approximate initial solution to the GS method, which is much closer to the final solution than the traditional zero-vector initial solution. We also propose a approximated method to compute log-likelihood ratios (LLRs) for soft channel decoding with a negligible performance loss. The analysis shows that the proposed GS-based algorithm can reduce the computational complexity from O(K^3) to O(K^2), where K is the number of users. Simulation results verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

• The paper proposes a novel low-complexity soft-output signal detection method for uplink large-scale MIMO systems using the Gauss-Seidel iterative method.
• It introduces a diagonal-approximate initial solution for the Gauss-Seidel iterations to accelerate convergence and avoid computationally expensive matrix inversion.
• The proposed method reduces computational complexity from O(K^3) to O(K^2), matching near-optimal MMSE performance within a few iterations.

Low-Complexity Soft-Output Signal Detection for Uplink Multi-User Large-Scale MIMO Systems

The paper "Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multi-User Large-Scale MIMO Systems" by Linglong Dai et al. addresses a critical challenge in the deployment of large-scale MIMO systems: efficient signal detection with minimal computational burden. This need arises from the increased multi-user interferences and the complexities associated with the high antenna count in large-scale MIMO, a technology heralded for its potential to significantly enhance spectral and energy efficiency in wireless communications.

Background and Motivation

In uplink scenarios of large-scale MIMO systems, where the base station (BS) must process signals arriving from multiple user equipments (UEs), an optimal signal detection approach is vital for maintaining high performance. The conventional Maximum Likelihood (ML) detector, despite its optimality, suffers from exponential complexity regarding the number of antennas, rendering it impractical. Linear alternatives, such as the Minimum Mean Square Error (MMSE) detector, offer a viable trade-off, achieving near-optimal performance with reduced complexity. However, MMSE still involves matrix inversion operations, which can be computationally prohibitive in large dimensions.

Proposed Methodology

The authors propose a novel detection algorithm that leverages the Gauss-Seidel (GS) iterative method to implement the MMSE estimation without the burden of direct matrix inversion. This approach capitalizes on the properties of the Hermitian positive definite structure inherent in the MMSE filtering matrix for large-scale MIMO systems. A key innovation is the introduction of a diagonal-approximate initial solution for the GS iterations, devised to quicken convergence by starting calculations from a point closer to the ultimate solution compared to a traditional zero-vector initial guess. By focusing on the diagonal dominance of the channel matrix and exploiting it to simplify calculations, the approach maintains performance while significantly lowering complexity.

Computational Efficiency and Results

Empirical analysis indicates a reduction in computational complexity from O(K^3) to O(K^2), representing a substantial efficiency gain, particularly relevant as the number of users K increases. Simulation results corroborate the efficacy of the method, with the GS-based algorithm matching the near-optimal performance of the traditional MMSE scheme within a few iterations. Moreover, it outperforms the Neumann series approximation approach, which has been previously proposed to tackle the matrix inversion dilemma with limited success in reducing complexity.

Implications and Future Directions

The proposed GS-based approach presents a significant advancement in the signal detection paradigm for large-scale MIMO systems, providing a pathway to deploying MIMO technology at scales conducive to next-generation wireless standards without prohibitive computational demands. This method opens avenues for further research in applying GS iterations beyond signal detection, potentially benefiting other areas of communication systems requiring efficient matrix inversions, such as downlink precoding.

Looking ahead, exploring more advanced initial conditions beyond diagonal approximations or hybrid techniques could further optimize convergence speed and reduce iteration counts. There is also scope to investigate the impact of different channel conditions and MIMO configurations to assess the broader applicability of this method across diverse network scenarios.

In conclusion, the paper by Linglong Dai et al. offers a noteworthy contribution to the ongoing quest for scalable and efficient implementations of large-scale MIMO systems, reinforcing its foundational role in future wireless communication systems.