Abstract
For multi-user uplink massive multiple input multiple output (MIMO) systems, minimum mean square error (MMSE) criterion-based linear signal detection algorithm achieves nearly optimal performance, on condition that the number of antennas at the base station is asymptotically large. However, it involves prohibitively high complexity in matrix inversion when the number of users is getting large. A low-complexity soft-output signal detection algorithm based on improved Kaczmarz method is proposed in this paper, which circumvents the matrix inversion operation and thus reduces the complexity by an order of magnitude. Meanwhile, an optimal relaxation parameter is introduced to further accelerate the convergence speed of the proposed algorithm and two approximate methods of calculating the log-likelihood ratios (LLRs) for channel decoding are obtained as well. Analysis and simulations verify that the proposed algorithm outperforms various typical low-complexity signal detection algorithms. The proposed algorithm converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.
Highlights
Massive multiple-input multiple-output (MIMO) technology dramatically expands the capacity of wireless communication systems without increasing the system bandwidth and transmit power and effectively resolves the contradiction between the limited spectrum resource and the rapid growth in capacity demand
A low complexity soft output signal detection algorithm based on Kaczmarz iteration is proposed
The algorithm is tailored for uplink massive multiple input multiple output (MIMO) system to avoid high-dimensional matrix inversion required by the minimum mean square error (MMSE) criterion
Summary
Massive multiple-input multiple-output (MIMO) technology dramatically expands the capacity of wireless communication systems without increasing the system bandwidth and transmit power and effectively resolves the contradiction between the limited spectrum resource and the rapid growth in capacity demand. To the authors’ best knowledge, the MMSE criterion-based low complexity signal detection algorithms can be basically categorized into three typical types, namely the approximate matrix inversion algorithms (AMIA), the iterative approaches for solving linear equations (IASLE), and the matrix gradient search methods (MGSM). In order to obtain an easy-to-implement multi-user signal detection scheme for the uplink massive MIMO system, we propose a soft decision algorithm based on the Kaczmarz iteration [20,21,22]. The Kaczmarz algorithm was proposed to serve as a matrix-inverse approximation method for implementing the MMSE criterion-based signal detection with reduced complexity [22].
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