Abstract
In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.
Highlights
Multiple-input multiple-output (MIMO) is a critical technique that enables independent data transmission of multiple streams to increase data throughput and gains diversity benefits to improve link reliability
Inspired by studies for massive MIMO detectors to deal with the implementation issue, this paper proposes a novel deep learning (DL)-based detection technique that can extend the detection performance and benefits of orthogonal approximate message passing network (OAMPNet) to massive MIMO systems
In this paper, a novel iterative detector is proposed for high reliability in massive MIMO systems
Summary
Multiple-input multiple-output (MIMO) is a critical technique that enables independent data transmission of multiple streams to increase data throughput and gains diversity benefits to improve link reliability. The non-linear detection technique as a more advanced technique includes more complex signal processing, e.g., the interference cancellation based MIMO detectors are known as ordered successive interference cancellation (OSIC) and decision feedback equalizer (DFE) [11], [13] The iterative detector, such as approximate message passing (AMP), provides asymptotically optimal detection performance for large independent and identically distributed (i.i.d.) Rayleigh fading channels [12]. To solve the aforementioned problems of considering the DL algorithm as a black box and requiring a large number of training samples, model-driven detectors are designed to improve detection performance by combining existing detection techniques with trainable parameters, and such architecture is mainly referred to as a network. This paper discusses the computational complexity analysis between the proposed detector and OAMPNet, and this supports that the proposed detector can significantly reduce the computational complexity compared to OAMPNet in massive MIMO systems
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