MIMO Assisted Space-Code-Division Multiple-Access: Linear Detectors and Performance over Multipath Fading Channels

In this contribution we propose and investigate a multiple-input multiple-output space-division, code-division multiple-access (MIMO SCDMA) scheme. The main objective is to improve the capacity of the existing DS-CDMA systems, for example, for supporting an increased number of users, by deploying multiple transmit and receive antennas in the corresponding systems and by using some advanced transmission and detection algorithms. In the proposed MIMO SCDMA system, each user can be distinguished jointly by its spreading code-signature and its unique channel impulse response (CIR) transfer function referred to as spatial-signature. Hence, the number of users might be supported by the MIMO SCDMA system and the corresponding achievable performance are determined by the degrees of freedom provided by both the code-signatures and the spatial-signatures, as well as by how efficiently the degrees of freedom are exploited. Specifically, the number of users supported by the proposed MIMO SCDMA can be significantly higher than the number of chips per bit, owing to the employment of space-division. In this contribution space-time spreading (STS) is employed for configuring the transmitted signals. Three types of low-complexity linear detectors, namely correlation, decorrelating and minimum mean-square error (MMSE), are considered for detecting the MIMO SCDMA signals. The BER performance of the MIMO SCDMA system associated with these linear detectors are evaluated by simulations, when assuming that the MIMO SCDMA signals are transmitted over multipath Rayleigh fading channels. Our study and simulation results show that MIMO SCDMA assisted by multiuser detection is capable of facilitating joint space-time de-spreading, multipath combining and receiver diversity combining, while simultaneously suppressing the multiuser interfering signals

Optimizing MIMO Detection with DM-Detnet in 6G Networks

Artificial intelligence (AI) has transformed multiple inputs multiple output (MIMO) technology into a key enabling technology for beyond fifth-generation (B5G) technology such as sixth-generation (6G) networks. However, MIMO technology faces some crucial research challenges, among which signal detection is a significant problem. This paper presents an optimized deep learning-based MIMO detection method called deep MIMO detection network (DM-Detnet) for MIMO detection. The light network architecture of DM-Detnet allows signal detection to be performed in a layer-by-layer manner. This work primarily concentrates on the effect of signal-to-noise ratio (SNR) points on network training and testing. We conducted a detailed simulation study to analyze the performance of our model based on specific low and high SNR points. With intensive training and optimization, the DM-Detnet model achieves better performance in MIMO scenarios. Simulation results show that the optimized DM-Detnet achieves better symbol error rate (SER) performance and is also able to achieve lower computational complexity than benchmark conventional MIMO detectors.</p

Integer-Forcing MIMO Linear Receivers Based on Lattice Reduction

A new architecture called integer-forcing (IF) linear receiver has been recently proposed for multiple-input multiple-output (MIMO) fading channels, wherein an appropriate integer linear combination of the received symbols has to be computed as a part of the decoding process. In this paper, we propose a method based on Hermite-Korkine-Zolotareff (HKZ) and Minkowski lattice basis reduction algorithms to obtain the integer coefficients for the IF receiver. We show that the proposed method provides a lower bound on the ergodic rate, and achieves the full receive diversity. Suitability of complex Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm (CLLL) to solve the problem is also investigated. Furthermore, we establish the connection between the proposed IF linear receivers and lattice reduction-aided MIMO detectors (with equivalent complexity), and point out the advantages of the former class of receivers over the latter. For the $2 \times 2$ and $4\times 4$ MIMO channels, we compare the coded-block error rate and bit error rate of the proposed approach with that of other linear receivers. Simulation results show that the proposed approach outperforms the zero-forcing (ZF) receiver, minimum mean square error (MMSE) receiver, and the lattice reduction-aided MIMO detectors.Comment: 9 figures and 11 pages. Modified the title, abstract and some parts of the paper. Major change from v1: Added new results on applicability of the CLLL reductio

Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.Comment: 10 figures, 3 tables; IEEE Transactions on Wireless Communications, 201