Uplink Multiuser MIMO Detection Scheme with Reduced Computational Complexity

The wireless communication systems with multiple antennas have recently received significant attention due to their higher capacity and better immunity to fading channels as compared to single antenna systems. A fast antenna selection scheme has been introduced for the uplink multiuser multiple-input multiple-output (MIMO) detection to achieve diversity gains, but the computational complexity of the fast antenna selection scheme in multiuser systems is very high due to repetitive pseudo-inversion computations. In this paper, a new uplink multiuser detection scheme is proposed adopting a switch-and-examine combining (SEC) scheme and the Cholesky decomposition to solve the computational complexity problem. K users are considered that each users is equipped with two transmit antennas for Alamouti space-time block code (STBC) over wireless Rayleigh fading channels. Simulation results show that the computational complexity of the proposed scheme is much lower than the systems with exhaustive and fast antenna selection, while the proposed scheme does not experience the degradations of bit error rate (BER) performances

Efficient MIMO detection for high-order QAM constellations in time dispersive channels

In this paper, we apply a generalized form of the alternating direction method of multipliers (ADMM) and derive a multiple-input multiple-output (MIMO) detection algorithm for single carrier transmissions in time dispersive channels. The proposed algorithm supports different penalty parameters for each individual subcarrier and antenna and also includes a relaxation coefficient in the iterations. Besides evaluating the impact of these parameters, a method is presented for the automatic selection of the penalty. It is shown through simulations that very competitive performances can be obtained with the proposed approach for systems with high-order modulation combined with large antenna settings.info:eu-repo/semantics/acceptedVersio

A lower complexity K best algorithm for multiple input and multiple output detection

This paper presents Multiple Input Multiple Output (MIMO) detection steps using tree search based method known as the ‘K’ best algorithm. This low complexity algorithm is based on probabilistic approach of sphere decoding with self adjustable capability depending on the levels (root, branch, leaf etc.) of a tree. While the tree was searched to estimate the transmitted symbols level by level, the algorithm took into account the effect of the undetected symbols in the search criteria. Simulation results showed that the proposed method reduced complexity (in terms of the average number of visited nodes) about 10% for higher (medium to high) signal to noise ratio (SNR) values without degrading the system BER performance

Learning Vector Quantization-Aided Detection for MIMO Systems

In this letter, the learning vector quantization (LVQ) from machine learning (ML) is adopted into the large-scale multiple-input multiple-output (MIMO) detection to improve the detection performance. Inspired by the decision region from lattice decoding, the random Gaussian noises are applied in the proposed learning vector quantization-aided detection (LVQD) algorithm for data generation. Then, based on the classification, supervised learning is activated to update the targeted prototype vector iteratively, so as to a better detection performance. Meanwhile, the decoding radius in lattices is also used to serve as a preprocessing for LVQD, which leads to an efficient detection without performance loss. Finally, simulation results confirm that considerable performance gain can be achieved by the proposed LVQD algorithm, which suits well for suboptimal detection schemes

Reduced Complexity by Combining Norm based Ordering MMSE-BSIDE Detection in MIMO Systems

The breadth first signal decoder (BSIDE) is well known for its optimal maximum likelihood (ML) performance with lesser complexity. In this paper, we analyze a multiple-input multiple-output (MIMO) detection scheme that combines; column norm based ordering minimum mean square error (MMSE) and BSIDE detection methods. The investigation is carried out with a breadth first tree traversal technique, where the computational complexity encountered at the lower layers of the tree is high. This can be eliminated by carrying detection in the lower half of the tree structure using MMSE and upper half using BSIDE, after rearranging the column of the channel using norm calculation. The simulation results show that this approach achieves 22% of complexity reduction for 2x2 and 50% for 4x4 MIMO systems without any degradation in the performance

Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter

A Novel VLSI Architecture of Fixed-complexity Sphere Decoder

Fixed-complexity Sphere Decoder (FSD) is a recently proposed technique for Multiple-Input Multiple-Output (MIMO) detection. It has several outstanding features such as constant throughput and large potential parallelism, which makes it suitable for efficient VLSI implementation. However, to our best knowledge, no VLSI implementation of FSD has been reported in the literature, although some FPGA prototypes of FSD with pipeline architecture have been developed. These solutions achieve very high throughput but at very high cost of hardware resources, making them impractical in real applications. In this paper, we present a novel four-nodes-per-cycle parallel architecture of FSD, with a breadth-first processing that allows for short critical path. The implementation achieves a throughput of 213.3 Mbps at 400 MHz clock frequency, at a cost of 0.18 mm2 Silicon area on 0.13{\mu}m CMOS technology. The proposed solution is much more economical compared with the existing FPGA implementations, and very suitable for practicl applications because of its balanced performance and hardware-complexity; moreover it has the flexibility to be expanded into an eight-nodes-per-cycle version in order to double the throughput.Comment: 8 pages, this paper has been accepted by the conference DSD 201