Multiuser Detection Research Articles

A novel multiuser detection based on honey bees mating optimisation and tabu search algorithm for SDMA-OFDM systems

A novel multiuser detection based on honey bees mating optimisation and tabu search algorithm for SDMA-OFDM systems

Iterative Multiuser Detection and Decoding for Sparse Code Multiple Access Combined With Spectrally Efficient Frequency Division Multiplexing

Sparse code multiple access (SCMA) combined with spectrally efficient frequency division multiplexing (SEFDM) is a wireless air-interface technology with dual non-orthogonal characteristic which can meet the high spectral efficiency requirement of the future Internet of Things (IoT). In this paper, an iterative multiuser detection/decoder (IMUDD) method for SCMA-SEFDM is proposed, which performs soft information iteration in a turbo style. IMUDD can effectively achieve the multiple-access interference (MAI), inter-symbol interference (ISI) and inter-carrier interference (ICI) cancellation through an external iteration and three internal iterative processes. At each iteration, extrinsic information is extracted from detection and decoding stages, then it is used as a priori information in the next iteration. Furthermore, to reduce the computational complexity of IMUDD scheme, a logarithm domain IMUDD is adopted and analyzed. The simulation results show that IMUDD has a noticeable performance improvement compared to the traditional turbo structured receivers over the multipath fading channel or the additive white Gaussian noise (AWGN) channel.

Low-Overhead Evaluation of Multiuser Detection Performance for Physical-Layer Multiple Access Systems

In order to facilitate massive connectivity in fifth-generation (5G) systems and make full use of advanced coding schemes, designing effective multiuser detection algorithms is necessary for mitigating the interference in multiple access systems. To evaluate the performance of different detection algorithms, the conventional method is implementing the Monte Carlo (MC) simulation to estimate the detection error rate. In this paper, we present a novel simulation scheme based on adaptive importance-sampling (AIS) theory, which accelerates the simulation speed for estimating the extremely low detection error rate in multiple access systems. Specifically, by restricting the generation of random codewords to the joint Gaussian distribution biased with scaling parameters, two algorithms are proposed to determine the optimal biased parameters such that the estimated variance or the cross-entropy resulted in the AIS simulation is minimized respectively. Our proposed simulation scheme is compared with the standard MC simulation in the performance evaluation of message passing algorithm (MPA) for uplink sparse code multiple access (SCMA) system. Numerical results show that our proposed scheme provides a feasible estimation of extremely low detection error rate and achieves significant performance gain with reduced simulation overhead.

An Uplink SCMA Codebook Design Combining Probabilistic Shaping and Geometric Shaping

Sparse Code Multiple Access (SCMA) is a promising non-orthogonal multiple access scheme owning to the shaping gain of its multi-dimensional codebook. Most of the existing designs of the codebook are based on geometric shaping (GS), while probabilistic shaping (PS) has important advantages in increasing channel capacity and reducing bit error rate. In this paper, an uplink SCMA codebook optimization algorithm which combines PS and GS is proposed. The algorithm adopts the bare bones particle swarm optimization algorithm based on maximizing the AMI. Based on the non-equiprobable codebook, a message passing algorithm with non-equalprobable distribution is introduced as the multiuser detection algorithm for SCMA scheme. We theoretically analyze the superiority of the codebooks combining PS and GS over others in terms of the average mutual information (AMI). Simulation results show that our proposed codebooks outperform the reference GS based codebooks under different overloading, different channels and different codebook sizes, and with the increase of overloading or codebook sizes, the gains are greater, which is up to 2.1dB gain in terms of block error rate performance. This observation also validates the theoretical analysis based on the AMI.

A Novel Multiuser Detection and Channel Estimation Method for Uplink Communication in MIMO-OFDM IDMA System

Demand of wireless cellular communication systems has grown rapidly which necessitates high speed and reliable communication for various multimedia applications. Several technologies have been introduced to meet the desired communication requirements such as 1G, 2G, 3G and 4G but these techniques suffer from multiple-access and interference issues. Hence in this work, we focus on these challenges to improve the cellular communication performance. The main objective of this work is to develop a novel approach for multiuser detection and introducing a new architecture of channel estimation. However, several techniques are present to achieve these objectives but computational complexity and reliable performance still remains an issue. Moreover, interference remains a challenging task. Hence, in order to overcome these issues, we present a joint approach of multiuser detection where we compute the LLRs, find the Gaussian distribution and measure the mean and variance of this distribution. Similarly, in next phase, channel estimation is performed with the help of ESE and DEC. The performance of proposed approach is measured in terms of BER for varied simulation configurations. The experimental analysis reported significant improvement in the performance of MIMO-OFDM IDMA system.

Uplink Transmit Power Control for Single-Carrier Grouped FDMA with Iterative Multiuser Detection

We consider an uplink power allocation scheme for single-carrier frequency-division multiple access (FDMA) with iterative multiuser detection, called single-carrier grouped FDMA (SC-GFDMA). SC-GFDMA is a non-orthogonal scheme in which several users share a single time-frequency resource. Hence, the uplink signal of a user can be regarded as both a signal and a source of interference. The signal power of each user should be sufficiently high to ensure reliable signal detection and sufficiently low to suppress inter-user interference. That is, the transmit power of each user should be adjusted appropriately to achieve high spectral efficiency. In this context, a power control method for an uplink SC-GFDMA system is proposed by analyzing the signal-to-interference-plus-noise ratios of users sharing each time-frequency resource. In particular, the uplink spectral efficiency is improved by limiting the transmit power of each user according to a criterion derived using a semi-analytic method called signal-to-noise ratio-variance density evolution. Simulation results demonstrate that the proposed method can significantly increase the spectral efficiency of the system, even with a considerably reduced total transmit power.

To Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design

Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing debate as to what additional values artificial intelligence (or machine learning) could bring to us, particularly on the physical layer design; and what penalties there may have? These questions motivate a fundamental rethinking of the wireless modem design in the artificial intelligence era. Through several physical-layer case studies, we argue for a significant role that machine learning could play, for instance in parallel error-control coding and decoding, channel equalization, interference cancellation, as well as multiuser and multiantenna detection. In addition, we will also discuss the fundamental bottlenecks of machine learning as well as their potential solutions in this paper.

CHIRRUP: a practical algorithm for unsourced multiple access

Abstract Unsourced multiple access abstracts grantless simultaneous communication of a large number of devices (messages) each of which transmits (is transmitted) infrequently. It provides a model for machine-to-machine communication in the Internet of Things, including the special case of radio-frequency identification, as well as neighbour discovery in ad hoc wireless networks. This paper presents a fast algorithm for unsourced multiple access that scales to ${\mathscr{C}}=2^{100}$ (active or non-active) devices (arbitrary $100$ bit messages). The primary building block is multiuser detection of binary chirps, which are simply codewords in the second-order Reed–Muller code. The chirp detection algorithm originally presented by Howard et al. (2008, 42nd Annual Conference on Information Sciences and Systems) is enhanced and integrated into a peeling decoder designed for a patching and slotting framework. In terms of both energy per bit and number of active devices (number of transmitted messages), the proposed algorithm is within a factor of $2$ of state-of-the-art approaches. A significant advantage of our algorithm is its computational efficiency. We prove that the worst-case complexity of the basic chirp reconstruction algorithm is ${\mathscr{O}}[nK(\log _2^2 n + K)]$, where $n$ is the codeword length and $K$ is the number of active users. Crucially, the complexity is sublinear in ${\mathscr{C}}$, which makes the reconstruction computationally feasible—a claim we support by reporting computing times for our algorithm. Our performance and computing time results represent a benchmark against which other practical algorithms can be measured.

Joint Channel Estimation and MUD for Scalable Grant-Free Random Access

A scalable grant-free access, which simultaneously reduces access collision rate and signaling overhead, can be enabled by allowing contending users to randomly select a preamble from a very large set of non-orthogonal preamble sequences. By casting the joint channel estimation and multi-user detection (CE-MUD) problem into a multiple measurement vector (MMV)-based compressive sensing problem and employing a two-stage active preambles search over a narrowed subspace, a scheme proposed in this letter shows significant performance improvement over conventional schemes. Particularly, the two-stage search allows the proposed scheme to support multiple order of more active users while reducing receiver’s complexity.

Capacity of Interference Exploitation Schemes in Multibeam Satellite Systems

In this paper, we propose a framework, based on the combined use of single- and multiuser detection, to jointly optimize the achievable rates of two signals sharing the same frequency in the forward link of a multibeam satellite system. We then propose the application of the described framework to two different scenarios of interest. First, we consider a uniform coverage scenario, aimed at maximizing the average throughput per beam in a realistic coverage condition. We compare different solutions based on alternative frequency reuse (FR) schemes and different receiver strategies. We demonstrate that the use of multiuser detection can achieve significant gains over a reference strategy based on single-user detection. Next, we analyze a “hotspot” case, where resources are pulled from empty beams to serve a beam with a high service demand. Also in this case, we compare several strategies and FR schemes. We show that the best performance is achieved by a scheme adopting three colors and single-user detection.

A Unified Multiuser Coding Framework for Multiple-Access Technologies

In this paper, multiple-access techniques are viewed as generalized coding schemes to support both multiuser separation and coding gain. In this sense, the traditional coding schemes, e.g., low-density parity-check (LDPC) code, can be viewed as a special case. Note that still a big difference exists between multiple-access techniques and LDPC codes. In particular, both binary and nonbinary LDPC codes are generated based on finite field theory, whereas multiple-access works based on not necessarily a finite field. This paper presents a mathematical framework, called “multiuser coding,” to unify multiple-access and traditional coding theories. To verify the proposed framework, we propose a uniquely decodable mapping based codebook for futuristic multiple-access communications. A three-dimensional Tanner graph based message passing algorithm is introduced for multiuser detection. Simulation results show that the proposed scheme can support a large number of users with a promising performance.

Multiuser detection for uplink non‐orthogonal multiple access system

Non-orthogonal multiple access (NOMA) makes multiple users to overload the same wireless resources by utilising the superposition coding principle, so that NOMA can improve the system capacity and spectral efficiency. Therefore, NOMA has recently received considerable attention as a promising candidate to address some of the challenges for 5G. Various NOMA schemes including the power-domain NOMA (PD-NOMA) are proposed, and successive interference cancellation (SIC) is applied for detection, which limits the promised performance gain brought by NOMA due to the error propagation. In this study, the authors introduce an iterative interference cancellation (IIC) detection scheme for uplink PD-NOMA system by using parallel interference cancellation (PIC) scheme. The interleave division multiple access is also employed for improving system performance. A new detection scheme based on IIC is proposed, which is called advanced IIC (AIIC). The simulation results show that the bit error rate performance of AIIC is much better than that of SIC, and is close to that of maximum a posteriori. The AIIC can obtain the promised performance gain brought by NOMA.

Deep Learning-Based Detection for Moderate-Density Code Multiple Access in IoT Networks

In the era of the Internet of Things, massive devices communications become a fundamental problem. To improve spectral efficiency and reduce latency, a new non-orthogonal multiple access scheme dubbed moderate-density code multiple access (MCMA) is presented. We also propose a new deep learning-based multi-user detection algorithm for MCMA systems, which is based upon a new graphic representation of the Tanner graph for the message passing algorithm (MPA). The proposed algorithm learns to adjust the weights of the edges of the neural network to realize multi-user detection without iterations as required in conventional MPA algorithms. Experimental results show that with an increase in the overloading factor and the number of users, the BER performance of the proposed scheme is better than that of deep learning-aided SCMA (DL-SCMA) with a lower computational complexity.

GMM-MUD: An Effective Multiuser Detection Algorithm for DS-UWB-Based Space Formation Flying Systems

In this paper, an effective multiuser detection (MUD) is proposed for direct sequence ultrawideband- (DS-UWB-) based space formation flying systems. The proposed method called GMM-MUD is based on Gaussian mixture models (GMMs) to suppress multiple access interference. The GMM describes probability distributions of the hypothesis testing problem which is used for bit classification. To reveal the difference between correct bits and error bits, the preprocessing operation applies a mapping function based on optimal multiuser detection. The parameters of GMM are estimated by using expectation-maximization (EM) algorithm. EM algorithm employs iterative operation to simplify the complexity of maximum likelihood estimation method and considers the mapping values of received bits as the observations. Simulation results demonstrate that the proposed GMM-MUD algorithm achieves good performances in terms of bit error rate performance, user capacity, and near-far resistance. Moreover, the computational complexity is low enough for space formation flying applications.

Research on Classification and Recognition for Multiuser Signal Detection

Research on Classification and Recognition for Multiuser Signal Detection

Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

Multiuser Activity and Data Detection via Sparsity-Blind Greedy Recovery for Uplink Grant-Free NOMA

Exploiting the sparse activity of users, compressed sensing (CS) has been of interest in multiuser detection (MUD) for non-orthogonal multiple access (NOMA), to enable a massive connection of users in machine-type communications (MTC). In this letter, we propose a greedy algorithm with unknown sparsity level for CS-based multiuser activity and data detection in uplink grant-free NOMA. To accommodate practical scenarios, the algorithm employs a criterion to stop the iteration with no prior knowledge of sparsity level. Also, it requires no knowledge of noise variance by computing the log-likelihood ratios (LLR) approximately in its operation. With no need of sparsity and noise levels, we perform CS-based MUD with low complexity. Simulation results demonstrate that the proposed algorithm outperforms conventional CS-based solutions, nearly achieving the oracle performance of fully known supports.

Near-Capacity Detection and Decoding: Code Design for Dynamic User Loads in Gaussian Multiple Access Channels

This paper considers the forward error correction (FEC) code design for approaching the capacity of a dynamic multiple access channel (MAC) where both the number of users and their respective signal powers keep constantly changing, resembling the scenario of an actual wireless cellular system. To obtain a low-complexity non-orthogonal multiple access (NOMA) scheme, we propose a serial concatenation of a low-density parity-check (LDPC) code and a repetition code (REP), this way achieving near Gaussian MAC (GMAC) capacity performance while coping with the dynamics of the MAC system. The joint optimization of the LDPC and REP codes is addressed by matching the analytical extrinsic information transfer (EXIT) functions of the sub-optimal multi-user detector (MUD) and the channel code for a specific and static MAC system, achieving near-GMAC capacity. We show that the near-capacity performance can be flexibly maintained with the same LDPC code regardless of the variations in the number of users and power levels. This flexibility (or elasticity) is provided by the REP code, acting as “user-load and power equalizer”, dramatically simplifying the practical implementation of NOMA schemes, as only a single LDPC code is needed to cope with the dynamics of the MAC system.

Research on Classification and Recognition for Multiuser Signal Detection

Research on Classification and Recognition for Multiuser Signal Detection

A Noncoherent Massive MIMO System Employing Beamspace Techniques

Noncoherent detection schemes are an appealing and low-complexity alternative in multi-user massive MIMO uplink systems compared to classical coherent detection algorithms, since no actual channel knowledge is required at the receiver. For noncoherent multi-user detection to function, the induced power at the base station is utilized to separate the different users. However, spatial separation is impossible when the users are located in the far-field of the receiving antenna array. Consequently, noncoherent detection fails in such scenarios. To this end, beamspace techniques can be applied, focusing the energy of the incident wave to a smaller subset of the receive antennas and enabling again the noncoherent detection scheme. This paper analyzes the beamspace capabilities of a dielectric lens and an analog beamforming network applied at the receiver. Furthermore, a sub-array architecture is proposed, relaxing the design requirements for practical implementation. It is shown that noncoherent detection in combination with beamspace techniques performs comparably to channel-estimation-based detection. In addition, the sub-array architecture revealed a significant performance enhancement accompanied by a reduced user separability.