Multiuser Detection Algorithm Research Articles

A low complexity grouping multi-user detection algorithm for the LDS multiple access system

The complexity of the multiuser detection algorithm of the low-density spreading multiple access system is need to reduced. Based on the difference in the channel quality and signal power at the base station of each user in the uplink system, we propose a multi-user detection algorithm that reduces the computational complexity. This algorithm adopts group detection based on the users instantaneous channel quality, and presents design criteria for user grouping and the corresponding indication matrix. The computational complexity of the algorithm is analyzed and the algorithm is simulated under different channel conditions. The simulation results show that the computational complexity of the algorithm is effectively reduced with a very small loss of user performance.

Deep Learning Network for Multiuser Detection in Satellite Mobile Communication System.

A multiuser detection (MUD) algorithm based on deep learning network is proposed for the satellite mobile communication system. Due to relative motion between the satellite and users, multiple access interference (MUI) introduced by multipath fading channel reduces system performance. The proposed MUD algorithm based on deep learning network firstly establishes the CINR optimal loss function according to the multiuser access mode and then obtains the best multiuser detection weight through the steepest gradient iteration. Multilayer nonlinear learning obtains interference cancellation sharing weights to achieve maximum signal-to-noise ratio through gradient iteration, which is superior than the traditional serial interference cancellation algorithm and parallel interference cancellation algorithm. Then, the weights with multiuser detection through multilayer network forward learning iteration are obtained with traditional multiuser detecting quality characteristics. The proposed multiuser access detection based on deep learning network algorithm improves the MUD accuracy and reduces the number of traditional multiusers. The performance of the satellite multifading uplink system shows that the proposed deep learning network can provide high precision and better iteration times.

Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, multi-user detection (MUD) algorithms play an important role in reducing the effect of multi-access interference (MAI). A combination of the estimation of channel and multi-user detection is proposed for eliminating various interferences and reduce the bit error rate (BER). First, a novel sparse based k-nearest neighbor classifier is proposed to estimate the unknown activity factor at a high data rate. The active users are continuously detected and their data are decoded at the base station (BS) receiver. The activity detection considers both the pilot and data symbols. Second, an optimal pilot allocation method is suggested to select the minimum mutual coherence in the measurement matrix for optimal pilot placement. The suggested algorithm for designing pilot patterns significantly improves the results in terms of mean square error (MSE), symbol error rate (SER) and bit error rate for channel detection. An optimal pilot placement reduces the computational complexity and maximizes the accuracy of the system. The performance of the channel estimation (CE) and MUD for the proposed scheme was good as it provided significant results, which were validated through simulations.

Low-Complexity Multiuser QAM Detection for Uplink 1-bit Massive MIMO

This work studies multiuser detection for one-bit massive multiple-input multiple-output (MIMO) systems in order to diminish the power consumption at the base station (BS). A low-complexity near-maximum-likelihood (nML) multiuser detection algorithm is designed, assuming that each BS antenna port is connected with a pair of single-bit resolution analog-to-digital converters (ADCs) and each user equipment (UE) transmits symbols from a quadrature amplitude modulation (QAM) constellation. First, a novel convex program is formulated as a convex surrogate of the ML detector and subsequently solved through an accelerated first-order method. Then, the solution of the convex optimization problem is harnessed to solve a refined combinatorial problem with reduced search space, requiring non-exponential complexity on the number of the UEs. Judicious simulation study corroborates the efficacy of the resulting two-phase detection algorithm. The proposed two-phase algorithm can achieve symbol error rate (SER) performance close to the ML detector, with significantly reduced computation cost compared to the nML detection schemes in prior art.

Improved MAX-Log MPA multiuser detection algorithm in SCMA system

Improved MAX-Log MPA multiuser detection algorithm in SCMA system

Stability and Throughput of Random Access With CS-Based MUD for MTC

Recently, compressive random access has been considered for machine-type communications (MTC) as it has a potential to support massive connectivity by exploiting the sparsity of device activity. However, its performance limitations are not well studied yet compared with other well-known candidates for MTC, e.g., multichannel ALOHA. In this paper, we investigate the stability of compressive random access with a controlled access probability strategy under ideal conditions, and derive the maximum stable throughput, which shows that the maximum stable throughput of compressive random access is higher than that of multichannel ALOHA by a factor of 2. We also carry out simulations to see the performance of compressive random access under more realistic conditions with low-complexity multiuser detection (MUD) algorithms. Based on analysis and simulation results, we can claim that compressive random access can support more devices than conventional multichannel ALOHA and is well-suited for MTC.

Convex optimization–based multi-user detection in underwater acoustic sensor networks

Multi-carrier code-division multiple access is an important technical means for high-performance underwater acoustic sensor networks. Nevertheless, severe multiple access interference is a huge challenge. As the core technology, multi-user detection is used to eliminate multiple access interference. The traditional optimal detection algorithms (e.g. maximum likelihood) have very high computational complexity, and the performances of suboptimal detection methods (i.e. zero forcing, minimum mean square error, etc.) are poor. Therefore, taking into account the characteristics of underwater acoustic sensor networks, it is of great significance to design multi-user detection algorithms for achieving a tradeoff between the detection performance and the computational complexity in multi-carrier code-division multiple access systems. In this article, we design a transmitter model of underwater multi-carrier code-division multiple access system and then implement a multi-user detection algorithm based on convex optimization, which is named convex optimization–based algorithm. Next, we conduct the detection performance and computational complexity comparisons of maximum likelihood, zero forcing, minimum mean square error, and convex optimization–based algorithm. The results show that the performance of convex optimization–based algorithm is close to that of maximum likelihood, and the complexity is close to that of zero forcing. Therefore, a tradeoff between the computational complexity and the detection performance is realized in convex optimization–based algorithm. It means that convex optimization–based algorithm is suitable for the multi-user detection in multi-carrier code-division multiple access systems of underwater acoustic sensor networks.

Kalman Filter-Based Chip Differential Blind Adaptive Multiuser Detection for Variably Mobile Asynchronous Underwater Multiuser Communications

To mitigate the inter-symbol interference (ISI), multiple access interference (MAI), and the effect of different Doppler shifts within a data package incurred in variably mobile asynchronous underwater multiuser communications (VMAUMC), a Kalman filter-based chip differential blind adaptive multiuser detection (D-KF-BAMUD) algorithm is proposed in this paper. The Kalman filter-based blind adaptive multiuser detection (KF-BAMUD) algorithm has been adopted to combat ISI and MAI in the scenario of static users, and underwater and under-ice multiuser communication experiments have been carried out to verify the effectiveness of the KF-BAMUD algorithm. To tackle the more challenging VMAUMC, a differential modulation and demodulation technique operating at the chip level is proposed to reduce the impact of Doppler shift residues after the Doppler compensation with an average Doppler frequency offset within each data package. The technique is combined with the KF-BAMUD algorithm, leading to the D-KF-BAMUD algorithm, which is able to effectively handle ISI, MAI, and the effect of Doppler shift residues simultaneously in VMAUMC. This paper reports our experiments on VMAUMC carried out in Songhua River in April 2016 (seven users, data rate 7.87 bit/s, up to 2 m/s variably relative velocity in each data package, and horizontal distance 200-m shallow-water channel between transducer and hydrophone), and the encouraging experimental results demonstrate the effectiveness of the proposed system and the D-KF-BAMUD algorithm.

Uplink Nonorthogonal Multiple Access Technologies Toward 5G: A Survey

Owing to the superior performance in spectral efficiency, connectivity, and flexibility, nonorthogonal multiple access (NOMA) is recognized as the promising access protocol and is now undergoing the standardization process in 5G. Specifically, dozens of NOMA schemes have been proposed and discussed as the candidate multiple access technologies for the future radio access networks. This paper aims to make a comprehensive overview about the promising NOMA schemes. First of all, we analyze the state‐of‐the‐art NOMA schemes by comparing the operations applied at the transmitter. Typical multiuser detection algorithms corresponding to these NOMA schemes are then introduced. Next, we focus on grant‐free NOMA, which incorporates the NOMA techniques with uplink uncoordinated access and is expected to address the massive connectivity requirement of 5G. We present the motivation of applying grant‐free NOMA, as well as the typical grant‐free NOMA schemes and the detection techniques. In addition, this paper discusses the implementation issues of NOMA for practical deployment. Finally, we envision the future research challenges deduced from the recently proposed NOMA technologies.

Efficient Multi-User Detection for Uplink Grant-Free NOMA: Prior-Information Aided Adaptive Compressive Sensing Perspective

Non-orthogonal multiple access (NOMA) is an emerging research topic in the future fifth generation wireless communication networks, which is expected to support massive connectivity for massive machine-type communications (mMTC). Due to the sporadic communication nature of mMTC, the grant-free transmission methodology is highly expected in uplink NOMA systems, to drastically reduce the transmission latency and signaling overhead. Exploiting the inherent sparsity nature of user activity, compressive sensing (CS) techniques have been applied for efficient multi-user detection in the uplink grant-free NOMA. In this paper, we propose a prior-information-aided adaptive subspace pursuit (PIA-ASP) algorithm to improve the multi-user detection performance. In this algorithm, a parameter evaluating the quality of the prior-information support set is introduced, in order to exploit the intrinsically temporal correlation of active user support sets in several continuous time slots adaptively. Then, to mitigate the incorrect estimation effect of the prior support quality information, a robust PIA-ASP algorithm is further proposed, which adaptively exploits the prior support based on the corresponding support quality information in a conservative way. It is noted that both of the two proposed algorithms do not require the knowledge of the user sparsity level, while most of the state-of-the-art CS-based multi-user detection algorithms usually need. Moreover, for the two proposed algorithms, the upper bound of the signal detection error and the computational complexity is derived. Simulation results demonstrate that the two proposed algorithms are capable of achieving much better performance than that of the existing CS-based multi-user detection algorithms with a similar computational complexity.

Fast Uplink Synchronization in LTE-Like Systems

We show a way to accelerate the successive multiuser detection (SMUD) algorithm for initial uplink synchronization (IUS) in an LTE-like system. We develop a new data model to pose IUS as a signal representation problem in a Fourier basis. We provide the theoretical guidelines for generating codes using the data model. Our method significantly reduces the complexity of computationally demanding multiplication steps of the SMUD algorithm.

Improved sparse multiuser detection based on modulation-alphabets exploitation

In this paper, we focus on sporadic random-access communications and consider compressed-sensing (CS) techniques to perform the multiuser detection (MUD). Since all the users do not necessarily transmit information, MUD consists in detecting the transmitting users (activity detection) and their corresponding transmitted data (data detection). The main results presented here rely on the exploitation of the user signal alphabet knowledge within the detection step. To this aim, several modifications of the group orthogonal matching pursuit (GOMP) algorithm were proposed, differing in the way the modulation alphabet knowledge is considered within the detection stage. These modifications can be extended to any greedy-based CS-MUD. To overcome the error floor occurring at high SNR with a higher number of active users, we then propose an iterative ℓ1 minimization-based MUD algorithm that alternates between activity and data detection. Compared to the existing GOMP-based CS-MUD, the proposed modified GOMP algorithms exhibit a significant gain with almost the same complexity. The iterative ℓ1 minimization-based MUD algorithm has a higher complexity but outperforms all the others without any observed error-floor.

Spectral-Efficient Band Allocation Scheme for Frequency-Domain Pulse-Shaping-Based SC-FDMA Systems

One major concern of employing frequency-domain pulse shaping (FDPS) to reduce the peak-to-average power ratio of single-carrier frequency-division multiple-access (SC-FDMA) signals is the decrease of system spectral efficiency due to the excess bandwidth wasted in the existing band allocation schemes (BASs). To improve the spectral efficiency of FDPS-based SC-FDMA systems, a novel BAS is proposed, where edge subcarriers for each user are overlapped with those of all its neighbors. However, the spectrum overlapping incurs multiuser interference and degrades the bit error rate (BER) performance. To address this problem, two iterative multiuser detection (MUD) algorithms are proposed based on expectation propagation (EP), termed joint EP (J-EP) and distributed EP (D-EP). Simulation results demonstrate that, aided by either of the EP-based MUD algorithms, the FDPS-based SC-FDMA system with the proposed BAS can work more spectral-efficiently without BER performance degradation compared to those with the existing BASs. In addition, the D-EP algorithm performs nearly the same as J-EP but requires much lower complexity, making it more appealing for large numbers of allocated subcarriers.

Novel Bayesian Inference Algorithms for Multiuser Detection in M2M Communications

The fifth generation and future wireless networks are expected to support massive machine-to-machine (M2M) communications. Due to the sporadic nature, massive M2M communications can be well supported by low-activity code division multiple access (LA-CDMA). In the literature, maximum a posteriori detector has been proposed to detect the active users in LA-CDMA when the user activity factor is known and small. However, such user activity factor is usually unknown and could be large in practice, which makes the multiuser detection for LA-CDMA a challenging task. In this paper, we first formulate the LA-CDMA uplink using single measurement vector (SMV) model and multiple measurement vector (MMV) model, then, propose novel Bayesian inference algorithms to recover the transmitted signals. For SMV model, we first introduce sparse Bayesian learning (SBL) that exploits the sparsity of the transmitted signals, then, add on the known finite-alphabet constraints and introduce Gaussian mixture model method to recover the transmitted signals. For MMV model, pattern coupled SBL (PCSBL) algorithm is introduced that takes into consideration the neighbor coherence of each device, then the block SBL is introduced through exploiting the row sparsity property and the column coherence of each device. The four Bayesian inference methods make use of various priors and hyperparameters, which can be autonomously learned through the training process via expectation maximization (EM) or variational EM iterative algorithms. Furthermore, the proposed Bayesian methods do not require the knowledge of user activity factor. Simulation results have shown that the proposed Bayesian inference methods outperform the classical algorithms.

Low Complexity ZF and MMSE Detectors for the Uplink MU-MIMO Systems With a Time-Varying Number of Active Users

The classical multiuser detection algorithms such as zero forcing (ZF) and minimum mean square error (MMSE) receivers are designed with the assumption that the number of active users is constant and known. When the number of the active users changes, the receiver may exhibit a serious performance loss if it does not react quickly to such variations. In this paper, we address the problem of reducing the complexity of the reevaluation of the popular ZF and MMSE detectors for multiuser multiple-input multiple-output (MU-MIMO) systems with a time-varying number of users in the channel. For each technique, we propose a detection approach with low complexity and without performance loss. The proposed algorithms avoid the direct computation of matrix inverses required by the ZF and MMSE detectors. Moreover, the performance losses, due to the use of the ZF and MMSE detectors intended for the scenario with a fixed number of active users, are evaluated with Monte Carlo simulation results.

Mitigation of co-channel interferences in cognitive multi-carrier code division multiple access system by singular value decomposition techniques

Efficient spectrum usage is important criteria for next generation wireless systems. In this paper, to utilize the spectrum in useful manner, cognitive radio approaches are used along with multi-carrier code division multiple access schemes (MC-CDMA). But MC-CDMA systems are greatly affected by co-channel interference (CCI). To mitigate CCI interference, multi-user detection (MUD) technique is the design choice. When the user population increases complexity level of MUD algorithms are increases linearly. Also power consumption of mobile station also increases. Hence as a design alternate, in this paper we developed a singular value decomposition based pre-processing approaches to mitigate CCI interferences. The proposed technique is tested with real time channel models and the result shows that superior performance obtained with proposed techniques.

Compressive Channel Division Multiple Access for MTC Under Frequency-Selective Fading

In this paper, we study random access for machine-type communications based on a multiple access scheme that exploits different channel state information (CSI) from active devices under frequency-selective fading, which is called channel division multiple access (ChDMA). In ChDMA, in order to use a low-complexity compressive sensing (CS) algorithm for multiuser detection (MUD) with a high transmission rate, we propose to use index modulation. Since the CSI is used as a signature for MUD, the CSI estimation becomes crucial. For the CSI estimation, pilot transmission can be considered where each active device randomly chooses a pilot from a set of pre-determined pilots. Since the CSI estimation suffers from pilot collision in this case, we propose multiple pilot transmissions and show that a low probability of collision can be achieved. A CS-based approach is derived not only to estimate the CSI, but also to detect randomly selected multiple pilot signals by exploiting the sparsity of active devices over frequency-selective fading channels.

A Low-Complexity CLSIC-LMMSE-Based Multi-User Detection Algorithm for Coded MIMO Systems with High Order Modulation

A Low-Complexity CLSIC-LMMSE-Based Multi-User Detection Algorithm for Coded MIMO Systems with High Order Modulation

Design and Implementation of a Low Complexity Multiuser Detector for Hybrid CDMA Systems

In hybrid CDMA systems, multiuser detection (MUD) algorithms are adopted at the base station to reduce both multiple access and inter-symbol interference by exploitingspace-time (ST) signal processing techniques. Linear ST-MUD algorithms solve a linear problem where the system matrix has a block-Toeplitz shape. While exact inversion techniques impose an intolerable computational load, reduced complexity algorithms may be efficiently employed even if they show suboptimal behavior introducing performance degradation and nearfar effects. The block-Fourier MUD algorithm is generally considered the most effective one. However, the block-Bareiss MUD algorithm, that has been recently reintroduced, shows also good performance and low computational complexity comparingfavorably with the block-Fourier one. In this paper, both MUD algorithms will be compared, along with other well known ones, in terms of complexity, performance figures, hardware feasibility and implementation issues. Finally a short hardware description of the block-Bareiss and block-Fourier algorithms will be presented along with the FPGA (Field Programmable Gate Array) implementation of the block-Fourier using standard VHDL (VHSIC Hardware Description Language) design.

Optimized multiuser detection algorithm for uplink SCMA system

Sparse code multiple access (SCMA) is a competitive nonorthogonal access scheme for the next mobile communication. As a multiuser sharing system, SCMA adopts message passing algorithm (MPA) for decoding scheme in receiver, but its iterative method leads to high computational complexity. Therefore, a serial message passing algorithm based on variable node (VMPA) is proposed in this paper. Making some subtle alterations to message update in original MPA, VMPA can greatly reduce overall computing complexity of decoding scheme. Furthermore, considering that serial structure may increase decoding delay, a novel grouping scheme based sparse matrix is applied to VMPA. Simulation results verify that the new algorithm, termed as grouping VMPA (G-VMPA), can achieve a better tradeoff between bit error rate (BER) and computing complexity than MPA.