Conventional Multiuser Detection Research Articles

Generalized Approximate Message Passing Based Bayesian Learning Detectors for Uplink Grant-Free NOMA

The existing sparse Bayesian learning (SBL) and pattern coupled sparse Bayesian learning (PCSBL) multiuser detection (MUD) algorithms for grant-free non-orthogonal multiple access (GF-NOMA) have high computational complexity, i.e. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N{K^{2}})$</tex-math></inline-formula> , considering mainly the calculation of posterior distribution of the transmitted signals. In this paper, we embed generalized approximate message passing (GAMP) to SBL and PCSBL, and develop two efficient Bayesian learning algorithms for GF-NOMA systems, that is, generalized approximate message passing sparse Bayesian learning (GAMP-SBL) and generalized approximate message passing pattern coupled sparse Bayesian learning (GAMP-PCSBL). It is shown that the Bayesian algorithms can significantly reduce the computational complexity from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N{K^{2}})$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(NK)$</tex-math></inline-formula> . Simulation results show that these two low complexity detectors still have superior recovery performance than the conventional MUD methods, and nearly have the same performance compared with SBL and PCSBL.

Asynchronous Multi-User Detection for Code-Domain NOMA: Expectation Propagation Over 3D Factor-Graph

Code-domain non-orthogonal multiple access (NOMA) is a promising technology to achieve ubiquitous massive connections for 5G and beyond. In application scenarios with high channel dynamics or simplified scheduling procedures, multi-user signals face the difficulty of accurate synchronization. The resulted asynchronous inter-user interference (IUI) is too complex to be modeled or alleviated by conventional multi-user detection algorithms. To effectively characterize the asynchronous IUI for code-domain NOMA, this paper constructs a three-dimensional (3D) factor-graph by introducing time delay as an additional dimension and develops the corresponding message passing mechanisms. Then, a novel 3D-expectation propagation algorithm (3D-EPA) is proposed for low-complexity asynchronous multi-user detection. The proposed 3D-EPA iterates between per-user estimation, which projects the asynchronous interference via Gaussian approximation, and inter-user message passing, which propagates the previous estimates among the users to refine the approximation. We also extend the 3D-EPA to multi-antenna scenarios and analyze its state evolution. Simulation results show that the proposed asynchronous NOMA system with 3D-EPA even outperforms its synchronous counterpart, especially under high overloadings.

Comparison and Analysis of Sub-optimal performance of OFDM/SDMA uplink System that use Conventional Multiuser Detection Techniques

The major challenge in the practical accomplishment of OFDM/SDMA system depends on the efficient implemen- tation of a multiuser detection (MUD) technique that separates the spatially multiplexed signal streams. Several algorithms for MIMO detection for spatially-multiplexed signals have been developed till today. The MIMO MUDs that are mostly used in previous research works and practically used are minimum mean squared error (MMSE), decorrelator (ZF), Vertical-Bell laboratories layered space-time (V-BLAST), successive inter- ference cancellation (SIC) and maximum likelihood detection (MLD). MMSE and ZF are linear MIMO detection techniques, while the remaining procedures are non linear techniques. All MUD techniques in general can be explained as a solution to a quadratic optimization problem in most cases. In this work, basic principles of ZF, MMSE, ZF-OSIC, MMSE-OSIC and ML have been explained briefly and a comparison of their performances have been done. Although the detection techniques evolved so far are matured in theory, their implementation in a real-time scenario is still challenging. It is analyzed in this paper that the poor BER results of sub-optimal detection method in comparison to MLD is due to bad channel effects.

Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

Sparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

An Efficient Matching Pursuit Based Compressive Sensing Detector For Uplink Grant-Free NOMA

In this paper, an efficient compressive sensing detector is proposed for uplink grant-free non-orthogonal multiple access (NOMA), where the indices and symbols of the active users are detected separately by using efficient matching pursuit (MP) based algorithms. Specifically, a low-complexity subspace MP (SMP) algorithm is designed for detecting the active user's indices, which is helpful for obtaining an initial estimated symbol vector. Then an efficient MP based symbol correcting mechanism (SCM) is designed for finding and correcting the erroneous results encountered in the initial result. Simulation results indicate that our proposed SMP based SCM (SMP-SCM) algorithm is capable of exhibiting better bit error rate (BER) performance over the oracle least squares (OLS) counterpart in the high SNR regime, which is always taken as a benchmark in conventional multi-user detection of grant-free NOMA system.

A new flower pollination algorithm for equalization in synchronous DS/CDMA multiuser communication systems

This work proposes a modified version of an emerging nature-inspired technique, named flower pollination algorithm, for equalizing digital multiuser channels. This equalization involves two different tasks: (1) estimation of the channel impulse response, and (2) estimation of the users’ transmitted symbols. The new algorithm is developed and applied in a direct sequence/code-division multiple-access multiuser communications system. Important issues such as robustness, convergence speed and population diversity control have been in deep investigated. A method based on the entropy of the flowers’ fitness is proposed for in-service monitoring and adjusting population diversity. Numerical simulations analyze the performance, showing comparisons with well-known conventional multiuser detectors such as matched filter, minimum mean square error estimator or several Bayesian schemes, as well as with other nature-inspired strategies. Numerical analysis shows that the proposed algorithm enables transmission at higher symbol rates under stronger fading and interference conditions, constituting an attractive alternative to previous algorithms, both conventional and nature-inspired, whose performance is frequently sensible to near–far effects and multiple-access interference problems. These results have been validated by running hypothesis tests to confirm statistical significance.

Iterative Detection Based on Consensus Alternating Direction Method of Multipliers in Massive Machine-Type Communications

In massive machine-type communication systems, only some of devices usually transmit signals while the others remain silent. The conventional multiuser detection methods have been used with the aid of compressive sensing techniques, but their performance is far from the optimal one. In this paper, we propose an iterative signal detection method that jointly identifies the non-zero support and detects modulated symbols based on consensus alternating direction method of multipliers. Simulation results show that its performance is much better than the conventional detection methods and close to the lower-bound performance of the ideal detector in the case of high SNR.

Low-Complexity Multi-User Detection Based on Gradient Information for Uplink Grant-Free NOMA

Massive machine type communication (mMTC) serves an irreplaceable role in the development process of the Internet of Things (IoT). Because of its characteristics of massive connection and sporadic transmission, compressed sensing (CS) has been applied in joint user activity and data detection in the uplink grant-free non-orthogonal multiple access (NOMA) system. In previous work, greedy iterative-based multi-user detection (MUD) algorithms were developed in mMTC scenarios because of the computational benefit and competitive performance. However, conventional greedy iterative-based MUD algorithms still suffer from high computational complexity due to the process of large-size matrix inversion with the accession of massive devices into the system. In this paper, gradient information is used to address this problem. A low-complexity gradient descent-based gradient pursuit MUD (GDGP-MUD) algorithm is proposed, which uses the gradient information of error function in the process of iteration as a new updating direction, instead of the matrix inversion process. Then, a multi-step quasi-Newton MUD (MSQN-MUD) algorithm is proposed to improve the precision of detection while maintaining low complexity. In the algorithm, high-order information in the process of adjacent iteration is used effectively to update data values more accurately. Moreover, the convergence and complexity analysis of both algorithms are derived. The analysis shows that both proposed algorithms have lower computational consumption than most of the state-of-the-art greedy-based MUD algorithms. It is worth noting that in comparison to most existing CS-based MUD algorithms, the two proposed algorithms do not require the exact user sparsity level and, thus, reduce the dependence on prior knowledge. The numerical experiments demonstrate that the proposed algorithms have better real-time performance than existing greedy-based MUD algorithms with similar symbol error rate performance.

A robust blind adaptive multiuser detection based on maximum correntropy criterion in satellite CDMA systems

AbstractSatellite code‐division multiple‐access systems use multiuser detection (MUD) methods to deal with multiple‐access interferences. Among the different kinds of MUD, blind adaptive MUD regarding the system as a state‐space model is widely used for its tracking capability and independence of training sequences. The innovation process noise of the state‐space model is usually assumed to follow the Gaussian distribution, and the minimum mean‐square error criterion is the default criterion for most conventional blind adaptive MUD methods. In the past few years, the impulsive noise of the innovation process has attracted attention, declaring that a Gaussian distribution is not suitable for simulating the impulsive noise. Thus, more criteria are proposed to enhance the robustness of the blind adaptive MUD. This paper improves the robustness of MUD methods under impulsive noise by bringing forward a new cost function based on the maximum correntropy criterion and deduces the expression of the blind adaptive detector. Simulation results show that the proposed method achieves superior performance in terms of bit error rate, convergence, and robustness compared with other methods.

Multiuser Detection for FEC-Coded Massive Spatial Modulation MIMO: An Iterative Interference Rejection Approach

In this paper, we investigate the issue of multiuser detection (MUD) in a massive spatial modulation (SM) multiple-input multiple-output (MIMO) system, where the base station is equipped with a large number of antennas and each user equipment uses single-carrier SM (SC-SM) for uplink transmission. Unlike the conventional MUD for massive SM-MIMO systems, the application of forward error correction (FEC) coding is explored in our design, which is utilized to combat the impact of multiuser interference (MUI). More specifically, we propose to utilize the soft information from the FEC decoder to iteratively improve the MUI rejection procedure and provide more accurate estimation of the transmitted signals. The soft information is exchanged between the decoder and the MUI cancellation unit, which constitutes a turbo structure and leads to simultaneous improvement of the mean square error and the bit error ratio performance. The performance gain is substantiated by theoretical analysis as well as numerical simulations.

Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) can support more users than OMA techniques using the same wireless resources, which is expected to support massive connectivity for Internet of Things in 5G. Furthermore, in order to reduce the transmission latency and signaling overhead, grant-free transmission is highly expected in the uplink NOMA systems, where user activity has to be detected. In this letter, by exploiting the temporal correlation of active user sets, we propose a dynamic compressive sensing (DCS)-based multi-user detection (MUD) to realize both user activity and data detection in several continuous time slots. In particular, as the temporal correlation of the active user sets between adjacent time slots exists, we can use the estimated active user set in the current time slot as the prior information to estimate the active user set in the next time slot. Simulation results show that the proposed DCS-based MUD can achieve much better performance than that of the conventional CS-based MUD in NOMA systems.

Low dimensional multiuser detection exploiting low user activity

In this paper, we propose new multiuser detectors (MUDs) based on compressed sensing approaches for the large-scale multiple antenna systems equipped with dozens of low-power antennas. We consider the scenarios where the number of receiver antennas is smaller than the total number of users, but the number of active users is relatively small. This prior information motivates sparsity-embracing MUDs such as sparsity-embracing linear/nonlinear MUDs where the detection of active users and their symbol detection are employed. In addition, sparsity-embracing MUDs with maximum a posteriori probability criterion (MAP-MUDs) are presented. They jointly detect active users and their symbols by exploiting the probability of user activity, and it can be solved efficiently by introducing convex relaxing senses. Furthermore, it is shown that sparsity-embracing MUDs exploiting common users' activity across multiple symbols, i.e., frame-by-frame, can be considered to improve performance. Also, in multiple multiple-input and multiple-output networks with aggressive frequency reuse, we propose the interference cancellation strategy for the proposed sparsity-embracing MUDs. That first cancels out the interference induced by adjacent networks and then recovers the desired users' information by exploiting the low user activity. In simulation studies for binary phase shift keying modulation, numerical evidences establish the effectiveness of our proposed MUDs exploiting low user activity, as compared with the conventional MUD.

Performance of multiuser detectors based on EM-like method for ultra-wideband communications systems in multipath fading channel

An adaptive multiuser detector (MUD) is proposed for direct-sequence ultra-wideband (DS-UWB) multiple access communication systems to suppress both multiple access interference (MAI) and inter-symbol interference (ISI). In this contribution, considering the MUD from a combination viewpoint, we proposed a MUD based on electromagnetism-like (EM) method, which applied the concept of EM search to Hopfield neural network (EMHNN) for solving optimization problems. We analyze the performance of the EMHNN MUD in multipath fading channel, and compare it with the optimum detector and several suboptimum schemes such as conventional, decorrelator detector (DD), minimum-mean-squared error (MMSE) and HNN MUD. Simulation results will demonstrate that the proposed EMHNN MUD, which alleviates the detrimental effects of the MAI problem, can significantly improve the system performance.

Artificial Fish Swarm Algorithm-Assisted and Receive-Diversity Aided Multi-user Detection for MC-CDMA Systems

Artificial fish swarm algorithm (AFSA) assisted multi-user detection (MUD) is proposed for the receive-antenna-diversity-aided multi-carrier code-division multiple-access (MC-CDMA) systems in frequency selective fading channel. Due to the receive-diversity, the signals received at the different antennas are faded independently, resulting in an independent objective function for each antenna. To resolve the multi-objective dilemma when choosing one signal estimation for multiple receive antenna-branches, the individuals associated with the AFSA are selected based on the concept of Pareto optimality, which uses the information from the antennas independently. Simulation results showed that: with the same computation complexity, the strategy has much better bit error rate (BER) performance than the convention one. Comparisons with the conventional multiuser detector and the decorrelator verified the effectiveness of the proposed scheme.

Imperfect cross-correlation and amplitude balance effects on conventional multiuser decoder with turbo encoding

The present work aims to illustrate the improvement that turbo encoding/decoding algorithms can bring to a multiuser detection system, evaluated in terms of bit error rate (BER). Extended simulations have been performed for conventional multiuser detection algorithms and are still under study for other algorithms, like the optimal, the MMSE and the classical MMSE ones. Simulations have been performed in both optimal conditions and imperfect ones, like non-equal power of users and non-zero correlations between them and for different number of users. Several interesting conclusions have been highlighted with respect to parameters influence on overall system performances.

Synchronous CDMA based on the cyclical translations of a CAZAC sequence

A new scheme of synchronous CDMA is introduced in the paper. The new scheme is based on a code made by all the cyclical translations of a basic sequence having constant amplitude and white discrete spectrum. Such a code is proposed here for the first time as a code for CDMA. According to the proposed scheme, a cyclic prefix is appended to the multiplexed signal. The proposed scheme has a property that none of the known CDMA schemes has: in a multipath environment, it allows multiuser interference to become cyclic intersymbol interference. Noticeably, the memory of the finite state machine that describes the ISI model is equal to memory of the multipath channel. The main advantage of our proposed scheme is that optimal and suboptimal detectors can be obtained from detectors proposed in the past for the ISI channel, which are much easier to implement than conventional multiuser detectors of classical CDMA schemes. Another advantage of our scheme is that it leads naturally to a signal processing architecture similar to that of OFDM systems, hence based on the efficient FFT/IFFT algorithm.

Interference Suppression for Memoryless Nonlinear Multiuser Systems Using Constellation Structure

A generalized concept of interference suppression is introduced for multiuser systems with a known memoryless transmit non-linearity, such as in the downlink amplifier of a satellite communication system. By considering the operation of commonly used multiuser detection techniques from a viewpoint of constellation structure, we extend these notions to nonlinear multiuser systems. We also analyze the performance of such multiuser detectors in terms of their asymptotic multiuser error exponents, which reduce to the asymptotic multiuser effective energies for the respective detectors in the absence of nonlinearity. The asymptotic conditional error exponent is introduced as a quantitative measure for evaluating nonlinear multiuser detectors based on the conditional probability of error at high signal-to-noise ratio (SNR). The optimal affine detector that maximizes the asymptotic error exponent for a given user is derived along with a successive interference annulment (SIA) scheme that maximizes the asymptotic conditional error exponent at each stage of successive detection. We have shown that the optimal affine detector (and hence the SIA detector) is a generalization of existing linear (and successive interference canceller) detectors to include the possibility of nonlinearity and therefore, nonadditive multiuser interference. We introduce the concept of joint successive interference annulment (JSIA) for nonlinear systems, which uses the joint structural relationship between two or more users in an ordered set to achieve higher performance gains in terms of the asymptotic conditional error exponent. We generalize the joint successive interference annulment detector to include more than two users at each stage of JSIA detection to scale the tradeoff between detection complexity and performance gain. We also introduce the decorrelating detector for nonlinear systems which essentially employs a successive interference canceller with a decorrelation kernel in an equivalent but higher-order linear system. Finally, we analyze the optimal affine detector and hence, the family of proposed joint successive interference annulment detectors when the multiuser system is perturbed with noise inside the nonlinearity. Simulation results demonstrate that the proposed detectors based on successive interference annulment consistently outperform conventional multiuser detectors based on successive interference cancellation.

Multi-user Detection of Nonlinearly Distorted MC-CDMA Symbols by Microstatistic Filtering

Multi-carrier code division multiple access is a powerful modulation technique that is being considered in many emerging broadband communication systems. In a downlink scenario orthogonal spreading sequences are used since they reduce multiple access interference compared to non-orthogonal. However, the nonlinear amplification of the transmitted signal destroys the orthogonality and, thus, reduces the system performance. In order to avoid performance degradation without requiring large back-offs in the transmitter amplifier, it becomes necessary to use multi-user detection techniques at the receiver side. Conventional multi-user detectors (MUD) are designed for linear environments and, as a result, might not exhibit enough performance improvement. In this paper a new MUD based on microstatistic filtering is proposed. The presented MUD uses piece-wise linear filtering in conjunction with threshold decomposition of the input signal, which introduces a nonlinear effect, to improve performance when a nonlinearity is present. Maximum performance improvement compared to conventional MUD is achieved for low spreading factors and user loads no greater than 50%.

Multiuser detection for DS-CDMA systems in non-Gaussian channels

An adaptive multi-user detector was developed for direct-sequence code division multiple access (DS-CDMA) systems corrupted by non-Gaussian channel noise, which can be quite detrimental to the performance of the multi-user detectors based on classical white Gaussian assumption. This receiver simultaneously combats multipleaccess interference (MAI) and non-Gaussian impulsive noise. The channel parameters are estimated and transmitted signals are jointly detected by a simple recursive algorithm derived from the EM/SAGE algorithm. Analytical and simulation results show that the proposed technique is robust with wider applicability than conventional multi-user detectors in terms of near-far resistance and bit-error ratio (BER) when either MAI or non-Gaussian impulsive noise is dominant.

Iterative reduced-complexity multiuser detection based on Chase decoding for synchronous turbo-coded CDMA system

The conventional multiuser detector (MUD) based on the a posteriori probability (APP) algorithm has an exponential computational complexity in terms of the number of users. In this paper, we propose a low-complexity iterative multiuser receiver for synchronous turbo-coded code-division multiple-access (CDMA) systems. The proposed receiver is based on the Chase decoding algorithm that was previously used to decode turbo product codes. Simulation results show that the proposed receiver can significantly reduce the computational complexity with slight performance degradation compared with the APP MUD over highly correlated channels. Moreover, in this paper, we develop a numerical approach to analyze the convergence behavior of iteratively decoded CDMA channels based on density evolution technique. Analytical results are presented and shown to provide a reasonable match with what is observed in simulation.