Optimum Multiuser Detection Research Articles

Robust Online Multiuser Detection: A Hybrid Model-Data Driven Approach

We study the problem of robust online multiuser detection in the context of non-orthogonal multiple access. The optimal multiuser detector (in terms of the uncoded bit error rate) is the nonlinear maximum a posteriori (MAP) filter. Learning good nonlinear functions of this type (e.g., with deep neural networks) typically requires a large number of training samples and complicated signal processing, which poses a fundamental problem in dynamic wireless environments. Furthermore, compared with linear approaches, nonlinear filters are generally less robust against changes in the environment. To overcome these problems, we first show that the optimal MAP filter belongs to reproducing kernel Hilbert spaces (RKHSs) associated with Gaussian kernels whose widths satisfy a condition related to the standard deviation of the receiver noise. Second, we show how to approximate the optimal MAP filter with a computationally simple signal processing algorithm using a relatively small number of training samples. Third, to make the filter robust against changes in the wireless environment, we design a partially linear filter in the sum of an RKHS containing the MAP filter and an RKHS of a linear kernel. Finally, based on this partially linear design, we propose a multiuser detection framework that, in contrast to some state-of-the-art approaches, has the following desired features: (i) it has low complexity; (ii) it can work with small sample sets; (iii) it shows better robustness than a purely nonlinear receiver; and (iv) it does not require user parameter estimation (e.g., channels), which is prone to errors and may not be possible in certain scenarios.

A Simplified Sphere Decoding-Based Detector for Generalized SCMA Codebooks

Sparse code multiple access (SCMA) is one of the promising schemes to meet high connectivity and spectral efficiency in the future wireless networks. The iterative detectors, for example message passing algorithm (MPA), can provide near optimal multiuser detection (MUD) performance but becomes infeasible when the codebook size is large or the overloading factor is high. Recently, sphere decoding (SD) has been considered in the MUD of SCMA by rewriting the generalized transmission into a linear system. In this work, we first review the state-of-the-art SD-based detectors for SCMA: sphere decoding for SCMA (SD-SCMA) and generalized SD-SCMA (GSD-SCMA). We not only explain the state-of-the-art in a comprehensive way, but also exploit the sorted QR decomposition and Schnorr-Euchner enumeration to accelerate the tree search. Although GSD-SCMA overcomes the codebook constraint of SD-SCMA, its computational complexity is extremely sensitive to the overloading factor. To satisfy the trade-off between complexity and MUD performance, we propose two pruning algorithms, PRUN1 and PRUN2, and introduce the simplified GSD-SCMA (SGSD-SCMA). In the paper, error probabilities of the proposed pruning algorithms are derived. Simulation results show that the proposed detector outperforms the iterative detectors and SD-based state-of-the-art when the overloading factor is moderate and the codebook size is large.

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.

A novel modified genetic algorithm for cooperative multiuser detection with minimum transmitter power and SINR constraint

An efficient solution to prevent multiple access interference and reduce transmitter power in cellular communication systems is cooperative communications. In multiuser cooperative communications, the users transmit their signals to the receivers through multiple relay nodes. Simultaneous optimum detection of multiuser signals considering the constraints of transmitter power and SINR satisfaction is the main challenge of these systems. As known, the optimum detector in multiuser communications is maximum likelihood (ML) detector but it is not applicable because of computational complexity. In this paper, to detect the desired transmitted signal in receivers, the problem of optimum multiuser detection is optimized using Lagrange multipliers and due to complexity of the resulted output, modified dimension-reduction genetic algorithm is applied based on dividing the problem with large number of parameters (users) into several simpler problems with less number of parameters (users). The main idea is the selection of more critical parameters in first-round optimization. The optimization is done subjected to two constraints as SINR and minimum transmitter power to guarantee the quality of service (QoS) in the cooperative communication system. Simulation results show that our proposed algorithm’s performance is near the optimum ML detector and it is more efficient in comparison to other well-known sub-optimum algorithms in the literature with satisfying the QoS of the system.

Incremental gradient algorithm for multiuser detection in multi-carrier DS-CDMA system under modulation schemes

Nowadays, Multicarrier Direct sequence code division multiple access (MC DS-CDMA) systems are used in mobile communication. Performance of these systems are limited by multiple access interference (MAI) created by spread-spectrum users in the channel as well as background channel noise. This paper proposes an incremental gradient descent (IGD) multi-user detection (MUD) for MC DS-CDMA system that can achieve near-optimum performance while the number of users is linear in its implementation complexity. The IGD algorithm make an effort to perform optimum MUD by updating one user's bit decision each iteration in the best way. This algorithm accelerates the gradient algorithm convergence by averaging. When a minimum mean square error (MMSE) MUD is employed to initialize the proposed algorithm, in all cases tested the gradient search converges to a solution with optimum performance. Further, the iterative tests denote that the proposed IGD algorithm provides significant performance for cases where other suboptimum algorithms perform poorly. Simulation compares the proposed IGD algorithm with the conventional detectors.

Low Complexity Detection Based on Dynamic Factor Graph for SCMA Systems

Sparse code multiple access (SCMA) has been proposed to support massive connections, and becomes a promising non-orthogonal multiple access in future fifth generation wireless communication systems. In SCMA, message passing algorithm (MPA) is utilized to achieve nearly optimal multiuser detection, but the high complexity of MPA detector still makes it hard to implement in practice. To tackle this issue, we present a low complexity MPA detector based on the dynamic factor graph, named DFG-MPA, in which the branches with large belief values in the factor graph model do not participate in the message propagation in current and later iterations. Moreover, choosing the number of partial branches with large belief values for each iteration adaptively depends on practical application scenarios. Numerical results show that the proposed detection scheme provides a commendable tradeoff between bit-error-rate (BER) performance and computational complexity compared with some existing MPA schemes.

Radio Resource Allocation for Multicarrier Low-Density-Spreading Multiple Access

Multicarrier-low density spreading multiple access (MC-LDSMA) is a promising multiple access technique that enables near optimum multiuser detection. In MC-LDSMA, each user's symbol spread on a small set of subcarriers, and each subcarrier is shared by multiple users. The unique structure of MC-LDSMA makes the radio resource allocation more challenging comparing to some well-known multiple access techniques. In this paper, we study the radio resource allocation for single-cell MC-LDSMA system. Firstly, we consider the single-user case, and derive the optimal power allocation and subcarriers partitioning schemes. Then, by capitalizing on the optimal power allocation of the Gaussian multiple access channel, we provide an optimal solution for MC-LDSMA that maximizes the users' weighted sum-rate under relaxed constraints. Due to the prohibitive complexity of the optimal solution, suboptimal algorithms are proposed based on the guidelines inferred by the optimal solution. The performance of the proposed algorithms and the effect of subcarrier loading and spreading are evaluated through Monte Carlo simulations. Numerical results show that the proposed algorithms significantly outperform conventional static resource allocation, and MC-LDSMA can improve the system performance in terms of spectral efficiency and fairness in comparison with OFDMA.

Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks.

High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity.

A Joint Multiuser Detection Scheme for UWB Sensor Networks Using Waveform Division Multiple Access

A joint multiuser detection (MUD) scheme for wireless sensor networks (WSNs) is proposed to suppress multiple access interference (MAI) caused by a large number of sensor nodes. In WSNs, waveform division multiple access ultra-wideband (WDMA-UWB) technology is well suited for robust communications. Multiple sensor nodes are allowed to transmit modulated signals by sharing the same time periods and frequency bands using orthogonal pulse waveforms. This paper employs a mapping function based on the optimal multiuser detection to map the received bits into the mapping space where error bits can be distinguished. In order to revise error bits caused by MAI, the proposed joint MUD scheme combines the mapping function with suboptimal algorithms. Numerical results demonstrate that the proposed MUD scheme provides good performances in terms of suppressing MAI and resisting near-far effect with low computational complexity.

Efficient DS-UWB MUD Algorithm Using Code Mapping and RVM

A hybrid multiuser detection (MUD) using code mapping and a wrong code recognition based on relevance vector machine (RVM) for direct sequence ultra wide band (DS-UWB) system is developed to cope with the multiple access interference (MAI) and the computational efficiency. A new MAI suppression mechanism is studied in the following steps: firstly, code mapping, an optimal decision function, is constructed and the output candidate code of the matched filter is mapped to a feature space by the function. In the feature space, simulation results show that the error codes caused by MAI and the single user mapped codes can be classified by a threshold which is related to SNR of the receiver. Then, on the base of code mapping, use RVM to distinguish the wrong codes from the right ones and finally correct them. Compared with the traditional MUD approaches, the proposed method can considerably improve the bit error ratio (BER) performance due to its special MAI suppression mechanism. Simulation results also show that the proposed method can approximately achieve the BER performance of optimal multiuser detection (OMD) and the computational complexity approximately equals the matched filter. Moreover, the proposed method is less sensitive to the number of users.

Channel Estimation and Data Detection for Multicell Massive MIMO Systems in Correlated Channels

In this study, multicell multiuser multiple-input multiple-output systems are considered with very large number of antennas at base station. We consider a physical channel model comprised finite number of angle of arrivals, and propose a channel estimation scheme based on the eigenvalue decomposition of the covariance matrix of the received signal vector. To improve the channel estimation performance under strong intercell interference, a joint channel estimation and optimal multiuser detection scheme is proposed. Furthermore, an optimal linear receiver is developed for the uplink by taking the channel estimation error into account, and derive a closed-form expression for the sum achievable rate of the optimal receiver. Moreover, using the channel reciprocity of a time-division duplex system, we present how to form an optimal precoding vector for the downlink, and how to compute the sum achievable rate in the downlink. Numerical results are provided to verify the analysis and evaluate the performance of the channel estimation and data detection.

Performance Analysis of Multistage Interference Cancellation in THUWB Systems Using Adaptive Differential Evolution Algorithm with Novel Mutation and Crossover Strategies

We study the application of differential evolution optimization algorithm to the problem of multiuser detection (MUD) and the suppression of multiple-access interference (MAI) in time-hopping UWB (TH-UWB) system is carried out considering the performance-complexity trade-off. The importance of MUD for achieving high data or low bit error rates in these systems has already been established in several studies. However, the optimum MUD can be characterized as a nondeterministic polynomial-time hard combinatorial optimization problem such that the computational complexity increases exponentially with number of user. In this paper, we proposed modified differential evolution (MDE) optimization algorithm with novel mutation and crossover strategy based MUD is investigated by simulations, when communicating over Saleh---Valenzuela (S---V) channel model. The RAKE detector is used as the first stage to initialize the MDE-based MUD. Then, the MDE algorithm is applied to detect the received data bit by optimizing an objective function incorporating the system of the RAKE detector. The performance evaluation with extensive simulations show that our proposed MDE based MUD can go to convergence rapidly under TH-UWB channel model, the bit error ratio performance is better than of the traditional MUD.

English

Smart antenna primarily uses the SDMA-OFDM based architecture of communication. Due to robustness and protection against interference provide by OFDM (Orthogonal Frequency division multiplexing). Although High data rate transmission is effectively achieved by using OFDM but detection techniques especially in over-loaded scenario poses many challenging issues. There are many optimization techniques presents for optimal multiuser detection process in SDMA-OFDM system, though each method is suffered from limitations. In these paper two popular evolutionary algorithms such as particle swarm optimization (PSO) hybrid with MMSE technique and genetic algorithm (GA) based SDMA-OFDM multi user detection (MUD) is hybrid with the MMSE technique. These optimization multiuser detection (MUD) techniques are simulated using three different modulation techniques these are PSK, QAM and its performance is compared against four existing MUDs such as MMSE(Minimum mean square) GA(Genetic Algorithm),PSO and ZF(Zero forcing) varying different parameters. Concatenated PSO-MMSE and GA-MMSE, the two methods are better in terms of simulation and reduces complexity. These techniques are proved to provide a very high performance when comparing with the existing detectors especially in a rank-deficient scenario in which numbers of users are very high as compare to transmitting antenna. From the experimental results it is crystal clear that GA-MMSE and PSO-MMSE improve the results by 29.8% than existing multiuser detection technique and BER reached to a level of 0.01.

On the Bit Error Probability of Optimal Multiuser Detectors in Cooperative Cellular Networks

In this paper, we present new theoretical analysis on the uncoded bit error probability (BEP) of optimal multiuser detectors in cooperative cellular networks. We consider the uplink of a cooperative network where an arbitrary number of receivers jointly detect the signals transmitted from multiple transmitters. For such a network, we derive accurate upper and lower bounds on the BEP with independent Rayleigh fading and arbitrary path loss. We observe that our lower bound accurately approximates the BEP at low signal-to-noise ratios (SNRs), whereas the upper bound is accurate at high SNRs. We further evaluate our bounds asymptotically to explicitly characterize the cooperative diversity order and array gains in the high-SNR regime.

Nonlinear Detector Design for CDMA Signals in the Presence of Unknown Interferers Using Maximum Entropy Method and Comparison with SIC

In this letter, we obtain the exact bit error rate (BER) of the successive interference cancellation (SIC). Then, the relationship between SIC and the optimal multiuser detector is determined. Next, a nonlinear scheme is proposed to detect code division multiple access (CDMA) signals in the presence of unknown interferers. The proposed scheme is derived based on maximum entropy estimation of the probability density function (PDF). Analysis and simulation show that the BER performance of the proposed scheme approaches that of optimal multiuser detector and also \sic for low signal to interference ratio values.

Multi-user detector based on improved binary artificial bee colony algorithm

Optimum Multi-user Detection(OMD) technique can achieve the theoretical minimum error probability,but it has been proven to be a Non-deterministic Polynomial(NP) problem.As a new swarm intelligence algorithm,Artificial Bee Colony(ABC) algorithm has been widely used in various optimization problems.However,the traditional Binary Artificial Bee Colony(BABC) algorithm has the shortcomings of slower convergence speed and falling into local optimum easily.Concerning the shortcomings,an improved binary artificial bee colony algorithm was proposed and used for optimum multi-user detection.The initialization process was simplified.The one-dimensional-reversal neighborhood search strategy was adopted.Compared with optimum multi-user detection,the computation complexity of the improved algorithm declines obviously.The simulation results show that the proposed scheme has significant performance improvement over the conventional detection in anti-multiple access interference and near-far resistance.

Achieving Full Diversity in Multi-Antenna Two-Way Relay Networks via Symbol-Based Physical-Layer Network Coding

This paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users' modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large.

Comparative study of various multiuser detection and base-station cooperation schemes for uplink multicell systems

In this contribution, we investigate and compare the spectral-efficiency of uplink multicell systems, when various BS cooperation strategies and detection schemes are employed. Associated with our studies, three base-station (BS) operational schemes are considered, which are the ideal BS cooperation, data exchange only and no BS cooperation, in conjunction with four multiuser detection (MUD) schemes, including the optimum MUD (OMUD), OMUD with parallel interference cancellation (OMUD-PIC), linear minimum mean-square error MUD (MMSE-MUD) and the MMSE-based successive interference cancellation (MMSE-SIC). Their spectral-efficiency is evaluated either by simulations for the multicell systems of limited size or based on the formulas derived by asymptotic analysis. In this article, the asymptotic spectral-efficiency (ASE) of multicell systems with various BS cooperation and MUD schemes is derived based on the asymptotic analysis of the channel autocorrelation matrix’s eigenvalue distribution or of the signal-to-interference-plus-noise ratio (SINR) attained by a concerned scheme. The impacts of system load, signal-to-noise ratio (SNR) and intercell interference strength on the spectral-efficiency are demonstrated. Our studies and performance results show that, when taking into account of the trade-off between complexity and performance, the MMSE-SIC supported by data exchange among BSs may constitute a promising multicell processing (MCP) scheme that is beneficial for implementation in practical systems.

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

Quantum Search Algorithms, Quantum Wireless, and a Low-Complexity Maximum Likelihood Iterative Quantum Multi-User Detector Design

The high complexity of numerous optimal classic communication schemes, such as the maximum likelihood (ML) multiuser detector (MUD), often prevents their practical implementation. In this paper, we present an extensive review and tutorial on quantum search algorithms (QSA) and their potential applications, and we employ a QSA that finds the minimum of a function in order to perform optimal hard MUD with a quadratic reduction in the computational complexity when compared to that of the ML MUD. Furthermore, we follow a quantum approach to achieve the same performance as the optimal soft-input soft-output classic detectors by replacing them with a quantum algorithm, which estimates the weighted sum of a function's evaluations. We propose a soft-input soft-output quantum-assisted MUD (QMUD) scheme, which is the quantum-domain equivalent of the ML MUD. We then demonstrate its application using the design example of a direct-sequence code division multiple access system employing bit-interleaved coded modulation relying on iterative decoding, and compare it with the optimal ML MUD in terms of its performance and complexity. Both our extrinsic information transfer charts and bit error ratio curves show that the performance of the proposed QMUD and that of the optimal classic MUD are equivalent, but the QMUD's computational complexity is significantly lower.