Multiuser Detection Problem Research Articles

Efficient Massive Machine Type Communication (mMTC) via AMP

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and nonseparable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

Boost Sum-Product Performance for Multiuser Detection in mMTC at Millimeter Wave

We consider the multiuser detection (MUD) problem, i.e., how to separate and decode colliding data streams, in the uplink of massive Machine Type Communications (mMTC) at millimeter wave (mmWave). Operating on factor-graphs by passing messages, the sum-product algorithm and its variants are widely applied in many other scenarios. However, in this paper, we find that their performance in mMTC at mmWave could be dramatically degraded due to the ill-conditioned MUD channel gain matrix and the existence of enormous short cycles in their corresponding factor-graphs, which are caused by the limited scattering of mmWave and the sharing of a same codebook for error correction among densely located user equipments. Assuming LDPC codes are used for error correction, we further propose a novel sum-product based approach to dealing with the MUD problem in mMTC at mmWave. It first leverages the propagation characteristics of mmWave to optimize the factor-graph for MUD by removing short cycles based on node-split and node-contraction, and then takes a dynamic-programming based method to approximate the messages passing on the resulted factor-graph, which can achieve a higher decoding accuracy. Extensive simulation results show that our approach outperforms the state-of-the-art sum-product based approaches significantly.

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.

Multi-user detection for CPM spread system with turbo in VDES

The multi-user detection technology uses the spread sequence, delay, amplitude and phase information of each user to jointly detect users, which can effectively suppress multiple access interference, It is one of the key technologies in satellite communication system to make full use of uplink spectrum resources and improve system performance and capacity. Continuous phase modulation spread signal can adapt to multi-user communication due to the random variation of spread sequence, which is gradually applied to satellite communication. QPSK-CPM spread modulation scheme is adopted in the random access channel of VHF data exchange system. Aiming at the multi-user detection problem of QPSK-CPM spread system, proposing a multi-user detection algorithm based on interference elimination joint Turbo iterative detection based on the characteristics of CPM spread sequence. Compared with the traditional serial interference cancellation algorithm, the algorithm can effectively eliminate the multiple access interference problem caused by user aliasing by using the characteristics of Turbo codes. The theoretical simulation shows that the algorithm has a good detection effect on multi-user aliased signals.

Bayesian Learning-Based Multiuser Detection for Grant-Free NOMA Systems

Grant-Free Non-Orthogonal Multiple Access (GF-NOMA) is considered as a promising technology to support the massive connectivity of Machine-Type Communications (MTC). The design of efficient and high-performance multi-user detection (MUD) scheme is a challenging issue of GF-NOMA, especially when the number of active users is unknown and relatively high. This paper adopts Sparse Bayesian Learning (SBL) approaches to solve the MUD problem of GF-NOMA in MTC. The MUD problem within a certain access slot is formulated as a Single Measurement Vector (SMV) model and efficiently solved via SBL-based methods. To further improve the MUD performance, we set up a Multiple Measurement Vector (MMV) model and develop block SBL-based MUD methods, by exploiting the temporal correlation of user activity over successive access slots. Then to extend the usage of the aforementioned algorithms to the scenarios with relatively high, or quasi-sparse, user activity, we propose novel SBL-based MUD algorithms via post sparse error recovery methodology, for both the SMV and MMV problem models. Simulation results show that the proposed SBL-based MUD algorithms achieve substantial performance gain over traditional ones, especially when the number of active users is unknown and relatively high.

Blind Constant Modulus Multiuser Detection With Regularization

The problem of blind constant modulus multiuser detection in code-division multiple-access (CDMA) systems is investigated in this letter. Different from traditional approaches which either assume the amplitude of the user of interest is known or arbitrarily set the value of dispersion constant, we consider a more general case where the amplitude is unknown and the dispersion constant is deemed as an unknown parameter jointly determined with the weight vector of the detector. Moreover, a regularization term of the weight vector is incorporated into the optimization to stabilize the problem. With these considerations, an iterative framework is thus introduced to tackle the formulated problem. It is shown that for a given weight vector, the dispersion constant can be analytically determined. To handle the more challenging subproblem with respect to the weight vector, we first present a semidefinite relaxation (SDR) based scheme. Then, a Newton’s method based approach is developed to release the computational burden. Simulations have been carried out to demonstrate the effectiveness of the proposed methods and superiority over conventional approaches.

Nonlinear MIMO Communication With π-Periodic Phase Measurements

This paper proposes a nonlinear MIMO scheme named as halved-phase only MIMO (HPO-MIMO), whose base station (BS) is equipped with multiple HPO radio-frequency (RF) chains extracting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -periodic phase measurements from RF signals directly by phase detectors, and single classical RF chain sampling the combination of all the RF signals from HPO-RF chains. Compared to MIMO, HPO-RF receivers are simplified significantly, and have much lower power consumption and fabrication cost. Two types of channel estimators and multiuser detectors are developed for HPO-MIMO from the perspectives of numerical optimization and Bayesian inference. Firstly, because <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -periodic phases provide tan-relationships between the Inphase (I) and Quadrature (Q) components, the channel estimation (CE) and multiuser detection (MUD) problems are resolved by minimizing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula> -norm cost functions with unit-norm constraint on the recovered vector. Their solutions are obtained by deriving the eigenvector corresponding to the minimum eigenvalue through shifted power method (SPW). Secondly, the CE and MUD problems are categorized as generalized linear mixing problems under (un-)quantized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -periodic phase measurements, and then handled by generalized approximate message passing (GAMP), where closed-form solutions for mean-and-variance messages involving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -periodic phases are derived. Finally, magnitude and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -phase ambiguities persisting in signal recovery are removed based on the complex-valued full measurements from the single classical RF chain. Simulation results show that GAMP-type algorithms outperform SPW-type ones, and handle nonlinear phase quantization losses. 16-sector quantized HPO-MIMO could work as well as its un-quantized correspondence. HPO-MIMO reserves the MIMO advantages, but is more energy-efficient than the latter.

Mean field analysis of reverse annealing for code-division multiple-access multiuser detection

Code-division multiple-access (CDMA) multiuser detection is a kind of signal recovery problem. The main problem of CDMA multiuser detection is to estimate the original signal from the degraded information. In CDMA multiuser detection, the first-order phase transition happens. The first-order phase transition degrades the estimation performance. To avoid or mitigate the first-order phase transition, we apply adiabatic reverse annealing (ARA) to CDMA multiuser detection. In ARA, we introduce the initial Hamiltonian, which corresponds to the prior information of the original signal into quantum annealing (QA) formulation. The ground state of the initial Hamiltonian is the initial candidate solution. By using the prior information of the original signal, ARA enhances the performance of QA for CDMA multiuser detection. We evaluate the typical ARA performance of CDMA multiuser detection by means of statistical mechanics using the replica method. At first, we consider the oracle cases where the initial candidate solution is randomly generated with a fixed fraction of the original signal in the initial state. In the oracle cases, the first-order phase transition can be avoided or mitigated by ARA if we prepare for the proper initial candidate solution. We validate our theoretical analysis with quantum Monte Carlo simulations. The theoretical results to avoid the first-order phase transition are consistent with the numerical results. Next, we consider the practical cases where we prepare for the initial candidate solution obtained by commonly used algorithms. We show that the practical algorithms can exceed the threshold to avoid the first-order phase transition. Finally, we test the performance of ARA with the initial candidate solution obtained by the practical algorithm. In this case, ARA cannot avoid the first-order phase transition even if the initial candidate solution exceeds the threshold to avoid the first-order phase transition.

Neural Network Based AMP Method for Multi-User Detection in Massive Machine-Type Communication

In massive machine-type communications (mMTC) scenarios, grant-free non-orthogonal multiple access becomes crucial due to the small transmission latency, limited signaling overhead and the ability to support massive connectivity. In a multi-user detection (MUD) problem, the base station (BS) is unaware of the active users and needs to detect active devices. With sporadic devices transmitting signals at any moment, the MUD problem can be formulated as a multiple measurement vector (MMV) sparse recovery problem. Through the Khatri–Rao product, we prove that the MMV problem is transformed into a single measurement vector (SMV) problem. Based on the basis pursuit de-noising approximate message passing (BPDN-AMP) algorithm, a novel learning AMP network (LAMPnet) algorithm is proposed, which is designed to reduce the false alarm probability when the required detection probability is high. Simulation results show that when the required detection probablity is high, the AMP algorithm based on LAMPnet noticeably outperforms the traditional algorithms with acceptable computational complexity.

A Novel Multi-user Detection Approach on Fluctuations of Autocorrelation Estimators in Non-Cooperative Communication

Recently, blind multi-user detection has become an important topic in code division multiple access (CDMA) systems. Direct-Sequence Spread Spectrum (DSSS) signals are well-known due to their low probability of detection, and secure communication. In this article, the problem of blind multi-user detection is studied in variable processing gain direct-sequence code division multiple access (VPG DS-CDMA). The method based on the fluctuations of autocorrelation estimators,which previously described in a single and multi-user context, is extended to an asynchronous variable processing gain (VPG) DS/CDMA systems without restrictions on the processing gains in multi-path channel condition. According to the proposed algorithm, not only the presence of the common peaks in the second-order statistic of the received signal is proved, but the relationship between these peaks and the spreading code length is also derived. The simulation results show that the proposed algorithm even in the presence a very low signal-to-noise ratio (SNR) is completely efficient in signal detection. It is remarkable that the minimum description length (MDL) method is used to determine the number of users.

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.

Enhanced Receiver Based on FEC Code Constraints for Uplink NOMA With Imperfect CSI

Non-orthogonal multiple access (NOMA) has been envisioned as a useful component of fifth generation (5G) mobile networks. As imperfect channel state information (CSI) due to channel estimation errors poses problems for most wireless receivers, it presents even greater challenges in successive interference cancellation (SIC) reception of NOMA signals. We present a novel approach to the multi-user detection problem by exploiting the important constraints of forward error correction (FEC) code word. We devise our new receiver based on the minimum output energy (MOE) criterion while preserving a distortionless response to the user equipment (UE) of interest. In particular, we efficiently adopt the UE signatures presented by the FEC channel codes under distinct permutations to separate desired signals of interest from interfering UEs. We formulate our receiver optimization into a quadratic-programming problem anchored with a set of code constraints. Our simulations demonstrate that the proposed code-anchored quadratic programming (CQP) receiver can accurately improve SIC performance and provide robustness to CSI errors better than other legacy schemes.

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.

Sparsity Learning-Based Multiuser Detection in Grant-Free Massive-Device Multiple Access

In this paper, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In RSL-MUD, active users generate and transmit data packets with random sparsity. In SSL-MUD, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing-based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of active users. The simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.

Quantum Multi-User Detection Based on Coherent State Signals

Multi-user detection is one of the important technical problems for modern communications. In the field of quantum communication, the multi-access channel on which we apply the technology of quantum information processing is still an open question. In this work, we investigate the multi-user detection problem based on the binary coherent-state signals whose communication way is supposed to be seen as a quantum channel. A binary phase shift keying model of this multi-access channel is studied and a novel method of quantum detection proposed according to the conclusion of the quantum measurement theory. As a result, the average interference between deferent users is presented and the average error probability of the quantum detection is derived theoretically. Finally, we show the maximum channel capacity of this effective detection for a two-access quantum channel

Compressive Sensing-Based Multiuser Detection via Iterative Reweighed Approach in M2M Communications

Massive machine-to-machine (M2M) is an important application for Internet of Things in 5G. In this letter, we focus on solving the multiuser detection problem supported by low-activity code division multiple access for M2M communications. To address the user activity factor unknown issue in the optimal maximum a posterior probability and improve the signal reconstruction ability, we propose iterative reweighed and minimum mean-square-error iterative reweighed algorithms based on compressive sensing theory. The simulation results demonstrate that the proposed algorithms achieve substantial performance gain over traditional detectors.

Low-Complexity Multi-User Detection for MBM in Uplink Large-Scale MIMO Systems

Media-based modulation (MBM) is a novel modulation scheme, which embeds information in different channel fading realizations created by changing the status of radio frequency mirrors. In this letter, we investigate the problem of multi-user MBM signal detection in a large-scale multiple-input multiple-output system, where the base station is equipped with a large number of antennas and several users use MBM for uplink transmission. We propose an iterative interference cancellation-based algorithm. The basic idea is to treat the multi-user interference as noise and cancel it iteratively. Computer simulation results show that the proposed algorithm has good performance with low complexity.

Compressive Channel Estimation and Multi-User Detection in C-RAN With Low-Complexity Methods

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). By taking into account of the sparsity of user activities in C-RAN, we solve the CE and MUD problems with compressed sensing to greatly reduce the large pilot overhead. A mixed $\ell _{2,1}$ -regularization penalty functional is proposed to exploit the inherent sparsity existing in both the user activities and remote radio heads with which active users are associated. An iteratively re-weighted strategy is adopted to further enhance the estimation accuracy, and empirical and theoretical guidelines are also provided to assist in choosing tuning parameters. To speed up the optimization procedure, three low-complexity methods under different computing setups are proposed to provide differentiated services. With a centralized setting at the baseband unit pool, we propose a sequential method based on block coordinate descent (BCD). With a modern distributed computing setup, we propose two parallel methods based on alternating direction method of multipliers (ADMM) and hybrid BCD (HBCD), respectively. Specifically, the ADMM is guaranteed to converge but has a high computational complexity, while the HBCD has low complexity but works under empirical guidance. Numerical results are provided to verify the effectiveness of the proposed functional and methods.

Phase Retrieval Motivated Nonlinear MIMO Communication With Magnitude Measurements

This paper proposes a multiuser magnitude-only (MO-)MIMO, whose base station acquires quantized magnitudes of the complex baseband signals through envelop detectors and low-resolution ADCs. Consequently, MO-MIMO enjoys much lower circuit power and cost in comparison with the conventional MIMO. Because the phase information is unavailable, all the existing MIMO baseband algorithms cannot be applied into MO-MIMO. Therefore, two types of channel estimators and multiuser detectors are constructed by first categorizing the channel estimation and multiuser detection problems as a quantized phase retrieval (PR) problem, and then solving the latter by developing two methods under the framework of generalized approximate message passing (GAMP). The first method directly applies GAMP to solve the quantized PR problem by exploiting the probability relationships between the quantized magnitude measurements and unknown complex signals. The second method iterates between the missing phase estimation and signal recovery, where the latter calls for GAMP to handle a linear mixing problem with quantized observations. The developed estimators and detectors call for matrix-vector multiplications and nonlinear function calculations as the most complex operations, handle the nonlinear quantization loss specially, and exploit the signal prior probability distributions. Finally, their effectiveness is validated experimentally.

Multi-User Detection for Sporadic IDMA Transmission Based on Compressed Sensing

The Internet of Things (IoT) is placing new demands on existing communication systems. The limited orthogonal resources do not meet the demands of massive connectivity of future IoT systems that require efficient multiple access. Interleave-division multiple access (IDMA) is a promising method of improving spectral efficiency and supporting massive connectivity for IoT networks. In a given time, not all sensors signal information to an aggregation node, but each node transmits a short frame on occasion, e.g., time-controlled or event-driven. The sporadic nature of the uplink transmission, low data rates, and massive connectivity in IoT scenarios necessitates minimal control overhead communication schemes. Therefore, sensor activity and data detection should be implemented on the receiver side. However, the current chip-by-chip (CBC) iterative multi-user detection (MUD) assumes that sensor activity is precisely known at the receiver. In this paper, we propose three schemes to solve the MUD problem in a sporadic IDMA uplink transmission system. Firstly, inspired by the observation of sensor sparsity, we incorporate compressed sensing (CS) to MUD in order to jointly perform activity and data detection. Secondly, as CS detection could provide reliable activity detection, we combine CS and CBC and propose a CS-CBC detector. In addition, a CBC-based MUD named CBC-AD is proposed to provide a comparable baseline scheme.