Multiuser Detection Research Articles

Retraction notice: Hybrid multibeam forming and multiuser detection technique for MU-MIMO system

Retraction notice: Hybrid multibeam forming and multiuser detection technique for MU-MIMO system

Progressive Pattern Interleaver with Multi-Carrier Modulation Schemes and Iterative Multi-User Detection in IoT 6G Environments with Multipath Channels.

Sixth-generation (6G) wireless networks demand a more efficient implementation of non-orthogonal multiple access (NOMA) schemes for severe multipath fading environments to serve multiple users. Using non-orthogonal multiple access (NOMA) schemes in IoT 6G networks is a promising solution to allow multiple users to share the same spectral and temporal resource, increasing spectral efficiency and improving the network's capacity. In this work, we have evaluated the performance of a novel progressive pattern interleaver (PPI) employed to distinguish the users in interleaved division multiple access (IDMA) schemes, suggested by 3GPP guidelines as a NOMA scheme, with two multi-carrier modulation schemes known as single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiplexing (OFDM), resulting in SC-FDMA-IDMA and OFDM-IDMA schemes. Both schemes are multi-carrier schemes with orthogonal sub-carriers to deal against inter-symbol interference (ISI) and orthogonal interleavers for the simultaneous access of multiple users. It has been suggested through simulation outcomes that PPI performance is adequate with SC-FDMA-IDMA and OFDM-IDMA schemes in terms of bit error rate (BER) under multipath channel conditions. Moreover, regarding bandwidth requirement and the implementation complexity of the transmitted interleaver structure, PPI is superior to the conventional random interleaver (RI).

Multi-User Detection Based on Improved Cheetah Optimization Algorithm

Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual update mechanism of the Cheetah Optimizer (CO) algorithm using a nonlinear strategy to improve the uniformity and discretization of the individual search range, and then dynamically introduces a differential evolutionary algorithm into the improved selection mechanism of the CO algorithm, which is utilized to fine-tune the solution space and maintain the local diversity during the fast search process. Simulation results demonstrate that this detection algorithm not only realizes fast convergence with a very low bit error rate (BER) at eight iterations but also has obvious advantages in terms of noise immunity, resistance to far and near effects, communication capacity, etc., which greatly improves the speed and accuracy of optimal position solving for multi-user detection and can achieve the purpose of accurate solving in complex environments.

Multimodal and Multiuser Semantic Communications for Channel-Level Information Fusion

Recently, semantic communication emerged as one of the enabling technologies for intelligent communications and multimodal information fusion plays an important role therein, where various sensors collect environment information to generate complementary multimodal data. In this paper, we propose a new multimodal information fusion scheme for multi-user semantic communications, namely channel-level information fusion, taking into account the nature of signal transmission in wireless channels. In the proposed scheme, wireless channel acts as a medium to fuse multimodal data and the received signal is viewed as the fused information, such that a receiver can retrieve semantic information without a need to perform multiuser signal detection. In addition, semantic precoding is used to mitigate the randomness effect of wireless channels in fusion. A case study is conducted to validate the effectiveness of the proposed fusion scheme, followed by a discussion on the open issues and challenges to be tackled.

Asynchronous multiuser detection for sparse code multiple access with unknown delays: A compressed sensing approach

SummaryDespite being the subject of a growing body of research, nonorthogonal multiple access has failed to garner sufficient support to be included in modern standards. One of the more promising approaches to nonorthogonal multiple access is sparse code multiple access, which seeks to utilize nonorthogonal, sparse spreading codes to share bandwidth among users more efficiently than traditional orthogonal methods. Nearly all of the studies regarding sparse code multiple access assume synchronization at the receiver, which may not always be a practical assumption. In this work, we aim to bring this promising technology closer to a practical realization by dropping the assumption of synchronization. We therefore propose a compressed sensing‐based delay estimation technique developed specifically for an uplink sparse code multiple access system. The proposed technique can be used with nearly all of the numerous decoding algorithms proposed in the existing literature, including the popular message passing approach. Furthermore, we derive a theoretical bound regarding the recovery performance of the proposed technique and use simulations to demonstrate its viability in a practical uplink system.

The Application of Artificial Intelligence Technology in Music Education

Artificial intelligence (AI) is transforming the field of education by emulating human cognitive processes, boosting adaptability, and improving efficiency across various domains. This integration of AI with music education, which involves a rich tapestry of history, culture, theory, and instrument instruction, facilitates the efficient utilization of musical resources. The growing demand for AI-driven online education is reshaping the landscape of learning and music instruction, particularly in the realm of music education, where the amalgamation of AI technology heralds innovative teaching approaches. This article reviews three significant AI-driven approaches: the "Flipped Classroom", which redefines traditional teaching paradigms by seamlessly combining online and offline learning experiences; the "Music Learning System Based on the RBF Algorithm", employing intricate neural network structures and large-scale music samples for personalized music education; and the "Music Learning System Based on the SCMA Algorithm", which utilizes advanced multiuser detection algorithms for tailored learning experiences. Each method leverages AI techniques, including Convolutional Neural Networks (CNN) and sophisticated algorithms, to enhance content delivery, automate feature extraction, and differentiate individual student needs. These approaches collectively signify a paradigm shift in music education, fostering personalized, interactive, and efficient learning environments for diverse learners. This article proposes future developments that could revolutionize the landscape of AI in music education. The "Singing Teaching System" mentioned in the article creates open-access AI music education software accessible to non-music majors.

Multiuser detection with accurate channel estimation for collided AIS packets

Multiuser detection with accurate channel estimation for collided AIS packets

Quantum Speedup for Multiuser Detection With Optimized Parameters in Grover Adaptive Search

Quantum Speedup for Multiuser Detection With Optimized Parameters in Grover Adaptive Search

Deep Learning Multi-User Detection for PD-SCMA

Deep Learning Multi-User Detection for PD-SCMA

Novel Hybrid SOR- and AOR-Based Multi-User Detection for Uplink M-MIMO B5G Systems

The Internet of Things (IoT) is one of the most important wireless sensor network (WSN) applications in 5G systems and requires a large amount of wireless data transmission. Therefore, massive multiple-input multiple-output (M-MIMO) has become a crucial type of technology and trend in the future of beyond fifth-generation (B5G) wireless network communication systems. However, as the number of antennas increases, this also causes a significant increase in complexity at the receiving end. This is a challenge that must be overcome. To reduce the BER, confine the computational complexity, and produce a form of detection suitable for 4G and B5G environments simultaneously, we propose a novel multi-user detection (MUD) scheme for the uplink of M-MIMO orthogonal frequency division multiplexing (OFDM) and universal filtered multi-carrier (UFMC) systems that combines the merits of successive over-relaxation (SOR) and accelerated over-relaxation (AOR) named mixed over-relaxation (MOR). Herein, we divide MOR into the initial and collaboration stages. The former will produce the appropriate initial parameters to improve feasibility and divergence risk. Then, the latter achieves rapid convergence and refinement performance through alternating iterations. The conducted simulations show that our proposed form of detection, compared with the BER performance of traditional SOR and AOR, can achieve 99.999% and 99.998% improvement, respectively, and keep the complexity at O(N2). It balances BER performance and complexity with fewer iterations.

Accumulation and Elimination: A Hard Decision-Based Multi-User Interference Cancellation Method in Satellite Communication System

With the increasing number of users in the Medium-Orbit (MEO) satellite communication system, multi-access interference (MAI) has become an important factor that restricts the reliability and capacity of the system. Additionally, the low carrier-power-to-noise-density ratio (C/N0) resulting from long-distance transmission poses a significant concern. The parallel interference cancellation (PIC) algorithm, utilized within the paradigm of multi-user detection (MUD), exhibits the capability to effectively mitigate the impact of MAI within the same system. Simultaneously, coherent accumulation serves as a means to substantially enhance the correct detection probability (Pcd) at low C/N0. In this study, a signal acquisition method for multi-user spread spectrum satellite receivers is proposed, which employs interference cancellation and coherent accumulation as its core mechanisms. Furthermore, we introduce a power estimation method based on the outcomes of signal acquisition, which can be integrated into the signal reconstruction module of PIC. Finally, we implement the aforementioned algorithms in both simulation and hardware platforms. Remarkably, we observe that when the interference-to-signal ratio (ISR) caused by MAI equals 20 dB, the improved algorithm attains a maximum Pcd of 0.95 within the high signal-to-noise ratio (SNR) region, closely approaching the theoretical limit for the bit error rate (BER). The experimental results prove the effectiveness and feasibility of the acquisition algorithm. In summary, the enhanced algorithm holds vast potential for widespread implementation in multi-user spread spectrum communication systems.

Uniquely Decodable Multi-Amplitude Sequence for Grant-Free Multiple-Access Adder Channels

Grant-free multiple-access (GFMA) is a valuable research topic, since it can support multiuser transmission with low latency. This paper constructs novel uniquely-decodable multi-amplitude sequence (UDAS) sets for GFMA systems, which can provide high spectrum efficiency (SE) without additional redundancy. We give the definition of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> -size UDAS set, and construct two kinds of UDAS sets based on cyclic and quasi-cyclic modes. Then, we propose an UDAS-based multi-dimensional bit interleaving coded modulation (MD-BICM) transceiver, with corresponding detection algorithms, i.e., a statistic of UDAS feature based active user detection algorithm (SoF-AUD) and a multiuser detection iteration decoding algorithm. In addition, the theoretical active user error rate (AUER) and Shannon limits of the proposed system are deduced. Simulation results show that our proposed system can simultaneously support four users with an AUER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , under the condition that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E_b</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is 0 dB and the length of transmit block is larger than a specified value, e.g., 784, verifying the validity of the proposed UDAS sets.

Joint Waveform Design and Detection in Symbiotic Ambient Backscatter NOMA Systems

Non-orthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBC) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this paper, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multi-user blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multi-user detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Simplified Deep Reinforcement Learning Approach for Channel Prediction in Power Domain NOMA System.

In this work, the impact of implementing Deep Reinforcement Learning (DRL) in predicting the channel parameters for user devices in a Power Domain Non-Orthogonal Multiple Access system (PD-NOMA) is investigated. In the channel prediction process, DRL based on deep Q networks (DQN) algorithm will be developed and incorporated into the NOMA system so that this developed DQN model can be employed to estimate the channel coefficients for each user device in NOMA system. The developed DQN scheme will be structured as a simplified approach to efficiently predict the channel parameters for each user in order to maximize the downlink sum rates for all users in the system. In order to approximate the channel parameters for each user device, this proposed DQN approach is first initialized using random channel statistics, and then the proposed DQN model will be dynamically updated based on the interaction with the environment. The predicted channel parameters will be utilized at the receiver side to recover the desired data. Furthermore, this work inspects how the channel estimation process based on the simplified DQN algorithm and the power allocation policy, can both be integrated for the purpose of multiuser detection in the examined NOMA system. Simulation results, based on several performance metrics, have demonstrated that the proposed simplified DQN algorithm can be a competitive algorithm for channel parameters estimation when compared to different benchmark schemes for channel estimation processes such as deep neural network (DNN) based long-short term memory (LSTM), RL based Q algorithm, and channel estimation scheme based on minimum mean square error (MMSE) procedure.

Multi-Service Oriented Joint Channel Estimation and Multi-User Detection Scheme for Grant-Free Massive MTC Networks

Multi-Service Oriented Joint Channel Estimation and Multi-User Detection Scheme for Grant-Free Massive MTC Networks

A teaching model of college ideological and political theory course based on multiuser detection algorithm

Abstract To prove that the hybrid teaching mode of ideological and political theory courses in colleges and universities is significantly helpful in improving the teaching effect of Civics and Political Science, this study builds a basic framework of the hybrid teaching mode of the course by using the smart teaching platform starting from the accurate teaching mode of information technology. For the user tracking of the smart teaching platform, the gradient tracking multi-user detection algorithm based on the multi-step proposed Newton method is used to realize the user data analysis and the convergence analysis is conducted for the GDGP-MUD algorithm and MSQN-MUD algorithm. The results show that the maximum average response time is 460ms when the number of concurrent users of the smart teaching platform is 350-400. The average performance of the experimental class under the comparative analysis is 11.41 points higher than that of the control class.

Deep neural network based downlink power domain multi-user NOMA-OFDM signal detection

Non-orthogonal multiple access-based orthogonal frequency division multiplexing (NOMA-OFDM) is a promising waveform-based multiple access technology for future wireless networks for multiple-user symbol transmission (MUST) in the same time-frequency resource block. However, it differs in the power domain, enhancing its spectrum efficiency. This is essential to meet the high data rate required for ever-increasing connected devices and the Internet of Things (IoT). However, NOMA-OFDM systems suffer from impairments such as imperfect successive interference cancellation (SIC) caused by channel impairments like channel fading, carrier frequency offset, and non-linearity caused by non-linear power amplifiers. This paper identifies and addresses the key impairments mentioned in the NOMA-OFDM system and proposes DNN-based estimation in offline training and detection in online testing for downlink power domain multi-user NOMA-OFDM symbols. The reported 2 dB SNR gain compared to least square-SIC/minimum mean square error SIC-based methods is a significant finding and demonstrates the robustness of the proposed DNN-aided approach against various channel impairments.

Deep learning-based sequential models for multi-user detection with M-PSK for downlink NOMA wireless communication systems

Deep learning-based sequential models for multi-user detection with M-PSK for downlink NOMA wireless communication systems

Receive Beamforming Designed for Massive Multi-User MIMO Detection via Gaussian Belief Propagation

Receive Beamforming Designed for Massive Multi-User MIMO Detection via Gaussian Belief Propagation

Enhancing Multi-User Detection in Multicarrier 5G and Beyond: A Space-Time Spreading Approach with Parallel Interference Cancellation

Enhancing Multi-User Detection in Multicarrier 5G and Beyond: A Space-Time Spreading Approach with Parallel Interference Cancellation