Multiuser Systems Research Articles

Joint online 3D trajectory optimisation and power allocation of UAVs as mobile access points in a downlink distributed multi-user MIMO system

Unmanned aerial vehicles (UAVs) are introduced as one of the key-enablers for 5G beyond networks due to their low cost and flexible deployment, and mobility degrees-of-freedom. Moreover, low sensitivity to blockage and uncorrelated channels in distributed multiple-input multiple-output (D-MIMO) systems, and their extension which is called cell-free massive MIMO, have made them an attractive research field. The location and number of access points (APs) in D-MIMO systems highly influence the system performance in terms of energy and spectral efficiency that can be handled by UAVs mobility and flexible deployment. In this paper, joint power allocation, and 3D trajectory design in a D-MIMO system for a downlink scenario where multiple UAVs serve as mobile APs (MbAPs) is investigated. Contrary to the existing works in multi-mobile base stations, we introduce space-division multiple access (SDMA) for the online trajectory design as a multi-access technology which does not suffer from the non-casual channel property in traditional trajectory designs. In the proposed design, the smallest user’s ergodic rate lower bound is maximised by jointly optimising MbAPs’ 3D trajectory and transmit power. The block coordinate descent (BCD) algorithm is deployed to break the main problem into two sub-problems and iteratively optimise over the two. Considering the non-convex nature of both of sub-problems, successive convex approximation is also exploited. For performance analysis, complexity and convergence property of the proposed algorithm are investigated. Finally, numerical results validate the improved performance of the proposed system in a practical scenario, where users can connect and disconnect to and from the network.

Transmit antenna selection for multi-user VASM systems: Simplicity and fairness

Variable Active Antenna Spatial Modulation (VASM) systems have been shown to offer higher spectral efficiency and greater flexibility than conventional spatial modulation systems. In VASM systems, since a part of the information is carried on the indices of activated antennas, the transmit antenna selection (TAS) problem significantly impacts the systems’ quality. In this article, we propose two new TAS methods for multi-user VASM systems, namely, Hi-TAS and ObO-TAS. Specifically, Hi-TAS aims to reduce the system’s computational complexity, which is a hierarchical combination of two popular TAS methods, i.e., channel gain-based TAS (CG-TAS) and Euclidean distance-based TAS (ED-TAS). Meanwhile, the ObO-TAS alternately chooses each transmit antenna one by one for every user to ensure fairness among them. In addition, we propose the idea of reusable TAs to improve the quality of the system. The calculation and simulation results show that the proposed Hi-TAS has a computational complexity of only 7.74% compared to ED-TAS while achieving better average bit error rate performance for the system than ED-TAS. The proposed ObO-TAS also demonstrates the advantage of ensuring fairness among users, namely that the difference in bit error rate among users is very small compared to other TAS methods.

A low-complexity iterative algorithm for multiuser millimeter-wave systems

A low-complexity iterative algorithm for multiuser millimeter-wave systems

Beyond Diagonal Reconfigurable Intelligent Surfaces: A Multi-Sector Mode Enabling Highly Directional Full-Space Wireless Coverage

Reconfigurable intelligent surface (RIS) has gained much traction due to its potential to manipulate the propagation environment via nearly-passive reconfigurable elements. In our previous work, we have analyzed and proposed a beyond diagonal RIS (BD-RIS) model, which is not limited to traditional diagonal phase shift matrices, to unify different RIS modes/architectures. In this paper, we create a new branch of BD-RIS supporting a multi-sector mode. A multi-sector BD-RIS is modeled as multiple antennas connected to a multi-port group-connected reconfigurable impedance network. More specifically, antennas are divided into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> ≥ 2) sectors and arranged as a polygon prism with each sector covering 1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> space. Different from the recently introduced concept of intelligent omni-surface (or simultaneously transmitting and reflecting RIS), the multi-sector BD-RIS not only achieves a full-space coverage, but also has significant performance gains thanks to the highly directional beam of each sector. We derive the constraint of the multi-sector BD-RIS and the corresponding channel model taking into account the relationship between antenna beamwidth and gain. With the proposed model, we first derive the scaling law of the received signal power for a multi-sector BD-RIS-assisted single-user system. We then propose efficient beamforming design algorithms to maximize the sum-rate of the multi-sector BD-RIS-assisted multiuser system. Simulation results verify the effectiveness of the proposed design and demonstrate the performance enhancement of the proposed multi-sector BD-RIS.

User Pairing and Power Allocation in Untrusted Multiuser NOMA for Internet of Things

In the Internet-of-Things (IoT), massive sensitive and confidential information is transmitted wirelessly, making security a serious concern. This is particularly true when technologies, such as non-orthogonal multiple access (NOMA), are used, making it possible for users to access each other’s data. This paper studies secure communications in multiuser NOMA downlink systems, where each user is potentially an eavesdropper. Resource allocation is formulated to achieve the maximum sum secrecy rate, meanwhile satisfying the users’ data requirements and power constraint. We solve this non-trivial, mixed-integer non-linear programming problem by decomposing it into power allocation with a closed-form solution, and user pairing obtained effectively using linear programming relaxation and barrier algorithm. These subproblems are solved iteratively until convergence, with the convergence rate rigorously analyzed. Simulations demonstrate that our approach outperforms its existing alternatives significantly in the sum secrecy rate and computational complexity.

Pattern-Division Multiplexing for Multi-User Continuous-Aperture MIMO

In recent years, thanks to the advances in meta-materials, the concept of continuous-aperture MIMO (CAP-MIMO) is reinvestigated to achieve improved communication performance with limited antenna apertures. Unlike the classical MIMO composed of discrete antennas, CAP-MIMO has a quasi-continuous antenna surface, which is expected to generate any current distribution (i.e., pattern) and induce controllable spatial electromagnetic (EM) waves. In this way, the information is directly modulated on the EM waves, which makes it promising to approach the ultimate capacity of finite apertures. The pattern design is the key factor to determine the communication performance of CAP-MIMO, but it has not been well studied in the literature. In this paper, we develop pattern-division multiplexing (PDM) to design the patterns for CAP-MIMO. Specifically, we first study and model a typical multi-user CAP-MIMO system, which allows us to formulate the sum-rate maximization problem. Then, we develop a general PDM technique to transform the design of the continuous pattern functions to the design of their projection lengths on finite orthogonal bases, which can overcome the challenge of functional programming. Utilizing PDM, we further propose a block coordinate descent (BCD) based pattern design scheme to solve the formulated sum-rate maximization problem. Simulation results show that, the sum-rate achieved by the proposed scheme is higher than that achieved by benchmark schemes, which demonstrates the effectiveness of the developed PDM for CAP-MIMO.

Passive Reflection Optimization for IRS-Aided Multicast Beamforming With Discrete Phase Shifts

Intelligent reflecting surface (IRS) can bring promising advantages to enhance the communication system performance by reconfiguring wireless channels dynamically. The design of passive IRS reflection has attracted high attention and most existing works have considered continuous phase shift for IRS reflection. However, this is difficult to realize in practice due to hardware limitations and discrete phase shifts are applied for practical IRSs instead. In this letter, the information multicast beamforming problem in an IRS-aided multi-user system is considered, where the minimum receive signal-to-noise-ratio among the users is maximized by optimizing the IRS reflections with discrete phase shifts. By leveraging semi-definite relaxation (SDR) and gradient descent/ascent (GDA), two solutions are presented with high/low computational complexity, respectively. Simulation results show the efficacy of the proposed GDA-based algorithm as compared to the SDR-based and other benchmark schemes.

Robust Joint Precoding/Combining Design for Multiuser MIMO Systems With Calibration Errors

We consider the downlink of a multiuser system operating in the time-division duplexing mode, for which base station (BS) and users are equipped with multiple antennas, and provide a robust precoding/combining design against imperfect channel state information (CSI) and calibration errors due to hardware mismatch. Towards this end, we first formulate a robust joint precoder and combiner design as a stochastic minimum mean squared error optimization problem. Then, employing an alternating optimization approach, we propose an algorithm to obtain the precoding and combining matrices assuming imperfect CSI and calibration errors at both the BS and the user sides. We also provide asymptotic closed-form expressions for the mean squared error (MSE) and the achievable sum-rate in the massive MIMO regime. The results indicate that while the MSE linearly increases with the calibration errors at the user side, the sum-rate is asymptotically independent of them. Extensive simulation results show that the proposed robust joint precoder/combiner outperforms the existing solutions while having the same order of complexity. Moreover, when the BS sends a quantized version of the combining coefficients to the users, it is observed that the proposed solution is more robust to the quantization errors than the existing algorithms.

Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning.

Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4-15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.

Joint task offloading and resource allocation for multi-user and multi-server MEC networks: A deep reinforcement learning approach with multi-branch architecture

Mobile Edge Computing (MEC) is a promising computing paradigm in the context of 5G networks, as it enables the migration of workloads from User Equipments (UEs) to nearby MEC servers, thereby providing additional computing resources to UEs. In this paper, we propose a joint optimization approach to offloading decisions and resource allocation in a multi-user and multi-server MEC system, which operates in a time-varying environment. Our objective is to minimize the average task latency and discard rate under the constraints of latency and limited computing resources of the server. While traditional optimization methods have been used to solve computational offloading problems in static environments, these methods are not suitable for time-varying systems. Deep reinforcement learning can be an effective method for solving optimization problems in time-varying environments, as it can be used to adjust strategies in real-time in response to changes in the environment. However, the increased number of UEs in the system leads to a combinatorial increase in the number of possible actions, making it difficult for the algorithm to learn. To address this issue, we propose a multi-branch network based Deep Q Network (DQN) algorithm called Branch Deep Q Network (BDQN), which modifies the action generation network into a multi-branch network structure, each branch generates one-dimensional action. This modification makes the number of network outputs increase linearly. Numerical results show that the BDQN algorithm outperforms other baseline algorithms in terms of average task latency and discard rate.

Energy Efficiency Optimization for SLIPT-Enabled NOMA System

For the upcoming sixth generation (6G) networks, the application of simultaneous lightwave information and power transfer (SLIPT) in a non-orthogonal multiple access (NOMA) system is a potential solution to improve energy efficiency (EE). In this paper, we propose a novel SLIPT-enabled NOMA multi-user system with power splitting (PS) protocol and investigate the effect of system parameters on EE. In addition, to enhance the energy harvesting and information receiving performance of the proposed system, we build up an optimization framework that aims to maximize the EE of the system by jointly optimizing the power allocation of the users and the PS coefficient. We introduce a two-step particle swarm optimization (PSO) algorithm to solve this problem while satisfying the constraints of maximum transmit power, the minimum achievable data rate, and the minimum harvested energy. The numerical results demonstrate the SLIPT-enabled NOMA system using PSO algorithm has significantly improved up to 3.83 ×106 bit/s/J in terms of EE over the traditional orthogonal multiple access (OMA) systems.

Enhanced deep soft interference cancellation for multiuser symbol detection

AbstractThe detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model‐based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data‐driven methods perform slightly worse than model‐based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning‐based method by improving a state‐of‐the‐art data‐driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error‐propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model‐based methods in perfect CSI environments and the best performance in imperfect CSI environments.

Wireless communications empowered by reconfigurable intelligent surfaces: Model-based vs model-free channel estimation

Reconfigurable intelligent surfaces (RISs) are lately being attractive for their great potential in future sixth generation wireless communications (6G), which is attributed to their affordable energy consumption and easy integration. However, the large numbers of low-cost reflecting elements comprising RISs impose challenges for channel acquisition in various RIS-based wireless applications, such as RIS-enhanced orthogonal frequency-division multiplexing and multi-user multiple-input multiple-output systems. In this article, we first overview the state-of-the-art RIS hardware architectures designed to assist channel estimation for RIS-empowered wireless communication systems. We also overview existing channel estimation approaches, which are categorized into model-based and model-free techniques, and discuss their advantages and limitations depending on the RIS deployment. Design challenges with RIS-empowered systems in terms of hardware and other parameter limitations are presented, together with future research directions for channel estimation in RIS-based wireless systems, such as RISs with extremely large numbers of elements, multi-hop communications with RISs, and frequency division duplexing for high mobility systems.

AL-aided AMC in a multi-user white-light OFDMA VLC system over a light-diffusing fiber loop.

To develop an adaptive modulation scheme for flexible high-speed multi-user visible light communication (VLC), automatic modulation classification (AMC) is adopted for monitoring the modulation formats of different subcarrier groups. An AMC scheme based on a joint convolutional neural network (CNN), active learning (AL), and data augmentation (DA) is demonstrated over an orthogonal frequency division multiplexing access (OFDMA) VLC system. The configuration of the diffuse white-light VLC system is combined with a pair integrated transceiver module, a light-diffusing fiber (LDF), and a wireless channel, which can provide white-light illumination and ubiquitous access. Within the forward error correction (FEC) threshold, the data rates of the white-light VLC links can reach 325.5 Mbps with a bit error rate (BER) of 2.163 × 10-3. An experiment with two-user access via the proposed VLC link with an unequal bandwidth allocation was demonstrated. The performance of the AL-aided CNN AMC scheme also shows a classification accuracy rate of 95.48% for the constellation diagrams of different subcarriers of the OFDMA signal over 240 training samples and faster convergence than a CNN-based AMC.

A Tutorial on Holographic MIMO Communications—Part II: Performance Analysis and Holographic Beamforming

As Part II of a three-part tutorial on holographic multiple-input multiple-output (HMIMO), this Letter focuses on the state-of-the-art in performance analysis and on holographic beamforming for HMIMO communications. We commence by discussing the spatial degrees of freedom (DoF) and ergodic capacity of a point-to-point HMIMO system, based on the channel model presented in Part I. Additionally, we also consider the sum-rate analysis of multi-user HMIMO systems. Moreover, we review the recent progress in holographic beamforming techniques developed for various HMIMO scenarios. Finally, we evaluate both the spatial DoF and the channel capacity through numerical simulations.

DCSaNet: Dilated Convolution and Self-Attention-Based Neural Network for Channel Estimation in IRS-Aided Multi-User Communication System

Channel estimation (CE) is a critical part for intelligent reflecting surface (IRS) aided multi-user communication (MUC) systems. However, the cascaded channel of the IRS-aided MUC (IRS-MUC) is a complex multi-dimensional channel, which is difficult to estimate the precise channel matrix in practice. In this letter, we propose a dilated convolution and self-attention based neural network (DCSaNet) to handle the CE in the IRS-MUC system. Specifically, the dilated convolution block is used to improve the feature extraction for CE during the network training. Further, the weighed features are obtained by the self-attention block. Last, a lightweight model, DCSaNet-l is applied to reduce the network parameters for the practical IRS deployment. Experimental results show that the proposed DCSaNet can significantly lower the normalized mean square error (NMSE), accelerate the training speed under different SNR cases and different channel dimensions. The results also verify that the lightweight DCSaNet-l can reach a near optimal performance of the proposed DCSaNet, but further significantly reduce the parameter amount by more than 83.7%.

Age of Information in Downlink Systems: Broadcast or Unicast Transmission?

We analytically decide whether the broadcast transmission scheme or the unicast transmission scheme achieves the optimal age of information (AoI) performance of a multiuser system where a base station (BS) generates and transmits status updates to multiple user equipments (UEs). In the broadcast transmission scheme, the status update for all UEs is jointly encoded into a packet for transmission, while in the unicast transmission scheme, the status update for each UE is encoded individually and transmitted by following the round robin policy. For both transmission schemes, we examine three packet management strategies, namely the non-preemption strategy, the preemption in buffer strategy, and the preemption in serving strategy. We first derive new closed-form expressions for the average AoI achieved by two transmission schemes with three packet management strategies. Based on them, we compare the AoI performance of two transmission schemes in two systems, namely, the remote control system and the dynamic system. Aided by simulation results, we verify our analysis and investigate the impact of system parameters on the average AoI. For example, the unicast transmission scheme is more appropriate for the system with a large number of UEs. Otherwise, the broadcast transmission scheme is more appropriate.

Low-Resolution Hybrid Beamforming in Millimeter-Wave Multi-User Systems

A new hybrid amalgam of analog and baseband digital beamforming is conceived for millimeter-wave (mmWave) multi-user networks. The base station is equipped with a large-scale antenna-array, which however relies on a limited number of radio frequency chains for mitigating the severe path-loss of mmWave band transmission. Each user is also equipped with a multi-antenna array. The analog beamformer's weight-resolution is set low for the sake of power efficient implementation. The hybrid beamformer design relies on the novel optimization criterion of directly maximizing the geometric means of users' rates, which is shown to result in fair rate-distributions for the users without imposing a minimum user-rate constraint. Furthermore, new computationally efficient algorithms are developed, which are purely based on closed-form low-complexity expressions. Hence these low-complexity solutions are eminently suitable for large-scale mmWave arrays. Numerical examples are provided for demonstrating their efficiency.

Low complexity nonlinear detection for multiuser hybrid MIMO systems and performance analysis

This paper proposes an analytical framework and a system of distortion-aware receiver structures for single-carrier uplink hybrid beamforming-based multiple-input-multiple-output (MIMO) channels. Proposed single-user processing units cooperate at the base station (BS) in an iterative manner for multi-user symbol detection under hardware impairments. Firstly, in the proposed system, an iterative nonlinear interference cancellation (nIC) based on feedforward neural network (NN) modeling is developed to eliminate the nonlinear distortion effects of power amplifiers (PAs). Furthermore, depending on the fading pattern of the instantaneous channel, it is analytically shown that a large gain may be applied to the distortion signal during the equalization stage, which yields distortion amplification causing a significant decrease in the signal-to-distortion-plus-noise ratio (SDNR). A reduced complexity distortion aware detector, based on a bank of fractionally delayed iterative block decision feedback equalization (IB-DFE) receivers, is introduced to adjust the combiner weights for fractionally delayed IB-DFE outputs so that distortion power is minimized before detection. Consequently, multi-user detection capability is enhanced considerably. Secondly, an asymptotic SDNR analysis is performed. The theoretical analysis, verified via numerical evaluations, clearly shows that distortion amplification limits the achievable SDNR at the BS, and the proposed reduced complexity multi-user receiver architecture improves the BER performance by effectively mitigating this effect.

Model division multiple access for semantic communications

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.