Multiuser Wireless Networks Research Articles

Joint Beamforming and Reflecting Elements Optimization for Segmented RIS Assisted Multi-User Wireless Networks

Joint Beamforming and Reflecting Elements Optimization for Segmented RIS Assisted Multi-User Wireless Networks

Beamforming Design for In-Band Full-Duplex Multi-Cell Multi-User MIMO Networks With Global and Local CSI

This paper investigates beamforming techniques for in-band full-duplex multi-cell multi-user (IBFD-MCMU) wireless networks considering hardware impairments (HWIs), channel uncertainty, and limited channel state information (CSI). For the global CSI scenario, we first enhance zero-forcing (ZF), maximum ratio transmission and combining (MRTC), and minimum mean-squared error (MMSE) beamforming to be compatible with multi-antenna users and IBFD base stations. Then, we investigate beamforming schemes for the local CSI assumption where only intra-cell channel knowledge is fully available at the base stations, and inter-cell channels are known statistically due to the limited training resources. With these limitations, two MMSE-based methods are proposed, which regard unknown CSI as random instances (i.e., eMMSE-RI) and noise (i.e., eMMSE-N). We explore the self-interference cancellation (SIC) capability of beamformers and evaluate the performance of these methods in 3GPP scenarios. Numerical results reveal that the enhanced MMSE beamforming can achieve the desired IBFD with low analog SIC depth, inspiring a low-cost IBFD transceiver design. The practical imperfections (e.g., transceiver HWIs, channel uncertainty, limited CSI) decrease the achievable sum rate but do not reduce the IBFD gain. With CSI limitations, eMMSE-RI outperforms eMMSE-N for microcells, where the probability of the presence of LOS is high, whereas eMMSE-N performs better when cell size is enlarged.

Resource Allocation of NOMA Communication Systems for Federated Learning

Federated learning is a new communication and computing concept that allows naturally distributed data sets (e.g., as in data acquisition sensors) to be used to train global models, and therefore successfully addresses privacy, power and bandwidth limitations in wireless networks. In this paper, we study the communications problem of latency minimization of a multi-user wireless network used to train a decentralized machine learning model. To facilitate low latency, the wireless stations (WSs) employ non-orthogonal multiple access (NOMA) for simultaneous transmission of local model parameters to the base station, subject to the users’ maximum CPU frequency, maximum transmit power, and maximum available energy. The proposed resource allocation scheme guarantees fair resource sharing among WSs by enforcing only a single WS to spend the maximum allowable energy or transmit at maximum power, whereas the rest of the WSs transmit at lower power and spend less energy. The closed-form analytical solution for the optimal values of resource allocation parameters is used for efficient online implementation of the proposed scheme with low computational complexity.

Securing Multiuser Communications via an Energy Harvesting Node: Jammer or Relay?

In this paper, we study the secure communication problem of a wireless multiuser network consisting of a base station (BS), multiple users, and an energy harvesting (EH) node in the face of an eavesdropper. We focus on the question that which role the EH node plays, as a friendly jammer or as a cooperative decode-and-forward (DF) relay. Accordingly, we propose two EH node aided multiuser scheduling schemes to help the secure communications between BS and multiple users, i.e., the EH jammer aided transmission (EHJT) scheme where the EH node acts as a friendly jammer and the EH relay aided transmission (EHRT) scheme where the EH node acts as a cooperative DF relay. For comparison purposes, the direct transmission (DT) scheme is also presented. We analyze the security-reliability trade-off (SRT) performance of the proposed EHJT and EHRT schemes as well as the DT scheme in terms of deriving their exact and asymptotic expressions of outage and intercept probabilities. Additionally, we propose an iterative algorithm based on the golden search to determine the optimal time allocation factor (OTAF) for the sake of minimizing the sum of outage and intercept probabilities for the proposed EHJT and EHRT schemes. Numerical results show that the EH node can improve the SRT performance of the multiuser network more significantly as a friendly jammer than as a cooperative DF relay. It is also shown that the best SRT performance of the EHJT and EHRT schemes can be achieved by adjusting the signal-to-noise ratio (SNR) and time allocation factor.

A Centralized Multi-User Anti-Composite Intelligent Interference Algorithm Based on Improved Q-Learning

This paper proposes a central anti-jamming algorithm (CAJA) based on improved Q-learning to further solve the communication challenges faced by multi-user wireless communication networks in terms of external complex malicious interference. This will also reduce the dual factors restricting wireless communication quality, the impact of inter-user interference within the network, and the effect of external malicious interference on the communication system to improve multi-user wireless communication transmission. Firstly, a central base station that coordinates and allocates channels for users within the network is set up using multi-user wireless communication network architecture to constitute a centralized wireless communication network. Secondly, the multi-user system is modeled using the single-user Markov decision process in which the central base station is the main body. Finally, an improved Q-learning algorithm is used to improve overall system transmission income using the central base station, based on the network user number sequential decision action for avoiding external malicious interference. It is designed to avoid the impact of internal network interference on transmission performance during the early stage of communication, achieving overall system transmission income improvement. Simulation results show that in comparison to the existing multi-user independent Q-learning anti-jamming algorithm and the traditional orthogonal frequency-hopping scheme, the proposed algorithm significantly improves overall system transmission performance.

Deep Recurrent Reinforcement Learning-Based Distributed Dynamic Spectrum Access in Multichannel Wireless Networks With Imperfect Feedback

This paper investigates Dynamic Spectrum Access (DSA) paradigm with imperfect feedback for multiuser wireless network. Each user selects an orthogonal channel in particular time slot to transmit packet with a certain transmission probability. In next time slot, the user who has transmitted a packet receives an ACK signal based on local observation. Bearing in mind the dynamic nature of wireless networks, it is appealing to develop a blended strategy to perform effective DSA. This paper aims to design a distributed Deep Reinforcement Learning (DRL) based scheme with an objective of maximizing network utility. In conventional DRL framework, it is assumed that the feedback received (ACK packet) is always correct but in wireless networks, it may be lost or corrupted due to noise. Furthermore, it is challenging to promise that the multiple agents will cooperate to make coherent decisions in order to accomplish the same objective, particularly under imperfect feedback. To tackle these challenges, this work proposes (i) Deep Recurrent Reinforcement Learning network with integrated GRU layer to optimize the network utility function and (ii) a feedback recovery mechanism using complete and incomplete replay buffers. Extensive simulations corroborate the success of proposed scheme in complex multiuser scenario and exhibit robustness against the detrimental effects of the imperfect feedback.

Multichannel Power Constraints-Based SNR Estimation for the non-cooperative and cooperative scenarios of multi-user Wireless Network

Cooperative communication holds a significant task in Wireless Networks (WNs) by enhancing communication reliability, power, efficiency, and network connectivity. In addition, cooperative communication also enables the progression of a well-organized method for improving the WN quality. Moreover, it facilitates the exploitation of communicating resources by permitting the pathways and nodes in a network to assist in the transmission of data. In this research, a Multi-Channel Power Constraints-Based SNR Estimation (MCPC-SNR Estimation) is considered with source node to destination node and relay node to destination node. Also, three probabilistic models like joint entropy, mutual information, and correlation are considered for SNR parameters. Rather than considering the signals with the current information of signal and channel, we consider signal-to-noise ratio combining the method with static and dynamic Signal to Noise Ratio (SNR) estimation for the non-cooperative and cooperative scenarios. Finally, an assessment of the Multi-Channel Power Constraints-Based SNR Estimation (MCPC-SNR) model shows that the power allocations at the source, as well as relay nodes for transmissions, and the destination and relay nodes, have a notable effect on the Bit Error Rate (BER) performance for power-constrained cooperative communications. The analysis proves that the proposed work ensures a much lower Bit Error Rate (BER) for almost all timing error variations, which probably reaches 0.005.

Opportunistic RIS-assisted rate splitting transmission in coordinated multiple points networks

Reconfigurable intelligent surfaces (RIS) have attracted a great deal of interests due to its potential contributions to the next-generation mobile networks. The deployment of RIS in multi-user wireless networks promises to reduce system hardware costs, signal processing complexity, as well as energy consumption due to its small size, lightweight and ability to actively shape the wireless propagation environment. Based on the ability to suitably adjust the phase shifts according to the dynamic wireless environment, the transmission rate and energy efficiency can be improved by deploying software-controlled meta-surfaces to reflect signals from the source to the destination. This paper proposes a RIS-assisted rate splitting-based transmission strategy in the Coordinated Multiple Points (CoMP) transmission network. The obtained results demonstrated that the proposed opportunistic RIS-assisted rate splitting transmission outperforms the conventional jointly transmission Non-Orthogonal Multiple Access (NOMA) strategy and the mutual-aided NOMA strategy in terms of outage probability under a target power level when the obstacle exists. Along with the one-off control RIS, the network spectral efficiency can be improved significantly by the proposed opportunistic RIS-assisted (O-RIS) rate splitting strategy under the target channel interference levels.

Optimization of IRS-Aided Sub-THz Communications Under Practical Design Constraints

We consider the optimization of a smart radio environment where meta-surfaces are employed to improve the performance of multiuser wireless networks working at sub-THz frequencies. Motivated by the extreme sparsity of the THz channel we propose to model each meta-surface as an electronically steerable reflector, by using only two parameters, regardless of its size. This assumption, although suboptimal in a general multiuser setup, allows for a significant complexity reduction when optimizing the environment and, despite its simplicity, is able to provide high communication rates. We derive a set of asymptotic results providing insight on the system behavior when both the number of antennas at the transmitter and the meta-surfaces area grow large. For the optimization we propose an algorithm based on the Newton-Raphson method and a simpler, yet effective, heuristic approach based on a map associating meta-surfaces and users. Through numerical results we provide insights on the system behavior and we assess the performance limits of the network in terms of supported users and spatial density of the meta-surfaces.

Bi-Objective Optimization of Information Rate and Harvested Power in RIS-Aided SWIPT Systems

The problem of simultaneously optimizing the information rate and the harvested power in a reconfigurable intelligent surface (RIS)-aided multiple-input single-output downlink multiuser wireless network with simultaneous wireless information and power transfer (SWIPT) is addressed. The beamforming vectors, RIS reflection coefficients, and power split ratios are jointly optimized subject to maximum power constraints, minimum harvested power constraints, and realistic constraints on the RIS reflection coefficients. A practical algorithm is developed through an interplay of alternating optimization, sequential optimization, and pricing-based methods. Numerical results show that the deployment of RISs jointly improves the information rate and the amount of harvested power.

Performance Analysis of Multi User Optical Wireless Network (IR, or UV light for signal carrying) under Different Environmental Conditions

<p>The field of optics is flourishing day by day on account of the wide usage of the internet and its associated technologies. Free Space Optics (FSO) is a data transmission technique that employs light propagation to send data. This technology is effective in those circumstances where fiber optic cables cannot be deployed. This paper provides a model and simulation of the Fiber Bragg Grating (FBG) based FSO network under different environmental conditions, such as modulation types and data rates. Also, investigation of the impact of Multiple Access Interference (MAI) on FSO under design parameters has been done. The designed system performance is analyzed under the multiuser optical wireless network by changing data rates and modulation types. The proposed model's security is achieved through the use of the FBG encoder/decoder and the FSO channel for transmission. It has been inferred that the CRZ modulation type provides the best BER and Q factor compared to other formats, and a data rate as high as 1Gbps can be achieved using the CRZ modulation format. It has been noticed that as the number of users grows, the BER value begins to deteriorate.</p>

Secure transmissions in wireless multiuser networks using message correlation

Due to the openness of wireless multiuser networks, the private information transmitted in uplink or downlink is vulnerable to eavesdropping. Especially, when the downlink transmissions use nonorthogonal multiple access (NOMA) techniques, the system further encounters interior eavesdropping. In order to address these security problems, we study the secret communication in multiuser networks with both uplink and downlink transmissions. Specifically, in uplink transmissions, the private messages transmitted in each slot are correlated, so any loss of the private information at the eavesdropper will prevent the eavesdropper from decoding the private information in later time slots. In downlink transmissions, the messages are correlated to the uplink information. In this way, any unexpected users who lose the expected user's uplink information cannot decode its downlink information. The intercept probability is used to measure security performance and we analyze it in theory. Finally, simulation results are provided to corroborate our theoretical analysis.

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.

New generalized zero forcing beamforming for serving more users in energy-harvesting enabled networks

This paper considers a multi-user wireless communication network supported by a multiple-antenna base station (BS), where the users who are located sufficiently close to the BS employ wireless energy harvesting (EH) to replenish their energy needs. The objective of this work is to design an efficient beamforming to maximize the minimum throughput among all the information users (IUs), subject to EH constraints. In this regard, transmit time-switching approach is employed, where energy and information are transmitted over different fractions of a time-slot. To achieve efficient EH, a conjugate beamforming (matched filtering) is applied. To design efficient information beamforming for max–min throughput optimization, conventional zero-forcing (ZF) beamforming can be adopted, however, it will not suppress multi-user interference if the number of users is greater than the number of antennas at the BS. To this end, different from the existing works which employ regularized zero-forcing (RZF) beamforming, this work proposes a new generalized zero-forcing (GZF) beamforming, which promises better max–min throughput compared to that achieved by the RZF beamforming. A new path-following algorithm is developed to achieve max–min throughput optimization by GZF beamforming, which is based on a simple convex quadratic program over each iteration.

A Novel Distributed Multi-Agent Reinforcement Learning Algorithm Against Jamming Attacks

In a multi-user wireless network under jamming attacks, self-interested users trying to learn their best anti-jamming strategy encounter a major problem, which is interference. An interesting solution is for users to learn anti-jamming techniques cooperatively, which can be achieved using a distributed learning algorithm. In this context, works proposing distributed learning algorithms to overcome jamming in multi-user applications, rely on the availability of a safe communication link to exchange information between users. However, if this communication link wireless, assuming that this link is safe against jamming attacks would not be accurate. Consequently, we propose a novel distributed multi-agent reinforcement learning algorithm for anti-jamming, namely Cross-Check Q-learning, where users build estimates of each other’s decision-making policies and adapt to them, therefore eliminating their need to communicate. When applied against both sweeping and smart jammers, our algorithm provides users with a better understanding of their environment and helps them learn the attacker’s policy and effectively avoid mutual interference. The proposed method’s transmission rates and interference levels is compared with the standard Q-learning, the collaborative multi-agent algorithm, and random policy. Simulation results show that our algorithm improves the users’ transmission rates, eliminates mutual interference, and has the highest convergence speed under the two considered jamming attacks.

Symbol-Level Precoding With Constellation Rotation in the Finite Block Length Regime

This letter tackles the problem of optimizing the parameters of a symbol-level precoder for downlink multi-antenna multi-user systems in the finite block length regime. Symbol-level precoding (SLP) is a non-linear technique for multi-user wireless networks, which exploits constructive interference among co-channel links. Current SLP designs, however, implicitly assume asymptotically infinite blocks, since they do not take into account that the design rules for finite and especially short blocks might significantly differ. This letter fills this gap by introducing a novel SLP design based on discrete constellation rotations. The rotations are the added degree of freedom that can be optimized for every block to be transmitted, for instance, to save transmit power. Numerical evaluations of the proposed method indicate substantial power savings, which might be over 99% compared to the traditional SLP, at the expense of a single additional pilot symbol per block for constellation de-rotation.

RIS Configuration, Beamformer Design, and Power Control in Single-Cell and Multi-Cell Wireless Networks

Reconfigurable Intelligent Surfaces (RISs) are recently attracting a wide interest due to their capability of tuning wireless propagation environments in order to increase the system performance of wireless networks. In this paper, a multiuser wireless network assisted by a RIS is studied and resource allocation algorithms are presented for several scenarios. First of all, the problem of channel estimation is considered, and an algorithm that permits separate estimation of the mobile user-to-RIS and RIS-to-base stations components is proposed. Then, for the special case of a single-user system, three possible approaches are shown in order to optimize the Signal-to-Noise Ratio with respect to the beamformer used at the base station and of the RIS phase shifts. Then, for a multiuser system with two cells, assuming channel-matched beamforming, the geometric mean of the downlink Signal-to-Interference plus Noise Ratios across users is maximized with respect to the base stations transmit powers and RIS phase shifts configurations. In this scenario, the RIS is placed at the cell-edge and some users are jointly served by two base stations to increase the system performance. Numerical results show that the proposed procedures are effective and that the RIS brings substantial performance improvements to wireless system.

Quasi-Degradation Probability of Two-User NOMA Over Rician Fading Channels

Non-orthogonal multiple access (NOMA) has a great potential to offer a higher spectral efficiency of multi-user wireless networks than orthogonal multiples access (OMA). Previous work has established the condition, referred to quasi-degradation (QD) probability, under which NOMA has no performance loss compared to the capacity-achieving dirty paper coding for the two-user case. Existing results assume Rayleigh fading channels without line-of-sight (LOS). In many practical scenarios, the channel LOS component is critical to the link quality where the channel gain follows a Rician distribution instead of a Rayleigh distribution. In this work, we analyze the QD probability over multi-input and single-output (MISO) channels subject to Rician fading. The QD probability heavily depends on the angle between two user channels, which involves a matrix quadratic form in random vectors and a stochastic matrix. With the deterministic LOS component, the distribution of the matrix quadratic form is non-central that dramatically complicates the derivation of the QD probability. To remedy this difficulty, a series of approximations is proposed that yields a closed-form expression for the QD probability over MISO Rician channels. Numerical results are presented to assess the analysis accuracy and get insights into the optimality of NOMA over Rician fading channels.

Double Deep Recurrent Reinforcement Learning for Centralized Dynamic Multichannel Access

We consider the problem of dynamic multichannel access for transmission maximization in multiuser wireless communication networks. The objective is to find a multiuser strategy that maximizes global channel utilization with a low collision in a centralized manner without any prior knowledge. Obtaining an optimal solution for centralized dynamic multichannel access is an extremely difficult problem due to the large‐state and large‐action space. To tackle this problem, we develop a centralized dynamic multichannel access framework based on double deep recurrent Q‐network. The centralized node first maps current state directly to channel assignment actions, which can overcome prohibitive computation compared with reinforcement learning. Then, the centralized node can be easy to select multiple channels by maximizing the sum of value functions based on a trained neural network. Finally, the proposed method avoids collisions between secondary users through centralized allocation policy.

Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

Reconfigurable intelligent surface (RIS)-aided communication is considered as an exciting research topic in academic and industrial communities since it provides an emerging affordable solution to achieve high quality and secure next-generation wireless systems. Especially, the deployment of RIS in multi-user wireless networks promises to reduce system hardware costs, signal processing complexity, as well as energy consumption due to small size, lightweight and ability to actively shape the wireless propagation environment. Further, by realizing a cost-effective radio environment, RIS-aided communication can be implemented to be an appealing technology for future integration with other emerging wireless applications and communication systems. Despite the positive appeal, RISs face new challenges that hinder integrating efficiently into wireless networks, such as network secrecy performance and system sum-rates, as well as achieving efficient deployment design in highly dynamic and time-varying wireless environments. To this end, we overview recent state-of-the-art techniques to address the above issues faced in the integration of RISs with various emerging multi-user communication techniques, such as Unmanned Aerial Vehicles (UAVs), Non-Orthogonal Multiple Access (NOMA), Millimeter Wave (mmWave) and Terahertz (THz) communications, Physical Layer Security (PLS), massive antennas, and Simultaneous Wireless Information and Power Transfer (SWIPT). Finally, we highlight promising future research directions of RIS-aided communication in Cell-Free Massive Multiple-Input-Multiple-Output (MIMO) systems, Rate-Splitting Multiple Access (RSMA), Light Fidelity (LiFi), and Cognitive Radio (CR) systems.