Multiuser Communication Networks Research Articles

Mode-pairing quantum key distribution based on wavelength division multiplexing in multi-user networks

Mode-pairing quantum key distribution (MP-QKD), a protocol that combines high performance and flexibility, not only eliminates the need for global phase locking but also beats the rate-transmittance bound. Such remarkable characteristics are poised to further advance the practical application of quantum communication networks. In this paper, MP-QKD is extended to the scenario of multi-user communication networks. MP-QKD enables concurrent operation across multiple channels by integrating wavelength division multiplexing (WDM) technology, facilitating secure communication among multiple users. The performance of MP-QKD in multi-channel concurrent operation is analyzed through simulating various experimental conditions. The asymmetric MP-QKD case is also considered and pulse intensity optimization improves performance for asymmetric network channels. These results delineate the performance of MP-QKD with WDM technology, highlighting its significant potential for application in quantum communication networks.

Orthogonal spatial coding with stimulated parametric down-conversion

Orthogonal optical coding is widely used in classical multi-user communication networks. Using the phase conjugation property of stimulated parametric down-conversion, we extend the current time-domain orthogonal optical coding scheme to the spatial domain to encode and decode image information. In this process, the idler beam inherits the complex conjugate of the field information encoded in the seed beam. An encoding phase mask introduced onto the input seed beam blurs the image transferred to the idler. The original image is restored by passing the coded transferred image through a corrective phase mask placed in the momentum space of the idler beam. We expect that this scheme can also inspire new techniques in secure image transmission, aberration cancellation, and frequency conversion imaging.

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.

Second-Order Statistics for IRS-Assisted Multiuser Vehicular Network With Co-Channel Interference

In this paper, we consider a downlink multi-user vehicular communication network empowered by an intelligent reflecting surface (IRS) with the base station (BS) employing the orthogonal frequency division multiple access (OFDMA) scheme. The performance of the considered system is evaluated in terms of second-order statistics (SOS) in the presence of multiple co-channel interferers with varying powers and speeds. In particular, we develop the analytical expressions of level crossing rate (LCR) and average outage duration (AOD) of the received signal envelope at a desired vehicle by considering a generalized fading model as - distribution. The impact of various system and channel parameters such as number of reflecting elements, amplitude of reflection coefficients, number of interferers, Doppler frequencies of desired and interfering vehicles, and different channel fading environments has been shown on the LCR and AOD performances. Moreover, we derive an expression for asymptotic LCR under high transmit power conditions. It has been shown that the asymptotic LCR is independent of the threshold and transmit power. In addition, we consider a stop and wait automatic repeat request (SW-ARQ) protocol for reliable data packet transmission and derive the expressions for packet error rate (PER) and throughput by utilizing the finite-state Markov channel (FSMC) model. The optimal packet length is also determined which maximizes the throughput under SW-ARQ protocol. The impact of various system parameters is also shown on the PER and throughput performances.

Rate-Splitting Multiple Access for UAV-Based RIS-Enabled Interference-Limited Vehicular Communication System

Rate-splitting multiple access (RSMA) has emerged as a novel generalized multiple access technology which can control the interference in multi-user communication systems. Reconfigurable intelligent surface (RIS) has also been proven to improve the spectral efficiency in the next-generation (beyond 5 G (B5G) and 6 G) wireless communication networks. Thus, in this paper, we explore the RSMA scheme for RIS-enabled unmanned aerial vehicle (UAV) based multi-user vehicular communication network in the presence of co-channel interference. The communication between the UAV and the desired vehicle takes place through an RIS along with the direct link and is interfered by multiple interfering vehicles operating in the same spectrum. After deriving the approximate cumulative distribution function (CDF) of common and private stream signal-to-interference-plus-noise ratios (SINRs) at the desired vehicle in the interference limited scenario, we obtain an expression for the average outage probability (AOP) over the complete flying duration of the UAV. Furthermore, the optimum RSMA power allocation coefficients in each time slot of UAV flight duration are obtained by minimizing the Sum-AOP over all the desired vehicles. The performance of the proposed RSMA based RIS-aided vehicular communication network is compared with (a) RSMA based network without RIS and (b) non-orthogonal multiple access (NOMA) based RIS-aided counterpart. The numerical results reveal the superiority of RSMA over NOMA and the significance of deploying RIS in considered vehicular communication network. The analytical results are corroborated with Monte Carlo simulations.

Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks

Inspired by mobile edge computing (MEC), edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center. However, the quality of model training will be affected by the limited communication and computing resources of network edge. In this paper, how to improve the training performance of a federated learning system aided by intelligent reflecting surface (IRS) over vehicle platooning networks is studied, where multiple platoons train a shared federated learning model. Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon. Meanwhile, IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner. Firstly, the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling, bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon. Specifically, in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks. Then, a method based on sequential optimization algorithm (SOA) and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided communication, respectively. Finally, a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons. Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks. The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.

Finite-Resolution Digital Beamforming for Multi-User Millimeter-Wave Networks

Recent studies have shown that low-resolution analog-to-digital-converters and digital-to-analog-converters (ADCs and DACs) can make fully-digital beamforming more power efficient than its analog or hybrid beamforming counterpart over wide-band millimeter-wave (mmWave) channels. Inspired by this, we propose a computationally efficient fully-digital beamformer relying on low-resolution ADCs/DACs for multi-user mmWave communication networks. Both a generalized (unstructured) beamformer (GB) and a structured zero-forcing beamformer (ZFB) are proposed. For maintaining fairness among all users in the network, specifically tailored objective functions are considered under sum-power constraints, namely that of maximizing the geometric mean (GM) of users’ rate and their max-min rate. These computationally challenging beamforming design problems are tackled by developing computationally efficient steep ascent algorithms, which have the radical benefit of relying on a closed-form solution at each iteration. Moreover, to facilitate the employment of low-cost amplifiers at each antenna, the GB design problem subject to the equal-gain transmission constraint is considered, which assigns equal transmit power to each transmit antenna. The proposed algorithms promise a user-rate distribution having a reduced deviation among the user-rates, i.e., improved rate-fairness. Our extensive simulation results show an approximately upto 45% reduction for the GM-rate of a 2-bit ADC (4-bin quantization) compared to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -resolution ADC.

On the Performance of Laser-Powered UAV-Assisted SWIPT Enabled Multiuser Communication Network With Hybrid NOMA

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network.

Long-Term CSI-Based Design for RIS-Aided Multiuser MISO Systems Exploiting Deep Reinforcement Learning

In this letter, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributions, we consider long-term CSI-based transmission design, where both the beamforming vectors at the base station (BS) and the phase shifts at the RIS are designed based on long-term CSI, which can significantly reduce the channel estimation overhead. Due to the lack of explicit ergodic data rate expression, we propose a novel deep deterministic policy gradient (DDPG) based algorithm to solve the optimization problem, which was trained by using the channel vectors generated in an offline manner. Simulation results demonstrate that the achievable net throughput is higher than that achieved by the conventional instantaneous-CSI based scheme when taking the channel estimation overhead into account.

Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems.

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.

Reconfigurable Intelligent Surfaces in 6G: Reflective, Transmissive, or Both?

Reconfigurable intelligent surfaces (RISs) have attracted wide interest from industry and academia since they can shape the wireless environment into a desirable form at low cost. In practice, RISs have three types of implementations: 1) reflective, where signals can be reflected to the users on the same side of the base station (BS), 2) transmissive, where signals can penetrate the RIS to serve the users on the opposite side of the BS, and 3) hybrid, where the RISs have a dual function of reflection and transmission. Most existing works focus on the reflective type RISs, and the other two types of RISs have not been well investigated. In this letter, a downlink multi-user RIS-assisted communication network is considered, where the RIS can be one of these types. We derive the system sum-rate, and discuss which type can yield the best performance under a specific user distribution. Numerical results verify our analysis.

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.

NOMA-aided generalized pre-coded quadrature spatial modulation for downlink communication systems

Generalised pre-coding quadrature spatial modulation (GPQSM) is recently proposed to increase the spectral efficiency (SE) of GPSM, which extends the transmitted symbols into in-phase/quadrature domains. In this paper, a novel scheme named non-orthogonal multiple access (NOMA)-aided GPQSM (NOMA-GPQSM), which incorporates the GPQSM scheme into the multi-user communication networks with assist of NOMA, is proposed to further improve the SE and system performance. In NOMA-GPQSM, one base station (BS) is set to serve K users, where user 1 is closest to the BS, and user K is farthest from the BS. In addition, a low-complexity detection method is proposed to reduce the high detection complexity of the maximum-likelihood (ML) detection in successive interference cancellation (SIC) method for all users by NOMA-GPQSM. The theoretical analysis of the BER performance for all users is also derived. Simulation results show that near users achieve relatively good performance, and far users achieve acceptable performance by adjusting power factors for all users in NOMA-GPQSM.

Analysis of Critical Damage in the Communication Network. II. Guaranteed Functional Performance Estimates

In the computational experiments on a model of a multiuser communication and control network, changes in the system’s performance under destructive effects are studied. The vector of the maximal flows that can be transmitted independently between all nodes is used for the multicriteria assessment of the system’s functionality. The set of damage of various types is considered, consisting of specially selected vertices and minimal cuts; when they are removed, the maximal possible flow turns out to be zero for at least one source–sink pair. To assess the consequences and impact on the quality of communication of a specific destructive effect for each pair of nodes, the characteristic vector of the maximal permissible flows is formed. Computational experiments are carried out for all the damage from the given set and make it possible to determine a representative set of characteristic vectors. To obtain guaranteed estimates, a component-by-component analysis, sorting, and comparison by several criteria of all the performance indicators is performed. Final diagrams are constructed, reflecting the changes in the initial functional characteristics for the given set of targeted destructive actions. The aggregated assessments and indicators allow comparing the vulnerability and survivability of network systems with different structural features.

Beamforming Design for Multiuser Transmission Through Reconfigurable Intelligent Surface

This article investigates the problem of resource allocation for multiuser communication networks with a reconfigurable intelligent surface (RIS)-assisted wireless transmitter. In this network, the sum transmit power of the network is minimized by controlling the phase beamforming of the RIS and transmit power of the base station. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to minimize the sum transmit power under signal-to-interference-plus-noise ratio (SINR) constraints of the users. To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form, while the optimal transmit power is obtained by using the standard interference function. Simulation results show that the proposed scheme can reduce up to 94% and 27% sum transmit power compared to the maximum ratio transmission (MRT) beamforming and zero-forcing (ZF) beamforming techniques, respectively.

Performance of Wireless Powered Multi-user Multi-relay Communication Networks with Outdated CSI

In this paper, we investigate the wireless powered multi-user multi-relay communication network, in which only relay nodes need energy harvesting with power splitting-based simultaneous wireless information and power transfer architecture. A low-complexity practical scheme, in which the best user selection and partial Nth best relay selection are adopted, is proposed and analyzed under the impact of outdated channel state information (CSI). The exact and lower bound closed-form expressions for the outage probability are derived over independent but not necessarily identical distributed (i.n.i.d) Rayleigh fading channel, and its asymptotic analysis is also given. The asymptotic analysis reveals that the diversity gain can achieve 2, which is different from the system without outdated CSI. The correctness of analytical result is validated by Monte Carlo simulations, and simulation results provide insights into the effect of system parameters, i.e., the impact of outdated CSI, the number of relays and users, average channel fading power, energy harvesting coefficient and power-splitting ratio, on the outage performance. At last, simulation results show that the outdated CSI and the number of relays also affect the optimal value of the power-splitting ratio.

Multi-User Communication Networks: A Coordinated Multi-Armed Bandit Approach

Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while keeping coordination overhead to a minimum. The proposed solution combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme, and ensures convergence to a stable allocation of resources. Our work considers single-user level algorithms for two scenarios: an unknown fixed number of users, and a dynamic number of users. Analytic performance guarantees, proving convergence to stable marriage configurations, are presented for both setups. The algorithms are designed based on a system-wide perspective, rather than focusing on single user welfare. Thus, maximal resource utilization is ensured. An extensive experimental analysis covers convergence to a stable configuration as well as reward maximization. Experiments are carried out over a wide range of setups, demonstrating the advantages of our approach over existing state-of-the-art methods.

Throughput Maximization With Rate-Dependent Power Consumption in Battery-Limited Multiuser Networks

This article proposes beamforming strategies for multiuser communication networks by jointly considering the data rate and battery lifetime of each user. We consider a practical power consumption model, where the decoding power of each user device is a nonlinear function of the data rate. We assume each user device is powered by a battery with limited capacity. The power consumption of the user device determines the expected battery lifetime which conforms to Peukert's law. Two non-convex optimization problems are considered based on two criteria: maximizing the minimum of user throughputs (MaxMin) and maximizing the weighted sum of user throughputs (MaxSum), during the battery lifetime. For the MaxMin problem, we solve it by using a bisection search algorithm. For the MaxSum problem, we propose an algorithm based on the successive convex approximation, which can find a stable point that approximates the optimum solution. Numerical simulations verify that the expected data throughput of the multiuser system can be greatly improved by properly choosing the beamforming strategies using the proposed algorithms.

Joint Code-Frequency Index Modulation for IoT and Multi-User Communications

In this paper we propose a family of index modulation systems which can operate with low-power consumption and low operational complexity for multi-user communication. This is particularly suitable for non-time sensitive Internet of Things (IoT) applications such as telemetry, smart metering, and soon. The proposed architecture reduces the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM)-based schemes without relegating the data rate. In the proposed scheme, we implement joint code-frequency-index modulation (CFIM) by considering code and frequency domains for index-modulation (IM). After introducing and analysing the structure of the CFIM, we derive closed-form expressions of the bit error rate (BER) performance over Rayleigh fading channels and we provide extensive simulation results to validate our outcomes. To better exhibit the particularities of the proposed scheme, the PAPR and complexity are thoroughly examined. The obtained results show that the PAPR is reduced compared to conventional OFDM-like IM-based schemes. Therefore, the proposed system is more likely to operate in the linear regime, which can in turn be implemented into low-cost devices with cost effective amplifiers. In addition, the concept is extended to synchronous multi-user communication networks, where full functionality is obtained by using orthogonal spreading codes. With the characteristics demonstrated in this work, the proposed system would constitute an exceptional nominee for IoT applications where low-complexity, low-power consumption and high data rate are paramount.