User's Achievable Rate Research Articles

Dynamic resource allocation in IRS-assisted UAV wideband cognitive radio networks: A DDQN-TD3 approach

Intelligent reflecting surface (IRS) technology has found extensive application in the wireless communication domain, offering significant enhancements in communication performance by manipulating the reflection of electromagnetic waves. This research article delves into the domain of UAV-assisted wideband cognitive radio networks augmented by IRS and underpinned by sensing-based spectrum sharing techniques. We introduce UAVs as alternative solutions for both primary and secondary base stations to optimize the management of spectral resources. Our primary objective centers around the joint optimization of both the trajectories of the primary and secondary UAVs, power allocation at the secondary UAVs, reflection coefficients of intelligent reflecting surfaces, sensing time, and subcarrier allocation, all aimed at maximizing the achievable rate of secondary users. Given that the problem at hand is non-convex, we employ deep reinforcement learning algorithms for resolution. To address the challenge of mixed-action spaces, we implement a novel Dueling DQN-Twin Delayed Deep Deterministic policy gradient (DDQN-TD3) algorithm. The simulation outcomes illustrate that the methodology introduced in this paper substantially amplifies the performance of CR system when contrasted with benchmark methods. This is evident in the improved spectrum efficiency, elevated data transmission rates, and the minimization of interference with the primary network.

CFLIT: Coexisting Federated Learning and Information Transfer

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexistence</i> of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexisting federated learning and information transfer (CFLIT)</i> communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

Toward Optimal Deployment of UAV Relays in UAV-Assisted Internet of Vehicles

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV networks. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this paper considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

Enhanced-Rate Iterative Beamformers for Active IRS-Assisted Wireless Communications

Compared to passive intelligent reflecting surface (IRS), active IRS is viewed as a more efficient promising technique to combat the double-fading impact in IRS-aided wireless network. In this paper, in order to boost the achievable rate of user in such a wireless network, three enhanced-rate iterative beamforming methods are proposed by designing the amplifying factors and the corresponding phases at active IRS. The first method, maximizing the simplified signal-to-noise ratio (Max-SSNR) is designed by omitting the cross-term in the definition of rate. Using the Rayleigh-Ritz (RR) theorem, Max-SSNR-RR is proposed to iteratively optimize the norm of beamforming vector and its associated normalized vector. In addition, generalized maximum ratio reflection (GMRR) is presented with a closed-form expression, which is motivated by the maximum ratio combining. To further improve rate, maximizing SNR (Max-SNR) is designed by fractional programming (FP), which is called Max-SNR-FP. Simulation results show that the proposed three methods make an obvious rate enhancement over Max-reflecting signal-to-noise ratio (Max-RSNR), maximum ratio reflection (MRR), selective ratio reflecting (SRR), equal gain reflection (EGR) and passive IRS, and are in increasing order of rate performance as follows: Max-SSNR-RR, GMRR, and Max-SNR-FP.

Multi‐agent reinforcement learning based transmission scheme for IRS‐assisted multi‐UAV systems

AbstractIn this paper, a transmission scheme based on multi‐agent reinforcement learning for intelligent reflecting surface (IRS)‐assisted multiple unmanned aerial vehicles (UAVs) systems is proposed. The proposed scheme is based on reinforcement learning and alternating optimization algorithm, which can effectively improve communication quality and ensure fairness. The scheme is divided into two parts. In the first part, the multi‐UAV cooperation problem is modeled as a markov decision process. The objective of each UAV is to maximize the minimum user channel gain. To achieve stable strategies for all agents, the Multi‐agent Deep Deterministic Policy Gradient (MADDPG) algorithm is applied to train UAVs trajectories to reach the Nash equilibrium. The MADDPG algorithm is centralized trained at the base station and executed in a distributed manner by each UAV, ensuring efficient and effective coordination among agents. In the second part, an alternating optimization algorithm is formulated to optimize active and passive beamforming. Considering the non‐convexity of the fairness objective, by using auxiliary variables and semi‐definite relaxation method, the problem of maximizing the minimum user achievable rate is transformed into a feasibility problem. Simulation results show that the proposed scheme can effectively train UAVs trajectories and improve the communication performance of all users fairly.

Outage Performance Analysis of Improper Gaussian Signaling for Two-User Downlink NOMA Systems with Imperfect Successive Interference Cancellation.

The improper Gaussian signaling (IGS) technique can improve the achievable rate of an interference-limited network by fully exploiting the second-order statistics of complex signaling. This paper addresses the outage performance analysis of a two-user downlink non-orthogonal multiple access (NOMA) system using the IGS technique in the presence of imperfect successive interference cancellation (SIC). The strong channel user (SU) adopts the IGS, while the weak channel user (WU) adopts the traditional proper Gaussian signaling (PGS). Considering a practical scenario where the transmitter has obtained the statistics of the channel coefficients instead of the instantaneous channel state information (CSI), the expressions of the achievable rates of both users under residual interference due to imperfect SIC are derived, together with their outage probabilities, subject to predetermined target rates and channel statistics. Given a fixed transmit power of the WU, both the transmit power and the degree of impropriety of the SU are optimized to minimize the outage probability subject to the outage constraint of the WU. Numerical results are provided to assess the benefits of the proposed IGS-based downlink NOMA system, which are consistent with the analysis.

Robust Beamforming Design for RIS-Aided Integrated Sensing and Communication System

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Massive MIMO Communication With Intelligent Reflecting Surface

This paper studies the feasibility of deploying intelligent reflecting surfaces (IRSs) in massive multiple input multiple-output (MIMO) systems to improve the performance of users in the service dead zone. One question of paramount importance is as follows: if the overhead of channel training and the computational complexity of algorithm design arising from the huge number of IRS reflecting elements and base station (BS) antennas have to be controlled, can we provide reasonable performance to the users with weak direct channels? This paper provides an affirm answer to this question. Specifically, to reduce the channel training overhead, we consider an appealing protocol for the uplink communication in the IRS-assisted massive MIMO systems. Under this protocol, the IRS reflection coefficients are optimized based on the channel covariance matrices, which are generally fixed for many coherence blocks, to boost the long-term performance. Then, given the IRS reflecting coefficients, the BS beamforming vectors are designed in each coherence block based on the effective channel of each user, which is the superposition of its direct and reflected user-IRS-BS channels, to improve the instantaneous performance. Since merely the user effective channels are estimated in each coherence block, the training overhead of this protocol is the same as that in the legacy wireless systems without IRSs. Moreover, in the asymptotic regime that the numbers of IRS elements and BS antennas both go to infinity with a fixed ratio, we manage to first characterize the minimum mean-squared error (MMSE) estimators of the user effective channels and then quantify the closed-form user achievable rates as functions of channel covariance matrices with channel training overhead and estimation error taken into account. Interestingly, it is shown that the properties of channel hardening and favorable propagation still hold for the user effective channels, and satisfactory user rates are thus achievable even if simple BS beamforming solutions, e.g., maximal-ratio combining, are employed. Finally, thanks to the rate characterization, we design a low-complexity algorithm to optimize the IRS reflection coefficients based on channel covariance matrices.

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

In Beyond the Fifth Generation (B5G) heterogeneous edge networks, numerous users are multiplexed on a channel or served on the same frequency resource block, in which case the transmitter applies coding and the receiver uses interference cancellation. Unfortunately, uncoordinated radio resource allocation can reduce system throughput and lead to user inequity, for this reason, in this paper, channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate. Since the construction model is non-convex and the response variables are high-dimensional, a distributed Deep Reinforcement Learning (DRL) framework called distributed Proximal Policy Optimization (PPO) is proposed to allocate or assign resources. Specifically, several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation. Moreover, agents in the collection stage slow down, which hinders the learning of other agents. Therefore, a preemption strategy is further proposed in this paper to optimize the distributed PPO, form DP-PPO and successfully mitigate the straggler problem. The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Power Allocation Optimization and Decoding Order Selection in Uplink C-NOMA Networks

In this letter, we investigate the power allocation and decoding order at the base station (BS) of two-user uplink (UL) cooperative non-orthogonal multiple access (C-NOMA)-based cellular networks. We formulate a joint optimization problem aiming at maximizing the minimum user achievable rate. Given that the problem at hand is non-convex, as an alternative, we derive low-complexity and near optimal analytical expressions for the power allocation coefficients. We demonstrate through numerical results that the proposed scheme provides superior performance in comparison with the traditional UL NOMA. We also demonstrate that, in UL C-NOMA, decoding the far NOMA user first at the BS provides the best performance.

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

Design of Hybrid Precoding for Millimeter-Wave MIMO-NOMA Systems Based on User Fairness

This letter studies the design of hybrid precoding in millimeter-Wave (mmWave) multiple input multiple output-non orthogonal multiple access (MIMO-NOMA) systems. To guarantee the rate performance for all served users, we formulate the optimization problem of joint analog precoding (AP) and digital precoding (DP) vectors based on maximum and minimum (Max-Min) fairness to maximize the minimal achievable rate of users, i.e., we design different HP vectors for each user from the perspective of fairness. This can be solved by the proposed two-stage optimization scheme. First, the AP matrix and the DP vectors of each user are alternately designed, where the AP and DP vectors can be obtained by using the Semi-Definite Relaxation algorithm and the proposed boundary-compressed particle swarm optimization (BC-PSO) algorithm, respectively. Then, the optimal scaling factors obtained based on the Karush-Kuhn-Tucker (KKT) condition are introduced to further to improve the Max-Min rate. Simulation results show that the proposed scheme performs better than existing schemes.

Downlink Power Control for Cell-Free Massive MIMO With Deep Reinforcement Learning

Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm.

UAV Trajectory and Beamforming Optimization for Integrated Periodic Sensing and Communication

Unmanned aerial vehicle (UAV) is expected to bring transformative improvement to the integrated sensing and communication (ISAC) system. However, due to shared spectrum resources, it is challenging to achieve a critical trade-off between these two integrated functionalities. To address this issue, we propose in this paper a new integrated \emph{periodic} sensing and communication mechanism for the UAV-enable ISAC system. Specifically, the user achievable rate is maximized via jointly optimizing UAV trajectory, transmit precoder, and sensing start instant, subject to the sensing frequency and beam pattern gain constraints. Despite that this problem is highly non-convex and involves an infinite number of variables, we obtain the optimal transmit precoder and derive the optimal achievable rate in closed-form for any given UAV location to facilitate the UAV trajectory design. Furthermore, we first prove the structural symmetry between optimal solutions in different ISAC frames without location constraints and then propose a high-quality UAV trajectory and sensing optimization algorithm for the general location-constrained case. Simulation results corroborate the effectiveness of the proposed design and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

Joint optimization for RIS-assisted multicast D2D communications

Multicast device-to-device (D2D) communication is an important technique to overcome the problem of spectrum shortage. Due to the channel gain difference between users in different locations, receivers in the same multicast D2D group still suffer from the problem of different quality of service (QoS) levels. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted multicast D2D communication system to enhance the QoS of users, in which RIS solves the problem of different QoS between users through intelligent resource optimization. To maximize the achievable sum rate of all multicast D2D users, we formulate a joint optimization problem. This problem combines channel assignment, power allocation, and reflection matrix design by considering the QoS fairness among different D2D users. To solve this problem, we propose a novel joint optimization algorithm, which has three steps. First, we propose a matching algorithm based on swap operations to solve the channel assignment problem. Then, we propose a successive convex approximation (SCA) method to solve the power allocation problem. Finally, we present the optimization of the RIS reflection coefficients with mathematical analysis. The numerical results demonstrate that the proposed algorithm can effectively enhance the QoS of users in the multicast D2D group. With the aid of RIS, the proposed algorithm decreases the service disparities between users in different locations. Furthermore, it reveals that it is helpful to deploy the RIS closer to the receivers or the transmitters.

Proportional rate constrained resource allocation in multicarrier nonorthogonal multiple access systems

AbstractIn multicarrier nonorthogonal multiple access (MC‐NOMA) systems, resource allocation for user rate fairness is an inherent challenge due to the huge difference in channel conditions among different users. As the general case of proportional fairness, proportional rate constraint is usually adopted in resource allocation optimization problems to ensure that achievable rates of users are distributed proportionally according to a set of arbitrarily predefined portion. In this article, we investigate joint subcarrier and power allocation in MC‐NOMA systems under proportional rate constraints. The formulated resource allocation optimization problem belongs to a mixed integer nonlinear programming (MINLP) problem due to its binary subcarrier allocation variables and nonaffine equality constraints. By introducing an auxiliary variable in proportional rate constraints and substituting power variable with rate variable, we convert this nonconvex problem into a convex optimization problem and solve it by using Karush‐Kuhn‐Tucker (KKT) conditions and the dual decomposition method. Based on the optimal solutions, we divide the resource allocation process into two phases, that is, subcarrier allocation and power allocation, and develop an optimal subcarrier allocation solution based resource allocation algorithm (OSSRA). Furthermore, we also propose a heuristic algorithm, referred to as proportional rate constrained subcarrier allocation algorithm (PCSAA), as a benchmark. Numerical results show that, substantial gains can be achieved by OSSRA over the existing work and the traditional orthogonal multiple access (OMA) schemes in terms of both sum rate and user fairness. Besides, PCSAA has better user fairness performance than OSSRA.

Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge.

Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead.

Max-Min Fairness for Beamspace MIMO-NOMA: From Single-Beam to Multi-Beam

With the help of non-orthogonal multiple access (NOMA), the number of connections of the beamspace multiple-input multiple-output (MIMO) systems can be improved with enhanced sum-rate performance, which constitutes beamspace MIMO-NOMA. Thus, most relevant papers focus on improving the system sum rate, which may inflict unbearable rate loss to weak users. To ensure the achievable rates of weak users, we maximize and analyze the minimal rate of the system in the single-beam case as well as the multi-beam case, where two completely different phenomena are revealed. Particularly, in the single-beam case, the maximized minimal rate of the beamspace MIMO-NOMA always grows rapidly with the signal-to-noise-ratio (SNR), and is larger than that of the beamspace MIMO using orthogonal multiple access (beamspace MIMO-OMA). However, in the multi-beam case, the maximized minimal rate of the beamspace MIMO-NOMA grows slower and slower in the high-SNR region, where it is smaller than that of the beamspace MIMO-OMA. To explain this difference, it is disclosed that the intra-beam interference in the single-beam case is of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">successive pattern</i> , which is proved to have no limit on the max-min rate. In contrast, the inter-beam interference in the multi-beam case is of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mutual pattern</i> , which is proved to restrict the max-min rate to a derived upper bound.

Exact capacity of downlink NOMA system

Non‐orthogonal multiple access (NOMA) is a key technology beyond 5G/6G wireless and mobile communication systems. Simply because of the high efficiency of resource utilization, the system can meet the ever‐increasing requirements of higher data rates and ultra‐reliability. Keeping that in mind, we seek in this paper to derive simple closed‐form expressions to compute the maximum achievable user rate and formulate the probability density function of the SNR and SINR in the system. In addition to that, we determine the outage probability of the system in a generalized and explicit form. All of that is in terms of the user's deterministic propagation channel parameters, such as power allocation factor, noise level, position, and pathloss coefficient. Simulation results validate the accuracy of the obtained formulae.

Sub-Band Assignment and Power Control for IoT Cellular Networks via Deep Learning

As various Internet of things (IoT) communication services have recently received great attention, the development of resource allocation scheme that can support the connection of a number of IoT devices becomes an important task for next-generation communication systems. Motivated this challenge, we propose deep learning-based optimization algorithms for a joint resource allocation problem in uplink IoT cellular networks, in which the base station uses multiple sub-bands to serve IoT users and inter sub-band interference exists due to spectral leakage. Specifically, to maximize the achievable sum rate of IoT users with low complexity, we develop a two-stage optimization method built on convolutional neural networks (CNNs) that sequentially optimizes sub-band assignment and transmit power control. Moreover, in order to examine the performance according to the neural network structure, the proposed scheme is also implemented through fully-connected neural networks (FNNs) and compared with the CNN-based scheme. Simulation results show that our proposed CNN-based algorithm significantly improves the sum rate and reduces the required computation time compared to previous schemes without deep learning.