Rate Maximization Research Articles

Secrecy-Rate Optimization of Double RIS-Aided Space–Ground Networks

The physical layer security (PLS) of a space-ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and the Dinkelbach’s method, which allow us to approximate non-convex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

Joint Information-Theoretic Secrecy and Covertness for UAV-Assisted Wireless Transmission With Finite Blocklength

In this paper, we study a novel joint information-theoretic secrecy and covertness model for an unmanned aerial vehicle (UAV)-assisted finite blocklength transmission system, in which an UAV delivers classified information to a ground user (Bob) in the presence of two unauthorized users (Eve and Willie). Specifically, Eve tries to decode the legitimate information while Willie tries to detect if the UAV's transmission is present or not. Firstly, we assume that the perfect location knowledge of both Eve and Willie is known at the UAV. In this scenario, to guarantee security and covertness, the average secrecy rate maximization problem is formulated via joint design of the UAV's transmit power and three dimensional (3D) trajectory under Willie's covertness constraint. Technically speaking, the optimization problem is non-convex and mathematically intractable to tackle owing to the coupling of multiple variables. To address this non-convex problem, an iterative alternating optimization (AO) algorithm is developed with the assistance of the successive convex approximation (SCA) technique. After that, we extend the system model to both Eve's and Willie's imperfect location knowledge case, which is more sophisticated compared to the perfect location knowledge case due to the uncertain location constraints. We still utilize the iterative AO algorithm combined with SCA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula> -procedure techniques to cope with the nonconvex problem. Numerical results indicate that our proposed 3D optimization algorithm performs better than the conventional two dimensional (2D) scheme and the benchmark scheme for both perfect and imperfect location knowledge cases.

Integrated Sensing and Communication for RIS-Assisted Backscatter Systems

To facilitate the development of Internet of Things (IoT) services, future networks are expected to simultaneously provide sensing functionality and support low-power communications. In this paper, we investigate the system sum rate maximization problem in an integrated sensing and RIS backscatter communication system, where the base station (BS) simultaneously detects backscattered signals from multiple IoT devices and senses targets based on the echo signals. We formulate a joint transmit beamforming, RIS phase shifts and receive beamforming design problem under the Cramér-Rao bound (CRB) constraint for target angle estimation. To solve the non-convex problem, we then propose a fractional programming based alternating optimization algorithm. In particular, the fractional programming technique is firstly employed to transform the formulated problem into a more tractable form, and the exact penalty method and manifold optimization are then utilized to address the CRB constraint and constant-modulus constraint, respectively. Numerical results have shown that the proposed design significantly improves the system sum rate and illustrate the trade-off between the communication and sensing performance.

Secrecy Rate Maximization by Cooperative Jamming for UAV-Enabled Relay System With Mobile Nodes

In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used in wireless communications due to their low cost, small size, flexible deployment and mobile controllability. However, because of the line-of-sight (LoS) communication links, the security threat is always a challenging problem to deal with. In particular, information stolen and leakage may happen in the presence of eavesdroppers. This paper proposes a UAV-enabled system with a relay UAV and a jammer UAV, and certain mobile source and destination nodes in the presence of an eavesdropper to solve the secrecy rate maximization problem. In this system, the relay UAV transmits information between pairs of moving source nodes and moving destination nodes with interrupted communication channels due to blockage or long distance, and the jammer UAV interferes with eavesdropper to reduce the milked information through sending jamming signals. We establish an average secrecy rate maximization problem with trajectory and transmit power optimization under certain constraints for this system. Since this problem is non-convex and reformulated as the Markov decision process (MDP), we use deep reinforcement learning (DRL) method to solve it. In this article, we adopt a proximal policy optimization (PPO) algorithm to find an optimal solution because it can deal with the model of continuous action space. According to our defined states, rewards and actions in this specified MDP, this algorithm can autonomously learn to optimize the trajectory and power allocation of the UAVs to realize our goal. Simulation results demonstrate that the proposed PPO-based average secrecy rate maximization algorithm is valid, effective and scalable.

Weighted Sum Secrecy Rate Maximization for Joint ITS- and IRS-Empowered System.

In this work, we investigate a novel intelligent surface-assisted multiuser multiple-input single-output multiple-eavesdropper (MU-MISOME) secure communication network where an intelligent reflecting surface (IRS) is deployed to enhance the secrecy performance and an intelligent transmission surface (ITS)-based transmitter is utilized to perform energy-efficient beamforming. A weighted sum secrecy rate (WSSR) maximization problem is developed by jointly optimizing transmit power allocation, ITS beamforming, and IRS phase shift. To solve this problem, we transform the objective function into an approximated concave form by using the successive convex approximation (SCA) technique. Then, we propose an efficient alternating optimization (AO) algorithm to solve the reformulated problem in an iterative way, where Karush-Kuhn-Tucker (KKT) conditions, the alternating direction method of the multiplier (ADMM), and majorization-minimization (MM) methods are adopted to derive the closed-form solution for each subproblem. Finally, simulation results are given to verify the convergence and secrecy performance of the proposed schemes.

Transfer learning for resource allotment in dynamic hybrid WiFi/ LiFi communication systems

The demand of newer technologies for fifth generation mobile communication systems is high in the current arena. A hybrid form of Free Space Optical (FSO) Communication, also called Light Fidelity (LiFi) Communication, and Wireless Fidelity (WiFi) Communication technologies, has emerged as a promising candidate to fulfill it. Such a system is termed as hybrid WiFi/LiFi Communication System. The joint optimization problem of bandwidth, user association and power for data rate maximization in these hybrid systems is non-concave. Deep Q Network (DQN) Learning based algorithms offer solution to non-concavity. However, existing DQN learning based solutions are often restricted to static networks. They face complexity issues in dynamic networks. In this paper, we address the dynamic hybrid WiFi/LiFi Communication system with DQN transfer learning algorithm. Transfer learning is used to gather information about a newly entering mobile user in the network, thereby improving the overall throughput of the network. Simulations show that the proposed algorithms perform well than the existing optimization algorithms in throughput maximization.

Robust Secrecy via Aerial Reflection and Jamming: Joint Optimization of Deployment and Transmission

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this paper, we consider the anti-eavesdropping communication established through a RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst-case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst-case security provisioning as compared with the baselines.

Integrated single target sensing and multiuser communications based on zero-forcing beamforming

Integrated sensing and communication (ISAC) is a promising approach that utilizes a single waveform for both information transmission and target object acquisition. In this study, we focus on the challenges posed by inter-user and inter-ISAC-antenna interference, specifically the self-interference from ISAC transmit antennas to receive antennas. Further, the benefit of a multi-functional ISAC scheme is clarified by comparing it with single function-aware schemes: sensing- and communication-aware approaches. To address these issues, we investigate the application of a zero-forcing (ZF)-based beamforming and power allocation (PA) technique. A sum rate maximization problem with a target object sensing signal-to-noise (SNR) threshold is formulated and split into two subproblems for improved tractability. To tackle the first subproblem of precoder design, we develop a constraint-aware greedy algorithm that employs communication SNR ordering. Subsequently, we propose a feasibility test-based algorithm to solve the second subproblem of PA.Through an extensive performance evaluation, encompassing computational complexity, sum rate, and ISAC received SNR, we verify that relying solely on a single function-aware scheme is insufficient for accommodating ISAC's multifunctional capabilities. Additionally, we demonstrate that a naive switching strategy between two single function-aware schemes can result in inefficient communication performance. Considering both sensing and communication functions, we identify the need for an adaptive balance between sensing and communication capabilities in a multi-functional ISAC system. This can be achieved by dynamically adjusting the target object sensing SNR threshold within the optimization problem's constraint. The proposed ZF-based ISAC with the designed PA scheme offers an optimal strategy when the transmit power of the ISAC is low. Conversely, an equal PA to multiple users emerges as the best strategy when the transmit power of the ISAC is sufficiently high. In conclusion, our study highlights the significance of integrating sensing and communication functionalities in ISAC systems. Moreover, by providing insights into the interplay between target object sensing, beamforming, and power allocation, we contribute to the development of efficient and adaptable ISAC architectures.

Joint Receive and Passive Beamforming Optimization for RIS-Assisted Uplink RSMA Systems

In this letter, we investigate the joint optimization of transmit power allocation and receive/passive beamforming in a reconfigurable intelligent surface (RIS)-assisted uplink two-user rate-splitting multiple access (RSMA) system. Aiming at maximizing the achievable rate of one user subject to another user’s quality of service (QoS) requirements, RIS is deployed to assist the uplink transmissions from both users to a multi-antenna base station (BS). We decouple the non-convex rate maximization problem into three sub-problems and propose an alternating optimization (AO) algorithm to optimize the receive/passive beamforming and transmit power allocation. A closed-form expression is derived for the sub-problem of transmit power allocation by using the optimal decoding order and the concave-convex fractional programming beamforming problems are converted to convex ones by using the Charnes-Cooper transform. Then, a penalty term approach is applied to effectively solve the rank-1 constrained beamforming subproblems. Simulation results verify that the proposed AO algorithm attains a higher achievable rate than the existing benchmark schemes.

Sum Rate Maximization for Cooperative NOMA System with IQ Imbalance

Sum Rate Maximization for Cooperative NOMA System with IQ Imbalance

Secure Transmission Design for Virtual Antenna Array-Aided Device-to-Device Multicast Communications

This paper investigates the physical-layer security in a Virtual Antenna Array (VAA)-aided Device-to-Device Multicast (D2DM) communication system, where the User Equipments (UEs) who cache the common content will form a VAA system and cooperatively multicast the content to UEs who desire it. Specifically, with the target of securing the VAA-aided D2DM communication under the threat of multiple eavesdroppers, we propose a secure beamforming scheme by jointly considering the formed VAA and the Base Station (BS). For obtaining the optimal beamforing vectors, a nonsmooth and nonconvex Weight Sum Rate Maximization Problem (WRMP) is formulated and solved using Successive Convex Approximation (SCA) approach. Furthermore, we consider the worst case that eavesdroppers cooperatively form a eavesdrop VAA to enhance the overhearing capacity. In the worst case, we modify the securing beamforming scheme, formulate the corresponding WRMP and solve it using a two-level optimization. Simulation results validate the improvements of the VAA-aided D2DM scheme in terms of communication security compared with conventional D2DM schemes.

Adapt and Aggregate: Adaptive OFDM Numerology and Carrier Aggregation for High Data Rate Terahertz Communications

We propose a communication framework suitable for data rate maximization in the (THz) bands using adaptive (OFDM) numerology and carrier aggregation. OFDM is a widely adopted waveform due to the simplicity of its implementation and its effectiveness in combating frequency selectivity when the numerology is carefully chosen. However, it suffers from a multitude of limitations, including phase noise due to local oscillator inaccuracies, high peak-to-average power ratio, and is particularly sensitive to time-frequency synchronization errors, which can considerably impact its performance. This is especially relevant at THZ frequencies where larger-than-usual bandwidth is available, and the choice of the numerology should be carefully made given the intrinsic transceiver constraints. Moreover, the abundance of frequency resources in the THZ band imposes new design challenges that should be addressed, especially since the bandwidth usability at these frequencies depends on the communication distance. Hence, we propose a dynamic OFDM numerology adaptation mechanism, where the bandwidth of a CC covered by a single OFDM waveform is changed. For each (CC), the Component Carrier Data Rate (CCDR) is evaluated while considering the effect of both hardware impairments and the wireless channel statistics. We further propose the adoption of a dynamic distance-aware CC allocation such that the available frequency resources are fully utilized, and maximize an (ADR) through the aggregation of several CCs. Simulation results show that the proposed approach yields the highest ADR out of all possible setups.

Outage-Minimization Coordinated Multi-Point for Millimeter-Wave OFDM With Random Blockages

We consider millimeter-wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems subjected to random propagation path blockages and propose a new Coordinated multi-point (CoMP) transmission scheme that minimizes the outage probability of users with respect to given target data rates. To this end, a stochastic sum-outage-probability minimization problem is formulated for joint beamforming design, data rate allocation, and power allocation over subcarriers. In order to solve this problem efficiently, a block statistic learning approach is introduced using training data generated from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of path blockage probabilities. To initialize the stochastic learning solver, the novel initial beamforming is also proposed based on the upper bound of the original objective function, which improves convergence without tuning hyper-parameters. Numerical results confirm the effectiveness of the proposed block stochastic learning approach in terms of both convergence behavior and outage probability. Furthermore, these results confirm that the proposed approach with only blockage probabilities is comparable to the outage performance of a CoMP sum rate maximization (SRM) transmission scheme with perfect channel state information (CSI) and perfect knowledge of blockages.

RSMA Precoding Design Based on Interference Nulling and Sum Rate Upper Bound

Rate splitting multiple access (RSMA), a recent generalization and merging of spatial division multiple access (SDMA) and superposition coding has exponential complexity. Hierarchical streams that transmit a subset of possible streams of encoded messages are proposed for rate splitting and analyzed. To reduce precoding complexity, RSMA with interference nulling (RSMA-IN), that nulls the portion of interference that is not decoded is investigated by formulating a sum rate maximization problem subject to a total power constraint. To solve this non-convex problem, a method that combines interference nulling and stream SNR maximization is proposed. A convex upper bound problem is formulated for the sum rate to compare existing algorithms, and conditions under which the upper bound problem is tight are determined. Taking advantage of the tight cases, we propose an upper bound aided (UBA) enhancement of existing precoding designs by exploiting optimal solutions in the tight cases. It is also shown that the existing augmented weighted mean square error (AWMSE) algorithms cannot converge to a local optimum in sum rate maximization. Simulation results indicate that the proposed approaches enhance performance over existing ones as well as approach their upper bounds in certain instances.

Unsupervised Learning-Based Joint Power Control and Fronthaul Capacity Allocation in Cell-Free Massive MIMO With Hardware Impairments

A deep learning-based resource allocation algorithm that maximizes the sum rate of a limited fronthaul cell-free massive MIMO network with transceiver hardware impairments is proposed in this paper. The sum rate maximization problem with user power constraints and total fronthaul capacity constraints for channel state information (CSI) and data transmission is considered. The deep neural network (DNN) PowerNet is proposed to learn solutions to the joint power control and capacity allocation problem in a low-complex, flexible, and scalable way. An unsupervised learning approach is used which eliminates the need of knowing the optimal resource allocation vectors during model training, hence having a simpler and more flexible model training stage. Numerical simulations show that PowerNet achieves close sum rate performance compared to the existing optimization-based approach, with a significantly lower time complexity which does not exponentially scale with the number of users and access points (APs) in the network. Furthermore, the addition of the online learning stage resulted in a better sum rate than the optimization-based method.

Sum Rate Maximization for Active RIS-Aided Uplink Multi-Antenna NOMA Systems

Both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are treated as promising technologies for future wireless communications. However, the performance gain achieved by the traditional passive RIS-aided NOMA system is limited due to the double-fading effect for the base station (BS)-RIS-user channel. In this letter, a sum rate maximization problem for active RIS-aided uplink multi-antenna NOMA system is studied, in which the reflecting elements (REs) of active RIS can manipulate the phase shifts and amplify the amplitudes of incident signals. To tackle the formulated sum rate maximization problem, the original optimization problem is decomposed into three subproblems, i.e., the equalizer optimization, the power allocation, and the active RIS beamforming design. Simulation results show that the proposed active RIS-aided NOMA system can effectively improve sum rate compared with several benchmark schemes.

STAR-RIS-Aided Mobile Edge Computing: Computation Rate Maximization With Binary Amplitude Coefficients

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

Joint Hovering Height, Power, and Rate Optimization for Air-to-Ground UAV-RSMA Covert Communications

In this paper, we investigate covert communications for an unmanned aerial vehicle (UAV)-aided rate-splitting multiple access (RSMA) system in which the UAV transmits to the covert and public users separately while shielding covert transmissions from a warden. Under the RSMA principles, the messages of the covert and public users are converted to common and private streams for air-to-ground transmissions. Subject to the quality of service (QoS) requirements of the public user and covertness constraint of the UAV-aid RSMA (UAV-RSMA) system, the aim of covert communication design is to maximize the covert rate by jointly optimizing the transmit power allocation, common rate allocation, and UAV hovering height, which each contribute to the uncertainty of the warden’s binary decision and attempts to enhance communication performance. To address the non-convex covert rate maximization problem in addition to the highly coupled system parameters, we decouple the original problem into three subproblems of transmit power allocation, common rate allocation, and UAV hovering height. We derive the optimal solutions for each of the subproblems of the transmit power and rate allocations and formulate a signomial programming problem to tackle UAV hovering height optimization. The simulation results indicate the superior covert rate performance achieved by the proposed AO algorithm and demonstrate that the proposed UAV-RSMA scheme achieves a higher covert rate than the benchmark schemes.

Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System

Ultra-reliable low-latency communication (URLLC) is an important application scenario in fifth generation (5G) communication. With the increasing deployment of 5G, spectrum resources are becoming increasingly scarce, and millimeter wave (mmWave) operating in a high frequency has garnered significant attention. However, the short wavelength of the mmWave makes the signals susceptible to fading. Precoding has emerged as a promising solution for mitigating the severe path loss in mmWave, which can enhance the system capacity and communication performance. In our system, an achievable rate maximization problem is established by jointly optimizing the analog and digital precoders at the base station, subject to the constraint of the total power and the constant modulus constraint of the subarrays. The simulation results demonstrate that the algorithm employed in our system can successfully solve the optimization problem in this new scenario. Furthermore, the subarray-structure hybrid precoder has a higher energy efficiency than that of a fully connected hybrid precoder or all-digital precoder. The proposed algorithm also outperforms the MM (Majorization–Minimization) algorithm.

A Robust Scheme for RIS-Assisted UAV Secure Communication in IoT

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) have been extensively studied on the Internet of Things (IoT) systems to improve communication performance. In this paper, we aimed to counter simultaneous jamming and eavesdropping attacks by jointly designing an active beamforming vector at the base station (BS) and reflect phase shifts at the RIS. Specifically, considering imperfect angular channel state information (CSI), the sum secrecy rate maximization problem in the worst case could be formulated, which is NP-hard and non-convex. To address this problem, we improved the robust enhanced signal-to-leakage-and-noise ratio (E-SLNR) beamforming to reduce the computational complexity and mitigate the impact of interference, eavesdropping and jamming. Furthermore, a genetic algorithm with a tabu search (GA-TS) method was proposed to efficiently obtain an approximate optimal solution. The simulation results demonstrated that the proposed GA-TS method converged faster with better results than conventional GA, while the proposed robust scheme could achieve higher sum secrecy rates than the zero-forcing (ZF) and SLNR schemes.