Physical Communication最新文献

In the edge computing network systems for the Internet of Things (IoT), there is growing attention to utilizing drones for collecting data and maintaining the freshness of data processing. This study focuses on analyzing the problems related to trajectory planning and task scheduling in a single drone-assisted edge computing network within a dense, three-dimensional urban environment. We first design an edge computing network architecture and establish an air-to-ground channel model between the drone and ground mobile devices to address the blockage caused by buildings in urban environments. Subsequently, to provide effective edge computing services, we structure the problem as a Partially Observable Markov Decision Process (POMDP) and introduce an optimization framework based on reinforcement learning. This improves data timeliness and reduces energy consumption.

Driven by the perception of IoT applications and advanced communication technologies, including beyond 5G and 6G, recent years have seen a paradigm shift from traditional cloud computing towards the local edge of the networks. Modern edge-centric networks have become autonomous and decentralized to expand IoT applications and corresponding data fusion. When edge networks are uncertain, network entities execute tasks locally to increase network performance. Over the past decade, Reinforcement Learning (RL) algorithms have been integrated into edge networks to generate optimal decisions and intelligent edge networks. However, complex edge networks with ample state and action space create several challenges in making optimal decisions with the RL technique. To address such shortcomings, Deep Reinforcement Learning (DRL) is combined with edge networks to build an intelligent edge framework. Concerning the benefits of edge intelligence, this paper summarizes the importance of traditional and advanced DRL methodologies in edge networks. Besides, we discuss different types of DRL-enabled libraries and state-of-the-art edge models for processing real-time IoT applications. Then, we review other emerging issues in edge networks regarding data offloading, caching, dynamic network access, edge information fusion, and data privacy. Moreover, we incorporate various DRL-enabled IoT applications in edge networks such as healthcare applications, industrial applications, traffic management, etc. Finally, we shed light on future trends of intelligent edge computing regarding system performance, security, and network management.

In this paper, we investigate the robust secure resource optimization for the active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) system under the imperfect eavesdroppers channel state information. Considering the fairness, a max–min secure rate optimization problem is formulated based on several practical constraints. To deal with the original non-convex problem, an alternative iteration algorithm is proposed. First, the original problem is decomposed into two non-convex sub-problems. Next, the continuous convex approximation and S-procedure techniques are applied to deal with the non-convex and uncertainty constraints, respectively. Then, the first-order Taylor expansion formula is utilized to approximate the convex difference form of the objective function. Finally, two sub-problems are transformed into convex ones and alternately solved until convergence. Simulation results show that the secure rate of the proposed scheme is higher than the conventional schemes.

In this paper, we propose a three-dimensional (3D) elliptical cylinder multiple-input multiple-output (MIMO) stochastic channel model assisted by double reconfigurable intelligent surfaces (RIS) for the vehicle-to-vehicle (V2V) propagation environment. The double-RIS is deployed on building surfaces to reduce signal attenuation and assist the mobile terminal (MT) in reflecting its signals towards the mobile receiver (MR). In the proposed channel model, we incorporate the complex channel impulse responses (CIRs) resulting from multi-path propagation for all four links, thereby deducing the complete channel matrix. Additionally, we derive statistical characteristics, including spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), and frequency correlation functions (FCFs). Simulation results are presented to illustrate the propagation characteristics of the double-RIS assisted MIMO V2V elliptical cylinder channel model, which clearly indicate that the double-RIS outperforms the single-RIS in channel characteristics, underscoring the importance of introducing double-RIS into the V2V channel model.

Unmanned aerial vehicle (UAV) can be deployed as aerial base station to provide communication services for the user equipments (UEs). However, in urban environments, the links between UAV and UEs might be frequently blocked by obstacles, leading to severely adverse effects on the quality of service (QoS) of UEs. Additionally, due to the limited energy of the UAV, it might not always be feasible to re-establish the line-of-sight (LoS) links by frequently adjusting the positions of the UAV. In this context, the reconfigurable intelligent surface (RIS) is utilized to enhance the transmission range of UAV-UE links by reflecting incident signals to UEs. In this paper, we investigate the RIS-assisted UAV communication systems with the goal of maximizing the energy efficiency of the UAV through a joint optimization of the UAV’s trajectory and the RIS’s phase shift. The formulated optimization problem is non-convex, and challenging to solve in a polynomial time. Therefore, an effective deep reinforcement learning (DRL)-based solution, named Dueling DQN-PER is proposed, which combines the Dueling DQN algorithm with the prioritized experience replay (PER) technique. To ensure the fairness among all UEs, we design a service fairness index, and integrate it into the reward function when designing the proposed algorithm. Numerical results demonstrate that: 1) the proposed Dueling DQN-PER algorithm is capable of improving the system energy efficiency and has a better training performance than benchmark schemes; 2) by devising the service fairness index, the fairness among all UEs is ensured while enhancing the system performance in energy efficiency; 3) the RIS-assisted UAV communication systems benefit from significant energy efficiency gain over the systems without RIS.

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

As a novel communication pattern, the asymmetric beams adopt a strategy of different beamwidths for a specific link to reduce the beam alignment overheads and energy consumption. A good and thorough knowledge of the radio propagation characteristics is pivotal for further network deployment and optimization of wireless mobile communication systems. In this paper, a multiple-bounce beam channel model is proposed based on the ray-tracing considering the beamforming effects. Besides, a space–time–frequency (STF) power density profile reconstruction method is proposed. Relevant simulations are conducted to emulate an urban micro-cellular (UMi) street scenario at 28 GHz under the case of perfect beam alignment. On this basis, the beam-dependent small-scale fading properties (including STF power density profiles, delay spread, Doppler frequency shift spread, and angular spread) together with the large-scale fading characteristics (involving path loss and shadow fading) are fully investigated. Results reveal that the downlink of asymmetric beams presents more dispersions in contrast to the uplink in the STF domains. Furthermore, the shadow fading variances are asymmetric over different transceivers array element numbers.

Metamaterials are a class of artificial materials that have exceptional physical properties that do not exist in nature. They are widely used in various fields, such as electromagnetics, optics, and acoustics. However, designing metamaterials can be a challenging and time-consuming task. Traditional methods rely on simulations and trial-and-error, which are inefficient and often require significant computational resources. Recently, deep learning has emerged as a promising tool to design metamaterials. Deep learning involves training neural networks to learn complex patterns and relationships in data, which can be used to predict the behavior of metamaterials under different conditions. This paper proposes a neural network that maps geometric parameters to frequency domain responses for optimized design. The network utilizes PCA (Principal Component Analysis) to reduce the training time by approximately 5%, and this combination method is far superior to similar algorithms in terms of prediction accuracy and generalization ability. Experimental results demonstrate that the designed network model can be used for optimized design, achieving a remarkably low RMSE (Root Mean Square Error) of 0.0408 and a prediction accuracy of 97.64% in the reverse network, outperforming similar articles. The proposed network model improves the design efficiency of metamaterials, providing a more efficient and effective approach for designing these metamaterials.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy).

This article has been retracted at the request of the Editor-in-Chief.

The authors plagiarised content from a manuscript that was submitted to another journal. The title of the original manuscript is, “Intra-class CutMix Data Augmentation based Deep Learning Side Channel Attacks”, and was submitted by authors, Runlian Zhanga, Yu Moa, Zhaoxuan Pana, Hailong Zhangb, Yongzhuang Weia, Xiaonian Wua.

One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original. Reuse of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

a Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology.

b State Key Laboratory of Information Security, Institute of Information Engineering Chinese Academy.

Covert throughput maximization for a non-orthogonal multiple access (NOMA)-based visible light covert communication (VLCC) network is investigated. The network consists of a light emitting diode (LED) transmitter, two NOMA users (one public, one covert), and a monitor tasked with detecting any covert transmissions between the LED and the covert user. The transmitter leverages its interaction with the public user to mask the covert communication with the covert user, adopting a random power transmission scheme. This strategy serves to amplify the monitor’s detection uncertainty and significantly enhance the covertness of the VLCC network. Two VLCC scenarios are covered: For the indoor static VLCC scenario where the LED is fixed, subject to the minimum detection error probability of the monitor (covertness constraint) and the outage probability of NOMA users (reliability constraint), the covert throughput is maximized by optimizing the ratio of the LED’s power allocation factor (PAF). For the mobile VLCC scenario where the LED is mounted on an unmanned aerial vehicle (UAV), subject to the constraints of the covertness, reliability and UAV’s flight region, the optimal LED’s PAF ratio and UAV’s location are jointly obtained via a graphical approach. Finally, simulations are carried out to analyze the influence of VLCC parameters on the maximum covert throughput, and results show that compared with benchmark schemes, the proposed scheme can greatly improve the covert throughput.