EURASIP Journal on Wireless Communications and Networking最新文献

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

A distributed ledger is a shared and synchronized database across multiple designated nodes, often referred to as miners, validators, or peers. These nodes record, distribute, and access data to ensure security and transparency. However, these nodes can be compromised and manipulated by selectively choosing which user transactions to include, exclude, or reorder, thereby gaining an unfair advantage. This is known as a miner/maximal extractable value (MEV) attack. Existing solutions can be classified into various categories, such as MEV auction platforms and time-based ordering properties, which rely on private transaction Mempools. In this paper, we first identify some architectural weaknesses inherent in the latest proposals that divide the block creation and execution roles into separate functions: block builders and block executors. The existing schemes mainly suffer from the verifiability of the decryption process, where a corrupted builder or executor can simply deny the inclusion of specific targeted transactions by exploiting the fact that all transactions are in plain format. To address this, we propose an enhanced version that incorporates a verifiable decryption process. On a very high level, within our proposal, whenever an Executor or a Builder performs a decryption, the decrypted values must be broadcasted. This enables any entity in the network to publicly verify whether the decryption was executed correctly, thus preventing malicious behavior by either party from going undetected. We also define a new adversary model for MEV and conduct a comprehensive security analysis of our protocol against all kinds of potential adversaries related to MEV. Finally, we present the performance analysis of the proposed solution.

Reflecting intelligent surface technology (RIS) is regarded as a key enabler of the sixth-generation (6G) communication system. It provides the ability to reshape radio channels through passively reflecting beams in a reconstructive manner. Furthermore, aerial RIS (ARIS) introduces more flexibility in providing line-of-sight (LOS) links. Unfortunately, most of the related research efforts supposed the system as a planar RIS mounted on a satellite, unmanned aerial vehicle (UAV), or balloon despite reported limitations of planar RISs. The essential problem in designing any planar RIS network resides in mutual orientation and alignment difficulty, especially under random fluctuation of position/orientation due to wind conditions or UAV wobbling in the hover state. So, this paper highlights spherical RIS (bubble) as the optimal choice for aerial beam routing where the orientation/rotation can be completely relaxed. It outperforms planar RIS in terms of RIS networking flexibility, dead zone relaxation, and coverage extension. Consequently, due to the added degrees of freedom, many new deployment scenarios/use cases are recommended such as introducing meta-bubbles as intermediate gateways between satellite and ground nodes and extending network infrastructure installation down to the client level to enhance its visibility and throughput. Simulations demonstrate the superiority of meta-bubbles in minimizing channel loss over successive multi-hop routing.

The future generations of communication technologies envision the transmission of signals across the millimeter wave and sub-THz spectrums. However, the characteristics of the propagation channel at such high frequencies differ from what is observed in the conventional low-frequency spectrum with for instance, the apparition of stronger phase noise (PN) induced by the Radio Frequency (RF) transceivers and more especially by the oscillators. That is why there is growing interest in evaluating and adapting the 5G new radio (5G-NR) physical layer to the presence of PN. This article is dedicated to the study of discrete Fourier transform-spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) under uncorrelated Gaussian PN (GPN) impairments. We show that the presence of GPN induces two distortions: (i) a frequency-dependent random rotation of data, namely the subcarrier phase error (SPE) and (ii) a frequency-dependent intercarrier interference (ICI) that are analytically expressed. Then, we investigate the design of the adapted and optimal detection criterion according to the baseband model we derived in this paper. We demonstrate that (i) the proposed polar detector outperforms the conventional Euclidean detector and (ii) contrary to legacy OFDM, DFT-s-OFDM is a promising solution when strong GPN is involved.

Simultaneous transmission and reflection reconfigurable intelligent surface (Star-RIS) has been recently introduced as one of the hopeful technologies in future communications. In this work, we investigate a beamforming design for Star-RIS-assisted secure wireless communications with two practical protocols, namely energy splitting (ES) and mode switching (MS). We assume that our transceivers are not ideal and have residual hardware impairments that lead to distortion noise. We aim to maximize the total secrecy rate by optimizing the beamforming in BS and Star-RIS under the limitation of transmission power consumption. The proposed non-convex optimization problem is solved via successive convex approximation and alternating optimization.

The security of vehicle communication becomes increasingly important due to the transmission requirement of large-scale security and privacy data in the Internet of Vehicles (IoV). This paper investigates the physical layer secure transmission with reconfigurable intelligent surface (RIS) assistance in the coexistence of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V). It establishes a communication model for physical layer secure transmission where the eavesdropping user exists. Based on this model, we propose a V2I physical layer secure transmission rate (PLSTR) maximization problem and solve it by employing an alternating optimization algorithm based on generalized Rayleigh entropy and semi-definite relaxation programming so as to obtain optimal V2I base station precoding and RIS reflection coefficient matrix. Simulation results further validate the superiority of the proposed algorithm and analyze the impact of relevant parameters on system performance.

Construction of wireless infrastructure using unmanned aerial vehicle (UAV) can effectively expand the coverage and support high-density traffic of next-generation communication systems. Designing wireless systems including UAVs as aerial base stations (ABSs) is a challenging task, due to the mobility of ABSs causing time-varying nature of environmental surroundings and relative propagation paths to user equipment (UE) devices. Therefore, it is essential to have an accurate estimate of the channel for varying positioning of the UAVs. In this paper, we propose to adopt a digital twin based performance evaluation procedure for wireless systems including ABSs, providing enhanced accuracy of channel modeling for specific target deployment areas. Using ray-tracing channel models reflecting detailed building and terrain information of the transmission environment, an UAV position optimization algorithm based on reinforcement learning is presented. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm calculates the overall throughput in the digital twin and determines the desired states of the UAV. Performance evaluation results demonstrate the trajectory training ability of the algorithm and the performance advantage of the system with a reduced amount of shadow area compared to those with ground base stations (GBSs).

Large intelligent surfaces arise as an emerging technology and critical building block for sixth-generation (6G) wireless networks. Reconfigurable holographic surfaces (RHSs) have been attracting significant attention recently as active antenna arrays with the capability of forming narrow beams at low cost and complexity. This paper introduces the directional modulation (DM) concept for RHS, where a large number of elements are exploited to control not only the signal’s power at the receiver but also its phase. Thus, a novel DM algorithm is proposed for RHS enables modulating a carrier wave to transmit information toward specific directions while broadcasting arbitrary signals toward other directions. Error vector magnitude results are reported for multiple users, where the directions of two users with respect to the RHS are varied. Mutual information result is also provided for 6 users to demonstrate the application of RHS for the physical layer security. Results are highly promising for future low-cost and secure spatially multiplexed communications.

Physical layer key generation (PLKG) is a technique of information-theoretic security to tackle the problem of key distribution between resource-constrained legitimate users and is a promising candidate for the one time pad (OTP) technique. However, in quasi-static, the key rate is greatly limited due to low entropy. Reconfigurable intelligent surface (RIS) is introduced to adaptively reconfigure the radio environment. However, how to allocate time slots in the OTP to counter the increasingly powerful adversary model is an urgent problem to be solved. In this paper, we propose a very powerful adversary model and give an attack strategy called eavesdropping channel search, which allows Eve to use its search and eavesdropping capabilities to maximize the probability of successful attacks. Meanwhile, we propose a time slot allocation algorithm in the OTP to ensure the security of the key. Simulations validate that our proposed attack strategy is more powerful than any existing adversary model and our proposed time slot allocation algorithm does not have any security loss.

Due to the spectrum and complexity efficiency, the integrated radar and communications (RadCom) systems have been widely favored, in which orthogonal frequency division multiplexing (OFDM) is the most popular signal to conduct the two functions simultaneously. However, an unoptimized pulse could suffer from severe inter-carrier interference (ICI) and high out-of-band emission (OOBE), which greatly degrades the system performance. In this paper, we introduce the pulse shaping scheme dedicated to RadCom systems, in which both transmitter and receiver can adaptively design pulses with the assistance of radar estimation. We first optimize the transmitting pulse with the weighted sum of signal-to-interference-plus-noise ratio (SINR) and OOBE by employing the popular genetic algorithm. Then, we design an improved-matched pulse at the receiver for maximizing the SINR with the fmincon solver. In this way, they both utilize the readily available radar information and keep the pulse optimal even in highly dynamic scenarios, which makes the most of RadCom systems while avoiding the overhead of channel estimation and feedback. Simulations prove the feasibility of proposed scheme and reveal that the radar image and communications SINR stay close to their optimum in most cases with much lower OOBE. An improved-matched pulse can further improve the communications performance when severe ICI occurs compared with a matched pulse.