Physical Communication最新文献

英文中文

A resource allocation algorithm based on hybrid spider wasp optimization for cognitive radio networks

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-15 DOI: 10.1016/j.phycom.2025.102625

Shuo Shang, Mingyue Zhou

Cognitive radio (CR) is an effective technology for addressing spectrum scarcity, which can improve the utilization of spectrum resources through intelligent sensing and dynamic parameter adjustment. Since traditional resource allocation algorithms are difficult to adapt to the dynamic characteristics of cognitive radio environment, more and more researchers are focusing on intelligent optimization algorithms. Our objective is to maximize the channel capacity of cognitive transmitters under interference constraint at primary receiver, total transmit power constraint and fairness constraint in underlay cognitive radio networks. To enhance the flexibility of the algorithm, we transform the original constrained optimization problem into an unconstrained penalty function form. Given that the proposed problem is non-convex, we present the Spider Wasp Optimization (SWO) algorithm to solve this optimization problem. To better search the solution space and avoid getting trapped in local optima, a hybrid Spider Wasp Optimization algorithm (HSWO) is proposed. This algorithm integrates genetic algorithm (GA) principles to help the SWO algorithm in achieving the global optimum. Additionally, three different dynamic response strategies were proposed to validate the adaptability and flexibility of the proposed algorithm in dynamic environments. Simulation results show that HSWO and SWO algorithm can obtain higher system capacity and higher flexibility compared with the particle swarm optimization (PSO).

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引用次数: 0

Secure energy efficiency maximization in cell-free networks with sub-connected active reconfigurable intelligent surface

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-15 DOI: 10.1016/j.phycom.2025.102624

Yi Wang , Junlei Zhi , Shuo Wang , Fei Zhao , Shaochuan Yang , Wanming Hao , Chunguo Li

We consider a cell-free network security energy efficiency (SEE) scheme based on sub-connected active reconfigurable intelligent surface (RIS), where the multiple phase shifters share a power amplifier at RIS to achieve the purpose of reducing system energy consumption and saving costs. A joint base station (BS) and RIS beamforming optimizing problem of maximizing SEE is constructed, which includes the constraints of maximum transmission power, active RIS maximum power, phase shifter amplitude and amplification factor. Since the optimization problem is non-convex, the fractional programming method is used to decouple the objection function, and then the successive convex approximation, Alternating Direction Method of Multipliers, and Majorization-Minimization techniques are employed to alternately optimize to obtain the solutions of the original problem. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

引用次数: 0

ADMM-RMCBF-Net: A neural network decision for distributed robust multi-cell beamforming

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-14 DOI: 10.1016/j.phycom.2025.102620

Jing Xu , Qiaozhi Wang , Chongbin Xu , Wujie Fan , Yizhai Zhang

In this paper, to construct a promising deep-learning architecture for the distributed robust multi-cell beamforming (RMCBF) decision, we reconsider the existing typical power-minimization problem. By thoroughly unfolding the conventional algorithm of the Alternating Direction Method of Multipliers (ADMM), we establish a novel neural network for fast distributed RMCBF design, namely ADMM-RMCBF-Net. The most important step in unfolding lies in that we explicitly solve the key semi-definite programming sub-problems by resorting to the primal–dual inter-point method instead of the encapsulated convex solvers. It is worth stressing that all parameters of the ADMM algorithm can be learned from end-to-end data-driven training in the proposed ADMM-RMCBF-Net, rather than being predetermined empirically in the conventional ADMM method. Simulation results confirm the advantages of the proposed deep-learning approach over its conventional optimization-based counterpart in terms of these distributed RMCBF decisions’ performance. Specifically, in addition to accelerating the convergence of the distributed RMCBF design, more importantly, the proposed ADMM-RMCBF-Net could adapt to the practical propagation environment quickly through small-sample learning.

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引用次数: 0

Design and optimization of uplink multi-user time-reversal DSSS systems

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-13 DOI: 10.1016/j.phycom.2025.102622

Weijia Lei, Hang Yang, Mengting Zou, Hong Tang, Hongjiang Lei

Time reversal (TR) filtering can enhance signal strength and reduce interference by exploring multipath transmission in wireless channels. In this paper, for uplink multi-user direct sequence spread spectrum (DSSS) communication systems, a multi-user detection performance enhancement scheme is designed using TR receive filtering and successive interference cancellation (SIC). Under the constraints of the minimum rate requirement and the maximum transmit power of each user, the TR filters and the transmit power of each user are jointly optimized to minimize the total system power consumption or to maximize the sum capacity of the system. In the optimization, the imperfect SIC is considered. The two optimization problems with different objectives are both non-convex. Each original problem is transformed into two optimization sub-problems about the TR filters and the transmit power of each user. The optimal impulse responses (IRs) of the TR filters are obtained by finding the eigenvector corresponding to the maximum eigenvalue of the equivalent channel matrix beam. The transmit power of each user is determined one by one according to the minimum rate requirement when the optimization is to minimize the transmit power, and is obtained by solving the transmit power optimization sub-problem with successive convex approximation when the optimization is to maximize the sum capacity. Simulation results show that the system performance can be effectively improved by using TR filters and SIC, and can be further promoted by jointly optimizing the TR filters and each user’s transmit power.

{"title":"Design and optimization of uplink multi-user time-reversal DSSS systems","authors":"Weijia Lei, Hang Yang, Mengting Zou, Hong Tang, Hongjiang Lei","doi":"10.1016/j.phycom.2025.102622","DOIUrl":"10.1016/j.phycom.2025.102622","url":null,"abstract":"<div><div>Time reversal (TR) filtering can enhance signal strength and reduce interference by exploring multipath transmission in wireless channels. In this paper, for uplink multi-user direct sequence spread spectrum (DSSS) communication systems, a multi-user detection performance enhancement scheme is designed using TR receive filtering and successive interference cancellation (SIC). Under the constraints of the minimum rate requirement and the maximum transmit power of each user, the TR filters and the transmit power of each user are jointly optimized to minimize the total system power consumption or to maximize the sum capacity of the system. In the optimization, the imperfect SIC is considered. The two optimization problems with different objectives are both non-convex. Each original problem is transformed into two optimization sub-problems about the TR filters and the transmit power of each user. The optimal impulse responses (IRs) of the TR filters are obtained by finding the eigenvector corresponding to the maximum eigenvalue of the equivalent channel matrix beam. The transmit power of each user is determined one by one according to the minimum rate requirement when the optimization is to minimize the transmit power, and is obtained by solving the transmit power optimization sub-problem with successive convex approximation when the optimization is to maximize the sum capacity. Simulation results show that the system performance can be effectively improved by using TR filters and SIC, and can be further promoted by jointly optimizing the TR filters and each user’s transmit power.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"70 ","pages":"Article 102622"},"PeriodicalIF":2.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analysis of Q-learning-based NOMA in Satellite–Terrestrial Relay Networks 卫星-地面中继网络中基于 Q 学习的 NOMA 性能分析

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-11 DOI: 10.1016/j.phycom.2025.102619

Leonardo Pacheco de Aguiar , Marcos Eduardo Pivaro Monteiro , Jamil Farhat , Guilherme de Santi Peron , Glauber Brante

In this paper, we analyze the performance of

Q

-learning-based Non-Orthogonal Multiple Access (NOMA) in Satellite–Terrestrial Relay Networks (STRNs), addressing key challenges in massive Internet of Things (IoT) communications. Specifically, we focus on energy efficiency and normalized throughput metrics in uplink scenarios. By integrating a distributed

Q

-learning algorithm with NOMA, IoT devices can autonomously optimize transmission parameters – such as time slots, channels, and power levels – enhancing overall network performance. The proposed scheme outperforms fixed-power strategies by achieving higher normalized throughput and energy efficiency under varying network densities, offering up to 73% improvement in energy efficiency. Simulation results validate the protocol’s effectiveness, demonstrating its potential for large-scale IoT deployments in STRNs through efficient power allocation and reduced collision rates.

引用次数: 0

Secure air-to-ground transmission with jamming links blocked by the eavesdropper

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-10 DOI: 10.1016/j.phycom.2025.102618

Jiawei Zhao, Mengwen Shi, Hongliang He

Secure air-to-ground communication when an eavesdropper is able to block the jamming link from a jammer is considered in this paper. A feedback-aided cooperative jamming scheme is proposed, where the transmitter, the destination, and the jammer alternately design and send artificial noise to protect the private information in three stages. It shows that even if the jamming links are blocked, the eavesdropper will inevitably be impacted by artificial noise. We limit the eavesdropping rate to a constant, and guarantee the minimum secrecy rate scales with the transmit power. We allocate power between the artificial noise and the private information, and obtain the optimal ratio to maximize the minimum secrecy rate. We analyze the symbol error probability of the destination and the eavesdropper when the transmitter exploits M-ary constellation modulation. It shows that a high symbol error floor is created at the eavesdropper but the symbol error probability at the legitimate destination decreases with the transmit power.

引用次数: 0

Optimizing UAV deployment for maximizing coverage and data rate efficiency using multi-agent deep deterministic policy gradient and Bayesian optimization

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-10 DOI: 10.1016/j.phycom.2025.102621

Dhinesh Kumar R. , Rammohan A.

The rise of connected vehicles has highlighted the crucial need to cater diverse Quality of Service (QoS) demands in intricate vehicular networks. To address this, the burgeoning utilization of Unmanned Aerial Vehicles (UAVs) across various applications has garnered significant attention. UAVs, acting as aerial Base Stations (BSs), improve network coverage and performance in critical communication scenarios. However, challenges such as limited coverage range and unpredictable QoS hinder UAVs from continuously covering urban or rural areas. To tackle these challenges, we introduce a novel multi-agent deep deterministic policy gradient (MADDPG) approach incorporating Bayesian Optimization to optimize UAV trajectories in both rural and urban environments. Our principal aim is to maximize vehicle coverage while ensuring efficient QoS. Comparative evaluations against benchmark algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Greedy methods. In rural environments, proposed framework achieves a mean coverage rate of 85.75%, surpassing MADDPG by 4.49%, GA by 8.72%, PSO by 10.02%, SCA by 8.90%, and Greedy by 14.06%. In urban settings, proposed framework maintains superior performance, with a mean coverage rate of 83.78%, outperforming MADDPG by 6.31%, GA by 9.81%, PSO by 9.38%, SCA by 12.78%, and Greedy by 19.53%. Additionally, the system achieves a 95.6% convergence rate, optimizing MADDPG hyperparameters efficiently. The implications of the energy penalty in the proposed algorithm have given the outlook on the tradeoff, that the overall energy consumption can reduce up to 8.3%, it may also result in a decrease in coverage efficiency by around 5.5%.

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引用次数: 0

Hybrid message passing for total variation regularized linear regression

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-08 DOI: 10.1016/j.phycom.2025.102616

Ying Chen , Haochuan Zhang , Hekun Zhang , Huimin Zhu

Total variation regularized linear regression has emerged as a dynamic research area in signal processing. Classical algorithms often struggle with high correlation in the unknown signal, which can impair their performance. This paper introduces a novel algorithm that addresses this challenge by integrating elements of traditional scalar-form message passing with recent innovations in vector-form message passing. The proposed approach not only captures the intricate structure within the signal but also efficiently handles high-dimensional inference tasks. When the prior of the target signal contains unknown parameters, the hybrid message-passing algorithm can be incorporated into a broader Expectation-Maximization framework, enabling iterative refinement of the parameter estimates. Furthermore, a set of state evolution (SE) equations is provided to describe the behavior of the proposed algorithm. Although derived heuristically, the SE equations empirically align with the algorithm’s mean squared error (MSE) performance with remarkable accuracy.

引用次数: 0

Enhancing spectral efficiency using a new MIMO WPT-NOMA system based on wavelet packet transform and convolutional complex neural network 利用基于小波包变换和卷积复合神经网络的新型多输入多输出 WPT-NOMA 系统提高频谱效率

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-04 DOI: 10.1016/j.phycom.2025.102617

Samar I. Farghaly , Sherine Nagy Saleh , Moustafa H. Aly , Amira I. Zaki

A hybrid combination of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) technologies solves various challenges beyond the fifth generation (5G) networks. These challenges include huge connectivity, low latency, and high dependability. Accordingly, this paper proposes a new strategy for NOMA Wavelet Packet Transform (WPT-NOMA) and a new Sequential Interference Cancellation (SIC) receiver based on a Complex valued Convolutional Neural Network (CVNN). In the proposed model it is assumed that the Channel State Information (CSI) is perfectly estimated by the Base Station (BS) and users. It is utilized to enhance the Spectral Efficiency (SE) and thus improve system performance. The WPT-NOMA embeds the signals of different users in one signal. Every two users are paired via a constant power allocation and then combined by WPT. WPT-NOMA has great advantages, where a low level of WPT is needed in comparison to other algorithms. Also, the proposed receiver for the WPT-NOMA system uses only one CVNN-SIC to retrieve data. Accordingly, the proposed system outperforms the conventional NOMA and other algorithms in terms of Bit Error Rate (BER), SE, Energy Efficiency (EE), and Outage Probability (OP). A CNN and a CVNN for SIC receivers are trained on simulated data to improve the accuracy of detecting signals at different Signal-to-Noise Ratios (SNRs).

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引用次数: 0

LDPC-coded OAM shift-keying FSO communication system with dual-pattern CNN demodulator

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Physical Communication

Pub Date : 2025-02-01 DOI: 10.1016/j.phycom.2024.102567

Zhaokun Li , Tao Shang , Xiongchao Liu , Peiheng Qian , Yinling Zhang

An LDPC-coded orbital angular momentum shift-keying (OAM-SK) free space optical (FSO) communication system with a dual-pattern convolutional neural network (CNN) demodulator is put forward to resist the adverse effect of atmospheric turbulence (AT). Diverging from the standard single-pattern CNN demodulator, the proposed method inputs a dual OAM light pattern into the demodulator. The first pattern stems from the Laguerre–Gaussian OAM-SK light focused by a convex lens, while the second is acquired post-cylindrical lens modulation. The dual-pattern CNN demodulator can extract richer features from the incoming light, enhancing the recognition accuracy for OAM-SK modes. This advancement notably enables the recognition of OAM-SK modes with opposite orbital quantum numbers, a challenging task for standard single-pattern CNN demodulators. We have refined communication reliability by integrating LDPC coding with OAM-SK, and LDPC decoding has also been explicitly designed for OAM-SK channels. Simulations validate our proposed method, achieving a recognition accuracy 0.869 under strong AT (

C_{n}^{2} = 5 \times 1 0^{- 14} m^{- 2 / 3}

). We use the image transmission as a benchmark; the dual-pattern CNN demodulator enhances the LDPC-coded OAM-SK FSO link, achieving a 30 dB boost in the image’s PSNR compared to the traditional CNN demodulation. The bit error rate (BER) drops to 3.8e−5, achieving significant advancement in comparison to the single-pattern CNN demodulators.

{"title":"LDPC-coded OAM shift-keying FSO communication system with dual-pattern CNN demodulator","authors":"Zhaokun Li , Tao Shang , Xiongchao Liu , Peiheng Qian , Yinling Zhang","doi":"10.1016/j.phycom.2024.102567","DOIUrl":"10.1016/j.phycom.2024.102567","url":null,"abstract":"<div><div>An LDPC-coded orbital angular momentum shift-keying (OAM-SK) free space optical (FSO) communication system with a dual-pattern convolutional neural network (CNN) demodulator is put forward to resist the adverse effect of atmospheric turbulence (AT). Diverging from the standard single-pattern CNN demodulator, the proposed method inputs a dual OAM light pattern into the demodulator. The first pattern stems from the Laguerre–Gaussian OAM-SK light focused by a convex lens, while the second is acquired post-cylindrical lens modulation. The dual-pattern CNN demodulator can extract richer features from the incoming light, enhancing the recognition accuracy for OAM-SK modes. This advancement notably enables the recognition of OAM-SK modes with opposite orbital quantum numbers, a challenging task for standard single-pattern CNN demodulators. We have refined communication reliability by integrating LDPC coding with OAM-SK, and LDPC decoding has also been explicitly designed for OAM-SK channels. Simulations validate our proposed method, achieving a recognition accuracy 0.869 under strong AT (<span><math><mrow><msubsup><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>=</mo><mn>5</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>14</mn></mrow></msup><msup><mrow><mi>m</mi></mrow><mrow><mo>−</mo><mn>2</mn><mo>/</mo><mn>3</mn></mrow></msup></mrow></math></span>). We use the image transmission as a benchmark; the dual-pattern CNN demodulator enhances the LDPC-coded OAM-SK FSO link, achieving a 30 dB boost in the image’s PSNR compared to the traditional CNN demodulation. The bit error rate (BER) drops to 3.8e−5, achieving significant advancement in comparison to the single-pattern CNN demodulators.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"68 ","pages":"Article 102567"},"PeriodicalIF":2.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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