Pub Date : 2026-01-13DOI: 10.1016/j.phycom.2026.103002
Ahmed S. Alwakeel , Mohamed H. Saad , Mohamed S. Elbakry
Fluid Antenna System (FAS) have emerged as a promising solution for improving wireless communication by allowing an antenna’s placement within a device to dynamically adjust to its surroundings. This flexibility improves signal quality, link stability, and spectrum efficiency without requiring the deployment of extra antennas. However, realizing the full potential of FAS necessitates determining the ideal antenna arrangement, which is a difficult, multidimensional challenge driven by user locations and signal propagation parameters. To address this issue, this research proposes using the Whale Optimization Algorithm (WHO) for efficient FAS tuning. WHO automatically searches the solution space for ideal antenna placements that improve network performance while reducing deployment complexity. Simulation results show that WHO outperforms traditional methods such as Gaussian approximation (GA) and Particle Swarm Optimization (PSO), achieving better connection with fewer antennas–only three vs four and five for GA and PSO, respectively. WHO improves convergence by 49.6% compared to GA and reduces inference time by 35% compared to Differential Evolution (DE), making it suitable for real-time, adaptive, and resource-efficient wireless networks.
{"title":"Adaptive fluid antenna deployment for improved wireless reliability","authors":"Ahmed S. Alwakeel , Mohamed H. Saad , Mohamed S. Elbakry","doi":"10.1016/j.phycom.2026.103002","DOIUrl":"10.1016/j.phycom.2026.103002","url":null,"abstract":"<div><div>Fluid Antenna System (FAS) have emerged as a promising solution for improving wireless communication by allowing an antenna’s placement within a device to dynamically adjust to its surroundings. This flexibility improves signal quality, link stability, and spectrum efficiency without requiring the deployment of extra antennas. However, realizing the full potential of FAS necessitates determining the ideal antenna arrangement, which is a difficult, multidimensional challenge driven by user locations and signal propagation parameters. To address this issue, this research proposes using the Whale Optimization Algorithm (WHO) for efficient FAS tuning. WHO automatically searches the solution space for ideal antenna placements that improve network performance while reducing deployment complexity. Simulation results show that WHO outperforms traditional methods such as Gaussian approximation (GA) and Particle Swarm Optimization (PSO), achieving better connection with fewer antennas–only three vs four and five for GA and PSO, respectively. WHO improves convergence by 49.6% compared to GA and reduces inference time by 35% compared to Differential Evolution (DE), making it suitable for real-time, adaptive, and resource-efficient wireless networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103002"},"PeriodicalIF":2.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980717","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}
Pub Date : 2026-01-13DOI: 10.1016/j.phycom.2025.102972
Zahid Zaman , Yousaf Khan , Farman Ali , Ammar Armghan , Muhammad Kamran Shereen , Sultan S. Aldkeelalah , Mardeni Roslee
Long-haul optical transmission (LHOT) systems are affected by nonlinear impairments (NIs), including self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), amplified spontaneous emission (ASE) noise, and Kerr nonlinearities, which limit achievable data rates and system reach. Conventional methods, such as digital back-propagation (DBP), optical phase conjugation (OPC), and DSP-assisted receivers, have demonstrated mitigation capabilities but suffer from high computational complexity, latency, and power consumption, making them impractical for large-scale networks. Machine learning (ML) approaches, including label propagation and transformer-based schemes, reduce some processing overhead yet do not perform dimensionality reduction for feature compression and lack a mechanism to jointly handle multiple nonlinear effects across LHOT. Furthermore, most reported works do not align with optical communication standards, such as ITU-T G.652.D or OS1/OS2 fibers, which limits their practical implementation in standardized infrastructures.
This work proposes an autoencoder-based pelican optimization algorithm (APOA) for NIs mitigation in LHOT systems. The autoencoder compresses high-dimensional signal distortions into a latent space that preserves nonlinear mappings, reducing computational load while maintaining representation accuracy. The POA performs parameter tuning to optimize signal recovery in the presence of nonlinear effects and noise. The transmission channel is modeled using the nonlinear Schrŏdinger equation (NLSE), with propagation distortions characterized by ITU-T G.652.D single-mode fiber (SMF) parameters: attenuation of 0.20 dB/km, chromatic dispersion of ∼ 17 ps/nm/km at 1550 nm, effective area of 80 µm2, and nonlinear coefficient γ ≈ 1.3 Wkm. Simulations are conducted using parameter settings aligned with OS1/OS2 fiber specifications (9 µm core diameter) and representative optical communication terminal (OCT) configurations, to reflect realistic long-haul transmission environments. Performance evaluation across multiple OSNR levels, fiber lengths, and modulation formats uses FEC thresholds and operating ranges that are consistent with IEEE 802.3 Ethernet and ITU-T G.709 OTN reference values, showing that APOA achieves BER values below the adopted FEC thresholds, increases spectral efficiency, and extends transmission reach.
{"title":"Autoencoder-Pelican optimization for nonlinear impairment mitigation in long-haul optical fiber systems","authors":"Zahid Zaman , Yousaf Khan , Farman Ali , Ammar Armghan , Muhammad Kamran Shereen , Sultan S. Aldkeelalah , Mardeni Roslee","doi":"10.1016/j.phycom.2025.102972","DOIUrl":"10.1016/j.phycom.2025.102972","url":null,"abstract":"<div><div>Long-haul optical transmission (LHOT) systems are affected by nonlinear impairments (NIs), including self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), amplified spontaneous emission (ASE) noise, and Kerr nonlinearities, which limit achievable data rates and system reach. Conventional methods, such as digital back-propagation (DBP), optical phase conjugation (OPC), and DSP-assisted receivers, have demonstrated mitigation capabilities but suffer from high computational complexity, latency, and power consumption, making them impractical for large-scale networks. Machine learning (ML) approaches, including label propagation and transformer-based schemes, reduce some processing overhead yet do not perform dimensionality reduction for feature compression and lack a mechanism to jointly handle multiple nonlinear effects across LHOT. Furthermore, most reported works do not align with optical communication standards, such as ITU-T G.652.D or OS1/OS2 fibers, which limits their practical implementation in standardized infrastructures.</div><div>This work proposes an autoencoder-based pelican optimization algorithm (APOA) for NIs mitigation in LHOT systems. The autoencoder compresses high-dimensional signal distortions into a latent space that preserves nonlinear mappings, reducing computational load while maintaining representation accuracy. The POA performs parameter tuning to optimize signal recovery in the presence of nonlinear effects and noise. The transmission channel is modeled using the nonlinear Schrŏdinger equation (NLSE), with propagation distortions characterized by ITU-T G.652.D single-mode fiber (SMF) parameters: attenuation of 0.20 dB/km, chromatic dispersion of ∼ 17 ps/nm/km at 1550 nm, effective area of 80 µm<sup>2</sup>, and nonlinear coefficient <em>γ</em> ≈ 1.3 W<span><math><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>km<span><math><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span>. Simulations are conducted using parameter settings aligned with OS1/OS2 fiber specifications (9 µm core diameter) and representative optical communication terminal (OCT) configurations, to reflect realistic long-haul transmission environments. Performance evaluation across multiple OSNR levels, fiber lengths, and modulation formats uses FEC thresholds and operating ranges that are consistent with IEEE 802.3 Ethernet and ITU-T G.709 OTN reference values, showing that APOA achieves BER values below the adopted FEC thresholds, increases spectral efficiency, and extends transmission reach.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102972"},"PeriodicalIF":2.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980712","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}
Pub Date : 2026-01-12DOI: 10.1016/j.phycom.2026.103006
Xiang Li , Peng Chen , Zhimin Chen , Zihang Li
The analog structure is widely used for the direction-of-arrival (DOA) estimation in millimeter-wave systems because of its low power consumption and efficient implementation. However, due to the limited number of radio frequency (RF) chains in the architecture, it is infeasible to independently acquire data from each antenna and the inevitably mutual coupling effects will also make accurate DOA estimation become harder. These issues increase the challenge of achieving high-precision DOA estimations. To address these problems, a novel DOA estimation procedure is proposed in this letter to reconstruct the covariance matrix with high precision and reduce the effects of the mutual coupling on the DOA estimation. By adjusting the weights of each antenna, including switches and phase shifters, the covariance matrix is reconstructed and transformed into a real-valued matrix. Subsequently, through matrix enhancement, the covariance matrix can be appropriately modified to improve the accuracy of the DOA estimation in the presence of unknown mutual coupling effects. The simulation results show that the proposed algorithm achieves better DOA estimation performance in scenarios with unknown mutual coupling effects.
{"title":"An improved matrix reconstruction method for the DOA estimation with unknown mutual coupling effects","authors":"Xiang Li , Peng Chen , Zhimin Chen , Zihang Li","doi":"10.1016/j.phycom.2026.103006","DOIUrl":"10.1016/j.phycom.2026.103006","url":null,"abstract":"<div><div>The analog structure is widely used for the direction-of-arrival (DOA) estimation in millimeter-wave systems because of its low power consumption and efficient implementation. However, due to the limited number of radio frequency (RF) chains in the architecture, it is infeasible to independently acquire data from each antenna and the inevitably mutual coupling effects will also make accurate DOA estimation become harder. These issues increase the challenge of achieving high-precision DOA estimations. To address these problems, a novel DOA estimation procedure is proposed in this letter to reconstruct the covariance matrix with high precision and reduce the effects of the mutual coupling on the DOA estimation. By adjusting the weights of each antenna, including switches and phase shifters, the covariance matrix is reconstructed and transformed into a real-valued matrix. Subsequently, through matrix enhancement, the covariance matrix can be appropriately modified to improve the accuracy of the DOA estimation in the presence of unknown mutual coupling effects. The simulation results show that the proposed algorithm achieves better DOA estimation performance in scenarios with unknown mutual coupling effects.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103006"},"PeriodicalIF":2.2,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980710","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}
Pub Date : 2026-01-10DOI: 10.1016/j.phycom.2026.102996
Ke Yang, Jiarui Yang, Mingyu Gao, Peng Lin, Xin Zhang
Magnetic induction (MI) communication across the air-sea boundary (transboundary MI) offers unique advantages for seamless information exchange between aerial and underwater platforms. However, its performance, quantified by the product of coverage range and data rate, is fundamentally constrained by the rapid attenuation of MI signals with distance and frequency. This paper presents a novel relay transmission framework to address such limitations by enabling distributed superposition of magnetic induction fields. The proposed method extends MI propagation from the near-field to medium/far-field regimes, thereby mitigating signal attenuation while enhancing transmission robustness. A comprehensive propagation model and channel characterization are developed, along with closed-form expressions for channel capacity. Through systematic simulations guided by underwater coverage threshold lines, the communication range, achievable bandwidth, and Coverage×Data-Rate performance limits are rigorously evaluated under diverse relay configurations. Numerical results demonstrate that optimized relay strategies not only enable transboundary MI signals to penetrate expected underwater depths but also elevate data rates by up to 10-fold compared to conventional non-relay systems. This breakthrough significantly extends the theoretical and practical performance boundaries of transboundary MI communication, establishing relay-aided architectures as a transformative paradigm for next-generation cross-domain UUV networks.
{"title":"Relay-driven magnetic induction communication with enhanced coverage-Data rate trade-offs for transboundary UUV control and information exchange","authors":"Ke Yang, Jiarui Yang, Mingyu Gao, Peng Lin, Xin Zhang","doi":"10.1016/j.phycom.2026.102996","DOIUrl":"10.1016/j.phycom.2026.102996","url":null,"abstract":"<div><div>Magnetic induction (MI) communication across the air-sea boundary (transboundary MI) offers unique advantages for seamless information exchange between aerial and underwater platforms. However, its performance, quantified by the product of coverage range and data rate, is fundamentally constrained by the rapid attenuation of MI signals with distance and frequency. This paper presents a novel relay transmission framework to address such limitations by enabling distributed superposition of magnetic induction fields. The proposed method extends MI propagation from the near-field to medium/far-field regimes, thereby mitigating signal attenuation while enhancing transmission robustness. A comprehensive propagation model and channel characterization are developed, along with closed-form expressions for channel capacity. Through systematic simulations guided by underwater coverage threshold lines, the communication range, achievable bandwidth, and Coverage×Data-Rate performance limits are rigorously evaluated under diverse relay configurations. Numerical results demonstrate that optimized relay strategies not only enable transboundary MI signals to penetrate expected underwater depths but also elevate data rates by up to 10-fold compared to conventional non-relay systems. This breakthrough significantly extends the theoretical and practical performance boundaries of transboundary MI communication, establishing relay-aided architectures as a transformative paradigm for next-generation cross-domain UUV networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102996"},"PeriodicalIF":2.2,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980711","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}
Pub Date : 2026-01-08DOI: 10.1016/j.phycom.2026.103004
Bin Li , Jie Ding , Hui Li , Jinlong Shi , Xin Zuo
With the rapid development of 5G and the forthcoming B5G/6G networks, unmanned aerial vehicles (UAVs) have been widely adopted in communication systems for their flexible deployment and integrated air-space-ground coverage capabilities. However, UAV communications are highly vulnerable to eavesdropping and jamming attacks, posing a severe threat to communication security. To address this problem, we construct a joint resource and position optimization framework based on the soft Actor-Critic (SAC) algorithm for a secure Two-Way Relay (TWR) system of UAV enabled with Non-Orthogonal Multiple Access (NOMA) technology. In this framework, NOMA technology is incorporated into the TWR relay transmission to achieve spectrum reuse and multi-user parallel communication. The UAV’s position and power allocation are modeled as a Markov Decision Process (MDP), which is intelligently optimized using deep reinforcement learning. We aim to maximize the overall secrecy rate of the system in a dynamic environment while minimizing constraint violations and eavesdropping risks. Simulation results demonstrate that, compared with A2C and PPO algorithms, the proposed SAC-based approach achieves superior convergence speed, stability, and anti-eavesdropping performance, providing technical references for NOMA-based secure UAV communications in B5G/6G networks.
{"title":"Joint optimization of resource and position for UAV secure two-Way relay systems using reinforcement learning","authors":"Bin Li , Jie Ding , Hui Li , Jinlong Shi , Xin Zuo","doi":"10.1016/j.phycom.2026.103004","DOIUrl":"10.1016/j.phycom.2026.103004","url":null,"abstract":"<div><div>With the rapid development of 5G and the forthcoming B5G/6G networks, unmanned aerial vehicles (UAVs) have been widely adopted in communication systems for their flexible deployment and integrated air-space-ground coverage capabilities. However, UAV communications are highly vulnerable to eavesdropping and jamming attacks, posing a severe threat to communication security. To address this problem, we construct a joint resource and position optimization framework based on the soft Actor-Critic (SAC) algorithm for a secure Two-Way Relay (TWR) system of UAV enabled with Non-Orthogonal Multiple Access (NOMA) technology. In this framework, NOMA technology is incorporated into the TWR relay transmission to achieve spectrum reuse and multi-user parallel communication. The UAV’s position and power allocation are modeled as a Markov Decision Process (MDP), which is intelligently optimized using deep reinforcement learning. We aim to maximize the overall secrecy rate of the system in a dynamic environment while minimizing constraint violations and eavesdropping risks. Simulation results demonstrate that, compared with A2C and PPO algorithms, the proposed SAC-based approach achieves superior convergence speed, stability, and anti-eavesdropping performance, providing technical references for NOMA-based secure UAV communications in B5G/6G networks.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103004"},"PeriodicalIF":2.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980716","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}
Pub Date : 2026-01-08DOI: 10.1016/j.phycom.2025.102981
Jiachi Zhang , Rongchen Sun , Dongmei Liu , Baoyue Meng , Liu Liu
This paper proposes a novel method for estimating channel quasi-stationary regions (QSRs) using joint Doppler-delay power profiles (DDPPs), with a focus on high-speed railway (HSR) channels. Conventional non-stationarity assessment methods, which rely primarily on power delay profiles (PDPs), may yield inaccurate QSR estimates, especially near track-side transceiver stations due to symmetric propagation conditions. By incorporating Doppler-delay information, the proposed DDPP-based approach significantly improves QSR identification accuracy. The method is validated using real channel measurements at 2.35 GHz from the Zhengzhou-Xi’an HSR line, covering both viaduct and cutting scenarios. Results indicate that the DDPP-based definition not only avoids false QSR estimations but also produces generally smaller QSR values than the PDP-based method for a given threshold. Moreover, the viaduct scenario exhibits larger QSRs than the cutting scenario. For instance, at a threshold of 0.7, the QSR values near the trackside receiver in the cutting scenario are 0.98 m (DDPP-based) versus 9.34 m (PDP-based), while in the viaduct scenario, the values are 2.03 m (DDPP-based) and 31.61 m (PDP-based), highlighting the method’s ability to capture environment-dependent stationarity characteristics.
{"title":"Joint doppler-delay quasi-stationarity region analysis for high-speed railway communication channels","authors":"Jiachi Zhang , Rongchen Sun , Dongmei Liu , Baoyue Meng , Liu Liu","doi":"10.1016/j.phycom.2025.102981","DOIUrl":"10.1016/j.phycom.2025.102981","url":null,"abstract":"<div><div>This paper proposes a novel method for estimating channel quasi-stationary regions (QSRs) using joint Doppler-delay power profiles (DDPPs), with a focus on high-speed railway (HSR) channels. Conventional non-stationarity assessment methods, which rely primarily on power delay profiles (PDPs), may yield inaccurate QSR estimates, especially near track-side transceiver stations due to symmetric propagation conditions. By incorporating Doppler-delay information, the proposed DDPP-based approach significantly improves QSR identification accuracy. The method is validated using real channel measurements at 2.35 GHz from the Zhengzhou-Xi’an HSR line, covering both viaduct and cutting scenarios. Results indicate that the DDPP-based definition not only avoids false QSR estimations but also produces generally smaller QSR values than the PDP-based method for a given threshold. Moreover, the viaduct scenario exhibits larger QSRs than the cutting scenario. For instance, at a threshold of 0.7, the QSR values near the trackside receiver in the cutting scenario are 0.98 m (DDPP-based) versus 9.34 m (PDP-based), while in the viaduct scenario, the values are 2.03 m (DDPP-based) and 31.61 m (PDP-based), highlighting the method’s ability to capture environment-dependent stationarity characteristics.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102981"},"PeriodicalIF":2.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980708","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}
Pub Date : 2026-01-07DOI: 10.1016/j.phycom.2026.103003
Cong Hung Dinh , Xuan Nghia Pham , Xuan Nam Tran , Ba Cao Nguyen
This article proposes a combination of emerging technologies, including rate-splitting multiple access (RSMA), full-duplex (FD) communications, unmanned aerial vehicle (UAV) communications, intelligent reflecting surfaces (IRS), and millimeter-wave (mmWave) communications, to enhance the performance of wireless systems in fifth-generation (5G) and beyond (B5G) networks with multiple clusters. We mathematically derive and present formulas for outage probability (OP), throughput, and ergodic capacity (EC) for the proposed IRS-aided UAV-RSMA system with transmit antenna selection (TAS), employing FD transmission over Nakagami-m channels. Numerical results demonstrate that the proposed system offers significant performance improvements over existing systems. Specifically, across different comparative scenarios, TAS provides higher performance than systems without TAS. Furthermore, RSMA outperforms non-orthogonal multiple access (NOMA), particularly in high-power regions, by reducing OP and preventing error floor saturation. Additionally, increasing the number of reflecting elements (REs) substantially enhances system performance. Moreover, key factors such as carrier frequency, number of REs, transmission rates, UAV speed and altitude, and residual self-interference (SI) levels play a crucial role in minimizing OP and maximizing throughput and EC. Finally, Monte-Carlo simulations are conducted to validate the accuracy of the theoretical formulas.
{"title":"Performance analysis of IRS-aided full-duplex mmWave UAV systems using RSMA and antenna selection","authors":"Cong Hung Dinh , Xuan Nghia Pham , Xuan Nam Tran , Ba Cao Nguyen","doi":"10.1016/j.phycom.2026.103003","DOIUrl":"10.1016/j.phycom.2026.103003","url":null,"abstract":"<div><div>This article proposes a combination of emerging technologies, including rate-splitting multiple access (RSMA), full-duplex (FD) communications, unmanned aerial vehicle (UAV) communications, intelligent reflecting surfaces (IRS), and millimeter-wave (mmWave) communications, to enhance the performance of wireless systems in fifth-generation (5G) and beyond (B5G) networks with multiple clusters. We mathematically derive and present formulas for outage probability (OP), throughput, and ergodic capacity (EC) for the proposed IRS-aided UAV-RSMA system with transmit antenna selection (TAS), employing FD transmission over Nakagami-<em>m</em> channels. Numerical results demonstrate that the proposed system offers significant performance improvements over existing systems. Specifically, across different comparative scenarios, TAS provides higher performance than systems without TAS. Furthermore, RSMA outperforms non-orthogonal multiple access (NOMA), particularly in high-power regions, by reducing OP and preventing error floor saturation. Additionally, increasing the number of reflecting elements (REs) substantially enhances system performance. Moreover, key factors such as carrier frequency, number of REs, transmission rates, UAV speed and altitude, and residual self-interference (SI) levels play a crucial role in minimizing OP and maximizing throughput and EC. Finally, Monte-Carlo simulations are conducted to validate the accuracy of the theoretical formulas.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103003"},"PeriodicalIF":2.2,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980707","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}
Pub Date : 2026-01-07DOI: 10.1016/j.phycom.2026.102999
Feng Liu, Jiahua Huang, Jun Gao
Shallow water acoustic communication plays an important role in offshore exploration but faces challenges from both adverse channel conditions and resource-constrained equipment capabilities. OTFS modulation improves BER and spectral efficiency in such environments; however, existing detection methods such as MPA and LMMSE remain computationally intensive. Specifically, even the relatively more efficient MPA and related frameworks like DD-MRC still impose heavy computational burdens, while LMMSE exhibits extremely high complexity that is impractical for resource-limited devices. To address this, we propose a low-complexity iterative rake detector with delay-time domain maximal ratio combining (DT-MRC), which reduces redundancy via intermediate storage and domain transformations. Built on the delay-Doppler domain MRC (DD-MRC) framework, our contributions include developing DT-MRC for efficient shallow water detection and analyzing complexity-BER trade-offs. Extensive simulation results show that the proposed DT-MRC maintains nearly optimal BER performance (comparable to MPA and LMMSE) while achieving significantly lower complexity-reducing computational overhead by approximately 80% compared to MPA, over 99.9% compared to LMMSE, and about 95% compared to DD-MRC-thus meeting the requirements of marine equipment.
{"title":"Shallow water-oriented low-complexity iterative detector for underwater acoustic OTFS systems","authors":"Feng Liu, Jiahua Huang, Jun Gao","doi":"10.1016/j.phycom.2026.102999","DOIUrl":"10.1016/j.phycom.2026.102999","url":null,"abstract":"<div><div>Shallow water acoustic communication plays an important role in offshore exploration but faces challenges from both adverse channel conditions and resource-constrained equipment capabilities. OTFS modulation improves BER and spectral efficiency in such environments; however, existing detection methods such as MPA and LMMSE remain computationally intensive. Specifically, even the relatively more efficient MPA and related frameworks like DD-MRC still impose heavy computational burdens, while LMMSE exhibits extremely high complexity that is impractical for resource-limited devices. To address this, we propose a low-complexity iterative rake detector with delay-time domain maximal ratio combining (DT-MRC), which reduces redundancy via intermediate storage and domain transformations. Built on the delay-Doppler domain MRC (DD-MRC) framework, our contributions include developing DT-MRC for efficient shallow water detection and analyzing complexity-BER trade-offs. Extensive simulation results show that the proposed DT-MRC maintains nearly optimal BER performance (comparable to MPA and LMMSE) while achieving significantly lower complexity-reducing computational overhead by approximately 80% compared to MPA, over 99.9% compared to LMMSE, and about 95% compared to DD-MRC-thus meeting the requirements of marine equipment.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102999"},"PeriodicalIF":2.2,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980709","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}
Pub Date : 2026-01-06DOI: 10.1016/j.phycom.2026.103001
Bowen Zhu, Pan Zhen, Zihao Pan, Wenming Zhu, Ning Yang, Daoxing Guo
Traditional multi-beam satellite systems typically employ fixed beam pointing and static directional designs, lacking the ability to adapt spatial-domain resources. Consequently, service load is often distributed unevenly among beams or users, causing substantial mismatches between requested and allocated capacities. With the rapid evolution of Space-Air-Ground Integrated Networks (SAGIN) and the convergence of 5G/6G communications, flexible payloads and reconfigurable beam technologies have emerged as promising approaches to improve capacity utilization and service fairness. Motivated by these advances, this paper introduces a unified beam-configuration optimization framework that jointly integrates beam-pattern and beam-pointing design.We proposed a hierarchical iterative optimization method to address beam configuration, user association, and power allocation simultaneously. The overall coupled optimization problem was decomposed into three synergistic subproblems: beam configuration, user association, and power allocation. Specifically, the outer layer used an exchange-matching mechanism for dynamic beam-user mapping, while the inner layer employed a successive convex approximation (SCA) algorithm for efficient power allocation. Simulation results demonstrated that the proposed framework substantially improved capacity-demand matching, achieving 10–25% higher capacity than optimizing beam pointing or pattern alone, with robust performance, fast convergence, and superior energy efficiency across diverse user distributions.
{"title":"Joint optimization of multi-beam configuration and resource allocation for low-earth orbit satellites","authors":"Bowen Zhu, Pan Zhen, Zihao Pan, Wenming Zhu, Ning Yang, Daoxing Guo","doi":"10.1016/j.phycom.2026.103001","DOIUrl":"10.1016/j.phycom.2026.103001","url":null,"abstract":"<div><div>Traditional multi-beam satellite systems typically employ fixed beam pointing and static directional designs, lacking the ability to adapt spatial-domain resources. Consequently, service load is often distributed unevenly among beams or users, causing substantial mismatches between requested and allocated capacities. With the rapid evolution of Space-Air-Ground Integrated Networks (SAGIN) and the convergence of 5G/6G communications, flexible payloads and reconfigurable beam technologies have emerged as promising approaches to improve capacity utilization and service fairness. Motivated by these advances, this paper introduces a unified beam-configuration optimization framework that jointly integrates beam-pattern and beam-pointing design.We proposed a hierarchical iterative optimization method to address beam configuration, user association, and power allocation simultaneously. The overall coupled optimization problem was decomposed into three synergistic subproblems: beam configuration, user association, and power allocation. Specifically, the outer layer used an exchange-matching mechanism for dynamic beam-user mapping, while the inner layer employed a successive convex approximation (SCA) algorithm for efficient power allocation. Simulation results demonstrated that the proposed framework substantially improved capacity-demand matching, achieving 10–25% higher capacity than optimizing beam pointing or pattern alone, with robust performance, fast convergence, and superior energy efficiency across diverse user distributions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103001"},"PeriodicalIF":2.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929047","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}
Pub Date : 2026-01-06DOI: 10.1016/j.phycom.2026.102994
Chen Shen , Tingting Lyu , Yu Li , Tianqi Lin , Yulong Liu
Automatic modulation classification (AMC) techniques are crucial for cognitive radio and communication systems. However, in low signal-to-noise ratio (SNR) conditions, transient shortwave signals are highly vulnerable to noise interference. This vulnerability leads to a reduction in identification accuracy. Medium time scale shortwave signals offer more stable characteristics. However, these signals are influenced by the time-varying SNR. This effect causes the energy density distribution to become discrete, thereby leading to lower recognition accuracy. To address this issue, this paper proposes a new architecture combining the adaptive time-frequency threshold denoising (ATFTD) algorithm and dual-modal feature fusion to enhance the modulation recognition accuracy of medium time scale shortwave signals. First, the signals are transformed into two types of time-frequency images (TFIs) using smoothed pseudo Wigner-Ville distribution (SPWVD) and Born-Jordan distribution (BJD). Subsequently, the ATFTD algorithm denoises these two TFIs. Next, the denoised TFIs are input into deep networks for feature extraction, and Jensen-Shannon divergence (JSD) is employed for fusion. Meanwhile, the time-domain statistical features of the signals are extracted and concatenated with the fused TFI features. Finally, the concatenated features are fed into a fully connected network for classification. Experimental results demonstrate that the proposed solution achieves over 90% recognition accuracy across six deep learning networks (AlexNet, ResNet18, VGGNet16, DenseNet121, ResNet50, and ResNet152), with the best performance observed in the ResNet152 network, ultimately reaching an average recognition accuracy of 99.625%.
{"title":"Shortwave signal modulation recognition method using adaptive time-Frequency threshold denoising and feature fusion","authors":"Chen Shen , Tingting Lyu , Yu Li , Tianqi Lin , Yulong Liu","doi":"10.1016/j.phycom.2026.102994","DOIUrl":"10.1016/j.phycom.2026.102994","url":null,"abstract":"<div><div>Automatic modulation classification (AMC) techniques are crucial for cognitive radio and communication systems. However, in low signal-to-noise ratio (SNR) conditions, transient shortwave signals are highly vulnerable to noise interference. This vulnerability leads to a reduction in identification accuracy. Medium time scale shortwave signals offer more stable characteristics. However, these signals are influenced by the time-varying SNR. This effect causes the energy density distribution to become discrete, thereby leading to lower recognition accuracy. To address this issue, this paper proposes a new architecture combining the adaptive time-frequency threshold denoising (ATFTD) algorithm and dual-modal feature fusion to enhance the modulation recognition accuracy of medium time scale shortwave signals. First, the signals are transformed into two types of time-frequency images (TFIs) using smoothed pseudo Wigner-Ville distribution (SPWVD) and Born-Jordan distribution (BJD). Subsequently, the ATFTD algorithm denoises these two TFIs. Next, the denoised TFIs are input into deep networks for feature extraction, and Jensen-Shannon divergence (JSD) is employed for fusion. Meanwhile, the time-domain statistical features of the signals are extracted and concatenated with the fused TFI features. Finally, the concatenated features are fed into a fully connected network for classification. Experimental results demonstrate that the proposed solution achieves over 90% recognition accuracy across six deep learning networks (AlexNet, ResNet18, VGGNet16, DenseNet121, ResNet50, and ResNet152), with the best performance observed in the ResNet152 network, ultimately reaching an average recognition accuracy of 99.625%.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 102994"},"PeriodicalIF":2.2,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980718","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号