Physical Communication最新文献

{"title":"Adaptive resource allocation for semantic-aware vehicular wireless networks with hierarchical codebooks","authors":"Zhen Wang ,&nbsp;Xiaobing Shi ,&nbsp;Yitong Yang","doi":"10.1016/j.phycom.2025.102876","DOIUrl":"10.1016/j.phycom.2025.102876","url":null,"abstract":"<div><div>Vehicular wireless networks must exchange task-relevant information, such as perception data, cooperative sensing, and driving intention, under constrained freshness constraints. Semantic communication can reduce the bandwidth by transmitting task-level representations rather than raw bits of information. However, the joint decision of radio allocation and semantic granularity remains open in high-mobility vehicle-to-everything (V2X) communication networks. This paper proposes an adaptive framework that couples hierarchical codebooks with deep reinforcement learning (DRL) for joint resource scheduling and codebook-level selection in V2X networks. Each codebook level offers a different distortion trade-off, and the DRL-based controller can adaptively select a codebook level and allocate resource blocks and power, so that the proposed method can ensure the age of information (AoI) for each vehicle within its maximum AoI tolerance. We propose a system model that captures OFDMA scheduling, power control, and semantic-level decisions, and formulate an AoI-constrained optimization that minimizes long-term weighted semantic distortion; and design a DRL-based online policy with feasibility projection to respect AoI limits in every timeslot. The approach decomposes into lightweight per-slot decisions and scales to multi-RSU deployments. Simulations with cooperative perception demonstrate significant reductions in semantic distortion at spectrum usage, while satisfying the stringent AoI constraints.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102876"},"PeriodicalIF":2.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267460","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}

{"title":"WB-CPRSN algorithm for mainlobe maintaining wideband beamforming","authors":"Fulai Liu ,&nbsp;Zhuoyi Yao ,&nbsp;Zhibo Su ,&nbsp;Ruiyan Du","doi":"10.1016/j.phycom.2025.102875","DOIUrl":"10.1016/j.phycom.2025.102875","url":null,"abstract":"<div><div>Aiming at the problem that most existing mainlobe interference suppression wideband beamforming algorithms fail to properly capture and process the complex-valued information of interference signals, which leads to a decrease in their mainlobe interference suppression performance, an effective mainlobe maintaining wideband beamforming algorithm, named WB-CPRSN, is proposed based on a complex-valued processing residual shrinkage network (CPRSN). Initially, Eigen-projection Processing and Focusing Reconstruction (EPFR) algorithm is introduced to generate the dataset for the proposed neural network. Subsequently, a CPRSN model is proposed, in which a Complex-valued Attention Module (CAM) is incorporated to extract the mainlobe interference features. It is used to perceive global features and fuse them with the original data to enhance more important channel features, thereby improving the prediction performance of the network. The above model effectively mitigates the impact of interference on the mainlobe array gain by fully leveraging the phase information of complex-valued data and extracting mainlobe interference features to obtain an optimal beamforming weight vector. Finally, the well-trained CPRSN can rapidly predict near-optimal mainlobe maintaining wideband beamforming weight vectors. Simulation results indicate that the proposed WB-CPRSN algorithm has satisfactory performance against mainlobe interference and modest computational time. For example, the WB-CPRSN algorithm can form an accurate and narrow mainlobe, and achieve nulls lower than -70 dB in the two side lobe interference directions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102875"},"PeriodicalIF":2.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267462","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}

{"title":"Low complexity equalizer with iterative interference cancellation for OTFS underwater acoustic communications","authors":"Tonghui Zheng ,&nbsp;Chengbing He ,&nbsp;Lianyou Jing ,&nbsp;Mingqi Jin ,&nbsp;Run Zhang","doi":"10.1016/j.phycom.2025.102872","DOIUrl":"10.1016/j.phycom.2025.102872","url":null,"abstract":"<div><div>Orthogonal Time–Frequency Space (OTFS) has emerged as a promising two-dimensional modulation technique that multiplexes information symbols in the delay-Doppler (DD) domain, demonstrating significant robustness in rapidly time-varying channels. In this paper, we propose a low-complexity equalizer with iterative interference cancellation (LC-IC) for OTFS-based underwater acoustic (UWA) communications. The proposed method employs a two-stage process: initial symbol estimation using the least-squares minimum residual (LSMR) algorithm, followed by iterative interference cancellation for symbol-wise refinement. Through reformulation of the interference cancellation process and efficient utilization of the LSMR algorithm, the proposed method achieves significant computational complexity reduction. Furthermore, the integration of soft estimation and decision-statistic combining (DSC) ensures rapid convergence and enhanced system performance. Extensive simulation and experimental results validate that the proposed LC-IC equalizer maintains excellent performance while substantially reducing computational complexity compared to conventional methods, making it particularly suitable for OTFS-based UWA communication systems. In a real-world UWA communication scenario with a communication distance of 1.5 km, error-free transmission is achieved at a data rate of 5.79 kbps.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102872"},"PeriodicalIF":2.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267475","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}

{"title":"WBGM-IC: Joint uplink–downlink resource optimization for many-to-many D2D sharing in 6G networks","authors":"Shuhan Yang ,&nbsp;Gaoli Yue ,&nbsp;Wenjun Yuan ,&nbsp;Bin Shen ,&nbsp;Xiaoge Huang","doi":"10.1016/j.phycom.2025.102873","DOIUrl":"10.1016/j.phycom.2025.102873","url":null,"abstract":"<div><div>Device-to-device (D2D) communication is envisioned to support distributed artificial intelligence in 6G networks by enabling data sharing and collaborative learning among terminals. To mitigate co-channel interference introduced by D2D communication, a complex “many-to-many” D2D resource-sharing scenario within cellular networks is investigated. We propose a joint uplink–downlink resource allocation mechanism based on weighted bipartite graph matching and interference clustering to optimize resource coordination. In the considered resource-sharing model, a single channel can be shared by various D2D users (DUs), and each DU is allowed to access multiple channels concurrently. A joint optimization framework for uplink–downlink channel assignment and power control is formulated, comprising two stages. In the first stage, a weighted bipartite graph matching-based resource allocation (WBGM-RA) algorithm is employed to allocate channels to cellular users (CUs) to maximize the system sum rate. In the second stage, an interference clustering-based resource allocation (IC-RA) algorithm is developed, where an interference matrix is constructed to represent inter-user interference relationships. Based on this, the transmit power of DUs is optimized while ensuring that the communication quality of CUs is not compromised. Experimental results demonstrate that, under the condition that the data rates of CUs and DUs are at least 2 bps/Hz, the proposed scheme significantly outperforms JUDRA and JUAD in terms of system sum rate, number of supported communication links, and DU channel access ratio—achieving at least 27% and 31% gain in sum rate, 62% and 112% increase in links support, and <span><math><mrow><mn>8</mn><mo>.</mo><mn>7</mn><mtext>%</mtext><mo>∼</mo><mn>21</mn><mo>.</mo><mn>5</mn><mtext>%</mtext></mrow></math></span> and <span><math><mrow><mn>13</mn><mo>.</mo><mn>6</mn><mtext>%</mtext><mo>∼</mo><mn>122</mn><mtext>%</mtext></mrow></math></span> improvement in DU access ratio, respectively. This work may serve as a preliminary step toward more efficient spectrum utilization and enhanced system capacity.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102873"},"PeriodicalIF":2.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267459","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}

{"title":"Semi-blind OTFS channel estimation based on DPS-BEM","authors":"Jiai He,&nbsp;Nan He","doi":"10.1016/j.phycom.2025.102874","DOIUrl":"10.1016/j.phycom.2025.102874","url":null,"abstract":"<div><div>In high-speed mobile scenarios like Internet of vehicles, UAVs, and LEO satellite communications, the large double dispersion dynamic range of continuous Doppler spread channels (CDSC) challenges traditional estimation methods by reducing accuracy and increasing pilot overhead. To address this, we propose a semi-blind channel estimation method based on a discrete prolate spheroidal sequences basic extension model (DPS-BEM). This approach embodies the concept of semi-blind estimation, which leverages a small number of embedded pilot frames alongside the channel’s dynamic characteristics to estimate and predict parameters, allowing most frames to be dedicated to data transmission, thus significantly reducing pilot overhead. A frame structure grouping pilots and data is first designed based on the channel’s quasi-static property. The channel is then modeled in the delay-Doppler (DD) domain using DPS-BEM, transforming high-dimensional parameter estimation into a low-dimensional problem of solving for basis coefficients. To capture the channel’s temporal evolution, a first-order time-varying auto-regressive model (TVARM) is constructed for these coefficients. Dynamic channel estimation is finally achieved using a particle filtering algorithm, which leverages Bayesian theory and the pilot information. Simulations on the MATLAB platform demonstrate that the proposed DPS-BEM-based scheme achieves superior channel estimation and bit error performance. It yields a gain of nearly 2 dB at a BER of <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span> compared to traditional methods. Furthermore, with low pilot overhead, its performance is competitive with recently proposed sparse learning-based techniques.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102874"},"PeriodicalIF":2.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267474","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}

{"title":"Polar code decoding using a learning-based rescue algorithm with a successive cancellation list decoder","authors":"Sunil Yadav Kshirsagar,&nbsp;Venkatrajam Marka","doi":"10.1016/j.phycom.2025.102871","DOIUrl":"10.1016/j.phycom.2025.102871","url":null,"abstract":"<div><div>Low-complexity node-based successive cancellation list (SCL) decoding has gained significant attention due to its potential use in 5G communication systems owing to the low latency and high-reliability requirements of 5G. Although SCL decoding has a high error-correction capability compared to other standard successive cancellation (SC) decoding methods, SCL decoding faces complexity because of its list-based method which limits the practical implications. This research proposes a SCL decoder using the learning-based rescue (LBR) algorithm to address this limitation and enhance the error-correction capability of polar codes in terms of bit error and frame error rates. The proposed method identifies weak additive white Gaussian noise (AWGN) channels that degrade decoding performance. By employing the LBR approach, the bit error rate of the weak channel can be quickly identified and fixed before subsequent decoding attempts. With LBR, it is possible to achieve considerable improvement in error-correction capability and reduced computing complexity for AWGN channels. Compared with state-of-the-art node-based polar decoding techniques, a SCL decoder employing the LBR algorithm significantly reduces the bit and frame error rates. The improvements are realized by combining a hybrid-based decoding scheme and implementing the LBR algorithm tailored for 5G New Radio (NR) polar codes. The attained bit error rate, binary phase shift keying bit error rate, and frame error rate, respectively are <span><math><mrow><mn>2</mn><mo>.</mo><mn>5</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></math></span>, <span><math><mrow><mn>2</mn><mo>.</mo><mn>6</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></math></span>, and <span><math><mrow><mn>4</mn><mo>.</mo><mn>6</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></math></span> for 4 dB signal-to-noise ratio (SNR) using the SCL decoder with the LBR algorithm.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102871"},"PeriodicalIF":2.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267457","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}

{"title":"Performance analysis of downlink NOMA short-packet communications with multiple antennas","authors":"Xinjie Wang,&nbsp;Yongchang Li,&nbsp;Xunxun Li,&nbsp;Enyu Li,&nbsp;Xuhu Wang","doi":"10.1016/j.phycom.2025.102867","DOIUrl":"10.1016/j.phycom.2025.102867","url":null,"abstract":"<div><div>This paper analyzes the performance of downlink NOMA short-packet system with multiple antennas at base station. The max–min antenna selection scheme is absorbed into this multi-antenna system, and the outdated channel side information (CSI) is also considered. The analytical expressions for the average block error rate (BLER) are derived. Meanwhile, the asymptotic behaviors of average BLER at high signal-to-noise ratio (SNR) are also studied. The results should that the outdated coefficient, antennas’ number and packet length have a large effect on the average BLER. Based on the asymptotic expressions, we find that there is only a linear correlation between the diversity order of the average BLER and the antennas number in a fixed range decided by packet length and rate threshold when the perfect CSI appears, otherwise the diversity order is only zero, one or two. The simulation results are provided to confirm the theoretical analysis, and they also show that the short-packet system reduces the scope of power allocation compared to the long-packet system when a low BLER is required.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102867"},"PeriodicalIF":2.2,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267455","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}

{"title":"DRL-based resource allocation for max–min fairness in V2V network","authors":"Daqian Liu,&nbsp;Yongkang Cao,&nbsp;Yuntao Shi,&nbsp;Zhenwu Lei","doi":"10.1016/j.phycom.2025.102870","DOIUrl":"10.1016/j.phycom.2025.102870","url":null,"abstract":"<div><div>As vehicle-to-everything (V2X) standards continue to develop, ensuring both high throughput and fair resource allocation for safety-critical vehicle-to-vehicle (V2V) data has become a pivotal challenge in vehicular network design. To address this issue, this study proposes a resource allocation scheme that incorporates fairness constraints into the joint optimization of spectrum and power, thereby enhancing overall system throughput while maintaining fairness among V2V links. To solve the multi-objective optimization problem in dynamic vehicular environments, a deep reinforcement learning (DRL)-based framework is developed, which introduces improvements in experience sampling and reward modeling to enhance the stability and convergence efficiency of the learning process. Simulation results demonstrate that the proposed algorithm not only achieves optimized aggregate system throughput but also significantly enhances the minimum V2V link rate. Compared to baseline methods, the algorithm exhibits superior adaptability across varying vehicle densities, further validating its practical efficacy in dynamic scenarios.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102870"},"PeriodicalIF":2.2,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267463","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}

{"title":"A novel joint Gaussian and impulse noise denoising method for very low-frequency communication over atmospheric channel","authors":"Rui Xue,&nbsp;Kefeng Deng","doi":"10.1016/j.phycom.2025.102868","DOIUrl":"10.1016/j.phycom.2025.102868","url":null,"abstract":"<div><div>Very low-frequency (VLF) communication systems are significantly degraded by the non-Gaussian noise (impulsive noise+Gaussian background noise) over the atmospheric channel. Conventional noise reduction methods, which rely on Gaussian assumptions, demonstrate limited efficacy in scenarios with mixed noise distributions. Therefore, a novel noise model that applies symmetric <span><math><mi>α</mi></math></span>-Stable (S<span><math><mi>α</mi></math></span>S) distribution to characterize VLF noise statistics is introduced in this paper. Subsequently, to address this noisy environment, we propose a novel joint denoising algorithm named AADMF+IWTFE. The algorithm consists of two complementary stages: (1) the received signal is denoised by adaptive absolute differences median filter (AADMF) to suppress impulsive noise, which can identify the noised samples and adaptively adjust the length of sliding window, by taking advantages of the absolute differences between the filtered sample and its neighbors, then (2) the filtered received signal is further denoised by Gaussian background noise reduction based on improved wavelet threshold function with exponential factors (IWTFE), of which the optimal parameters are obtained by the recent swarm intelligence algorithm, namely population quality improvement golden jackal optimization (PQI-GJO) algorithm. In order to evaluate the performance of the proposed method on denoising VLF communication signals, minimum shift keying (MSK) signals and continuous phase modulation with prolate spheroidal wave function (CPM-PSWF) signals are used in the simulation experiments. Numerical experiments indicate that the proposed AADMF+IWTFE outperforms conventional and state-of-the-art denoising approaches with higher signal-to-noise ratio (SNR), normalized correlation coefficient (NCC), and lower mean square error (MSE).</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102868"},"PeriodicalIF":2.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267456","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}

{"title":"REAFL: Accelerating federated learning with personalized iteration allocation and peer learning","authors":"Peng Pi ,&nbsp;Dewen Qiao ,&nbsp;Hongli Zhang","doi":"10.1016/j.phycom.2025.102861","DOIUrl":"10.1016/j.phycom.2025.102861","url":null,"abstract":"<div><div>In recent years, federated learning (FL) has made significant promise for wireless communication edge computing (WCEC) environments. However, challenges like device heterogeneity, limited edge resources, and Non-IID data disrupt symmetry in data distribution and model updates, which is crucial FL efficiency and performance. To address these issues, we propose Resource-Efficient Accelerating Federated Learning (REAFL), which combines Particle Swarm Optimization (PSO) and Momentum Gradient Descent (MGD) to enhance model training efficiency while maximizing resource utilization in WCEC environments. Our approach innovatively leverages PSO’s inherent self-learning and peer-learning capabilities to facilitate robust local model training on individual devices, subsequently refined by MGD optimization. Through a clear toy example, we demonstrate the importance of dynamically adjusting the local iteration quantities for heterogeneous devices during FL training. Theoretical analysis of REAFL’s convergence under a fixed time budget reveals the relationship between local iteration quantities and the optimal global model. Building on this, we propose a novel FL framework to dynamically adjust the local iteration quantities for devices. Extensive experiments show that REAFL outperforms existing benchmarks, offering improvements in accuracy, resource efficiency, and resilience to Non-IID data distributions.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"73 ","pages":"Article 102861"},"PeriodicalIF":2.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221020","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}