Physical Communication最新文献_第3页

Boosted HyperNet neural Min-Sum decoder: LDPC error floor mitigation in Space-Air-Ground integrated networks 增强型超网络神经最小和解码器：空-空-地集成网络中的LDPC误差层降低

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.phycom.2026.103017

Peiying Zhang , Zheng Feng , Wei Zhang , Huiling Shi , Neeraj Kumar , Jian Wang

{"title":"Boosted HyperNet neural Min-Sum decoder: LDPC error floor mitigation in Space-Air-Ground integrated networks","authors":"Peiying Zhang , Zheng Feng , Wei Zhang , Huiling Shi , Neeraj Kumar , Jian Wang","doi":"10.1016/j.phycom.2026.103017","DOIUrl":"10.1016/j.phycom.2026.103017","url":null,"abstract":"<div><div>Ensuring extremely high reliability and efficiency of communication in dynamic heterogeneous environments is critical for Space-Air-Ground Integrated Networks (SAGIN). To address the error floor phenomenon encountered in low-density parity check (LDPC) codes, we design an innovative decoder based on HyperNet and boosted learning: boosted HyperNet Neural Min-Sum (BHNMS) decoder. This decoder features: (1) Layered Activation Mechanism dynamically activates HyperNet to solve the explosion of training resources due to forward computation required for HyperNet, multi-stage training for boosted learning, and block-wise iterative chunk training; (2) Quantization-aware weight generation relies on differentiable quantization gates, dynamic precision-aware training, and quantization domain consistency constraints to solve the compatibility disconnect between HyperNet’s floating-point computation and quantization weight generation; (3) Cross-stage State Relay achieves continuous reconstruction of node evolution trajectories across stages through spatiotemporal compression transmission and adaptive accuracy control to solve the dynamic feedback imbalance issue; (4) Block Gradient Bridge reconstructs the global gradient propagation path through learnable bridge parameters and cross-block consistency constraints, ensuring the end-to-end trainability of dynamic weight generators to solve the problem of gradient propagation disruption. Simulation shows that BHNMS does not exhibit significant error floors, and its performance far exceeds that of traditional decoders, reaching <span><math><msup><mn>10</mn><mrow><mo>−</mo><mn>9</mn></mrow></msup></math></span> earlier</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103017"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397950","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

Improving rainfall retrieval accuracy using cross-Modal deep learning: Merging wifi with commercial microwave link 利用跨模态深度学习提高降雨检索精度：将wifi与商用微波链路融合

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.phycom.2026.103014

Weitao Tao , Bin Lian , Zhongcheng Wei , Luming Song , Lili Huang , Jijun Zhao

{"title":"Improving rainfall retrieval accuracy using cross-Modal deep learning: Merging wifi with commercial microwave link","authors":"Weitao Tao , Bin Lian , Zhongcheng Wei , Luming Song , Lili Huang , Jijun Zhao","doi":"10.1016/j.phycom.2026.103014","DOIUrl":"10.1016/j.phycom.2026.103014","url":null,"abstract":"<div><div>Commercial microwave links (CMLs) have attracted increasing attention in meteorology and hydrology as a cost-effective method for rainfall retrieval. Nevertheless, the typical single-link and high-frequency structure of CMLs often limits the accuracy of rainfall retrieval. This study proposes a cross-modal deep learning model that merges channel state information (CSI) from WiFi with received signal level (RSL) from CMLs for high-precision rainfall retrieval. First, a fast dynamic time warping algorithm is applied to align the RSL and CSI data. The CSI data are then denoised and filtered using wavelet denoising and Savitzky-Golay filtering. At the same time, the RSL data undergo feature engineering, including gradient extraction, rolling statistics, and wavelet-based features. A gated cross-modal attention mechanism is then utilized for feature-level fusion, and a dynamic weighted network adaptively adjusts the contribution of each modality. Finally, a decision-level fusion module with consistency regularization generates the final rainfall intensity estimates. Experimental results demonstrate that the proposed model achieves a pearson correlation coefficient of 0.9541, a root mean square error of 0.0271 mm/h, a coefficient of variation of 1.8948, and a relative bias of -30.4%, confirming its effectiveness in improving rainfall retrieval accuracy through WiFi-CML data fusion.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103014"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397952","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

Robust secure transmission for STAR-RIS assisted ISAC against collaborative eavesdropper with imperfect CSI STAR-RIS辅助ISAC抗不完美CSI协同窃听的鲁棒安全传输

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.phycom.2026.103011

Jialing Xu , Jianbin Xue , Han Zhang , Xiangrui Guan

{"title":"Robust secure transmission for STAR-RIS assisted ISAC against collaborative eavesdropper with imperfect CSI","authors":"Jialing Xu , Jianbin Xue , Han Zhang , Xiangrui Guan","doi":"10.1016/j.phycom.2026.103011","DOIUrl":"10.1016/j.phycom.2026.103011","url":null,"abstract":"<div><div>Integrated sensing and communication (ISAC), especially when assisted by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS), is a key technology for improving spectrum efficiency in future sixth-generation (6 G) networks. However, the inherent open and broadcast nature of wireless channels makes such systems vulnerable to eavesdropping by malicious users. Existing secure transmission schemes often overlook the severe threats posed by cooperative eavesdroppers (Eve), and many studies commonly assume that the channel state information (CSI) of Eve is perfectly known, which is unrealistic in practice. This paper investigates the problem of physical layer security (PLS) transmission in a downlink ISAC system assisted by a STAR-RIS. We consider a challenging scenario in which the dual regions of the STAR-RIS serve a sensing target (ST) and communication users, respectively, while facing the worst-case security threat caused by cooperative Eves on both sides. In addition, due to the practical difficulty in obtaining perfect CSI of the Eves, we formulate a secrecy rate maximization problem under imperfect CSI, while considering both the quality of service (QoS) requirement of communication users and the sensing performance constraint. To solve this highly non-convex optimization problem, a deep reinforcement learning (DRL) method based on the soft actor–critic (SAC) algorithm is developed, which can dynamically search for the optimal strategy for secure transmission. Numerical results illustrate that the proposed scheme keeps a good balance between sensing, communication, and security performance. Moreover, it exhibits strong robustness against cooperative Eves and uncertain CSI.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103011"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397956","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

STAR-RIS-empowered hybrid NOMA/OMA with adaptive modulation for URLLC in finite-blocklength V2X-communications 基于star - ris的混合NOMA/OMA自适应调制在有限块长度v2x通信中的URLLC

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.phycom.2026.103041

Sanjay Bhardwaj, Won Jae Ryu, Dong-Seong Kim

{"title":"STAR-RIS-empowered hybrid NOMA/OMA with adaptive modulation for URLLC in finite-blocklength V2X-communications","authors":"Sanjay Bhardwaj, Won Jae Ryu, Dong-Seong Kim","doi":"10.1016/j.phycom.2026.103041","DOIUrl":"10.1016/j.phycom.2026.103041","url":null,"abstract":"<div><div>This paper proposes a novel STAR-RIS-assisted hybrid NOMA/OMA framework with adaptive modulation (AM) for ultra-reliable low-latency vehicle-to-everything (V2X) communications under finite blocklength (FBL) constraints. Unlike existing works that separately address STAR-RIS optimization, hybrid multiple access, or URLLC modeling, this study jointly integrates AM, dynamic NOMA/OMA mode switching, and mobility-aware STAR-RIS beam control into a unified FBL-aware design. A roadside unit (RSU) serves a pair of vehicular users with heterogeneous channel conditions, where a STAR-RIS simultaneously transmits and reflects signals to enhance coverage and reliability in high-mobility environments. The core motivation is to overcome the inaccuracy of infinite blocklength assumptions and the rigidity of fixed-access schemes when meeting stringent URLLC latency–reliability requirements. Closed-form reliability expressions are derived for both NOMA and OMA modes under short-packet transmission, explicitly capturing decoding errors, channel dispersion, and imperfect channel state information (CSI). A joint optimization framework based on fractional programming and K-means user clustering is developed to adapt power allocation, modulation order, and access mode selection.Simulation results demonstrate that the proposed SR-AM-URLLC-V2X framework achieves up to 25% higher sum rate, ultra-high reliability (<span><math><mrow><msub><mi>ϵ</mi><mi>k</mi></msub><mo>≤</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></math></span>) at practical SNR levels, and robust performance under vehicular speeds up to 100 km/h, compared to conventional RIS-assisted and fixed-access V2X schemes. These results highlight its effectiveness for safety-critical URLLC-driven V2X communications.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103041"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397959","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

Covert underwater acoustic communication: Joint spectral mimicry and soft-Limiting peak suppression 隐蔽水声通信：联合频谱模拟和软限制峰值抑制

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-17 DOI: 10.1016/j.phycom.2026.103009

Ebrahim Raeisian Dashtaki , Ehsan Moradi , Mohammadreza Jalili

{"title":"Covert underwater acoustic communication: Joint spectral mimicry and soft-Limiting peak suppression","authors":"Ebrahim Raeisian Dashtaki , Ehsan Moradi , Mohammadreza Jalili","doi":"10.1016/j.phycom.2026.103009","DOIUrl":"10.1016/j.phycom.2026.103009","url":null,"abstract":"<div><div>The broadcast nature of underwater acoustic channels necessitates low probability of detection communication to secure critical sensor networks. This paper proposes a hardware-aware steganographic transceiver that reconciles high reliability with strict covertness constraints under practical hardware impairments. To ensure reliable transmission amidst severe multipath fading, we adopt an orthogonal frequency division multiplexing framework. However, practical deployment faces challenges from the high peak-to-average power ratio and hardware non-idealities, including in-phase/quadrature imbalance and residual hardware impairments. Our architecture addresses these by disguising the signal via Wenz-based spectral amplitude shaping and random phase scrambling to mimic ambient ocean noise. Furthermore, we introduce a symmetric frequency diversity scheme that transforms in-phase/quadrature imbalance-induced interference into constructive diversity gain and employ a soft-limiting suppression mechanism based on a hyperbolic-tangent profile. This technique smoothly compresses signal peaks to a target of 7 dB, mitigating non-linear distortion while preserving the Gaussian statistical integrity of the cover signal. Simulation results demonstrate that the proposed system can achieve a bit error rate <span><math><mrow><mo><</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></math></span> across transmission ranges from 100 m to 7 km. The effective data rate is scalable between 3.31 kbps and 52 bps, while maintaining a negligible Kullback-Leibler divergence ( ≈ 0.03) relative to the Gaussian background noise, validating its feasibility for hardware-constrained underwater covert operations.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103009"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024417","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

Effective degrees of freedom maximization for XL-RIS-assisted near-field communication via hybrid learning-driven optimization 通过混合学习驱动优化实现xml - ris辅助近场通信的有效自由度最大化

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.phycom.2026.103012

Yaru Li , Wengen Gao , Yunfei Li , Wenlong Jiang

{"title":"Effective degrees of freedom maximization for XL-RIS-assisted near-field communication via hybrid learning-driven optimization","authors":"Yaru Li , Wengen Gao , Yunfei Li , Wenlong Jiang","doi":"10.1016/j.phycom.2026.103012","DOIUrl":"10.1016/j.phycom.2026.103012","url":null,"abstract":"<div><div>Extremely Large Reconfigurable Intelligent Surfaces (XL-RIS) have emerged as a transformative technology for controlling electromagnetic propagation in near-field wireless communication. However, optimizing their performance is challenging due to the complex spatial coupling and polarization effects in this regime-physical phenomena that are not fully captured by conventional models and result in intractable high-dimensional optimization problems. This paper proposes a hybrid learning-driven framework for maximizing the Effective Degrees of Freedom (EDoF) of XL-RIS-assisted systems. The proposed framework is grounded in an electromagnetically rigorous dyadic Green’s function-based channel model that accurately captures these critical near-field environment. To tackle the high-dimensional optimization problem efficiently, we introduce a novel method that combines a Multi-Layer Perceptron (MLP) as a fast performance surrogate with a Genetic Algorithm (GA) for global search. Comprehensive simulations demonstrate that the proposed framework achieves superior performance in achievable EDoF and channel capacity compared to existing benchmarks, effectively reveals the saturation behavior of spatial degrees of freedom and highlights the substantial gains enabled by polarization diversity. The results indicate that the integration of precise physical modeling with learning-based optimization offers an efficient and scalable approach for enhancing the performance of near-field XL-RIS.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103012"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024340","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

LSTM-based deep reinforcement learning for ISI mitigation in maritime LoRaWAN 基于lstm的海上LoRaWAN ISI缓解深度强化学习

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-02-28 DOI: 10.1016/j.phycom.2026.103063

Martine Lyimo, Bonny Mgawe, Judith Leo, Mussa Dida, Kisangiri Michael

{"title":"LSTM-based deep reinforcement learning for ISI mitigation in maritime LoRaWAN","authors":"Martine Lyimo, Bonny Mgawe, Judith Leo, Mussa Dida, Kisangiri Michael","doi":"10.1016/j.phycom.2026.103063","DOIUrl":"10.1016/j.phycom.2026.103063","url":null,"abstract":"<div><div>For reliable, long-range, low-power Maritime Internet of Things (MIoT) communication (e.g., vessel tracking, ocean monitoring, and offshore automation), LoRaWAN offers attractive coverage and energy efficiency. However, sea-surface reflections, wave motion, and platform mobility create time-varying multipath with large delay spread, which induces inter-symbol interference (ISI) and degrades packet delivery ratio (PDR) and energy performance. This paper proposes a Long Short-Term Memory (LSTM)-assisted Deep Reinforcement Learning (DRL) framework LSTM–DDPG Adaptive Modulation and Coding (LD-AMC) that proactively mitigates ISI by predicting short-term channel evolution and adapting the LoRaWAN physical-layer parameters. An LSTM predictor learns temporal correlations in observed link metrics (RSSI, SNR, PER, and RMS delay spread) and provides one-step-ahead forecasts, which are appended to the agent state. A Deep Deterministic Policy Gradient (DDPG) controller then selects the spreading factor (SF), coding rate (CR), bandwidth (BW), and transmit power (P<sub>t</sub>) within LoRaWAN constraints to maximize a reward that favors reliable delivery and throughput while penalizing energy cost and ISI severity. MATLAB/Simulink simulations under coastal and offshore two-ray maritime channels show that LD-AMC reduces ISI-induced symbol errors by up to 58%, improving PDR by up to 47% and reducing energy per delivered packet by up to 32% compared with standard and enhanced ADR baselines.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103063"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147397949","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

The detection method for UE-Codebook Mismatch in grant-free SCMA IoT systems 无授权SCMA物联网系统中ue -码本不匹配检测方法

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-28 DOI: 10.1016/j.phycom.2026.103026

Hua He, Yanxia Liang, Qiong Zhao

引用次数: 0

Investigation of MIMO channel model for ultra supersonic unmanned aerial vehicles 超声速无人机MIMO信道模型研究

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.phycom.2026.103005

Lei Xu , Jie Zhou , Rui Liu , Manyan Zhao , Gen-Fu Shao

{"title":"Investigation of MIMO channel model for ultra supersonic unmanned aerial vehicles","authors":"Lei Xu , Jie Zhou , Rui Liu , Manyan Zhao , Gen-Fu Shao","doi":"10.1016/j.phycom.2026.103005","DOIUrl":"10.1016/j.phycom.2026.103005","url":null,"abstract":"<div><div>In this paper, a three-dimensional multi-input multi-output (MIMO) random channel model based on unmanned aerial vehicle (UAV) is proposed to address the communication challenges between supersonic UAVs and ground stations. During the flight of supersonic UAVs, a unique plasma sheath phenomenon is observed. The plasma sheath, located beneath the UAV, initially affects the signal emitted by the UAVs transmitter before it reaches the scatterers or ground receiver. Consequently, we develop a model that incorporates the signal passing through the plasma sheath and couples it with the air-to-ground channel model. The envelope and phase correlation coefficients of the channel, as well as the spatial-temporal frequency correlation function, are derived and described in detail. The statistical characteristics of the channel are examined by varying the incidence angles of the plasma layer and the UAV flight altitude. The results demonstrate that the plasma sheath significantly reduces the channel correlation coefficient within a short time frame, while a higher channel correlation coefficient is observed when the signal vertically impacts the plasma layer.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103005"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024413","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

A hybrid GAN and attention-based sparse autoencoder framework for robust end-to-end wireless communication 一种用于端到端无线通信的混合GAN和基于注意力的稀疏自编码器框架

IF 2.2 4区计算机科学

Physical Communication Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.phycom.2026.103008

Safalata S. Sindal, Y.N. Trivedi

{"title":"A hybrid GAN and attention-based sparse autoencoder framework for robust end-to-end wireless communication","authors":"Safalata S. Sindal, Y.N. Trivedi","doi":"10.1016/j.phycom.2026.103008","DOIUrl":"10.1016/j.phycom.2026.103008","url":null,"abstract":"<div><div>This paper presents a hybrid deep learning framework that integrates a Generative Adversarial Network (GAN) with an Attention-based Sparse Autoencoder (GAN-AAE) for end-to-end wireless communication over Rayleigh fading channels with imperfect channel state information at the receiver (CSIR). Traditional autoencoder models lack the ability to learn underlying signal distributions or correct distortions caused by fading and noise. The proposed GAN-AAE addresses these limitations by using a generator as a learnable channel surrogate to refine encoded signals and an attention mechanism to dynamically prioritize relevant features for improved decoding. The imperfection in the CSI is quantified by a correlation coefficient <em>ρ</em>, where 0 ≤ <em>ρ</em> ≤ 1. Perfect channel knowledge is denoted by <em>ρ</em> = 1, and decreasing values of <em>ρ</em> correspond to increasingly inaccurate CSIR. The model is jointly trained using adversarial and reconstruction losses to enhance its adaptability. Simulation results show that the GAN-AAE framework significantly outperforms conventional Maximum Likelihood Detection and baseline deep and convolutional neural network-based models in terms of bit error rate (BER). The model is evaluated over <em>M</em>-ary phase shift keying (<em>M</em>-PSK) and <em>M</em>-ary Quadrature Amplitude Modulation (<em>M</em>-QAM) with Rayleigh fading channel. At <em>ρ</em> = 0.9 and a signal-to-noise ratio (SNR) of 10 dB, the conventional baseline model achieves a BER of 0.072, whereas the proposed GAN-AAE achieves a lower BER of 0.02404 for Binary phase shift keying (BPSK). A complexity analysis indicates that although the GAN-AAE model introduces some additional computational overhead, the performance gains in reconstruction justify the trade-off. Overall, the GAN-AAE offers a resilient and adaptive solution for end-to-end communication under realistic wireless impairments.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"75 ","pages":"Article 103008"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024339","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