In underground emergency rescues, the inherent narrow bandwidth of magnetic induction (MI) communication severely conflicts with the delay-sensitive nature of critical data transmission. While existing works assume infinite-length coding to maximize throughput, they inevitably incur unacceptable transmission latency. To directly address this issue, we introduce finite blocklength coding (FBC) into MI communication to achieve ultra-low latency, while employing magnetic beamforming to compensate for the short-packet penalty and maintain high throughput. Based on perfect channel state information (CSI), we propose a joint beamforming and blocklength configuration algorithm that minimizes the transmission latency while satisfying the constraints on total energy consumption and throughput. For the case of imperfect CSI, a robust joint beamforming and blocklength configuration algorithm is proposed. This paper provides a novel framework integrating FBC with magnetic beamforming to address the trade-off between throughput and transmission latency. Numerical results show that the proposed methods can effectively reduce the transmission latency while satisfying the constraints on total energy consumption and throughput.
{"title":"Low-Latency Underground Emergency Communication with Magnetic Beamforming","authors":"Jianyu Wang;Tianrui Hou;Wenchi Cheng;Xiaoqing Xiao;Hailin Zhang","doi":"10.23919/JCIN.2026.11737690","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737690","url":null,"abstract":"In underground emergency rescues, the inherent narrow bandwidth of magnetic induction (MI) communication severely conflicts with the delay-sensitive nature of critical data transmission. While existing works assume infinite-length coding to maximize throughput, they inevitably incur unacceptable transmission latency. To directly address this issue, we introduce finite blocklength coding (FBC) into MI communication to achieve ultra-low latency, while employing magnetic beamforming to compensate for the short-packet penalty and maintain high throughput. Based on perfect channel state information (CSI), we propose a joint beamforming and blocklength configuration algorithm that minimizes the transmission latency while satisfying the constraints on total energy consumption and throughput. For the case of imperfect CSI, a robust joint beamforming and blocklength configuration algorithm is proposed. This paper provides a novel framework integrating FBC with magnetic beamforming to address the trade-off between throughput and transmission latency. Numerical results show that the proposed methods can effectively reduce the transmission latency while satisfying the constraints on total energy consumption and throughput.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"283-292"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737694
Dandan Che;Ying Li;Wenshuai Wang;Yuanhao He;Yi Gong
Spatial reconstruction of the ionospheric F2-layer (foF2) critical frequency is essential for high-frequency communication and ionospheric modeling, yet remains challenging due to the sparse distribution of ground-based ionosondes. This paper proposes a graph attention network framework that reconstructs spatially continuous foF2 fields by treating global navigation satellite system (GNSS) total electron content (TEC) as a direct spatial covariate, bypassing intermediate conversion steps. The framework is named TEC-driven graph-based spatial reconstruction of continuous foF2 fields (TGR-foF2). Each ionosonde station is modeled as a graph node with co-located TEC, coordinates, and solar-geomagnetic features; three physically motivated edge types (geographic proximity, TEC similarity, geomagnetic latitude conjugacy) govern inter-station message passing; and a physics-guided output head encodes the first-order physical scaling between foF2 and the square root of TEC while learning residual corrections. Evaluated via leave-one-station-out cross-validation across eight Australian stations using 2020 full-year data, TGR-foF2 achieves a mean root mean squared error (RMSE) of 0.840 MHz, outperforming inverse distance weighting (0.912 MHz, −7.9%), IRI-2020 (0.976 MHz, −13.9%), and polynomial TEC-to-foF2 conversion (1.227 MHz, −31.5%). Ablation experiments confirm that TEC features, graph topology, and the attention mechanism are the three most impactful components. The strong correlation between station isolation distance and RMSE (r = 0.886, p = 0.003) identifies network sparsity as the primary remaining limitation.
{"title":"TGR-foF2: TEC-Driven Graph-Based Spatial Reconstruction of Continuous foF2 Fields with Physics-Guided Learning","authors":"Dandan Che;Ying Li;Wenshuai Wang;Yuanhao He;Yi Gong","doi":"10.23919/JCIN.2026.11737694","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737694","url":null,"abstract":"Spatial reconstruction of the ionospheric F2-layer (foF2) critical frequency is essential for high-frequency communication and ionospheric modeling, yet remains challenging due to the sparse distribution of ground-based ionosondes. This paper proposes a graph attention network framework that reconstructs spatially continuous foF2 fields by treating global navigation satellite system (GNSS) total electron content (TEC) as a direct spatial covariate, bypassing intermediate conversion steps. The framework is named TEC-driven graph-based spatial reconstruction of continuous foF2 fields (TGR-foF2). Each ionosonde station is modeled as a graph node with co-located TEC, coordinates, and solar-geomagnetic features; three physically motivated edge types (geographic proximity, TEC similarity, geomagnetic latitude conjugacy) govern inter-station message passing; and a physics-guided output head encodes the first-order physical scaling between foF2 and the square root of TEC while learning residual corrections. Evaluated via leave-one-station-out cross-validation across eight Australian stations using 2020 full-year data, TGR-foF2 achieves a mean root mean squared error (RMSE) of 0.840 MHz, outperforming inverse distance weighting (0.912 MHz, −7.9%), IRI-2020 (0.976 MHz, −13.9%), and polynomial TEC-to-foF2 conversion (1.227 MHz, −31.5%). Ablation experiments confirm that TEC features, graph topology, and the attention mechanism are the three most impactful components. The strong correlation between station isolation distance and RMSE (r = 0.886, p = 0.003) identifies network sparsity as the primary remaining limitation.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"334-349"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737696
Penghui Hu;Jian Wang
Age of information (AoI) is a freshness metric for time-critical wireless systems. We study periodic deterministic all-to-all status broadcasting under protocol interference and generate-at-will sampling. In each period, every source samples a fresh status at its first transmission, and each destination updates its timestamp at the first reception of that packet. This convention decomposes the network-average AoI into a half-period term and a mean first-reception delay term. The problem captures a tradeoff: spatial reuse shortens the update cycle, while rebroadcast waiting and interference shape dissemination delay. We develop three complementary solution methods. Complete-state packet-knowledge A∗ search certifies global optima on 992 connected nonisomorphic graphs, and an independent compact mixed-integer linear programming (MILP) reproduces their objective values. Bounded-state A∗ search constructs medium-scale schedules. For large graphs, a time-indexed MILP and its Dantzig-Wolfe reformulation motivate three completion-window policies: coverage-first, age-first, and dual-guided. Local greedy scheduling (LGS) implements each policy through distributed request/winner contention. Experiments on connected small graphs, medium-scale parameter regimes, and sparse random graphs with hundreds to thousands of nodes quantify the cycle-delay tradeoff and scheduling scalability.
{"title":"Minimum-AoI Scheduling for All-to-All Wireless Broadcasting: From Exact Search to Scalable Scheduling","authors":"Penghui Hu;Jian Wang","doi":"10.23919/JCIN.2026.11737696","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737696","url":null,"abstract":"Age of information (AoI) is a freshness metric for time-critical wireless systems. We study periodic deterministic all-to-all status broadcasting under protocol interference and generate-at-will sampling. In each period, every source samples a fresh status at its first transmission, and each destination updates its timestamp at the first reception of that packet. This convention decomposes the network-average AoI into a half-period term and a mean first-reception delay term. The problem captures a tradeoff: spatial reuse shortens the update cycle, while rebroadcast waiting and interference shape dissemination delay. We develop three complementary solution methods. Complete-state packet-knowledge A∗ search certifies global optima on 992 connected nonisomorphic graphs, and an independent compact mixed-integer linear programming (MILP) reproduces their objective values. Bounded-state A∗ search constructs medium-scale schedules. For large graphs, a time-indexed MILP and its Dantzig-Wolfe reformulation motivate three completion-window policies: coverage-first, age-first, and dual-guided. Local greedy scheduling (LGS) implements each policy through distributed request/winner contention. Experiments on connected small graphs, medium-scale parameter regimes, and sparse random graphs with hundreds to thousands of nodes quantify the cycle-delay tradeoff and scheduling scalability.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"360-386"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737699
Kaijun Zeng;Chao Wang;Han Lin;Yixuan Wu;Dingcheng Yang
This paper studies a surrogate-assisted two-base station (BS)-site deployment problem for low-altitude unmanned aerial vehicle (UAV) communication in dense urban environments. The study focuses on a fixed representative UAV altitude of 120 m and a fixed sectorized BS antenna configuration, so that the resulting signal-to-interference-plus-noise ratio (SINR) field is mainly determined by large-scale air-to-ground propagation, urban geometry, BS-site placement, and the prescribed antenna pattern. To avoid repeated ray-tracing simulations during deployment search, we synthesize dual-BS SINR maps from WinProp-generated path-loss (PL) maps and train RadioUNet++, an interference-aware cascaded U-Net surrogate, to predict dense SINR maps from the urban layout and two-BS-site location masks. Based on the trained surrogate, the two-BS-site placement problem is formulated as a surrogate-based continuous optimization problem and solved using proximal policy optimization (PPO) with a coverage-based reward. Simulation results over four urban scenarios in Nanchang, China show that RadioUNet++ achieves Pearson correlation above 0.95 and root mean square error (RMSE) below 3.3 dB on BS-pair-level held-out test sets, supporting its use as a fast SINR predictor under the considered setting. For deployment optimization, PPO reaches coverage ratios within 1.67–8.63 percentage points of exhaustive brute-force (BF) while using only 8 × 104 surrogate evaluations, roughly one order of magnitude fewer than BF (∼7 × 105), and achieves competitive performance compared with the genetic algorithm (GA) baseline. These results indicate that the proposed framework can serve as an efficient planning tool for two-BS low-altitude coverage optimization under the considered fixed-altitude and fixed-sector-antenna urban setting.
{"title":"RadioUNet++: Fast SINR Map Prediction and BS Deployment Optimization for Low-Altitude Wireless Coverage via Reinforcement Learning","authors":"Kaijun Zeng;Chao Wang;Han Lin;Yixuan Wu;Dingcheng Yang","doi":"10.23919/JCIN.2026.11737699","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737699","url":null,"abstract":"This paper studies a surrogate-assisted two-base station (BS)-site deployment problem for low-altitude unmanned aerial vehicle (UAV) communication in dense urban environments. The study focuses on a fixed representative UAV altitude of 120 m and a fixed sectorized BS antenna configuration, so that the resulting signal-to-interference-plus-noise ratio (SINR) field is mainly determined by large-scale air-to-ground propagation, urban geometry, BS-site placement, and the prescribed antenna pattern. To avoid repeated ray-tracing simulations during deployment search, we synthesize dual-BS SINR maps from WinProp-generated path-loss (PL) maps and train RadioUNet++, an interference-aware cascaded U-Net surrogate, to predict dense SINR maps from the urban layout and two-BS-site location masks. Based on the trained surrogate, the two-BS-site placement problem is formulated as a surrogate-based continuous optimization problem and solved using proximal policy optimization (PPO) with a coverage-based reward. Simulation results over four urban scenarios in Nanchang, China show that RadioUNet++ achieves Pearson correlation above 0.95 and root mean square error (RMSE) below 3.3 dB on BS-pair-level held-out test sets, supporting its use as a fast SINR predictor under the considered setting. For deployment optimization, PPO reaches coverage ratios within 1.67–8.63 percentage points of exhaustive brute-force (BF) while using only 8 × 10<sup>4</sup> surrogate evaluations, roughly one order of magnitude fewer than BF (∼7 × 10<sup>5</sup>), and achieves competitive performance compared with the genetic algorithm (GA) baseline. These results indicate that the proposed framework can serve as an efficient planning tool for two-BS low-altitude coverage optimization under the considered fixed-altitude and fixed-sector-antenna urban setting.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"416-429"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reconfigurable intelligent surface (RIS)-assisted communication system is a promising technology for secure wireless communication. However, achieving the maximum communication rate requires perfect channel state information, which is particularly challenging in practical scenarios for RIS-assisted communication. Although various channel estimation methods have been proposed, they typically demand high training overhead, which increases with the number of RIS elements. To address this challenge, a novel method is proposed by using alternating optimization (AO) and channel knowledge map techniques. This method does not require real-time CSI, reducing the complexity of RIS-assisted communication in practical applications, and significantly lowering the training overhead while ensuring the secrecy rate. It innovatively uses a regional communication rate visualization map to analyze the secrecy rate while considering user location errors. Simulation results show that this proposed scheme surpasses those based on channel estimation, especially exhibiting strong robustness to errors related to terminal positioning in practical applications.
{"title":"Environment-Aware Beam Selection for RIS-Assisted Secure Wireless Communication with Channel Knowledge Map","authors":"Chunlong He;Huimin Zhou;Chiya Zhang;Xin Wen;Xinyi Chen;Gongbin Qian","doi":"10.23919/JCIN.2026.11737695","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737695","url":null,"abstract":"Reconfigurable intelligent surface (RIS)-assisted communication system is a promising technology for secure wireless communication. However, achieving the maximum communication rate requires perfect channel state information, which is particularly challenging in practical scenarios for RIS-assisted communication. Although various channel estimation methods have been proposed, they typically demand high training overhead, which increases with the number of RIS elements. To address this challenge, a novel method is proposed by using alternating optimization (AO) and channel knowledge map techniques. This method does not require real-time CSI, reducing the complexity of RIS-assisted communication in practical applications, and significantly lowering the training overhead while ensuring the secrecy rate. It innovatively uses a regional communication rate visualization map to analyze the secrecy rate while considering user location errors. Simulation results show that this proposed scheme surpasses those based on channel estimation, especially exhibiting strong robustness to errors related to terminal positioning in practical applications.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"350-359"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149175997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737700
Ruixing Ren;Shan Chen;Junhui Zhao;Xiaoke Sun
For cooperative perception in the Internet of vehicles (IoV), this paper proposes a generative adversarial network (GAN)-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional communication systems in visual data transmission under limited bandwidth and dynamic channel conditions. At the transmitter, the framework adopts a pyramid attention network to extract semantic label maps and introduces a semantic priority preservation mechanism. It assigns differentiated weights to distinct semantic categories based on driving safety, guiding bit allocation and loss function design. At the receiver, an image reconstruction module integrating a coarse-to-fine multi-resolution generator and multi-scale discriminator is designed. Combined with the temporal consistency branch, spatial pyramid pooling and class-aware convolutional layers, it achieves high-fidelity reconstruction of high quality images from corrupted semantic labels. The model is trained with combined adversarial, feature matching and perceptual losses, effectively improving semantic consistency and visual realism of generated images. Experimental results on the Cityscapes dataset show that the proposed method outperforms existing counterparts in both semantic segmentation accuracy and reconstructed image quality, and maintains stable reconstruction performance under additive white Gaussian noise (AWGN) and Rayleigh fading channels.
{"title":"GAN-Based Semantic Communication for Image Transmission in IoV","authors":"Ruixing Ren;Shan Chen;Junhui Zhao;Xiaoke Sun","doi":"10.23919/JCIN.2026.11737700","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737700","url":null,"abstract":"For cooperative perception in the Internet of vehicles (IoV), this paper proposes a generative adversarial network (GAN)-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional communication systems in visual data transmission under limited bandwidth and dynamic channel conditions. At the transmitter, the framework adopts a pyramid attention network to extract semantic label maps and introduces a semantic priority preservation mechanism. It assigns differentiated weights to distinct semantic categories based on driving safety, guiding bit allocation and loss function design. At the receiver, an image reconstruction module integrating a coarse-to-fine multi-resolution generator and multi-scale discriminator is designed. Combined with the temporal consistency branch, spatial pyramid pooling and class-aware convolutional layers, it achieves high-fidelity reconstruction of high quality images from corrupted semantic labels. The model is trained with combined adversarial, feature matching and perceptual losses, effectively improving semantic consistency and visual realism of generated images. Experimental results on the Cityscapes dataset show that the proposed method outperforms existing counterparts in both semantic segmentation accuracy and reconstructed image quality, and maintains stable reconstruction performance under additive white Gaussian noise (AWGN) and Rayleigh fading channels.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"430-438"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737697
Xiaolin Liang;Wang Jia;Zili Chen;Qingqing Zhao
Reconfigurable intelligent surface (RIS)-assisted vehicle-to-everything (V2X) communication has come to be a key driver for 6G networks. Nevertheless, guaranteeing both timeliness and security in vehicular information exchange remains a challenge, primarily owing to the substantial vehicle mobility and the severe environmental intricacy within V2X network. Thus, a resource allocation scheme to minimize the age of information (AoI), maximize the sum rates, and improve the reliability of payload transmission for RIS-assisted V2X networks is proposed. Moreover, the target problem is converted into a Markov decision process, and an algorithm based on deep reinforcement learning (DRL) termed as stochastic reinforced soft learning network (SRSLNet) is proposed. The proposed DRL model establishes a collaborative optimization framework integrating value and policy functions, which can not only efficiently use the replay buffer data, but also ensure the stability of policy update. Based on the proposed algorithm, the AoI, sum rates of the V2X network, and the probability of successful vehicle-to-vehicle (V2V) payload transmission are analyzed. Simulation results demonstrate that the proposed SRSLNet algorithm outperforms benchmark schemes across various scenarios and parameter settings, yielding a 14.58% reduction in AoI, a 10.34% increase in V2V transmission success probability, and a 46.34% improvement in sum rate.
可重构智能表面（RIS）辅助的车联网（V2X）通信已成为6G网络的关键驱动力。然而，保证车辆信息交换的及时性和安全性仍然是一个挑战，这主要是由于V2X网络中大量的车辆移动性和严峻的环境复杂性。为此，提出了一种最小化信息年龄（age of information, AoI）、最大化求和速率、提高ris辅助V2X网络有效载荷传输可靠性的资源分配方案。将目标问题转化为马尔可夫决策过程，提出了一种基于深度强化学习（DRL）的随机强化软学习网络（SRSLNet）算法。提出的DRL模型建立了一个集值函数和策略函数于一体的协同优化框架，既能高效利用重播缓冲数据，又能保证策略更新的稳定性。基于该算法，分析了V2X网络的AoI、和率以及V2V有效载荷传输成功概率。仿真结果表明，提出的SRSLNet算法在各种场景和参数设置下都优于基准方案，AoI降低了14.58%，V2V传输成功率提高了10.34%，求和速率提高了46.34%。
{"title":"DRL-Based AoI-Aware Resource Allocation for RIS-Assisted V2X Networks","authors":"Xiaolin Liang;Wang Jia;Zili Chen;Qingqing Zhao","doi":"10.23919/JCIN.2026.11737697","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737697","url":null,"abstract":"Reconfigurable intelligent surface (RIS)-assisted vehicle-to-everything (V2X) communication has come to be a key driver for 6G networks. Nevertheless, guaranteeing both timeliness and security in vehicular information exchange remains a challenge, primarily owing to the substantial vehicle mobility and the severe environmental intricacy within V2X network. Thus, a resource allocation scheme to minimize the age of information (AoI), maximize the sum rates, and improve the reliability of payload transmission for RIS-assisted V2X networks is proposed. Moreover, the target problem is converted into a Markov decision process, and an algorithm based on deep reinforcement learning (DRL) termed as stochastic reinforced soft learning network (SRSLNet) is proposed. The proposed DRL model establishes a collaborative optimization framework integrating value and policy functions, which can not only efficiently use the replay buffer data, but also ensure the stability of policy update. Based on the proposed algorithm, the AoI, sum rates of the V2X network, and the probability of successful vehicle-to-vehicle (V2V) payload transmission are analyzed. Simulation results demonstrate that the proposed SRSLNet algorithm outperforms benchmark schemes across various scenarios and parameter settings, yielding a 14.58% reduction in AoI, a 10.34% increase in V2V transmission success probability, and a 46.34% improvement in sum rate.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"387-400"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent low-altitude economy networks demand reliable, low delay, and energy-efficient unmanned aerial vehicle (UAV) collaboration under highly dynamic wireless conditions and stringent resource constraints. This setting induces a multi-objective coordination problem with non-trivial trade-offs. To address it, we propose a large language model (LLM)-guided meta-regulation framework for multi-agent reinforcement learning (MARL). The framework adopts a dual-timescale architecture: MARL agents conduct fast-timescale policy optimization, while a slow-timescale meta-controller adaptively tunes reward weights to steer learning toward desirable trade-offs among reliability, delay, and energy consumption. In addition, we introduce a meta early-exit mechanism to balance training stability, efficiency, and peak performance. Extensive simulations show that the proposed method delivers faster convergence and smoother learning dynamics than fixed-weight MARL baselines, improves task success rate and delay, and remains robust as the UAV swarm scales. Overall, LLM-guided meta-regulation offers an effective and flexible solution for multi-objective coordination in intelligent low-altitude networking scenarios.
{"title":"LLM-Guided Dual-Timescale Meta-Regulated MARL for Intelligent Low-Altitude Economy Networks","authors":"Boquan Sun;Zitong Yu;Heling Zhang;Zetao Cheng;Wenbo Wang;Xing Zhang","doi":"10.23919/JCIN.2026.11737691","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737691","url":null,"abstract":"Intelligent low-altitude economy networks demand reliable, low delay, and energy-efficient unmanned aerial vehicle (UAV) collaboration under highly dynamic wireless conditions and stringent resource constraints. This setting induces a multi-objective coordination problem with non-trivial trade-offs. To address it, we propose a large language model (LLM)-guided meta-regulation framework for multi-agent reinforcement learning (MARL). The framework adopts a dual-timescale architecture: MARL agents conduct fast-timescale policy optimization, while a slow-timescale meta-controller adaptively tunes reward weights to steer learning toward desirable trade-offs among reliability, delay, and energy consumption. In addition, we introduce a meta early-exit mechanism to balance training stability, efficiency, and peak performance. Extensive simulations show that the proposed method delivers faster convergence and smoother learning dynamics than fixed-weight MARL baselines, improves task success rate and delay, and remains robust as the UAV swarm scales. Overall, LLM-guided meta-regulation offers an effective and flexible solution for multi-objective coordination in intelligent low-altitude networking scenarios.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"293-305"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-09-01Epub Date: 2026-10-14DOI: 10.23919/JCIN.2026.11737692
Tong Zhang;Yu Gou;Jun Liu;Yu Wang
Underwater acoustic networks (UANs) are essential for long-term ocean monitoring, offshore exploration, and submerged target sensing, but their resource management is challenged by limited bandwidth, long propagation delay, time-varying noise, multi-path spreading, and shared-channel interference. Existing flat reinforcement learning (RL)-based schemes usually encode link scheduling, transmission mode selection, and power control into a single composite action, resulting in enlarged action spaces and ambiguous credit assignment among heterogeneous resource decisions. This paper proposes H2MARL-UAN, a heterogeneous hierarchical multi-agent reinforcement learning (MARL) framework for adaptive resource management in UANs. H2MARL-UAN decomposes each resource control decision into three physically meaningful layers: Q-mixing (QMIX)-based link scheduling, value decomposition network (VDN)-based transmission mode selection, and independent Q-learning (IQL)-based power control, thereby matching the cooperation mechanism to the coupling strength of each layer. Experiments in a physically consistent UAN simulator show that H2MARL-UAN improves throughput and packet delivery performance under random and bursty traffic while maintaining competitive energy efficiency. The results also reveal the trade-off between service capability and information freshness.
{"title":"Heterogeneous Hierarchical Multi-Agent Reinforcement Learning for Adaptive Resource Management in Underwater Acoustic Networks","authors":"Tong Zhang;Yu Gou;Jun Liu;Yu Wang","doi":"10.23919/JCIN.2026.11737692","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737692","url":null,"abstract":"Underwater acoustic networks (UANs) are essential for long-term ocean monitoring, offshore exploration, and submerged target sensing, but their resource management is challenged by limited bandwidth, long propagation delay, time-varying noise, multi-path spreading, and shared-channel interference. Existing flat reinforcement learning (RL)-based schemes usually encode link scheduling, transmission mode selection, and power control into a single composite action, resulting in enlarged action spaces and ambiguous credit assignment among heterogeneous resource decisions. This paper proposes H2MARL-UAN, a heterogeneous hierarchical multi-agent reinforcement learning (MARL) framework for adaptive resource management in UANs. H2MARL-UAN decomposes each resource control decision into three physically meaningful layers: Q-mixing (QMIX)-based link scheduling, value decomposition network (VDN)-based transmission mode selection, and independent Q-learning (IQL)-based power control, thereby matching the cooperation mechanism to the coupling strength of each layer. Experiments in a physically consistent UAN simulator show that H2MARL-UAN improves throughput and packet delivery performance under random and bursty traffic while maintaining competitive energy efficiency. The results also reveal the trade-off between service capability and information freshness.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"306-319"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a resource-aware sparse federated image semantic communication framework with dual-side U-Net fusion to improve reconstruction quality and reduce federated training overhead in distributed image transmission scenarios. Firstly, lightweight U-Net modules are introduced at both the transmitter and receiver to extract and exploit useful semantic and structural information for feature fusion and reconstruction compensation. An information noise-contrastive estimation (InfoNCE)-based feature-level consistency constraint is further incorporated, extending the optimization from pixel-level reconstruction to feature-level semantic representation preservation. Secondly, to address the communication burden caused by parameter-intensive semantic communication models in federated training, a resource-aware regularization term is designed to guide local optimization toward communication-efficient update distributions while preserving reconstruction-relevant parameter changes. Thirdly, a sparse federated optimization mechanism is developed by combining importance-guided update control, magnitude-aware sparse transmission, dense warm-up, and residual accumulation, thereby reducing both uplink and downlink communication costs. Experimental results show that the proposed framework improves reconstruction quality and feature-level semantic consistency while achieving more efficient federated training.
{"title":"Resource-Aware Sparse Federated Image Semantic Communication with Dual-Side U-Net Fusion","authors":"Xingyu Du;Xiaoming Wang;Rui Jiang;Liu Liu;Youyun Xu","doi":"10.23919/JCIN.2026.11737693","DOIUrl":"https://doi.org/10.23919/JCIN.2026.11737693","url":null,"abstract":"This paper proposes a resource-aware sparse federated image semantic communication framework with dual-side U-Net fusion to improve reconstruction quality and reduce federated training overhead in distributed image transmission scenarios. Firstly, lightweight U-Net modules are introduced at both the transmitter and receiver to extract and exploit useful semantic and structural information for feature fusion and reconstruction compensation. An information noise-contrastive estimation (InfoNCE)-based feature-level consistency constraint is further incorporated, extending the optimization from pixel-level reconstruction to feature-level semantic representation preservation. Secondly, to address the communication burden caused by parameter-intensive semantic communication models in federated training, a resource-aware regularization term is designed to guide local optimization toward communication-efficient update distributions while preserving reconstruction-relevant parameter changes. Thirdly, a sparse federated optimization mechanism is developed by combining importance-guided update control, magnitude-aware sparse transmission, dense warm-up, and residual accumulation, thereby reducing both uplink and downlink communication costs. Experimental results show that the proposed framework improves reconstruction quality and feature-level semantic consistency while achieving more efficient federated training.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"11 3","pages":"320-333"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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