In this research, we investigate the long-term secure content delivery problem in uncrewed aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks. We consider that ISAC-assisted UAVs are allowed to store user-requested contents, provide content delivering service to users, and perform eavesdropper detection. However, the openness of UAV-enabled networks makes the content delivery network more susceptible to security threats. To address the eavesdropper detection, we propose a Cramér-Rao Lower Bound (CRLB) and an extended Kalman Filter (EKF)-based location estimation algorithm. We then examine the secrecy throughput of users and formulate the joint user association, UAV deployment, content caching, communication, and sensing beamforming problem as a long-term secure throughput maximization problem. As the formulated problem is a mixed-integer non-linear programming problem (MINLP) and cannot be solved conveniently, we decompose it into two subproblems, namely, a long-timescale content caching subproblem, and a short-timescale user association, UAV deployment, communication and sensing beamforming subproblem. To solve the subproblems, we transform it into a Markov decision process (MDP) and we propose an attention-based hierarchical deep reinforcement learning (HDRL) with an action mask and design a double deep Q-network (DDQN) algorithm to obtain the long-timescale and an attention-based DDQN with an action mask for short-timescale strategies. Specifically, we first obtain a long-timescale strategy for content caching. Given the long-timescale strategy, we then obtain the short-timescale user association, UAV deployment, communication, and sensing beamforming strategy. Simulation results demonstrate the effectiveness of the proposed algorithms.
{"title":"Attention-Based Hierarchical-DRL With Mask for Multi-Timescale Caching, Association, and Secure Content Delivery in UAV-Enabled ISAC Networks","authors":"Gezahegn Abdissa Bayessa;Rong Chai;Chengchao Liang;Qinyuan Wang;Jun Li;Qianbin Chen","doi":"10.1109/TGCN.2026.3651458","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3651458","url":null,"abstract":"In this research, we investigate the long-term secure content delivery problem in uncrewed aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks. We consider that ISAC-assisted UAVs are allowed to store user-requested contents, provide content delivering service to users, and perform eavesdropper detection. However, the openness of UAV-enabled networks makes the content delivery network more susceptible to security threats. To address the eavesdropper detection, we propose a Cramér-Rao Lower Bound (CRLB) and an extended Kalman Filter (EKF)-based location estimation algorithm. We then examine the secrecy throughput of users and formulate the joint user association, UAV deployment, content caching, communication, and sensing beamforming problem as a long-term secure throughput maximization problem. As the formulated problem is a mixed-integer non-linear programming problem (MINLP) and cannot be solved conveniently, we decompose it into two subproblems, namely, a long-timescale content caching subproblem, and a short-timescale user association, UAV deployment, communication and sensing beamforming subproblem. To solve the subproblems, we transform it into a Markov decision process (MDP) and we propose an attention-based hierarchical deep reinforcement learning (HDRL) with an action mask and design a double deep Q-network (DDQN) algorithm to obtain the long-timescale and an attention-based DDQN with an action mask for short-timescale strategies. Specifically, we first obtain a long-timescale strategy for content caching. Given the long-timescale strategy, we then obtain the short-timescale user association, UAV deployment, communication, and sensing beamforming strategy. Simulation results demonstrate the effectiveness of the proposed algorithms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1779-1794"},"PeriodicalIF":6.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 10.1109/TGCN.2026.3651522
Baoxin Su;Shufeng Li;Junwei Zhang;Libiao Jin
Multimodal semantic communication enables efficient and accurate cross-modal information transmission, yet precisely integrating semantic information from different modalities in dynamic communication environments remains challenging. To address this, this paper proposes a non-binary polar coding and decoding scheme for multimodal semantic feature fusion. This approach limits channel coding during the training phase to reduce overhead, while leveraging the high coding gain of non-binary polar codes and a deep neural network-based fast decoder to decrease complexity and power consumption. In the semantic module, we design a feature fusion network based on attention mechanisms and residual modules to more accurately capture cross-modal semantic features. For transmission, an adaptive non-binary polar (ANP) encoding strategy is introduced, while a neural network-driven non-binary polar (DNP) decoder is employed in the decoding part. Simulation results demonstrate that the proposed system significantly improves transmission efficiency and reliability while reducing bit error rate, and effectively lowers transmission power consumption.
{"title":"A Low-Power Semantic Communication Framework for Multimodal Fusion via Non-Binary Polar Codes","authors":"Baoxin Su;Shufeng Li;Junwei Zhang;Libiao Jin","doi":"10.1109/TGCN.2026.3651522","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3651522","url":null,"abstract":"Multimodal semantic communication enables efficient and accurate cross-modal information transmission, yet precisely integrating semantic information from different modalities in dynamic communication environments remains challenging. To address this, this paper proposes a non-binary polar coding and decoding scheme for multimodal semantic feature fusion. This approach limits channel coding during the training phase to reduce overhead, while leveraging the high coding gain of non-binary polar codes and a deep neural network-based fast decoder to decrease complexity and power consumption. In the semantic module, we design a feature fusion network based on attention mechanisms and residual modules to more accurately capture cross-modal semantic features. For transmission, an adaptive non-binary polar (ANP) encoding strategy is introduced, while a neural network-driven non-binary polar (DNP) decoder is employed in the decoding part. Simulation results demonstrate that the proposed system significantly improves transmission efficiency and reliability while reducing bit error rate, and effectively lowers transmission power consumption.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1867-1878"},"PeriodicalIF":6.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-05DOI: 10.1109/TGCN.2026.3650899
Puxi Yu;Dingyou Ma;Qixun Zhang;Zhiyong Feng
The growing demand for robust, green, and wide-area sensing in applications like the low-altitude economy necessitates scalable, low-cost, and energy-efficient network architectures. To meet this need, this paper proposes a one-bit bi-static integrated sensing and communication (ISAC) system architecture designed to cover sensing blind spots in a cost-effective and energy-efficient manner. The system employs micro receiving nodes equipped with one-bit analog-to-digital converters to minimize hardware cost and power consumption. However, this design introduces the dual challenges: severe signal distortion from one-bit quantization and parameter ambiguity from clock asynchronism between the transmitter and receiver. These issues render conventional synchronization and parameter estimation methods ineffective. This paper presents a novel algorithm that directly tackles these challenges by processing the one-bit temporal data. Our method first estimates the direction of arrival (DOA) using the one-bit MUSIC algorithm, and leverages it to coherently accumulate signal energy in the spatial domain for each preamble. Aided by the DOA, a compressive sensing-based approach then estimates the combined delay and timing offset, followed by the extraction of the Doppler shift mixed with carrier frequency offset. By systematically processing these estimates, our algorithm obtains absolute target delays and relative Doppler shifts without requiring a dedicated reference path. The Cramér-Rao lower bound (CRLB) is derived, and simulations validate that the proposed method achieves high-accuracy parameter estimation, i.e., the optimization result reaches the CRLB. In addition, simulation under practical settings illustrates that the sensing range is extended from 400 m to 900 m, increased by 125%, demonstrating the feasibility of low-cost, one-bit bi-static ISAC systems.
{"title":"Parameter Estimation for Energy-Efficient 1-bit Bi-Static ISAC Network","authors":"Puxi Yu;Dingyou Ma;Qixun Zhang;Zhiyong Feng","doi":"10.1109/TGCN.2026.3650899","DOIUrl":"https://doi.org/10.1109/TGCN.2026.3650899","url":null,"abstract":"The growing demand for robust, green, and wide-area sensing in applications like the low-altitude economy necessitates scalable, low-cost, and energy-efficient network architectures. To meet this need, this paper proposes a one-bit bi-static integrated sensing and communication (ISAC) system architecture designed to cover sensing blind spots in a cost-effective and energy-efficient manner. The system employs micro receiving nodes equipped with one-bit analog-to-digital converters to minimize hardware cost and power consumption. However, this design introduces the dual challenges: severe signal distortion from one-bit quantization and parameter ambiguity from clock asynchronism between the transmitter and receiver. These issues render conventional synchronization and parameter estimation methods ineffective. This paper presents a novel algorithm that directly tackles these challenges by processing the one-bit temporal data. Our method first estimates the direction of arrival (DOA) using the one-bit MUSIC algorithm, and leverages it to coherently accumulate signal energy in the spatial domain for each preamble. Aided by the DOA, a compressive sensing-based approach then estimates the combined delay and timing offset, followed by the extraction of the Doppler shift mixed with carrier frequency offset. By systematically processing these estimates, our algorithm obtains absolute target delays and relative Doppler shifts without requiring a dedicated reference path. The Cramér-Rao lower bound (CRLB) is derived, and simulations validate that the proposed method achieves high-accuracy parameter estimation, i.e., the optimization result reaches the CRLB. In addition, simulation under practical settings illustrates that the sensing range is extended from 400 m to 900 m, increased by 125%, demonstrating the feasibility of low-cost, one-bit bi-static ISAC systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1705-1719"},"PeriodicalIF":6.7,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-29DOI: 10.1109/TGCN.2025.3648868
Yinuo Hao;Liang Jin;Honghao Zheng;Jiale Bai;Xiaoming Xu;Lizhe Liu
This paper investigates the security degrees of freedom (SDoF) of a reconfigurable intelligent surface (RIS)-assisted multiple-input-multiple-output (MIMO) wiretap channel, where a multi-antenna eavesdropper is present and the RIS antenna array is integrated into the receiver as a multi-antenna system. A RIS-based MIMO spatial-temporal rapid reconfigurable secure transmission strategy is proposed to fully exploit the spatial degrees of freedom (DoF) by dynamically adjusting the radiation response of each metasurface element within a single symbol period, thereby expanding the equivalent receiver subspace dimension. We analyze the theoretical SDoF of this system, deriving an explicit relationship between the SDoF and rapid reconfiguration frequency (number of radiation response adjustments per symbol period) and the number of RIS antennas. Our analysis reveals that the proposed strategy can significantly improve the SDoF when the number of receive antennas is fewer than that of the transmit or eavesdropper antennas. Furthermore, we optimize the rapid reconfigurable radiation responses to maximize the effective SDoF (ESDoF), which provides a more precise characterization of the system’s secrecy capacity. A joint design of the transmit precoding (TPC) matrix and radiation responses is performed to maximize secrecy capacity, considering varying levels of eavesdropper’s channel information availability. Simulation results validate the theoretical SDoF enhancement and demonstrate the impact of metasurface element density and reconfiguration frequency on the ESDoF.
{"title":"RIS-Assisted MIMO Wiretap Channel: Theoretical SDoF Bounds and Practical Optimization","authors":"Yinuo Hao;Liang Jin;Honghao Zheng;Jiale Bai;Xiaoming Xu;Lizhe Liu","doi":"10.1109/TGCN.2025.3648868","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3648868","url":null,"abstract":"This paper investigates the security degrees of freedom (SDoF) of a reconfigurable intelligent surface (RIS)-assisted multiple-input-multiple-output (MIMO) wiretap channel, where a multi-antenna eavesdropper is present and the RIS antenna array is integrated into the receiver as a multi-antenna system. A RIS-based MIMO spatial-temporal rapid reconfigurable secure transmission strategy is proposed to fully exploit the spatial degrees of freedom (DoF) by dynamically adjusting the radiation response of each metasurface element within a single symbol period, thereby expanding the equivalent receiver subspace dimension. We analyze the theoretical SDoF of this system, deriving an explicit relationship between the SDoF and rapid reconfiguration frequency (number of radiation response adjustments per symbol period) and the number of RIS antennas. Our analysis reveals that the proposed strategy can significantly improve the SDoF when the number of receive antennas is fewer than that of the transmit or eavesdropper antennas. Furthermore, we optimize the rapid reconfigurable radiation responses to maximize the effective SDoF (ESDoF), which provides a more precise characterization of the system’s secrecy capacity. A joint design of the transmit precoding (TPC) matrix and radiation responses is performed to maximize secrecy capacity, considering varying levels of eavesdropper’s channel information availability. Simulation results validate the theoretical SDoF enhancement and demonstrate the impact of metasurface element density and reconfiguration frequency on the ESDoF.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1720-1733"},"PeriodicalIF":6.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-29DOI: 10.1109/TGCN.2025.3649278
Changtong Liu;Xin Xin;Yueyue Dai;Du Xu
Unmanned aerial vehicles (UAVs) are a promising solution for emergency communications due to their rapid deployment and capability of flexible network formation. This flexibility enables UAVs to dynamically adjust their positions and link configurations to form stable multi-hop networks, thereby establishing resilient data links from isolated disaster areas to remote base stations. However, sustaining such a persistent UAV swarm network is challenging due to limited onboard energy, the scarcity of available UAVs, and complex coordination in emergencies. This paper aims to minimize the number of UAVs required while ensuring continuous multi-hop connectivity for all target areas under energy constraints. We propose a UAV swarm planning strategy based on non-fixed relay points (USP-NFRP), jointly optimizing UAV-to-target associations, trajectories, and the backhaul topology connecting target areas to the base station. First, we propose a periodic rotation path (PRP) method to efficiently manage UAV replacements and assignments at disaster sites. We also provide a mathematical proof of its effectiveness. Second, we propose a dynamic tree backhaul link (DTBL) method that ensures persistent and seamless network connectivity. It is achieved by adjusting the positional roles of relay nodes (fixed or non-fixed) during path planning and dynamically configuring tree-based backhaul links during UAV missions. Finally, we develop a max-min ant system-based path planning algorithm (MMAS-PP) to optimize UAV trajectories and the sequence of task executions. Simulation results show that the proposed strategy reduces the number of UAVs by up to 30.9% compared with baselines.
{"title":"Cost Optimization of UAV Swarm Network for Persistent Emergency Communication","authors":"Changtong Liu;Xin Xin;Yueyue Dai;Du Xu","doi":"10.1109/TGCN.2025.3649278","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3649278","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are a promising solution for emergency communications due to their rapid deployment and capability of flexible network formation. This flexibility enables UAVs to dynamically adjust their positions and link configurations to form stable multi-hop networks, thereby establishing resilient data links from isolated disaster areas to remote base stations. However, sustaining such a persistent UAV swarm network is challenging due to limited onboard energy, the scarcity of available UAVs, and complex coordination in emergencies. This paper aims to minimize the number of UAVs required while ensuring continuous multi-hop connectivity for all target areas under energy constraints. We propose a UAV swarm planning strategy based on non-fixed relay points (USP-NFRP), jointly optimizing UAV-to-target associations, trajectories, and the backhaul topology connecting target areas to the base station. First, we propose a periodic rotation path (PRP) method to efficiently manage UAV replacements and assignments at disaster sites. We also provide a mathematical proof of its effectiveness. Second, we propose a dynamic tree backhaul link (DTBL) method that ensures persistent and seamless network connectivity. It is achieved by adjusting the positional roles of relay nodes (fixed or non-fixed) during path planning and dynamically configuring tree-based backhaul links during UAV missions. Finally, we develop a max-min ant system-based path planning algorithm (MMAS-PP) to optimize UAV trajectories and the sequence of task executions. Simulation results show that the proposed strategy reduces the number of UAVs by up to 30.9% compared with baselines.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1734-1748"},"PeriodicalIF":6.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-24DOI: 10.1109/TGCN.2025.3647851
Pravindra Kumar;Navneet Sharma;Harsh Choudhary;Hem Dutt Joshi;Atul Kumar;Kapal Dev
Quantum-optical communication systems offer a high data rate with unmatched security aspects. This paper presents a novel approach to implement the superdense coding technique using Chua’s oscillator in a quantum-optical channel environment. To enhance the encoding process, the chaotic behaviour of Chua’s oscillator is utilized. The high data transmission rate is maintained while the resistance against the eavesdropping attempts has been increased due to the principle of quantum mechanics. In comparison to the classical channel, the double speed data rate in the quantum-optical channel will be achieved due to the quantum entanglement-enabled model. In this, two informative classical bits are sent with a single photon. The realization of the proposed model is performed on Qiskit simulator. The noise analysis of the proposed quantum circuit is discussed in detail, along with fidelity and decoherence measures. The security aspect and computational & hardware complexity are also analyzed. This work contributes to the advancement of secure quantum communications to help in the design of next-generation cryptographic systems.
{"title":"Secure Quantum-Optical Communication Using Chua’s Oscillator-Based Superdense Coding","authors":"Pravindra Kumar;Navneet Sharma;Harsh Choudhary;Hem Dutt Joshi;Atul Kumar;Kapal Dev","doi":"10.1109/TGCN.2025.3647851","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3647851","url":null,"abstract":"Quantum-optical communication systems offer a high data rate with unmatched security aspects. This paper presents a novel approach to implement the superdense coding technique using Chua’s oscillator in a quantum-optical channel environment. To enhance the encoding process, the chaotic behaviour of Chua’s oscillator is utilized. The high data transmission rate is maintained while the resistance against the eavesdropping attempts has been increased due to the principle of quantum mechanics. In comparison to the classical channel, the double speed data rate in the quantum-optical channel will be achieved due to the quantum entanglement-enabled model. In this, two informative classical bits are sent with a single photon. The realization of the proposed model is performed on Qiskit simulator. The noise analysis of the proposed quantum circuit is discussed in detail, along with fidelity and decoherence measures. The security aspect and computational & hardware complexity are also analyzed. This work contributes to the advancement of secure quantum communications to help in the design of next-generation cryptographic systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1694-1704"},"PeriodicalIF":6.7,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-23DOI: 10.1109/TGCN.2025.3647430
Wen-Fu Wang;Jang-Ping Sheu;Nguyen Van Cuong
Semantic communication (SC) has emerged as a novel communication paradigm for enhancing transmission efficiency in wireless networks by prioritizing data semantics over raw bits. Meanwhile, mobile delay-sensitive users demand strict low-latency transmissions for critical tasks such as emergency response. Supporting both user types under limited resources is challenging. This paper proposes a joint resource allocation framework that integrates time-slot allocation and relay assignment to serve semantic- and delay-sensitive users simultaneously. The formulated problem is hard to solve optimally. We develop a Joint Matching, Time Slot, and Relay Allocation Algorithm (JMTRA) that combines stable matching, knapsack-based scheduling, and linear-programming-based relay selection to find a suboptimal solution. Simulation results demonstrate that our approach improves semantic performance while meeting delay constraints.
{"title":"Joint Scheduling and Relay Allocation for Semantic and Delay-Sensitive Wireless Communications","authors":"Wen-Fu Wang;Jang-Ping Sheu;Nguyen Van Cuong","doi":"10.1109/TGCN.2025.3647430","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3647430","url":null,"abstract":"Semantic communication (SC) has emerged as a novel communication paradigm for enhancing transmission efficiency in wireless networks by prioritizing data semantics over raw bits. Meanwhile, mobile delay-sensitive users demand strict low-latency transmissions for critical tasks such as emergency response. Supporting both user types under limited resources is challenging. This paper proposes a joint resource allocation framework that integrates time-slot allocation and relay assignment to serve semantic- and delay-sensitive users simultaneously. The formulated problem is hard to solve optimally. We develop a Joint Matching, Time Slot, and Relay Allocation Algorithm (JMTRA) that combines stable matching, knapsack-based scheduling, and linear-programming-based relay selection to find a suboptimal solution. Simulation results demonstrate that our approach improves semantic performance while meeting delay constraints.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1666-1678"},"PeriodicalIF":6.7,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-22DOI: 10.1109/TGCN.2025.3646959
Ruiyan Du;Yadong Yue;Huifang Wang;Luyao Suo;Fulai Liu
Aiming at the problem that the bit error rate (BER) performance degrades due to the multi-dimensional complex data for generalized space-frequency with IM (GSF-IM) signal detection, a detection algorithm is proposed based on ghost bottleneck convolutional neural network (GBCNN), referred to as GBCNN-GSF-IM. The algorithm reduces model complexity while ensuring detection accuracy, providing a effcient solution for signal detection in GSF-IM systems. Firstly, the GSF-IM signal is equalized to reduce the inter-symbol interference, and then adapted to the network input requirements through data conversion. Secondly, a GBCNN signal detection model is constructed containing a space subnetwork and a frequency subnetwork. Specifically, to improve BER performance, the frequency subnetwork employs ghost convolution, which can effectively enhance the capability of extracting frequency domain information and significantly strengthen the representation of frequencyn domain features. The space subnetwork extracts the space features of the GSF-IM signal using the convolutional neural network. Finally, complete bit information is obtained through the fusion of spatial and frequency domain information to achieve GSF-IM signal detection. Simulation results show that the proposed algorithm can significantly improve the BER performance compared with the related algorithms, and the BER is reduced by at least 14.67% when the SNR is greater than or equal to 15 dB.
{"title":"GBCNN-Based Detection Algorithm for Generalized Space-Frequency With Index Modulation","authors":"Ruiyan Du;Yadong Yue;Huifang Wang;Luyao Suo;Fulai Liu","doi":"10.1109/TGCN.2025.3646959","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3646959","url":null,"abstract":"Aiming at the problem that the bit error rate (BER) performance degrades due to the multi-dimensional complex data for generalized space-frequency with IM (GSF-IM) signal detection, a detection algorithm is proposed based on ghost bottleneck convolutional neural network (GBCNN), referred to as GBCNN-GSF-IM. The algorithm reduces model complexity while ensuring detection accuracy, providing a effcient solution for signal detection in GSF-IM systems. Firstly, the GSF-IM signal is equalized to reduce the inter-symbol interference, and then adapted to the network input requirements through data conversion. Secondly, a GBCNN signal detection model is constructed containing a space subnetwork and a frequency subnetwork. Specifically, to improve BER performance, the frequency subnetwork employs ghost convolution, which can effectively enhance the capability of extracting frequency domain information and significantly strengthen the representation of frequencyn domain features. The space subnetwork extracts the space features of the GSF-IM signal using the convolutional neural network. Finally, complete bit information is obtained through the fusion of spatial and frequency domain information to achieve GSF-IM signal detection. Simulation results show that the proposed algorithm can significantly improve the BER performance compared with the related algorithms, and the BER is reduced by at least 14.67% when the SNR is greater than or equal to 15 dB.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1633-1641"},"PeriodicalIF":6.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-22DOI: 10.1109/TGCN.2025.3646873
Yihui Wang;Shanzhi Chen;Li Chen;Shaoli Kang
The rapid development of 6G and the growing demands for customized services drive the User-Centric Access Network (UCAN) to address the limitations of traditional cellular architecture. Leveraging advanced architecture and user-centric cell-free design, UCAN effectively improves network coverage and spectral efficiency (SE). However, its practical deployment in large-scale cell-free scenarios faces challenges such as scalability issues, excessive energy consumption, and high processing complexity. Existing clustering methods inadequately balance user SE and system energy efficiency (EE), while overlooking some practical factors including multi-antenna access points (APs) deployment, cluster size constraints, and imperfect coordination. This paper proposes a non-overlapping clustering framework for large-scale UCAN equipped with multi-antenna APs, aiming to reduce network power consumption and processing complexity while guaranteeing individual SE requirements. The closed-form expression for the achievable user SE is derived under practical conditions such as finite APs and imperfect channel state information. To minimize the network power consumption and optimize on-demand AP activation, a clustering problem is formulated under per-user SE thresholds and cluster size constraints. An enhanced genetic clustering algorithm (GAC) is introduced to efficiently solve this NP-hard problem. Numerical evaluations show that the proposed GAC outperforms existing methods in balancing SE, EE, and computational complexity, while demonstrating robustness against pilot contamination, cluster number variations, and power allocation strategies. The proposed framework addresses the challenges of practical implementation and provides a scalable solution for future user-centric networks.
{"title":"Energy-Efficient and Scalable Clustering for User-Centric Access Network (UCAN) of 6G","authors":"Yihui Wang;Shanzhi Chen;Li Chen;Shaoli Kang","doi":"10.1109/TGCN.2025.3646873","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3646873","url":null,"abstract":"The rapid development of 6G and the growing demands for customized services drive the User-Centric Access Network (UCAN) to address the limitations of traditional cellular architecture. Leveraging advanced architecture and user-centric cell-free design, UCAN effectively improves network coverage and spectral efficiency (SE). However, its practical deployment in large-scale cell-free scenarios faces challenges such as scalability issues, excessive energy consumption, and high processing complexity. Existing clustering methods inadequately balance user SE and system energy efficiency (EE), while overlooking some practical factors including multi-antenna access points (APs) deployment, cluster size constraints, and imperfect coordination. This paper proposes a non-overlapping clustering framework for large-scale UCAN equipped with multi-antenna APs, aiming to reduce network power consumption and processing complexity while guaranteeing individual SE requirements. The closed-form expression for the achievable user SE is derived under practical conditions such as finite APs and imperfect channel state information. To minimize the network power consumption and optimize on-demand AP activation, a clustering problem is formulated under per-user SE thresholds and cluster size constraints. An enhanced genetic clustering algorithm (GAC) is introduced to efficiently solve this NP-hard problem. Numerical evaluations show that the proposed GAC outperforms existing methods in balancing SE, EE, and computational complexity, while demonstrating robustness against pilot contamination, cluster number variations, and power allocation strategies. The proposed framework addresses the challenges of practical implementation and provides a scalable solution for future user-centric networks.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1679-1693"},"PeriodicalIF":6.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-12-15DOI: 10.1109/TGCN.2025.3644128
Kaiqiang Lin;Yijie Mao;Onel Luis Alcaraz López;Mohamed-Slim Alouini
Wireless-powered underground communication networks (WPUCNs), which allow underground devices (UDs) to harvest energy from wireless signals for battery-free communication, offer a promising solution for sustainable underground monitoring. However, the severe wireless signal attenuation in challenging underground environments and the costly acquisition of channel state information (CSI) make large-scale WPUCNs economically infeasible in practice. To address this challenge, we introduce flexible uncrewed aerial vehicles (UAVs) into WPUCNs, leading to UAV-enabled WPUCN systems. In this system, a UAV is first charged by a terrestrial hybrid access point (HAP), then flies to the monitoring area to wirelessly charge UDs. Afterwards, the UAV collects data from the UDs and finally returns to the HAP for data offloading. Based on the proposed UAV-enabled WPUCN system, we first propose its energy consumption model and a hybrid wireless energy transfer (WET) approach (i.e., UDs can harvest energy from both the HAP and the UAV) relying on full-CSI and CSI-free multi-antenna beamforming. Then, we formulate and address a time allocation problem to minimize the energy consumption of UAV, while ensuring that the throughput requirements of all UDs are met and all sensor data is offloaded. Through simulations of a realistic farming scenario, we demonstrate that the proposed hybrid WET approach outperforms other WET approaches, with performance gains influenced by the number of antennas, communication distance, number of UDs, and underground conditions. Additionally, under the optimized time allocation, we found that the proposed hybrid WET approach based on a CSI-free multi-antenna scheme achieves the lowest UAV’s energy consumption among all WET mechanisms, thereby enabling sustainable underground monitoring in WPUCNs.
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