Pub Date : 2025-10-01DOI: 10.1016/j.dcan.2024.11.009
Wei Liu , Muhammad Bilal , Yuzhe Shi , Xiaolong Xu
Increasing reliance on large-scale AI models has led to rising demand for intelligent services. The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time, and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption. However, finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services. To deal with this, we propose a distributed service caching scheme integrating deep reinforcement learning (DRL) with mobility prediction, which we refer to as DSDM. Specifically, we employ the D3QN (Deep Double Dueling Q-Network) framework to integrate Long Short-Term Memory (LSTM) predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training. Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.
{"title":"Distributed service caching with deep reinforcement learning for sustainable edge computing in large-scale AI","authors":"Wei Liu , Muhammad Bilal , Yuzhe Shi , Xiaolong Xu","doi":"10.1016/j.dcan.2024.11.009","DOIUrl":"10.1016/j.dcan.2024.11.009","url":null,"abstract":"<div><div>Increasing reliance on large-scale AI models has led to rising demand for intelligent services. The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time, and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption. However, finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services. To deal with this, we propose a distributed service caching scheme integrating deep reinforcement learning (DRL) with mobility prediction, which we refer to as DSDM. Specifically, we employ the D3QN (Deep Double Dueling Q-Network) framework to integrate Long Short-Term Memory (LSTM) predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training. Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1447-1456"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529496","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.04.006
Jieun Lee , JooSung Kim , Seong Ki Yoo , Tarik Taleb , JaeSeung Song
Edge computing is swiftly gaining traction and is being standardised by the European Telecommunications Standards Institute (ETSI) as Multi-access Edge Computing (MEC). Simultaneously, oneM2M has been actively developing standards for dynamic data management and IoT services at the edge, particularly for applications that require real-time support and security. Integrating MEC and oneM2M offers a unique opportunity to maximize their individual strengths. Therefore, this article proposes a framework that integrates MEC and oneM2M standard platforms for IoT applications, demonstrating how the synergy of these architectures can leverage the geographically distributed computing resources at base stations, enabling efficient deployment and added value for time-sensitive IoT applications. In addition, this study offers a concept of potential interworking models between oneM2M and the MEC architectures. The adoption of these standard architectures can enable various IoT edge services, such as smart city mobility and real-time analytics functions, by leveraging the oneM2M common service layer instantiated on the MEC host.
{"title":"Standardised interworking and deployment of IoT and edge computing platforms","authors":"Jieun Lee , JooSung Kim , Seong Ki Yoo , Tarik Taleb , JaeSeung Song","doi":"10.1016/j.dcan.2025.04.006","DOIUrl":"10.1016/j.dcan.2025.04.006","url":null,"abstract":"<div><div>Edge computing is swiftly gaining traction and is being standardised by the European Telecommunications Standards Institute (ETSI) as Multi-access Edge Computing (MEC). Simultaneously, oneM2M has been actively developing standards for dynamic data management and IoT services at the edge, particularly for applications that require real-time support and security. Integrating MEC and oneM2M offers a unique opportunity to maximize their individual strengths. Therefore, this article proposes a framework that integrates MEC and oneM2M standard platforms for IoT applications, demonstrating how the synergy of these architectures can leverage the geographically distributed computing resources at base stations, enabling efficient deployment and added value for time-sensitive IoT applications. In addition, this study offers a concept of potential interworking models between oneM2M and the MEC architectures. The adoption of these standard architectures can enable various IoT edge services, such as smart city mobility and real-time analytics functions, by leveraging the oneM2M common service layer instantiated on the MEC host.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1578-1587"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529001","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 : 2025-10-01DOI: 10.1016/j.dcan.2024.10.007
Xiaoke Li, Zufan Zhang
Speech Emotion Recognition (SER) has received widespread attention as a crucial way for understanding human emotional states. However, the impact of irrelevant information on speech signals and data sparsity limit the development of SER system. To address these issues, this paper proposes a framework that incorporates the Attentive Mask Residual Network (AM-ResNet) and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively, together with a cross-attention module to interact and fuse these two features. The AM-ResNet branch mainly consists of maximum amplitude difference detection, mask residual block, and an attention mechanism. Among them, the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network, respectively, to reduce the impact of silent frames, and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech. In the Wav2vec 2.0 branch, this model is introduced as a feature extractor to obtain general speech features (Wav2vec 2.0 features) through pre-training with a large amount of unlabeled speech data, which can assist the SER task and cope with data sparsity problems. In the cross-attention module, AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features, which are used to predict the final emotion. Furthermore, multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations. Finally, experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.
{"title":"Cross-feature fusion speech emotion recognition based on attention mask residual network and Wav2vec 2.0","authors":"Xiaoke Li, Zufan Zhang","doi":"10.1016/j.dcan.2024.10.007","DOIUrl":"10.1016/j.dcan.2024.10.007","url":null,"abstract":"<div><div>Speech Emotion Recognition (SER) has received widespread attention as a crucial way for understanding human emotional states. However, the impact of irrelevant information on speech signals and data sparsity limit the development of SER system. To address these issues, this paper proposes a framework that incorporates the Attentive Mask Residual Network (AM-ResNet) and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively, together with a cross-attention module to interact and fuse these two features. The AM-ResNet branch mainly consists of maximum amplitude difference detection, mask residual block, and an attention mechanism. Among them, the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network, respectively, to reduce the impact of silent frames, and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech. In the Wav2vec 2.0 branch, this model is introduced as a feature extractor to obtain general speech features (Wav2vec 2.0 features) through pre-training with a large amount of unlabeled speech data, which can assist the SER task and cope with data sparsity problems. In the cross-attention module, AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features, which are used to predict the final emotion. Furthermore, multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations. Finally, experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1567-1577"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529002","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.06.008
Umar Ghafoor, Adil Masood Siddiqui
The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation. Sixth-Generation (6G) networks, utilizing Cognitive Radio (CR) technology within CR Network (CRN), can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment (PME) is inactive or served by a Primary Base Station (PrBS). Secondary Mobile Equipment (SME) accesses this spectrum through a Secondary Base Station (SrBS) using opportunistic access, i.e., spectrum sensing. Hybrid Multiple Access (HMA), combining Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), can enhance Energy Efficiency (EE). Additionally, SME Clustering (SMEC) reduces inter-cluster interference, enhancing EE further. Despite these advancements, the integration of CR technology, HMA, and SMEC in CRN for better bandwidth utilization and EE remains unexplored. This paper introduces a new CR-assisted SMEC-based Downlink HMA (CR-SMEC-DHMA) method for 6G CRN, aimed at jointly optimizing SME admission, SME association, sum rate, and EE subject to imperfect sensing, collision, and Quality of Service (QoS). A novel optimization problem, formulated as a non-linear fractional programming problem, is solved using the Charnes-Cooper Transformation (CCT) to convert into a concave optimization problem, and an ϵ-optimal Outer Approximation Algorithm (OAA) is employed to solve the concave optimization problem. Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA, surpassing the performance of current OMA-enabled CRN, NOMA-enabled CRN, SMEC-OMA enabled CRN, and SMEC-NOMA enabled CRN methods, with ϵ-optimal results obtained at , while satisfying Performance Measures (PMs) including SME admission in SMEC, SME association with SrBS, SME-channel opportunistic allocation through spectrum sensing, sum rate and overall EE within the 6G CRN.
{"title":"Maximizing energy efficiency in 6G cognitive radio network","authors":"Umar Ghafoor, Adil Masood Siddiqui","doi":"10.1016/j.dcan.2025.06.008","DOIUrl":"10.1016/j.dcan.2025.06.008","url":null,"abstract":"<div><div>The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation. Sixth-Generation (6G) networks, utilizing Cognitive Radio (CR) technology within CR Network (CRN), can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment (PME) is inactive or served by a Primary Base Station (PrBS). Secondary Mobile Equipment (SME) accesses this spectrum through a Secondary Base Station (SrBS) using opportunistic access, i.e., spectrum sensing. Hybrid Multiple Access (HMA), combining Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), can enhance Energy Efficiency (EE). Additionally, SME Clustering (SMEC) reduces inter-cluster interference, enhancing EE further. Despite these advancements, the integration of CR technology, HMA, and SMEC in CRN for better bandwidth utilization and EE remains unexplored. This paper introduces a new CR-assisted SMEC-based Downlink HMA (CR-SMEC-DHMA) method for 6G CRN, aimed at jointly optimizing SME admission, SME association, sum rate, and EE subject to imperfect sensing, collision, and Quality of Service (QoS). A novel optimization problem, formulated as a non-linear fractional programming problem, is solved using the Charnes-Cooper Transformation (CCT) to convert into a concave optimization problem, and an <em>ϵ</em>-optimal Outer Approximation Algorithm (OAA) is employed to solve the concave optimization problem. Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA, surpassing the performance of current OMA-enabled CRN, NOMA-enabled CRN, SMEC-OMA enabled CRN, and SMEC-NOMA enabled CRN methods, with <em>ϵ</em>-optimal results obtained at <span><math><mi>ϵ</mi><mo>=</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></math></span>, while satisfying Performance Measures (PMs) including SME admission in SMEC, SME association with SrBS, SME-channel opportunistic allocation through spectrum sensing, sum rate and overall EE within the 6G CRN.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1356-1369"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529394","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.05.002
Yuanzhi He , Zhiqiang Li , Zheng Dou
As Satellite Frequency and Orbit (SFO) constitute scarce natural resources, constructing a Satellite Frequency and Orbit Knowledge Graph (SFO-KG) becomes crucial for optimizing their utilization. In the process of building the SFO-KG from Chinese unstructured data, extracting Chinese entity relations is the fundamental step. Although Relation Extraction (RE) methods in the English field have been extensively studied and developed earlier than their Chinese counterparts, their direct application to Chinese texts faces significant challenges due to linguistic distinctions such as unique grammar, pictographic characters, and prevalent polysemy. The absence of comprehensive reviews on Chinese RE research progress necessitates a systematic investigation. A thorough review of Chinese RE has been conducted from four methodological approaches: pipeline RE, joint entity-relation extraction, open domain RE, and multimodal RE techniques. In addition, we further analyze the essential research infrastructure, including specialized datasets, evaluation benchmarks, and competitions within Chinese RE research. Finally, the current research challenges and development trends in the field of Chinese RE were summarized and analyzed from the perspectives of ecological construction methods for datasets, open domain RE, N-ary RE, and RE based on large language models. This comprehensive review aims to facilitate SFO-KG construction and its practical applications in SFO resource management.
{"title":"Chinese relation extraction for constructing satellite frequency and orbit knowledge graph: A survey","authors":"Yuanzhi He , Zhiqiang Li , Zheng Dou","doi":"10.1016/j.dcan.2025.05.002","DOIUrl":"10.1016/j.dcan.2025.05.002","url":null,"abstract":"<div><div>As Satellite Frequency and Orbit (SFO) constitute scarce natural resources, constructing a Satellite Frequency and Orbit Knowledge Graph (SFO-KG) becomes crucial for optimizing their utilization. In the process of building the SFO-KG from Chinese unstructured data, extracting Chinese entity relations is the fundamental step. Although Relation Extraction (RE) methods in the English field have been extensively studied and developed earlier than their Chinese counterparts, their direct application to Chinese texts faces significant challenges due to linguistic distinctions such as unique grammar, pictographic characters, and prevalent polysemy. The absence of comprehensive reviews on Chinese RE research progress necessitates a systematic investigation. A thorough review of Chinese RE has been conducted from four methodological approaches: pipeline RE, joint entity-relation extraction, open domain RE, and multimodal RE techniques. In addition, we further analyze the essential research infrastructure, including specialized datasets, evaluation benchmarks, and competitions within Chinese RE research. Finally, the current research challenges and development trends in the field of Chinese RE were summarized and analyzed from the perspectives of ecological construction methods for datasets, open domain RE, N-ary RE, and RE based on large language models. This comprehensive review aims to facilitate SFO-KG construction and its practical applications in SFO resource management.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1305-1317"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529501","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.06.006
Yuxiang Zhang , Jianhua Zhang , Jiwei Zhang , Yuanpeng Pei , Yameng Liu , Lei Tian , Tao Jiang , Guangyi Liu
Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an Extended-GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model's effectiveness, measurements of target and background channels are conducted across a wide range of indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, unmanned aerial vehicles, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.
{"title":"Research and experimental validation for 3GPP ISAC channel modeling standardization","authors":"Yuxiang Zhang , Jianhua Zhang , Jiwei Zhang , Yuanpeng Pei , Yameng Liu , Lei Tian , Tao Jiang , Guangyi Liu","doi":"10.1016/j.dcan.2025.06.006","DOIUrl":"10.1016/j.dcan.2025.06.006","url":null,"abstract":"<div><div>Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an Extended-GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model's effectiveness, measurements of target and background channels are conducted across a wide range of indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, unmanned aerial vehicles, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1601-1613"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528980","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}
Vehicular Ad-hoc Network (VANET) is a platform that facilitates Vehicle-to-Everything (V2X) interconnection. However, its open communication channels and high-speed mobility introduce security and privacy vulnerabilities. Anonymous authentication is crucial in ensuring secure communication and privacy protection in VANET. However, existing anonymous authentication schemes are prone to single points of failure and often overlook the efficient tracking of the true identities of malicious vehicles after pseudonym changes. To address these challenges, we propose an efficient anonymous authentication scheme for blockchain-based VANET. By leveraging blockchain technology, our approach addresses the challenges of single points of failure and high latency, thereby enhancing the service stability and scalability of VANET. The scheme integrates homomorphic encryption and elliptic curve cryptography, allowing vehicles to independently generate new pseudonyms when entering a new domain without third-party assistance. Security analyses and simulation results demonstrate that our scheme achieves effective anonymous authentication in VANET. Moreover, the roadside unit can process 500 messages per 19 ms. As the number of vehicles in the communication domain grows, our scheme exhibits superior message-processing capabilities.
{"title":"A blockchain-based efficient traceability authentication scheme in VANET","authors":"Junhui Zhao , Yingxuan Guo , Longxia Liao , Dongming Wang","doi":"10.1016/j.dcan.2025.04.013","DOIUrl":"10.1016/j.dcan.2025.04.013","url":null,"abstract":"<div><div>Vehicular Ad-hoc Network (VANET) is a platform that facilitates Vehicle-to-Everything (V2X) interconnection. However, its open communication channels and high-speed mobility introduce security and privacy vulnerabilities. Anonymous authentication is crucial in ensuring secure communication and privacy protection in VANET. However, existing anonymous authentication schemes are prone to single points of failure and often overlook the efficient tracking of the true identities of malicious vehicles after pseudonym changes. To address these challenges, we propose an efficient anonymous authentication scheme for blockchain-based VANET. By leveraging blockchain technology, our approach addresses the challenges of single points of failure and high latency, thereby enhancing the service stability and scalability of VANET. The scheme integrates homomorphic encryption and elliptic curve cryptography, allowing vehicles to independently generate new pseudonyms when entering a new domain without third-party assistance. Security analyses and simulation results demonstrate that our scheme achieves effective anonymous authentication in VANET. Moreover, the roadside unit can process 500 messages per 19 ms. As the number of vehicles in the communication domain grows, our scheme exhibits superior message-processing capabilities.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1410-1420"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529391","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.05.005
Ailin Deng , Xiaoqian Li , Gang Feng , Lu Guan
Terahertz (THz) and millimeter Wave (mmWave) have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates. However, indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss. Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems. However, base station dense deployment leads to a significant increase in system energy consumption. In this paper, we develop a novel ultra-efficient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios. Unlike the existing relevant protocol framework of 3GPP, which operates the cellular system based on constant system signaling messages (including cell ID, cell reselection information, etc.), the proposed mechanism eliminates the need for system messages. The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high, hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios. Specifically, we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch off the main communication module when there are no active users for energy saving. The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications, and the protocol parameters involved are optimized. Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts. Compared to traditional energy-saving schemes, the proposed mechanism achieves an average energy-saving gain of 58%, with a peak energy-saving gain of 90%.
{"title":"An ultra energy-saving mechanism based on beacon signals for 6G networks","authors":"Ailin Deng , Xiaoqian Li , Gang Feng , Lu Guan","doi":"10.1016/j.dcan.2025.05.005","DOIUrl":"10.1016/j.dcan.2025.05.005","url":null,"abstract":"<div><div>Terahertz (THz) and millimeter Wave (mmWave) have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates. However, indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss. Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems. However, base station dense deployment leads to a significant increase in system energy consumption. In this paper, we develop a novel ultra-efficient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios. Unlike the existing relevant protocol framework of 3GPP, which operates the cellular system based on constant system signaling messages (including cell ID, cell reselection information, etc.), the proposed mechanism eliminates the need for system messages. The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high, hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios. Specifically, we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch off the main communication module when there are no active users for energy saving. The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications, and the protocol parameters involved are optimized. Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts. Compared to traditional energy-saving schemes, the proposed mechanism achieves an average energy-saving gain of 58%, with a peak energy-saving gain of 90%.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1330-1342"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529393","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.05.016
Mingliang Pang , Wupeng Xie , Chaowei Wang , Jiabin Chen , Shuai Yan , Fan Jiang , Lexi Xu , Junyi Zhang , Kuoye Han
As key technologies in 6G, Space-Air-Ground Integrated Networks (SAGIN) promises to provide seamless global coverage through a comprehensive, ubiquitous communication system, while Integrated Sensing and Communications (ISAC) effectively addresses spectrum congestion by sharing spectrum resources and transceivers for simultaneous communication and sensing operations. However, existing ISAC research has primarily focused on terrestrial networks, with limited exploration of its applications in SAGIN environments. This paper proposes a novel SAGIN-ISAC scheme leveraging High-Altitude Platform Stations (HAPS). In this scheme, HAPS serves as a relay node that not only amplifies and forwards communication signals but also receives and processes target echo signals for parameter estimation. The satellite employs Resilient Massive Access (RMA) to provide communication services to different User Terminals (UTs). To address scenarios with an unknown number of targets, we develop a Two-threshold Detection and Parameter Multiple Signal Classification (MUSIC) algorithm (TDPM), which employs dual-threshold correlation detection to determine the number of targets and utilizes the MUSIC algorithm to estimate targets' Angle of Arrival (AoA), range, and relative velocity. Furthermore, we establish a joint optimization framework that considers both communication and sensing performance, optimizing energy efficiency, detection probability, and the Cramér-Rao bound. The power allocation coefficients are derived through Nash equilibrium, while the precoding matrix is optimized using Sequential Convex Approximation (SCA) to address the non-convex nature of the optimization problem. Experimental results demonstrate that our proposed scheme significantly enhances the overall performance of the SAGIN-ISAC system.
{"title":"Integrated sensing and communication empowered by resilient massive access in SAGIN: An energy efficient perspective","authors":"Mingliang Pang , Wupeng Xie , Chaowei Wang , Jiabin Chen , Shuai Yan , Fan Jiang , Lexi Xu , Junyi Zhang , Kuoye Han","doi":"10.1016/j.dcan.2025.05.016","DOIUrl":"10.1016/j.dcan.2025.05.016","url":null,"abstract":"<div><div>As key technologies in 6G, Space-Air-Ground Integrated Networks (SAGIN) promises to provide seamless global coverage through a comprehensive, ubiquitous communication system, while Integrated Sensing and Communications (ISAC) effectively addresses spectrum congestion by sharing spectrum resources and transceivers for simultaneous communication and sensing operations. However, existing ISAC research has primarily focused on terrestrial networks, with limited exploration of its applications in SAGIN environments. This paper proposes a novel SAGIN-ISAC scheme leveraging High-Altitude Platform Stations (HAPS). In this scheme, HAPS serves as a relay node that not only amplifies and forwards communication signals but also receives and processes target echo signals for parameter estimation. The satellite employs Resilient Massive Access (RMA) to provide communication services to different User Terminals (UTs). To address scenarios with an unknown number of targets, we develop a Two-threshold Detection and Parameter Multiple Signal Classification (MUSIC) algorithm (TDPM), which employs dual-threshold correlation detection to determine the number of targets and utilizes the MUSIC algorithm to estimate targets' Angle of Arrival (AoA), range, and relative velocity. Furthermore, we establish a joint optimization framework that considers both communication and sensing performance, optimizing energy efficiency, detection probability, and the Cramér-Rao bound. The power allocation coefficients are derived through Nash equilibrium, while the precoding matrix is optimized using Sequential Convex Approximation (SCA) to address the non-convex nature of the optimization problem. Experimental results demonstrate that our proposed scheme significantly enhances the overall performance of the SAGIN-ISAC system.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1588-1600"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529003","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 : 2025-10-01DOI: 10.1016/j.dcan.2025.05.006
Xin Su , Zijian Qin , Weikang Feng , Ziyang Gong , Christian Esposito , Sokjoon Lee
Satellite communication technology has emerged as a key solution to address the challenges of data transmission in remote areas. By overcoming the limitations of traditional terrestrial communication networks, it enables long-distance data transmission anytime and anywhere, ensuring the timely and accurate delivery of water level data, which is particularly crucial for fishway water level monitoring. To enhance the effectiveness of fishway water level monitoring, this study proposes a multi-task learning model, AS-SOMTF, designed for real-time and comprehensive prediction. The model integrates auxiliary sequences with primary input sequences to capture complex relationships and dependencies, thereby improving representational capacity. In addition, a novel time-series embedding algorithm, AS-SOM, is introduced, which combines generative inference and pooling operations to optimize prediction efficiency for long sequences. This innovation not only ensures the timely transmission of water level data but also enhances the accuracy of real-time monitoring. Compared with traditional models such as Transformer and Long Short-Term Memory (LSTM) networks, the proposed model achieves improvements of 3.8% and 1.4% in prediction accuracy, respectively. These advancements provide more precise technical support for water level forecasting and resource management in the Diqing Tibetan Autonomous Prefecture of the Lancang River, contributing to ecosystem protection and improved operational safety.
{"title":"AS-SOMTF: A novel multi-task learning model for water level prediction by satellite remoting","authors":"Xin Su , Zijian Qin , Weikang Feng , Ziyang Gong , Christian Esposito , Sokjoon Lee","doi":"10.1016/j.dcan.2025.05.006","DOIUrl":"10.1016/j.dcan.2025.05.006","url":null,"abstract":"<div><div>Satellite communication technology has emerged as a key solution to address the challenges of data transmission in remote areas. By overcoming the limitations of traditional terrestrial communication networks, it enables long-distance data transmission anytime and anywhere, ensuring the timely and accurate delivery of water level data, which is particularly crucial for fishway water level monitoring. To enhance the effectiveness of fishway water level monitoring, this study proposes a multi-task learning model, AS-SOMTF, designed for real-time and comprehensive prediction. The model integrates auxiliary sequences with primary input sequences to capture complex relationships and dependencies, thereby improving representational capacity. In addition, a novel time-series embedding algorithm, AS-SOM, is introduced, which combines generative inference and pooling operations to optimize prediction efficiency for long sequences. This innovation not only ensures the timely transmission of water level data but also enhances the accuracy of real-time monitoring. Compared with traditional models such as Transformer and Long Short-Term Memory (LSTM) networks, the proposed model achieves improvements of 3.8% and 1.4% in prediction accuracy, respectively. These advancements provide more precise technical support for water level forecasting and resource management in the Diqing Tibetan Autonomous Prefecture of the Lancang River, contributing to ecosystem protection and improved operational safety.</div></div>","PeriodicalId":48631,"journal":{"name":"Digital Communications and Networks","volume":"11 5","pages":"Pages 1554-1566"},"PeriodicalIF":7.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529004","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}
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