Pub Date : 2025-12-09DOI: 10.1109/TGCN.2025.3642128
Zhaotao Zhang;Xiaohan Li;Xinjiang Xia;Chenfei Fan;Pengcheng Zhu;Dongming Wang;Tiecheng Song
The traditional massive multiple-input multiple-output (MIMO) system faces spectral efficiency limitations due to half-duplex constraints and centralized processing bottlenecks. This paper proposes a network-assisted free duplex (NA-FD) architecture in a cell-free radio access network (CF-RAN) system. In this architecture, user equipments (UEs) operate in half-duplex mode, while access points (APs) can support both full-duplex and half-duplex modes, significantly reducing inter-link interference. The distributed framework features edge distributed units (EDUs) that handle both uplink (demodulating signals for central processing unit (CPU) to aggregate) and downlink (precoding data for AP to transmit) processing, reducing backhaul load and enhancing scalability. For CF-RAN NA-FD, the following three works synergistically to improve system spectral efficiency and reduce computational overhead by optimizing the distributed association: 1) discrete differential evolution (DDE)-based EDU-AP association, 2) evolutionary and coalition game-theoretic AP mode selection, 3) experience replay (ER)-enhanced distributed Q-learning for capacity-constrained EDU-UE association. Simulation results demonstrate the effectiveness of our proposed architecture in achieving efficient resource allocation, significantly improving system performance and user quality of service.
{"title":"Duplex Mode Selection and Distributed Association for Cell-Free RAN With Network-Assisted Free Duplex","authors":"Zhaotao Zhang;Xiaohan Li;Xinjiang Xia;Chenfei Fan;Pengcheng Zhu;Dongming Wang;Tiecheng Song","doi":"10.1109/TGCN.2025.3642128","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3642128","url":null,"abstract":"The traditional massive multiple-input multiple-output (MIMO) system faces spectral efficiency limitations due to half-duplex constraints and centralized processing bottlenecks. This paper proposes a network-assisted free duplex (NA-FD) architecture in a cell-free radio access network (CF-RAN) system. In this architecture, user equipments (UEs) operate in half-duplex mode, while access points (APs) can support both full-duplex and half-duplex modes, significantly reducing inter-link interference. The distributed framework features edge distributed units (EDUs) that handle both uplink (demodulating signals for central processing unit (CPU) to aggregate) and downlink (precoding data for AP to transmit) processing, reducing backhaul load and enhancing scalability. For CF-RAN NA-FD, the following three works synergistically to improve system spectral efficiency and reduce computational overhead by optimizing the distributed association: 1) discrete differential evolution (DDE)-based EDU-AP association, 2) evolutionary and coalition game-theoretic AP mode selection, 3) experience replay (ER)-enhanced distributed Q-learning for capacity-constrained EDU-UE association. Simulation results demonstrate the effectiveness of our proposed architecture in achieving efficient resource allocation, significantly improving system performance and user quality of service.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1603-1617"},"PeriodicalIF":6.7,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830872","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 : 2025-12-08DOI: 10.1109/TGCN.2025.3641183
Afan Ali;Abdelali Arous;Hüseyin Arslan
The high Peak-to-average-power ratio (PAPR) is still a common issue in multicarrier signal modulation systems such as Orthogonal Chirp Division Multiplexing (OCDM) and Affine Frequency Division Multiplexing (AFDM), which are expected to play a central role in 6G networks. This paper presents a novel unified premodulation data spreading framework that repurposes four well-established transforms—Walsh-Hadamard Transform (WHT), Discrete Cosine Transform (DCT), Zadoff-Chu (ZC) sequences, and Interleaved Discrete Fourier Transform (IDFT)—to achieve up to 4 dB PAPR reduction with lower complexity and zero side information. Conventional PAPR reduction frameworks such as Partial transmission Sequence (PTS) or Selected Mapping (SLM) have a high complexity drawback, as they require extensive search and signaling overhead. In contrast, our framework leverages fast transforms and the inherent chirp structure to redistribute energy before modulation without additional overhead, delivering not only superior PAPR performance but also enhanced phase selectivity and interference resilience. Extensive simulations and analytical derivations confirm its energy efficiency and scalability in large-scale IoT deployments.
{"title":"Spreading the Wave: Low-Complexity PAPR Reduction for AFDM and OCDM in 6G Networks","authors":"Afan Ali;Abdelali Arous;Hüseyin Arslan","doi":"10.1109/TGCN.2025.3641183","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3641183","url":null,"abstract":"The high Peak-to-average-power ratio (PAPR) is still a common issue in multicarrier signal modulation systems such as Orthogonal Chirp Division Multiplexing (OCDM) and Affine Frequency Division Multiplexing (AFDM), which are expected to play a central role in 6G networks. This paper presents a novel unified premodulation data spreading framework that repurposes four well-established transforms—Walsh-Hadamard Transform (WHT), Discrete Cosine Transform (DCT), Zadoff-Chu (ZC) sequences, and Interleaved Discrete Fourier Transform (IDFT)—to achieve up to 4 dB PAPR reduction with lower complexity and zero side information. Conventional PAPR reduction frameworks such as Partial transmission Sequence (PTS) or Selected Mapping (SLM) have a high complexity drawback, as they require extensive search and signaling overhead. In contrast, our framework leverages fast transforms and the inherent chirp structure to redistribute energy before modulation without additional overhead, delivering not only superior PAPR performance but also enhanced phase selectivity and interference resilience. Extensive simulations and analytical derivations confirm its energy efficiency and scalability in large-scale IoT deployments.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1565-1577"},"PeriodicalIF":6.7,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830940","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}
This paper proposes a novel OTFS (orthogonal time frequency space)-based MIMO Integrated Sensing and Communication (ISAC) system that achieves real-time channel reconstruction through advanced target parameter estimation. However, existing OTFS techniques still encounter challenges in sensing performance when applied to scenarios characterized by fractional delays and fractional Doppler frequency shifts. A novel radar channel matrix model in the delay-Doppler (DD) domain is proposed in this paper with dual fractional features effectively captured. Furthermore, to address the orthogonality issue of transmitted signals in the MIMO-OTFS model, a matched filter is designed based on the proposed matrix model to achieve signal separation in DD domain and extract the channel information vector associated with the target state. By exploiting the obtained channel information vector, target angle estimation is readily accomplished. For range and velocity estimation, two scenarios are considered. In separable multi-target cases, we propose the Rapid Second-Order Inversion (RSOI) algorithm to efficiently decouple and estimate individual target parameters. The Joint Particle Swarm Optimization-based Super-Resolution Algorithm (JPSO-SRA) is proposed for off-grid estimation of target parameters in inseparable multi-target cases. Extensive simulation results demonstrate that the proposed algorithms achieve more accurate target parameter sensing with lower computational complexity compared to existing methods, thereby enabling channel reconstruction in MIMO-OTFS-based ISAC systems.
{"title":"Integrated Sensing and Communication With MIMO-OTFS: Energy-Conscious Channel Reconstruction via Efficient Target Parameter Estimation","authors":"Tongmin Xiong;Yi Liao;Songjun Han;Ying-Chang Liang","doi":"10.1109/TGCN.2025.3641228","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3641228","url":null,"abstract":"This paper proposes a novel OTFS (orthogonal time frequency space)-based MIMO Integrated Sensing and Communication (ISAC) system that achieves real-time channel reconstruction through advanced target parameter estimation. However, existing OTFS techniques still encounter challenges in sensing performance when applied to scenarios characterized by fractional delays and fractional Doppler frequency shifts. A novel radar channel matrix model in the delay-Doppler (DD) domain is proposed in this paper with dual fractional features effectively captured. Furthermore, to address the orthogonality issue of transmitted signals in the MIMO-OTFS model, a matched filter is designed based on the proposed matrix model to achieve signal separation in DD domain and extract the channel information vector associated with the target state. By exploiting the obtained channel information vector, target angle estimation is readily accomplished. For range and velocity estimation, two scenarios are considered. In separable multi-target cases, we propose the Rapid Second-Order Inversion (RSOI) algorithm to efficiently decouple and estimate individual target parameters. The Joint Particle Swarm Optimization-based Super-Resolution Algorithm (JPSO-SRA) is proposed for off-grid estimation of target parameters in inseparable multi-target cases. Extensive simulation results demonstrate that the proposed algorithms achieve more accurate target parameter sensing with lower computational complexity compared to existing methods, thereby enabling channel reconstruction in MIMO-OTFS-based ISAC systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1552-1564"},"PeriodicalIF":6.7,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830795","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}
Intelligent reflecting surface (IRS) assisted data aggregation in Internet of Things (IoT) networks is considered in this paper, where IoT devices harvest energy from a hybrid access point (HAP) and transmit sensory data to the HAP using over-the-air computation (AirComp). Taking into account a practical non-linear energy harvesting (EH) model, we formulate an energy minimization problem under certain data aggregation accuracy requirements, which involves jointly optimizing the time allocation, beamforming at the HAP and IoT devices, and downlink/uplink IRS phase-shifts. This problem is highly intractable due to the non-convexity and variable coupling, which necessitates an alternating optimization algorithm. We first derive the time allocation ratio in closed-form and then transform the subproblem for energy beamforming to a solvable convex problem. Subsequently, the aggregation beamforming vector is optimized via majorization minimization, and the IRS phase-shifts are optimized by developing a sequential rank-one constraint relaxation based algorithm. Moreover, we investigate the optimization of both dynamic and static IRS passive beamforming. Results demonstrate that the proposed algorithm achieves a lower energy consumption than baseline algorithms. Meanwhile, the dynamic IRS beamforming possesses a superiority of reducing the energy consumption while the static IRS beamforming requires a lower computational complexity.
{"title":"IRS-Assisted Data Aggregation With Over-the-Air Computation and Non-Linear Energy Harvesting","authors":"Jiaqi Jin;Shaojun Wan;Zhibin Wang;Yuanming Shi;Yong Zhou","doi":"10.1109/TGCN.2025.3640387","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3640387","url":null,"abstract":"Intelligent reflecting surface (IRS) assisted data aggregation in Internet of Things (IoT) networks is considered in this paper, where IoT devices harvest energy from a hybrid access point (HAP) and transmit sensory data to the HAP using over-the-air computation (AirComp). Taking into account a practical non-linear energy harvesting (EH) model, we formulate an energy minimization problem under certain data aggregation accuracy requirements, which involves jointly optimizing the time allocation, beamforming at the HAP and IoT devices, and downlink/uplink IRS phase-shifts. This problem is highly intractable due to the non-convexity and variable coupling, which necessitates an alternating optimization algorithm. We first derive the time allocation ratio in closed-form and then transform the subproblem for energy beamforming to a solvable convex problem. Subsequently, the aggregation beamforming vector is optimized via majorization minimization, and the IRS phase-shifts are optimized by developing a sequential rank-one constraint relaxation based algorithm. Moreover, we investigate the optimization of both dynamic and static IRS passive beamforming. Results demonstrate that the proposed algorithm achieves a lower energy consumption than baseline algorithms. Meanwhile, the dynamic IRS beamforming possesses a superiority of reducing the energy consumption while the static IRS beamforming requires a lower computational complexity.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1591-1602"},"PeriodicalIF":6.7,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830920","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}
The industrial Internet of things (IIoT) is increasingly employing blockchain-assisted drone swarms to execute critical missions. However, due to the high energy consumption, traditional blockchain mechanisms with substantial computation overhead cannot be deployed on drones with limited power resources. To address this issue, we propose a novel renewable energy-aware consensus mechanism, named proof of green (PoG). In particular, based on the hybrid energy supply framework, we design a dynamic election strategy. To jointly optimize renewable energy consumption and historical reputation of drones, we formulate a multi-objective optimization problem to determine the optimal node selection scheme for drones. Moreover, to enhance the fairness of the election process, we introduce a verifiable random function (VRF)-based perturbation factor and integrate PoG into the Byzantine fault tolerance (BFT) voting process. Simulation results demonstrate that compared to traditional mechanisms, our scheme reduces drone battery consumption and provides robust resistance against common security threats in drone swarms.
{"title":"Renewable Energy-Aware Blockchain Consensus Architecture for Hybrid-Powered Drone Swarms","authors":"Wei Long;Jingjing Wang;Xin Zhang;Jianrui Chen;Chunxiao Jiang","doi":"10.1109/TGCN.2025.3639437","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3639437","url":null,"abstract":"The industrial Internet of things (IIoT) is increasingly employing blockchain-assisted drone swarms to execute critical missions. However, due to the high energy consumption, traditional blockchain mechanisms with substantial computation overhead cannot be deployed on drones with limited power resources. To address this issue, we propose a novel renewable energy-aware consensus mechanism, named proof of green (PoG). In particular, based on the hybrid energy supply framework, we design a dynamic election strategy. To jointly optimize renewable energy consumption and historical reputation of drones, we formulate a multi-objective optimization problem to determine the optimal node selection scheme for drones. Moreover, to enhance the fairness of the election process, we introduce a verifiable random function (VRF)-based perturbation factor and integrate PoG into the Byzantine fault tolerance (BFT) voting process. Simulation results demonstrate that compared to traditional mechanisms, our scheme reduces drone battery consumption and provides robust resistance against common security threats in drone swarms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1525-1537"},"PeriodicalIF":6.7,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802349","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 : 2025-12-01DOI: 10.1109/TGCN.2025.3639186
Anastasios Valkanis;Georgia A. Beletsioti;Konstantinos F. Kantelis;Petros Nicopolitidis;Georgios I. Papadimitriou;Malamati Louta
LoRa networks are a reliable and efficient technology for the deployment of long-range IoT networks. Their operating characteristics make them an ideal choice for the low-cost installation of sensor networks in hard-to-reach wide areas. The densification of the deployed gateways is considered as a solution to the reliability and scalability issues facing LoRa networks. However, the energy needs of the gateways require their connection and supply with the power grid. This requirement increases both installation costs and the carbon footprint of LoRa networks. The prospect of battery operated gateways connected to renewable energy resources simplifies the installation and reduces their carbon footprint. In this paper we propose a hybrid protocol that improves both the energy efficiency of the gateways and the reliability of Lora networks, making their green operation feasible. The main differentiation that the proposed protocol provides in the operation of LoRa networks, compared to the existing LoRaWAN protocol, is the dynamic and intelligent activation/deactivation of the gateways. The simulation results show that the proposed protocol greatly improves key performance metrics of LoRa networks over the existing protocol, as well as significantly reduces their carbon footprint.
{"title":"A Hybrid Protocol for Reliable and Green Operation of Multigateway LoRa Networks","authors":"Anastasios Valkanis;Georgia A. Beletsioti;Konstantinos F. Kantelis;Petros Nicopolitidis;Georgios I. Papadimitriou;Malamati Louta","doi":"10.1109/TGCN.2025.3639186","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3639186","url":null,"abstract":"LoRa networks are a reliable and efficient technology for the deployment of long-range IoT networks. Their operating characteristics make them an ideal choice for the low-cost installation of sensor networks in hard-to-reach wide areas. The densification of the deployed gateways is considered as a solution to the reliability and scalability issues facing LoRa networks. However, the energy needs of the gateways require their connection and supply with the power grid. This requirement increases both installation costs and the carbon footprint of LoRa networks. The prospect of battery operated gateways connected to renewable energy resources simplifies the installation and reduces their carbon footprint. In this paper we propose a hybrid protocol that improves both the energy efficiency of the gateways and the reliability of Lora networks, making their green operation feasible. The main differentiation that the proposed protocol provides in the operation of LoRa networks, compared to the existing LoRaWAN protocol, is the dynamic and intelligent activation/deactivation of the gateways. The simulation results show that the proposed protocol greatly improves key performance metrics of LoRa networks over the existing protocol, as well as significantly reduces their carbon footprint.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1512-1524"},"PeriodicalIF":6.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802357","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 : 2025-12-01DOI: 10.1109/TGCN.2025.3634008
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/TGCN.2025.3634008","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3634008","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 4","pages":"C3-C3"},"PeriodicalIF":6.7,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271726","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145646088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1109/TGCN.2025.3638812
Saad Masrur;İsmail Güvenç;David López-Pérez
Dynamic sleep mode optimization (SMO) in millimeter-wave (mmWave) networks is essential for maximizing energy efficiency (EE) under stringent quality-of-service (QoS) constraints. However, existing optimization and reinforcement learning (RL)-based approaches rely on aggregated, static base station (BS) traffic models that fail to capture non-stationary traffic dynamics and suffer from prohibitively large state–action spaces, limiting their real-world deployment. To address these challenges, this paper proposes a Multi-Agent Deep Reinforcement Learning (MARL) framework employing a Double Deep Q-Network (DDQN), referred to as MARL-DDQN, for adaptive SMO in a 3D urban environment using a time-varying and community-based user equipment (UE) mobility model. Unlike conventional single-agent RL, the proposed MARL-DDQN enables scalable, distributed decision-making with minimal signaling overhead. A realistic BS power consumption model and beamforming are integrated to accurately quantify EE, while QoS is uniquely defined in terms of throughput. The proposed method adaptively learns SMO policies to maximize EE while mitigating inter-cell interference and ensuring throughput fairness. Extensive simulations demonstrate that MARL-DDQN consistently outperforms state-of-the-art SM strategies, including the All On, iterative QoS-aware load-based (IT-QoS-LB), MARL-DDPG, and MARL-PPO, achieving up to 0.60 Mbit/Joule EE, 8.5 Mbps 10th-percentile throughput, and satisfying QoS constraints 95% of the time under dynamic network scenarios.
{"title":"Energy-Efficient Sleep Mode Optimization in 5G mmWave Networks via Multi-Agent Deep Reinforcement Learning","authors":"Saad Masrur;İsmail Güvenç;David López-Pérez","doi":"10.1109/TGCN.2025.3638812","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3638812","url":null,"abstract":"Dynamic sleep mode optimization (SMO) in millimeter-wave (mmWave) networks is essential for maximizing energy efficiency (EE) under stringent quality-of-service (QoS) constraints. However, existing optimization and reinforcement learning (RL)-based approaches rely on aggregated, static base station (BS) traffic models that fail to capture non-stationary traffic dynamics and suffer from prohibitively large state–action spaces, limiting their real-world deployment. To address these challenges, this paper proposes a Multi-Agent Deep Reinforcement Learning (MARL) framework employing a Double Deep Q-Network (DDQN), referred to as <monospace>MARL-DDQN</monospace>, for adaptive SMO in a 3D urban environment using a time-varying and community-based user equipment (UE) mobility model. Unlike conventional single-agent RL, the proposed <monospace>MARL-DDQN</monospace> enables scalable, distributed decision-making with minimal signaling overhead. A realistic BS power consumption model and beamforming are integrated to accurately quantify EE, while QoS is uniquely defined in terms of throughput. The proposed method adaptively learns SMO policies to maximize EE while mitigating inter-cell interference and ensuring throughput fairness. Extensive simulations demonstrate that <monospace>MARL-DDQN</monospace> consistently outperforms state-of-the-art SM strategies, including the <monospace>All On</monospace>, iterative QoS-aware load-based (<monospace>IT-QoS-LB</monospace>), <monospace>MARL-DDPG</monospace>, and <monospace>MARL-PPO</monospace>, achieving up to 0.60 Mbit/Joule EE, 8.5 Mbps 10th-percentile throughput, and satisfying QoS constraints 95% of the time under dynamic network scenarios.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1495-1511"},"PeriodicalIF":6.7,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802342","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 : 2025-11-20DOI: 10.1109/TGCN.2025.3635206
Yue Ren;Huasen He;Yunpeng Hou;Xiaofeng Jiang;Shuangwu Chen;Jian Yang
With the increasing application of Unmanned Aerial Vehicles (UAVs) in urban areas, employing ground Base Stations (BSs) to serve UAVs has been proposed as a low-cost and promising solution. However, the Quality of Service (QoS) of UAVs served by ground BSs is impacted by multiple factors, including blockage, UAV height, transmit power, BS density, BS selection strategy, and so on. Efficient and accurate evaluation of the communication performance between UAVs and BSs is critical for network planning and optimization. In this paper, we analyze the communication performance of UAVs served by ground BSs in urban environments, where millimeter wave communication is employed. In contrast with existing works adopting a simplified Line-of-Sight (LOS) probability model, we adopt a more practical yet complex LOS model proposed by 3GPP for characterizing the densely distributed buildings in urban environments. Moreover, the distribution of BSs is modeled as a Matérn Hard-Core Point Process (MHCPP) with a minimum distance constraint to reflect real scenarios. The analytical expressions of outage probability and ergodic capacity under different BS selection strategies are derived for enabling efficient performance evaluation. We verify the accuracy of the analytical results through simulation experiments under both the Urban Macro (UMa) and Urban Micro (UMi) scenarios, while the impacts of multiple parameters are analyzed. The results indicate that the outage probability decreases as the UAV height increases, but the ergodic capacity shows an opposite trend. Moreover, we show that our analytical results can be used to select the optimal flight height for UAVs with a given required outage probability.
{"title":"Performance Analysis of Distributed UAVs in Urban Environments Using a Practical Line-of-Sight Model","authors":"Yue Ren;Huasen He;Yunpeng Hou;Xiaofeng Jiang;Shuangwu Chen;Jian Yang","doi":"10.1109/TGCN.2025.3635206","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3635206","url":null,"abstract":"With the increasing application of Unmanned Aerial Vehicles (UAVs) in urban areas, employing ground Base Stations (BSs) to serve UAVs has been proposed as a low-cost and promising solution. However, the Quality of Service (QoS) of UAVs served by ground BSs is impacted by multiple factors, including blockage, UAV height, transmit power, BS density, BS selection strategy, and so on. Efficient and accurate evaluation of the communication performance between UAVs and BSs is critical for network planning and optimization. In this paper, we analyze the communication performance of UAVs served by ground BSs in urban environments, where millimeter wave communication is employed. In contrast with existing works adopting a simplified Line-of-Sight (LOS) probability model, we adopt a more practical yet complex LOS model proposed by 3GPP for characterizing the densely distributed buildings in urban environments. Moreover, the distribution of BSs is modeled as a Matérn Hard-Core Point Process (MHCPP) with a minimum distance constraint to reflect real scenarios. The analytical expressions of outage probability and ergodic capacity under different BS selection strategies are derived for enabling efficient performance evaluation. We verify the accuracy of the analytical results through simulation experiments under both the Urban Macro (UMa) and Urban Micro (UMi) scenarios, while the impacts of multiple parameters are analyzed. The results indicate that the outage probability decreases as the UAV height increases, but the ergodic capacity shows an opposite trend. Moreover, we show that our analytical results can be used to select the optimal flight height for UAVs with a given required outage probability.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1478-1494"},"PeriodicalIF":6.7,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802378","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 : 2025-11-19DOI: 10.1109/TGCN.2025.3634827
Jinhao Wang;Xiao Zhou;Chengyou Wang;Zhiquan Bai
Reconfigurable intelligent surface (RIS) can effectively overcome the limited coverage of millimeter wave (mmWave). The effective operation of RIS depends on the accurate channel state information (CSI) obtained through channel estimation. However, accurate channel estimation is challenging due to the passive nature and the presence of high pilot overhead. Recently, deep learning (DL)-based channel estimation methods have shown potential. However, high model complexity and low estimation accuracy remain key issues. This paper proposes a user partitioning channel estimation method based on reparameterization and coordinate attention network (RCANet). The proposed method uses user partitioning to achieve angle segmentation for accurate estimation and obtains the estimated channel by the well trained RCANet. Initially, partial CSI is obtained by RIS elements grouping and least square (LS) algorithms. Subsequently, the data of users in the partition is utilized to train RCANet in the offline training part. Finally, using well-trained network allows estimating the channel with low complexity in the online test part. Our comprehensive simulation results show that the proposed method has lower normalized mean square error (NMSE) as well as lower complexity compared with other channel estimation algorithms.
{"title":"RCANet-Based User Partitioning Channel Estimation for RIS-Assisted mmWave MIMO Systems","authors":"Jinhao Wang;Xiao Zhou;Chengyou Wang;Zhiquan Bai","doi":"10.1109/TGCN.2025.3634827","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3634827","url":null,"abstract":"Reconfigurable intelligent surface (RIS) can effectively overcome the limited coverage of millimeter wave (mmWave). The effective operation of RIS depends on the accurate channel state information (CSI) obtained through channel estimation. However, accurate channel estimation is challenging due to the passive nature and the presence of high pilot overhead. Recently, deep learning (DL)-based channel estimation methods have shown potential. However, high model complexity and low estimation accuracy remain key issues. This paper proposes a user partitioning channel estimation method based on reparameterization and coordinate attention network (RCANet). The proposed method uses user partitioning to achieve angle segmentation for accurate estimation and obtains the estimated channel by the well trained RCANet. Initially, partial CSI is obtained by RIS elements grouping and least square (LS) algorithms. Subsequently, the data of users in the partition is utilized to train RCANet in the offline training part. Finally, using well-trained network allows estimating the channel with low complexity in the online test part. Our comprehensive simulation results show that the proposed method has lower normalized mean square error (NMSE) as well as lower complexity compared with other channel estimation algorithms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"10 ","pages":"1419-1432"},"PeriodicalIF":6.7,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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