Pub Date : 2025-02-12DOI: 10.1109/TGCN.2025.3541338
Siya Chen;Chee Wei Tan;Xiangping Bryce Zhai;H. Vincent Poor
The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep-learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements.
{"title":"OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation With Optimization-Based Deep Learning","authors":"Siya Chen;Chee Wei Tan;Xiangping Bryce Zhai;H. Vincent Poor","doi":"10.1109/TGCN.2025.3541338","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3541338","url":null,"abstract":"The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep-learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 4","pages":"2169-2183"},"PeriodicalIF":6.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145646078","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 : 2025-02-11DOI: 10.1109/TGCN.2025.3540872
Xiaonan Wang;Xilan Chen
Underwater Wireless Sensor Networks (UWSNs) have enormous potential for exploring oceans that cover more than 70% of the earth. In UWSN, sensor nodes are deployed in a three-dimensional water region which is characterized by water depth, and continuously disseminate real-time underwater data to sink nodes through multi-hop communication. Due to mobility and limited energy of sink/sensor nodes, multi-hop marine data routing and dissemination suffer from long data transmission latency and low delivery ratios. In this paper, we propose a learning automata based routing and data dissemination framework for UWSN, and aim to reduce real-time underwater data access delays and improve data delivery ratios. The framework leverages learning automata to construct routing paths so that underwater data can be continuously delivered to sink nodes by reusing routing paths and supporting sink/sensor node mobility. The simulation results demonstrate the feasibility and superiority of the proposed framework.
{"title":"Learning Automata-Based Routing and Data Dissemination for Underwater Wireless Sensor Networks","authors":"Xiaonan Wang;Xilan Chen","doi":"10.1109/TGCN.2025.3540872","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3540872","url":null,"abstract":"Underwater Wireless Sensor Networks (UWSNs) have enormous potential for exploring oceans that cover more than 70% of the earth. In UWSN, sensor nodes are deployed in a three-dimensional water region which is characterized by water depth, and continuously disseminate real-time underwater data to sink nodes through multi-hop communication. Due to mobility and limited energy of sink/sensor nodes, multi-hop marine data routing and dissemination suffer from long data transmission latency and low delivery ratios. In this paper, we propose a learning automata based routing and data dissemination framework for UWSN, and aim to reduce real-time underwater data access delays and improve data delivery ratios. The framework leverages learning automata to construct routing paths so that underwater data can be continuously delivered to sink nodes by reusing routing paths and supporting sink/sensor node mobility. The simulation results demonstrate the feasibility and superiority of the proposed framework.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 4","pages":"2160-2168"},"PeriodicalIF":6.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145646083","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 : 2025-01-20DOI: 10.1109/TGCN.2025.3531902
Zheng Zhang;Qiong Wu;Pingyi Fan;Nan Cheng;Wen Chen;Khaled B. Letaief
To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI.
{"title":"DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV","authors":"Zheng Zhang;Qiong Wu;Pingyi Fan;Nan Cheng;Wen Chen;Khaled B. Letaief","doi":"10.1109/TGCN.2025.3531902","DOIUrl":"https://doi.org/10.1109/TGCN.2025.3531902","url":null,"abstract":"To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 4","pages":"2144-2159"},"PeriodicalIF":6.7,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145646092","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 research advances the application of Quantum Key Distribution (QKD) in Free-Space Optics (FSO) satellite-based quantum communication. It proposes an innovative satellite quantum channel model and derives the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, this model incorporates a hybrid quantum noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise under collective attack with reverse-reconciliation (RR) setting, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This work delves into the variation of asymptotic SKR with the Signal-to-Noise Ratio (SNR) and satellite altitudes under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, electrical noise, transmission coefficient, detection efficiency, transmission efficiency, excess noise, and the quantum Poissonian noise parameter impacting the SKR. These parameters are pivotal in determining the asymptotic SKR in FSO satellite quantum channels, highlighting the challenges of satellite-based quantum communication. A comparative study has been provided based on the finite-size and asymptotic SKR. It provides a comprehensive framework for understanding and optimizing asymptotic SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.
{"title":"A Hybrid Noise Approach to Modeling of Free-Space Satellite Quantum Communication Channel for Continuous-Variable QKD","authors":"Mouli Chakraborty;Anshu Mukherjee;Ioannis Krikidis;Avishek Nag;Subhash Chandra","doi":"10.1109/TGCN.2024.3525297","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3525297","url":null,"abstract":"This research advances the application of Quantum Key Distribution (QKD) in Free-Space Optics (FSO) satellite-based quantum communication. It proposes an innovative satellite quantum channel model and derives the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, this model incorporates a hybrid quantum noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise under collective attack with reverse-reconciliation (RR) setting, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This work delves into the variation of asymptotic SKR with the Signal-to-Noise Ratio (SNR) and satellite altitudes under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, electrical noise, transmission coefficient, detection efficiency, transmission efficiency, excess noise, and the quantum Poissonian noise parameter impacting the SKR. These parameters are pivotal in determining the asymptotic SKR in FSO satellite quantum channels, highlighting the challenges of satellite-based quantum communication. A comparative study has been provided based on the finite-size and asymptotic SKR. It provides a comprehensive framework for understanding and optimizing asymptotic SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1311-1325"},"PeriodicalIF":6.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880607","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 : 2025-01-01DOI: 10.1109/TGCN.2024.3524623
Ashutosh Kumar Yadav;Suneel Yadav;Radhika Gour;Devendra Singh Gurjar;Xingwang Li
This paper examines the secrecy performance of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered wireless communication system, where a base station sends its confidential data through a STAR-RIS to trusted outdoor and indoor users while facing threats from outdoor and indoor eavesdroppers. We also leverage the benefits of direct links between BS and outdoor users along with the STAR-RIS link, whereas indoor users only rely on the STAR-RIS link due to the severe blockages. We derive the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) expressions for both the users over Nakagami-m fading channels. In addition, we present the asymptotic SOP expressions in high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regimes to reveal more insights into the secrecy diversity orders of both users. We then analytically discuss that the high SNR slopes of ESCs for both users are equal to zero. Additionally, we provide an analytical framework to demonstrate the impact of STAR elements on the SOP and ESC performance under two cases of interest: 1) when STAR-RIS is out of service and 2) when RIS consists of a very large number of STAR elements. A tradeoff between the energy efficiency and secrecy capacity is also discussed. Finally, numerical and simulation studies verify our analytical findings.
{"title":"Exploiting Direct Links for Secure STAR-RIS Aided Wireless Communications: Outage and Ergodic Capacity Analysis Over Nakagami-m Fading Channels","authors":"Ashutosh Kumar Yadav;Suneel Yadav;Radhika Gour;Devendra Singh Gurjar;Xingwang Li","doi":"10.1109/TGCN.2024.3524623","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3524623","url":null,"abstract":"This paper examines the secrecy performance of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered wireless communication system, where a base station sends its confidential data through a STAR-RIS to trusted outdoor and indoor users while facing threats from outdoor and indoor eavesdroppers. We also leverage the benefits of direct links between BS and outdoor users along with the STAR-RIS link, whereas indoor users only rely on the STAR-RIS link due to the severe blockages. We derive the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) expressions for both the users over Nakagami-m fading channels. In addition, we present the asymptotic SOP expressions in high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regimes to reveal more insights into the secrecy diversity orders of both users. We then analytically discuss that the high SNR slopes of ESCs for both users are equal to zero. Additionally, we provide an analytical framework to demonstrate the impact of STAR elements on the SOP and ESC performance under two cases of interest: 1) when STAR-RIS is out of service and 2) when RIS consists of a very large number of STAR elements. A tradeoff between the energy efficiency and secrecy capacity is also discussed. Finally, numerical and simulation studies verify our analytical findings.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1294-1310"},"PeriodicalIF":6.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880622","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 : 2024-12-30DOI: 10.1109/TGCN.2024.3523673
Sukriti Gautam;Suman Kumar
Advertising extensions introduced in Bluetooth Core Specification version 5.0 brought many feature enhancements like larger payload size, options for longer range, larger number of channels used, as compared to legacy advertisements introduced in version 4.0. Based on real-time energy consumption analysis, this paper deems Bluetooth Low Energy (BLE) extended advertisements as the more energy-efficient choice as compared to legacy advertisements for applications with motion data generated at high sampling rates, and also highlights the range of data size for which legacy advertisements are more energy-efficient. For sensor networks using high data sampling rates, taking receiving link layer’s behavior into account, the paper analysis the merits and demerits of utilizing all 1650 bytes of an extended advertising event which is the maximum allowed limit. Extensive real-time experimentation carried out for varying amount of network congestion reveals that data loss mostly remains between 2-4% even in highly congested channel for large amount of data sent in each extended advertising event. However, because receiving link layer rejects partially received extended advertising events, sometimes, hiked losses are observed. Sending smaller data in each event yields more stable losses across the sensor network, but these losses are larger in higher network congestion, and energy efficiency is poorer. The paper highlights the potential and limitations of extended advertisements in applications under consideration, and emphasizes the need of the receiving link layer’s capability to process partially received secondary channel packet chains.
{"title":"BLE Extended Advertisements for Energy Efficient and Reliable Transfer of Large Sensor Data in Monitoring Applications","authors":"Sukriti Gautam;Suman Kumar","doi":"10.1109/TGCN.2024.3523673","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3523673","url":null,"abstract":"Advertising extensions introduced in Bluetooth Core Specification version 5.0 brought many feature enhancements like larger payload size, options for longer range, larger number of channels used, as compared to legacy advertisements introduced in version 4.0. Based on real-time energy consumption analysis, this paper deems Bluetooth Low Energy (BLE) extended advertisements as the more energy-efficient choice as compared to legacy advertisements for applications with motion data generated at high sampling rates, and also highlights the range of data size for which legacy advertisements are more energy-efficient. For sensor networks using high data sampling rates, taking receiving link layer’s behavior into account, the paper analysis the merits and demerits of utilizing all 1650 bytes of an extended advertising event which is the maximum allowed limit. Extensive real-time experimentation carried out for varying amount of network congestion reveals that data loss mostly remains between 2-4% even in highly congested channel for large amount of data sent in each extended advertising event. However, because receiving link layer rejects partially received extended advertising events, sometimes, hiked losses are observed. Sending smaller data in each event yields more stable losses across the sensor network, but these losses are larger in higher network congestion, and energy efficiency is poorer. The paper highlights the potential and limitations of extended advertisements in applications under consideration, and emphasizes the need of the receiving link layer’s capability to process partially received secondary channel packet chains.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1092-1106"},"PeriodicalIF":6.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880582","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 : 2024-12-30DOI: 10.1109/TGCN.2024.3524003
Farzad H. Panahi;Fereidoun H. Panahi
We explore the use of an uncrewed aerial vehicle (UAV) flying on a circular path to offload mobile data from a ground base station (GBS) to enhance cellular network capacity. The UAV’s performance is constrained by battery life and energy-intensive radio frequency communications. To address this, we jointly optimize energy efficiency (EE) and spectrum efficiency (SE) by adjusting the UAV’s trajectory, speed, and minimum user throughput. The multi-objective optimization problem we propose is complex and non-convex, presenting substantial challenges in finding an optimal solution. We develop a tailored deep reinforcement learning (DRL) approach to address this specific problem. Simulations show that our method effectively balances EE and SE, enhancing UAV-based cellular offloading while maintaining robust performance, even in uncertain and dynamic conditions.
{"title":"Intelligent UAV-Based Mobile Offloading: A Multi-Objective Optimization Approach","authors":"Farzad H. Panahi;Fereidoun H. Panahi","doi":"10.1109/TGCN.2024.3524003","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3524003","url":null,"abstract":"We explore the use of an uncrewed aerial vehicle (UAV) flying on a circular path to offload mobile data from a ground base station (GBS) to enhance cellular network capacity. The UAV’s performance is constrained by battery life and energy-intensive radio frequency communications. To address this, we jointly optimize energy efficiency (EE) and spectrum efficiency (SE) by adjusting the UAV’s trajectory, speed, and minimum user throughput. The multi-objective optimization problem we propose is complex and non-convex, presenting substantial challenges in finding an optimal solution. We develop a tailored deep reinforcement learning (DRL) approach to address this specific problem. Simulations show that our method effectively balances EE and SE, enhancing UAV-based cellular offloading while maintaining robust performance, even in uncertain and dynamic conditions.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"900-909"},"PeriodicalIF":6.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887547","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 : 2024-12-27DOI: 10.1109/TGCN.2024.3523337
Ruidongxue Wang;Jianhua Bao
Wireless communication is highly susceptible to various forms of noise and interference, leading to potential errors in received data. Consequently, this study proposes a novel anti-interference LoRa physical layer communication model to reduce the Bit Error Rate (BER) under extremely low Signal-to-Noise Ratio (SNR) conditions in LoRa communication. The model incorporates Low-Density Parity-Check (LDPC) codes as the channel coding scheme and integrates soft Chirp Spread Spectrum (CSS) demodulation with the Log-Likelihood Ratios Belief Propagation (LLR-BP) decoding algorithm. A key component of the model is the Enhanced LLR-BP (ELLR-BP) algorithm, which dynamically adjusts LLR values during iterative decoding based on variations in SNR and Spreading Factor (SF), enabling enhanced BER performance in challenging environments. Additionally, the model introduces realistic channel effects, including fading and Co-SF interference, into the simulation framework. MATLAB simulations demonstrate the model’s effectiveness: LDPC coding improves the SNR threshold by approximately 1 dB and reduces the BER by more than 3% compared to traditional Hamming coding. When BER ${=} ,, 10{^{-}4 }$ , the proposed ELLR-BP algorithm achieves a gain of 0.4097 dB and reduces the BER by 0.54% to 0.81% compared with the LLR-BP algorithm. Similarly, the proposed anti-interference model significantly improves performance under Rayleigh channels and provides better resistance to Co-SF interference than standard LoRa.
无线通信极易受到各种形式的噪声和干扰，从而导致接收数据的潜在错误。因此，本研究提出了一种新的抗干扰LoRa物理层通信模型，以降低LoRa通信在极低信噪比（SNR）条件下的误码率。该模型采用低密度奇偶校验码（LDPC）作为信道编码方案，将软啁啾扩频（CSS）解调与对数似然比置信传播（LLR-BP）译码算法相结合。该模型的一个关键组成部分是增强型LLR- bp （ELLR-BP）算法，该算法在迭代解码过程中根据信噪比和扩频因子（SF）的变化动态调整LLR值，从而在具有挑战性的环境中增强误码率性能。此外，该模型在仿真框架中引入了真实的信道效应，包括衰落和Co-SF干扰。MATLAB仿真证明了该模型的有效性：与传统的汉明编码相比，LDPC编码将信噪比阈值提高了约1 dB，将误码率降低了3%以上。当误码率${=},,10{^{-}4}$时，与LLR-BP算法相比，ELLR-BP算法的增益为0.4097 dB，误码率降低0.54% ~ 0.81%。同样，所提出的抗干扰模型显著提高了瑞利信道下的性能，并且比标准LoRa具有更好的抗Co-SF干扰能力。
{"title":"ELLR-BP: An Enhanced LLR-BP Algorithm Based on LDPC Coding for LoRa Physical Layer","authors":"Ruidongxue Wang;Jianhua Bao","doi":"10.1109/TGCN.2024.3523337","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3523337","url":null,"abstract":"Wireless communication is highly susceptible to various forms of noise and interference, leading to potential errors in received data. Consequently, this study proposes a novel anti-interference LoRa physical layer communication model to reduce the Bit Error Rate (BER) under extremely low Signal-to-Noise Ratio (SNR) conditions in LoRa communication. The model incorporates Low-Density Parity-Check (LDPC) codes as the channel coding scheme and integrates soft Chirp Spread Spectrum (CSS) demodulation with the Log-Likelihood Ratios Belief Propagation (LLR-BP) decoding algorithm. A key component of the model is the Enhanced LLR-BP (ELLR-BP) algorithm, which dynamically adjusts LLR values during iterative decoding based on variations in SNR and Spreading Factor (SF), enabling enhanced BER performance in challenging environments. Additionally, the model introduces realistic channel effects, including fading and Co-SF interference, into the simulation framework. MATLAB simulations demonstrate the model’s effectiveness: LDPC coding improves the SNR threshold by approximately 1 dB and reduces the BER by more than 3% compared to traditional Hamming coding. When BER <inline-formula> <tex-math>${=} ,, 10{^{-}4 }$ </tex-math></inline-formula>, the proposed ELLR-BP algorithm achieves a gain of 0.4097 dB and reduces the BER by 0.54% to 0.81% compared with the LLR-BP algorithm. Similarly, the proposed anti-interference model significantly improves performance under Rayleigh channels and provides better resistance to Co-SF interference than standard LoRa.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"889-899"},"PeriodicalIF":6.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887537","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 : 2024-12-11DOI: 10.1109/TGCN.2024.3515637
Youyun Xu;Weinan Wang;Tianyou Li
In communication confrontation, the capabilities of jamming and resisting jamming play a pivotal role in achieving victory. Addressing the lack of dynamism in decision-making during communications counter operations, in this paper, we customize the confrontation process between the communicator and the jammer as a Stackelberg game, where the communicator is the leader and the jammer is the follower. The game process is partitioned into time slots, and to obtain the best strategies of both in each time slot, we propose metrics to evaluate their utilities. The optimization problem is then constructed with the objective function of maximizing their utility metrics. Intelligent reflecting surface (IRS) is introduced to the communication process in order to improve the leader’s immunity to interference. A new optimization method is proposed in this paper to decompose and transform the optimization problem to finally obtain an asymptotic optimal solution. In addition, this paper proves that at least one Stackelberg equation exists, and the communicating party can always make the best decision after the interfering party makes the best decision. Finally, the empirical findings presented in this paper further substantiate that the gaming process ultimately reaches equilibrium. With the assistance of IRS, the communication pair saves power consumption while enhancing its anti-jamming capability.
{"title":"A Communication Confrontation Based on the Stackelberg Game: IRS Aids for Improving Anti-Jamming Capability","authors":"Youyun Xu;Weinan Wang;Tianyou Li","doi":"10.1109/TGCN.2024.3515637","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3515637","url":null,"abstract":"In communication confrontation, the capabilities of jamming and resisting jamming play a pivotal role in achieving victory. Addressing the lack of dynamism in decision-making during communications counter operations, in this paper, we customize the confrontation process between the communicator and the jammer as a Stackelberg game, where the communicator is the leader and the jammer is the follower. The game process is partitioned into time slots, and to obtain the best strategies of both in each time slot, we propose metrics to evaluate their utilities. The optimization problem is then constructed with the objective function of maximizing their utility metrics. Intelligent reflecting surface (IRS) is introduced to the communication process in order to improve the leader’s immunity to interference. A new optimization method is proposed in this paper to decompose and transform the optimization problem to finally obtain an asymptotic optimal solution. In addition, this paper proves that at least one Stackelberg equation exists, and the communicating party can always make the best decision after the interfering party makes the best decision. Finally, the empirical findings presented in this paper further substantiate that the gaming process ultimately reaches equilibrium. With the assistance of IRS, the communication pair saves power consumption while enhancing its anti-jamming capability.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1280-1293"},"PeriodicalIF":6.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880610","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 : 2024-12-11DOI: 10.1109/TGCN.2024.3516509
Xiangsen Chen;Wenbo Xu;Yue Wang;David K. Y. Yau;Yingshu Li;Zhipeng Cai
In wireless sensor networks (WSNs), the energy consumption of sensors and the system performance are inevitably contradictory. For the distributed detection problem in WSN, the censoring technique has been proposed as an energy-efficient transmission strategy. However, most current detection schemes with censoring do not consider the quantization operations in actual communication systems to further save the transmission energy in actual communication systems. In this paper, we propose two energy-efficient detection schemes including a common censoring-quantization strategy and two different detectors. This censoring-quantization strategy aims to reduce the energy consumption of transmitting observations. It is modeled as a three-state equivalent transmission strategy at sensors, with the states being keeping silent, transmitting “1”, and transmitting “−1”. To fully explore the advantage of this strategy, we design two detectors, namely the Censoring-Quantization Maximum-Likelihood (CQ-ML) detector and the Censoring-Quantization Locally Most Powerful Test (CQ-LMPT) detector, respectively for two scenarios, which correspond to whether the fusion center (FC) knows the parameters of the Phenomenon of Interest (POI). We further analyze the theoretical performance of our schemes to provide the optimal censoring-quantization thresholds. Finally, experimental results demonstrate the effectiveness of our schemes and highlight the significant energy consumption reduction with negligible performance loss.
{"title":"Distributed Sparse Signal Detection With Energy-Efficient Censoring-Quantization in Wireless Sensor Networks","authors":"Xiangsen Chen;Wenbo Xu;Yue Wang;David K. Y. Yau;Yingshu Li;Zhipeng Cai","doi":"10.1109/TGCN.2024.3516509","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3516509","url":null,"abstract":"In wireless sensor networks (WSNs), the energy consumption of sensors and the system performance are inevitably contradictory. For the distributed detection problem in WSN, the censoring technique has been proposed as an energy-efficient transmission strategy. However, most current detection schemes with censoring do not consider the quantization operations in actual communication systems to further save the transmission energy in actual communication systems. In this paper, we propose two energy-efficient detection schemes including a common censoring-quantization strategy and two different detectors. This censoring-quantization strategy aims to reduce the energy consumption of transmitting observations. It is modeled as a three-state equivalent transmission strategy at sensors, with the states being keeping silent, transmitting “1”, and transmitting “−1”. To fully explore the advantage of this strategy, we design two detectors, namely the Censoring-Quantization Maximum-Likelihood (CQ-ML) detector and the Censoring-Quantization Locally Most Powerful Test (CQ-LMPT) detector, respectively for two scenarios, which correspond to whether the fusion center (FC) knows the parameters of the Phenomenon of Interest (POI). We further analyze the theoretical performance of our schemes to provide the optimal censoring-quantization thresholds. Finally, experimental results demonstrate the effectiveness of our schemes and highlight the significant energy consumption reduction with negligible performance loss.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"9 3","pages":"1079-1091"},"PeriodicalIF":6.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880560","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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