More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.
{"title":"Resource Management for QoS-Guaranteed Marine Data Feedback Based on Space–Air–Ground–Sea Network","authors":"Yuanmo Lin;Zhiyong Xu;Jianhua Li;Jingyuan Wang;Cheng Li;Zhonghu Huang;Yanli Xu","doi":"10.1109/JSYST.2024.3439343","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3439343","url":null,"abstract":"More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1741-1752"},"PeriodicalIF":4.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 growing number of electric vehicles (EVs) on the roads led to a wide deployment of public EV charging stations (EVCSs). Recent reports revealed that both EVs and EVCSs are targets of cyber-attacks. In this context, a malware attack on vehicle-to-grid (V2G) communications increases the risk of malware spread among EVs and public EVCSs. However, the existing literature lacks practical studies on malware spread in power-transportation systems. Hence, this article demonstrates malicious traffic injection and proposes strategies to identify target EVCSs that can maximize physical malware spread within power-transportation systems. We first show the feasibility of injecting malicious traffic into the front-end V2G communication. Next, we establish a model that reflects the logical connectivity among the EVCSs, based on a realistic framework for large-scale EV commute and charge simulation. The logical connectivity is then translated into a malware spread probability, which we use to design an optimal attack strategy that identifies the locations of target EVCSs that maximize the malware spread. We compare malware spread due to random, cluster-based, and optimal attack strategies in both urban (Nashville) and rural (Cookeville) U.S. cities. Our results reveal that optimal attack strategies can accelerate malware spread by $10%$ – $33%$ .
{"title":"Attack Design for Maximum Malware Spread Through EVs Commute and Charge in Power-Transportation Systems","authors":"Sushil Poudel;Mahmoud Abouyoussef;J. Eileen Baugh;Muhammad Ismail","doi":"10.1109/JSYST.2024.3446231","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3446231","url":null,"abstract":"The growing number of electric vehicles (EVs) on the roads led to a wide deployment of public EV charging stations (EVCSs). Recent reports revealed that both EVs and EVCSs are targets of cyber-attacks. In this context, a malware attack on vehicle-to-grid (V2G) communications increases the risk of malware spread among EVs and public EVCSs. However, the existing literature lacks practical studies on malware spread in power-transportation systems. Hence, this article demonstrates malicious traffic injection and proposes strategies to identify target EVCSs that can maximize physical malware spread within power-transportation systems. We first show the feasibility of injecting malicious traffic into the front-end V2G communication. Next, we establish a model that reflects the logical connectivity among the EVCSs, based on a realistic framework for large-scale EV commute and charge simulation. The logical connectivity is then translated into a malware spread probability, which we use to design an optimal attack strategy that identifies the locations of target EVCSs that maximize the malware spread. We compare malware spread due to random, cluster-based, and optimal attack strategies in both urban (Nashville) and rural (Cookeville) U.S. cities. Our results reveal that optimal attack strategies can accelerate malware spread by \u0000<inline-formula><tex-math>$10%$</tex-math></inline-formula>\u0000–\u0000<inline-formula><tex-math>$33%$</tex-math></inline-formula>\u0000.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1809-1820"},"PeriodicalIF":4.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-28DOI: 10.1109/JSYST.2024.3443541
Ziyi Zhou;Oluwakayode Onireti;Xinyi Lin;Lei Zhang;Muhammad Ali Imran
The ever-increasing mobile users pose a great challenge to the current centralized structure, where the failure of the network center significantly compromises privacy and data security. Practical byzantine fault tolerant (PBFT), a voting-based consensus blockchain, is a plausible solution to the wireless network for its distributed and decentralized traits, and it is not computation-intensive. The view change mechanism guarantees the liveness and resilience of PBFT, but it also causes delay. Moreover, spectrum is usually shared in the wireless network, introducing additional channel contention. Hence, we develop a framework investigating the impacts of channel contention on the wireless PBFT network using the IEEE 802.11 protocol. Based on the Markov model, we derive the throughput, transmission success probability, and transaction confirmation delay of such a network. Furthermore, we derive the view change delay in reference to the transaction confirmation delay. The analysis and results show that channel contention impacts in two ways. It impairs the success probability and increases the chance of view change. Optimal pairs of packet arrival rate and contention window size are formulated to maximize the consensus's success probability without sacrificing the overall network performance. Further, the optimal pairs under different network sizes are demonstrated for straightforward guidance.
{"title":"On the Performance of Wireless PBFT-Based Blockchain Network With IEEE 802.11","authors":"Ziyi Zhou;Oluwakayode Onireti;Xinyi Lin;Lei Zhang;Muhammad Ali Imran","doi":"10.1109/JSYST.2024.3443541","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3443541","url":null,"abstract":"The ever-increasing mobile users pose a great challenge to the current centralized structure, where the failure of the network center significantly compromises privacy and data security. Practical byzantine fault tolerant (PBFT), a voting-based consensus blockchain, is a plausible solution to the wireless network for its distributed and decentralized traits, and it is not computation-intensive. The view change mechanism guarantees the liveness and resilience of PBFT, but it also causes delay. Moreover, spectrum is usually shared in the wireless network, introducing additional channel contention. Hence, we develop a framework investigating the impacts of channel contention on the wireless PBFT network using the IEEE 802.11 protocol. Based on the Markov model, we derive the throughput, transmission success probability, and transaction confirmation delay of such a network. Furthermore, we derive the view change delay in reference to the transaction confirmation delay. The analysis and results show that channel contention impacts in two ways. It impairs the success probability and increases the chance of view change. Optimal pairs of packet arrival rate and contention window size are formulated to maximize the consensus's success probability without sacrificing the overall network performance. Further, the optimal pairs under different network sizes are demonstrated for straightforward guidance.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1786-1797"},"PeriodicalIF":4.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-28DOI: 10.1109/JSYST.2024.3444049
Xin Cai;Bingpeng Gao;Xinyuan Nan;Jie Yuan
For islanded ac microgrids with constrained digital communication networks under denial-of-service (DoS) attacks, this article proposes a resilient distributed secondary control strategy with a discrete-time quantization communication scheme to enhance the resilience to DoS attacks and to reduce communication loads. To cope with the time-constrained DoS attacks, the proposed discrete-time communication scheme combines a periodic communication attempt in the time intervals in presence of DoS attacks, and an event-triggered communication in the time intervals in absence of DoS attacks. Moreover, a dynamic quantization scheme for data transmission is introduced in the distributed control for the stability of the frequency and voltage at the reference. Then, a sufficient stability condition is established for the microgrid and the tradeoff between the communication attempt period and DoS attacks is obtained. Simulations of a microgrid are presented to verify the proposed resilient secondary control strategy.
对于在拒绝服务（DoS）攻击下具有受限数字通信网络的孤岛交流微电网，本文提出了一种具有离散时间量化通信方案的弹性分布式二次控制策略，以增强对 DoS 攻击的弹性并减少通信负载。为应对时间受限的 DoS 攻击，所提出的离散时间通信方案结合了在存在 DoS 攻击的时间间隔内的周期性通信尝试和在不存在 DoS 攻击的时间间隔内的事件触发通信。此外，在分布式控制中引入了数据传输动态量化方案，以确保频率和电压在基准点的稳定性。然后，建立了微电网的充分稳定条件，并得到了通信尝试周期与 DoS 攻击之间的权衡。本文还对微电网进行了仿真，以验证所提出的弹性二次控制策略。
{"title":"Resilient Discrete-Time Quantization Communication for Distributed Secondary Control of AC Microgrids Under DoS Attacks","authors":"Xin Cai;Bingpeng Gao;Xinyuan Nan;Jie Yuan","doi":"10.1109/JSYST.2024.3444049","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3444049","url":null,"abstract":"For islanded ac microgrids with constrained digital communication networks under denial-of-service (DoS) attacks, this article proposes a resilient distributed secondary control strategy with a discrete-time quantization communication scheme to enhance the resilience to DoS attacks and to reduce communication loads. To cope with the time-constrained DoS attacks, the proposed discrete-time communication scheme combines a periodic communication attempt in the time intervals in presence of DoS attacks, and an event-triggered communication in the time intervals in absence of DoS attacks. Moreover, a dynamic quantization scheme for data transmission is introduced in the distributed control for the stability of the frequency and voltage at the reference. Then, a sufficient stability condition is established for the microgrid and the tradeoff between the communication attempt period and DoS attacks is obtained. Simulations of a microgrid are presented to verify the proposed resilient secondary control strategy.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1798-1808"},"PeriodicalIF":4.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-27DOI: 10.1109/JSYST.2024.3440472
Xihe Qiu;Haoyu Wang;Xiaoyu Tan
Accurately understanding intentions is crucial in various real-world multiagent scenarios, which helps comprehend motives and predict actions within these contexts. Existing methods tend to either concentrate too much on single agents' isolated characteristics or model complex interactions among multiple agents, failing to adequately address both aspects simultaneously. To address this challenge, we propose a novel framework called integrative multiagent behavior prediction framework to systematically incorporate individual features and interagent relational dynamics. Our approach not only models multiagent interactions by learning from visual data, but also integrates mining of imagery and videos to leverage intrinsic invariant and variant qualities of each agent's extrinsic morphology. Meanwhile, inspired by time -series forecasting, we represent interagent history and connections as seasonal and trend features in time-series patterns, capturing past behavioral influences that are often ignored. We also design an encoder that efficiently learns time-dependencies and concatenates individual invariant–variant feature learning modules with multiagent interaction representations to accurately infer intentions and trajectory predictions. Our approach not only models multiagent interactions by learning from visual data, but also integrates mining of imagery and videos to leverage intrinsic invariant qualities of each agent's extrinsic morphology (e.g., body shape, color) and variant qualities (e.g., pose, expression, attire). Extensive experiments demonstrate that, compared to current state-of-the-art intention analysis models, our framework improves behavioral prediction performance in multiagent environments.
{"title":"Inferring Intents From Equivariant–Invariant Representations and Relational Learning in Multiagent Systems","authors":"Xihe Qiu;Haoyu Wang;Xiaoyu Tan","doi":"10.1109/JSYST.2024.3440472","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3440472","url":null,"abstract":"Accurately understanding intentions is crucial in various real-world multiagent scenarios, which helps comprehend motives and predict actions within these contexts. Existing methods tend to either concentrate too much on single agents' isolated characteristics or model complex interactions among multiple agents, failing to adequately address both aspects simultaneously. To address this challenge, we propose a novel framework called integrative multiagent behavior prediction framework to systematically incorporate individual features and interagent relational dynamics. Our approach not only models multiagent interactions by learning from visual data, but also integrates mining of imagery and videos to leverage intrinsic invariant and variant qualities of each agent's extrinsic morphology. Meanwhile, inspired by time -series forecasting, we represent interagent history and connections as seasonal and trend features in time-series patterns, capturing past behavioral influences that are often ignored. We also design an encoder that efficiently learns time-dependencies and concatenates individual invariant–variant feature learning modules with multiagent interaction representations to accurately infer intentions and trajectory predictions. Our approach not only models multiagent interactions by learning from visual data, but also integrates mining of imagery and videos to leverage intrinsic invariant qualities of each agent's extrinsic morphology (e.g., body shape, color) and variant qualities (e.g., pose, expression, attire). Extensive experiments demonstrate that, compared to current state-of-the-art intention analysis models, our framework improves behavioral prediction performance in multiagent environments.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1765-1775"},"PeriodicalIF":4.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-26DOI: 10.1109/JSYST.2024.3442017
Deemah H. Tashman;Soumaya Cherkaoui;Walaa Hamouda
This article presents a reinforcement learning-based approach to improve the physical layer security of an underlay cognitive radio network over cascaded channels. These channels are utilized in highly mobile networks such as cognitive vehicular networks (CVN). In addition, an eavesdropper aims to intercept the communications between secondary users (SUs). The SU receiver has full-duplex and energy harvesting capabilities to generate jamming signals to confound the eavesdropper and enhance security. Moreover, the SU transmitter extracts energy from ambient radio frequency signals in order to power subsequent transmissions to its intended receiver. To optimize the privacy and reliability of the SUs in a CVN, a deep Q-network (DQN) strategy is utilized where multiple DQN agents are required such that an agent is assigned at each SU transmitter. The objective for the SUs is to determine the optimal transmission power and decide whether to collect energy or transmit messages during each time period in order to maximize their secrecy rate. Thereafter, we propose a DQN approach to maximize the throughput of the SUs while respecting the interference threshold acceptable at the receiver of the primary user. According to our findings, our strategy outperforms two other baseline strategies in terms of security and reliability.
{"title":"Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels","authors":"Deemah H. Tashman;Soumaya Cherkaoui;Walaa Hamouda","doi":"10.1109/JSYST.2024.3442017","DOIUrl":"10.1109/JSYST.2024.3442017","url":null,"abstract":"This article presents a reinforcement learning-based approach to improve the physical layer security of an underlay cognitive radio network over cascaded channels. These channels are utilized in highly mobile networks such as cognitive vehicular networks (CVN). In addition, an eavesdropper aims to intercept the communications between secondary users (SUs). The SU receiver has full-duplex and energy harvesting capabilities to generate jamming signals to confound the eavesdropper and enhance security. Moreover, the SU transmitter extracts energy from ambient radio frequency signals in order to power subsequent transmissions to its intended receiver. To optimize the privacy and reliability of the SUs in a CVN, a deep Q-network (DQN) strategy is utilized where multiple DQN agents are required such that an agent is assigned at each SU transmitter. The objective for the SUs is to determine the optimal transmission power and decide whether to collect energy or transmit messages during each time period in order to maximize their secrecy rate. Thereafter, we propose a DQN approach to maximize the throughput of the SUs while respecting the interference threshold acceptable at the receiver of the primary user. According to our findings, our strategy outperforms two other baseline strategies in terms of security and reliability.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1839-1848"},"PeriodicalIF":4.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-26DOI: 10.1109/JSYST.2024.3443332
Yongsheng Chen;Shi Li;Yujing Yan;Guobao Liu
In this letter, a distributed periodic event-triggered (PET) consensus protocol is proposed for nonlinear multiagent systems under directed communication topology switchings. Only at the sampling instants, the information exchanges and the modes of topologies are used. Then, a distributed PET controller with a discrete event-triggering mechanism is proposed to reduce the burden of communication. By deriving a novel average dwell time constraint and constructing multiple Lyapunov functions, the leader–following consensus objective can be achieved. Finally, the proposed method's effectiveness is demonstrated by simulation results.
本文针对定向通信拓扑切换下的非线性多代理系统提出了一种分布式周期事件触发（PET）共识协议。该协议仅在采样时刻使用信息交换和拓扑模式。然后，提出了一种具有离散事件触发机制的分布式 PET 控制器，以减轻通信负担。通过推导新的平均停留时间约束条件和构建多个 Lyapunov 函数，可以实现领导-跟随共识目标。最后，模拟结果证明了所提方法的有效性。
{"title":"Distributed Periodic Event-Triggered Consensus of Second-Order Nonlinear Multiagent Systems With Switching Topologies","authors":"Yongsheng Chen;Shi Li;Yujing Yan;Guobao Liu","doi":"10.1109/JSYST.2024.3443332","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3443332","url":null,"abstract":"In this letter, a distributed periodic event-triggered (PET) consensus protocol is proposed for nonlinear multiagent systems under directed communication topology switchings. Only at the sampling instants, the information exchanges and the modes of topologies are used. Then, a distributed PET controller with a discrete event-triggering mechanism is proposed to reduce the burden of communication. By deriving a novel average dwell time constraint and constructing multiple Lyapunov functions, the leader–following consensus objective can be achieved. Finally, the proposed method's effectiveness is demonstrated by simulation results.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1821-1824"},"PeriodicalIF":4.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-23DOI: 10.1109/JSYST.2024.3437232
Han Deng;Dengfeng Xia;Han Cai;Qifa Yan;Peng Xu;Bin Dai
In this article, the relay network (RN) with receiver-transmitter feedback is investigated. First, we propose an efficient feedback coding scheme for the additive white Gaussian noise (AWGN) RN with noiseless receiver-transmitter feedback, which generalizes the Schalkwijk–Kailath (SK) type scheme for the AWGN channel with a single relay and noiseless receiver-transmitter feedback. The corresponding achievable rate of our proposed scheme is obtained under fixed coding block length and the receiver's decoding error probability, and it is shown that channel feedback significantly enhances the achievable rate of the same model without feedback. Then, we extend the above scheme to the same model with an AWGN feedback channel, where a modulo lattice function is applied to mitigate the impact of the feedback channel noise on the performance of the SK-type scheme. Finally, we further extend our proposed scheme to the quasi-static Rayleigh fading RN by using a precoding strategy. The results of this article are further explained via numerical examples, and this article provides a new method to construct low complexity coding schemes for relay networks.
{"title":"An Efficient Coding Scheme for the AWGN Relay Network With Receiver–Transmitter Feedback","authors":"Han Deng;Dengfeng Xia;Han Cai;Qifa Yan;Peng Xu;Bin Dai","doi":"10.1109/JSYST.2024.3437232","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3437232","url":null,"abstract":"In this article, the relay network (RN) with receiver-transmitter feedback is investigated. First, we propose an efficient feedback coding scheme for the additive white Gaussian noise (AWGN) RN with noiseless receiver-transmitter feedback, which generalizes the Schalkwijk–Kailath (SK) type scheme for the AWGN channel with a single relay and noiseless receiver-transmitter feedback. The corresponding achievable rate of our proposed scheme is obtained under fixed coding block length and the receiver's decoding error probability, and it is shown that channel feedback significantly enhances the achievable rate of the same model without feedback. Then, we extend the above scheme to the same model with an AWGN feedback channel, where a modulo lattice function is applied to mitigate the impact of the feedback channel noise on the performance of the SK-type scheme. Finally, we further extend our proposed scheme to the quasi-static Rayleigh fading RN by using a precoding strategy. The results of this article are further explained via numerical examples, and this article provides a new method to construct low complexity coding schemes for relay networks.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1717-1728"},"PeriodicalIF":4.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Edge caching is expected to alleviate the traffic consumption in next-generation communications. In this article, we consider the transmission delay in wideband communications deteriorated by rapid user movements, where the frequency-selective wideband fading channels become fast time-varying and hence doubly-selective due to the user movements. To preferably allocate the caching resource in such circumstance, we introduce a coordinated caching network and accordingly formulate an allocation problem. However, the formulated problem is shown to be NP-hard. By considering the extremely high computational complexity to solve the NP-hard problem by traditional optimization algorithm, and considering only a few samples can be obtained for each training instance due to shortened coherence-time in the dynamical doubly selective fading channels, we propose a model-agnostic meta-reinforcement learning method to address the formulated problem. Particularly, the proposed method can efficiently recognize the unstable mobile channels and accordingly cache to reduce the overall transmission delay while only requires a few training samples. Numerical simulations are performed to verify the effectiveness of the proposed method and results show that the proposed one outperforms the commonly adopted existing method of deep-deterministic-policy-gradient learning in terms of average delay and cache hit rate.
{"title":"Caching for Doubly Selective Fading Channels via Model-Agnostic Meta-Reinforcement Learning","authors":"Weibao He;Fasheng Zhou;Dong Tang;Fang Fang;Wei Chen","doi":"10.1109/JSYST.2024.3442958","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3442958","url":null,"abstract":"Edge caching is expected to alleviate the traffic consumption in next-generation communications. In this article, we consider the transmission delay in wideband communications deteriorated by rapid user movements, where the frequency-selective wideband fading channels become fast time-varying and hence \u0000<italic>doubly-selective</i>\u0000 due to the user movements. To preferably allocate the caching resource in such circumstance, we introduce a coordinated caching network and accordingly formulate an allocation problem. However, the formulated problem is shown to be NP-hard. By considering the extremely high computational complexity to solve the NP-hard problem by traditional optimization algorithm, and considering only a few samples can be obtained for each training instance due to shortened coherence-time in the dynamical doubly selective fading channels, we propose a model-agnostic meta-reinforcement learning method to address the formulated problem. Particularly, the proposed method can efficiently recognize the unstable mobile channels and accordingly cache to reduce the overall transmission delay while only requires a few training samples. Numerical simulations are performed to verify the effectiveness of the proposed method and results show that the proposed one outperforms the commonly adopted existing method of deep-deterministic-policy-gradient learning in terms of average delay and cache hit rate.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1776-1785"},"PeriodicalIF":4.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-08-21DOI: 10.1109/JSYST.2024.3437769
Siyi Li;Xiaojie Fang;Heng Dong;Wei Wu;Fugang Liu;Zhuoming Li
The integration of communication technologies is regarded as a potential direction of future network. Combining cell-free massive multiple-input multiple-output (CF-mMIMO) with nonorthogonal multiple access (NOMA) can achieve substantial access and uniform coverage, but it also poses challenges on channel state information (CSI) acquisition. Although systems operating in time division duplex can approximate uplink CSI for downlink precoding design, the performance degradation resulting from nonreciprocity is still nonnegligible. This article proposes a calibration method to improve system sum rate of CF-mMIMO-NOMA with nonreciprocal channels. The proposed method exploits the impact of all access points (APs) on achievable rate to provide a prioritization and calibrates the selected APs orderly, finding an optimal compromise between pilot overhead and performance gain. The order and number of calibrated APs can be adjusted according to the parameters including coherence interval, number of users, etc. Based on the calibration method, a power allocation algorithm focusing on maximizing the minimum rate is presented. The nonconvex initial model is transformed into a standard second-order cone programming problem through slack variables for bisection solution. Simulation results validate the importance of calibration and indicate that the proposed method provides enhanced achievable rate with flexibility and adaptability.
{"title":"Cell-Free Massive MIMO-NOMA Systems With Nonreciprocal Channels: Performance Analysis and Calibration Method Design","authors":"Siyi Li;Xiaojie Fang;Heng Dong;Wei Wu;Fugang Liu;Zhuoming Li","doi":"10.1109/JSYST.2024.3437769","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3437769","url":null,"abstract":"The integration of communication technologies is regarded as a potential direction of future network. Combining cell-free massive multiple-input multiple-output (CF-mMIMO) with nonorthogonal multiple access (NOMA) can achieve substantial access and uniform coverage, but it also poses challenges on channel state information (CSI) acquisition. Although systems operating in time division duplex can approximate uplink CSI for downlink precoding design, the performance degradation resulting from nonreciprocity is still nonnegligible. This article proposes a calibration method to improve system sum rate of CF-mMIMO-NOMA with nonreciprocal channels. The proposed method exploits the impact of all access points (APs) on achievable rate to provide a prioritization and calibrates the selected APs orderly, finding an optimal compromise between pilot overhead and performance gain. The order and number of calibrated APs can be adjusted according to the parameters including coherence interval, number of users, etc. Based on the calibration method, a power allocation algorithm focusing on maximizing the minimum rate is presented. The nonconvex initial model is transformed into a standard second-order cone programming problem through slack variables for bisection solution. Simulation results validate the importance of calibration and indicate that the proposed method provides enhanced achievable rate with flexibility and adaptability.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1729-1740"},"PeriodicalIF":4.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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