Pub Date : 2024-03-30DOI: 10.1109/TGCN.2024.3407523
Hamad Yahya;Emad Alsusa;Arafat Al-Dweik
This work considers the design of a generalized Gray-mapping process to multilayer multicast non-orthogonal multiple access (NOMA) transmission with arbitrary modulation orders. Unlike orthogonal multiple access (OMA), joint-multilayer Gray-mapping (J-Gray) can provide significant energy savings and bit error rate (BER) improvements, which can be used to alleviate the degradation caused by the inherent multilayer interference in NOMA. The obtained improvement is due to the increased Euclidean distance that Gray-mapping provides for certain layers/symbols. To evaluate the impact of Gray-mapping, closed-form expressions are derived for the exact BER with imperfect successive interference cancellation (SIC). The obtained analytical and simulation results demonstrate that the proposed scheme can offer up to 10 dB gain and 94% energy saving compared to conventional NOMA in certain scenarios, where such a performance gain can be shared between the layers by selecting an appropriate power assignment (PA). Moreover, feasibility maps are generated to demonstrate the additional flexibility that Gray-mapping can offer in terms of quality of service (QoS) satisfaction for the various layers at lower signal to noise ratio (SNR).
这项研究考虑了设计一种通用的灰色映射过程，用于具有任意调制阶的多层多播非正交多址（NOMA）传输。与正交多址（OMA）不同，联合多层灰色映射（J-Gray）可显著节省能量和提高误码率（BER），用于减轻 NOMA 中固有的多层干扰造成的性能下降。所获得的改进是由于灰色映射为某些层/符号提供了更大的欧氏距离。为了评估灰色映射的影响，推导出了不完全连续干扰消除（SIC）情况下精确误码率的闭式表达式。分析和仿真结果表明，在某些情况下，与传统的 NOMA 相比，建议的方案可提供高达 10 dB 的增益和 94% 的节能。此外，还生成了可行性图，以证明在较低信噪比（SNR）条件下，灰色映射可在满足各层服务质量（QoS）方面提供额外的灵活性。
{"title":"Joint Gray-Mapping for Multilayer Multicast NOMA With Arbitrary Modulation Orders","authors":"Hamad Yahya;Emad Alsusa;Arafat Al-Dweik","doi":"10.1109/TGCN.2024.3407523","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3407523","url":null,"abstract":"This work considers the design of a generalized Gray-mapping process to multilayer multicast non-orthogonal multiple access (NOMA) transmission with arbitrary modulation orders. Unlike orthogonal multiple access (OMA), joint-multilayer Gray-mapping (J-Gray) can provide significant energy savings and bit error rate (BER) improvements, which can be used to alleviate the degradation caused by the inherent multilayer interference in NOMA. The obtained improvement is due to the increased Euclidean distance that Gray-mapping provides for certain layers/symbols. To evaluate the impact of Gray-mapping, closed-form expressions are derived for the exact BER with imperfect successive interference cancellation (SIC). The obtained analytical and simulation results demonstrate that the proposed scheme can offer up to 10 dB gain and 94% energy saving compared to conventional NOMA in certain scenarios, where such a performance gain can be shared between the layers by selecting an appropriate power assignment (PA). Moreover, feasibility maps are generated to demonstrate the additional flexibility that Gray-mapping can offer in terms of quality of service (QoS) satisfaction for the various layers at lower signal to noise ratio (SNR).","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1762-1780"},"PeriodicalIF":5.3,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713891","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 : 2024-03-29DOI: 10.1109/TGCN.2024.3406803
Shaima Abidrabbu;Sawaira Rafaqat Ali;H. M. Furqan;Hüseyin Arslan
In rate-splitting multiple access (RSMA) networks, high reliability is critical, and one efficient way to improve reliability is to use a hybrid automated repeat request (HARQ) protocol. However, the challenge lies in designing the HARQ in RSMA networks due to the combining and splitting procedures at the transmitter side. In this paper, we propose a novel transmission approach based on incremental redundancy (IR) that is also equivalent to the HARQ protocol for RSMA networks. The proposed strategy opens up a new path for RSMA networks where HARQ is challenging or cannot be applied. In particular, a redundancy version (RV) of each user’s private data is constructed and placed in the common stream by making use of common stream properties such as transmitting it non-orthogonally with higher power, alongside the private stream. The efficacy of the proposed design is examined through various metrics, encompassing average transmission rate, outage analysis, energy efficiency (EE), and diversity order. Simulation results validate the superior performance of the proposed protocol, showcasing a significant improvement of up to 60%, 23%, and 79% in terms of packet error rate, average waiting time, and EE respectively, compared to the conventional HARQ RSMA.
{"title":"An IR-HARQ Based RSMA for Reliable and Low-Latency Communication","authors":"Shaima Abidrabbu;Sawaira Rafaqat Ali;H. M. Furqan;Hüseyin Arslan","doi":"10.1109/TGCN.2024.3406803","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3406803","url":null,"abstract":"In rate-splitting multiple access (RSMA) networks, high reliability is critical, and one efficient way to improve reliability is to use a hybrid automated repeat request (HARQ) protocol. However, the challenge lies in designing the HARQ in RSMA networks due to the combining and splitting procedures at the transmitter side. In this paper, we propose a novel transmission approach based on incremental redundancy (IR) that is also equivalent to the HARQ protocol for RSMA networks. The proposed strategy opens up a new path for RSMA networks where HARQ is challenging or cannot be applied. In particular, a redundancy version (RV) of each user’s private data is constructed and placed in the common stream by making use of common stream properties such as transmitting it non-orthogonally with higher power, alongside the private stream. The efficacy of the proposed design is examined through various metrics, encompassing average transmission rate, outage analysis, energy efficiency (EE), and diversity order. Simulation results validate the superior performance of the proposed protocol, showcasing a significant improvement of up to 60%, 23%, and 79% in terms of packet error rate, average waiting time, and EE respectively, compared to the conventional HARQ RSMA.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1308-1317"},"PeriodicalIF":5.3,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672044","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 : 2024-03-27DOI: 10.1109/TGCN.2024.3405627
Santiago Fernández;F. Javier López-Martínez;Fernando H. Gregorio;Juan Cousseau
In this work, we analyze how the use of companding techniques, together with digital predistortion (DPD), can be leveraged to improve system efficiency and performance in simultaneous wireless information and power transfer (SWIPT) systems based on power splitting. By taking advantage of the benefits of each of these well-known techniques to mitigate non-linear effects due to power amplifier (PA) and energy harvesting (EH) operation, we illustrate how DPD and companding can be effectively combined to improve the EH efficiency while keeping unalterable the information transfer performance. We establish design criteria that allow the PA to operate in a higher efficiency region so that the reduction in peak-to-average power ratio over the transmitted signal is translated into an increase in the average radiated power and EH efficiency. The performance of DPD and companding techniques is evaluated in a number of scenarios, showing that a combination of both techniques allows to significantly increase the power transfer efficiency in SWIPT systems.
{"title":"Companding and Predistortion Techniques for Improved Efficiency and Performance in SWIPT","authors":"Santiago Fernández;F. Javier López-Martínez;Fernando H. Gregorio;Juan Cousseau","doi":"10.1109/TGCN.2024.3405627","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3405627","url":null,"abstract":"In this work, we analyze how the use of companding techniques, together with digital predistortion (DPD), can be leveraged to improve system efficiency and performance in simultaneous wireless information and power transfer (SWIPT) systems based on power splitting. By taking advantage of the benefits of each of these well-known techniques to mitigate non-linear effects due to power amplifier (PA) and energy harvesting (EH) operation, we illustrate how DPD and companding can be effectively combined to improve the EH efficiency while keeping unalterable the information transfer performance. We establish design criteria that allow the PA to operate in a higher efficiency region so that the reduction in peak-to-average power ratio over the transmitted signal is translated into an increase in the average radiated power and EH efficiency. The performance of DPD and companding techniques is evaluated in a number of scenarios, showing that a combination of both techniques allows to significantly increase the power transfer efficiency in SWIPT systems.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1676-1691"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713890","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}
Satellite mobile edge computing (SMEC) deployed on ultra-dense low Earth orbit (LEO) satellites with high throughput and low latency can provide ubiquitous computing services closer to the user side. However, considering the highly dynamic and limited resources of LEO constellations, a joint strategy for accessing and offloading of ground users becomes difficult under overlapping satellite coverage. In this paper, a joint optimization method of dynamic user association and computation offloading for SMEC is proposed. Terrestrial users with random and diverse tasks adaptively access the optimal associated satellite under time-varying channel conditions, and offload to a satellite with sufficient remaining computing capability for load balancing in the SMEC network with inter-satellite cooperation. Furthermore, an evolutionary algorithm based on deep Q-network (DQN) is designed to jointly optimize the decisions of associated and offloading satellites and the allocation of computing resources, which enables energy-efficient strategies while meeting task latency and SMEC resource constraints. The method learns multi-dimensional actions intelligently and synchronously by improving network structure. The simulation results show that the proposed scheme can effectively reduce the system energy consumption by ensuring that the task is completed on demand, and outperform the benchmark algorithms.
{"title":"Dynamic User Association and Computation Offloading in Satellite Edge Computing Networks via Deep Reinforcement Learning","authors":"Hangyu Zhang;Hongbo Zhao;Rongke Liu;Xiangqiang Gao;Shenzhan Xu","doi":"10.1109/TGCN.2024.3357813","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3357813","url":null,"abstract":"Satellite mobile edge computing (SMEC) deployed on ultra-dense low Earth orbit (LEO) satellites with high throughput and low latency can provide ubiquitous computing services closer to the user side. However, considering the highly dynamic and limited resources of LEO constellations, a joint strategy for accessing and offloading of ground users becomes difficult under overlapping satellite coverage. In this paper, a joint optimization method of dynamic user association and computation offloading for SMEC is proposed. Terrestrial users with random and diverse tasks adaptively access the optimal associated satellite under time-varying channel conditions, and offload to a satellite with sufficient remaining computing capability for load balancing in the SMEC network with inter-satellite cooperation. Furthermore, an evolutionary algorithm based on deep Q-network (DQN) is designed to jointly optimize the decisions of associated and offloading satellites and the allocation of computing resources, which enables energy-efficient strategies while meeting task latency and SMEC resource constraints. The method learns multi-dimensional actions intelligently and synchronously by improving network structure. The simulation results show that the proposed scheme can effectively reduce the system energy consumption by ensuring that the task is completed on demand, and outperform the benchmark algorithms.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1888-1901"},"PeriodicalIF":5.3,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713939","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 : 2024-03-25DOI: 10.1109/TGCN.2024.3381346
Ning Zhao;Michael Shi;Xudong Zhao;Guangdeng Zong;Huiyan Zhang
This paper addresses the issue of secure distributed consensus tracking control for uncertain heterogeneous multi-agent systems (MASs) under synchronous sampled communication and intermittent denial-of-service (DoS) attacks. Considering that DoS attacks intermittently block the sampled data signal, a new packet update sequence is established to describe the transmitted signal to the neighbor agents. By virtue of the leader’s dynamics, the sampled-data-based distributed observer is established to observe the leader’s information for all followers. Then, based on the estimated signals and employing neural network approximation approach, secure adaptive sampled-data control strategy is proposed to compensate for the effects of uncertainty and DoS attacks. By utilizing novel Lyapunov-Krasovskii approach, the consensus tracking errors are regulated in the neighborhood around the origin. Finally, a numerical example with the coupled pendulums is provided to substantiate the efficiency of the proposed approach to achieve tracking security performance.
本文探讨了在同步采样通信和间歇性拒绝服务（DoS）攻击下，不确定异构多代理系统（MAS）的安全分布式共识跟踪控制问题。考虑到 DoS 攻击会间歇性地阻断采样数据信号，因此需要建立一个新的数据包更新序列，以向相邻代理描述传输信号。利用领导者的动态性，建立基于采样数据的分布式观测器，为所有跟随者观测领导者的信息。然后，根据估计的信号并采用神经网络近似方法，提出安全的自适应采样数据控制策略，以补偿不确定性和 DoS 攻击的影响。通过利用新颖的 Lyapunov-Krasovskii 方法，在原点附近调节了共识跟踪误差。最后，提供了一个耦合摆的数值示例，以证实所提方法在实现跟踪安全性能方面的效率。
{"title":"Distributed Adaptive Sampled-Data Security Tracking Control for Uncertain Heterogeneous Multi-Agents Systems Under DoS Attacks","authors":"Ning Zhao;Michael Shi;Xudong Zhao;Guangdeng Zong;Huiyan Zhang","doi":"10.1109/TGCN.2024.3381346","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3381346","url":null,"abstract":"This paper addresses the issue of secure distributed consensus tracking control for uncertain heterogeneous multi-agent systems (MASs) under synchronous sampled communication and intermittent denial-of-service (DoS) attacks. Considering that DoS attacks intermittently block the sampled data signal, a new packet update sequence is established to describe the transmitted signal to the neighbor agents. By virtue of the leader’s dynamics, the sampled-data-based distributed observer is established to observe the leader’s information for all followers. Then, based on the estimated signals and employing neural network approximation approach, secure adaptive sampled-data control strategy is proposed to compensate for the effects of uncertainty and DoS attacks. By utilizing novel Lyapunov-Krasovskii approach, the consensus tracking errors are regulated in the neighborhood around the origin. Finally, a numerical example with the coupled pendulums is provided to substantiate the efficiency of the proposed approach to achieve tracking security performance.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1385-1397"},"PeriodicalIF":5.3,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672050","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 : 2024-03-23DOI: 10.1109/TGCN.2024.3404891
Carla E. Garcia;Mario R. Camana;Jorge Querol;Symeon Chatzinotas
In this paper, we investigate a secure transmission for a rate-splitting multiple-access (RSMA)-based multiple-input single-output (MISO) underlay cognitive radio (CR) system. The proposed network is composed of a set of secondary users (SUs) that utilize simultaneous wireless information and power transfer (SWIPT) technology and an additional set of non-linear energy harvesting (EH) users. Moreover, the system model under consideration is exposed to multiple eavesdroppers. Thus, we propose to minimize the transmit power intended to the SUs and EH users while maximizing the artificial noise (AN) generated by the secondary transmitter, aiming to counter eavesdroppers’ wiretaps while satisfying the quality-of-service constraints. Therefore, we develop a novel approach based on ant colony regression (ACOR) and semidefinite relaxation (SDR) methods to solve the challenging and non-convex problem which is further transformed into a bilevel optimization problem. Afterward, we investigate a comparative solution based on the particle swarm optimization (PSO) algorithm, the successive convex approximation (SCA) technique, and analyze the incidence of linear and non-linear EH designs. In addition, we compare the RSMA-based scheme with non-orthogonal multiple-access (NOMA), space-division multiple access (SDMA), and zero-forcing (ZF) techniques. Satisfactorily, simulation results prove the proposed ACOR-SDR framework achieves better performance and lower complexity than its counterparts.
{"title":"Rate-Splitting Multiple Access for Secure Communications Over CR MISO SWIPT Systems With Non-Linear EH Users","authors":"Carla E. Garcia;Mario R. Camana;Jorge Querol;Symeon Chatzinotas","doi":"10.1109/TGCN.2024.3404891","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3404891","url":null,"abstract":"In this paper, we investigate a secure transmission for a rate-splitting multiple-access (RSMA)-based multiple-input single-output (MISO) underlay cognitive radio (CR) system. The proposed network is composed of a set of secondary users (SUs) that utilize simultaneous wireless information and power transfer (SWIPT) technology and an additional set of non-linear energy harvesting (EH) users. Moreover, the system model under consideration is exposed to multiple eavesdroppers. Thus, we propose to minimize the transmit power intended to the SUs and EH users while maximizing the artificial noise (AN) generated by the secondary transmitter, aiming to counter eavesdroppers’ wiretaps while satisfying the quality-of-service constraints. Therefore, we develop a novel approach based on ant colony regression (ACOR) and semidefinite relaxation (SDR) methods to solve the challenging and non-convex problem which is further transformed into a bilevel optimization problem. Afterward, we investigate a comparative solution based on the particle swarm optimization (PSO) algorithm, the successive convex approximation (SCA) technique, and analyze the incidence of linear and non-linear EH designs. In addition, we compare the RSMA-based scheme with non-orthogonal multiple-access (NOMA), space-division multiple access (SDMA), and zero-forcing (ZF) techniques. Satisfactorily, simulation results prove the proposed ACOR-SDR framework achieves better performance and lower complexity than its counterparts.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1332-1347"},"PeriodicalIF":5.3,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672002","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 : 2024-03-23DOI: 10.1109/TGCN.2024.3404500
Andreas Andreou;Constandinos X. Mavromoustakis
This study explores the integration of sustainable practices in the advancing domain of sixth-generation and beyond (6G+) network technologies, with a particular focus on enhancing the efficiency of search and rescue operations. It presents a comprehensive strategy for network slicing designed to bolster seamless communication and operational efficacy of emergency response teams in varied and ever-changing conditions. It presents an innovative approach to managing workload fluctuations in network slicing. Also, it introduces a new slice configuration mechanism to prioritize signals for devices within the complex, compelling, hierarchical network systems. Incorporating a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is central to the approach, tackling the complexity of implementing effective communication strategies across multiple network layers. Our findings demonstrate a highly adaptable and real-time slice configuration technique within System of Systems (SoS) environments, offering significant enhancements in systems engineering and emergency communication management. This approach contributes to the robustness and reliability of emergency response communications and underscores the importance of integrating environmental sustainability in developing next-generation network technologies.
{"title":"6G⁺ Networks Through Enhanced Efficiency and Sustainability With MADDPG-Driven Network Slicing in SoS Environments","authors":"Andreas Andreou;Constandinos X. Mavromoustakis","doi":"10.1109/TGCN.2024.3404500","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3404500","url":null,"abstract":"This study explores the integration of sustainable practices in the advancing domain of sixth-generation and beyond (6G+) network technologies, with a particular focus on enhancing the efficiency of search and rescue operations. It presents a comprehensive strategy for network slicing designed to bolster seamless communication and operational efficacy of emergency response teams in varied and ever-changing conditions. It presents an innovative approach to managing workload fluctuations in network slicing. Also, it introduces a new slice configuration mechanism to prioritize signals for devices within the complex, compelling, hierarchical network systems. Incorporating a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is central to the approach, tackling the complexity of implementing effective communication strategies across multiple network layers. Our findings demonstrate a highly adaptable and real-time slice configuration technique within System of Systems (SoS) environments, offering significant enhancements in systems engineering and emergency communication management. This approach contributes to the robustness and reliability of emergency response communications and underscores the importance of integrating environmental sustainability in developing next-generation network technologies.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1752-1761"},"PeriodicalIF":5.3,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713765","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 : 2024-03-21DOI: 10.1109/TGCN.2024.3403843
Ruiyan Du;Huifang Wang;Shiyi Wang;Baozhu Shi;Zhuoyao Duan;Fulai Liu
As a key emerging green communication technology, signal detection based on deep learning can improve communication performance for orthogonal frequency division multiplexing with index modulation (OFDM-IM). However, it may lead to an increase in the bit error rate (BER) when the index and carrier are detected as a whole. To tackle this problem, a two-stage dilated convolutional neural network based on OFDM-IM (TS-DCNN-IM) is presented to improve signal detection performance in this paper. Through the two-stage design, the index and carrier can be processed separately by different subnetworks, thereby achieving better detection performance. In the first stage, an index subnetwork based on CNN is designed to obtain the index information of the carriers. Specifically, a dilated convolution module is introduced into the index subnetwork to better extract the carrier features, which is achieved by enlarging the receptive field without adding the network parameters. In the second stage, a deep neural network is constructed to predict the transmitted signal bits. Finally, the well-trained TS-DCNN-IM model is used to directly output the transmitted signal bits. Simulation results show that compared to the related algorithms, the TS-DCNN-IM algorithm can achieve better BER performance and higher computational efficiency.
{"title":"Two-Stage Dilated Convolutional Neural Network-Based Detector for OFDM-IM","authors":"Ruiyan Du;Huifang Wang;Shiyi Wang;Baozhu Shi;Zhuoyao Duan;Fulai Liu","doi":"10.1109/TGCN.2024.3403843","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403843","url":null,"abstract":"As a key emerging green communication technology, signal detection based on deep learning can improve communication performance for orthogonal frequency division multiplexing with index modulation (OFDM-IM). However, it may lead to an increase in the bit error rate (BER) when the index and carrier are detected as a whole. To tackle this problem, a two-stage dilated convolutional neural network based on OFDM-IM (TS-DCNN-IM) is presented to improve signal detection performance in this paper. Through the two-stage design, the index and carrier can be processed separately by different subnetworks, thereby achieving better detection performance. In the first stage, an index subnetwork based on CNN is designed to obtain the index information of the carriers. Specifically, a dilated convolution module is introduced into the index subnetwork to better extract the carrier features, which is achieved by enlarging the receptive field without adding the network parameters. In the second stage, a deep neural network is constructed to predict the transmitted signal bits. Finally, the well-trained TS-DCNN-IM model is used to directly output the transmitted signal bits. Simulation results show that compared to the related algorithms, the TS-DCNN-IM algorithm can achieve better BER performance and higher computational efficiency.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 4","pages":"1852-1861"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713794","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 : 2024-03-21DOI: 10.1109/TGCN.2024.3403662
Ankur Jaiswal;Salla Shivateja;Abhishek Hazra;Nabajyoti Mazumdar;Jagpreet Singh;Varun G. Menon
This article introduces a novel approach for Unmanned Aerial Vehicles (UAV) assisted wireless power transfer (WPT) within a Radio Access Network (RAN) provisioned Internet of Things (IoT) network. The goal is to efficiently charge scattered IoT Nodes (INs) within their respective energy deadlines. The proposed methodology combines the concepts of Radio Frequency Energy Transfer (RFET) zones, K-means clustering, and Ant Colony Optimization (ACO) to optimize the charging process. Initially, RFET zones are formed around the INs, and K-means clustering is applied to group nodes based on their spatial proximity and energy requirements. Subsequently modified ACO algorithm is employed to construct efficient paths for UAVs to visit these clusters. This is achieved by taking into account several aspects such as node deadlines and UAV capacity, thereby assuring the timely and efficient transmission of energy. After comparative analysis with EUP-ACS and IA-DRL, the proposed algorithm achieves a substantial reduction of 22.22% and 36.36% respectively in UAV usage, while also exhibiting significant improvements in RFET zones, energy efficiency, and survival rate, confirming its effectiveness in enhancing charging performance, reducing energy waste, and meeting deadlines.
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The development of the Internet of Things (IoT) causes most industrial applications to utilize IoT devices to improve their productivity. Applications such as smart cities, energy management, smart homes, smart cars, and supply chain management widely utilize the IoT to manage the industries’ efficiency. Industrial IoT devices are frequently affected by cybercriminals and damage information and productivity. Criminal activities can be overcome by applying various machine-learning techniques. Existing methods can process intermediate attacks; however, traditional machine learning techniques have difficulties predicting adversarial and catastrophic attacks. In addition, most of the AI-based industrial applications have heterogeneous and mixed data, requiring robust intruder detection systems. The research issues are addressed by introducing the Meta-Heuristic Optimized Deep Random Neural Networks (MH-DRNN). The system uses the optimization process in feature selection and classification, reducing the heterogeneous data analysis issues. The optimization method selects the features from the feature set according to the sunflower movement, which minimizes the difficulties in computation. In addition, three MLP and three recurrent layers are incorporated into this system to maximize the prediction rate up to 99.2% accuracy.
{"title":"Anomaly Detection Algorithm of Industrial Internet of Things Data Platform Based on Deep Learning","authors":"Xing Li;Chao Xie;Zhijia Zhao;Chunbao Wang;Huajun Yu","doi":"10.1109/TGCN.2024.3403102","DOIUrl":"https://doi.org/10.1109/TGCN.2024.3403102","url":null,"abstract":"The development of the Internet of Things (IoT) causes most industrial applications to utilize IoT devices to improve their productivity. Applications such as smart cities, energy management, smart homes, smart cars, and supply chain management widely utilize the IoT to manage the industries’ efficiency. Industrial IoT devices are frequently affected by cybercriminals and damage information and productivity. Criminal activities can be overcome by applying various machine-learning techniques. Existing methods can process intermediate attacks; however, traditional machine learning techniques have difficulties predicting adversarial and catastrophic attacks. In addition, most of the AI-based industrial applications have heterogeneous and mixed data, requiring robust intruder detection systems. The research issues are addressed by introducing the Meta-Heuristic Optimized Deep Random Neural Networks (MH-DRNN). The system uses the optimization process in feature selection and classification, reducing the heterogeneous data analysis issues. The optimization method selects the features from the feature set according to the sunflower movement, which minimizes the difficulties in computation. In addition, three MLP and three recurrent layers are incorporated into this system to maximize the prediction rate up to 99.2% accuracy.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":"8 3","pages":"1037-1048"},"PeriodicalIF":5.3,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090948","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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