Pub Date : 2023-12-19DOI: 10.1109/TGCN.2023.3344318
Sudhanshu Arya;Yeon Ho Chung
This paper presents a new and unique approach that significantly increases the channel capacity of multiuser indoor optical wireless communication systems. In particular, we consider an orbital angular momentum (OAM) based multiuser indoor communication with a unique optical geometric transformation (OGT) technique. We derive the channel impulse response for OAM carrying Laguerre-Gaussian beams using the angular spectrum method. The optical transceiver is designed on the principle of space variance and parallel processing by realizing a geometric transformation in structured light. Specifically, each transmitter transforms transverse positions in the input beams into multiplexed azimuthal positions at the output. The transmitter uses the orthogonality of OAM modes to encode many optical channels on the same wavelength. As a proof of concept, multiple signals are encoded on each transmitter and multicast to various receivers simultaneously. The amplitude function and the spatial dependence of the OAM field vectors are analyzed. We visualize the combined effects of the dispersion, optical channel noise immunity, accuracy of timing extraction, and intersymbol interference on the performance of the proposed system. In addition, we analyze the impact of multiuser interference on performance. The channel equalization condition for an interference-free transmission is also presented. Moreover, we present the impacts of amplification gain and reflectivity on the ripples. The results illustrate that reflectivity has a strong impact on ripples. The proposed transceiver design can also easily distinguish the true mode from other neighboring modes, as we expect the energy of an OAM-carrying beam to spread from the true mode symmetrically to its neighbors. In addition, the proposed transceiver design enables the detection of existing all multiple OAM modes through a single transformation. Finally, it is shown from a series of results and comparative analyses that the proposed system can offer very high channel capacity in indoor optical multiuser communication systems, while maintaining an arbitrary low bit error rate.
{"title":"Optical Geometric Transformation-Based Orbital Angular Momentum for Indoor Multiuser Communications","authors":"Sudhanshu Arya;Yeon Ho Chung","doi":"10.1109/TGCN.2023.3344318","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3344318","url":null,"abstract":"This paper presents a new and unique approach that significantly increases the channel capacity of multiuser indoor optical wireless communication systems. In particular, we consider an orbital angular momentum (OAM) based multiuser indoor communication with a unique optical geometric transformation (OGT) technique. We derive the channel impulse response for OAM carrying Laguerre-Gaussian beams using the angular spectrum method. The optical transceiver is designed on the principle of space variance and parallel processing by realizing a geometric transformation in structured light. Specifically, each transmitter transforms transverse positions in the input beams into multiplexed azimuthal positions at the output. The transmitter uses the orthogonality of OAM modes to encode many optical channels on the same wavelength. As a proof of concept, multiple signals are encoded on each transmitter and multicast to various receivers simultaneously. The amplitude function and the spatial dependence of the OAM field vectors are analyzed. We visualize the combined effects of the dispersion, optical channel noise immunity, accuracy of timing extraction, and intersymbol interference on the performance of the proposed system. In addition, we analyze the impact of multiuser interference on performance. The channel equalization condition for an interference-free transmission is also presented. Moreover, we present the impacts of amplification gain and reflectivity on the ripples. The results illustrate that reflectivity has a strong impact on ripples. The proposed transceiver design can also easily distinguish the true mode from other neighboring modes, as we expect the energy of an OAM-carrying beam to spread from the true mode symmetrically to its neighbors. In addition, the proposed transceiver design enables the detection of existing all multiple OAM modes through a single transformation. Finally, it is shown from a series of results and comparative analyses that the proposed system can offer very high channel capacity in indoor optical multiuser communication systems, while maintaining an arbitrary low bit error rate.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078755","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}
Low Earth Orbit (LEO) satellite networks are expected to play a crucial role in providing high-speed Internet access and low-latency communication worldwide. However, some challenges can affect the performance of LEO satellite networks. For example, they can face energy and spectral efficiency challenges, such as high power consumption and spectral congestion, due to the increasing number of satellites. Furthermore, mobile ground users tend to operate with low directive antennas, which pose significant challenges in closing the LEO-to-ground communication link, especially when operating at a high-frequency range. To overcome these challenges, energy-efficient technologies like reconfigurable intelligent surfaces (RIS) and advanced spectrum management techniques like non-orthogonal multiple access (NOMA) can be employed. RIS can improve signal quality and reduce power consumption, while NOMA can enhance spectral efficiency by sharing the same resources among multiple users. This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks while ensuring the quality of services. The proposed framework simultaneously optimizes the NOMA transmit power of the LEO satellite and the passive beamforming of RIS, considering the assumption of imperfect successive interference cancellation. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47% and 54.9% higher energy efficiency compared to the benchmark and conventional frameworks.
{"title":"RIS-Assisted Energy-Efficient LEO Satellite Communications With NOMA","authors":"Wali Ullah Khan;Eva Lagunas;Asad Mahmood;Symeon Chatzinotas;Björn Ottersten","doi":"10.1109/TGCN.2023.3344102","DOIUrl":"10.1109/TGCN.2023.3344102","url":null,"abstract":"Low Earth Orbit (LEO) satellite networks are expected to play a crucial role in providing high-speed Internet access and low-latency communication worldwide. However, some challenges can affect the performance of LEO satellite networks. For example, they can face energy and spectral efficiency challenges, such as high power consumption and spectral congestion, due to the increasing number of satellites. Furthermore, mobile ground users tend to operate with low directive antennas, which pose significant challenges in closing the LEO-to-ground communication link, especially when operating at a high-frequency range. To overcome these challenges, energy-efficient technologies like reconfigurable intelligent surfaces (RIS) and advanced spectrum management techniques like non-orthogonal multiple access (NOMA) can be employed. RIS can improve signal quality and reduce power consumption, while NOMA can enhance spectral efficiency by sharing the same resources among multiple users. This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks while ensuring the quality of services. The proposed framework simultaneously optimizes the NOMA transmit power of the LEO satellite and the passive beamforming of RIS, considering the assumption of imperfect successive interference cancellation. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47% and 54.9% higher energy efficiency compared to the benchmark and conventional frameworks.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139369532","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 : 2023-12-15DOI: 10.1109/TGCN.2023.3343491
Kapila W. S. Palitharathna;Nirmal D. Wickramasinghe;Anna M. Vegni;Himal A. Suraweera
In this paper, we consider a simultaneous lightwave and power transfer-enabled indoor visible light communication system and aim to investigate how to manage the transmission power from multiple transmitters to provide both information and energy harvesting. We formulate three different optimization problems, all aiming to minimize the total average transmit power at the luminaries, assuming different performance constraints, such as data rate, energy harvest, and illumination requirements. The first problem aims to find the optimal beamforming matrix and the transmit powers at light emitting diodes (LEDs), while the second problem aims to use zero-forcing beamforming and finds the optimal transmit powers. Finally, the third problem aims to find the minimum number of LEDs required to satisfy the given constraints. Relying on a Machine Learning approach, our solution is capable of predicting the user mobility patterns, and receiver orientation angles and accordingly optimizing parameters leading to a near-optimal result under different blockage conditions with low computational complexity. Moreover, a comparison with other approaches shows the effectiveness of the proposed solution in terms of significantly reducing the transmit power in a wide range of orientation errors. Specifically, up to 50% of the average transmit power can be minimized using the presented approach.
{"title":"Neural Network-Based Optimization for SLIPT-Enabled Indoor VLC Systems With Energy Constraints","authors":"Kapila W. S. Palitharathna;Nirmal D. Wickramasinghe;Anna M. Vegni;Himal A. Suraweera","doi":"10.1109/TGCN.2023.3343491","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3343491","url":null,"abstract":"In this paper, we consider a simultaneous lightwave and power transfer-enabled indoor visible light communication system and aim to investigate how to manage the transmission power from multiple transmitters to provide both information and energy harvesting. We formulate three different optimization problems, all aiming to minimize the total average transmit power at the luminaries, assuming different performance constraints, such as data rate, energy harvest, and illumination requirements. The first problem aims to find the optimal beamforming matrix and the transmit powers at light emitting diodes (LEDs), while the second problem aims to use zero-forcing beamforming and finds the optimal transmit powers. Finally, the third problem aims to find the minimum number of LEDs required to satisfy the given constraints. Relying on a Machine Learning approach, our solution is capable of predicting the user mobility patterns, and receiver orientation angles and accordingly optimizing parameters leading to a near-optimal result under different blockage conditions with low computational complexity. Moreover, a comparison with other approaches shows the effectiveness of the proposed solution in terms of significantly reducing the transmit power in a wide range of orientation errors. Specifically, up to 50% of the average transmit power can be minimized using the presented approach.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078736","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 : 2023-12-14DOI: 10.1109/TGCN.2023.3343186
George A. Ropokis;Petros S. Bithas
We investigate Wireless Powered Multi-Relay Networks (WPRNs) equipped with multiple antennas both at the Source and the Relay and propose two different communication schemes. These schemes are based on the combination of Time Switching (TS) and Self-Energy Recycling (SER) and extend existing ones that have been developed for single-antenna sources. Following that, by adopting only the very generic assumption that the Energy Harvesting Model (EHM) is described by any non-decreasing function, we focus on the instantaneous rate maximization problem and design near-optimal beamforming and wireless power transfer-time determination algorithms for our schemes. A common characteristic of the presented algorithms is their low complexity and implementation simplicity. Given the generality of our EHM assumptions, our algorithms are applicable for all popular EHMs found in the literature, which are normally described using non-decreasing functions, without being specific to any of them. Various simulation results are presented that allow to evaluate the two schemes and compare them with existing benchmarks for different popular EHMs and relay availability scenarios. Finally, we bound the suboptimality of our solutions and verify their near-optimal performance for different EHMs.
{"title":"Multi-Antenna Wireless Powered Relaying: Low Complexity and Near Optimal Techniques for Generic EH Models","authors":"George A. Ropokis;Petros S. Bithas","doi":"10.1109/TGCN.2023.3343186","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3343186","url":null,"abstract":"We investigate Wireless Powered Multi-Relay Networks (WPRNs) equipped with multiple antennas both at the Source and the Relay and propose two different communication schemes. These schemes are based on the combination of Time Switching (TS) and Self-Energy Recycling (SER) and extend existing ones that have been developed for single-antenna sources. Following that, by adopting only the very generic assumption that the Energy Harvesting Model (EHM) is described by any non-decreasing function, we focus on the instantaneous rate maximization problem and design near-optimal beamforming and wireless power transfer-time determination algorithms for our schemes. A common characteristic of the presented algorithms is their low complexity and implementation simplicity. Given the generality of our EHM assumptions, our algorithms are applicable for all popular EHMs found in the literature, which are normally described using non-decreasing functions, without being specific to any of them. Various simulation results are presented that allow to evaluate the two schemes and compare them with existing benchmarks for different popular EHMs and relay availability scenarios. Finally, we bound the suboptimality of our solutions and verify their near-optimal performance for different EHMs.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078854","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 : 2023-12-12DOI: 10.1109/TGCN.2023.3341874
Jiliang Zhang;Jun Wang;Xingyi Li;Shanghui Li;Zi Yuan;Gaofeng Pan
In this study, secrecy performance for unmanned aerial vehicle (UAV) relaying multi-antenna satellite-terrestrial simultaneous wireless information and power transfer systems with non-orthogonal multiple access (NOMA) is investigated. Specifically, a multi-antenna satellite communicates with two multi-antenna terrestrial NOMA users via a UAV relay under the wiretapping of a multi-antenna eavesdropper, which is randomly distributed. In addition, a maximum ratio transmission scheme is adopted at the satellite to transmit the information, and both a maximum ratio combining and a power splitting schemes are taken into account to process the multiple copies of the received signals. Considering the satellite channel is subject to the Shadowed-Rician distribution and the terrestrial channels are with Nakagami- ${m}$ distributed, the analytical expressions of secrecy outage probability as well as the probability of strictly positive secrecy capacity for both NOMA users with perfect and imperfect successive interference cancellation are obtained by using the stochastic geometry method, and verified with Monte-Carlo simulations.
{"title":"Secrecy Analysis for NOMA-Based Multi-Antenna Satellite-UAV-Terrestrial SWIPT Systems","authors":"Jiliang Zhang;Jun Wang;Xingyi Li;Shanghui Li;Zi Yuan;Gaofeng Pan","doi":"10.1109/TGCN.2023.3341874","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3341874","url":null,"abstract":"In this study, secrecy performance for unmanned aerial vehicle (UAV) relaying multi-antenna satellite-terrestrial simultaneous wireless information and power transfer systems with non-orthogonal multiple access (NOMA) is investigated. Specifically, a multi-antenna satellite communicates with two multi-antenna terrestrial NOMA users via a UAV relay under the wiretapping of a multi-antenna eavesdropper, which is randomly distributed. In addition, a maximum ratio transmission scheme is adopted at the satellite to transmit the information, and both a maximum ratio combining and a power splitting schemes are taken into account to process the multiple copies of the received signals. Considering the satellite channel is subject to the Shadowed-Rician distribution and the terrestrial channels are with Nakagami-\u0000<inline-formula> <tex-math>${m}$ </tex-math></inline-formula>\u0000 distributed, the analytical expressions of secrecy outage probability as well as the probability of strictly positive secrecy capacity for both NOMA users with perfect and imperfect successive interference cancellation are obtained by using the stochastic geometry method, and verified with Monte-Carlo simulations.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078816","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 : 2023-12-12DOI: 10.1109/TGCN.2023.3341443
Shuaifei Ma;Yizhi Feng;Miaowen Wen;Fei Ji
In this paper, we present a secure transmission optimization scheme for intelligent reflecting surface (IRS) aided wireless powered communication networks (WPCNs) in the presence of eavesdropping in the uplink, where the linear energy harvesting (EH) model and imperfect channel state information (CSI) are considered. We aim to maximize the secrecy rate at the hybrid access point (HAP) by jointly optimizing the transmission time allocation factor, HAP transmit beamforming vector, IRS energy reflection coefficients, and IRS information reflection coefficients, which is formulated as a joint multivariate non-convex optimization problem. By using the alternatively iterative optimization method and after several manipulations on the subproblems, the optimal solutions of time allocation factor and HAP transmit beamforming vector are obtained with standard convex optimization tools, while the sub-optimal solutions of IRS energy and information reflection coefficients are obtained with semidefinite relaxation (SDR) and Gaussian randomization procedure. Numerical results show that the proposed algorithm achieves substantial performance enhancement while maintaining high robustness, and significantly outperforms the benchmark schemes under various CSI error variance. It is also shown that optimizing IRS phase shifts or increasing the number of IRS reflection elements is better than optimizing HAP transmit beamforming or increasing the number of HAP antennas, respectively.
{"title":"Secure Transmission Optimization for IRS-Aided WPCNs With Linear EH Model","authors":"Shuaifei Ma;Yizhi Feng;Miaowen Wen;Fei Ji","doi":"10.1109/TGCN.2023.3341443","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3341443","url":null,"abstract":"In this paper, we present a secure transmission optimization scheme for intelligent reflecting surface (IRS) aided wireless powered communication networks (WPCNs) in the presence of eavesdropping in the uplink, where the linear energy harvesting (EH) model and imperfect channel state information (CSI) are considered. We aim to maximize the secrecy rate at the hybrid access point (HAP) by jointly optimizing the transmission time allocation factor, HAP transmit beamforming vector, IRS energy reflection coefficients, and IRS information reflection coefficients, which is formulated as a joint multivariate non-convex optimization problem. By using the alternatively iterative optimization method and after several manipulations on the subproblems, the optimal solutions of time allocation factor and HAP transmit beamforming vector are obtained with standard convex optimization tools, while the sub-optimal solutions of IRS energy and information reflection coefficients are obtained with semidefinite relaxation (SDR) and Gaussian randomization procedure. Numerical results show that the proposed algorithm achieves substantial performance enhancement while maintaining high robustness, and significantly outperforms the benchmark schemes under various CSI error variance. It is also shown that optimizing IRS phase shifts or increasing the number of IRS reflection elements is better than optimizing HAP transmit beamforming or increasing the number of HAP antennas, respectively.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078764","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 : 2023-12-06DOI: 10.1109/TGCN.2023.3339819
Shatakshi Singh;Aditya Trivedi;Divya Saxena
Intelligent reflecting surface (IRS) is a potential technology for enhancing communication systems’ performance. Accurate cascaded channel estimation between the base station (BS), IRS, and the user is vital for optimal system performance. However, incorporating IRS increases channel estimation complexity due to additional dimensions from each element, leading to higher training overhead. To reduce training overhead, existing approaches assume the sparse cascaded channel which may not be valid in dense multipath propagation and non-line-of-sight settings. We propose a novel technique to address this issue by leveraging the spatial correlation among IRS elements’ channels. By dividing the IRS surface into groups, we estimate the channel for some groups via the least square (LS) method. To estimate the channels for the remaining groups, a graph transformer-based IRS channel estimation (G-TIRC) model is proposed, which includes a graph neural network (GNN) and transformer model. The GNN finds the correlations among the different groups by embedding the channel information. Then, the attention mechanism within the transformer extracts useful correlations to accurately predict the channels for the unknown groups. The experiments demonstrate the effectiveness of the G-TIRC model in achieving accurate channel estimation with reduced pilot overhead compared to other state-of-the-art methods.
{"title":"Channel Estimation for Intelligent Reflecting Surface Aided Communication via Graph Transformer","authors":"Shatakshi Singh;Aditya Trivedi;Divya Saxena","doi":"10.1109/TGCN.2023.3339819","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3339819","url":null,"abstract":"Intelligent reflecting surface (IRS) is a potential technology for enhancing communication systems’ performance. Accurate cascaded channel estimation between the base station (BS), IRS, and the user is vital for optimal system performance. However, incorporating IRS increases channel estimation complexity due to additional dimensions from each element, leading to higher training overhead. To reduce training overhead, existing approaches assume the sparse cascaded channel which may not be valid in dense multipath propagation and non-line-of-sight settings. We propose a novel technique to address this issue by leveraging the spatial correlation among IRS elements’ channels. By dividing the IRS surface into groups, we estimate the channel for some groups via the least square (LS) method. To estimate the channels for the remaining groups, a graph transformer-based IRS channel estimation (G-TIRC) model is proposed, which includes a graph neural network (GNN) and transformer model. The GNN finds the correlations among the different groups by embedding the channel information. Then, the attention mechanism within the transformer extracts useful correlations to accurately predict the channels for the unknown groups. The experiments demonstrate the effectiveness of the G-TIRC model in achieving accurate channel estimation with reduced pilot overhead compared to other state-of-the-art methods.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078859","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 : 2023-12-05DOI: 10.1109/TGCN.2023.3339477
Kai Chen;Yi Sun;Shunlin Zheng;Hongyue Yang;Peng Yu
The operations of IoT devices (IoTD) and 5G base station (BS) contribute to most of carbon emission and on-power energy consumption in wireless edge network. To reduce operational costs and achieve low-carbon computing, this paper investigates a long-term average operational expenditure (OPEX) minimization problem, and proposes an online joint energy-network resource scheduling algorithm, including computation offloading, wireless power transmission, energy sharing, and task migration in wireless edge network powered by renewable energy, energy storage, and power grid. Differing from existing works, ours consider the energy loss of battery, dynamic carbon emission related with on-power energy, and constraint of spatial electric network into our model. Then, we apply the Lyapunov technique to decompose the proposed problem into three real-time sub-problems. Furthermore, a federal gradient descent based full-distributed online algorithm is proposed to obtain solution while protecting the privacy of network operators. We also prove the convergence of proposed algorithm and provide the tradeoff between network stability and optimal OPEX. Simulation results reveal that the proposed algorithm outperforms existing benchmarks in reducing on-grid power dependence and OPEX.
{"title":"Online Collaborative Energy-Network Resource Scheduling for WPT-Enabled Green Edge Computing","authors":"Kai Chen;Yi Sun;Shunlin Zheng;Hongyue Yang;Peng Yu","doi":"10.1109/TGCN.2023.3339477","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3339477","url":null,"abstract":"The operations of IoT devices (IoTD) and 5G base station (BS) contribute to most of carbon emission and on-power energy consumption in wireless edge network. To reduce operational costs and achieve low-carbon computing, this paper investigates a long-term average operational expenditure (OPEX) minimization problem, and proposes an online joint energy-network resource scheduling algorithm, including computation offloading, wireless power transmission, energy sharing, and task migration in wireless edge network powered by renewable energy, energy storage, and power grid. Differing from existing works, ours consider the energy loss of battery, dynamic carbon emission related with on-power energy, and constraint of spatial electric network into our model. Then, we apply the Lyapunov technique to decompose the proposed problem into three real-time sub-problems. Furthermore, a federal gradient descent based full-distributed online algorithm is proposed to obtain solution while protecting the privacy of network operators. We also prove the convergence of proposed algorithm and provide the tradeoff between network stability and optimal OPEX. Simulation results reveal that the proposed algorithm outperforms existing benchmarks in reducing on-grid power dependence and OPEX.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078765","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 : 2023-11-29DOI: 10.1109/TGCN.2023.3337748
Shiya Liu;Nima Mohammadi;Yang Yi
Spectrum sensing is a technique used to identify idle/busy bandwidths in cognitive radio. Energy-efficient spectrum sensing is critical for multiple-input-multiple-output (MIMO) orthogonal-frequency-division multiplexing (OFDM) systems. In this paper, we propose the use of spiking neural networks (SNNs), which are more biologically plausible and energy-efficient than deep neural networks (DNNs), for spectrum sensing. The SNN models are implemented on the Loihi chip, which is better suited for SNNs than GPUs. Quantization is an effective technique to reduce the memory and energy consumption of SNNs. However, previous quantization methods for SNNs have suffered from accuracy degradation when compared to full-precision models. This degradation can be attributed to errors introduced by the coarse estimation of gradients in non-differentiable quantization layers. To address this issue, we introduce a quantization-aware training algorithm for SNNs running on Loihi. To mitigate errors caused by the poor estimation of gradients, we do not use a fixed configuration for the quantizer, as is common in existing SNN quantization methods. Instead, we make the scale parameters of the quantizer trainable. Furthermore, our proposed method adopts a probability-based scheme to selectively quantize individual layers within the network, rather than quantizing all layers simultaneously. Our experimental results demonstrate that high-performance and energy-efficient spectrum sensing can be achieved using Loihi.
{"title":"Quantization-Aware Training of Spiking Neural Networks for Energy-Efficient Spectrum Sensing on Loihi Chip","authors":"Shiya Liu;Nima Mohammadi;Yang Yi","doi":"10.1109/TGCN.2023.3337748","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3337748","url":null,"abstract":"Spectrum sensing is a technique used to identify idle/busy bandwidths in cognitive radio. Energy-efficient spectrum sensing is critical for multiple-input-multiple-output (MIMO) orthogonal-frequency-division multiplexing (OFDM) systems. In this paper, we propose the use of spiking neural networks (SNNs), which are more biologically plausible and energy-efficient than deep neural networks (DNNs), for spectrum sensing. The SNN models are implemented on the Loihi chip, which is better suited for SNNs than GPUs. Quantization is an effective technique to reduce the memory and energy consumption of SNNs. However, previous quantization methods for SNNs have suffered from accuracy degradation when compared to full-precision models. This degradation can be attributed to errors introduced by the coarse estimation of gradients in non-differentiable quantization layers. To address this issue, we introduce a quantization-aware training algorithm for SNNs running on Loihi. To mitigate errors caused by the poor estimation of gradients, we do not use a fixed configuration for the quantizer, as is common in existing SNN quantization methods. Instead, we make the scale parameters of the quantizer trainable. Furthermore, our proposed method adopts a probability-based scheme to selectively quantize individual layers within the network, rather than quantizing all layers simultaneously. Our experimental results demonstrate that high-performance and energy-efficient spectrum sensing can be achieved using Loihi.","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141078777","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 : 2023-11-28DOI: 10.1109/TGCN.2023.3336398
{"title":"2023 Index IEEE Transactions on Green Communications and Networking Vol. 7","authors":"","doi":"10.1109/TGCN.2023.3336398","DOIUrl":"https://doi.org/10.1109/TGCN.2023.3336398","url":null,"abstract":"","PeriodicalId":13052,"journal":{"name":"IEEE Transactions on Green Communications and Networking","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10329484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138454423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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