Pub Date : 2024-07-01DOI: 10.23919/JCC.ea.2022-0222.202401
Zhang Hang, Li Bo, Zhongjiang Yan, Yang Mao, Li Xinru
In this paper, we propose a Multi-token Sector Antenna Neighbor Discovery (M-SAND) protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks. The central concept of our work involves maintaining multiple tokens across the network. To prevent mutual interference among multi-token holders, we introduce the time and space non-interference theorems. Furthermore, we propose a master-slave strategy between tokens. When the master token holder (MTH) performs the neighbor discovery, it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders (STHs). Using this approach, the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other. Building on this foundation, we provide a comprehensive procedure for the M-SAND protocol. We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability. Finally, simulation results demonstrate the time efficiency of the M-SAND protocol. When compared to the Q-SAND protocol, which uses only one token, the total neighbor discovery time is reduced by 28% when 6 beams and 112 nodes are employed.
{"title":"A multi-token sector antenna neighbor discovery protocol for directional ad hoc networks","authors":"Zhang Hang, Li Bo, Zhongjiang Yan, Yang Mao, Li Xinru","doi":"10.23919/JCC.ea.2022-0222.202401","DOIUrl":"https://doi.org/10.23919/JCC.ea.2022-0222.202401","url":null,"abstract":"In this paper, we propose a Multi-token Sector Antenna Neighbor Discovery (M-SAND) protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks. The central concept of our work involves maintaining multiple tokens across the network. To prevent mutual interference among multi-token holders, we introduce the time and space non-interference theorems. Furthermore, we propose a master-slave strategy between tokens. When the master token holder (MTH) performs the neighbor discovery, it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders (STHs). Using this approach, the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other. Building on this foundation, we provide a comprehensive procedure for the M-SAND protocol. We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability. Finally, simulation results demonstrate the time efficiency of the M-SAND protocol. When compared to the Q-SAND protocol, which uses only one token, the total neighbor discovery time is reduced by 28% when 6 beams and 112 nodes are employed.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0495.202404
Shao Liwei, Liping Qian, Mengru Wu, Wu Yuan
With the development of the Internet of Things (IoT), it requires better performance from wireless sensor networks (WSNs), such as larger coverage, longer lifetime, and lower latency. However, a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power. For this, we investigate an unmanned aerial vehicles assisted mobile wireless sensor network (UAV-assisted WSN) to prolong the network lifetime in this paper. Specifically, we use UAVs to assist the WSN in collecting data. In the current UAV-assisted WSN, the clustering and routing schemes are determined sequentially. However, such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing. To efficiently prolong the lifetime of the WSN, we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together. In the whole network space, it is intractable to efficiently obtain the optimal integrated clustering and routing scheme. Therefore, we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas, which can generate the chain matrix to guide the algorithm to find the solution faster. Unnecessary point-to-point collection leads to long collection paths, so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes. To avoid the coverage hole caused by the death of sensor nodes, the deployment of mobile sensor nodes and the preventive mechanism design are indispensable. An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs. Compared with the existing schemes, the proposed scheme can prolong the lifetime of the UAV-assisted WSN at least by 360%, and shorten the collection path of UAVs by 56.24%.
{"title":"Integrated clustering and routing design and triangle path optimization for UAV-assisted wireless sensor networks","authors":"Shao Liwei, Liping Qian, Mengru Wu, Wu Yuan","doi":"10.23919/JCC.fa.2023-0495.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0495.202404","url":null,"abstract":"With the development of the Internet of Things (IoT), it requires better performance from wireless sensor networks (WSNs), such as larger coverage, longer lifetime, and lower latency. However, a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power. For this, we investigate an unmanned aerial vehicles assisted mobile wireless sensor network (UAV-assisted WSN) to prolong the network lifetime in this paper. Specifically, we use UAVs to assist the WSN in collecting data. In the current UAV-assisted WSN, the clustering and routing schemes are determined sequentially. However, such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing. To efficiently prolong the lifetime of the WSN, we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together. In the whole network space, it is intractable to efficiently obtain the optimal integrated clustering and routing scheme. Therefore, we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas, which can generate the chain matrix to guide the algorithm to find the solution faster. Unnecessary point-to-point collection leads to long collection paths, so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes. To avoid the coverage hole caused by the death of sensor nodes, the deployment of mobile sensor nodes and the preventive mechanism design are indispensable. An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs. Compared with the existing schemes, the proposed scheme can prolong the lifetime of the UAV-assisted WSN at least by 360%, and shorten the collection path of UAVs by 56.24%.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper aimed to propose two algorithms, DA-M and RF-M, of reducing the impact of multipath interference (MPI) on intensity modulation direct detection (IM-DD) systems, particularly for four-level pulse amplitude modulation (PAM4) systems. DA-M reduced the fluctuation by averaging the signal in blocks, RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block, and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting factor. This paper firstly proposed to separate the signal before decision — making into multiple blocks, which significantly reduced the complexity of DA-M and RF-M. Simulation results showed that the MPI noise of 28 GBaud IM-DD system under the linewidths of 1e5 Hz, 1e6 Hz and 10e6 Hz can be effectively alleviated.
{"title":"Mitigation of optical multipath interference algorithms in IM-DD transmission system","authors":"Jiahao Huo, Zhu Jin, Zuqing Zhu, Xiaoying Zhang, Shaonan Liu, Jianlong Tao, Huangfu Wei","doi":"10.23919/JCC.fa.2023-0482.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0482.202404","url":null,"abstract":"This paper aimed to propose two algorithms, DA-M and RF-M, of reducing the impact of multipath interference (MPI) on intensity modulation direct detection (IM-DD) systems, particularly for four-level pulse amplitude modulation (PAM4) systems. DA-M reduced the fluctuation by averaging the signal in blocks, RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block, and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting factor. This paper firstly proposed to separate the signal before decision — making into multiple blocks, which significantly reduced the complexity of DA-M and RF-M. Simulation results showed that the MPI noise of 28 GBaud IM-DD system under the linewidths of 1e5 Hz, 1e6 Hz and 10e6 Hz can be effectively alleviated.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140792396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As modern electromagnetic environments are more and more complex, the anti-interference performance of the synchronization acquisition is becoming vital in wireless communications. With the rapid development of the digital signal processing technologies, some synchronization acquisition algorithms for hybrid direct-sequence (DS)/frequency hopping (FH) spread spectrum communications have been proposed. However, these algorithms do not focus on the analysis and the design of the synchronization acquisition under typical interferences. In this paper, a synchronization acquisition algorithm based on the frequency hopping pulses combining (FHPC) is proposed. Specifically, the proposed algorithm is composed of two modules: an adaptive interference suppression (IS) module and an adaptive combining decision module. The adaptive IS module mitigates the effect of the interfered samples in the time-domain or the frequency-domain, and the adaptive combining decision module can utilize each frequency hopping pulse to construct an anti-interference decision metric and generate an adaptive acquisition decision threshold to complete the acquisition. Theory and simulation demonstrate that the proposed algorithm significantly enhances the anti-interference and anti-noise performances of the synchronization acquisition for hybrid DS/FH communications.
{"title":"A synchronization acquisition algorithm based on the frequency hopping pulses combining","authors":"Honghan She, Cheng Yufan, Zhang Wenzihan, Yaohui Zhang, Yuheng Zhao, Haoran Shen, Mou Ying","doi":"10.23919/JCC.fa.2023-0505.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0505.202404","url":null,"abstract":"As modern electromagnetic environments are more and more complex, the anti-interference performance of the synchronization acquisition is becoming vital in wireless communications. With the rapid development of the digital signal processing technologies, some synchronization acquisition algorithms for hybrid direct-sequence (DS)/frequency hopping (FH) spread spectrum communications have been proposed. However, these algorithms do not focus on the analysis and the design of the synchronization acquisition under typical interferences. In this paper, a synchronization acquisition algorithm based on the frequency hopping pulses combining (FHPC) is proposed. Specifically, the proposed algorithm is composed of two modules: an adaptive interference suppression (IS) module and an adaptive combining decision module. The adaptive IS module mitigates the effect of the interfered samples in the time-domain or the frequency-domain, and the adaptive combining decision module can utilize each frequency hopping pulse to construct an anti-interference decision metric and generate an adaptive acquisition decision threshold to complete the acquisition. Theory and simulation demonstrate that the proposed algorithm significantly enhances the anti-interference and anti-noise performances of the synchronization acquisition for hybrid DS/FH communications.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2022-0660.202404
Xiaohan Lin, Liu Yuan, Fangjiong Chen, Huang Yang, Xiaohu Ge
As a mature distributed machine learning paradigm, federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent (SGD). However, devices need to upload high-dimensional stochastic gradients to edge server in training, which cause severe communication bottleneck. To address this problem, we compress the communication by sparsifying and quantizing the stochastic gradients of edge devices. We first derive a closed form of the communication compression in terms of sparsification and quantization factors. Then, the convergence rate of this communication-compressed system is analyzed and several insights are obtained. Finally, we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound, under the constraint of multiple-access channel capacity. Simulations show that the proposed scheme outperforms the benchmarks.
{"title":"Stochastic gradient compression for federated learning over wireless network","authors":"Xiaohan Lin, Liu Yuan, Fangjiong Chen, Huang Yang, Xiaohu Ge","doi":"10.23919/JCC.fa.2022-0660.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0660.202404","url":null,"abstract":"As a mature distributed machine learning paradigm, federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent (SGD). However, devices need to upload high-dimensional stochastic gradients to edge server in training, which cause severe communication bottleneck. To address this problem, we compress the communication by sparsifying and quantizing the stochastic gradients of edge devices. We first derive a closed form of the communication compression in terms of sparsification and quantization factors. Then, the convergence rate of this communication-compressed system is analyzed and several insights are obtained. Finally, we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound, under the constraint of multiple-access channel capacity. Simulations show that the proposed scheme outperforms the benchmarks.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140774120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2022-0748.202404
Wenjing Xu, Wang Wei, Zuguang Li, Wu Qihui, Xianbin Wang
Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks. However, edge computing servers (ECSs) from different operators may not trust each other, and thus the incentives for collaboration cannot be guaranteed. In this paper, we propose a consortium blockchain enabled collaborative edge computing framework, where users can offload computing tasks to ECSs from different operators. To minimize the total delay of users, we formulate a joint task offloading and resource optimization problem, under the constraint of the computing capability of each ECS. We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution. Finally, we propose a reputation based node selection approach to facilitate the consensus process, and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain. Simulation results validate the effectiveness of the proposed algorithm, and the total delay can be reduced by up to 40% compared with the non-cooperative case.
{"title":"Joint task allocation and resource optimization for blockchain enabled collaborative edge computing","authors":"Wenjing Xu, Wang Wei, Zuguang Li, Wu Qihui, Xianbin Wang","doi":"10.23919/JCC.fa.2022-0748.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0748.202404","url":null,"abstract":"Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks. However, edge computing servers (ECSs) from different operators may not trust each other, and thus the incentives for collaboration cannot be guaranteed. In this paper, we propose a consortium blockchain enabled collaborative edge computing framework, where users can offload computing tasks to ECSs from different operators. To minimize the total delay of users, we formulate a joint task offloading and resource optimization problem, under the constraint of the computing capability of each ECS. We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution. Finally, we propose a reputation based node selection approach to facilitate the consensus process, and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain. Simulation results validate the effectiveness of the proposed algorithm, and the total delay can be reduced by up to 40% compared with the non-cooperative case.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140771132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0383.202404
Feng Chuan, Zhang Xu, Pengchao Han, Tianchun Ma, Xiaoxue Gong
The Multi-access Edge Cloud (MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources, and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G. However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multi-resource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue, we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features (NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.
{"title":"Policy network-based dual-agent deep reinforcement learning for multi-resource task offloading in multi-access edge cloud networks","authors":"Feng Chuan, Zhang Xu, Pengchao Han, Tianchun Ma, Xiaoxue Gong","doi":"10.23919/JCC.fa.2023-0383.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0383.202404","url":null,"abstract":"The Multi-access Edge Cloud (MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources, and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G. However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multi-resource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue, we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features (NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140776058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2022-0507.202404
Liu Jie, Chen Sibo, Liu Yuqin, Zhiwei Mo, Lin Yilin, Hongmei Zhu, Yufeng He
To ensure the extreme performances of the new 6G services, applications will be deployed at deep edge, resulting in a serious challenge of distributed application addressing. This paper traces back the latest development of mobile network application addressing, analyzes two novel addressing methods in carrier network, and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture, combining the new 6G capabilities of computing & network convergence, endogenous intelligence, and communication-sensing integration. This paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.
{"title":"Distributed application addressing in 6G network","authors":"Liu Jie, Chen Sibo, Liu Yuqin, Zhiwei Mo, Lin Yilin, Hongmei Zhu, Yufeng He","doi":"10.23919/JCC.fa.2022-0507.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0507.202404","url":null,"abstract":"To ensure the extreme performances of the new 6G services, applications will be deployed at deep edge, resulting in a serious challenge of distributed application addressing. This paper traces back the latest development of mobile network application addressing, analyzes two novel addressing methods in carrier network, and puts forward a 6G endogenous application addressing scheme by integrating some of their essence into the 6G network architecture, combining the new 6G capabilities of computing & network convergence, endogenous intelligence, and communication-sensing integration. This paper further illustrates how that the proposed method works in 6G networks and gives preliminary experimental verification.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140761927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0504.202404
Xinxin He, Qi Xuan, Meng Wei, Liu Wei, Changchuan Yin
Wireless Power Transfer (WPT) technology can provide real-time power for many terminal devices in Internet of Things (IoT) through millimeter Wave (mmWave) to support applications with large capacity and low latency. Although the intelligent reflecting surface (IRS) can be adopted to create effective virtual links to address the mmWave blockage problem, the conventional solutions only adopt IRS in the downlink from the Base Station (BS) to the users to enhance the received signal strength. In practice, the reflection of IRS is also applicable to the uplink to improve the spectral efficiency. It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission. In this paper, we first design a Low-Energy Adaptive Clustering Hierarchy (LEACH) clustering protocol for clustering and data collection. Then, the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices, the uplink and downlink transmission times, the active beamforming at the BS, and the IRS dynamic beamforming. To solve this non-convex optimization problem, we propose an alternating optimization (AO)-based joint solution algorithm. Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system, and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.
{"title":"Intelligent reflecting surface assisted transmission optimization strategies in wireless networks","authors":"Xinxin He, Qi Xuan, Meng Wei, Liu Wei, Changchuan Yin","doi":"10.23919/JCC.fa.2023-0504.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0504.202404","url":null,"abstract":"Wireless Power Transfer (WPT) technology can provide real-time power for many terminal devices in Internet of Things (IoT) through millimeter Wave (mmWave) to support applications with large capacity and low latency. Although the intelligent reflecting surface (IRS) can be adopted to create effective virtual links to address the mmWave blockage problem, the conventional solutions only adopt IRS in the downlink from the Base Station (BS) to the users to enhance the received signal strength. In practice, the reflection of IRS is also applicable to the uplink to improve the spectral efficiency. It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission. In this paper, we first design a Low-Energy Adaptive Clustering Hierarchy (LEACH) clustering protocol for clustering and data collection. Then, the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices, the uplink and downlink transmission times, the active beamforming at the BS, and the IRS dynamic beamforming. To solve this non-convex optimization problem, we propose an alternating optimization (AO)-based joint solution algorithm. Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system, and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCC.fa.2023-0492.202404
Xiaoge Huang, Lingzhi Wang, He Yong, Qianbin Chen
Wireless Sensor Network (WSN) is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation. However, WSN data collection encounters challenges in scenarios lacking communication infrastructure. Unmanned aerial vehicle (UAV) offers a novel solution for WSN data collection, leveraging their high mobility. In this paper, we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN. Firstly, a two-layer UAV-assisted data collection model is introduced, including the ground and aerial layers. The ground layer senses the environmental data by the cluster members (CMs), and the CMs transmit the data to the cluster heads (CHs), which forward the collected data to the UAVs. The aerial network layer consists of multiple UAVs that collect, store, and forward data from the CHs to the data center for analysis. Secondly, an improved clustering algorithm based on K-Means++ is proposed to optimize the number and locations of CHs. Moreover, an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs. Finally, simulation results verify the effectiveness of the proposed algorithms.
{"title":"Actor-Critic-based UAV-assisted data collection in the wireless sensor network","authors":"Xiaoge Huang, Lingzhi Wang, He Yong, Qianbin Chen","doi":"10.23919/JCC.fa.2023-0492.202404","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0492.202404","url":null,"abstract":"Wireless Sensor Network (WSN) is widely utilized in large-scale distributed unmanned detection scenarios due to its low cost and flexible installation. However, WSN data collection encounters challenges in scenarios lacking communication infrastructure. Unmanned aerial vehicle (UAV) offers a novel solution for WSN data collection, leveraging their high mobility. In this paper, we present an efficient UAV-assisted data collection algorithm aimed at minimizing the overall power consumption of the WSN. Firstly, a two-layer UAV-assisted data collection model is introduced, including the ground and aerial layers. The ground layer senses the environmental data by the cluster members (CMs), and the CMs transmit the data to the cluster heads (CHs), which forward the collected data to the UAVs. The aerial network layer consists of multiple UAVs that collect, store, and forward data from the CHs to the data center for analysis. Secondly, an improved clustering algorithm based on K-Means++ is proposed to optimize the number and locations of CHs. Moreover, an Actor-Critic based algorithm is introduced to optimize the UAV deployment and the association with CHs. Finally, simulation results verify the effectiveness of the proposed algorithms.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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