Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2023-0017.202308
Congzhou Zhou, Shuo Shi, Chenyu Wu, Zhenyu Xu
As the sixth generation network (6G) emerges, the Internet of remote things (IoRT) has become a critical issue. However, conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks, and the Space-Air-Ground integrated network (SAGIN) holds promise. We propose a novel setup that integrates non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) to collect latency-sensitive data from IoRT networks. To extend the lifetime of devices, we aim to minimize the maximum energy consumption among all IoRT devices. Due to the coupling between variables, the resulting problem is non-convex. We first decouple the variables and split the original problem into four subproblems. Then, we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation (SCA) techniques and slack variables. Finally, simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency, providing valuable insights.
{"title":"NOMA empowered energy efficient data collection and wireless power transfer in space-air-ground integrated networks","authors":"Congzhou Zhou, Shuo Shi, Chenyu Wu, Zhenyu Xu","doi":"10.23919/JCC.fa.2023-0017.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0017.202308","url":null,"abstract":"As the sixth generation network (6G) emerges, the Internet of remote things (IoRT) has become a critical issue. However, conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks, and the Space-Air-Ground integrated network (SAGIN) holds promise. We propose a novel setup that integrates non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) to collect latency-sensitive data from IoRT networks. To extend the lifetime of devices, we aim to minimize the maximum energy consumption among all IoRT devices. Due to the coupling between variables, the resulting problem is non-convex. We first decouple the variables and split the original problem into four subproblems. Then, we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation (SCA) techniques and slack variables. Finally, simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency, providing valuable insights.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"17-31"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42507906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2023-0206.202308
Yuxin Zhang, R. He, B. Ai, Mi Yang, Ruifeng Chen, Chenlong Wang, Zhengyu Zhang, Z. Zhong
Integration of digital twin (DT) and wireless channel provides new solution of channel modeling and simulation, and can assist to design, optimize and evaluate intelligent wireless communication system and networks. With DT channel modeling, the generated channel data can be closer to realistic channel measurements without requiring a prior channel model, and amount of channel data can be significantly increased. Artificial intelligence (AI) based modeling approach shows outstanding performance to solve such problems. In this work, a channel modeling method based on generative adversarial networks is proposed for DT channel, which can generate identical statistical distribution with measured channel. Model validation is conducted by comparing DT channel characteristics with measurements, and results show that DT channel leads to fairly good agreement with measured channel. Finally, a link-layer simulation is implemented based on DT channel. It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data. The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications, as well as improving the performance and reliability of intelligent communication networking.
{"title":"Generative adversarial networks based digital twin channel modeling for intelligent communication networks","authors":"Yuxin Zhang, R. He, B. Ai, Mi Yang, Ruifeng Chen, Chenlong Wang, Zhengyu Zhang, Z. Zhong","doi":"10.23919/JCC.fa.2023-0206.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0206.202308","url":null,"abstract":"Integration of digital twin (DT) and wireless channel provides new solution of channel modeling and simulation, and can assist to design, optimize and evaluate intelligent wireless communication system and networks. With DT channel modeling, the generated channel data can be closer to realistic channel measurements without requiring a prior channel model, and amount of channel data can be significantly increased. Artificial intelligence (AI) based modeling approach shows outstanding performance to solve such problems. In this work, a channel modeling method based on generative adversarial networks is proposed for DT channel, which can generate identical statistical distribution with measured channel. Model validation is conducted by comparing DT channel characteristics with measurements, and results show that DT channel leads to fairly good agreement with measured channel. Finally, a link-layer simulation is implemented based on DT channel. It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data. The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications, as well as improving the performance and reliability of intelligent communication networking.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"32-43"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42876027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2021-0843.202308
Yueheng Li, Sven Bettinga, Lucas Giroto de Oliveira, Mohamad Basim Alabd, J. Eisenbeis, X. Wan, Xueyun Long, T. Cui, T. Zwick
The programmable metasurface (PM) is an antenna array architecture that realizes flexible beam steering. This functionality is achieved by controlling the unit cells designed with micro components such as positive-intrinsic-negative (PIN) diodes, which offers potential cost reductions in the next generation wireless communication systems. Although PM has been a popular topic in antenna design, its implementations in real-time systems accompanied by signal processing algorithms are challenging. In this paper, novel predictive tracking algorithms for mobile communication scenarios using a PM are created and implemented in a real-time system operating at 28 GHz. An angular speed prediction (ASP) algorithm is proposed to compute the position of user equipment (UE) based on the previously recorded beam directions. As another solution, an angle correction (AC) algorithm is proposed to further improve the prediction and tracking accuracy. As a benchmark, the comparisons to a previous PM tracking algorithm without prediction are presented. Both simulation and measurement results show that the prediction algorithms successfully improve the tracking performance, which also prove the feasibilities of PM-based systems to solve complex real-time signal processing problems.
{"title":"Predictive tracking implementation for mobile communication using programmable metasurface","authors":"Yueheng Li, Sven Bettinga, Lucas Giroto de Oliveira, Mohamad Basim Alabd, J. Eisenbeis, X. Wan, Xueyun Long, T. Cui, T. Zwick","doi":"10.23919/JCC.fa.2021-0843.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2021-0843.202308","url":null,"abstract":"The programmable metasurface (PM) is an antenna array architecture that realizes flexible beam steering. This functionality is achieved by controlling the unit cells designed with micro components such as positive-intrinsic-negative (PIN) diodes, which offers potential cost reductions in the next generation wireless communication systems. Although PM has been a popular topic in antenna design, its implementations in real-time systems accompanied by signal processing algorithms are challenging. In this paper, novel predictive tracking algorithms for mobile communication scenarios using a PM are created and implemented in a real-time system operating at 28 GHz. An angular speed prediction (ASP) algorithm is proposed to compute the position of user equipment (UE) based on the previously recorded beam directions. As another solution, an angle correction (AC) algorithm is proposed to further improve the prediction and tracking accuracy. As a benchmark, the comparisons to a previous PM tracking algorithm without prediction are presented. Both simulation and measurement results show that the prediction algorithms successfully improve the tracking performance, which also prove the feasibilities of PM-based systems to solve complex real-time signal processing problems.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"137-152"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47234151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2022-0806.202308
Yi Wang, Kanqi Wang, Maosheng Zhang, Hongzhi Zheng, Hui Zhang
Wireless sensor networks (WSN) are widely used in many situations, but the disordered and random deployment mode will waste a lot of sensor resources. This paper proposes a multi-topology hierarchical collaborative particle swarm optimization (MHCHPSO) to optimize sensor deployment location and improve the coverage of WSN. MHCHPSO divides the population into three types topology: diversity topology for global exploration, fast convergence topology for local development, and collaboration topology for exploration and development. All topologies are optimized in parallel to overcome the precocious convergence of PSO. This paper compares with various heuristic algorithms at CEC 2013, CEC 2015, and CEC 2017. The experimental results show that MHCHPSO outperforms the comparison algorithms. In addition, MHCHPSO is applied to the WSN localization optimization, and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.
{"title":"Multi-topology hierarchical collaborative hybrid particle swarm optimization algorithm for WSN","authors":"Yi Wang, Kanqi Wang, Maosheng Zhang, Hongzhi Zheng, Hui Zhang","doi":"10.23919/JCC.fa.2022-0806.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0806.202308","url":null,"abstract":"Wireless sensor networks (WSN) are widely used in many situations, but the disordered and random deployment mode will waste a lot of sensor resources. This paper proposes a multi-topology hierarchical collaborative particle swarm optimization (MHCHPSO) to optimize sensor deployment location and improve the coverage of WSN. MHCHPSO divides the population into three types topology: diversity topology for global exploration, fast convergence topology for local development, and collaboration topology for exploration and development. All topologies are optimized in parallel to overcome the precocious convergence of PSO. This paper compares with various heuristic algorithms at CEC 2013, CEC 2015, and CEC 2017. The experimental results show that MHCHPSO outperforms the comparison algorithms. In addition, MHCHPSO is applied to the WSN localization optimization, and the experimental results confirm the optimization ability of MHCHPSO in practical engineering problems.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"254-275"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45617385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an overview of dielectric patch (DP) antennas developed in recent years. The employed DP resonator composed of a DP and a bottom substrate is analyzed comprehensively here, enabling the easy realization of a quasi-planar DP antenna. It combines the dual advantages of the conventional microstrip patch (MP) antenna and dielectric resonator (DR) antenna in terms of profile, gain, bandwidth, radiation efficiency, and design freedom. Furthermore, the DP antenna inherits the multi-mode characteristic of the DR antenna, thus it has a large number of high-order modes, including TMmn mode and TEmn mode. The high-order modes are widely applied, for example, by combining with the dominantTMio mode to expand the bandwidth, or selecting multiple higher-order modes to implement a high-gain antenna. Additionally, the non-radiation high-order modes are also utilized to produce natural radiation null in filtering antenna design. In this paper, the design theories and techniques of DP antenna are introduced and investigated, including calculation and control methods of the resonant mode frequencies, analysis of the radiation mechanism, and applications of the multi-mode characteristic. This overview could provide guidance for the subsequent antenna design, thus effectively avoid time-consuming optimization.
{"title":"Dielectric patch resonator and antenna","authors":"Jianxin Chen, Xue‐Ying Wang, Shichang Tang, Yongle Wu","doi":"10.23919/JCC.fa.2021-0507.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2021-0507.202308","url":null,"abstract":"This paper presents an overview of dielectric patch (DP) antennas developed in recent years. The employed DP resonator composed of a DP and a bottom substrate is analyzed comprehensively here, enabling the easy realization of a quasi-planar DP antenna. It combines the dual advantages of the conventional microstrip patch (MP) antenna and dielectric resonator (DR) antenna in terms of profile, gain, bandwidth, radiation efficiency, and design freedom. Furthermore, the DP antenna inherits the multi-mode characteristic of the DR antenna, thus it has a large number of high-order modes, including TMmn mode and TEmn mode. The high-order modes are widely applied, for example, by combining with the dominantTMio mode to expand the bandwidth, or selecting multiple higher-order modes to implement a high-gain antenna. Additionally, the non-radiation high-order modes are also utilized to produce natural radiation null in filtering antenna design. In this paper, the design theories and techniques of DP antenna are introduced and investigated, including calculation and control methods of the resonant mode frequencies, analysis of the radiation mechanism, and applications of the multi-mode characteristic. This overview could provide guidance for the subsequent antenna design, thus effectively avoid time-consuming optimization.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"209-219"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44202722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2022-0128.202308
Dongting Lin, Yuan Liu
Reconfigurable intelligent surface (RIS) for wireless networks have drawn lots of attention in both academic and industry communities. RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction, thus it provides supplementary links for wireless networks. Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts, but phase shifts of RIS are discrete in practical hardware. Thus we focus on the actual discrete phase shifts on RIS in this paper. Using the advanced deep reinforcement learning (DRL), we jointly optimize the transmit beamforming matrix from the discrete Fourier transform (DFT) codebook at the base station (BS) and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio (SINR). Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts, the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network. Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.
{"title":"Discrete phase shifts control and beam selection in RIS-aided MISO system via deep reinforcement learning","authors":"Dongting Lin, Yuan Liu","doi":"10.23919/JCC.fa.2022-0128.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0128.202308","url":null,"abstract":"Reconfigurable intelligent surface (RIS) for wireless networks have drawn lots of attention in both academic and industry communities. RIS can dynamically control the phases of the reflection elements to send the signal in the desired direction, thus it provides supplementary links for wireless networks. Most of prior works on RIS-aided wireless communication systems consider continuous phase shifts, but phase shifts of RIS are discrete in practical hardware. Thus we focus on the actual discrete phase shifts on RIS in this paper. Using the advanced deep reinforcement learning (DRL), we jointly optimize the transmit beamforming matrix from the discrete Fourier transform (DFT) codebook at the base station (BS) and the discrete phase shifts at the RIS to maximize the received signal-to-interference plus noise ratio (SINR). Unlike the traditional schemes usually using alternate optimization methods to solve the transmit beamforming and phase shifts, the DRL algorithm proposed in the paper can jointly design the transmit beamforming and phase shifts as the output of the DRL neural network. Numerical results indicate that the DRL proposed can dispose the complicated optimization problem with low computational complexity.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"198-208"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41927253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2022-0705.202308
J. Ren, Peng Zhu, Zhiyuan Ren
With the rapid development of the Industrial Internet of Things (IIoT), the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks. This paper aims to propose a low-latency and low-energy path computing scheme for the above problems. This scheme is based on the cloud-fog network architecture. The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center. A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization (PDBPSO) algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably. The task in the form of a directed acyclic graph (DAG) is mapped to a factory fog network in the form of an undirected graph (UG) to find the appropriate computing path for the task, significantly reducing the task processing latency under energy consumption constraints. Simulation experiments show that this scheme's latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.
{"title":"Path computing scheme with low-latency and low-power in hybrid cloud-fog network for IIoT","authors":"J. Ren, Peng Zhu, Zhiyuan Ren","doi":"10.23919/JCC.fa.2022-0705.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2022-0705.202308","url":null,"abstract":"With the rapid development of the Industrial Internet of Things (IIoT), the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks. This paper aims to propose a low-latency and low-energy path computing scheme for the above problems. This scheme is based on the cloud-fog network architecture. The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center. A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization (PDBPSO) algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably. The task in the form of a directed acyclic graph (DAG) is mapped to a factory fog network in the form of an undirected graph (UG) to find the appropriate computing path for the task, significantly reducing the task processing latency under energy consumption constraints. Simulation experiments show that this scheme's latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"1-16"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46146642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2021-0762.202308
Ali Sanagooy Aghdam, A. T. Eshlaghy, M. Kazemi, Amir Danehsvar
The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification (RFID) network planning in an emergency department of a hospital. Accordingly, though genetic algorithm (GA) and simulated annealing (SA) have advantages and disadvantages, but they are also complementary. Hence, the combined algorithm not only takes advantages of the two methods, but also avoids their disadvantages. The simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID readers. Besides, the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.
{"title":"RFID network planning optimization using a genetic-simulated annealing combined algorithm","authors":"Ali Sanagooy Aghdam, A. T. Eshlaghy, M. Kazemi, Amir Danehsvar","doi":"10.23919/JCC.fa.2021-0762.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2021-0762.202308","url":null,"abstract":"The main purpose of this paper is to present and apply a genetic and simulated annealing combined algorithm to solve an optimization problem of Radio Frequency Identification (RFID) network planning in an emergency department of a hospital. Accordingly, though genetic algorithm (GA) and simulated annealing (SA) have advantages and disadvantages, but they are also complementary. Hence, the combined algorithm not only takes advantages of the two methods, but also avoids their disadvantages. The simulation results in an emergency department of a hospital present that the proposed method provides minimum total cost and maximum RFID network coverage in a simultaneous way with the efficient use of multi-antenna RFID readers. Besides, the results of comparison of two scenarios of the model with the results of other existing models in the relevant literature show that the proposed model has better outcomes.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"234-253"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43013052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2021-0792.202308
Yuanni Liu, Xi Liu, Xin Li, Mingxin Li, Yi Li
Mobile Crowd Sensing (MCS) is an emerging paradigm that leverages sensor-equipped smart devices to collect data. The introduction of MCS also poses some challenges such as providing high-quality data for upper layer MCS applications, which requires adequate participants. However, recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget, which may lead to a low coverage ratio of sensing area. This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users. The method consists of two steps: (1) A second-order Markov chain is used to predict the next positions of users, and select users whose next places are in the target sensing area to form a candidate pool. (2) The Average Entropy (DAE) is proposed to measure the distribution of participants. The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area. Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.
{"title":"Participants recruitment for coverage maximization by mobility predicting in mobile crowd sensing","authors":"Yuanni Liu, Xi Liu, Xin Li, Mingxin Li, Yi Li","doi":"10.23919/JCC.fa.2021-0792.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2021-0792.202308","url":null,"abstract":"Mobile Crowd Sensing (MCS) is an emerging paradigm that leverages sensor-equipped smart devices to collect data. The introduction of MCS also poses some challenges such as providing high-quality data for upper layer MCS applications, which requires adequate participants. However, recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget, which may lead to a low coverage ratio of sensing area. This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users. The method consists of two steps: (1) A second-order Markov chain is used to predict the next positions of users, and select users whose next places are in the target sensing area to form a candidate pool. (2) The Average Entropy (DAE) is proposed to measure the distribution of participants. The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area. Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"163-176"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42835580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-01DOI: 10.23919/JCC.fa.2023-0034.202308
Jiadai Wang, Chaochao Xing, Jiajia Liu
The emergence of various commercial and industrial Internet of Things (IoT) devices has brought great convenience to people's life and production. Both low-power, massively connected mMTC devices (MDs) and highly reliable, low-latency URLLC devices (UDs) play an important role in different application scenarios. However, when dense MDs and UDs periodically initiate random access (RA) to connect the base station and send data, due to the limited preamble resources, preamble collisions are likely to occur, resulting in device access failure and data transmission delay. At the same time, due to the high-reliability demands of UDs, which require smooth access and fast data transmission, it is necessary to reduce the failure rate of their RA process. To this end, we propose an intelligent preamble allocation scheme, which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side. In particular, considering the limited processing capacity and energy of IoT devices, we adopt the lightweight Q-learning algorithm on the device side and design simple states and actions for them. Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.
{"title":"Intelligent preamble allocation for coexistence of mMTC/URLLC devices: A hierarchical Q-learning based approach","authors":"Jiadai Wang, Chaochao Xing, Jiajia Liu","doi":"10.23919/JCC.fa.2023-0034.202308","DOIUrl":"https://doi.org/10.23919/JCC.fa.2023-0034.202308","url":null,"abstract":"The emergence of various commercial and industrial Internet of Things (IoT) devices has brought great convenience to people's life and production. Both low-power, massively connected mMTC devices (MDs) and highly reliable, low-latency URLLC devices (UDs) play an important role in different application scenarios. However, when dense MDs and UDs periodically initiate random access (RA) to connect the base station and send data, due to the limited preamble resources, preamble collisions are likely to occur, resulting in device access failure and data transmission delay. At the same time, due to the high-reliability demands of UDs, which require smooth access and fast data transmission, it is necessary to reduce the failure rate of their RA process. To this end, we propose an intelligent preamble allocation scheme, which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side. In particular, considering the limited processing capacity and energy of IoT devices, we adopt the lightweight Q-learning algorithm on the device side and design simple states and actions for them. Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"20 1","pages":"44-53"},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42334726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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