Pub Date : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473678
Nadia Yoza-Mitsuishi, P. Mathys
In the United States, the 6 GHz band (5925-7125 MHz) has recently been opened for unlicensed use, such as Radio Local Area Networks (RLANs), while sharing the spectrum with current incumbents. This paper proposes a novel aggregate interference model to analyze the impact of RLANs, specifically Wi-Fi devices, on fixed and mobile terrestrial incumbents, based on a spatial, time and frequency-domain approach using Monte Carlo simulations and real data. We simulate low-power indoor and standard-power operations based on the rules authorized by the Federal Communications Commission (FCC). In addition, we analyze the effect of increasing the maximum power spectral density by 3 dB for low-power indoor operations, as inquired by the FCC. Urban, suburban and rural scenarios were simulated and compared. The results show that Wi-Fi devices can coexist with terrestrial incumbents without causing harmful interference.
{"title":"Spectrum sharing between RLANs and terrestrial links in the 6 GHz band","authors":"Nadia Yoza-Mitsuishi, P. Mathys","doi":"10.1109/ICCWorkshops50388.2021.9473678","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473678","url":null,"abstract":"In the United States, the 6 GHz band (5925-7125 MHz) has recently been opened for unlicensed use, such as Radio Local Area Networks (RLANs), while sharing the spectrum with current incumbents. This paper proposes a novel aggregate interference model to analyze the impact of RLANs, specifically Wi-Fi devices, on fixed and mobile terrestrial incumbents, based on a spatial, time and frequency-domain approach using Monte Carlo simulations and real data. We simulate low-power indoor and standard-power operations based on the rules authorized by the Federal Communications Commission (FCC). In addition, we analyze the effect of increasing the maximum power spectral density by 3 dB for low-power indoor operations, as inquired by the FCC. Urban, suburban and rural scenarios were simulated and compared. The results show that Wi-Fi devices can coexist with terrestrial incumbents without causing harmful interference.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115706473","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473602
Yihan Liu, Zhanqi Xu, F. Yang, Liwei Kuang
To solve the resource allocation problem for the scenario where node resources and users’ demands are heterogeneous in network function virtualization (NFV) more effectively and practically, we formulate this problem as an integer linear programming (ILP) model with weighted node importance and on-demand requests. Then we propose a node-resource- and user-demand-aware mapping (NUAM) algorithm with a specially designed virtual network functions (VNFs) reusing and deploying mechanism so as to reuse VNFs instances and reduce bandwidth consumption jointly. Simulation results show that the proposed NUAM algorithm can reduce bandwidth usage and adapt to users’ demands effectively compared with existing methods.
{"title":"Node-Resource- and User-Demand-Aware Resource Allocation in NFV-enabled Elastic Optical Networks","authors":"Yihan Liu, Zhanqi Xu, F. Yang, Liwei Kuang","doi":"10.1109/ICCWorkshops50388.2021.9473602","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473602","url":null,"abstract":"To solve the resource allocation problem for the scenario where node resources and users’ demands are heterogeneous in network function virtualization (NFV) more effectively and practically, we formulate this problem as an integer linear programming (ILP) model with weighted node importance and on-demand requests. Then we propose a node-resource- and user-demand-aware mapping (NUAM) algorithm with a specially designed virtual network functions (VNFs) reusing and deploying mechanism so as to reuse VNFs instances and reduce bandwidth consumption jointly. Simulation results show that the proposed NUAM algorithm can reduce bandwidth usage and adapt to users’ demands effectively compared with existing methods.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123103784","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473849
Bing Li, Rongqing Zhang, Liuqing Yang
As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this paper, we investigate a non-orthogonal multiple access (NOMA) based UAV relaying network that helps data transmission from a base station (BS) to remote multiple users. To improve spectral efficiency of the investigated UAV relaying network, we provide a generalized user grouping protocol that partitions users into multiple groups and transmit data via NOMA within the same group. Then, we further formulate a joint optimization problem of the NOMA user grouping, the UAV position, and the UAV transmit power to maximize the system throughput. The formulated problem is non-convex which makes it difficult to solve directly, hence, we propose an iterative algorithm to obtain an approximate optimal solution. Simulation results demonstrate that our proposed NOMA-based UAV relaying scheme achieves significant throughput gains compared with other benchmark schemes.
{"title":"Joint User Grouping and Power Allocation for NOMA-Based UAV Relaying Networks","authors":"Bing Li, Rongqing Zhang, Liuqing Yang","doi":"10.1109/ICCWorkshops50388.2021.9473849","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473849","url":null,"abstract":"As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this paper, we investigate a non-orthogonal multiple access (NOMA) based UAV relaying network that helps data transmission from a base station (BS) to remote multiple users. To improve spectral efficiency of the investigated UAV relaying network, we provide a generalized user grouping protocol that partitions users into multiple groups and transmit data via NOMA within the same group. Then, we further formulate a joint optimization problem of the NOMA user grouping, the UAV position, and the UAV transmit power to maximize the system throughput. The formulated problem is non-convex which makes it difficult to solve directly, hence, we propose an iterative algorithm to obtain an approximate optimal solution. Simulation results demonstrate that our proposed NOMA-based UAV relaying scheme achieves significant throughput gains compared with other benchmark schemes.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123157650","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473513
Qiang Li, Pinyi Ren, Dongyang Xu, Yuncong Xie
The collaborative transmission of device-to-device (D2D) devices with the wireless networks enables the spectrum- efficient communication, which yet causes the interference of the wireless networks to D2D devices. It is fatal to the transmission of privacy content. To tackle the issue, we establish a collaborative D2D and uplink relay transmission model, in which D2D receiver eliminates the interference of the networks under the assist of the relay. Particularly, the relay and D2D receiver of the model can capture energy from the signals they received to relay the signals and facilitate the future D2D transmission, respectively. In order to guarantee the efficiency of D2D transmission, a novel energy- efficient D2D privacy content transmission protocol is proposed. In the protocol, a D2D transmission rate maximization problem is formulated subjected to several key rate and energy efficiency constraints. By solving the problem, the exact analytic solutions with respect to energy allocation parameters are acquired. Furthermore, the outage probabilities of the relay network and D2D transmission are characterized to verify the performance of the protocol. Finally, numerical results are provided to validate the analytical results of the protocol.
{"title":"Energy-Efficient Device-to-Device Privacy Content Transmission in Uplink Relay Networks","authors":"Qiang Li, Pinyi Ren, Dongyang Xu, Yuncong Xie","doi":"10.1109/ICCWorkshops50388.2021.9473513","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473513","url":null,"abstract":"The collaborative transmission of device-to-device (D2D) devices with the wireless networks enables the spectrum- efficient communication, which yet causes the interference of the wireless networks to D2D devices. It is fatal to the transmission of privacy content. To tackle the issue, we establish a collaborative D2D and uplink relay transmission model, in which D2D receiver eliminates the interference of the networks under the assist of the relay. Particularly, the relay and D2D receiver of the model can capture energy from the signals they received to relay the signals and facilitate the future D2D transmission, respectively. In order to guarantee the efficiency of D2D transmission, a novel energy- efficient D2D privacy content transmission protocol is proposed. In the protocol, a D2D transmission rate maximization problem is formulated subjected to several key rate and energy efficiency constraints. By solving the problem, the exact analytic solutions with respect to energy allocation parameters are acquired. Furthermore, the outage probabilities of the relay network and D2D transmission are characterized to verify the performance of the protocol. Finally, numerical results are provided to validate the analytical results of the protocol.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115824809","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473625
N. Piatkowski, J. Mueller-Roemer, P. Hasse, A. Bachorek, Tim Werner, Pascal Birnstill, A. Morgenstern, L. Stobbe
Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system—allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible—e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.
{"title":"Generative Machine Learning for Resource-Aware 5G and IoT Systems","authors":"N. Piatkowski, J. Mueller-Roemer, P. Hasse, A. Bachorek, Tim Werner, Pascal Birnstill, A. Morgenstern, L. Stobbe","doi":"10.1109/ICCWorkshops50388.2021.9473625","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473625","url":null,"abstract":"Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system—allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible—e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836887","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473610
A. Dowhuszko, M. C. Ilter, P. Pinho, R. Wichman, Jyri Hämäläinen
This paper studies the effect that the color temperature of an LED lamp has on the ability of a Visible Light Communication (VLC) system to detect different events, which could be the presence, position, and/or color of an object in the sensing area. The proposed VLC-based monitoring system takes advantage of the Channel State Information (CSI) that the VLC receiver estimates regularly for OFDM equalization, and makes use of K-means++ clustering to estimate the number of events that can be identified in the collected CSI data. The color temperature of the LED lighting is varied by changing the fraction of the total radiant flux emitted by Cool-White and Red-Orange LEDs, respectively, enabling to obtain a complete palette of white light that ranges from warm reddish (2600 K) to cool blueish (6200 K). The experimental evaluation is carried out with the aid of a software-defined VLC demonstrator, and shows that the sensing performance when using the reflected VLC signal to estimate the position of the object does not vary notably with the color temperature of the LED lamp. In contrast, the use of white light with high Color Rendering Index provides better results when the objective is to identify the color signature that different objects create when placed in the sensing area.
{"title":"Effect of the Color Temperature of LED lighting on the sensing ability of Visible Light Communications","authors":"A. Dowhuszko, M. C. Ilter, P. Pinho, R. Wichman, Jyri Hämäläinen","doi":"10.1109/ICCWorkshops50388.2021.9473610","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473610","url":null,"abstract":"This paper studies the effect that the color temperature of an LED lamp has on the ability of a Visible Light Communication (VLC) system to detect different events, which could be the presence, position, and/or color of an object in the sensing area. The proposed VLC-based monitoring system takes advantage of the Channel State Information (CSI) that the VLC receiver estimates regularly for OFDM equalization, and makes use of K-means++ clustering to estimate the number of events that can be identified in the collected CSI data. The color temperature of the LED lighting is varied by changing the fraction of the total radiant flux emitted by Cool-White and Red-Orange LEDs, respectively, enabling to obtain a complete palette of white light that ranges from warm reddish (2600 K) to cool blueish (6200 K). The experimental evaluation is carried out with the aid of a software-defined VLC demonstrator, and shows that the sensing performance when using the reflected VLC signal to estimate the position of the object does not vary notably with the color temperature of the LED lamp. In contrast, the use of white light with high Color Rendering Index provides better results when the objective is to identify the color signature that different objects create when placed in the sensing area.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125828503","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473810
Ammad Ali Syed, S. Ayaz, T. Leinmüller, Madhu Chandra
The future autonomous vehicle is not only processing the copious amount of indispensable data generated by its onboard sensors but also utilizing the data from other vehicles, roadside unit (RSU) etc. Managing the mixed-criticality data requires intelligent time-sensitive scheduling and routing within the in-vehicle network (IVN) infrastructure. Use-cases related to self-adaptivity (including vehicular communication), partial networking and embedded virtualization require to change the configuration of the IVN at runtime. State-of-the-art IEEE Time-Sensitive Networking (TSN) standards possess a grave challenge in handling runtime reconfigurations. Above mentioned use-cases foster the development of scalable and efficient dynamic scheduling and routing algorithms for TSN based IVN. In this paper, four meticulously designed heuristics are analyzed for dynamic scheduling and routing on-the-fly in TSN based IVN. One of the algorithms, Bottleneck heuristic outperforms others in term of schedulability and response time. It schedules around 16 − 22% more flows as compared to other developed heuristics depending on the network load.
{"title":"Dynamic Scheduling and Routing for TSN based In-vehicle Networks","authors":"Ammad Ali Syed, S. Ayaz, T. Leinmüller, Madhu Chandra","doi":"10.1109/ICCWorkshops50388.2021.9473810","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473810","url":null,"abstract":"The future autonomous vehicle is not only processing the copious amount of indispensable data generated by its onboard sensors but also utilizing the data from other vehicles, roadside unit (RSU) etc. Managing the mixed-criticality data requires intelligent time-sensitive scheduling and routing within the in-vehicle network (IVN) infrastructure. Use-cases related to self-adaptivity (including vehicular communication), partial networking and embedded virtualization require to change the configuration of the IVN at runtime. State-of-the-art IEEE Time-Sensitive Networking (TSN) standards possess a grave challenge in handling runtime reconfigurations. Above mentioned use-cases foster the development of scalable and efficient dynamic scheduling and routing algorithms for TSN based IVN. In this paper, four meticulously designed heuristics are analyzed for dynamic scheduling and routing on-the-fly in TSN based IVN. One of the algorithms, Bottleneck heuristic outperforms others in term of schedulability and response time. It schedules around 16 − 22% more flows as compared to other developed heuristics depending on the network load.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115218039","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473800
Kejia Ji, Shuo Chang, Sai Huang, Hao Chen, Shao Jia, Hua Lu
The traditional modulation classification method is difficult to cope with the changing wireless electromagnetic environment and the complex signal model. On this basis, this paper proposes a data-driven automatic modulation classification (AMC) method using a global pooling-based convolutional neural network (GP-CNN). Stepping convolution is used to replace the pooling layer to avoid loss of signal details and global pooling (GP) is utilized to replace the fully-connected for a lower computational complexity. Simulations verify the superiority of the proposed method, which outperforms other deep neural network methods and approaches the optimal bound of the maximum likelihood method. Moreover, the influence of the network parameters on performance is also explored.
{"title":"Modulation Classification of Active Attack Signals for Internet of Things Using GP-CNN Network","authors":"Kejia Ji, Shuo Chang, Sai Huang, Hao Chen, Shao Jia, Hua Lu","doi":"10.1109/ICCWorkshops50388.2021.9473800","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473800","url":null,"abstract":"The traditional modulation classification method is difficult to cope with the changing wireless electromagnetic environment and the complex signal model. On this basis, this paper proposes a data-driven automatic modulation classification (AMC) method using a global pooling-based convolutional neural network (GP-CNN). Stepping convolution is used to replace the pooling layer to avoid loss of signal details and global pooling (GP) is utilized to replace the fully-connected for a lower computational complexity. Simulations verify the superiority of the proposed method, which outperforms other deep neural network methods and approaches the optimal bound of the maximum likelihood method. Moreover, the influence of the network parameters on performance is also explored.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122765876","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473897
Susruth Sudhakaran, Vincent Mageshkumar, Amit S. Baxi, D. Cavalcanti
This paper addresses the challenges in guaranteeing accurate time synchronization and deterministic data delivery over wireless using collaborative robotics as an example application. The paper describes a methodology to map application layer QoS requirements from the ROS2 (Robotics Operating System 2) and DDS (Data Distribution System) middleware, used to develop robotics applications, to the link layer transport based on Wireless Time-Sensitive Networking (TSN) capabilities built on Wi-Fi. The paper provides experimental results from a prototype implementation of a collaborative task between two robots enabled by WTSN time synchronization and traffic shaping over Wi-Fi. The results demonstrate how time synchronized time-aware scheduling over Wi-Fi can be configured to meet the QoS requirements of the robotics application even in the presence of background traffic sharing the same channel.
{"title":"Enabling QoS for Collaborative Robotics Applications with Wireless TSN","authors":"Susruth Sudhakaran, Vincent Mageshkumar, Amit S. Baxi, D. Cavalcanti","doi":"10.1109/ICCWorkshops50388.2021.9473897","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473897","url":null,"abstract":"This paper addresses the challenges in guaranteeing accurate time synchronization and deterministic data delivery over wireless using collaborative robotics as an example application. The paper describes a methodology to map application layer QoS requirements from the ROS2 (Robotics Operating System 2) and DDS (Data Distribution System) middleware, used to develop robotics applications, to the link layer transport based on Wireless Time-Sensitive Networking (TSN) capabilities built on Wi-Fi. The paper provides experimental results from a prototype implementation of a collaborative task between two robots enabled by WTSN time synchronization and traffic shaping over Wi-Fi. The results demonstrate how time synchronized time-aware scheduling over Wi-Fi can be configured to meet the QoS requirements of the robotics application even in the presence of background traffic sharing the same channel.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122538745","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 : 2021-06-01DOI: 10.1109/ICCWorkshops50388.2021.9473767
G. Prasad, Deepak Mishra
Energy transfer (ET) in RF powered harvesting as well as information transfer (IT) in end-to-end communication is obstructed by range of transmission in the network under consideration. This can be resolved by employing a cooperative relay in both ET and IT operations. However, involving the composite operations in a practically unknown environment require a learning based algorithms to obtain an optimal policy for efficient energy management and data communication together. To confront it, here, we propose a deep learning algorithm based on deep deterministic policy gradient (DDPG), providing continuous course of actions under optimal online policy for integrated information and energy relaying (i2ER) network. In the designed nonconvex problem, the long-term average net bit rate of the end-to-end communication is maximized in four phases of operations under the given constraints on the harvested energy at relay and source nodes. Via extensive simulations, various insights are obtained on the performance of the proposed algorithm in different used modulation for transmission and learning rate while and after learning. Lastly, the achieved bit rate in the i2ER network is compared with the performance of a greedy benchmark scheme and get an improvement upto 62%.
{"title":"Deep Learning Based Integrated Information and Energy Relaying in RF Powered Communication","authors":"G. Prasad, Deepak Mishra","doi":"10.1109/ICCWorkshops50388.2021.9473767","DOIUrl":"https://doi.org/10.1109/ICCWorkshops50388.2021.9473767","url":null,"abstract":"Energy transfer (ET) in RF powered harvesting as well as information transfer (IT) in end-to-end communication is obstructed by range of transmission in the network under consideration. This can be resolved by employing a cooperative relay in both ET and IT operations. However, involving the composite operations in a practically unknown environment require a learning based algorithms to obtain an optimal policy for efficient energy management and data communication together. To confront it, here, we propose a deep learning algorithm based on deep deterministic policy gradient (DDPG), providing continuous course of actions under optimal online policy for integrated information and energy relaying (i2ER) network. In the designed nonconvex problem, the long-term average net bit rate of the end-to-end communication is maximized in four phases of operations under the given constraints on the harvested energy at relay and source nodes. Via extensive simulations, various insights are obtained on the performance of the proposed algorithm in different used modulation for transmission and learning rate while and after learning. Lastly, the achieved bit rate in the i2ER network is compared with the performance of a greedy benchmark scheme and get an improvement upto 62%.","PeriodicalId":127186,"journal":{"name":"2021 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114509907","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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