Pub Date : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814574
A. Pfadler, Peter Jung, Vlerar Shala, Martin Kasparick, M. Adrat, Sławomir Stańczak
In this paper, we investigate the ability of recurrent neural networks to perform channel predictions for orthogonal time frequency and space modulation (OTFS). Due to 2D orthogonal precoding, OTFS promises high time-frequency (TF) diversity which turns out to enable robust communication even in high mobility scenarios. To exploit high diversity gain, knowledge of accurate channel state information (CSI) is essential. In OTFS, the CSI can directly be estimated in the delay-Doppler (DD) domain. Vehicular channels however are considered to be doubly-dispersive and therefore require a channel estimation on a per frame basis. This motivates the investigation of short-term channel prediction. We propose a scheme to estimate the channel coefficients collected on vehicular trajectory and predict them into the future using 2D-convolutional long short-term memory network (2D-ConvLSTM). First numerical results show that a prediction of the channel coefficients is possible.
{"title":"Short-Term Prediction of Doubly-Dispersive Channels for Pulse-Shaped OTFS using 2D-ConvLSTM","authors":"A. Pfadler, Peter Jung, Vlerar Shala, Martin Kasparick, M. Adrat, Sławomir Stańczak","doi":"10.1109/iccworkshops53468.2022.9814574","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814574","url":null,"abstract":"In this paper, we investigate the ability of recurrent neural networks to perform channel predictions for orthogonal time frequency and space modulation (OTFS). Due to 2D orthogonal precoding, OTFS promises high time-frequency (TF) diversity which turns out to enable robust communication even in high mobility scenarios. To exploit high diversity gain, knowledge of accurate channel state information (CSI) is essential. In OTFS, the CSI can directly be estimated in the delay-Doppler (DD) domain. Vehicular channels however are considered to be doubly-dispersive and therefore require a channel estimation on a per frame basis. This motivates the investigation of short-term channel prediction. We propose a scheme to estimate the channel coefficients collected on vehicular trajectory and predict them into the future using 2D-convolutional long short-term memory network (2D-ConvLSTM). First numerical results show that a prediction of the channel coefficients is possible.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130686687","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814552
Dawei Nie, Wenjuan Yu, Q. Ni, H. Pervaiz
Empirical studies have observed that the spec-trum usage in practice follows regular patterns. Machine learning (ML)-based spectrum prediction techniques can thus be used jointly with cooperative sensing in cognitive radio networks (CRNs). In this paper, we propose a novel cluster-based sensing-after-prediction scheme and aim to reduce the total energy consumption of a CRN. An integer programming problem is formulated that minimizes the cluster size and optimizes the decision threshold, while guaranteeing the system accuracy requirement. To solve this challenging optimization problem, the relaxation technique is used which transforms the optimization problem into a tractable problem. The solution to the relaxed problem serves as a foundation for the solution to the original integer programming. Finally, a low-complexity search algorithm is proposed which achieves the global optimum, as it obtains the same performance with exhaustive search. Simulation results demonstrate that the total energy consumption of CRN is greatly reduced by applying our clustered sensing-after-prediction scheme.
{"title":"Optimization for Prediction-Driven Cooperative Spectrum Sensing in Cognitive Radio Networks","authors":"Dawei Nie, Wenjuan Yu, Q. Ni, H. Pervaiz","doi":"10.1109/iccworkshops53468.2022.9814552","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814552","url":null,"abstract":"Empirical studies have observed that the spec-trum usage in practice follows regular patterns. Machine learning (ML)-based spectrum prediction techniques can thus be used jointly with cooperative sensing in cognitive radio networks (CRNs). In this paper, we propose a novel cluster-based sensing-after-prediction scheme and aim to reduce the total energy consumption of a CRN. An integer programming problem is formulated that minimizes the cluster size and optimizes the decision threshold, while guaranteeing the system accuracy requirement. To solve this challenging optimization problem, the relaxation technique is used which transforms the optimization problem into a tractable problem. The solution to the relaxed problem serves as a foundation for the solution to the original integer programming. Finally, a low-complexity search algorithm is proposed which achieves the global optimum, as it obtains the same performance with exhaustive search. Simulation results demonstrate that the total energy consumption of CRN is greatly reduced by applying our clustered sensing-after-prediction scheme.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131245950","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 : 2022-05-16DOI: 10.1109/ICCWorkshops53468.2022.9882173
Solomon Satche, D. Rawat
Terahertz band appears as a solution to the spectrum scarcity for wireless communications. But, it presents beam pointing error, multipath interference, and atmospheric hurdle challenges. In this paper, we analyse the impacts of these constraints on the communication link in the THz band based on the mobile speeds of an end users in vehicles. We use a generic approach to assess the performance in terms of link failure probability. To that end we use the Mellin transform theorem approach to derive the joint pdf of the channel coefficient h and derive their joint probability density function that would be useful to assess the performance of the mobile vehicular communication link. The performance as a function of speeds is analyzed for pointing error, joint pointing and multipath error, and joint pointing, multi-path, and atmospheric error. The simulated results show severe and rapid degradation at higher speeds of the THz link when the SNIR is below a certain threshold. We then infer the performance with Doppler effects.
{"title":"On the Performance of Terahertz Communications for Vehicular Wireless Networks","authors":"Solomon Satche, D. Rawat","doi":"10.1109/ICCWorkshops53468.2022.9882173","DOIUrl":"https://doi.org/10.1109/ICCWorkshops53468.2022.9882173","url":null,"abstract":"Terahertz band appears as a solution to the spectrum scarcity for wireless communications. But, it presents beam pointing error, multipath interference, and atmospheric hurdle challenges. In this paper, we analyse the impacts of these constraints on the communication link in the THz band based on the mobile speeds of an end users in vehicles. We use a generic approach to assess the performance in terms of link failure probability. To that end we use the Mellin transform theorem approach to derive the joint pdf of the channel coefficient h and derive their joint probability density function that would be useful to assess the performance of the mobile vehicular communication link. The performance as a function of speeds is analyzed for pointing error, joint pointing and multipath error, and joint pointing, multi-path, and atmospheric error. The simulated results show severe and rapid degradation at higher speeds of the THz link when the SNIR is below a certain threshold. We then infer the performance with Doppler effects.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131400967","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814706
Randhir Kumar, Prabhat Kumar, Abhinav Kumar, A. Franklin, A. Jolfaei
The softwarized infrastructure of Software-Defined Industrial Internet of Things (SDIIoT) offers a cost-effective solution to improve flexibility and reliability in network management but faces several critical challenges. First, th Majority of SDIIoT entities operate over wireless channel, which expose them to a variety of attacks (e.g., man-in-the-middle, replay, and impersonation attacks) and also the centralized nature of SDN controller is prone to single point attacks. Second, network traffic in the SDIIoT is associated with large scale, high dimension and redundant data, all of which present significant hurdles in the development of efficient flow analyzer. In this regard, we present a novel blockchain and Deep Learning (DL) integrated framework for protecting confidential information and hunting cyber threats against SDIIoT and their network traffic. First the blockchain module is proposed to securely transmit industrial data from IIoT sensors to controllers of SDN via forwarding nodes (i.e., OpenFLow switches) using Clique Proof-of-Authority (C-PoA) consensus mechanism. A novel flow analyzer based on DL architecture named LSTMSCAE-AGRU is designed by combining Long Short-Term Memory Stacked Contractive AutoEncoder (LSTMSCAE) with Attention-based Gated Recurrent Unit (AGRU) at the control plane. The latter first extracts low-dimensional features in an unsupervised manner, which is then fed to AGRU for hunting anomalous switch requests. The proposed framework can withstand a variety of well-known cyber threats and mitigate the single point of controller failure problem in SDIIoT.
{"title":"Blockchain and Deep Learning for Cyber Threat-Hunting in Software-Defined Industrial IoT","authors":"Randhir Kumar, Prabhat Kumar, Abhinav Kumar, A. Franklin, A. Jolfaei","doi":"10.1109/iccworkshops53468.2022.9814706","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814706","url":null,"abstract":"The softwarized infrastructure of Software-Defined Industrial Internet of Things (SDIIoT) offers a cost-effective solution to improve flexibility and reliability in network management but faces several critical challenges. First, th Majority of SDIIoT entities operate over wireless channel, which expose them to a variety of attacks (e.g., man-in-the-middle, replay, and impersonation attacks) and also the centralized nature of SDN controller is prone to single point attacks. Second, network traffic in the SDIIoT is associated with large scale, high dimension and redundant data, all of which present significant hurdles in the development of efficient flow analyzer. In this regard, we present a novel blockchain and Deep Learning (DL) integrated framework for protecting confidential information and hunting cyber threats against SDIIoT and their network traffic. First the blockchain module is proposed to securely transmit industrial data from IIoT sensors to controllers of SDN via forwarding nodes (i.e., OpenFLow switches) using Clique Proof-of-Authority (C-PoA) consensus mechanism. A novel flow analyzer based on DL architecture named LSTMSCAE-AGRU is designed by combining Long Short-Term Memory Stacked Contractive AutoEncoder (LSTMSCAE) with Attention-based Gated Recurrent Unit (AGRU) at the control plane. The latter first extracts low-dimensional features in an unsupervised manner, which is then fed to AGRU for hunting anomalous switch requests. The proposed framework can withstand a variety of well-known cyber threats and mitigate the single point of controller failure problem in SDIIoT.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134147715","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814498
H. Farooq, Julien Forgeat, Shruti Bothe, Maxime Bouton, P. Karlsson
Energy Efficient operation of ultra-dense hetero-geneous network deployments is a big challenge for mobile networks. AI-assisted energy saving is one of the potential self-organizing network use cases for radio access network intelli-gence that can be used to predict the service load. This prediction can in turn be leveraged for proactively turning OFF/ON the capacity booster small cells within the coverage of always ON macro cells. These ML workloads can reside in macro cell base stations as opposed to conventional cloud-centric architecture to meet beyond 5G ambitious requirements of ultra-low latency, highest reliability, and scalability. However, the power-hungry hyperparameter search of ML workloads distributed at edges of the radio access network is a major challenge that can have substantial effect on the overall energy -efficiency of the network. In this paper, we illustrate how coordinated efficient training of distributed edge- ML models driven energy saving functions can enhance network energy efficiency. We validate the proposed method through a data-driven simulation methodology augmenting real traffic traces and comparing it with variants of legacy edge-ML hyper-parameter search techniques.
{"title":"Edge-distributed Coordinated Hyper-Parameter Search for Energy Saving SON Use-Case","authors":"H. Farooq, Julien Forgeat, Shruti Bothe, Maxime Bouton, P. Karlsson","doi":"10.1109/iccworkshops53468.2022.9814498","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814498","url":null,"abstract":"Energy Efficient operation of ultra-dense hetero-geneous network deployments is a big challenge for mobile networks. AI-assisted energy saving is one of the potential self-organizing network use cases for radio access network intelli-gence that can be used to predict the service load. This prediction can in turn be leveraged for proactively turning OFF/ON the capacity booster small cells within the coverage of always ON macro cells. These ML workloads can reside in macro cell base stations as opposed to conventional cloud-centric architecture to meet beyond 5G ambitious requirements of ultra-low latency, highest reliability, and scalability. However, the power-hungry hyperparameter search of ML workloads distributed at edges of the radio access network is a major challenge that can have substantial effect on the overall energy -efficiency of the network. In this paper, we illustrate how coordinated efficient training of distributed edge- ML models driven energy saving functions can enhance network energy efficiency. We validate the proposed method through a data-driven simulation methodology augmenting real traffic traces and comparing it with variants of legacy edge-ML hyper-parameter search techniques.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121285334","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814577
Mi Yang, B. Ai, R. He, Zhangfeng Ma, Z. Zhong
Vehicular communication, as one of the most important supporting technologies of intelligent transportation system, has been widely concerned by academia and industry. Wireless channel characterization and modeling are the foundation of communication systems. Compared with typical road scenarios such as urban and suburban areas, wireless channel characterization in intersections is a challenging task. It is necessary to carry out measurement, characterization, and modeling for intersection channels as the basic theoretical support for vehicular communication system solutions. In this paper, channel measurements at 5.9 GHz in street intersection scenarios are carried out and provide data for the characterization and modeling of time-varying vehicular channels. Based on the measured data, this paper extracts and analyzes the time-varying power, delay and spatial characteristics and quantitatively models the influence of building obstruction on key channel parameters. The research in this paper can enrich the investigation of vehicular channels and enable the analysis and design of vehicular communication systems.
{"title":"Vehicle-to-Vehicle Channel Characteristics in Intersection Environment","authors":"Mi Yang, B. Ai, R. He, Zhangfeng Ma, Z. Zhong","doi":"10.1109/iccworkshops53468.2022.9814577","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814577","url":null,"abstract":"Vehicular communication, as one of the most important supporting technologies of intelligent transportation system, has been widely concerned by academia and industry. Wireless channel characterization and modeling are the foundation of communication systems. Compared with typical road scenarios such as urban and suburban areas, wireless channel characterization in intersections is a challenging task. It is necessary to carry out measurement, characterization, and modeling for intersection channels as the basic theoretical support for vehicular communication system solutions. In this paper, channel measurements at 5.9 GHz in street intersection scenarios are carried out and provide data for the characterization and modeling of time-varying vehicular channels. Based on the measured data, this paper extracts and analyzes the time-varying power, delay and spatial characteristics and quantitatively models the influence of building obstruction on key channel parameters. The research in this paper can enrich the investigation of vehicular channels and enable the analysis and design of vehicular communication systems.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128929341","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814520
Maosheng Zhu, Xi Li, Hong Ji, Heli Zhang
Industrial private networks (IPNs), having enhanced communication characteristics in a specific area, emerge to fulfill the demanding industrial use cases. However, users' demands for flexibility and low latency in the B5G era are in inevitable conflict with limited and isolated resources in IPNs; thus, scalability and flexibility of resources are required, which can be realized by jointly funneling users' traffic and resource allocation spanning IPN s to promote load balancing and resource efficiency. In this paper, we devise a cost-aware resource allocation (CARA) approach embedded with a proba-bilistic latency guarantee for resource efficiency achievement and low latency fulfillment. Specifically, we first establish a unified cost model for coupling funneled traffic and resource allocated in each lPN, avoiding the optimization penalty of alternating them. Then, to solve the conflict between limited computing and communication resources, we propose the CARA approach based on the non-dominated sorting genetic algorithm-III (NSGA-III). Furthermore, a probabilistic latency guarantee sub-algorithm is embedded in CARA to fulfill the latency constraint and relax it for advanced industrial implementation. Additionally, compared with other existing algorithms, simulation results reveal that our proposed algorithm not only globally minimizes unified cost across IPNs, but also individually balances the funneled traffic.
工业专用网络(IPNs)在特定领域具有增强的通信特性，可以满足苛刻的工业用例。然而，B5G时代用户对灵活性和低时延的需求与IPNs有限、孤立的资源不可避免地发生冲突;因此，对资源的可扩展性和灵活性提出了更高的要求，这可以通过跨IPN共同汇集用户流量和资源分配来实现，以促进负载均衡和资源效率。在本文中，我们设计了一种成本感知资源分配(CARA)方法，该方法嵌入了概率延迟保证，以实现资源效率和低延迟实现。具体而言，我们首先建立了一个统一的成本模型，将漏斗流量和资源分配耦合到每个lPN中，避免了它们交替的优化惩罚。然后，为了解决有限的计算资源和通信资源之间的冲突，我们提出了基于非支配排序遗传算法- iii (NSGA-III)的CARA方法。此外，在CARA中嵌入了概率延迟保证子算法，以满足延迟约束，并放宽延迟约束，以便于高级工业实现。此外，与其他现有算法相比，仿真结果表明，我们提出的算法不仅在全局上最大限度地降低了ipn之间的统一成本，而且在各个ipn之间实现了流量均衡。
{"title":"Cost-Aware Resource Allocation with Probabilistic Latency Guarantee in B5G Industrial Private Networks","authors":"Maosheng Zhu, Xi Li, Hong Ji, Heli Zhang","doi":"10.1109/iccworkshops53468.2022.9814520","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814520","url":null,"abstract":"Industrial private networks (IPNs), having enhanced communication characteristics in a specific area, emerge to fulfill the demanding industrial use cases. However, users' demands for flexibility and low latency in the B5G era are in inevitable conflict with limited and isolated resources in IPNs; thus, scalability and flexibility of resources are required, which can be realized by jointly funneling users' traffic and resource allocation spanning IPN s to promote load balancing and resource efficiency. In this paper, we devise a cost-aware resource allocation (CARA) approach embedded with a proba-bilistic latency guarantee for resource efficiency achievement and low latency fulfillment. Specifically, we first establish a unified cost model for coupling funneled traffic and resource allocated in each lPN, avoiding the optimization penalty of alternating them. Then, to solve the conflict between limited computing and communication resources, we propose the CARA approach based on the non-dominated sorting genetic algorithm-III (NSGA-III). Furthermore, a probabilistic latency guarantee sub-algorithm is embedded in CARA to fulfill the latency constraint and relax it for advanced industrial implementation. Additionally, compared with other existing algorithms, simulation results reveal that our proposed algorithm not only globally minimizes unified cost across IPNs, but also individually balances the funneled traffic.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129140658","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814522
Yucheng Dai, Wooseok Nam, Tao Luo, A. Kannan
Owing to the development in both hardware man-ufacturing and signal processing techniques, a User Equipment (UE) has evolved into not just a communication tool but also an advanced device which can perceive the environment. Although current sensing designs at UE side mainly focus on the electro-magnetic environment or the existence of the device which can transmit signal, the 5G/6G standards plan to extend the sensing function to perceive the physical environment and the ‘device-free’ object in the near future. This paper proposes a cooperative device-free wireless sensing method for estimating the position of a target, aiming to exploit the existing Transmit/Receive Points (TRPs) and UEs in the cellular system. A 3D ray-tracing channel model of Qualcomm Morehouse campus has been constructed for sensing performance evaluation. The results show that, for a cuboidal target with dimensions $1times 1times 0.2$ meters, a projected distance error less than 0.34 meter and 0.26 meter in 90 percent cases can be achieved with a single TRP and 3 TRPs, respectively.
{"title":"A Cooperative Device Free Wireless Sensing Design and Analysis for Target Position Estimation","authors":"Yucheng Dai, Wooseok Nam, Tao Luo, A. Kannan","doi":"10.1109/iccworkshops53468.2022.9814522","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814522","url":null,"abstract":"Owing to the development in both hardware man-ufacturing and signal processing techniques, a User Equipment (UE) has evolved into not just a communication tool but also an advanced device which can perceive the environment. Although current sensing designs at UE side mainly focus on the electro-magnetic environment or the existence of the device which can transmit signal, the 5G/6G standards plan to extend the sensing function to perceive the physical environment and the ‘device-free’ object in the near future. This paper proposes a cooperative device-free wireless sensing method for estimating the position of a target, aiming to exploit the existing Transmit/Receive Points (TRPs) and UEs in the cellular system. A 3D ray-tracing channel model of Qualcomm Morehouse campus has been constructed for sensing performance evaluation. The results show that, for a cuboidal target with dimensions $1times 1times 0.2$ meters, a projected distance error less than 0.34 meter and 0.26 meter in 90 percent cases can be achieved with a single TRP and 3 TRPs, respectively.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115941356","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 : 2022-05-16DOI: 10.1109/iccworkshops53468.2022.9814518
Hao Song, Kyeong Jin Kim, Jianlin Guo, P. Orlik, K. Parsons
To meet the quality of service requirements of pri-vate applications, the private fifth-generation (5G) networks are required to provide low-latency and high reliability transmissions. Thus, a new semi-persistent scheduling (SPS) scheme is proposed to enable “grant-free” and immediate uplink access for users. Re-scheduling SPS users at the beginning of each SPS period, the scheduling frequency can be significantly reduced. By allocating users to the same wireless channels without requesting wireless resources and waiting for scheduling, the uplink transmission latency and the system complexity can be greatly reduced. To maintain reliability over a changing wireless environment caused by mobility, the proposed SPS scheme employs stochastic geometry for the derivation of the distance distribution within the SPS period, modulation and code scheme (MCS) selection, and scheduling optimization. Based on the MCS selection and the data expectation on an SPS channel, an optimization problem is formulated for reliability and fairness enhancement by jointly taking into account the current channel states and potential channel states in the SPS period. Finally, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the proposed SPS scheme.
{"title":"Semi-Persistent Scheduling Scheme for Low-Latency and High-Reliability Transmissions in Private 5G Networks","authors":"Hao Song, Kyeong Jin Kim, Jianlin Guo, P. Orlik, K. Parsons","doi":"10.1109/iccworkshops53468.2022.9814518","DOIUrl":"https://doi.org/10.1109/iccworkshops53468.2022.9814518","url":null,"abstract":"To meet the quality of service requirements of pri-vate applications, the private fifth-generation (5G) networks are required to provide low-latency and high reliability transmissions. Thus, a new semi-persistent scheduling (SPS) scheme is proposed to enable “grant-free” and immediate uplink access for users. Re-scheduling SPS users at the beginning of each SPS period, the scheduling frequency can be significantly reduced. By allocating users to the same wireless channels without requesting wireless resources and waiting for scheduling, the uplink transmission latency and the system complexity can be greatly reduced. To maintain reliability over a changing wireless environment caused by mobility, the proposed SPS scheme employs stochastic geometry for the derivation of the distance distribution within the SPS period, modulation and code scheme (MCS) selection, and scheduling optimization. Based on the MCS selection and the data expectation on an SPS channel, an optimization problem is formulated for reliability and fairness enhancement by jointly taking into account the current channel states and potential channel states in the SPS period. Finally, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the proposed SPS scheme.","PeriodicalId":102261,"journal":{"name":"2022 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"PP 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126708399","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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