Pub Date : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348155
Vineeth Kumar, N. Mehta
Differential channel quality indicator (CQI) and wideband CQI are key components of the 4G and 5G standards. They enable a base station (BS) to acquire channel state information that is essential for rate adaptation and scheduling without overwhelming the uplink. We present a novel throughput-optimal rate adaptation rule, which exploits the correlation between the differential and wideband CQIs to improve throughput without any additional feedback, and a computationally efficient approach to evaluate it. We then propose a novel flexible-overhead differential CQI feedback scheme, in which the number of feedback bits can be subband-specific. The combination of the two approaches provides a new flexibility to the BS to control the feedback overhead and achieves a throughput comparable to conventional approaches with much less feedback.
{"title":"Exploiting Correlation Between Wideband and Differential CQIs for Adaptation and Feedback","authors":"Vineeth Kumar, N. Mehta","doi":"10.1109/GLOBECOM42002.2020.9348155","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348155","url":null,"abstract":"Differential channel quality indicator (CQI) and wideband CQI are key components of the 4G and 5G standards. They enable a base station (BS) to acquire channel state information that is essential for rate adaptation and scheduling without overwhelming the uplink. We present a novel throughput-optimal rate adaptation rule, which exploits the correlation between the differential and wideband CQIs to improve throughput without any additional feedback, and a computationally efficient approach to evaluate it. We then propose a novel flexible-overhead differential CQI feedback scheme, in which the number of feedback bits can be subband-specific. The combination of the two approaches provides a new flexibility to the BS to control the feedback overhead and achieves a throughput comparable to conventional approaches with much less feedback.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"295 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79453803","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348064
M. Mukherjee, Vikas Kumar, D. Maity, Rakesh Matam, C. Mavromoustakis, Qi Zhang, G. Mastorakis
In this paper, we study the priority-aware task data offloading in edge computing-assisted healthcare service provisioning. The edge server aims to provide additional computing resources to the end-users for processing the delay-sensitive tasks. However, at the same time, it becomes a challenging issue when some of the tasks demand lower response time compared to the other tasks. We present a priority-aware task offloading and scheduling strategy that allocates the computing resources to the high-priority tasks. The hard-deadline tasks are processed first. Later, the remaining computing resources are used to tolerate longer average response time for the soft-deadline tasks. Moreover, we derive a lower bound of the average response time for all hard- and soft-deadline tasks. Through extensive simulations, we show that the proposed task scheduling manages to allocate the computing resources of both end-users and edge server to the hard-deadline tasks while scheduling the soft-deadline tasks with low priority.
{"title":"Delay-sensitive and Priority-aware Task Offloading for Edge Computing-assisted Healthcare Services","authors":"M. Mukherjee, Vikas Kumar, D. Maity, Rakesh Matam, C. Mavromoustakis, Qi Zhang, G. Mastorakis","doi":"10.1109/GLOBECOM42002.2020.9348064","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348064","url":null,"abstract":"In this paper, we study the priority-aware task data offloading in edge computing-assisted healthcare service provisioning. The edge server aims to provide additional computing resources to the end-users for processing the delay-sensitive tasks. However, at the same time, it becomes a challenging issue when some of the tasks demand lower response time compared to the other tasks. We present a priority-aware task offloading and scheduling strategy that allocates the computing resources to the high-priority tasks. The hard-deadline tasks are processed first. Later, the remaining computing resources are used to tolerate longer average response time for the soft-deadline tasks. Moreover, we derive a lower bound of the average response time for all hard- and soft-deadline tasks. Through extensive simulations, we show that the proposed task scheduling manages to allocate the computing resources of both end-users and edge server to the hard-deadline tasks while scheduling the soft-deadline tasks with low priority.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"3 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81912912","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322637
Moisés Nuñez Ochoa, M. Maman, A. Duda
In this paper, we investigate how to provide good transmission quality in massive deployments of LoRa networks by considering all parameters such as device heterogeneity, network topology, and deployment density. We consider the scenario with nodes progressively joining the network, i.e., new nodes joining the network are configured based on measured metrics and without modifying the configuration of nodes that already joined the network. Based on this assumption, we propose an algorithm to improve network performance by effectively allocating a spreading factor (SF) to end-devices in realistic multi-gateway deployments. The algorithm performs better than the Adaptive Data Rate (ADR) of LoRaWAN (e.g., it almost doubles the packet delivery ratio (PDR) in scenarios with 10k nodes) and enhances LoRa deployments by adapting the communication parameters of end-devices according to the network size and estimated metrics. The allocation decision is based on different metrics: link PDR, network PDR, and network distribution of SF per gateway. Nodes can easily derive the estimated metrics from gateway measurements.
{"title":"Spreading Factor Allocation for LoRa Nodes Progressively Joining a Multi-Gateway Adaptive Network","authors":"Moisés Nuñez Ochoa, M. Maman, A. Duda","doi":"10.1109/GLOBECOM42002.2020.9322637","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322637","url":null,"abstract":"In this paper, we investigate how to provide good transmission quality in massive deployments of LoRa networks by considering all parameters such as device heterogeneity, network topology, and deployment density. We consider the scenario with nodes progressively joining the network, i.e., new nodes joining the network are configured based on measured metrics and without modifying the configuration of nodes that already joined the network. Based on this assumption, we propose an algorithm to improve network performance by effectively allocating a spreading factor (SF) to end-devices in realistic multi-gateway deployments. The algorithm performs better than the Adaptive Data Rate (ADR) of LoRaWAN (e.g., it almost doubles the packet delivery ratio (PDR) in scenarios with 10k nodes) and enhances LoRa deployments by adapting the communication parameters of end-devices according to the network size and estimated metrics. The allocation decision is based on different metrics: link PDR, network PDR, and network distribution of SF per gateway. Nodes can easily derive the estimated metrics from gateway measurements.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"9 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84269433","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}
New radio (NR) and non-orthogonal multiple access (NOMA) have emerged for more scalable and efficient resource utilization in 5G. NR implements mixed numerology with a flexible radio frame structure to ensure forward compatibility for future services, whereas NOMA allows multiple users with different channel states to share identical radio resources. However, the resource allocation in the NOMA-based mixed numerology system is challenging due to the naturally different shapes of Physical Resource Block (PRB) for NR and the reused locations of PRBs in a radio frame for NOMA. In this paper, we formulate a new optimization problem Multi-Dimensional Resource Allocation Problem (MDRAP) and prove that MDRAP is NP-hard. To solve the problem, we propose an approximation algorithm to maximize the weighted sum rate under the heterogeneity of users. The algorithm includes Zone Displacement to displace the locations of allocated PRBs in different layers of the radio frame, and Zone Allocation to change the location of the bounded rectangles (i.e., zones) for the allocation in each layer. We design Layer Dissimilarity to examine the location and shape of PRBs for avoiding inter-numerology interference between different layers. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms regarding throughput and fairness.
{"title":"Resource Allocation in 5G with NOMA-Based Mixed Numerology Systems","authors":"Ru-Jun Wang, Chih-Hang Wang, Guang-Siang Lee, De-Nian Yang, Wen-Tsuen Chen, J. Sheu","doi":"10.1109/GLOBECOM42002.2020.9322172","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322172","url":null,"abstract":"New radio (NR) and non-orthogonal multiple access (NOMA) have emerged for more scalable and efficient resource utilization in 5G. NR implements mixed numerology with a flexible radio frame structure to ensure forward compatibility for future services, whereas NOMA allows multiple users with different channel states to share identical radio resources. However, the resource allocation in the NOMA-based mixed numerology system is challenging due to the naturally different shapes of Physical Resource Block (PRB) for NR and the reused locations of PRBs in a radio frame for NOMA. In this paper, we formulate a new optimization problem Multi-Dimensional Resource Allocation Problem (MDRAP) and prove that MDRAP is NP-hard. To solve the problem, we propose an approximation algorithm to maximize the weighted sum rate under the heterogeneity of users. The algorithm includes Zone Displacement to displace the locations of allocated PRBs in different layers of the radio frame, and Zone Allocation to change the location of the bounded rectangles (i.e., zones) for the allocation in each layer. We design Layer Dissimilarity to examine the location and shape of PRBs for avoiding inter-numerology interference between different layers. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms regarding throughput and fairness.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"29 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84405545","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9347962
Sai Siddartha Maram, Neeraj Kumar, J. Rodrigues, S. Tanwar, Arjav Jain
In this paper, we propose a framework to generate cricket highlights from broadcasted cricket matches. Generating cricket highlights is a difficult problem, due to the duration and rules of the game. We formulate the highlight generation problem as a key-event initialization and key-event-closure identification problem. We propose an Inverse Hierarchical Framework, which is generic and capable of automatically generating highlights of a broadcasted cricket match. We introduce a novel context-aware approach for event-initialization and a Structural Similarity Index-based approach for event-closure detection. Despite the quality of highlights being a subjective measure we provide an evaluation of our framework by comparing it with official highlights on various metrics. We also perform a user-survey on the generated highlights. The approval of the users and overlap between the generated highlights and official highlights indicate the robustness of our framework.
{"title":"Images to Signals, Signals to Highlights","authors":"Sai Siddartha Maram, Neeraj Kumar, J. Rodrigues, S. Tanwar, Arjav Jain","doi":"10.1109/GLOBECOM42002.2020.9347962","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9347962","url":null,"abstract":"In this paper, we propose a framework to generate cricket highlights from broadcasted cricket matches. Generating cricket highlights is a difficult problem, due to the duration and rules of the game. We formulate the highlight generation problem as a key-event initialization and key-event-closure identification problem. We propose an Inverse Hierarchical Framework, which is generic and capable of automatically generating highlights of a broadcasted cricket match. We introduce a novel context-aware approach for event-initialization and a Structural Similarity Index-based approach for event-closure detection. Despite the quality of highlights being a subjective measure we provide an evaluation of our framework by comparing it with official highlights on various metrics. We also perform a user-survey on the generated highlights. The approval of the users and overlap between the generated highlights and official highlights indicate the robustness of our framework.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"48 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85486732","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348218
Mgeni Makambi Mashauri, M. Lentmaier
In this paper we consider spatial coupling in turbo equalization and demonstrate that the code design trade-off between the performance in waterfall and error floor regions can be avoided. We introduce three coupling schemes and compare their performances, where the first method introduces coupling between the encoder and the channel, while the second uses a spatially coupled (SC) code. In the third scheme we use both a coupled code and couple between the code and the channel. We show by computer simulations that, with spatial coupling, we can have good performance in both the error floor and the waterfall region with reasonable decoding latency by using a window decoder. We show this for both the maximum a posteriori (MAP) and linear minimum mean square (MMSE) equalizers.
{"title":"Spatial Coupling In Turbo Equalization","authors":"Mgeni Makambi Mashauri, M. Lentmaier","doi":"10.1109/GLOBECOM42002.2020.9348218","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348218","url":null,"abstract":"In this paper we consider spatial coupling in turbo equalization and demonstrate that the code design trade-off between the performance in waterfall and error floor regions can be avoided. We introduce three coupling schemes and compare their performances, where the first method introduces coupling between the encoder and the channel, while the second uses a spatially coupled (SC) code. In the third scheme we use both a coupled code and couple between the code and the channel. We show by computer simulations that, with spatial coupling, we can have good performance in both the error floor and the waterfall region with reasonable decoding latency by using a window decoder. We show this for both the maximum a posteriori (MAP) and linear minimum mean square (MMSE) equalizers.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76999950","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322505
Mohammad Karimzadeh, Mai H. Vu
We introduce a process for designing the optimal cyclic redundancy check (CRC) code for each input of a given $kappa$ input convolutional code $(kappa ge 1)$. Using the free distance on each input and considering that each input sequence can correspond to multiple error events in a $kappa -$ input CC, the process efficiently narrows down from the set of polynomials with the same degree the best CRC that provides the minimum frame error rate (FER) for each input. We also extend the efficient and low complexity serial list Viterbi algorithm (SLVA) for single-input CCs in [1] to the $kappa -$ input case. We discuss different ways of integrating CRCs in a $kappa -$ input CC and derive the truncated union bound on the FER for each input. Numerical examples on a two-input CC illustrate the effectiveness of the proposed CRC design and SLVA decoder for $kappa -$ input CCs.
我们介绍了为给定$kappa$输入卷积码$(kappa ge 1)$的每个输入设计最优循环冗余校验(CRC)码的过程。利用每个输入的自由距离，并考虑到每个输入序列可以对应$kappa -$输入CC中的多个错误事件，该过程有效地从具有相同程度的多项式集中缩小为每个输入提供最小帧错误率(FER)的最佳CRC。我们还将[1]中针对单输入cc的高效低复杂度串行列表Viterbi算法(SLVA)扩展到$kappa -$输入情况。我们讨论了在$kappa -$输入CC中积分crc的不同方法，并推导了每个输入的FER上的截断联合界。在双输入CC上的数值示例说明了所提出的CRC设计和用于$kappa -$输入CC的SLVA解码器的有效性。
{"title":"Optimal CRC Design and Serial List Viterbi Decoding for Multi-Input Convolutional Codes","authors":"Mohammad Karimzadeh, Mai H. Vu","doi":"10.1109/GLOBECOM42002.2020.9322505","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322505","url":null,"abstract":"We introduce a process for designing the optimal cyclic redundancy check (CRC) code for each input of a given $kappa$ input convolutional code $(kappa ge 1)$. Using the free distance on each input and considering that each input sequence can correspond to multiple error events in a $kappa -$ input CC, the process efficiently narrows down from the set of polynomials with the same degree the best CRC that provides the minimum frame error rate (FER) for each input. We also extend the efficient and low complexity serial list Viterbi algorithm (SLVA) for single-input CCs in [1] to the $kappa -$ input case. We discuss different ways of integrating CRCs in a $kappa -$ input CC and derive the truncated union bound on the FER for each input. Numerical examples on a two-input CC illustrate the effectiveness of the proposed CRC design and SLVA decoder for $kappa -$ input CCs.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"37 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80829558","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348270
Sandra Hofmann, D. Schupke, F. Fitzek
While connectivity is available almost anytime and anywhere on ground, aircraft during flight still lack high-throughput communication. We investigate air-to-ground networks consisting of direct air-to-ground, air-to-air and satellite links for providing high throughput to aircraft. We formulate an optimization problem to maximize the minimum throughput of all aircraft. We solve the problem using realistic aircraft and base station positions and also model physical limitations such as maximum number of antennas per aircraft and interference. We investigate different scenarios and parameters and analyze the influence of the parameters on the max-min throughput per aircraft. We show that the satellite and direct air-to-ground links are the bottleneck, as all throughput can be distributed among aircraft. Furthermore we show that air-to-air communication is dispensable for achieving a high throughput when having direct air-to-ground coverage.
{"title":"Optimal Throughput Allocation in Air-to-Ground Networks","authors":"Sandra Hofmann, D. Schupke, F. Fitzek","doi":"10.1109/GLOBECOM42002.2020.9348270","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348270","url":null,"abstract":"While connectivity is available almost anytime and anywhere on ground, aircraft during flight still lack high-throughput communication. We investigate air-to-ground networks consisting of direct air-to-ground, air-to-air and satellite links for providing high throughput to aircraft. We formulate an optimization problem to maximize the minimum throughput of all aircraft. We solve the problem using realistic aircraft and base station positions and also model physical limitations such as maximum number of antennas per aircraft and interference. We investigate different scenarios and parameters and analyze the influence of the parameters on the max-min throughput per aircraft. We show that the satellite and direct air-to-ground links are the bottleneck, as all throughput can be distributed among aircraft. Furthermore we show that air-to-air communication is dispensable for achieving a high throughput when having direct air-to-ground coverage.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"24 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85531368","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9322625
Zhiyan Chen, Murat Simsek, B. Kantarci
Mobile crowdsensing (MCS) is a distributed sensing concept that enables ubiquitous sensing services via various builtin sensors in smart devices. However, MCS systems are vulnerable because of being non-dedicated. Especially, submission of fake tasks with the aim of clogging participants device resources as well as MCS servers is a crucial threat to MCS platforms. In this paper, we propose an ensemble learning-based solution for MCS platforms to mitigate illegitimate tasks. Furthermore, we also integrate k-means-based classification with the proposed method to extract region-specific features as input to the machine learningbased fake task detection. Through simulations, we compare the ensemble method to a previously proposed Deep Belief Network (DBN)-based fake task detection, which is also shown to improve performance in terms of accuracy, F1 score, recall, precision and geometric mean score (G-mean) with the integration of regionawareness. Our validation results show that the ensemble machine learning-based detection can eliminate majority of the fake tasks, with up to 0.995 precision, 0.997 recall, 0.996 F1, 0.993 accuracy and 0.982 G-Mean. Furthermore, the proposed solution introduces savings up to 12.18% battery of mobile devices while reducing the impacted recruits to 0.25% and protecting up to 10.59% participants against malicious sensing tasks.
{"title":"Region-Aware Bagging and Deep Learning-Based Fake Task Detection in Mobile Crowdsensing Platforms","authors":"Zhiyan Chen, Murat Simsek, B. Kantarci","doi":"10.1109/GLOBECOM42002.2020.9322625","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322625","url":null,"abstract":"Mobile crowdsensing (MCS) is a distributed sensing concept that enables ubiquitous sensing services via various builtin sensors in smart devices. However, MCS systems are vulnerable because of being non-dedicated. Especially, submission of fake tasks with the aim of clogging participants device resources as well as MCS servers is a crucial threat to MCS platforms. In this paper, we propose an ensemble learning-based solution for MCS platforms to mitigate illegitimate tasks. Furthermore, we also integrate k-means-based classification with the proposed method to extract region-specific features as input to the machine learningbased fake task detection. Through simulations, we compare the ensemble method to a previously proposed Deep Belief Network (DBN)-based fake task detection, which is also shown to improve performance in terms of accuracy, F1 score, recall, precision and geometric mean score (G-mean) with the integration of regionawareness. Our validation results show that the ensemble machine learning-based detection can eliminate majority of the fake tasks, with up to 0.995 precision, 0.997 recall, 0.996 F1, 0.993 accuracy and 0.982 G-Mean. Furthermore, the proposed solution introduces savings up to 12.18% battery of mobile devices while reducing the impacted recruits to 0.25% and protecting up to 10.59% participants against malicious sensing tasks.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"100 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85796178","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 : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9347980
A. Yastrebova, Ilari Angervuori, N. Okati, Mikko Vehkaperä, M. Höyhtyä, R. Wichman, T. Riihonen
The integration of satellite-terrestrial networks is beneficial in terms of the increase of the network capacity and coverage. In such a heterogeneous network, highly efficient spectrum utilization is extremely important. This could be achieved by the single frequency reuse which allows increasing the capacity at the cost of increased interference. Interference is one of the main parameters that limits the link-level performance in such a network. In this paper, we examine the frequency reuse scenario by analyzing the impact of terrestrial interference to the uplink of a low Earth orbiting (LEO) satellite constellation in the high International Mobile Telecommunications (IMT) frequency bands. To this end, we propose a novel stochastic geometry based analytical framework that is able to accommodate various aspects of realistic satellite networks. The accuracy of the analysis is verified by using advanced simulation tools.
{"title":"Theoretical and Simulation-based Analysis of Terrestrial Interference to LEO Satellite Uplinks","authors":"A. Yastrebova, Ilari Angervuori, N. Okati, Mikko Vehkaperä, M. Höyhtyä, R. Wichman, T. Riihonen","doi":"10.1109/GLOBECOM42002.2020.9347980","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9347980","url":null,"abstract":"The integration of satellite-terrestrial networks is beneficial in terms of the increase of the network capacity and coverage. In such a heterogeneous network, highly efficient spectrum utilization is extremely important. This could be achieved by the single frequency reuse which allows increasing the capacity at the cost of increased interference. Interference is one of the main parameters that limits the link-level performance in such a network. In this paper, we examine the frequency reuse scenario by analyzing the impact of terrestrial interference to the uplink of a low Earth orbiting (LEO) satellite constellation in the high International Mobile Telecommunications (IMT) frequency bands. To this end, we propose a novel stochastic geometry based analytical framework that is able to accommodate various aspects of realistic satellite networks. The accuracy of the analysis is verified by using advanced simulation tools.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"44 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84061149","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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