Pub Date : 2020-12-01DOI: 10.1109/GLOBECOM42002.2020.9348247
Zezu Liang, Yuan Liu, T. Lok, Kaibin Huang
Mobile-edge computing (MEC) enhances the capacities and features of mobile devices via offloading computation-intensive tasks over wireless networks to the edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility, so that the offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The policy design is formulated as a multi-objective optimization problem that maximizes the sum offloading rate, quantifying MEC throughput, and minimizes the migration cost, where the issues of virtualization, I/O interference between virtual machines (VMs), and wireless multi-access are taken into account. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based approach, including an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design that exploits the derived problem properties. The simulation results show the close-to-optimal performance of the proposed migration policies under various settings, validating their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.
{"title":"Service Migration for Multi-Cell Mobile Edge Computing","authors":"Zezu Liang, Yuan Liu, T. Lok, Kaibin Huang","doi":"10.1109/GLOBECOM42002.2020.9348247","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348247","url":null,"abstract":"Mobile-edge computing (MEC) enhances the capacities and features of mobile devices via offloading computation-intensive tasks over wireless networks to the edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility, so that the offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The policy design is formulated as a multi-objective optimization problem that maximizes the sum offloading rate, quantifying MEC throughput, and minimizes the migration cost, where the issues of virtualization, I/O interference between virtual machines (VMs), and wireless multi-access are taken into account. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based approach, including an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design that exploits the derived problem properties. The simulation results show the close-to-optimal performance of the proposed migration policies under various settings, validating their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"124 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":"90983213","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/GLOBECOM42002.2020.9322490
Xinliang Zhang, M. Vaezi
This paper investigates a precoding design for a two-user multiple-input multiple-output (MIMO) network with various objectives, including simultaneous wireless information and power transfer, energy harvesting, and security. Conventionally, precoding and power allocation matrices for these objectives are obtained via different solutions. While in some cases analytic solutions are known, in other cases only time-consuming iterative methods are available. To overcome this issue and unify the solutions for multi-objective networks, a deep learning-enabled framework is proposed in this paper. The proposed deep neural network (DNN)-based precoding learns how to optimize multiple objective functions and find their corresponding input covariance matrices concurrently, efficiently, and reliably. Compared to conventional iterative precoding methods, the proposed approach reduces on-the-fly computational complexity 91.19% while reaching near-optimal performance (99.64% of the optimal solution). The proposed DNN-based precoding can flexibly adapt itself to the different needs of the network and is faster and more robust than transitional approaches, making it an attractive solution for current and future communication networks.
{"title":"A DNN-based Multi-Objective Precoding for Gaussian MIMO Networks","authors":"Xinliang Zhang, M. Vaezi","doi":"10.1109/GLOBECOM42002.2020.9322490","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322490","url":null,"abstract":"This paper investigates a precoding design for a two-user multiple-input multiple-output (MIMO) network with various objectives, including simultaneous wireless information and power transfer, energy harvesting, and security. Conventionally, precoding and power allocation matrices for these objectives are obtained via different solutions. While in some cases analytic solutions are known, in other cases only time-consuming iterative methods are available. To overcome this issue and unify the solutions for multi-objective networks, a deep learning-enabled framework is proposed in this paper. The proposed deep neural network (DNN)-based precoding learns how to optimize multiple objective functions and find their corresponding input covariance matrices concurrently, efficiently, and reliably. Compared to conventional iterative precoding methods, the proposed approach reduces on-the-fly computational complexity 91.19% while reaching near-optimal performance (99.64% of the optimal solution). The proposed DNN-based precoding can flexibly adapt itself to the different needs of the network and is faster and more robust than transitional approaches, making it an attractive solution for current and future communication networks.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"1 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":"91292715","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/GLOBECOM42002.2020.9348002
Jiun-Ting Huang, Young-Han Kim
Monte Carlo Markov chain (MCMC) decoding is a randomized algorithm which has been proven to be near-optimal in terms of decoding error probability. However, the exponentially slow mixing rate of Markov chains seems to preclude MCMC decoding from applications concerning even short blocklength codes. In contrast, belief propagation (BP) is a deterministic algorithm that is empirically fast but sub-optimal in error rate when it is used to decode low-density parity-check (LDPC) codes. In this paper, a code-independent BP–MCMC hybrid decoder is devised for short-blocklength LDPC codes. Theoretical error analysis of the hybrid algorithm is provided. Preliminary experiments show that the preprocessing of BP successfully reduces the time complexity of MCMC decoding and hence significantly improves the applicability of MCMC decoders to short LDPC codes.
{"title":"MCMC Decoding of LDPC Codes with BP Preprocessing","authors":"Jiun-Ting Huang, Young-Han Kim","doi":"10.1109/GLOBECOM42002.2020.9348002","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348002","url":null,"abstract":"Monte Carlo Markov chain (MCMC) decoding is a randomized algorithm which has been proven to be near-optimal in terms of decoding error probability. However, the exponentially slow mixing rate of Markov chains seems to preclude MCMC decoding from applications concerning even short blocklength codes. In contrast, belief propagation (BP) is a deterministic algorithm that is empirically fast but sub-optimal in error rate when it is used to decode low-density parity-check (LDPC) codes. In this paper, a code-independent BP–MCMC hybrid decoder is devised for short-blocklength LDPC codes. Theoretical error analysis of the hybrid algorithm is provided. Preliminary experiments show that the preprocessing of BP successfully reduces the time complexity of MCMC decoding and hence significantly improves the applicability of MCMC decoders to short LDPC codes.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"6 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":"89850694","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/GLOBECOM42002.2020.9348201
Aimin Tang, Xudong Wang
The IEEE 802.11ad based joint communication and radar sensing has attracted great attentions for vehicles in recent years. The existing studies all assume full duplex communications between the transmitter and radar receiver based on perfect self-interference cancellation. However, the self-interference may not be fully cancelled due to the limitation of self-interference cancellation capability in practical cases, which will significantly degrade the sensing capability of the radar function, especially for the detection range. In this paper, the imperfect self-interference cancellation is considered and a novel joint communication and automotive long range radar sensing design is proposed based on OFDM frame structure in 802.11ad standard. The received signal model in the frequency domain synchronized with the self-interference is derived, in which the target reflection signal suffers inter-carrier-interference (ICI) and inter-symbol-interference (ISI). However, we show that the ISI can be leveraged for enhancing radar parameter estimation. Based on the received signal model, a novel pilot signal design is first developed to combat the self-interference for accurate velocity and coarse range estimation. Then, a few self-interference-free OFDM symbols at the end of the data frame are utilized to achieve accurate range estimation. Simulation results show that the decimeter-per-second level velocity estimation and centimeter level range estimation can be achieved for up to 200-meter radar sensing.
{"title":"Self-Interference-Resistant IEEE 802.11ad-Based Joint Communication and Automotive Long Range Radar","authors":"Aimin Tang, Xudong Wang","doi":"10.1109/GLOBECOM42002.2020.9348201","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348201","url":null,"abstract":"The IEEE 802.11ad based joint communication and radar sensing has attracted great attentions for vehicles in recent years. The existing studies all assume full duplex communications between the transmitter and radar receiver based on perfect self-interference cancellation. However, the self-interference may not be fully cancelled due to the limitation of self-interference cancellation capability in practical cases, which will significantly degrade the sensing capability of the radar function, especially for the detection range. In this paper, the imperfect self-interference cancellation is considered and a novel joint communication and automotive long range radar sensing design is proposed based on OFDM frame structure in 802.11ad standard. The received signal model in the frequency domain synchronized with the self-interference is derived, in which the target reflection signal suffers inter-carrier-interference (ICI) and inter-symbol-interference (ISI). However, we show that the ISI can be leveraged for enhancing radar parameter estimation. Based on the received signal model, a novel pilot signal design is first developed to combat the self-interference for accurate velocity and coarse range estimation. Then, a few self-interference-free OFDM symbols at the end of the data frame are utilized to achieve accurate range estimation. Simulation results show that the decimeter-per-second level velocity estimation and centimeter level range estimation can be achieved for up to 200-meter radar sensing.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"18 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":"89860964","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/GLOBECOM42002.2020.9348177
Zitong Wang, Deze Zeng, Lin Gu, Song Guo
Compute First Networking (CFN) recently is proposed as an in-network computing paradigm for well balancing between the networking and computation resource scheduling. Thanks to the proliferation of network functions virtualization, the virtualized network functions can coexist with the computing services on a shared platform like edge computing environment. Thus, one critical issue incurred by CFN is how to manage and schedule the resources among various services from different over-the-top service provider (OSP) with different resource requirements, i.e., network slicing. In this paper, we first formulate the network slicing problem as a Stackelberg game problem and prove that there exists a Nash equilibrium beneficial to both the Network Slice Broker (NSB) and OSP. Furthermore, we propose a cooperative game model on the networking and computation resource allocation within each slice and invent a Nash bargaining solution to resolve the intra-slice resource competition for slice performance promotion. Simulation results are provided to validate the effectiveness and high efficiency of the our proposed game based network slicing and resource scheduling algorithm.
计算优先网络(CFN)是最近提出的一种网络内计算模式，可以很好地平衡网络和计算资源调度之间的关系。由于网络功能虚拟化的普及，虚拟化的网络功能可以与计算服务在边缘计算环境等共享平台上共存。因此，CFN产生的一个关键问题是如何管理和调度来自具有不同资源需求的不同over- top service provider (OSP)的各种服务之间的资源，即网络切片。本文首先将网络切片问题表述为一个Stackelberg博弈问题，并证明存在一个对网络切片代理(NSB)和OSP都有利的纳什均衡。在此基础上，提出了一种基于网络和计算资源分配的合作博弈模型，并提出了一种纳什议价解决方案，以解决分片内资源竞争对分片性能提升的影响。仿真结果验证了本文提出的基于游戏的网络切片和资源调度算法的有效性和高效性。
{"title":"A Game-based Network Slicing and Resource Scheduling for Compute First Networking","authors":"Zitong Wang, Deze Zeng, Lin Gu, Song Guo","doi":"10.1109/GLOBECOM42002.2020.9348177","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9348177","url":null,"abstract":"Compute First Networking (CFN) recently is proposed as an in-network computing paradigm for well balancing between the networking and computation resource scheduling. Thanks to the proliferation of network functions virtualization, the virtualized network functions can coexist with the computing services on a shared platform like edge computing environment. Thus, one critical issue incurred by CFN is how to manage and schedule the resources among various services from different over-the-top service provider (OSP) with different resource requirements, i.e., network slicing. In this paper, we first formulate the network slicing problem as a Stackelberg game problem and prove that there exists a Nash equilibrium beneficial to both the Network Slice Broker (NSB) and OSP. Furthermore, we propose a cooperative game model on the networking and computation resource allocation within each slice and invent a Nash bargaining solution to resolve the intra-slice resource competition for slice performance promotion. Simulation results are provided to validate the effectiveness and high efficiency of the our proposed game based network slicing and resource scheduling algorithm.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"19 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":"89603306","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/GLOBECOM42002.2020.9322150
Ning Yang, Haijun Zhang, R. Berry
In this paper, the problem of dynamic resource management in a cognitive radio network (CRN) with multiple primary users (PUs), multiple secondary users (SUs), and multiple channels is investigated. An optimization problem is formulated as a multi-agent partially observable Markov decision process (POMDP) problem in a dynamic and not fully observable environment. We consider using deep reinforcement learning (DRL) to address this problem. Based on the channel occupancy of PUs, a multi-agent deep Q-network (DQN)-based dynamic joint spectrum access and mode selection (SAMS) scheme is proposed for the SUs in the partially observable environment. The current observation of each SU is mapped to a suitable action. Each secondary user (SU) takes its own decision without exchanging information with other SUs. It seeks to maximize the total sum rate. Simulation results verify the effectiveness of our proposed schemes.
{"title":"Partially Observable Multi-Agent Deep Reinforcement Learning for Cognitive Resource Management","authors":"Ning Yang, Haijun Zhang, R. Berry","doi":"10.1109/GLOBECOM42002.2020.9322150","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322150","url":null,"abstract":"In this paper, the problem of dynamic resource management in a cognitive radio network (CRN) with multiple primary users (PUs), multiple secondary users (SUs), and multiple channels is investigated. An optimization problem is formulated as a multi-agent partially observable Markov decision process (POMDP) problem in a dynamic and not fully observable environment. We consider using deep reinforcement learning (DRL) to address this problem. Based on the channel occupancy of PUs, a multi-agent deep Q-network (DQN)-based dynamic joint spectrum access and mode selection (SAMS) scheme is proposed for the SUs in the partially observable environment. The current observation of each SU is mapped to a suitable action. Each secondary user (SU) takes its own decision without exchanging information with other SUs. It seeks to maximize the total sum rate. Simulation results verify the effectiveness of our proposed schemes.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"1 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":"89762419","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/GLOBECOM42002.2020.9322146
Apostolos Galanopoulos, J. Ayala-Romero, G. Iosifidis, D. Leith
Real-time object recognition is becoming an essen-tial part of many emerging services, such as augmented reality, which require accurate inference in a timely fashion with low delay. We consider an edge-assisted object recognition system that can be configured in ways that have diverse impacts on these key performance criteria. Our goal is to design an online algorithm that learns the optimal configuration of the system by observing the outcomes of configurations applied in the past. We leverage the structure of the problem and combine a Gaussian process with a multi-armed bandit framework to efficiently solve the problem at hand. Our results indicate that our solution makes better configuration choices compared to other bandit algorithms, resulting in lower regret.
{"title":"Bayesian Online Learning for MEC Object Recognition Systems","authors":"Apostolos Galanopoulos, J. Ayala-Romero, G. Iosifidis, D. Leith","doi":"10.1109/GLOBECOM42002.2020.9322146","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322146","url":null,"abstract":"Real-time object recognition is becoming an essen-tial part of many emerging services, such as augmented reality, which require accurate inference in a timely fashion with low delay. We consider an edge-assisted object recognition system that can be configured in ways that have diverse impacts on these key performance criteria. Our goal is to design an online algorithm that learns the optimal configuration of the system by observing the outcomes of configurations applied in the past. We leverage the structure of the problem and combine a Gaussian process with a multi-armed bandit framework to efficiently solve the problem at hand. Our results indicate that our solution makes better configuration choices compared to other bandit algorithms, resulting in lower regret.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"66 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":"90260838","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/GLOBECOM42002.2020.9322068
B. Correia, R. Sadeghi, E. Virgillito, A. Napoli, N. Costa, J. Pedro, V. Curri
Both spatial-division multiplexing (SDM) and band-division multiplexing (BDM) emerge as possible solutions to increase the optical network capacity to support the traffic demand which has been rising over time. In this work, two different ROADM (Re-configurable Optical Add Drop Multiplexer) switching techniques, namely SDM-InS (Independent switching) and SDM-CCC (Core Continue Constant) are investigated and the resulting network capacity is compared with the BDM approach. In the BDM case, both L- and S-bands have been used in addition to C-band to increase the network capacity. The launch power is optimized to control the QoT (Quality of Transmission) summarized by the generalized SNR (GSNR) per channel. Due to: stimulated Raman scattering, frequency variation of loss, frequency variation of dispersion coefficient and noise figures, an optimum power tilt and offset are calculated for each band. We show that the total network capacity increased by $sim 2 times$ and $sim 3 times$, when using the L-band and L+S-bands in addition to the C-band, respectively, in both a reference German and a reference US network. Additionally, it was also shown that using additional bands, the increase in network capacity is close to the result of using additional optical fibers in the SDM case.
{"title":"Networking Performance of Power Optimized C+L+S Multiband Transmission","authors":"B. Correia, R. Sadeghi, E. Virgillito, A. Napoli, N. Costa, J. Pedro, V. Curri","doi":"10.1109/GLOBECOM42002.2020.9322068","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322068","url":null,"abstract":"Both spatial-division multiplexing (SDM) and band-division multiplexing (BDM) emerge as possible solutions to increase the optical network capacity to support the traffic demand which has been rising over time. In this work, two different ROADM (Re-configurable Optical Add Drop Multiplexer) switching techniques, namely SDM-InS (Independent switching) and SDM-CCC (Core Continue Constant) are investigated and the resulting network capacity is compared with the BDM approach. In the BDM case, both L- and S-bands have been used in addition to C-band to increase the network capacity. The launch power is optimized to control the QoT (Quality of Transmission) summarized by the generalized SNR (GSNR) per channel. Due to: stimulated Raman scattering, frequency variation of loss, frequency variation of dispersion coefficient and noise figures, an optimum power tilt and offset are calculated for each band. We show that the total network capacity increased by $sim 2 times$ and $sim 3 times$, when using the L-band and L+S-bands in addition to the C-band, respectively, in both a reference German and a reference US network. Additionally, it was also shown that using additional bands, the increase in network capacity is close to the result of using additional optical fibers in the SDM case.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"36 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":"90309923","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/GLOBECOM42002.2020.9322418
Ngoc-Tan Nguyen, Diep N. Nguyen, D. Hoang, Nguyen Van Huynh, H. Nguyen, Q. Nguyen, E. Dutkiewicz
In this paper, an economic model is proposed to jointly optimize profits for participants in a heterogeneous IoT wireless-powered backscatter communication network. In the network under considerations, a power beacon and IoT devices (with various communication types and energy constraints) are assumed to belong to different service providers, i.e., energy service provider (ESP) and IoT service provider (ISP), respectively. To jointly maximize the utility for both service providers in terms of energy efficiency and network throughput, a Stackelberg game model is proposed to study the strategic interaction between the ISP and ESP. In particular, the ISP first evaluates its benefits from providing IoT services to its customers and then sends its requested price together with the service time to the ESP. Based on the request from the ISP, the ESP offers an optimized transmission power that maximizes its utility while meeting energy demands of the ISP. To study the Stackelberg equilibrium, we first obtain a closed-form solution for the ESP and propose a low-complexity iterative method based on block coordinate descent (BCD) to address the non-convex optimization problem for the ISP. Through simulation results, we show that our approach can significantly improve the profits for both providers compared with those of conventional transmission methods, e.g., bistatic backscatter and harvest-then-transmit communication methods.
{"title":"Energy Trading and Time Scheduling for Energy-Efficient Heterogeneous Low-Power IoT Networks","authors":"Ngoc-Tan Nguyen, Diep N. Nguyen, D. Hoang, Nguyen Van Huynh, H. Nguyen, Q. Nguyen, E. Dutkiewicz","doi":"10.1109/GLOBECOM42002.2020.9322418","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322418","url":null,"abstract":"In this paper, an economic model is proposed to jointly optimize profits for participants in a heterogeneous IoT wireless-powered backscatter communication network. In the network under considerations, a power beacon and IoT devices (with various communication types and energy constraints) are assumed to belong to different service providers, i.e., energy service provider (ESP) and IoT service provider (ISP), respectively. To jointly maximize the utility for both service providers in terms of energy efficiency and network throughput, a Stackelberg game model is proposed to study the strategic interaction between the ISP and ESP. In particular, the ISP first evaluates its benefits from providing IoT services to its customers and then sends its requested price together with the service time to the ESP. Based on the request from the ISP, the ESP offers an optimized transmission power that maximizes its utility while meeting energy demands of the ISP. To study the Stackelberg equilibrium, we first obtain a closed-form solution for the ESP and propose a low-complexity iterative method based on block coordinate descent (BCD) to address the non-convex optimization problem for the ISP. Through simulation results, we show that our approach can significantly improve the profits for both providers compared with those of conventional transmission methods, e.g., bistatic backscatter and harvest-then-transmit communication methods.","PeriodicalId":12759,"journal":{"name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","volume":"3 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":"89261452","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/GLOBECOM42002.2020.9322261
Jait Purohit, Xuyu Wang, S. Mao, Xiaoyan Sun, Chao Yang
This paper aims at predicting accurate outdoor and indoor locations using deep neural networks, for the data collected using the Long-Range Wide-Area Network (LoRaWAN) communication protocol. First, we propose an interpolation aided fingerprinting-based localization system architecture. We propose a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks. We also leverage three different deep learning models, i.e., the Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN), for fingerprinting based location regression. The superior localization performance of the proposed system is validated by our experimental study using a publicly available outdoor dataset and an indoor LoRa testbed.
{"title":"Fingerprinting-based Indoor and Outdoor Localization with LoRa and Deep Learning","authors":"Jait Purohit, Xuyu Wang, S. Mao, Xiaoyan Sun, Chao Yang","doi":"10.1109/GLOBECOM42002.2020.9322261","DOIUrl":"https://doi.org/10.1109/GLOBECOM42002.2020.9322261","url":null,"abstract":"This paper aims at predicting accurate outdoor and indoor locations using deep neural networks, for the data collected using the Long-Range Wide-Area Network (LoRaWAN) communication protocol. First, we propose an interpolation aided fingerprinting-based localization system architecture. We propose a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks. We also leverage three different deep learning models, i.e., the Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN), for fingerprinting based location regression. The superior localization performance of the proposed system is validated by our experimental study using a publicly available outdoor dataset and an indoor LoRa testbed.","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":"89263049","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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