Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685991
P. Chhikara, Rajkumar Tekchandani, Neeraj Kumar, S. Tanwar, J. Rodrigues
People need to breathe, and so do other living beings, including plants and animals. It is impossible to overlook the impact of air pollution on nature, human well-being, and concerned countries' economies. Monitoring of air pollution and future predictions of air quality have lately displayed a vital concern. There is a need to predict the air quality index with high accuracy; on a real-time basis to prevent people from health issues caused by air pollution. With the help of Unmanned Aerial Vehicle's onboard sensors, we can collect air quality data easily. The paper proposes a distributed and decentralized Federated Learning approach within a UAV swarm. The accumulated data by the sensors are used as an input to the Long Short Term Memory (LSTM) model. Each UAV used its locally gathered data to train a model before transmitting the local model to the central base station. The central base station creates a master model by combining all the UAV's local model weights of the participating UAVs in the FL process and transmits it to all UAV s in the subsequent cycles. The effectiveness of the proposed model is evaluated with other machine learning models using various evaluation metrics using test data from the capital city of India, i.e., Delhi.
{"title":"Federated Learning for Air Quality Index Prediction using UAV Swarm Networks","authors":"P. Chhikara, Rajkumar Tekchandani, Neeraj Kumar, S. Tanwar, J. Rodrigues","doi":"10.1109/GLOBECOM46510.2021.9685991","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685991","url":null,"abstract":"People need to breathe, and so do other living beings, including plants and animals. It is impossible to overlook the impact of air pollution on nature, human well-being, and concerned countries' economies. Monitoring of air pollution and future predictions of air quality have lately displayed a vital concern. There is a need to predict the air quality index with high accuracy; on a real-time basis to prevent people from health issues caused by air pollution. With the help of Unmanned Aerial Vehicle's onboard sensors, we can collect air quality data easily. The paper proposes a distributed and decentralized Federated Learning approach within a UAV swarm. The accumulated data by the sensors are used as an input to the Long Short Term Memory (LSTM) model. Each UAV used its locally gathered data to train a model before transmitting the local model to the central base station. The central base station creates a master model by combining all the UAV's local model weights of the participating UAVs in the FL process and transmits it to all UAV s in the subsequent cycles. The effectiveness of the proposed model is evaluated with other machine learning models using various evaluation metrics using test data from the capital city of India, i.e., Delhi.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117239835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685324
Qiang Liu, Chenhao Qi, Xianghao Yu, Geoffrey Y. Li
To reduce the number of phase shifters for analog precoding in millimeter wave massive multiple-input multiple-output communications, we investigate the hybrid use of expensive phase shifters and low-cost switches. Different from the existing fixed phase shifter (FPS) architecture where the phases are fixed and independent of the channel state information, we consider variable phase shifter (VPS) whose phases are variable and subject to the hardware constraint. Based on the VPS architecture, a hybrid precoding design (HPD) scheme named VPS-HPD is proposed to optimize the phases according to the channel state information. Specifically, we alternately optimize the analog precoder and the digital precoder, where the former is converted into several subproblems and each subproblem further includes the alternating optimization of the phase matrix and switch matrix. Simulation results show that the spectral efficiency of the VPS-HPD scheme is very close to that of the fully digital precoding, higher than that of the existing MO-AltMin scheme for the fully-connected architecture with much fewer phase shifters, and substantially higher than that of the existing FPS-AltMin scheme for the FPS architecture with the same number of phase shifters.
{"title":"MmWave MIMO Hybrid Precoding Design Using Phase Shifters and Switches","authors":"Qiang Liu, Chenhao Qi, Xianghao Yu, Geoffrey Y. Li","doi":"10.1109/GLOBECOM46510.2021.9685324","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685324","url":null,"abstract":"To reduce the number of phase shifters for analog precoding in millimeter wave massive multiple-input multiple-output communications, we investigate the hybrid use of expensive phase shifters and low-cost switches. Different from the existing fixed phase shifter (FPS) architecture where the phases are fixed and independent of the channel state information, we consider variable phase shifter (VPS) whose phases are variable and subject to the hardware constraint. Based on the VPS architecture, a hybrid precoding design (HPD) scheme named VPS-HPD is proposed to optimize the phases according to the channel state information. Specifically, we alternately optimize the analog precoder and the digital precoder, where the former is converted into several subproblems and each subproblem further includes the alternating optimization of the phase matrix and switch matrix. Simulation results show that the spectral efficiency of the VPS-HPD scheme is very close to that of the fully digital precoding, higher than that of the existing MO-AltMin scheme for the fully-connected architecture with much fewer phase shifters, and substantially higher than that of the existing FPS-AltMin scheme for the FPS architecture with the same number of phase shifters.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"227 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120878943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685289
F. Penna, H. Kwon, Dongwoon Bai
This paper investigates the problem of noise vari-ance estimation in orthogonal frequency domain multiplexing (OFDM)-based systems such as 5G New Radio (NR). Accurate estimation of the noise variance is critical for the receiver performance, especially when applied with linear minimum mean square error (LMMSE) channel estimation (CE). A commonly used method estimates the noise variance from the power of the residual signal at the CE output. In this paper, we prove that such conventional estimator is biased, resulting in underestimation of the noise variance; then, we derive a bias correction method. Simulation results show that the proposed bias correction can significantly improve LMMSE CE performance, achieving up to 1dB gain in terms of block error rate (BLER).
{"title":"Noise Variance Estimation in 5G NR Receivers: Bias Analysis and Compensation","authors":"F. Penna, H. Kwon, Dongwoon Bai","doi":"10.1109/GLOBECOM46510.2021.9685289","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685289","url":null,"abstract":"This paper investigates the problem of noise vari-ance estimation in orthogonal frequency domain multiplexing (OFDM)-based systems such as 5G New Radio (NR). Accurate estimation of the noise variance is critical for the receiver performance, especially when applied with linear minimum mean square error (LMMSE) channel estimation (CE). A commonly used method estimates the noise variance from the power of the residual signal at the CE output. In this paper, we prove that such conventional estimator is biased, resulting in underestimation of the noise variance; then, we derive a bias correction method. Simulation results show that the proposed bias correction can significantly improve LMMSE CE performance, achieving up to 1dB gain in terms of block error rate (BLER).","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121112546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685095
Lenart Ibraimi, Mennan Selimi, Felix Freitag
Inference with trained machine learning models is now pos-sible with small computing devices while only a few years ago it was run mostly in the cloud only. The recent technique of Federated Learning offers now a way to do also the training of the machine learning models on small devices by distributing the computing effort needed for the training over many distributed machines. But, the training on these low-capacity devices takes a long time and often consumes all the available CPU resource of the device. Therefore, for Federated Learning to be done by low-capacity devices in practical environments, the training process must not only target for the highest accuracy, but also on reducing the training time and the resource consumption. In this paper, we present an approach which uses a dynamic epoch parameter in the model training. We propose the BePOCH (Best Epoch) algorithm to identify what is the best number of epochs per training round in Federated Learning. We show in experiments with medical datasets how with the BePOCH suggested number of epochs, the training time and resource consumption decreases while keeping the level of accuracy. Thus, BePOCH makes machine learning model training on low-capacity devices more feasible and furthermore, decreases the overall resource consumption of the training process, which is an important asnect towards greener machine learning techniques.
{"title":"BePOCH: Improving Federated Learning Performance in Resource-Constrained Computing Devices","authors":"Lenart Ibraimi, Mennan Selimi, Felix Freitag","doi":"10.1109/GLOBECOM46510.2021.9685095","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685095","url":null,"abstract":"Inference with trained machine learning models is now pos-sible with small computing devices while only a few years ago it was run mostly in the cloud only. The recent technique of Federated Learning offers now a way to do also the training of the machine learning models on small devices by distributing the computing effort needed for the training over many distributed machines. But, the training on these low-capacity devices takes a long time and often consumes all the available CPU resource of the device. Therefore, for Federated Learning to be done by low-capacity devices in practical environments, the training process must not only target for the highest accuracy, but also on reducing the training time and the resource consumption. In this paper, we present an approach which uses a dynamic epoch parameter in the model training. We propose the BePOCH (Best Epoch) algorithm to identify what is the best number of epochs per training round in Federated Learning. We show in experiments with medical datasets how with the BePOCH suggested number of epochs, the training time and resource consumption decreases while keeping the level of accuracy. Thus, BePOCH makes machine learning model training on low-capacity devices more feasible and furthermore, decreases the overall resource consumption of the training process, which is an important asnect towards greener machine learning techniques.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127116508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685347
Ziyang Lu, M. C. Gursoy
In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications, especially with the dramatic increase in the number of devices in networks. Recently, inspired by the success of deep reinforcement learning (DRL), extensive studies have been conducted in addressing wireless resource allocation problems via DRL. However, training DRL algorithms usually requires a massive amount of data collected from the environment for each specific task and the well-trained model may fail if there is a small variation in the environment. In this work, in order to address these challenges, we propose a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). In the proposed framework, we train a common initialization for similar channel selection tasks. From the initialization, we show that only a few gradient descents are required for adapting to different tasks drawn from the same distribution. We demonstrate the performance improvements via simulation results.
{"title":"Dynamic Channel Access via Meta-Reinforcement Learning","authors":"Ziyang Lu, M. C. Gursoy","doi":"10.1109/GLOBECOM46510.2021.9685347","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685347","url":null,"abstract":"In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications, especially with the dramatic increase in the number of devices in networks. Recently, inspired by the success of deep reinforcement learning (DRL), extensive studies have been conducted in addressing wireless resource allocation problems via DRL. However, training DRL algorithms usually requires a massive amount of data collected from the environment for each specific task and the well-trained model may fail if there is a small variation in the environment. In this work, in order to address these challenges, we propose a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). In the proposed framework, we train a common initialization for similar channel selection tasks. From the initialization, we show that only a few gradient descents are required for adapting to different tasks drawn from the same distribution. We demonstrate the performance improvements via simulation results.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125120683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685294
Di Wu, Jikun Kang, Yi Tian Xu, Hang Li, Jimmy Li, Xi Chen, D. Rivkin, Michael Jenkin, Taeseop Lee, Intaik Park, Xue Liu, Gregory Dudek
Within a cellular network, load balancing between different cells is of critical importance to network performance and quality of service. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. These rule-based meth-ods are difficult to adapt quickly to traffic changes in real-world environments. Given the success of Reinforcement Learning (RL) algorithms in many application domains, there have been a number of efforts to tackle load balancing for communication systems using RL-based methods. To our knowledge, none of these efforts have addressed the need for data efficiency within the RL framework, which is one of the main obstacles in applying RL to wireless network load balancing. In this paper, we formulate the communication load balancing problem as a Markov Decision Process and propose a data-efficient transfer deep reinforcement learning algorithm to address it. Experimental results show that the proposed method can significantly improve the system performance over other baselines and is more robust to environmental changes.
{"title":"Load Balancing for Communication Networks via Data-Efficient Deep Reinforcement Learning","authors":"Di Wu, Jikun Kang, Yi Tian Xu, Hang Li, Jimmy Li, Xi Chen, D. Rivkin, Michael Jenkin, Taeseop Lee, Intaik Park, Xue Liu, Gregory Dudek","doi":"10.1109/GLOBECOM46510.2021.9685294","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685294","url":null,"abstract":"Within a cellular network, load balancing between different cells is of critical importance to network performance and quality of service. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. These rule-based meth-ods are difficult to adapt quickly to traffic changes in real-world environments. Given the success of Reinforcement Learning (RL) algorithms in many application domains, there have been a number of efforts to tackle load balancing for communication systems using RL-based methods. To our knowledge, none of these efforts have addressed the need for data efficiency within the RL framework, which is one of the main obstacles in applying RL to wireless network load balancing. In this paper, we formulate the communication load balancing problem as a Markov Decision Process and propose a data-efficient transfer deep reinforcement learning algorithm to address it. Experimental results show that the proposed method can significantly improve the system performance over other baselines and is more robust to environmental changes.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125926716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685010
B. Liu, Andrea P. Guevara, L. V. D. Perre, S. Pollin
In this paper, we experimentally analyze the spatial distribution of nonlinear distortion in massive MIMO systems with various array topologies and user locations. With an indoor channel measurement, we reveal the spatial distortion distribution of the in-band (IB) and out-of-band (OOB) power leakage in a real-life scenario. We further investigate the power leakage under different antenna array topologies: including uniform linear array (ULA), uniform rectangular array (URA), distributed linear subarrays (DIS). The impact of user location on the per antenna distortion is also visualized. The results indicate that the DIS array configuration achieves the lowest in-band and out-of-band power leakage, which renders the distributed array a potential to reduce the linearity requirement of PAs when scaling up a practical massive MIMO system.
{"title":"Nonlinear Distortion in Distributed Massive MIMO Systems: An Indoor Channel Measurement Analysis","authors":"B. Liu, Andrea P. Guevara, L. V. D. Perre, S. Pollin","doi":"10.1109/GLOBECOM46510.2021.9685010","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685010","url":null,"abstract":"In this paper, we experimentally analyze the spatial distribution of nonlinear distortion in massive MIMO systems with various array topologies and user locations. With an indoor channel measurement, we reveal the spatial distortion distribution of the in-band (IB) and out-of-band (OOB) power leakage in a real-life scenario. We further investigate the power leakage under different antenna array topologies: including uniform linear array (ULA), uniform rectangular array (URA), distributed linear subarrays (DIS). The impact of user location on the per antenna distortion is also visualized. The results indicate that the DIS array configuration achieves the lowest in-band and out-of-band power leakage, which renders the distributed array a potential to reduce the linearity requirement of PAs when scaling up a practical massive MIMO system.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126047255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685995
Juan Luis Herrera, J. Galán-Jiménez, P. Bellavista, L. Foschini, J. G. Alonso, J. M. Murillo, J. Berrocal
The application of Internet of Things (IoT)-based solutions to intensive domains has enabled the automation of real-world processes. The critical nature of these domains requires for very high Quality of Service (QoS) to work properly. These applications often use computing paradigms such as fog computing and software architectures such as the Microservices Architecture (MSA). Moreover, the need for transparent service discovery in MSAs, combined with the need for network scalability and flexibility, motivates the use of Software-Defined Networking (SDN) in these infrastructures. However, optimizing QoS in these scenarios implies an optimal deployment of microservices, fog nodes, and SDN controllers. Moreover, the deployment of each of the different elements affects the optimality of the others, which calls for a joint solution. In this paper, we motivate the joining of these three optimization problems into a single effort and we present Umizatou, a holistic deployment optimization solution that makes use of Mixed Integer Linear Programming. Finally, we evaluate Umizatou over a healthcare case study, showing its scalability in topologies of different sizes.
{"title":"Optimal Deployment of Fog Nodes, Microservices and SDN Controllers in Time-Sensitive IoT Scenarios","authors":"Juan Luis Herrera, J. Galán-Jiménez, P. Bellavista, L. Foschini, J. G. Alonso, J. M. Murillo, J. Berrocal","doi":"10.1109/GLOBECOM46510.2021.9685995","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685995","url":null,"abstract":"The application of Internet of Things (IoT)-based solutions to intensive domains has enabled the automation of real-world processes. The critical nature of these domains requires for very high Quality of Service (QoS) to work properly. These applications often use computing paradigms such as fog computing and software architectures such as the Microservices Architecture (MSA). Moreover, the need for transparent service discovery in MSAs, combined with the need for network scalability and flexibility, motivates the use of Software-Defined Networking (SDN) in these infrastructures. However, optimizing QoS in these scenarios implies an optimal deployment of microservices, fog nodes, and SDN controllers. Moreover, the deployment of each of the different elements affects the optimality of the others, which calls for a joint solution. In this paper, we motivate the joining of these three optimization problems into a single effort and we present Umizatou, a holistic deployment optimization solution that makes use of Mixed Integer Linear Programming. Finally, we evaluate Umizatou over a healthcare case study, showing its scalability in topologies of different sizes.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685208
Raejoon Jung, P. Levis
This paper presents symbol querying and symbol SIC, two techniques which allow LoRa receivers to recover colliding packets. A symbol querying receiver allows the demodulator and channel decoder to jointly search for the correct set of symbols during a collision. By operating in the frequency domain, both symbol querying and symbol SIC greatly limit the search space of possible packets, allowing for efficient implementations. Experimental results show that these techniques allow LoRa to elevate error detection to correction and outperform a BICM-ID receiver, receiving 3.8x more frames than a traditional LoRa receiver in a low SINR setting.
{"title":"Receiving Colliding LoRa Packets with Hard Information Iterative Decoding","authors":"Raejoon Jung, P. Levis","doi":"10.1109/GLOBECOM46510.2021.9685208","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685208","url":null,"abstract":"This paper presents symbol querying and symbol SIC, two techniques which allow LoRa receivers to recover colliding packets. A symbol querying receiver allows the demodulator and channel decoder to jointly search for the correct set of symbols during a collision. By operating in the frequency domain, both symbol querying and symbol SIC greatly limit the search space of possible packets, allowing for efficient implementations. Experimental results show that these techniques allow LoRa to elevate error detection to correction and outperform a BICM-ID receiver, receiving 3.8x more frames than a traditional LoRa receiver in a low SINR setting.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126144193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/GLOBECOM46510.2021.9685526
Linzhi Shen, Shaowei Wang
In this paper, we investigate the network planning problem in mmWave mobile communication systems, where the narrow-beam antennas can adjust azimuths and downtilts of antennas so as to maximize the power coverage of the network, as well as the system throughput. Searching for the optimal configurations of antennas generally yields a combinatorial opti-mization problem, which cannot be addressed even for a medium scale antenna set case. We formulate this optimization task as a finite Markov decision process, and develop a multi-layer Monte Carlo tree search method to produce a promising solution with reasonable complexity, which evaluates the outcome of given azimuth and downtilt settings without acquiring all configurations of antennas. Experiments in a real urban environment show that our proposed scheme outperforms the state-of-the-art algorithms over 10% in terms of system throughput while guaranteeing high power coverage.
{"title":"Monte Carlo Tree Search for Network Planning for Next Generation Mobile Communication Networks","authors":"Linzhi Shen, Shaowei Wang","doi":"10.1109/GLOBECOM46510.2021.9685526","DOIUrl":"https://doi.org/10.1109/GLOBECOM46510.2021.9685526","url":null,"abstract":"In this paper, we investigate the network planning problem in mmWave mobile communication systems, where the narrow-beam antennas can adjust azimuths and downtilts of antennas so as to maximize the power coverage of the network, as well as the system throughput. Searching for the optimal configurations of antennas generally yields a combinatorial opti-mization problem, which cannot be addressed even for a medium scale antenna set case. We formulate this optimization task as a finite Markov decision process, and develop a multi-layer Monte Carlo tree search method to produce a promising solution with reasonable complexity, which evaluates the outcome of given azimuth and downtilt settings without acquiring all configurations of antennas. Experiments in a real urban environment show that our proposed scheme outperforms the state-of-the-art algorithms over 10% in terms of system throughput while guaranteeing high power coverage.","PeriodicalId":200641,"journal":{"name":"2021 IEEE Global Communications Conference (GLOBECOM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126180548","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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