Pub Date : 2021-07-28DOI: 10.1109/iccc52777.2021.9580272
Yihao Luo, Yang Yang, Zhen Gao, Dazhong He, Long Zhang
In millimeter-wave (mmWave) heterogeneous networks (HetNets), a variety of mmWave base stations (mBSs) are usually deployed with massive MIMO to form directional analog beams. Each mobile user equipment (MUE) can be served by multiple mBSs simultaneously with concurrent transmissions. However, as the number of mBSs and MUEs increase, it becomes a big challenge for the mBS to quickly and precisely select the analog beams. Thus, this paper propose an machine learning (ML) method to improve the analog beam selection. First, we use stochastic geometry to model the distribution of HetNets, where the probabilities that multiple mBSs serve every MUE are further derived and get the average throughput (AT) for mmWave HetNets. Based on ML, we adopt the support vector machine (SVM) to iteratively select the analog beam, where a promotional sequential minimal optimization (Pro-SMO) algorithm is proposed to train data sets of all the links, where the computational complexity and algorithm convergence are also discussed. Simulation results at last proofed that the proposed ML algorithm not only gets a higher AT than the traditional channel estimation (CE) algorithm, but also achieves a very substantial reduction of calculation complexity.
{"title":"Machine Learning based Analog Beam Selection for Concurrent Transmissions in mmWave Heterogeneous Networks","authors":"Yihao Luo, Yang Yang, Zhen Gao, Dazhong He, Long Zhang","doi":"10.1109/iccc52777.2021.9580272","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580272","url":null,"abstract":"In millimeter-wave (mmWave) heterogeneous networks (HetNets), a variety of mmWave base stations (mBSs) are usually deployed with massive MIMO to form directional analog beams. Each mobile user equipment (MUE) can be served by multiple mBSs simultaneously with concurrent transmissions. However, as the number of mBSs and MUEs increase, it becomes a big challenge for the mBS to quickly and precisely select the analog beams. Thus, this paper propose an machine learning (ML) method to improve the analog beam selection. First, we use stochastic geometry to model the distribution of HetNets, where the probabilities that multiple mBSs serve every MUE are further derived and get the average throughput (AT) for mmWave HetNets. Based on ML, we adopt the support vector machine (SVM) to iteratively select the analog beam, where a promotional sequential minimal optimization (Pro-SMO) algorithm is proposed to train data sets of all the links, where the computational complexity and algorithm convergence are also discussed. Simulation results at last proofed that the proposed ML algorithm not only gets a higher AT than the traditional channel estimation (CE) algorithm, but also achieves a very substantial reduction of calculation complexity.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129721993","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580309
Nansen Wang, Tian Lin, Yu Zhou, Yu Zhu
Intelligent reflecting surfaces (IRSs) have been regarded as promising enablers for future wireless communications thanks to their ability to customize favorable propagation environments. Meanwhile, the solution of large-scale antenna arrays with low-resolution analog-to-digital converters (ADCs), is supposed to achieve a good performance-complexity trade-off. In this paper, we investigate the channel estimation issue of IRS-aided systems with one-bit ADCs. By utilizing the Bussgang decomposition, we reformulate the non-linear one-bit quantization operation as a statistically equivalent linear model and propose a linear minimum mean square error (LMMSE) channel estimator. Simulation results reveal that the proposed LMMSE estimator can effectively reduce the impact of the quantization distortion, and therefore significantly outperforms the conventional least square estimator.
{"title":"Channel Estimation for Intelligent Reflecting Surface-Aided Communication Systems with One-bit ADCs","authors":"Nansen Wang, Tian Lin, Yu Zhou, Yu Zhu","doi":"10.1109/iccc52777.2021.9580309","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580309","url":null,"abstract":"Intelligent reflecting surfaces (IRSs) have been regarded as promising enablers for future wireless communications thanks to their ability to customize favorable propagation environments. Meanwhile, the solution of large-scale antenna arrays with low-resolution analog-to-digital converters (ADCs), is supposed to achieve a good performance-complexity trade-off. In this paper, we investigate the channel estimation issue of IRS-aided systems with one-bit ADCs. By utilizing the Bussgang decomposition, we reformulate the non-linear one-bit quantization operation as a statistically equivalent linear model and propose a linear minimum mean square error (LMMSE) channel estimator. Simulation results reveal that the proposed LMMSE estimator can effectively reduce the impact of the quantization distortion, and therefore significantly outperforms the conventional least square estimator.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126326124","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}
This paper proposes a three dimensional (3D) nonstationary geometry-based stochastic model (GBSM) for mobile-to-mobile (M2M) underwater acoustic (UWA) communication channels. In this proposed model, the border reverberations are modeled as a series of specular reflection paths and the volume reverberations are approximated as the twin-cluster birth-death model. Moreover, this model supports dual mobility both of transmitter (Tx) and receiver (Rx) in the 3D body of water. Based on the analytical model, the corresponding channel statistical properties such as the time-frequency correlation function (TF-CF), power delay profile (PDP), average delay, and root mean square delay spread (RMS- DS) are derived. The results show a good fit between the analytical model and the simulation model. Finally, the reliability of the model is validated by comparing the statistical characteristics with the measurement results.
{"title":"A 3D Non-Stationary GBSM for Mobile-to-Mobile Underwater Acoustic Communication Channels","authors":"Yihan Wang, Chengxiang Wang, Xiuming Zhu, Yubei He, Hengtai Chang, Jian Sun, Wensheng Zhang","doi":"10.1109/iccc52777.2021.9580250","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580250","url":null,"abstract":"This paper proposes a three dimensional (3D) nonstationary geometry-based stochastic model (GBSM) for mobile-to-mobile (M2M) underwater acoustic (UWA) communication channels. In this proposed model, the border reverberations are modeled as a series of specular reflection paths and the volume reverberations are approximated as the twin-cluster birth-death model. Moreover, this model supports dual mobility both of transmitter (Tx) and receiver (Rx) in the 3D body of water. Based on the analytical model, the corresponding channel statistical properties such as the time-frequency correlation function (TF-CF), power delay profile (PDP), average delay, and root mean square delay spread (RMS- DS) are derived. The results show a good fit between the analytical model and the simulation model. Finally, the reliability of the model is validated by comparing the statistical characteristics with the measurement results.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126583315","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580316
Zhenfeng Gao, Wei Liu, Long Suo, Jiandong Li, Yijun Lu
Recently the huge amount of energy consumption has become a barrier to the widespread deployment of data centers serving various Internet of Things applications. The reasonable allocation of compute-intensive workloads to physical servers is an efficient way to improve the data center's energy efficiency. Though existing works has proposed some algorithms to manage workloads or virtual machines for energy saving, most of them did not comprehensively consider the high dynamics of server states, and lacked in high scalability in their implementation. In this paper, the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS) is proposed, which can both guarantee the Quality of Service (QoS) of workloads and reduce the computational energy consumption of physical servers. To achieve rational workload allocation, AC-CIWAS captures the dynamic feature of server states continuously, and takes the impact of different workloads on energy consumption into consideration. AC-CIWAS employs the Deep Reinforcement Learning (DRL) based Actor Critic (AC) algorithm to evaluate the expected cumulative return over time, while the cumulative return guides to allocate workloads with high energy efficiency. Simulation results have demonstrated that compared to existing baseline allocation methods, the proposed AC-CIWAS can achieve an approximately 20 percent decrease in server power consumption with QoS guarantee.
{"title":"Deep Reinforcement Learning based Compute-Intensive Workload Allocation in Data Centers with High Energy Efficiency","authors":"Zhenfeng Gao, Wei Liu, Long Suo, Jiandong Li, Yijun Lu","doi":"10.1109/iccc52777.2021.9580316","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580316","url":null,"abstract":"Recently the huge amount of energy consumption has become a barrier to the widespread deployment of data centers serving various Internet of Things applications. The reasonable allocation of compute-intensive workloads to physical servers is an efficient way to improve the data center's energy efficiency. Though existing works has proposed some algorithms to manage workloads or virtual machines for energy saving, most of them did not comprehensively consider the high dynamics of server states, and lacked in high scalability in their implementation. In this paper, the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS) is proposed, which can both guarantee the Quality of Service (QoS) of workloads and reduce the computational energy consumption of physical servers. To achieve rational workload allocation, AC-CIWAS captures the dynamic feature of server states continuously, and takes the impact of different workloads on energy consumption into consideration. AC-CIWAS employs the Deep Reinforcement Learning (DRL) based Actor Critic (AC) algorithm to evaluate the expected cumulative return over time, while the cumulative return guides to allocate workloads with high energy efficiency. Simulation results have demonstrated that compared to existing baseline allocation methods, the proposed AC-CIWAS can achieve an approximately 20 percent decrease in server power consumption with QoS guarantee.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128895785","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580235
Tianshun Wang, Xumin Huang, Yuxiao Song, Yuan Wu, L. Qian, Bin Lin
Federated learning (FL) has been considered as an efficient yet privacy-preserving approach for enabling the distributed learning. There have been many studies investigating the applications of FL in different scenarios, e.g., Internet of Things, Internet of Vehicles, and UAV systems. However, due to delivering the trained model via wireless links, FL may suffer from a potential issue, i.e., some malicious users may intentionally overhear the trained model delivered through the wireless links. In this paper, we investigate the energy optimization for nonorthogonal multiple access (NOMA) assisted with secrecy provisioning. Specifically, we consider that the wireless devices (WDs) adopt NOMA to deliver their respectively trained local models to a base station (BS) which serves a parameter-server, and there exists a malicious node that overhears the parameter-server when delivering the aggregated global model to all WDs. We adopt the physical layer security to quantify the secrecy throughput under the eavesdropping attack and formulate an optimization problem to minimize the overall energy consumption of all the WDs in FL, by jointly optimizing the uplink time, the downlink time, the local model accuracy, and the uplink decoding order of NOMA. In spite of the non-convexity of this joint optimization problem, we propose an efficient algorithm, which is based on the theory of monotonic optimization, for finding the solution. Numerical results show that our proposed algorithm can achieve the almost same solutions as the LINGO's global-solver while reducing more than 90% computation-time than LINGO. Moreover, the results also show that our proposed NOMA decoding scheme can outperform some heuristic decoding schemes.
{"title":"Energy Optimization for NOMA assisted Federated Learning with Secrecy Provisioning","authors":"Tianshun Wang, Xumin Huang, Yuxiao Song, Yuan Wu, L. Qian, Bin Lin","doi":"10.1109/iccc52777.2021.9580235","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580235","url":null,"abstract":"Federated learning (FL) has been considered as an efficient yet privacy-preserving approach for enabling the distributed learning. There have been many studies investigating the applications of FL in different scenarios, e.g., Internet of Things, Internet of Vehicles, and UAV systems. However, due to delivering the trained model via wireless links, FL may suffer from a potential issue, i.e., some malicious users may intentionally overhear the trained model delivered through the wireless links. In this paper, we investigate the energy optimization for nonorthogonal multiple access (NOMA) assisted with secrecy provisioning. Specifically, we consider that the wireless devices (WDs) adopt NOMA to deliver their respectively trained local models to a base station (BS) which serves a parameter-server, and there exists a malicious node that overhears the parameter-server when delivering the aggregated global model to all WDs. We adopt the physical layer security to quantify the secrecy throughput under the eavesdropping attack and formulate an optimization problem to minimize the overall energy consumption of all the WDs in FL, by jointly optimizing the uplink time, the downlink time, the local model accuracy, and the uplink decoding order of NOMA. In spite of the non-convexity of this joint optimization problem, we propose an efficient algorithm, which is based on the theory of monotonic optimization, for finding the solution. Numerical results show that our proposed algorithm can achieve the almost same solutions as the LINGO's global-solver while reducing more than 90% computation-time than LINGO. Moreover, the results also show that our proposed NOMA decoding scheme can outperform some heuristic decoding schemes.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127563278","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580368
Xuanheng Li, Jiahong Liu, Nan Zhao, Nianmin Yao
With the soaring growth of data traffic, unmanned aerial vehicle (UAV) based edge caching has been regarded as a promising solution to alleviate network congestion and enable users to obtain their desired contents with reduced delay. For the UAV-based edge caching, how to jointly plan the trajectory and caching strategy is the key, which determines how much benefit can achieve accordingly. Such a joint strategy design highly depends on the content demands in the network. However, the content demands are usually heterogeneous both temporally and spatially, and hardly known in advance. Such demand uncertainty makes the joint strategy design extremely challenging. In this paper, aiming at maximizing the reduced delay brought by the UAV-based edge caching, we propose a proactive joint trajectory and caching strategy under uncertain content demands. We formulate it into a risk-averse stochastic optimization problem to guarantee the maximal benefit with a high probability. Furthermore, considering the fact that the precise distributional information might be unavailable in practice, we focus on the worst case and develop a data-driven distributionally robust solution, making the strategy trustworthy. Simulation results demonstrate the effectiveness of the proposed strategy.
{"title":"A Proactive Joint Strategy on Trajectory and Caching for UAV-Assisted Networks: A Data-Driven Distributionally Robust Approach","authors":"Xuanheng Li, Jiahong Liu, Nan Zhao, Nianmin Yao","doi":"10.1109/iccc52777.2021.9580368","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580368","url":null,"abstract":"With the soaring growth of data traffic, unmanned aerial vehicle (UAV) based edge caching has been regarded as a promising solution to alleviate network congestion and enable users to obtain their desired contents with reduced delay. For the UAV-based edge caching, how to jointly plan the trajectory and caching strategy is the key, which determines how much benefit can achieve accordingly. Such a joint strategy design highly depends on the content demands in the network. However, the content demands are usually heterogeneous both temporally and spatially, and hardly known in advance. Such demand uncertainty makes the joint strategy design extremely challenging. In this paper, aiming at maximizing the reduced delay brought by the UAV-based edge caching, we propose a proactive joint trajectory and caching strategy under uncertain content demands. We formulate it into a risk-averse stochastic optimization problem to guarantee the maximal benefit with a high probability. Furthermore, considering the fact that the precise distributional information might be unavailable in practice, we focus on the worst case and develop a data-driven distributionally robust solution, making the strategy trustworthy. Simulation results demonstrate the effectiveness of the proposed strategy.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121016379","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580273
Xiaohan Zhou, Yunhui Yi, Geng Jia
We study the problem of learning knowledge representations of entities and relations in knowledge graphs to predict missing links. The key to precisely accomplish a such task is modeling and inferring the diverse patterns of the relations. In this paper, we present a new rotation-based knowledge representation learning model named Path-RotatE, which considers additional paths to model rich inference patterns between entities. In addition, this paper considers the correlation between the path and the direct relation. In this way, we improve reliability of the path, making it more suitable to train. Finally, this paper conducts entity prediction experiments on datasets such as FB15k, FB15-237, WN18 and WN18RR. The results show that the Path-RotatE model has a certain improvement in MR, MRR and Hits@N compared to RotatE, PTransE and other baseline models.
{"title":"Path-RotatE: Knowledge Graph Embedding by Relational Rotation of Path in Complex Space","authors":"Xiaohan Zhou, Yunhui Yi, Geng Jia","doi":"10.1109/iccc52777.2021.9580273","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580273","url":null,"abstract":"We study the problem of learning knowledge representations of entities and relations in knowledge graphs to predict missing links. The key to precisely accomplish a such task is modeling and inferring the diverse patterns of the relations. In this paper, we present a new rotation-based knowledge representation learning model named Path-RotatE, which considers additional paths to model rich inference patterns between entities. In addition, this paper considers the correlation between the path and the direct relation. In this way, we improve reliability of the path, making it more suitable to train. Finally, this paper conducts entity prediction experiments on datasets such as FB15k, FB15-237, WN18 and WN18RR. The results show that the Path-RotatE model has a certain improvement in MR, MRR and Hits@N compared to RotatE, PTransE and other baseline models.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115606228","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580229
Yuge Zhang
Message authentication based on wireless physical layer channel information has gained significant attention in recent years. In existing studies, there are several channel based authentication methods to deal with the single attacker scenario. However, in the real wireless environment, there may be several attackers and we do not know the exact number of the attackers. To solve the physical layer authentication problem in such a multi-attackers scenario, we propose a variational Bayesian algorithm based authentication scheme using Gaussian mixture model. We show that even without having a complete prior knowledge and the number of the attackers, our algorithm can identify the received messages to determine whether they are from the legitimate transmitter or the attackers. We experimentally demonstrate the performance of our proposed method and show that the variational Bayesian algorithm has a low miss detection rate.
{"title":"Physical Layer Authentication Based on Gaussian Mixture Model Under Unknown Number of Attackers","authors":"Yuge Zhang","doi":"10.1109/iccc52777.2021.9580229","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580229","url":null,"abstract":"Message authentication based on wireless physical layer channel information has gained significant attention in recent years. In existing studies, there are several channel based authentication methods to deal with the single attacker scenario. However, in the real wireless environment, there may be several attackers and we do not know the exact number of the attackers. To solve the physical layer authentication problem in such a multi-attackers scenario, we propose a variational Bayesian algorithm based authentication scheme using Gaussian mixture model. We show that even without having a complete prior knowledge and the number of the attackers, our algorithm can identify the received messages to determine whether they are from the legitimate transmitter or the attackers. We experimentally demonstrate the performance of our proposed method and show that the variational Bayesian algorithm has a low miss detection rate.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122473365","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580196
Yu Wang, Zhenyu Liu, Zhiyong Chen, Ling Luo, Min Hua, Wenqing Li, Bin Xia
Timely transmission of sensor perception information and continuous working time are two very important performance metrics of wireless sensor networks. Motivated by this, we allocate the transmission power of sensors and the transmission bandwidth for each sensor to minimize the age of information (AoI) and the energy consumption in the wireless sensor network. We derive the closed-form expression of the average AoI of the wireless sensor network. The optimization problem with the objective of simultaneously minimizing AoI and energy consumption is then formulated. The optimization problem is non-convex, and we design a gradient descent (GD) approach and an iterative convex optimization (ICO) approach to effectively solve the problem. Numerical results reveal that the proposed methods can achieve a satisfying performance on AoI and energy with enough bandwidth resource, and have an excellent result of AoI with insufficient bandwidth resource.
传感器感知信息的及时传输和连续工作时间是无线传感器网络的两个非常重要的性能指标。在此基础上，我们对各传感器的传输功率和传输带宽进行分配，使无线传感器网络中的信息年龄(age of information, AoI)和能量消耗最小。导出了无线传感器网络平均AoI的封闭表达式。提出了以AoI和能耗同时最小化为目标的优化问题。针对非凸优化问题，设计了梯度下降法(GD)和迭代凸优化法(ICO)来有效解决该问题。数值结果表明，在带宽资源充足的情况下，所提方法在AoI和能量上都能获得满意的性能，在带宽资源不足的情况下，所提方法在AoI上也有很好的效果。
{"title":"Joint Age of Information and Energy Minimization in Wireless Sensor Systems","authors":"Yu Wang, Zhenyu Liu, Zhiyong Chen, Ling Luo, Min Hua, Wenqing Li, Bin Xia","doi":"10.1109/iccc52777.2021.9580196","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580196","url":null,"abstract":"Timely transmission of sensor perception information and continuous working time are two very important performance metrics of wireless sensor networks. Motivated by this, we allocate the transmission power of sensors and the transmission bandwidth for each sensor to minimize the age of information (AoI) and the energy consumption in the wireless sensor network. We derive the closed-form expression of the average AoI of the wireless sensor network. The optimization problem with the objective of simultaneously minimizing AoI and energy consumption is then formulated. The optimization problem is non-convex, and we design a gradient descent (GD) approach and an iterative convex optimization (ICO) approach to effectively solve the problem. Numerical results reveal that the proposed methods can achieve a satisfying performance on AoI and energy with enough bandwidth resource, and have an excellent result of AoI with insufficient bandwidth resource.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825787","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 : 2021-07-28DOI: 10.1109/iccc52777.2021.9580317
Jie Yang, Qinghe Du, Yi Jiang
In recent years, the machine learning assisted communication system design has drawn a lot of attentions. As a remarkable progress, a recent work proposed to incorporate a neural network (NN) into the traditional algorithms for symbol detection under intersymbol interference (ISI), e.g. the Viterbi algorithm and the BCJR algorithm, to achieve robustness against channel estimation errors. This paper presents an improved design over the state-of-the-art by using a neural network to approximate the likelihood of the received sample given different state transitions of the trellis diagram. The simulation results show that the proposed method performs similarly to the conventional methods in the channel model-matched scenarios, but is significantly more robust against channel estimation errors. Our design is superior to the state-of-art NN -assisted methods in two aspects: it requires significantly smaller training overhead and is robust against non-Gaussian noise.
{"title":"Neural Network-Assisted Robust Symbol Detection Under Intersymbol Interference","authors":"Jie Yang, Qinghe Du, Yi Jiang","doi":"10.1109/iccc52777.2021.9580317","DOIUrl":"https://doi.org/10.1109/iccc52777.2021.9580317","url":null,"abstract":"In recent years, the machine learning assisted communication system design has drawn a lot of attentions. As a remarkable progress, a recent work proposed to incorporate a neural network (NN) into the traditional algorithms for symbol detection under intersymbol interference (ISI), e.g. the Viterbi algorithm and the BCJR algorithm, to achieve robustness against channel estimation errors. This paper presents an improved design over the state-of-the-art by using a neural network to approximate the likelihood of the received sample given different state transitions of the trellis diagram. The simulation results show that the proposed method performs similarly to the conventional methods in the channel model-matched scenarios, but is significantly more robust against channel estimation errors. Our design is superior to the state-of-art NN -assisted methods in two aspects: it requires significantly smaller training overhead and is robust against non-Gaussian noise.","PeriodicalId":425118,"journal":{"name":"2021 IEEE/CIC International Conference on Communications in China (ICCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128439866","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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