{"title":"MADDPG-Based Power Allocation Algorithm for Network-Assisted Full-Duplex Cell-Free MmWave Massive MIMO Systems with DAC Quantization","authors":"Q. Fan, Yu Zhang, Zhaoye Wang, Jiamin Li, Pengcheng Zhu, Dongmin Wang","doi":"10.1109/WCSP55476.2022.10039231","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039231","url":null,"abstract":"Network-assisted full-duplex (NAFD) systems reduce the cross-link interference (CLI) by dividing the remote antenna unit (RAU) into the transmitting RAU (T-RAU) and receiving RAU (R-RAU), keeping them geographically separated and flexibly utilizing duplex modes, which further improves the system performance. The NAFD cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems with digital-to-analog converter (DAC) quantization is investigated in this paper. We propose an optimization problem of jointly power allocation of the T-RAUs and uplink users to maximize the weighted uplink and downlink sum rate, in which bidirectional power constraints need to be satisfied. To handle this intractable problem, we further apply a deep reinforcement learning algorithm based on multi-agent deep deterministic policy gradient (MADDPG) instead of the conventional convex optimization approach. The simulation results verify the convergence of the proposed MADDPG-based algorithm, explore the learning performance of each agent, analyze the impact of DAC quantization on NAFD cell-free mmWave massive MIMO systems, and compare the performance of the MADDPG-based algorithm in static and dynamic environments.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123066714","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}
{"title":"Signal Detection for HF Skywave Massive MIMO-OFDM with Slepian Transform","authors":"L. Song, D. Shi, Lu Gan, Xiqi Gao","doi":"10.1109/WCSP55476.2022.10039433","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039433","url":null,"abstract":"In this paper, we investigate signal detection for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM) modulation. We first reveal the relationship of sparse supports between the beam domain channel and the Fourier spectrum of the space domain channel for HF skywave massive MIMO-OFDM channels. We then propose a separate Slepian transform (SST) based detector, where a set of modulated Slepian sequences are designed separately for user terminals (UTs). Before the MMSE detection, the Slepian transform is performed to reduce the dimension for each UT, thus avoiding high di-mensional matrices multiplications and inversions. To further reduce the computational complexity, we propose a joint Slepian transform (JST) based detector, where a fixed set of modulated Slepian sequences are designed, and Slepian transforms of the observation vector can be efficiently implemented based on low-dimensional fast Fourier transform (FFT). Simulation results demonstrate the attractive performance and excellent complexity advantage of proposed detectors.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116785478","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}
{"title":"Joint Optimization of Energy Consumption and Latency Based on DRL: An Edge Server Activation and Task Scheduling Scheme in IIoT","authors":"Rui Ma, Xiaotian Zhou, Haixia Zhang, Dongfeng Yuan","doi":"10.1109/WCSP55476.2022.10039283","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039283","url":null,"abstract":"Edge computing has been proposed as a promising solution to alleviate the computation intensive requirement of Industrial Internet of Things (IIoT) scenarios. In edge computing based network, task latency and energy consumption are two key metrics, while the tradeoff of them is of great importance on impacting the overall performance of the system. In this paper, we formulate a joint optimization problem to minimize the weighted summation of latency and energy consumption in the network where the task scheduling and server dormant mode are both taken into account. To solve this problem, we designed a Deep Reinforcement Learning (DRL) based algorithm considering both the number of active edge servers and the task scheduling scheme per time slot. Simulation results show that our algorithm has advantages compared with other algorithms and reduces the overall cost of the system.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117158526","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}
Ruocheng Huang, Yong Li, Wei Feng, Xin Zhang, Tao Shan, Yun Liu
{"title":"An Edge Based Framework for Risk Assessment of Communicable Disease","authors":"Ruocheng Huang, Yong Li, Wei Feng, Xin Zhang, Tao Shan, Yun Liu","doi":"10.1109/WCSP55476.2022.10039130","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039130","url":null,"abstract":"Along with the development of edge computing and Artificial Intelligence (AI), there has been an explosion of health-care system. As COVID-19 spread globally, the pandemic created significant challenges for the global health system. Therefore, we proposed an edge-based framework for risk assessment of communicable disease called CDM-FL. The CDM-FL consists of two modules, the common data model (CDM) and federated learning (FL). The CDM can process and store multi-source heterogeneous data with standardized semantics and schema. This provides more data for model training using medical data globally. The model is deployed on edge nodes that can measure patients' status locally and with low latency. It also keeps patient privacy from being disclosed that patient are more likely to share their medical data. The results based on real-world data show that CDM-FL can help physicians to evaluate the risk of communicable disease as well as save lives during severe epidemic situations.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124843513","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}
Hongxing Wang, Mei Wu, Yu Song, Xin Zhang, Weiping Mao
{"title":"Defect Reconstruction for Class-imbalanced Power System Defect Recognition","authors":"Hongxing Wang, Mei Wu, Yu Song, Xin Zhang, Weiping Mao","doi":"10.1109/WCSP55476.2022.10039235","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039235","url":null,"abstract":"Performing fault diagnosis is an important routine to keep power systems functioning properly. Since most facilities of power systems are located in the wild, unmanned aerial vehicles (UAV) are used to collect potentially damaged components of power systems by taking pictures. Those pictures are categorized into a certain type to take corresponding actions to repair the damaged components. It is vital to classify collected images accurately. However, the collected images distribute in a class-imbalanced style, which degrades the performance of the classifier if directly used for training. In this paper, we make use of the generative adversarial network (GAN) to generate extra images for classes that have fewer images. Our method achieves decent improvements on 4 different scenes, showing the effectiveness of GAN-generated images on the class-imbalanced power system defect classification task.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125834290","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}
Wendong Gao, Lei Guan, Zan Li, Jiangbo Si, Pei Hui, Hang Fu
{"title":"An Extended Family of No-Hit-Zone Frequency Hopping Sequences for UAVs Cluster Communication","authors":"Wendong Gao, Lei Guan, Zan Li, Jiangbo Si, Pei Hui, Hang Fu","doi":"10.1109/WCSP55476.2022.10039124","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039124","url":null,"abstract":"This paper studies the optimal design and performance analysis of a novel class of no-hit-zone frequency hopping sequence (NHZ-FHS) set in large-scale UAV clusters using quasi-synchronous frequency hopping multiple access (QS-FHMA) communication. Although the NHZ-FHS set can offer interference-free FHMA performance when the arrival signal delay $(tau)$ does not exceed the NHZ $(Z)$, i.e., $vert tauvert leq Z$, the number of users in the conventional NHZ-FHS set is strictly limited, thus limiting their application in UAV clusters. Therefore, we consider an extended family of NHZ-FHSs consists of several conventional $mathbf{NHZ}$ -FHS subsets to form a new FHS set. Moreover, collisions are guaranteed to occur between users using different subsets of frequency hopping sequences only in the zero time delay region, and it is also essential to minimize the Hamming correlation in the zero delay region in this paper. In this paper, such FHS sets are called extended NHZ-FHS (ENHZ-FHS) sets. We derive a lower bound on the maximum Hamming correlation and then provide a construction of the optimal ENHZ-FHS set meeting our proposed lower bound with equality. The theoretical analysis and simulation results show that the ENHZ-FHS set proposed in this paper can meet the communication requirements of UAV clusters with large capacities.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125906142","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}
{"title":"Performance of Multiple Intelligent Reflecting Surfaces Assisted Communication","authors":"Donglin Jia, Yi Zhong, R. Qiu, Xiaohu Ge","doi":"10.1109/WCSP55476.2022.10039187","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039187","url":null,"abstract":"Intelligent Reflecting Surface (IRS) is an emerging passive device that implements passive beamforming by adjusting the amplitude and the phase of incident signal through a large number of passive reflection units on its surface. Many propagation paths can be constructed from a transmitter to an IRS, and then from the IRS to a receiver, thereby enhancing the received signal and improving the communication quality. In this paper, we evaluate the performance of wireless propagation paths without direct link assisted by multiple IRSs by leveraging stochastic geometry to model the locations of base stations (BSs) and IRSs. By analyzing the coverage probability and average rate of a typical user at a blind spot assisted by multiple IRSs which are modeled by stochastic geometry, we observe that increasing the number of IRSs will effectively improve the performance, however, the improvement will be smaller and smaller.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586179","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}
{"title":"Machine Learning-based Multi-classification for First-Episode Schizophrenics, Ultra-high risk Schizophrenics, and Healthy Controls","authors":"Wenmei Li, Nuoya Yu, Wei Yan, Rongrong Zhang","doi":"10.1109/WCSP55476.2022.10039279","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039279","url":null,"abstract":"Schizophrenia is a severe chronic disabling disease. Prompt treatment of ultra-high-risk individuals in the prodromal phase is of great significance for preventing the development of schizophrenia. The purpose of this study is to find a way to effectively distinguish ultra-high-risk individuals with schizophrenia, and to analyze important biomarkers of schizophrenia. There are 101 first-episode drug-naive schizophrenia patients, 49 ultra-high-risk individuals and 94 healthy people participated in our study. The cognition data, cortical thickness and the local gyrification index of these participants were collected for the identification of schizophrenia using various machine learning methods. Meanwhile, biological markers that indicate mental illness are identified by analyzing their relationship among different categories of individuals. Support vector machine performed best among the machine learning methods, with a classification accuracy of 86.4%. And the results indicate that the critical features for the identification of the three-type subject are executive function, the right cingulate gyrus, and the left temporal pole.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768131","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}
{"title":"Channel Modeling Based On Quantum Generative Adversarial Network","authors":"Zhairui Gong, Xinling He, Zhifan Wan, Zetong Li, Xianchao Zhang, Xutao Yu","doi":"10.1109/WCSP55476.2022.10039327","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039327","url":null,"abstract":"Channel modeling is indispensable in a communication system. In this paper, a novel scheme for channel modeling using quantum generative adversarial model was proposed. A quantum generative adversarial network is a generative adversarial model with a quantum circuit as the generative module and a deep neural network as the discriminant module, thereby exploiting the privilege of quantum algorithms in simulating probability distributions to stochastic channel models. Experiments were conducted on IBM QX quantum computing platform. The gradient descent of the cost function and Kullback-Leibler divergence were analyzed. Results verify the feasibility and superiority of the quantum generative adversarial network for channel modeling.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129485490","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}
{"title":"Deep Reinforcement Learning Based Resource Allocation for URLLC User-Centric Network","authors":"Fajin Hu, Junhui Zhao, Jieyu Liao, Huan Zhang","doi":"10.1109/WCSP55476.2022.10039329","DOIUrl":"https://doi.org/10.1109/WCSP55476.2022.10039329","url":null,"abstract":"In this paper, we solve the resource allocation problem by deep reinforcement learning (DRL) for diverse ultra-reliable low-latency communication (URLLC) services under the user-centric downlink transmission. Firstly, to meet the constraint of reliability, we model the channel decoding error rate by using the finite blocklength coding (FBC) according to the short packet characteristics of URLLC services. Then, we model the queue of different URLLC services in the temporal dimension to describe the delay violation problem. Furthermore, we adopt the DRL scheme that transforms the maximizing system availability and transmission efficiency problem into maximizing system reward problems. Simulation results show that the proposed algorithm achieves superior availability for diverse URLLC services compared with the baselines.","PeriodicalId":199421,"journal":{"name":"2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130613989","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}