Shuo Wang, Yantong Zhang, Shuzhen Shi, Ran Li, Fangzhou Liu, Ziheng He, Sisi Che, Hongyan Liu, Yuchen Wang
{"title":"Adaptive Modulation and Coding Based on Multi-Agent Reinforcement Learning for Power Emergency Communications","authors":"Shuo Wang, Yantong Zhang, Shuzhen Shi, Ran Li, Fangzhou Liu, Ziheng He, Sisi Che, Hongyan Liu, Yuchen Wang","doi":"10.1109/ICCET58756.2023.00039","DOIUrl":"https://doi.org/10.1109/ICCET58756.2023.00039","url":null,"abstract":"Power emergency communications are critical to rescue work when some disasters happen. For the sake of alleviating the shortage of spectrum resources and maintaining system connections, an adaptive modulation and coding scheme is studied in this paper. For the target system, the principle of cognitive radio networks (CRNs) is involved and the users in power emergency communications are modelled as primary users (PUs) and secondary users (SUs) according to their communication requirements. A maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm in deep reinforcement learning is proposed to train the system and achieve an optimal policy, in which different users can access the system with varying modulation and coding schemes. The simulation results show that the proposed ME-MAAC algorithm outperforms the Deep Q-Network (DQN) algorithm in accordance with efficiency and performance. The proposed adaptive modulation and coding (AMC) scheme can improve system connection rate and spectrum efficiency, that is, the users in power emergency communications can obtain more communications with limited power and spectrum resources. This paper provides an useful guidance for the design of practical power emergency communications.","PeriodicalId":170939,"journal":{"name":"2023 6th International Conference on Communication Engineering and Technology (ICCET)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188825","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":"An Adaptive Method for Linearizing Wideband Power Amplifiers with Band-Limited Feedback Path","authors":"Yuekai Zhang, W. Gao, Baojian Gao, Yuhui Ren","doi":"10.1109/ICCET58756.2023.00021","DOIUrl":"https://doi.org/10.1109/ICCET58756.2023.00021","url":null,"abstract":"This paper proposes a Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm based on dualobjective optimization of Normalized Mean Squared Error (NMSE) and Adjacent Channel Power Ratio (ACPR) called SPSA-DO. Since NMSE is mainly affected by in-band distortion and less by out-of-band distortion, the SPSA-DO algorithm can iteratively minimize NMSE and ACPR simultaneously in the time and frequency domains, respectively. Additionally, using a small number of FFT bins close to the desired channel in the ACPR calculation allows the use of a lowpass filter (LPF) in the feedback path to limit the bandwidth, thereby reducing the feedback sampling rate of analog-to-digital converter (ADC) and current consumption. Simulation results show that the SPSA-DO algorithm converges faster than the existing SPSA algorithm in reducing NMSE and ACPR.","PeriodicalId":170939,"journal":{"name":"2023 6th International Conference on Communication Engineering and Technology (ICCET)","volume":"99 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671672","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}
Ziheng He, Hongyan Liu, Rui Du, Lili Sun, Fangzhou Liu, Sisi Che, Shuo Wang, Yuchen Wang, Ran Li
{"title":"Intelligent Spectrum Allocation Based on Deep Reinforcement Learning for Power Emergency Communications","authors":"Ziheng He, Hongyan Liu, Rui Du, Lili Sun, Fangzhou Liu, Sisi Che, Shuo Wang, Yuchen Wang, Ran Li","doi":"10.1109/ICCET58756.2023.00018","DOIUrl":"https://doi.org/10.1109/ICCET58756.2023.00018","url":null,"abstract":"In order to improve system performance of power emergency communication systems, this paper studies an intelligent spectrum allocation scheme based on multi-agent reinforcement learning (MARL) to allocate limited spectrum resources to different users according to their spectrum requirements. When the users access communication channels, whether the communication is successful is judged according to the channel feedback information, which provides rewards for learning training process. A spectrum allocation scheme based on MARL is proposed to intelligently share the limited spectrum resources among different users. Simulation results show that the proposed MARL scheme can achieve better system performance compared to traditional reinforcement schemes such as Deep Q Network (DQN). The proposed scheme provides an efficient spectrum usage paradigm for power emergency communications.","PeriodicalId":170939,"journal":{"name":"2023 6th International Conference on Communication Engineering and Technology (ICCET)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126091091","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}