Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can ...Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.展开更多
Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investmen...Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck.This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data,augmented with memory of recent upand-down fluctuations occur in the data of short-term stock movement.The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process,which take care of decision-making in an ever-changing dynamic environment.Three trading strategies were implemented in this model;namely,a Price Model Strategy,a Probabilistic Model Strategy,and a Bayesian Gated Recurrent Unit Strategy,each leveraging the respective model’s outputs to optimize trading decisions.The experimental results show that,compared with the standard Gated Recurrent Unit models,the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment.The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.展开更多
基金supported in part by the National Natural Science Foundation of China(grant nos.61971365,61871339,62171392)Digital Fujian Province Key Laboratory of IoT Communication,Architecture and Safety Technology(grant no.2010499)+1 种基金the State Key Program of the National Natural Science Foundation of China(grant no.61731012)the Natural Science Foundation of Fujian Province of China No.2021J01004.
文摘Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.
文摘Machine learning,a revolutionary and advanced technology,has been widely applied in the field of stock trading.However,training an autonomous trading strategy which can effectively balance risk and Return On Investment without human supervision in the stock market with high uncertainty is still a bottleneck.This paper constructs a Bayesian-inferenced Gated Recurrent Unit architecture to support long-term stock price prediction based on characteristics of the stock information learned from historical data,augmented with memory of recent upand-down fluctuations occur in the data of short-term stock movement.The Gated Recurrent Unit architecture incorporates uncertainty estimation into the prediction process,which take care of decision-making in an ever-changing dynamic environment.Three trading strategies were implemented in this model;namely,a Price Model Strategy,a Probabilistic Model Strategy,and a Bayesian Gated Recurrent Unit Strategy,each leveraging the respective model’s outputs to optimize trading decisions.The experimental results show that,compared with the standard Gated Recurrent Unit models,the modified model exhibits a huge tremendous/dramatic advantage in managing volatility and improving return on investment Return On Investment.The results and findings underscore the significant potential of combining Bayesian inference with machine learning to operate effectively in chaotic decision-making environments.
