The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applicatio...The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.展开更多
Satellite mobile system and space-airground integrated network have a prominent superiority in global coverage which plays a critical role in remote and non-land regions, as well as emergency communications. However, ...Satellite mobile system and space-airground integrated network have a prominent superiority in global coverage which plays a critical role in remote and non-land regions, as well as emergency communications. However, due to the gradual angle attenuations of the satellite antennas, it is difficult to achieve full frequency multiplex among different beams as terrestrial 5G network. Multi-color frequency reuse is widely adopted in both academic and industry. Beam hopping scheme has attracted the attention of researchers recently due to the allocation flexibility. In this paper, we focus on analyzing the performance benefits of beam hopping compared with multi-color frequency reuse scheme in non-uniform user and traffic distributions in satellite system. Aerial networks are also introduced to form a space-airground integrated network for coverage enhancement,and the capacity improvement is analyzed. Besides,additional improved techniques are provided to make comprehensive analysis and comparisons. Theoretical analysis and simulation results indicate that the beam hopping scheme has a prominent superiority in the system capacity compared with the traditional multicolor frequency reuse scheme in both satellite mobile system and future space-air-ground integrated network.展开更多
Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.How...Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.However,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes.In this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all episodes.Through planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed algorithm.We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments.Furthermore,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.展开更多
基金supported by Key Program of Natural Science Foundation of China(Grant No.61631018)Anhui Provincial Natural Science Foundation(Grant No.1908085MF177)Huawei Technology Innovative Research(YBN2018095087)。
文摘The 5 th generation(5 G)mobile networks has been put into services across a number of markets,which aims at providing subscribers with high bit rates,low latency,high capacity,many new services and vertical applications.Therefore the research and development on 6 G have been put on the agenda.Regarding demands and characteristics of future 6 G,artificial intelligence(A),big data(B)and cloud computing(C)will play indispensable roles in achieving the highest efficiency and the largest benefits.Interestingly,the initials of these three aspects remind us the significance of vitamin ABC to human body.In this article we specifically expound on the three elements of ABC and relationships in between.We analyze the basic characteristics of wireless big data(WBD)and the corresponding technical action in A and C,which are the high dimensional feature and spatial separation,the predictive ability,and the characteristics of knowledge.Based on the abilities of WBD,a new learning approach for wireless AI called knowledge+data-driven deep learning(KD-DL)method,and a layered computing architecture of mobile network integrating cloud/edge/terminal computing,is proposed,and their achievable efficiency is discussed.These progress will be conducive to the development of future 6 G.
基金the Natural Science Foundation of China under Grant 61801319Sichuan Science and Technology Program under Grant 2020JDJQ0061+1 种基金the Education Agency Project of Sichuan Province under Grant 18ZB0419the Sichuan University of Science and Engineering Talent Introduction Project under Grant 2020RC33。
文摘Satellite mobile system and space-airground integrated network have a prominent superiority in global coverage which plays a critical role in remote and non-land regions, as well as emergency communications. However, due to the gradual angle attenuations of the satellite antennas, it is difficult to achieve full frequency multiplex among different beams as terrestrial 5G network. Multi-color frequency reuse is widely adopted in both academic and industry. Beam hopping scheme has attracted the attention of researchers recently due to the allocation flexibility. In this paper, we focus on analyzing the performance benefits of beam hopping compared with multi-color frequency reuse scheme in non-uniform user and traffic distributions in satellite system. Aerial networks are also introduced to form a space-airground integrated network for coverage enhancement,and the capacity improvement is analyzed. Besides,additional improved techniques are provided to make comprehensive analysis and comparisons. Theoretical analysis and simulation results indicate that the beam hopping scheme has a prominent superiority in the system capacity compared with the traditional multicolor frequency reuse scheme in both satellite mobile system and future space-air-ground integrated network.
基金This work is supported by the National Key Research and Development Program of China,2018YFA0701603 and Natural Science Foundation of Anhui Province,2008085MF213.
文摘Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.However,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes.In this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all episodes.Through planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed algorithm.We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments.Furthermore,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.
