Software defined intelligent satellite-terrestrial integrated networks:Insights and challenges

Satellite-Terrestrial integrated Networks(STNs)have been advocated by both academia and industry as a promising network paradigm to achieve service continuity and ubiquity.However,STNs suffer from problems including poor flexibility of network architecture,low adaptability to dynamic environments,the lack of network intelligence,and low resource utilization.To handle these challenges,a Software defined Intelligent STN(SISTN)architecture is introduced.Specifically,the hierarchical architecture of the proposal is described and a distributed deployment scheme for SISTNs controllers is proposed to realize agile and effective network management and control.Moreover,three use cases in SISTNs are discussed.Meanwhile,key techniques and their corresponding solutions are presented,followed by the identification of several open issues in SISTNs including compatibility with existing networks,the tradeoff between network flexibility and performance,and so on.

Joint Resource Allocation and Admission Control in Sliced Fog Radio Access Networks

Network slicing based fog radio access network(F-RAN) has emerged as a promising architecture to support various novel applications in 5 G-and-beyond wireless networks. However, the co-existence of multiple network slices in F-RANs may lead to significant performance degradation due to the resource competitions among different network slices. In this paper, the downlink F-RANs with a hotspot slice and an Internet of Things(Io T) slice are considered, in which the user equipments(UEs) of different slices share the same spectrum. A novel joint resource allocation and admission control scheme is developed to maximize the number of UEs in the hotspot slice that can be supported with desired quality-of-service, while satisfying the interference constraint of the UEs in the Io T slice. Specifically, the admission control and beamforming vector optimization are performed in the hotspot slice to maximize the number of admitted UEs, while the joint sub-channel and power allocation is performed in the Io T slice to maximize the capability of the UEs in the Io T slice tolerating the interference from the hotspot slice. Numerical results show that our proposed scheme can effectively boost the number of UEs in the hotspot slice compared to the existing baselines.

Edge Intelligence Assisted Resource Management for Satellite Communication

Satellite communication has been seen as a vital part of the sixth generation communication,which greatly extends network coverage.In satellite communication,resource management is a key problem attracting many research interests.However,previous study mainly focuses on throughput improvement via power allocation and spectrum assignment and the proposed approaches are mostly model-based and dedicated to specific problem structures.Fortunately,with the trend of edge intelligence,complex resource management problems can be efficiently resolved in a model-free manner.In this paper,a joint beam activation,user-beam association and time resource allocation approach is proposed.The core idea is using stochastic learning at the ground station to identify active user-link beams to meet user rate demand.In addition,the convergence,optimality and complexity of our proposal are rigorously discussed.By simulation,it is shown that the rate goal of most of the users can be met and meanwhile satellite energy is saved owing to much less active beams.

Intelligent radio access networks:architectures,key techniques,and experimental platforms

Intelligent radio access networks(RANs)have been seen as a promising paradigm aiming to better satisfy diverse application demands and support various service scenarios.In this paper,a comprehensive survey of recent advances in intelligent RANs is conducted.First,the efforts made by standard organizations and vendors are summarized,and several intelligent RAN architectures proposed by the academic community are presented,such as intent-driven RAN and network with enhanced data analytic.Then,several enabling techniques are introduced which include AI-driven network slicing,intent perception,intelligent operation and maintenance,AI-based cloud-edge collaborative networking,and intelligent multi-dimensional resource allocation.Furthermore,the recent progress achieved in developing experimental platforms is described.Finally,given the extensiveness of the research area,several promising future directions are outlined,in terms of standard open data sets,enabling AI with a computing power network,realization of edge intelligence,and software-defined intelligent satellite-terrestrial integrated network.