Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Epidemic-Like Proximity-Based Traffic Offloading

Cellular networks are overloaded due to the mobile traffic surge,and mobile social networks(MSNets) can be leveraged for traffic offloading.In this paper,we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks.We introduce a gossip-style social cascade(GSC) model to model the epidemic-like information diffusion process in MSNets.For static-case and mobile-case networks,we establish an equivalent view and a temporal mapping of the information diffusion process,respectively.We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading,yielding a sub-optimal solution to the NP-hard traffic offloading maximization(TOM) problem.Experiments are carried out to study the offloading performance,illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms,and user mobility can help further reduce cellular load.

Anchor Vertex Selection for Enhanced Reliability of Traffic Offloading Service in Edge-Enabled Mobile P2P Social Networks

As the rapid growth of mobile social networks,mobile peer-to-peer(P2P)communications and mobile edge computing(MEC)have been developed to reduce the traffic load and improve the computation capacity of cellular networks.However,the stability of social network is largely ignored in the advances of P2P and MEC,which is related to the social relations between users.It plays a vital role in improving the efficiency and reliability of traffic offloading service.In this paper,we integrate an edge node and the nearby P2P users as a mobile P2P social network and introduce the problem of adaptive anchored(k,r)-core to maintain the stability of multiple mobile P2P networks.It aims to adaptively select and retain a set of critical users for each network,whose participation is critical to overall stability of the network,and allocate certain resource for them so that the maximum number of users of all networks will remain engaged and the traffic of cellular network can be minimized.We called the retained users as anchor vertices.To address it,we devise a peer-edge-cloud framework to achieve the adaptive allocation of resources.We also develop a similarity based onion layers anchored(k,r)-core(S-OLAK)algorithm to explore the anchor vertices.Experimental results based on a real large-scale mobile P2P data set demonstrate the effectiveness of our method.

Cellular traffic offloading utilizing set-cover based caching in mobile social networks

To cope with the explosive data demands, offloading cellular traffic through mobile social networks（MSNs） has become a promising approach to alleviate traffic load. Indeed, the repeated data transmission results in a great deal of unnecessary traffic. Existing solutions generally adopt proactive caching and achieve traffic shifting by exploiting opportunistic contacts. The key challenge to maximize the offloading utility needs leveraging the trade-off between the offloaded traffic and the users＇ delay requirement. Since current caching scheme rarely address this challenge, in this paper, we first quantitatively interpret the offloading revenues on the cellular operator side associated with the scale of caching users, then develop a centralized caching protocol to maximize the offloading revenues, which includes the selective algorithm of caching location based on set-cover, the cached-data dissemination strategy based on multi-path routing and the cache replacement policy based on data popularity. The experimental results on real-world mobility traces show that the proposed caching protocol outperforms existing schemes in offloading scenario.

SWN: An SDN Based Framework for Carrier Grade WiFi Networks

With the rapid growth of mobile data traffic and vast traffic offloaded from cellular network, Wi-Fi has been considered as an essential component to cope with the tremendous growth of mobile data traffic. Although operators have deployed a lot of carrier grade Wi-Fi networks, but there are still a multitude of arrears for nowadays Wi-Fi networks, such as supporting seamless handover between APs, automatic network access and unified authentication, etc. In this paper, we propose an SDN based carrier grade Wi-Fi network framework, namely SWN. The key conceptual contribution of SWN is a principled refactoring of Wi-Fi networks into control and data planes. The control plane has a centralized global view of the whole network, can perceive the underlying network state by network situation awareness(NAS) technique, and bundles the perceived information and network management operations into northbound Application Programming Interface(API) for upper applications. In the data plane, we construct software access point(SAP) to abstract the connection between user equipment(UE) and access point(AP). Network operators can design network applications by utilizing these APIs and the SAP abstraction to configure and manage the whole network, which makes carrier grade Wi-Fi networks more flexible, user-friendly, and scalable.