IMPLEMENTATION OF BOOTSTRAPPING FOR P2P OVERLAYS IN MANETS

ABSTRACT Peer-to-peer networks are immensely popular, but bootstrapping them usually requires centralized infrastructure. In fully decentralized environments such as mobile ad hoc networks, the use of centralized solutions are not possible. This paper presents a method of bootstrapping P2P overlay networks running on MANETs, and demonstrates a Java implementation of the algorithm. Simulation results show that the algorithm performs well and the implementation confirms the feasibility of the algorithm

Bootstrapping P2P Overlays in MANETs

Abstract-Peer-to-peer networks are very popular but the problem of bootstrapping them has largely been ignored. In a fully decentralized environment such as a mobile ad hoc network (MANET) the usual bootstrapping solutions, which typically require a centralized service, are not possible. We present a method of bootstrapping P2P overlay networks running on MANETs which involves multicasting P2P overlay join queries and responses, and caching results at all nodes. Node choose which overlay members to join to based on a utility function that considers both the distance in hops and the overlay neighbors' available energy. Simulation results show that the P2P overlay can closely reflect the underlying topology, which reduces energy consumption, that caching the join requests reduces the number of messages required to join the overlay, and that compared to Random Address Probing, there is less overhead and significantly less delay

How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?

The success of immersive applications such as virtual reality (VR) gaming and metaverse services depends on low latency and reliable connectivity. To provide seamless user experiences, the open radio access network (O-RAN) architecture and 6G networks are expected to play a crucial role. RAN slicing, a critical component of the O-RAN paradigm, enables network resources to be allocated based on the needs of immersive services, creating multiple virtual networks on a single physical infrastructure. In the O-RAN literature, deep reinforcement learning (DRL) algorithms are commonly used to optimize resource allocation. However, the practical adoption of DRL in live deployments has been sluggish. This is primarily due to the slow convergence and performance instabilities suffered by the DRL agents both upon initial deployment and when there are significant changes in network conditions. In this paper, we investigate the impact of time series forecasting of traffic demands on the convergence of the DRL-based slicing agents. For that, we conduct an exhaustive experiment that supports multiple services including real VR gaming traffic. We then propose a novel forecasting-aided DRL approach and its respective O-RAN practical deployment workflow to enhance DRL convergence. Our approach shows up to 22.8%, 86.3%, and 300% improvements in the average initial reward value, convergence rate, and number of converged scenarios respectively, enhancing the generalizability of the DRL agents compared with the implemented baselines. The results also indicate that our approach is robust against forecasting errors and that forecasting models do not have to be ideal.Comment: This article has been accepted for presentation in IEEE GLOBECOM 202

Congestion relief in CDMA cellular networks using multihop inter-cell relay

Abstract-Multihop communication has been proposed in cellular networks to overcome some inherent limitations. Congestion relief is amongst the promised gains. In this paper, the concept of inter-cell relay, which uses multihop communication to divert calls from heavy loaded cells to less loaded adjacent cells, is introduced. We show that using inter-cell relay, the number of supported calls inside a congested cell can be significantly increased. We devise two approaches for congestion relief based on the conditions of the network, to maximize the number of supported calls inside a congested cell. The distribution-based approach determines the number of extra hops for inter-cell relay based on call distribution. On the other hand, the delay sensitive approach assumes that the number of extra hops for inter-cell relay is limited by calls quality of service requirements. By imposing a limit on the number of extra hops, the approach decides the number of inter-cell relayed calls and the number of calls connected to the congested BS. Our results illustrate the benefits gained from inter-cell relay in congestion relief. We demonstrate that inter-cell relay can decrease congestion of a cell by fully utilizing the available resources in surrounding cells