Integrated Sensing and Communication Neighbor Discovery for MANET with Gossip Mechanism
Zhiqing Wei, Chenfei Li, Yanpeng Cui, Xu Chen, Zeyang Meng, and, Zhiyong Feng

TL;DR
This paper proposes an ISAC-enabled gossip mechanism combined with reinforcement learning to significantly reduce neighbor discovery convergence time in MANETs, especially for 6G applications.
Contribution
It introduces a novel ISAC-assisted gossip-based ND algorithm with reinforcement learning to improve speed and reliability in MANET neighbor discovery.
Findings
Reduces ND convergence time by approximately 66.4%.
Theoretical derivation matches simulation results.
Enhances ND completeness and speed with GQ-nRnS algorithm.
Abstract
Mobile Ad hoc Network (MANET), supporting Machine-Type Communication(MTC), has a strong demand for rapid networking. Neighbor Discovery (ND) is a key initial step in configuring MANETs and faces a serious challenge in decreasing convergence time. Integrated Sensing and Communication (ISAC), as one of the potential key technologies in the 6th Generation (6G) mobile networks, can obtain the sensing data as the priori information to accelerate ND convergence. In order to further reduce the convergence time of ND, this paper introduces the ISAC-enabled gossip mechanism into the ND algorithm. The prior information acquired by ISAC reduces the information redundancy brought by the gossip mechanism and thus decreases the probability of collision, which further improves convergence speed. The average number of discovered nodes within a given period is derived, which is applied as the critical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsHigh-Order Consensuses · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Q-Learning
