24,834 research outputs found
A Q-Learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11p vehicular networks
Vehicular Ad hoc Networks (VANETs) are wireless networks formed of moving vehicle-stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimising channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-Learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based Medium Access Control (MAC) protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that "learn" how to act optimally in a network in order to maximise their achieved packet delivery and minimise bandwidth wastage. Additionally, via a Collective Contention Estimation (CCE) mechanism which we embed on the Q-Learning agent, faster convergence, higher throughput and short-term fairness are achieved
V2X Sidelink Positioning in FR1: Scenarios, Algorithms, and Performance Evaluation
In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in
5G vehicular networks through a comprehensive end-to-end methodology
encompassing ray-tracing-based channel modeling, novel theoretical performance
bounds, high-resolution channel parameter estimation, and geometric positioning
using a round-trip-time (RTT) protocol. We first derive a novel, approximate
Cram\'er-Rao bound (CRB) on the connected road user (CRU) position, explicitly
taking into account multipath interference, path merging, and the RTT protocol.
Capitalizing on tensor decomposition and ESPRIT methods, we propose
high-resolution channel parameter estimation algorithms specifically tailored
to dense multipath V2X sidelink environments, designed to detect multipath
components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using
realistic ray-tracing data and antenna patterns, comprehensive simulations are
conducted to evaluate channel estimation and positioning performance,
indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the
proposed algorithms
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