Learning Aided Auctioning based Spectrum Access System in a Wireless Optical Network

TL;DR

This paper presents a learning-based auctioning spectrum access system for wireless optical networks that optimizes bandwidth allocation and payments using VCG auctions and learning automata, enhancing QoS for nomadic users.

Contribution

It introduces a novel learning-aided auctioning approach for spectrum access in integrated wireless optical networks, combining VCG auctions with learning automata for dynamic bandwidth management.

Findings

Effective opportunistic bandwidth allocation for nomadic users.

Users can decide their payments to optimize bandwidth access.

Simulation results validate the proposed system's efficiency.

Abstract

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution (LTE) based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.