Cost-Aware Learning and Optimization for Opportunistic Spectrum Access

Chao Gan, Ruida Zhou, Jing Yang, Cong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we investigate cost-aware joint learning and optimization for multi-channel opportunistic spectrum access in a cognitive radio system. We investigate a discrete-time model where the time axis is partitioned into frames. Each frame consists of a sensing phase, followed by a transmission phase. During the sensing phase, the user is able to sense a subset of channels sequentially before it decides to use one of them in the following transmission phase. We assume the channel states alternate between busy and idle according to independent Bernoulli random processes from frame to frame. To capture the inherent uncertainty in channel sensing, we assume the reward of each transmission when the channel is idle is a random variable. We also associate random costs with sensing and transmission actions. Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i.e., reward-minus-cost).We start with an offline setting where the statistics of the channel status, costs and reward are known beforehand. We show that the the optimal policy exhibits a recursive double-threshold structure, and the user needs to compare the channel statistics with those thresholds sequentially in order to decide its actions. With such insights, we then study the online setting, where the statistical information of the channels, costs and reward are unknown a priori. We judiciously balance exploration and exploitation, and show that the cumulative regret scales in O(log T). We also establish a matched lower bound, which implies that our online algorithm is order-optimal. Simulation results corroborate our theoretical analysis.

Original languageEnglish (US)
Title of host publication2020 Information Theory and Applications Workshop, ITA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141909
StatePublished - Feb 2 2020
Event2020 Information Theory and Applications Workshop, ITA 2020 - San Diego, United States
Duration: Feb 2 2020Feb 7 2020

Publication series

Name2020 Information Theory and Applications Workshop, ITA 2020


Conference2020 Information Theory and Applications Workshop, ITA 2020
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems and Management
  • Control and Optimization


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