English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper describes a sequential, or online, learning scheme for adaptiveradar transmissions that facilitate spectrum sharing with a non-cooperativecellular network. First, the interference channel between the radar and aspatially distant cellular network is modeled. Then, a linear Contextual Bandit(CB) learning framework is applied to drive the radar's behavior. Thefundamental trade-off between exploration and exploitation is balanced by aproposed Thompson Sampling (TS) algorithm, a pseudo-Bayesian approach whichselects waveform parameters based on the posterior probability that a specificwaveform is optimal, given discounted channel information as context. It isshown that the contextual TS approach converges more rapidly to behavior thatminimizes mutual interference and maximizes spectrum utilization thancomparable contextual bandit algorithms. Additionally, we show that the TSlearning scheme results in a favorable SINR distribution compared to otheronline learning algorithms. Finally, the proposed TS algorithm is compared to adeep reinforcement learning model. We show that the TS algorithm maintainscompetitive performance with a more complex Deep Q-Network (DQN).

Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling