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

TL;DR

This paper introduces a novel online learning method using Contextual Thompson Sampling for adaptive radar transmissions that improve spectrum sharing with cellular networks, achieving faster convergence and better interference management.

Contribution

The paper presents a new TS-based online learning framework for cognitive radar that outperforms existing algorithms in convergence speed and spectrum efficiency.

Findings

Faster convergence to interference-minimizing behavior.

Improved SINR distribution compared to other online algorithms.

Competitive performance with deep reinforcement learning models.

Abstract

This paper describes a sequential, or online, learning scheme for adaptive radar transmissions that facilitate spectrum sharing with a non-cooperative cellular network. First, the interference channel between the radar and a spatially distant cellular network is modeled. Then, a linear Contextual Bandit (CB) learning framework is applied to drive the radar's behavior. The fundamental trade-off between exploration and exploitation is balanced by a proposed Thompson Sampling (TS) algorithm, a pseudo-Bayesian approach which selects waveform parameters based on the posterior probability that a specific waveform is optimal, given discounted channel information as context. It is shown that the contextual TS approach converges more rapidly to behavior that minimizes mutual interference and maximizes spectrum utilization than comparable contextual bandit algorithms. Additionally, we show that the TS learning scheme results in a favorable SINR distribution compared to other online learning algorithms. Finally, the proposed TS algorithm is compared to a deep reinforcement learning model. We show that the TS algorithm maintains competitive performance with a more complex Deep Q-Network (DQN).