Adaptive Energy-Efficient Waveform Design For Joint Communication and Sensing using Multiobjective Multiarmed Bandits

We study the transmission power adaptation problem for waveform design in a JCAS system. The challenge arises due to the information shortage about crucial factors such as channel quality and their time-varying nature. First, we formalize two performance metrics of such a system, namely, the probability of detection (PD) and the data information rate (DIR) of fading channels. We then optimize the transmission energy efficiency given constraints on the minimum values for the metrics mentioned above. Under uncertainty about the channel quality and dynamicity in a network, the problem is solvable as a piecewise-stationary multi-objective multi-armed bandit. We develop a sliding window Pareto generic upper confidence bound (UCB)-based algorithm to solve this problem. We establish an upper bound for the expected regret of our proposed algorithm. Numerical experiments validate the effectiveness of the designed waveforms and show that our policy outperforms state-of-the-art methods.