A Multi-Objective Learning Approach for Adaptive Waveform Selection in Integrated Sensing and Communications Systems

TL;DR

This paper introduces a multi-objective learning framework for adaptive waveform selection in integrated sensing and communications systems, enabling efficient handling of diverse service demands in 6G networks.

Contribution

It proposes a novel multi-objective learning approach that models waveform performance in a multi-dimensional space and uses machine learning for fast, adaptive selection in heterogeneous environments.

Findings

Effective adaptation to diverse service demands demonstrated

Pareto optimal waveform candidates identified for various scenarios

Machine learning models accurately predict optimal waveforms

Abstract

Integrated Sensing and Communications (ISAC) has emerged as a key enabler for sixth generation (6G) wireless systems by jointly supporting data transmission and environmental awareness within a unified framework. However, communication and sensing functionalities impose inherently conflicting performance requirements, particularly in heterogeneous networks where users may demand sensing only, communication only, or joint services. Selecting a waveform that satisfies diverse service demands therefore becomes a challenging multi objective decision problem. In this paper, a multi objective learning approach for adaptive waveform selection in ISAC systems is proposed. A simulation driven evaluation framework is developed to assess multiple waveform candidates across communication, sensing, and joint performance metrics. Instead of enforcing scalar utility aggregation, waveform performance is represented in a multi dimensional objective space where Pareto optimal candidates are identified for each scenario. A dataset is generated by varying user demand distributions and channel conditions, and multi-label targets are constructed based on Pareto dominance. Machine learning models are trained to learn the mapping between network conditions and Pareto optimal waveform sets, enabling fast waveform selection under dynamic network states. Simulation results demonstrate that the proposed framework effectively adapts waveform selection to heterogeneous service requirements while preserving sensing communication trade offs, providing a forward-looking perspective for 6G and beyond ISAC deployments.