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

Ahmet Yazar, Yusuf Islam Demir, Ahmed Naeem, Seyit Karatepe

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.

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

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.
Paper Structure (32 sections, 34 equations, 10 figures, 1 table)

This paper contains 32 sections, 34 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. SYSTEM MODEL
  3. Network Topology, User Model, and Coverage Assumptions
  4. Communication and Sensing Channel Assumptions
  5. Scenario Abstraction and Sample Generation Principle
  6. Considered ISAC Waveforms
  7. OFDM
  8. OCDM
  9. OTFS
  10. FMCW
  11. Single-Carrier
  12. Multi-Objective Waveform Evaluation Framework
  13. System Scenario Representation
  14. Performance Metric Definition
  15. Sensing Objective
  16. ...and 17 more sections

Figures (10)

  • Figure 1: An example of the network coverage layout with 30 users with different requirements.
  • Figure 2: Overview of the proposed multi-objective learning framework for adaptive waveform selection in ISAC systems.
  • Figure 3: Distribution of Pareto set sizes across generated scenarios.
  • Figure 4: Pareto inclusion ratios of waveform candidates across generated scenarios.
  • Figure 5: Demand-aware waveform selection maps under low Doppler conditions. Each heatmap shows the regions of the service demand space where the corresponding waveform appears among the top-two predicted candidates.
  • ...and 5 more figures