Resource Management and Beamforming for Joint Communication and Sensing

Abstract

Joint Communication and Sensing (JCAS) represents a key paradigm shift for future wireless systems, enabling efficient use of hardware and spectral resources. However, the integration of these two functions creates a fundamental challenge in resource management, as communication and sensing have conflicting performance objectives. This thesis addresses this challenge by developing a practical, simulation-based framework to optimize resource allocation in a communication-centric, mmWave JCAS system. A comprehensive system-level model is developed, which integrates time-frequency allocation, gain sharing, and beamforming at the Physical Resource Block - Transmission Time Interval level. Using this model, extensive Monte Carlo simulations are performed to characterize the trade-off between communication throughput and sensing accuracy, evaluated using practical metrics such as Root Mean Square Error (RMSE) and throughput. The results reveal that allocating joint resources in the center of the time-frequency grid outperforms the edge-based allocations using practical periodogram estimators. The proposed optimization tool successfully identifies the optimal resource parameters that maximize system performance under specific constraints. Furthermore, the analysis of an interference-nulling receiver provides critical insights into the trade-offs between interference suppression, noise enhancement, and beam robustness. Ultimately, this work provides a valuable design tool and a set of clear strategies for configuring the operational parameters of practical JCAS systems.