Overview
This research focuses on optimizing the sum-rate in an Integrated Sensing and Communication (ISAC) system. The system incorporates recently developed pinching antennas (PAs) and movable antennas (MAs). A key objective is to satisfy minimal sensing requirements while maximizing the sum-rate. A deep learning (DL) network is proposed to determine optimal values for several system variables.
Research Context
Integrated sensing and communication (ISAC) is identified as a significant technology for future wireless networks. Its perceived benefits include providing accurate environment awareness and enhancing energy, spectral, and hardware efficiency. The introduction of pinching antennas (PAs) and movable antennas (MAs) is considered to potentially further improve the performance gains achievable with ISAC systems.
Approach
The study's goal was to optimize the sum-rate for an ISAC system equipped with PAs and MAs, specifically aiming to satisfy minimal sensing requirements. The approach involved two main components:
- Derivation of a closed-form solution for the optimal sensing receive combiner, which was determined to depend on other optimization variables.
- Development of a deep learning (DL) network to find optimal values for the remaining variables. These variables include the positions of the transmit PAs, the positions of the users' MAs, the communication precoding matrices, and the sensing transmit beamformer.
The DL network was trained in an unsupervised manner. Its loss function was formulated to include the objective function alongside penalty terms related to the constraints for the PAs and MAs positions.
Findings
- A closed-form solution was derived for the optimal sensing receive combiner, which is determined by other optimization variables.
- Simulation results indicated that the use of PAs and MAs in ISAC systems yielded a larger sum-rate when compared to ISAC systems that only employ fixed antennas.
- The performance advantage gained by using PAs and MAs increased with the maximum transmit power.
- It was observed that the communication performance of the considered system is slightly more affected by the sensing signal-to-interference-plus-noise ratio (SINR) threshold than the sensing performance.
Why This Matters
The integration of pinching and movable antennas into ISAC systems, combined with deep learning optimization, offers a pathway to potentially enhance the overall efficiency and performance of future wireless networks. By increasing the sum-rate and improving spectral, energy, and hardware efficiency, these advancements could contribute to more robust and capable communication and sensing functionalities.