Deep-Reinforcement-Learning-Empowered-OFDM-Subcarrier-Allocation-in-ISAC

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Deep-Reinforcement-Learning-Empowered-OFDM-Subcarrier-Allocation-in-ISAC

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# Deep Reinforcement Learning Empowered OFDM Subcarrier Allocation in Integrated Sensing and Communication Networks
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**This is a project done during my last year as an undergraduate in HUST. It is a naive attempt on OFDM subcarrier allocation in ISAC 6G**
## Abstract
With the development of B5G/6G networks, integrated sensing and communication (ISAC) has been considered as a key enabler for future applications such as smart vehicle networks, smart cities and smart homes. However, the high demand for faster speeds, lower latency, and greater connection capacity in the next-generation wireless networks makes the coexistence of communication and sensing should be carefully designed. Resource scheduling has been envisioned as an important way for communication and sensing to coexist in ISAC networks. Many previous works focus on subcarrier allocation problems in OFDM-based ISAC. However, did not consider when OFDM subcarrier is insufficient, which means sensing and communication have to be reused on some subcarriers. We mainly focus on the scenario that subcarrier sharing occurs between sensing and communication, and then propose a reinforcement learning empowered subcarrier allocation algorithm to maximize the system energy efficiency.

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