Abstract

The next generation of wireless network communication requires high data rates and low latency, posing significant challenges in resource allocation. In next-generation networks, resource allocation remains a major issue, with recent approaches focusing on both dynamic and static allocation strategies. The proposed approach utilizes deep learning models, particularly Long Short-Term Memory (LSTM) networks, to optimize power and spectrum allocation in real-time. By leveraging deep learning's ability to handle complex, high-dimensional data, the algorithm adapts to varying channel conditions and user requirements while minimizing energy consumption. A key feature of the proposed model is its capability to dynamically allocate resources based on Channel State Information (CSI) and Quality of Service (QoS) constraints, ensuring the efficient utilization of available bandwidth.

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