IRA-DL: Resource Allocation Optimization Using Deep Learning in Massive MIMO Networks
Keywords:
Channel State Information Deep Q-Network Deep Reinforcement Learning Interference MitigationMassive MIMO Resource Allocation.Abstract
Accurate channel state information (CSI) is crucial for enhancing spectral efficiency and ensuring reliable communication in massive multiple-input multiple-output (M-MIMO) networks. However, conventional CSI estimation techniques often face limitations such as high estimation error, excessive feedback overhead, and increased interference, adversely impacting overall network performance. This paper presents IRA-DL, a novel deep learning-based resource allocation framework that employs Deep Q-Network Reinforcement Learning (DQRL) to enhance CSI estimation, reduce feedback and interference, and maximize the system sum-rate. By intelligently learning optimal resource allocation policies, IRA-DL dynamically adjusts CSI feedback mechanisms to improve accuracy and reduce collisions. Experimental evaluations demonstrate that IRA-DL achieves a 27.63% improvement in normalized mean square error (NMSE), a 41.87% gain in sum-rate, a 27.91% reduction in collision occurrences, and a 73.93% decrease in bit error rate (BER) compared to Compressive Sensing and CSI-L2O baselines. These results confirm the effectiveness of IRA-DL in improving the robustness and efficiency of M-MIMO systems.
Downloads
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
