Kama Ramudu on Muck Rack

Kama Ramudu

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MMU-STCNN-BDQ: a deep reinforcement learning framework for secure and energy-efficient beamforming in 6G mMIMO networks

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By Sivasubramanyam Medasani, Tathababu Addepalli, Manish Sharma, Ashish Pandey, Kama Ramudu, Manumula Srinubabu
| Nature Verified
Abstract To achieve energy-efficient and secure beamforming in 6G massive MIMO networks, especially in millimeter-wave (mmWave) bands. This research work introduces a hybrid framework utilizing the Massive Multi-User Spatiotemporal Convolutional Neural Network (MMU-STCNN) and Bi-Gated Deep Q-Learning (BDQ). The MMU-STCNN extracts secure beamforming prediction vectors from channel state information (CSI) using spatiotemporal and pooling layers enhanced by Multi-User Attention (MUA) mechanisms.
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