No abstract

In this paper, we propose a ResNet based neural architecture to solve the problem of Automatic Modulation Classification. We showed that our architecture outperforms the stateof-the-art (SOTA) architectures. We further propose to binarize the network to deploy it in the Edge network where the devices are resource-constrained i.e. have limited memory and computing power. Instead of simple binarization, rotated binarization is applied to the network which helps to close the significant performance gap between the real and the binarized network. Because of the immense representation capability or the real network, its rotated binarized version achieves 85.33% accuracy compared to 95.76% accuracy of the proposed real network with 2.33 and 16 times lesser computing power than two of the SOTA architectures, MCNet and RMLResNet respectively, and approximately 16 times less memory than both. The performance can be improved further to 87.74% by taking an ensemble of four such rotated binarized networks.

Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology. Beam alignment using brute-force search of the space introduces time overhead while location aided blind beam alignment adds additional hardware requirements to the system. In this paper, we introduce a method for blind alignment based on the RF fingerprints of user equipment obtained by the base stations. The proposed system performs blind beamforming on a multiple base station cellular environment with multiple mobile users using deep reinforcement learning. We present a novel neural network architecture that can handle a mix of both continuous and discrete actions and use policy gradient methods to train the model. Our results show that the proposed model is able to give a considerable improvement in data rates over traditional methods.

No abstract