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pro vyhledávání: '"Mourya, Sharan"'
In the realm of 5G communication systems, the accuracy of Channel State Information (CSI) prediction is vital for optimizing performance. This letter introduces a pioneering approach: the Spectral-Temporal Graph Neural Network (STEM GNN), which fuses
Externí odkaz:
http://arxiv.org/abs/2312.02159
Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image pro
Externí odkaz:
http://arxiv.org/abs/2306.00717
Deep learning-based massive MIMO CSI feedback has received a lot of attention in recent years. Now, there exists a plethora of CSI feedback models mostly based on auto-encoders (AE) architecture with an encoder network at the user equipment (UE) and
Externí odkaz:
http://arxiv.org/abs/2211.08173
Autor:
Mourya, Sharan
Quantum computers can outperform classical computers in certain tasks. However, there are still many challenges to the current quantum computers such as decoherence and fault tolerance, and other drawbacks such as portability and accessibility. In th
Externí odkaz:
http://arxiv.org/abs/2211.03212
Autor:
Mourya, Sharan, Dutta, Amit Kumar
Maximum likelihood (ML) detection is an optimal signal detection scheme, which is often difficult to implement due to its high computational complexity, especially in a multiple-input multiple-output (MIMO) scenario. In a system with $N_t$ transmit a
Externí odkaz:
http://arxiv.org/abs/2208.05194
Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback ove
Externí odkaz:
http://arxiv.org/abs/2208.03369
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