May 18, 2020 · This paper focuses on the design of beamforming codebooks that maximize the average normalized beamforming gain for any underlying channel distribution.
Abstract—This paper focuses on the design of beamforming codebooks that maximize the average normalized beamforming gain for any underlying channel ...
Simulation results demonstrate the capability of the proposed design criterion in learning the codebooks, reducing the codebook size and producing ...
May 20, 2020 · This paper focuses on the design of beamforming codebooks that maximize the average normalized beamforming gain for any underlying channel distribution.
An autoencoder operates on the hypothesis that the data possesses a representation on a lower dimensional manifold (referred to as feature space), ...
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Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems. K Bhogi, C Saha, HS Dhillon. 2020 54th Asilomar Conference on Signals, Systems ...
May 18, 2020 · Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems ... This paper focuses on the design of beamforming codebooks that ...
Apr 25, 2024 · Learning on a Grassmann Manifold: CSI Quantization for Massive MIMO Systems. ACSSC 2020: 179-186. [i1]. view. electronic edition @ arxiv.org ...
An adaptive quantization algorithm for subspace tracking on the Grassmann-manifold of p-dimensional subspaces in the n-dimensional Euclidean space is proposed.
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