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Found 8 papers, 1 papers with code
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular values.
Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models
We consider the problem of reconstructing the signal and the hidden variables from observations coming from a multi-layer network with rotationally invariant weight matrices.
Spectrum of non-Hermitian deep-Hebbian neural networks
Neural networks with recurrent asymmetric couplings are important to understand how episodic memories are encoded in the brain.
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms perform when the noise statistics is unknown and hence Gaussian noise is assumed?
Statistical physics of unsupervised learning with prior knowledge in neural networks
Here, we propose a statistical physics model of unsupervised learning with prior knowledge, revealing that the sensory inputs drive a series of continuous phase transitions related to spontaneous intrinsic-symmetry breaking.
Minimal model of permutation symmetry in unsupervised learning
Remarkably, we find that the embedded correlation between two receptive fields of hidden units reduces the critical data size.
