DeepAI

# Learning The MMSE Channel Predictor

We present a neural network based predictor which is derived by starting from the linear minimum mean squared error (LMMSE) predictor and by further making two key assumptions. With these assumptions, we first derive a weighted sum of LMMSE predictors which is motivated by the structure of the optimal MMSE predictor. This predictor provides an initialization (weight matrices, biases and activation function) to a feed-forward neural network based predictor. With a properly learned neural network, we show that under the given channel model assumptions it is possible to easily outperform the LMMSE predictor based on the Jakes assumption of the underlying Doppler spectrum.

• 8 publications
• 34 publications
11/28/2019

### Reproducible Evaluation of Neural Network Based Channel Estimators And Predictors Using A Generic Dataset

A low-complexity neural network based approach for channel estimation wa...
07/13/2021

### Geometry and Generalization: Eigenvalues as predictors of where a network will fail to generalize

We study the deformation of the input space by a trained autoencoder via...
02/09/2023

### The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Self-predictive unsupervised learning methods such as BYOL or SimSiam ha...
07/27/2018

### AXNet: ApproXimate computing using an end-to-end trainable neural network

Neural network based approximate computing is a universal architecture p...
08/04/2020

### Out-of-Distribution Generalization with Maximal Invariant Predictor

Out-of-Distribution (OOD) generalization problem is a problem of seeking...
12/20/2018

### Finite-time optimality of Bayesian predictors

The problem of sequential probability forecasting is considered in the m...
02/21/2017

### Best Linear Predictor with Missing Response: Locally Robust Approach

This paper provides asymptotic theory for Inverse Probability Weighing (...