US 2025/0141502 A1Published application

Machine learning-based receiver in wireless communication network

AssigneeNokia Solutions and Networks Oy

InventorsMikko Johannes HONKALA, Janne Matti Juhani HUTTUNEN, Dani Johannes KORPI

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Abstract

The present disclosure relates to a machine learning (ML)-based receiver that is invariant to the number of Multiple Input Multiple Output (MIMO) layers it processes. The ML-based receiver is configured, for each MIMO layer, to obtain a set of intermediate estimates by performing an equalization operation based on channel information and an array of symbols received over the MIMO layers, and to obtain a set of final estimates for the array of symbols by using a layer-associated block of a ML model. Each layer-associated block of the ML model is configured to receive the set of intermediate estimates as input data and output the set of final estimates. The layer-associated blocks are configured operate in parallel and exchange weights between each other during a training phase. In some embodiments, the layer-associated blocks may be further configured to exchange the input data between each other during the training and inference phases.

Claims15

Dependent claims

A Machine Learning (ML)-based receiver in a wireless communication network, comprising:

at least one processor;
and
at least one memory storing instructions that, when executed by the at least one processor, cause the ML-based receiver at least to:
receive an array of symbols over a plurality of Multiple Input Multiple Output (MIMO) layers associated with a MIMO channel;
receive a set of reference signals over the plurality of MIMO layers;
based on the set of reference signals and the array of symbols, obtain channel information that is indicative of a state of the MIMO channel;
and
for each MIMO layer of the plurality of MIMO layers:
obtain a set of intermediate estimates for the array of symbols by performing an equalization operation based on the channel information and the array of symbols;
and
obtain a set of final estimates for the array of symbols by using a layer-associated block of a ML model, the layer-associated block having a set of weights and configured to receive the set of intermediate estimates as input data and output the set of final estimates;
wherein the layer-associated blocks of the ML model are further configured to operate in parallel and exchange the sets of weights between each other during a training phase of the ML model.

9 more elements

A method for operating a Machine Learning (ML)-based receiver in a wireless communication network, comprising:

receiving an array of symbols over a plurality of Multiple Input Multiple Output (MIMO) layers associated with a MIMO channel;
receiving a set of reference signals over the plurality of MIMO layers;
based on the set of reference signals and the array of symbols, obtaining channel information that is indicative of a state of the MIMO channel;
and
for each MIMO layer of the plurality of MIMO layers:
obtaining a set of intermediate estimates for the array of symbols by performing an equalization operation based on the channel information and the array of symbols;
and
obtaining a set of final estimates for the array of symbols by using a layer-associated block of a ML model, the layer-associated block having a set of weights and configured to receive the set of intermediate estimates as input data and output the set of final estimates;
wherein the layer-associated blocks of the ML model are further configured to operate in parallel and exchange the sets of weights between each other during a training phase of the ML model.

6 more elements

Dependencies are inferred from the claim text.