EP-4736335-A1 - SIGNAL DETECTION FOR MIMO

Abstract

A method comprises obtaining data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted over a plurality of layers; generating, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and generating, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers.

Inventors

• QI, Wen Liang
• YE, Chen Hui
• KORPI, Dani Johannes

Assignees

• Nokia Solutions and Networks Oy

Dates

Publication Date

20260506

Application Date

20230627

Claims (19)

1. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: obtain data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted over a plurality of layers; generate, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and generate, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers.
2. The apparatus of claim 1, wherein the signal is transmitted in a Discrete Fourier Transform-Spread-Orthogonal Frequency Division Multiplexing waveform and the second sub-model comprises at least one module for Inverse Discrete Fourier Transform.
3. The apparatus of claim 1, wherein the second sub-model comprises a plurality of branches and a branch of the plurality of branches corresponds to a layer of the plurality of layers, and the instructions, when executed by the at least one processor, cause the apparatus to: feed the shared feature representations into each branch of the plurality of branches to generate the prediction on bits for the corresponding layer.
4. The apparatus of claim 3, wherein the branch of the plurality of branches comprises: a first neural network used to extract, from the shared feature representations, feature representations for the layer corresponding to the branch; a second neural network used to perform joint channel estimation and equalization specific to the layer corresponding to the branch; and a third neural network used to perform demodulation specific to the layer corresponding to the branch.
5. The apparatus of claim 4, wherein the data is part of a training sample for the machine learning model and the training sample further comprises respective transmitted bit sequences for the plurality of layers of the signal, and the instructions, when executed by the at least one processor, further cause the apparatus to: for a layer of the plurality of layers, determine a loss based on a difference between the prediction on bits for the layer and the transmitted bit sequence for the layer; update one or more neural networks of the corresponding branch based on the loss; and update the first sub-model based on a sum of respective losses determined for the plurality of layers.
6. The apparatus of claim 1, wherein the instructions, when executed by the at least one processor, cause the apparatus to: divide the shared feature representations into a plurality of groups of feature representations, a group of the plurality of groups corresponding to a layer of the plurality of layers; and feed each group of feature representations into the second sub-model to generate the prediction on bits for the corresponding layer.
7. The apparatus of claim 6, wherein the second sub-model comprises: a fourth neural network used to perform joint channel estimation and equalization common to the plurality of layers; and a fifth neural network used to perform demodulation common to the plurality of layers.
8. The apparatus of claim 6, wherein the data is part of a training sample for the machine learning model and the training sample further comprises respective transmitted bit sequences for the plurality of layers of the signal, and the instructions, when executed by the at least one processor, further cause the apparatus to: for a layer of the plurality of layers, determine a loss based on a difference between the prediction on bits for the layer and the transmitted bit sequence for the layer; update one or more neural networks of the second sub-model based on respective losses determined for the plurality of layers; and update the first sub-model based on a sum of the respective losses.
9. The apparatus of claim 1, wherein the instructions, when executed by the at least one processor, cause the apparatus to: feed a concatenation of the data and the channel estimation into a neural network of the first sub-model to generate the shared feature representations.
10. The apparatus of claim 1, wherein the instructions, when executed by the at least one processor, cause the apparatus to: feed to the data into a first neural network of the first sub-model to generate first feature representations for the data; feed the channel estimation into a second neural network of the first sub-model to generate second feature representations for the channel estimation; and transform the first and second feature representations to the shared feature representation.
11. The apparatus of claim 1, wherein the apparatus comprises a training entity for the machine learning model, and the data and the channel estimation are obtained from the first device for training the machine learning model.
12. The apparatus of claim 11, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: transmit the trained machine learning model to the first device.
13. The apparatus of claim 1, wherein the apparatus comprises the first device, and the instructions, when executed by the at least one processor, further cause the apparatus to: select the machine learning model from a plurality of candidate machine learning models based on a configuration of the signal, wherein the configuration at least comprises: a rank for determining the number of the plurality of layers, and a modulation scheme for determining the number of the bits for a layer of the plurality of layers.
14. The apparatus of claim 13, wherein the configuration further comprises at least one of: the number of subcarriers for transmitting the signal, or whether transform-precoding is enabled for the signal.
15. The apparatus of claim 13, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: replace the machine learning model by a non-machine learning approach to decode a further signal.
16. The apparatus of claim 15, wherein the instructions, when executed by the at least one processor, further cause the apparatus to: in response to the replacement of the machine learning model by the non-machine learning approach, update the machine learning model.
17. A method comprising: obtaining data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted over a plurality of layers; generating, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and generating, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers.
18. An apparatus comprising: means for obtaining data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted over a plurality of layers; means for generating, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and means for generating, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers.
19. A computer readable medium comprising instructions stored thereon for causing an apparatus at least to perform the method of claim 17.

Description

SIGNAL DETECTION FOR MIMO FIELDS Various example embodiments of the present disclosure generally relate to the field of telecommunication and in particular, to methods, devices, apparatuses and computer readable storage medium for signal detection for Multiple-Input-Multiple-Output (MIMO) . BACKGROUND MIMO refers to the type of wireless transmission and reception scheme where both a transmitter and a receiver employ more than one antenna. MIMO allows for spatial diversity to transmit data by use of a plurality of antennas in both uplink (UL) and downlink (DL) directions. The obtained spatial diversity offers a more efficient utilization of the frequency spectrum. Moreover, MIMO can reduce the inter-cell and intra-cell interferences which in turn, leads to more frequency re-use. Therefore, MIMO scheme with very high spectrum efficiency is an important technology of wireless communication systems. A receiver based on machine learning, which is also referred to as a machine learning receiver, leverages the customized artificial intelligence (AI) /machine learning (ML) techniques to further boost the data transmission from physical layer perspective and is playing the key role in the forthcoming communication. Currently, the System on Chip (SoC) platform compatible with the machine learning receiver solution is under intensive development. A machine learning receiver for MIMO system has been proposed. SUMMARY In a first aspect of the present disclosure, there is provided an apparatus. The apparatus comprises at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to: obtain data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted  over a plurality of layers; generate, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and generate, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers. In a second aspect of the present disclosure, there is provided a method. The method comprises: obtaining data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted over a plurality of layers; generating, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and generating, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers. In a third aspect of the present disclosure, there is provided an apparatus. The apparatus comprises means for obtaining data representing a signal received at a first device transmitted by a second device and channel estimation between the first and second devices, wherein the signal is transmitted over a plurality of layers; means for generating, by a first sub-model of a machine learning model, shared feature representations for the plurality of layers based on the data and the channel estimation; and means for generating, by a second sub-model of the machine learning model based on the shared feature representations, respective predictions on bits for the plurality of layers. In a fourth aspect of the present disclosure, there is provided a computer readable medium. The computer readable medium comprises instructions stored thereon for causing an apparatus to perform at least the method according to the second aspect. It is to be understood that the Summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description. BRIEF DESCRIPTION OF THE DRAWINGS Some example embodiments will now be described with reference to the accompanying drawings, where: FIG. 1 illustrates an example communication environment in which example  embodiments of the present disclosure can be implemented; FIG. 2 illustrates an example of an architecture of a machine learning model for signal detection in MIMO system according to some example embodiments of the present disclosure; FIG. 3 illustrates an example signal processing workflow by using the machine learning model according to some example embodiments of the present disclosure; FIG. 4A illustrates an example structure for generating the shared feature representations according to some example embodiments of the present disclosure; FIG. 4B illustrates another example structure for generating the share