Nokia Technologies Oy

Nokia Solutions and Networks Oy

Janne Matti Juhani Huttunen

Mikko Johannes Honkala

Dani Johannes Korpi

Mikko Aleksi Uusitalo

Jakob Hoydis

Faycal Ait Aoudia

Leo Mikko Johannes Kärkkäinen

Vesa Starck

In some examples, a node for a telecommunication network includes a neural-network-based receiver for uplink communications. The node is configured to modify the neural-network-based receiver to generate a set of modified receiver frameworks defining respective different versions for the receiver, using each of the modified receiver frameworks, generate respective measures representing bits encoded by a signal received at the node, calculate a value representing a variance of the measures, and on the basis of the value, determine whether to select the signal received at the node for use as part of a training set of data for the neural-network-based receiver.

Neural-network-based receivers

According to an example embodiment, a radio receiver includes at least one processor and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the at least one processor, cause the radio receiver to: obtain a data array including a plurality of elements, wherein each element in the plurality of elements in the data array corresponds to a subcarrier in a plurality of subcarriers and to a timeslot in a time interval; obtain a reference signal array representing a reference signal configuration applied during the time interval; implement a neural network; input data into the neural network, wherein the data includes at least the data array and the reference signal array. A radio receiver, a method and a computer program product are disclosed.

Radio receiver

Disclosed is a method comprising providing a first resource grid as input to a first machine learning algorithm, obtaining a second resource grid as output from the first machine learning algorithm, and transmitting a signal comprising the second resource grid by using orthogonal frequency-division multiplexing modulation.

Machine learning for orthogonal frequency-division multiplexing based communication

According to an aspect, a method in the radio head comprises receiving a reference signal from a terminal device, and computing a channel estimate on the basis of the reference signal and transferring the channel estimate with a first precision level to the network node over a link between the radio head and the network node. The first precision level defines a first number of bits carrying the channel estimate, then the method determines to change the precision level and, in response to the determining, transfers the channel estimate or a further channel estimate with a second precision level to the network node. The second precision level defines a second number of bits carrying the channel estimate or the further channel estimate, the second number being different from the first number.

Growing community of inventors

Espoo, Finland

Janne Matti Juhani Huttunen