Method and Apparatus for Beamforming with Neural Network

A computer-implemented method for training a beamforming module for a multi-antenna arrangement, the beamforming module comprising a neural network adapted to determine beamforming weights W for precoding for the multi-antenna arrangement, wherein the multi-antenna arrangement is adapted to establish N communication links with M user equipments (UEs), the method comprising:

• collecting a set of training data points, ϑ, each of the training data points comprising a channel state information (CSI) for one of the N established communication links between the multi-antenna arrangement and a UE, the set of training data points comprising a CSI for each of the N established communication links,
• training the neural network of the beamforming module on the training data ϑ, to determine a first set of beamforming weights W1,
• calculating a radiation metric fR and a beamforming metric fBF as functions of the first set of beamforming weights, where fR is a function evaluating levels of electromagnetic radiation emissions of the multi-antenna arrangement in different spatial directions, and fBF is a function evaluating a communication link quality of each established communication link, for all the N established communication links,
• applying a first loss function to the calculated radiation metric to obtain a first loss associated to the radiation metric, and applying a second loss function to the calculated beamforming metric to obtain a second loss associated to the beamforming metric,
• calculating a total loss by applying a total loss function to the calculated first loss associated to the radiation metric and the calculated second loss associated to the beamforming metric, where the total loss function is such that finding a global optimal value of the total loss function minimizes the radiation metric and maximizes the beamforming metric,
• using backpropagation to compute gradients of the total loss function, and using the computed gradients to determine a second set of improved beamforming weights W2;
• and
• updating the first set of beamforming weights W1 of the beamforming module with the second set of improved beamforming weights W2.

An apparatus comprising a multi-antenna arrangement, the multi-antenna arrangement adapted to establish N communication links with M user equipments (UEs), and a beamforming module, the beamforming module comprising a memory and a processor configured to:

• collect a set of training data, ϑ, each of the training data points comprising a channel state information (CSI) for one of the N established communication links between the multi-antenna arrangement and a UE the set of training data points comprising a CSI for each of the N established communication links,
• train the beamforming module on the training data ϑ, to obtain a trained beamforming module, wherein the beamforming module comprises a neural network adapted to determine a first set of beamforming weights W for the multi-antenna arrangement,
• calculate a radiation metric fR and a beamforming metric fBF as functions of the first set of beamforming weights, where fR is a function evaluating levels of electromagnetic radiation emissions of the multi-antenna arrangement in different spatial directions, and fBF is a function evaluating a communication link quality of each established communication link, for all the N established communication links,
• apply a first loss function to the calculated radiation metric to obtain a first loss associated to the radiation metric and apply a second loss function to the calculated beamforming metric to obtain a second loss associated to the beamforming metric,
• calculate a total loss by applying a total loss function to the calculated first loss associated to the radiation metric and the calculated second loss associated to the beamforming metric, where the total loss is such that finding a global optimal value of the total loss function minimizes the radiation metric and maximizes the beamforming metric,
• use backpropagation to compute gradients of the total loss function, and use the computed gradients to predict a second set of improved beamforming weights and update the first set of beamforming weights of the beamforming module with the second set of improved beamforming weights.