Nokia Solutions and Networks Oy

Deepak Kumar Nayak

Suresh Kalyanasundaram

Afef Feki

Veronique Capdevielle

Lorenzo Maggi

Claudiu Mihailescu

Anand Bedekar

Rajeev Agrawal

Nidham Ben Rached

Richa Gupta

Hua Xu

Shalini Gulati

Chandrashekhar Thejaswi

Boris Kouassi

Rakshak Agrawal

Maggi Lorenzo

Beam configuration optimization is disclosed. A network node device may determine a grid of beams. The grid of beams is determined to maximize a fairness function of an estimated received power of at least two of the beams of the grid of beams that are optimal for each client device of a plurality of client devices.

Beam configuration optimization

Beam and antenna array split configuration optimization is disclosed. A network node device generates a beam dictionary defining a set of beams. The network node device estimates a traffic density distribution for a radio channel based on obtained channel quality information. The network node device determines, for each of at least two array split configurations associated with a transceiver antenna array of the network node device, a set of optimal beams from the beam dictionary that optimizes a utility function, based on the estimated traffic density distribution. The network node device selects an array split configuration that maximizes the utility function across a coverage area of a radio cell associated with the network node device. The network node device applies the selected array split configuration to the transceiver antenna array in response to evaluating that the selected array split configuration improves downlink performance.

Beam and antenna array split configuration optimization

An apparatus including: a MIMO antenna for transmitting beamformed signals on a plurality of beams using a common frequency- and time-limited physical channel resource; circuitry for computing a beamforming gain of each beam in each sub-sector in a coverage area of said plurality of beams; circuitry for determining a beam dominance region of each beam within the coverage area of said plurality of beams; circuitry for determining, within the dominance region of each beam, an average of the beamforming gain of each of the other beams at least partially co-locating within said beam dominance region; circuitry for determining inter-beam interference estimations as an average interference of each beam from each of said other beams; and circuitry for scheduling transmissions of the beams by said MIMO antenna on said common frequency- and time-limited physical channel resource based on said inter-beam interference estimations.

Method for determining beam interference

Systems, methods, apparatuses, and computer program products for determining a grid-of-beams (GoB) are provided. One method may include collecting network data for training a neural network, train the neural network, using the collected data, to learn a non-discounted cumulative reward Q that evaluates a benefit of including a given beam into a grid-of-beams (GoB), iteratively applying the trained neural network to select at least one optimal beam to include in the grid-of-beams (GoB), and selecting one or more beams from the grid-of-beams (GoB) to transmit to a user equipment or to receive transmission from the user equipment.

Apparatus and method for designing a grid-of-beams using machine learning

A solution for allocating N radio beam configurations to access nodes including, in some embodiments, a method comprising storing a beam library defining a set of radio beam configurations for a coverage area. A coverage area is divided into sub-areas, and a set of channel quality metrics is collected, as is a traffic density parameter for each sub-area. The following steps are performed: selecting N radio beam configurations from the set of radio beam configurations; mapping each sub-area with one of the selected N radio beam configurations; computing a service performance for the selected N radio beam configurations; the service performance is weighted by the traffic density parameters. The service performance are stored as linked to each radio beam configuration of the selected N radio beam configurations. Based on the stored service performances, a set of N radio beam configurations is selected and the N radio beam configurations are allocated.

Beam selection for cellular access nodes

In some embodiments, a computing system maintains, in a memory, information on one or more terminal devices, a dictionary of beams and information on a spatial grid. The computing system maps each terminal device to the spatial grid based on up-to-date results of radio measurements and calculates, for each spatial element, a load caused by the one or more terminal devices based on the mapping. The computing system evaluates, for each combination of a beam and a terminal device, a reference signal received power, RSRP, based on results of the radio measurements. The computing system calculates a first map of expected spatial distribution of traffic based on the calculated loads and beam-specific second maps of expected RSRP based on the values of the RSRP. Finally, the computing system causes performing beamforming optimization based on the first map and second maps.

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Bengaluru, India

Deepak Kumar Nayak