Nokia Solutions and Networks Oy

Nokia Technologies Oy

Motorola Mobility LLC

Google Inc.

Motorola Corporation

Nokia Siemens Networks Oy

Anand Bedekar

Rajeev Agrawal

Suresh Kalyanasundaram

Venkat Gopikanth

Vishnu Ram Ov

Guang Han

Xiaoyi Wang

Hua Xu

Vinh Van Phan

Ling Yu

Hans Juergen Kroener

Vaibhav Singh

Jun He

Rangsan Leelahakriengkrai

Anna Sillanpaa

Vijay G Subramanian

Samuli Heikki Turtinen

Troels Emil Kolding

Eugene Visotsky

Umamaheswar Achari Kakinada

Amaanat Ali

Paul M Erickson

Bishwarup Mondal

Narayanan Venkitaraman

Afef Feki

Veronique Capdevielle

Steven D Upp

Paul Boudreaux

Andrea Cattoni

Jani Matti Johannes Moilanen

Chandrasekar Sankaran

Omar Salvador

Balamurali N Natarajan

Igor Filipovich

Mikko Tapani Suni

Jamil Shihab

Paul Stephens

Parijat Bhattacharjee

Thomas Gemmer

Stavros Tzavidas

Shivanand Kadadi

Vishnu Ram Omanakutty Amma Vijayaraghavan Nair

Nir Zinger

Prasanna Mudlappa

Claudiu Mihailescu

Ian Dexter Garcia

Qiaobing Xie

Deepak Kumar Nayak

Naveen Arulselvan

Joseph Thaliath

James A Butler

Chandrashekhar Thejaswi

Paula Tjandra

Eric Desorbay

Dalia-Georgiana Popescu

Carlos Manzanares Sancho

Heikki-Stefan Almay

Meir Cohen

Nurit Sprecher

Rajeev Ranjan

Prasanna Km

Joao Gustavo Kluck Gomes

Giuseppe Targia

Raul Pombo

Kevin Dombkowski

Ram O V Vishnu

Joao Gustavo Gomes

Boris Kouassi

Charles Filiatrault

Philippe Picquet

Methods and apparatuses are provided for hardware acceleration for a frequency domain scheduler. An example method includes receiving an instruction from a processor core to process a job for determining an allocation of a set of resources to one or more users, the set of resources corresponding to a transmission layer in a transmission time interval; processing the job by determining which of the one or more users is to be allocated on each of the respective resources in the set based on a first array stored in a memory associated with a metric of each of one or more users for each of the resources in the set, and a second array stored in the memory associated with a buffer size of each of the one or more users; and in response to successfully completing the job, sending an indication of the determined allocation to the processor core.

Hardware acceleration for frequency domain scheduler in wireless networks

To maximize power saving in a radio access network comprising cells, an optimal action amongst actions comprising switching on one or more cells, switching off one or more cells, and doing nothing is determined using a trained model, which maximizes a long term reward on tradeoff between throughput and power, the trained model taking as input a load estimate. The trained model may be updated online using measurement results on load, throughput and power consumption.

Power saving in radio access network

An apparatus comprising means for performing: receiving policy information for one or more transmissions in a radio access network; receiving data from the radio access network; determining an adjustment to one or more properties of the one or more transmissions in the radio access network, based on the received policy information and the data received from the radio access network; communicating an indication of the adjustment to the radio access network; wherein the transmissions comprise internet of things transmissions.

Method, apparatus and medium for adjusting transmission property

Apparatuses and methods in a communication system for identifying transient blockage are presented. Transmission to one or more RAN nodes of parameters for the one or more RAN node is controlled to detect occurrences of sharp channel degradation events in connections served by the node. Data is received from the one or more RAN nodes, the data being related to channel or transmission events related to detected sharp channel degradation in connections served by the node. Based on the received data, likelihood is determined that one or more RAN nodes have a high level of transient blockages.

Identifying transient blockage

An apparatus, method and computer program product of a new functional entity, namely an RRC-Inactive Context Hosting (RICH) entity, receiving, from a radio access node, a Push UE context notification or a Fetch UE Context Request. In response to the receiving of the Push UE context notification, the RICH entity sends the radio access node an ACK or NACK, while in response to the receiving of the Fetch UE Context Request, the RICH entity retrieves information requested. In response to the sending of the ACK, the RICH entity stores information contained in the notification, while in response to the retrieving, the RICH entity sends the radio access node the retrieved information.

Efficient context handling for RRC-inactive in 5G

Various communication systems may benefit from adaptive handling of communication loads. For example, certain wireless communication systems may benefit from a method and application programming interface between a radio access network and a controller to optimize inter-frequency load balancing (IFLB). A method can include receiving policy inputs regarding how the IFLB should be operated. The method can also include negotiating a functional split of IFLB-related functions for the purposes of executing inter-frequency load-balancing. The method can further include receiving a set of data/attributes related to facilitate determination of optimal settings for IFLB.

Radio access network controller methods and systems to optimize inter frequency load balancing

An apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: transmit an indication of a transformation function to a user equipment, wherein at least one input to the transformation function comprises a location of a user equipment and at least one measurement of a radio channel; receive a representation from the user equipment, the representation being a result of a calculation using the transformation function and the at least one input to the transformation function; and determine, using the representation, a channel quality of at least one secondary cell.

Spectral efficiency prediction with artificial intelligence for enhancing carrier aggregation and proactive radio resource management

Systems, methods, apparatuses, and computer program products for multi-user (MU) multiple-input multiple-output (MIMO) user pairing selection are provided. One method may include selecting multi-user multiple input multiple output (MU MIMO) candidate beams using deep neural network(s) (DNNs), and selecting paired users based on the selected beams. The deep neural network(s) (DNNs) are trained to maximize multi-user priority metric (MU-PM) or a heuristic of the multi-user priority metric (MU-PM).

Methods and apparatuses of multi-user multiple-input multiple-output beam selection and user pairing using deep learning

A method includes receiving, from a radio access network (RAN) node within a wireless network, connection event information for one or more user devices and associated channel condition information; and determining a connection behavior status of a user device based on at least a portion of the connection event information and associated channel condition information and a connection behavior status model, wherein the connection behavior status model provides a mapping or association between connection event information and associated channel condition information for one or more user devices and a connection behavior status.

Connection behavior identification for wireless networks

An apparatus including an input configured to receive at least one notification from at least one radio access network node controller, the at least one notification caused to notify the apparatus of at least one physical channel assignment policy indication; an assigner configured to implement at least one physical channel assignment based on the at least one physical channel assignment policy indication; an output configured to generate and transmit at least one assignment notification to at least one user equipment associated with the apparatus, the assignment notification caused to notify the at least one user equipment of the at least one physical channel assignment.

Resource allocation policy signaling

Apparatuses and methods in a communication system are provided. A method comprises receiving from network instructions of how to control multi-user multiple input multiple output connections maintained by one or more radio access nodes, the connections utilising one or more slices. One or more radio access nodes perform, based at least in part on the received network instructions, multi-user pairing of terminal devices of the same or different slices, and determine, based at least in part on the received network instructions, slice-based quota taking multi-user pairing and interference arising from paired allocations into account.

Control of multi-user multiple input multiple output connections

An apparatus includes a beam selection hardware accelerator for determining a set of beams to be used for data scheduling by a wireless node for a transmission time interval, the beam selection hardware accelerator being configured to: receive a beam information and information indicating an angular separation of beams for a set of selected beams; create a graph based on the beam information and the minimum angular separation; select a set of beams, out of a plurality of beams, as a maximum weight independent set of beams based on the created graph; and provide a notification of the selected set of beams.

Beam selection accelerator for wireless node scheduler

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

Growing community of inventors

Arlington Heights, IL, United States of America

Anand Bedekar