Telefonaktiebolaget Lm Ericsson (publ)

Sago Strategic Solutions LLC

Abdulrahman Alabbasi

Zhenhua Zou

Torsten Dudda

Jonas Fröberg Olsson

Yufei W Blankenship

Henrik Enbuske

John Walter Diachina

Jose Luis Pradas

Kittipong Kittichokechai

Mattias Andersson

Alexey Shapin

Robert Baldemair

Paul Schliwa-Bertling

Yi-Pin Eric Wang

Sorour Falahati

Min Wang

Pradeepa Ramachandra

Miguel Lopez

Kumar Balachandran

Joachim Sachs

Andreas Höglund

Osman Nuri Can Yilmaz

Niklas Andgart

Reem Karaki

Sándor Rácz

János Farkas

Ying Sun

Dinand Roeland

György Miklós

Henrik Rydén

Stefano Ruffini

Patrik Salmela

Bernard Smeets

Nianshan Shi

Reza Moosavi

José Araújo

Sara Sandberg

Dennis Sundman

Anna Larmo

Konstantinos Vandikas

Balázs Varga

Magnus Sandgren

Ali El Essaili

Geza Szabo

Harald Gustafsson

Mai-Anh Phan

Kimmo Juhani Hiltunen

Joel Berglund

Per Persson

Vijaya Yajnanarayana

Norbert Reider

András Kern

Joakim Persson

Massimo Condoluci

Alexandros Palaios

Gabor Nemeth

Zsolt Kenesi

Ola Angelsmark

Kun Wang

Johan Lundsjö

Bikramjit Singh

Majid Gerami

Hubertus Andreas Munz

Dhruvin Patel

Hjalmar Olsson

Mårten Wahlström

Heunchul Lee

Gábor Németh

Géza Szabó

Johan Kristofer Olsson

Fredrik Svensson

Johannes Nygren

Osama Al-Saadeh

Ricardo Da Silva Souza

Muhammad Ikram Ashraf

Anders Höglund

Per Skarin

Henrik RydÉn

Torgny Holmberg

Amin Azari

Marina Petrova

Karl Viktor Berggren

Roman Zhohov

Malgorzata Svensson

Fedor Chernogorov

Erik Sanders

Mazen Abdel Latif

Rodrigo Berg

Wolfgang Tonutti

Hossein Shokri Ghadikolaei

Milad Ganjalizadeh

Jonas Olsson Fröberg

Norberg Reider

This document presents one or more advantageous approaches for Reinforcement Learning (RL) powered management of one or more transmission parameters, such as transmit power and diversity, for maximizing the application-layer reliability and availability of a Cyber-Physical System (CPS) with a minimized level of radio/power resource consumption. Example mathematical models are also disclosed and are useful for transforming high-level 'intents' (e.g., KPIs that are applicable to industrial automation and control systems) into low-level orchestration objectives that drive the RL-based control. These objectives are subsequently employed in the definition of an RL-powered “orchestrator,” which may comprise an appropriately configured network node or other computing platform associated with the wireless communication network used to provide inter-device communications for a CPS comprising a population of devices. Further, the disclosure details example communication—e.g., observations and corresponding control signaling—between the orchestrator and the environment being managed.

Methods and apparatus for controlling one or more transmission parameters used by a wireless communication network for a population of devices comprising a cyber-physical system

Methods and nodes in a communications network for using a machine learning model that has been trained using training data from a first radio environment, in a second radio environment, wherein the machine learning model takes a first set of features as input and outputs a label based on a first labelling scheme, the first labelling scheme having been determined for the first radio environment. A method comprises obtaining () a dataset in the second radio environment comprising a plurality of data points comprising values of the first set of features, labelling () each data point in the dataset using the first labelling scheme, and determining () from the labelled dataset whether the first labelling scheme is suitable for use in the second radio environment. Responsive to determining that the first labelling scheme is unsuitable for use in the second radio environment, the method further comprises determining () a second labelling scheme for the machine learning model, using an unsupervised labelling method.

Dynamic labeling for machine learning models for use in dynamic radio environments of a communications network

Techniques for enhancing performance in Industrial Internet-of-Things (IIoT) scenarios, including techniques for time-sensitive networking (TSN) and 5G wireless network integration. An example method, performed by a wireless device, comprises receiving system information (SI) from a radio base station (RBS) of a radio access network (RAN), the SI being indicative of support for TSN through the RBS, and establishing at least one TSN stream with an external data network, through the RBS. The example method further includes receiving a first timing signal from the wireless communications network, via the RBS, receiving a second timing signal from the external TSN data network to which the wireless device is connected, comparing the first timing signal to the second timing signal to determine an offset, and transmitting the offset to the wireless communications network.

Industrial automation with 5G and beyond

According to certain embodiments, a method performed by a wireless device comprises receiving a first grant and a second grant from a network node. The first grant and the second grant are overlapping. The method comprises determining a prioritized one of the first grant and the second grant and constructing a Medium Access Control (MAC) Protocol Description Unit (PDU) for the prioritized grant of the first grant and the second grant. According to certain embodiments, a method performed by a network node comprises transmitting a first grant and a second grant to a wireless device. The first grant and the second grant are overlapping. The method comprises receiving a transmission from the wireless device on a prioritized one of the first grant and the second grant.

Multiple grant handling in mixed services scenarios

Apparatuses and methods are disclosed for pre-emption priority level for Uplink Control Information (UCI) and/or Physical Uplink Shared Channel (PUSCH) conflict resolution. In one embodiment, a method implemented in a network node includes indicating to a wireless device (WD) a priority associated with at least one uplink (UL) channel; and receiving at least one UL signal based at least in part on the indicated priority. In another embodiment, a method implemented in a WD includes determining a priority associated with at least one of at least two uplink (UL) channels; and resolving a conflict between the at least two uplink channels based at least in part on the determined priority.

Pre-emption priority level for uplink control information (UCI) and physical uplink shared channel (PUSCH) conflict resolution

According to certain embodiments, a method performed by a wireless device comprises receiving a first grant and a second grant from a network node. The first grant and the second grant are overlapping. The method comprises pre-empting a transmission for the first grant based on prioritizing a transmission for the second grant. The method comprises controlling a timer used in a hybrid automatic repeat request (HARQ) process associated with the first grant based on pre-empting the transmission for the first grant.

Configured grant timer in intra-user equipment multiplexing

A method performed by a wireless device comprises receiving () a first grant that allocates first radio resources for use by the wireless device and a second grant that allocates second radio resources for use by the wireless device, where the first radio resources partially overlap in time with the second radio resources. The method further comprises splitting () the first grant into a first non-overlapping grant and a first overlapping grant, where the first non-overlapping grant allocates a portion of the first radio resources which do not overlap in time with the second radio resources and the first overlapping grant allocates another portion of the first radio resources which overlap in time with the second radio resources.

Transmission scheduling in a wireless communication network

Embodiments herein disclose e.g. a method performed by user equipment, UE, () for handling uplink, UL, communications in a wireless communication network. The UE () receives from a radio network node (), an indication with an uplink grant, wherein the indication indicates that a radio resource scheduled by the UL grant for UL transmission is for a type of data. The UE () uses the radio resource for transmission of that type of data.

Radio network node, user equipment (UE) and methods performed in a wireless communication network

A method performed by a network node for a wireless telecommunications network performs operations including providing a resource allocation model that corresponds to a base station and that provides a recommendation regarding resource allocation for a user equipment, UE, that is in an operating zone of the base station during a limited resource condition of the base station. Operations may include identifying, based on the resource allocation model, a reduced priority zone in the operating zone of the base station that corresponds to the UE having a high risk of reduced service from the base station relative to other UEs in the operating range of the base station.

Methods for intelligent resource allocation based on throttling of user equipment traffic and related apparatus

A technique for determining segmentation parameters for use in segmenting Radio Link Control, RLC, Protocol Data Units, PDUs, in an RLC preprocessing procedure performed by a User Equipment, UE, prior to receiving an Uplink, UL, grant for transmission of the RLC PDUs is disclosed, wherein the segmentation parameters comprise a size of RLC PDU segments and a number of RLC PDU segments to be included into a physical layer Transport Block, TB. A method implementation of the technique comprises determining (S) a TBS prediction of an expected physical layer Transport Block Size, TBS, determining (S) an observation prediction of at least one of an expected channel quality, an expected traffic volume and an expected available bandwidth observable during transmission of the RLC PDUs, and determining (S) the size of RLC PDU segments and the number of RLC PDU segments based on the TBS prediction and the observation prediction, wherein the size of RLC PDU segments and the number of RLC PDU segments are determined to maximize the size of RLC PDU segments while keeping a total size of the number of RLC PDU segments within the expected TBS.

Technique for determining segmentation parameters for use in segmenting RLC PDUs

A computer-implemented method for acquiring new data samples and for maintaining a set of data samples in a database, wherein the set of data samples are configured to form input to a function associated with a predictive performance, the method including obtaining at least one relevance metric (M), where the relevance metric is indicative of an increase in the predictive performance of the function when using a data sample as input together with the set of data samples compared to when not using the data sample, obtaining a relevance criterion (C), where the relevance criterion identifies relevant data samples in a set of data samples based on the at least one relevance metric, signaling the at least one relevance metric (M) and the relevance criterion (C) to a data collecting network node.

Methods for processing data samples in communication networks

A method, system and apparatus are disclosed. According to one or more embodiments, a wireless device is provided. The wireless device includes processing circuitry configured to determine a first priority of UCI based at least in part on a UCI type where the UCI is preempted by a preempting scheduled uplink shared channel data transmission, and determine whether to include the UCI in the preempting scheduled data transmission based at least in part on the first priority.

Uplink control information pre-emption

A hybrid automatic repeat request (HARQ) process for cells configurable for multiple uplink grants is provided. A new rule to define the HARQ process identifiers (IDs) for configured grant is introduced, in cases when multiple configured grant configurations are used. In some embodiments, a method performed by a wireless device for a HARQ process in a cell configurable for multiple configured uplink grants is provided. The method comprises receiving an uplink grant comprising a HARQ process ID; determining that a HARQ process associated with the HARQ process ID is configured for a configured uplink grant based on a HARQ process ID offset; and starting or restarting a configured grant timer for the HARQ process. Selectively starting the configured grant timer depending on whether an identified HARQ process belongs to one of the multiple configured grant configurations, enables the gNB flexibility to schedule retransmissions or not.

HARQ process for cells configured for multiple configured uplink grants

A method of and a network function arranged for enabling data exchange between a first communication network having first data communication capabilities, such as a 5G network, and a second communication network, such as a TSN, having second data communication capabilities different from the first data communication capabilities. Communication attributes of corresponding first and second data communication capabilities are mapped (). Communication capabilities available in the first communication network are allocated () for use by the second communication network, and the allocated data communication capabilities are advertised () to the second communication network, represented in accordance with the attributes mapping ().

Enabling data exchange between first and second communication networks having different data communication capabilities

Systems and methods for dynamic and flexible configurations for configured grants are provided. In some embodiments, a method performed by a wireless device for configuring uplink grants includes receiving, from a network node, one or more indications to enable one or more configured grant configurations per cell and/or per BWP where at least one configured grant configuration indicating a plurality of transmission occasions within a period. The method also includes transmitting at least one transmission based on the at least one configured grant configuration. This may enable the use of different periodicity of mixed Time Sensitive Network (TSN), random Ultra-Reliable Low-Latency Communication (URLLC), and enhanced Mobile Broadband (eMBB) traffics; reduce the misalignment delay of the arrived data; improve the spectral efficiency of the eMBB traffic while satisfying the URLLC requirement; and/or reduce the number of Hybrid Automatic Repeat Request processes needed to transmit a considerable number of Configured Grant configurations.

Dynamic and flexible configurations for configured grants

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Kista, Sweden

Abdulrahman Alabbasi