WO2021121590A1 - Informations de commande pour attributions de liaison montante conflictuelles - Google Patents

Informations de commande pour attributions de liaison montante conflictuelles Download PDF

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Publication number
WO2021121590A1
WO2021121590A1 PCT/EP2019/086129 EP2019086129W WO2021121590A1 WO 2021121590 A1 WO2021121590 A1 WO 2021121590A1 EP 2019086129 W EP2019086129 W EP 2019086129W WO 2021121590 A1 WO2021121590 A1 WO 2021121590A1
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WIPO (PCT)
Prior art keywords
grant
wireless device
grants
predicted
control information
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PCT/EP2019/086129
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English (en)
Inventor
Abdulrahman ALABBASI
Original Assignee
Telefonaktiebolaget Lm Ericsson (Publ)
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Application filed by Telefonaktiebolaget Lm Ericsson (Publ) filed Critical Telefonaktiebolaget Lm Ericsson (Publ)
Priority to PCT/EP2019/086129 priority Critical patent/WO2021121590A1/fr
Publication of WO2021121590A1 publication Critical patent/WO2021121590A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/02—Selection of wireless resources by user or terminal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/56—Allocation or scheduling criteria for wireless resources based on priority criteria
    • H04W72/566—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient
    • H04W72/569—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient of the traffic information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/02—Terminal devices
    • H04W88/06—Terminal devices adapted for operation in multiple networks or having at least two operational modes, e.g. multi-mode terminals

Definitions

  • This invention relates to processing control information for conflicting uplink grants in a communications system.
  • the Third Generation Partnership Project (3GPP) develops technical standards defining protocols, requirements and radio technologies for wireless communication networks. These standards include the 4 th generation (4G) standard, also known as Long Term Evolution (LTE), and the 5 th generation (5G) standard, also known as New Radio (NR).
  • 4G 4 th generation
  • 5G 5 th generation
  • DG dynamic grant
  • FIG. 1 shows a schematic illustration of the dynamic grant procedure between a wireless device 101 and a network node 103.
  • the wireless device 101 transmits a scheduling request (SR) 105 to the network node 103.
  • the SR can be transmitted on configured SR resources.
  • the SR can be transmitted on a Physical Uplink Control Channel (PUCCH).
  • the network node 103 transmits a scheduling uplink grant to the wireless device 101.
  • the uplink grant is communicated in downlink control information (DCI) 107.
  • the DCI is transmitted on the Physical Downlink Control Channel (PDCCH).
  • the DCI can indicate the time and frequency- domain resources for the uplink data transmission.
  • DCI downlink control information
  • the uplink grant indicates time and frequency resources for the uplink transmission.
  • the wireless device transmits data on the Physical Uplink Shared Channel (PUSCH) 109.
  • the wireless device transmits the data on the PUSCH resources indicated by the uplink grant.
  • PUSCH Physical Uplink Shared Channel
  • NR provides support for a variety of traffic types including enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low-Latency Communications (URLLC) and massive Machine- Type Communications (mMTC).
  • eMBB enhanced Mobile Broadband
  • URLLC Ultra-Reliable and Low-Latency Communications
  • mMTC massive Machine- Type Communications
  • CG configured grants
  • Figure 2 shows a schematic illustration of the configured grant procedure.
  • a PUSCH resource is configured to the wireless device, enabling the wireless device to transit data on the PUSCH resource 201 when traffic data is available, without the need to transmit a scheduling request.
  • a method implemented at a wireless device for processing control information for conflicting uplink, UL, grants comprises performing a computational prediction process to predict a selected UL grant from at least two UL grants determined to overlap in time, and processing control information for the predicted selected grant.
  • the computational prediction process may be performed by implementing one or more machine learning models.
  • the method may comprise performing the computational prediction process to predict the selected UL grant from a set of one or more inputs relating to a communication network over which the wireless device communicates.
  • the set of one or more inputs may comprise one or more of:
  • Performing the computational prediction process may comprise predicting the selected UL grant based on a predicted priority associated with each of the at least two UL grants determined to overlap in time.
  • the computational prediction process may comprise predicting the priority associated with each of the at least two UL grants determined to overlap in time.
  • Performing the computational prediction process may comprise predicting the at least two UL grants are to overlap in time.
  • Performing the computational prediction process may comprise predicting data availability at one or more logical channels, LCHs, and predicting the at least two UL grants are to overlap in time using the predicted data availability at the one or more logical channels.
  • the priority associated with each of the at least two UL grants determined to overlap in time may be predicted using a PHY layer priority for that UL grant and a MAC layer priority for each LCH mapped to that UL grant.
  • the step of performing the computational prediction process can be performed at the media access control, MAC, layer and the step of processing the control information can be performed at the physical layer.
  • the at least two UL grants may comprise a configured grant, CG, and a dynamic grant, DG.
  • the at least two UL grants may comprise a first configured grant and a second configured grant.
  • the method may comprise processing the control information for the selected predicted grant to be multiplexed in an uplink channel of that predicted selected grant.
  • the method may further comprise transmitting the uplink channel carrying the control information in resources indicated by the predicted selected grant.
  • the uplink channel can be a physical uplink shared channel, PUSCH.
  • the control information can be uplink control information, UCI.
  • the method may further comprise processing the control information for the predicted selected grant in advance of a scheduled transmission time for the predicted selected grant.
  • a wireless device configured to resolve conflicting uplink, UL, grants.
  • the wireless device is configured to perform a computational prediction process to predict a selected UL grant from at least two UL grants determined to overlap in time, and process control information for the predicted selected grant.
  • a wireless device comprising a processor and a memory storing instructions which, when executed by the processor, cause the wireless device to perform a method for resolving uplink, UL, grants.
  • the method comprises performing a computational prediction process to predict a selected UL grant from at least two UL grants determined to overlap in time, and processing control information for the predicted selected grant.
  • a non-transitory computer-readable storage medium storing instructions that, when executed by a processor of a wireless device, cause the wireless device to perform a method for resolving uplink, UL, grants.
  • the method comprises performing a computational prediction process to predict a selected UL grant from at least two UL grants determined to overlap in time, and processing control information for the predicted selected grant.
  • Figure 1 illustrates a dynamic grant scheduling procedure
  • Figure 2 illustrates a configured grant scheduling procedure
  • Figure 3 is a schematic illustration of a sequential approach to preparing uplink control information when there are conflicting overlapping grants.
  • Figure 4 is a schematic illustration of a parallel approach to preparing uplink control information when there are conflicting overlapping grants.
  • Figure 5 is a schematic diagram of an exemplary network architecture.
  • Figure 6 is a schematic diagram illustrating components of a host computer, wireless device and a network node.
  • Figure 7 is a flowchart of steps for a method of processing control information according to embodiments of the present disclosure.
  • Figure 8 is a schematic illustration of components for performing a method of processing control information according to embodiments of the present disclosure.
  • Figure 9 is a schematic diagram of a sub-system of Figure 8 implemented as a reinforcement agent.
  • Figure 10 is a flowchart illustrating methods implemented in a communication system including a host computer, a base station and a user equipment.
  • Figure 11 is a further flowchart illustrating methods implemented in a communication system including a host computer, a base station and a user equipment.
  • Figure 12 is a further flowchart illustrating methods implemented in a communication system including a host computer, a base station and a user equipment.
  • Figure 13 is a further flowchart illustrating methods implemented in a communication system including a host computer, a base station and a user equipment.
  • 3GPP work items have identified objectives of enhancing approaches to resolving conflicts between multiple uplink grants through grant selection and prioritization schemes, with the aim of maintaining spectral efficiency whilst maintaining critical and mixed-services quality of service (QoS).
  • QoS quality of service
  • FIG. 3 is a schematic illustration of the processing performed at a wireless device in the case of overlapping uplink grants.
  • overlapping grants refers to two or more grants that have overlapping resources (e.g. that have overlapping time resources, or overlapping time and frequency resources). Such grants may also be referred to as conflicting grants.
  • FIG. 3 illustrates an example use case where the wireless device is allocated a dynamic uplink grant following a scheduling request (not shown) that conflicts with a configured grant for the device.
  • the processing begins with the wireless device processing the downlink control information (DCI) for the dynamic grant (shown at 301).
  • DCI downlink control information
  • the DCI is processed at the physical layer (PHY) of the wireless device.
  • the processed DCI information is then used to resolve the conflicting dynamic and configured grants, for example according to some prioritization or selection scheme (303).
  • the selection of the grant from the conflicting grants is performed at the media access control (MAC) layer of the wireless device.
  • the processed DCI information is passed from the PHY to the MAC layer.
  • the wireless device is configured with a configured grant
  • preparation of the uplink control information (UCI) to be multiplexed in the PUSCH for that configured grant is performed at the PHY layer (305). This processing of the UCI is performed before the grant conflict is resolved at the MAC layer.
  • UCI uplink control information
  • the wireless device implements the prioritization scheme and, in this example use case, prioritizes the dynamic grant and de-prioritizes the configured grant.
  • the grant conflict is resolved by selecting, or prioritizing, the dynamic grant over the configured grant.
  • An indication of the prioritized grant is passed from the MAC layer to the PHY layer. This indication causes the UCI processing for the configured grant to be interrupted (307). Following interruption of the UCI multiplexing preparation for the configured grant, preparation of the uplink control information (UCI) to be multiplexed in the PUSCH for the dynamic grant is performed at the PHY layer at 309.
  • UCI uplink control information
  • the transport block for the dynamic grant is processed at the MAC layer at 311 to form a MAC protocol data unit (PDU).
  • PDU MAC protocol data unit
  • the MAC PDU for the dynamic grant is passed to the PHY layer where it is processed to form a PHY PDU for transmission on the PUSCH resource indicated by the dynamic grant (313).
  • Figure 3 illustrates an example where prioritization between multiple conflicting grants delays the preparation of the UCI to be multiplexed in the allocated PUSCH due to the sequential preparation of UCI for both conflicting grants. This can lead to an increased end-to-end transmission delay, or cause the allocated uplink transmission time to be missed.
  • Figure 4 illustrates an example use case where the wireless device is allocated a dynamic uplink grant following a scheduling request (not shown) that conflicts with a configured grant for the device.
  • the processing begins with the wireless device processing the downlink control information (DCI) for the dynamic grant (shown at 401).
  • DCI downlink control information
  • the processed DCI information is then used to resolve the conflicting dynamic and configured grants, for example according to some prioritization or selection scheme (403).
  • the wireless device begins preparing in parallel the UCI to be multiplexed in the PUSCH for the configured grant and dynamic grant (405 and 407).
  • the wireless device implements the prioritization scheme and, in this example use case, prioritizes the dynamic grant and de-prioritizes the configured grant.
  • the grant conflict is resolved by selecting, or prioritizing, the dynamic grant over the configured grant.
  • An indication of the prioritized grant is passed from the MAC layer to the PHY layer. This indication causes the UCI processing for the configured grant to be interrupted (409).
  • the transport block for the dynamic grant is processed at the MAC layer at 411 to form a MAC protocol data unit (PDU).
  • PDU MAC protocol data unit
  • the MAC PDU for the dynamic grant is passed to the PHY layer where it is processed to form a PHY PDU for transmission on the PUSCH resource indicated by the dynamic grant (413).
  • Embodiments of the present disclosure are directed to techniques for handling control information processing (e.g. preparing the UCI for multiplexing on a PUSCH for an allocated grant) for overlapping grants.
  • a computational prediction process is performed to predict a selected uplink grant from multiple grants determined to conflict. According to some aspects, the computational prediction process anticipates two or more uplink grants will overlap in time, and selects one of the overlapping grants as the predicted grant.
  • the selection may be made based on predicted priorities associated with each of the anticipated overlapping grants.
  • the associated uplink control information for the predicted grant is then processed, for example by preparing the control information to be multiplexed on an uplink channel resource for the predicted grant.
  • the computational prediction process predicts the selected grant from a set of one or more parameters related to the radio environment of the wireless device.
  • the computational prediction process may use one or more machine learning models, statistical prediction algorithms and/or reinforcement learning agents.
  • the corresponding uplink control information for the selected grant can be processed without the additional delay introduced through the sequential UCI processing illustrated in Figure 3, and without the additional computational power of the parallel UCI processing shown in Figure 4.
  • relational terms such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements.
  • the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein.
  • the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.
  • the joining term, “in communication with” and the like may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • electrical or data communication may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • Coupled may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.
  • network node can be any kind of network node forming part of a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi-standard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS)
  • BS base station
  • wireless device herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals.
  • the WD may also be a radio communication device, target device, a user equipment (UE), a device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low- cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (IoT) device, or a Narrowband IoT (NB-IOT) device, etc.
  • UE user equipment
  • D2D device to device
  • M2M machine to machine communication
  • M2M machine to machine communication
  • a sensor equipped with WD Tablet
  • mobile terminals smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • CPE Customer Premises Equipment
  • IoT
  • radio network node can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).
  • RNC evolved Node B
  • MCE Multi-cell/multicast Coordination Entity
  • RRU Remote Radio Unit
  • RRH Remote Radio Head
  • WCDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • a time resource used may correspond to any type of physical resource or radio resource expressed in terms of length of time. Examples of time resources are: symbol, slot, subframe, radio frame, TTI, interleaving time, etc.
  • Implicit indication may for example be based on position and/or resource used for transmission.
  • Explicit indication may for example be based on a parametrization with one or more parameters, and/or one or more index or indices, and/or one or more bit patterns representing the information. It may in particular be considered that control signaling as described herein, based on the utilized resource sequence, implicitly indicates the control signaling type.
  • At least one uplink (UL) connection and/or channel and/or carrier and at least one downlink (DL) connection and/or channel and/or carrier e.g., via and/or defining a cell, which may be provided by a network node, in particular a base station, gNB or eNodeB.
  • An uplink direction may refer to a data transfer direction from a terminal/wireless device to a network node, e.g., base station and/or relay station.
  • a downlink direction may refer to a data transfer direction from a network node, e.g., base station and/or relay node, to a terminal/wireless device.
  • UL and DL may be associated to different frequency resources, e.g., carriers and/or spectral bands.
  • a cell may comprise at least one uplink carrier and at least one downlink carrier, which may have different frequency bands.
  • a network node e.g., a base station, gNB or eNodeB, may be adapted to provide and/or define and/or control one or more cells.
  • configuring may include determining configuration data representing the configuration and providing, e.g. transmitting, it to one or more other nodes (parallel and/or sequentially), which may transmit it further to the radio node (or another node, which may be repeated until it reaches the wireless device).
  • configuring a radio node e.g., by a network node or other device, may include receiving configuration data and/or data pertaining to configuration data, e.g., from another node like a network node, which may be a higher-level node of the network, and/or transmitting received configuration data to the radio node.
  • determining a configuration and transmitting the configuration data to the radio node may be performed by different network nodes or entities, which may be able to communicate via a suitable interface, e.g., an X2 interface in the case of LTE or a corresponding interface for NR.
  • a suitable interface e.g., an X2 interface in the case of LTE or a corresponding interface for NR.
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes.
  • the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • a communication system includes a telecommunication network 510, such as a 3GPP-type cellular network that may support standards such as 4G (LTE) and/or 5G (NR), which comprises an access network 511, such as a radio access network, and a core network 514.
  • the access network 511 comprises a plurality of network nodes 512a, 512b, 512c.
  • the network nodes are in this example base stations, such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 513a, 513b, 513c.
  • Each base station 512a, 512b, 512c is connectable to the core network 514 over a wired or wireless connection 515.
  • a first wireless device 591 located in coverage area 513c is configured to wirelessly connect to, or be paged by, the corresponding base station 512c.
  • a second wireless device 592 in coverage area 513a is wirelessly connectable to the corresponding base station 512a. While a plurality of wireless devices 591, 592 are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole wireless device is in the coverage area or where a sole wireless device is connecting to the corresponding base station 512. In this example, the wireless devices are UEs.
  • the telecommunication network 510 is itself connected to a host computer 530, which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm.
  • the host computer 530 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 521, 522 between the telecommunication network 510 and the host computer 530 may extend directly from the core network 514 to the host computer 530 or may go via an optional intermediate network 520.
  • the intermediate network 520 may be one of, or a combination of more than one of, a public, private or hosted network; the intermediate network 520, if any, may be a backbone network or the Internet; in particular, the intermediate network 520 may comprise two or more sub-networks (not shown).
  • the communication system of Figure 5 as a whole enables connectivity between one of the connected UEs 591, 592 and the host computer 530.
  • the connectivity may be described as an over-the-top (OTT) connection 550.
  • the host computer 530 and the connected UEs 591, 592 are configured to communicate data and/or signaling via the OTT connection 550, using the access network 511, the core network 514, any intermediate network 520 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection 550 may be transparent in the sense that the participating communication devices through which the OTT connection 550 passes are unaware of routing of uplink and downlink communications. For example, a base station 512 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 530 to be forwarded (e.g., handed over) to a connected UE 591. Similarly, the base station 512 need not be aware of the future routing of an outgoing uplink communication originating from the UE 591 towards the host computer 530.
  • a host computer 610 comprises hardware 615 including a communication interface 616 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 600.
  • the host computer 610 further comprises processing circuitry 618, which may have storage and/or processing capabilities.
  • the processing circuitry 618 may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions.
  • the host computer 610 further comprises software 611, which is stored in or accessible by the host computer 610 and executable by the processing circuitry 618.
  • the software 611 includes a host application 612.
  • the host application 612 may be operable to provide a service to a remote user, such as a wireless device 630 connecting via an OTT connection 650 terminating at the wireless device 630 and the host computer 610. In providing the service to the remote user, the host application 612 may provide user data which is transmitted using the OTT connection 650.
  • the communication system 600 further includes a network node (e.g. base station) 620 provided in a telecommunication system and comprising hardware 625 enabling it to communicate with the host computer 610 and with the UE 630.
  • the hardware 625 may include a communication interface 626 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 600, as well as a radio interface 627 for setting up and maintaining at least a wireless connection 670 with a wireless device 630 located in a coverage area (not shown in Figure 6) served by the network node 620.
  • the communication interface 626 may be configured to facilitate a connection 660 to the host computer 610.
  • connection 660 may be direct or it may pass through a core network (not shown in Figure 6) of the telecommunication system and/or through one or more intermediate networks outside the telecommunication system.
  • the hardware 625 of the network node 620 further includes processing circuitry 628, which may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions stored in memory 629.
  • the processing circuitry 628 can execute instructions stored in the memory 629 to cause the network node 620 to perform any of the associated methods disclosed herein.
  • the network node 620 further has software 621 stored internally or accessible via an external connection.
  • the communication system 600 further includes the wireless device (e.g. UE) 630 already referred to.
  • Its hardware 635 may include a radio interface 637 configured to set up and maintain a wireless connection 670 with a base station serving a coverage area in which the wireless device 630 is currently located.
  • the hardware 635 of the wireless device 630 further includes processing circuitry 638, which may comprise one or more programmable processors, application-specific integrated circuits, field programmable gate arrays or combinations of these (not shown) adapted to execute instructions stored in memory 639.
  • the processing circuitry 638 can execute instructions stored in the memory 639 to cause the wireless device 630 to perform any of the associated methods disclosed herein.
  • the wireless device 630 further comprises software 631, which is stored in or accessible by the wireless device 630 and executable by the processing circuitry 638.
  • the software 631 includes a client application 632.
  • the client application 632 may be operable to provide a service to a human or non-human user via the wireless device 630, with the support of the host computer 610.
  • an executing host application 612 may communicate with the executing client application 632 via the OTT connection 650 terminating at the wireless device 630 and the host computer 610.
  • the client application 632 may receive request data from the host application 612 and provide user data in response to the request data.
  • the OTT connection 650 may transfer both the request data and the user data.
  • the client application 632 may interact with the user to generate the user data that it provides.
  • the host computer 610, network node 620 and wireless device 630 illustrated in Figure 6 may be identical to the host computer 530, one of the network nodes 512a, 512b, 512c and one of the wireless devices 591, 592 of Figure 5, respectively.
  • the inner workings of these entities may be as shown in Figure 6 and independently, the surrounding network topology may be that of Figure 5.
  • the OTT connection 650 has been drawn abstractly to illustrate the communication between the host computer 610 and the wireless device 630 via the network node 620, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the wireless device 630 or from the service provider operating the host computer 610, or both. While the OTT connection 650 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 670 between the wireless device 630 and the network node 620 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments can improve the performance of OTT services provided to the wireless device 630 using the OTT connection 650, in which the wireless connection 670 forms the last segment. More precisely, the teachings of these embodiments may improve the latency and power consumption and thereby provide benefits such as reduced user waiting time and extended battery lifetime.
  • the wireless device 630 anticipates an upcoming conflicting uplink grant and predicts which of the conflicting grants to select. Control information for the predicted selected grant is then processed.
  • the wireless device 630 performs a computational prediction process to predict a selected uplink grant from at least two uplink grants determined to conflict.
  • the computational prediction process is performed by implementing one or more machine learning models; statistical prediction models and/or reinforcement learning agents. These models and/or agents can be implemented in software (e.g. by processing circuity 638 executing suitable instructions stored in memory 639) or by dedicated hardware units, or a combination thereof.
  • the conflicting uplink grants may comprise a dynamic grant and a configured grant, or may comprise two or more configured grants.
  • the computational prediction process is performed to select a predicted grant using a set of one or more inputs.
  • the one or more inputs may relate to the radio environment of the wireless device 630; in other words, the one or more inputs may be values for parameters relating to the communication network over which the wireless device communicates, for example communication network 510.
  • Example parameters suitable as inputs to the computational prediction process include:
  • the traffic patterns may relate to historical arrival times for different types of traffic for transmission from the wireless device.
  • the ‘arrival’ of a type of traffic may refer to data of that traffic type becoming available at the wireless device for transmission.
  • Types of traffic include eMBB traffic, URLLC traffic and mMTC traffic.
  • the radio access technology (RAT) of the communication network over which the wireless device 630 communicates (for example, LTE or NR),
  • parameters relating to the MIMO technology may be the number of transmit and/or receive antennas, and indication of whether beamforming is supported and, if so, beamforming parameters.
  • These parameters may be monitored by the wireless device 630 over time (e.g. the traffic patterns and available bandwidth), or in some cases the device may be provided or otherwise configured with the parameter values (e.g. the QoS requirements, the RAT and the MIMO technology).
  • the wireless device might be configured with the parameter values by the network node 620.
  • the set of inputs may comprise input values for a single time resource or for multiple time resources.
  • the set of inputs might comprise input values for a set of time resources.
  • a time resource may refer to, e.g. a subframe or slot.
  • the set of time resources may for some parameters may be a set of past time resources (e.g. for traffic patterns) or future time resources (e.g. for QoS requirements, RAT and MIMO and antenna technology).
  • the wireless device 630 processes the uplink control information (UCI) for the selected predicted grant.
  • This stage may involve preparing the UCI to be multiplexed on the uplink channel resource for the selected grant.
  • the UCI to be multiplexed on the uplink channel resource indicated by the predicted grant selected at step 701 is prepared, or assembled.
  • the uplink channel is a PUSCH.
  • step 701 is implemented at the MAC layer and step 703 at the PHY layer of the device 630.
  • an indication of the selected grant at step 701 is passed from the MAC to the PHY layer for the associated UCI preparation to be performed at the PHY layer.
  • the MAC PDU is assembled at the MAC layer and passed to the PHY layer for preparation of the transmission message.
  • Step 701 of performing the computational prediction process will now be described in more detail with reference to Figure 8.
  • Figure 8 shows an example prediction unit 801 coupled to a processing unit 803.
  • the prediction unit 801 and processing unit 803 form part of the wireless device 630.
  • the prediction unit 801 comprises a number of interconnected sub-systems A to E.
  • the sub-systems may be implemented in software, e.g. as software modules, or as hardware units.
  • prediction unit 801 and processing unit 803 may be implemented in software, e.g. in the form of software modules executable by processing circuitry 638.
  • units 801 and/or 803 can be implemented in hardware, for example as hardware units forming part of processing circuitry 638.
  • one or more of the sub-systems may be implemented in software and one or more of the sub-systems may be implemented in hardware, e.g. as dedicated units.
  • the prediction unit 801 receives the set of one or more inputs described above and denoted at 815. From these inputs, sub-system A 805 predicts the channel state information (CSI). This is step 705. The predicted CSI is input into sub-system B 807 and sub-system C 809.
  • CSI channel state information
  • sub-system B predicts the availability of data at one or more logical channels (LCH).
  • the one or more logical channels may be logical channels between the wireless device 630 and network node 620.
  • the one or more logical channels may be mapped to, or associated with, one or more uplink grants, for example as specified in 3GPP TS 38.321 Section 5.4.
  • the association between the uplink channels and logical channels may vary by implementation. For example, each LCH may be associated with a single uplink grant; one LCH may be associated with multiple uplink grants; or one uplink grant might be associated with multiple LCHs.
  • Sub system B may predict the availability for each type of data (e.g. eMBB data; URLLC data; mMTC data etc.).
  • the sub-system B can predict the availability of data for each data type at the associated LCH.
  • the data availability at the LCHs may be predicted for a set of one or more upcoming time resources, i.e. a set of one or more future time resources.
  • a time resource may refer to, e.g. a subframe or slot.
  • Sub-system B may implement a machine learning model.
  • the machine learning model may be a classification model or a regression model.
  • the machine learning model might be a random forest learning model.
  • the models may implement a computational neural network such as a Feed Forward Neural Network or Recurrent Neural Network, or may implement computational reinforcement learning.
  • the sub-system B might observe the outcome of its data availability predictions and use those observations in future predictions.
  • the learning model implemented by sub-system B may be trained, or learnt, or adapt, from observations on past predictions. This can improve the accuracy of the predictions over time and reduce error rates.
  • Inputs to the machine learning model implemented by sub-system B can include the system inputs 815.
  • the machine learning model may use the system input values for multiple time resources (e.g. multiple time slots).
  • the machine learning model may make a data availability prediction for a current time resource using the system input values for the current time resource and a set of values for a plurality of previous time resources along with data availability predictions made by the sub-system B for
  • the output of sub-system B might indicate whether data availability is predicted or not for each LCH, for each of a set of one or more time resources.
  • the output might for example be a set of binary values each associated with a LCH and a time resource, with one value indicating data is predicted to be available for the associated LCH at the associated time resource, and the other value indicating no data is predicted to be available for the associated LCH at the associated time resource.
  • sub-system B 807 The output of sub-system B 807 is input into sub-system D 811.
  • sub-system D 811 predicts two or more uplink grants will overlap. That is, sub system D predicts the allocation of two or more overlapping grants to the wireless device.
  • the two or more overlapping grants might be configured grants or a combination of configured grant(s) and dynamic grant(s).
  • Sub-system D may predict future grant allocations for a set of one or more time resources. This might be the same set of time resources for which sub-system B predicts data availability for the LCHs.
  • the sub-system D can predict whether two or more grants are scheduled to overlap for each of the set of time resources.
  • the output of sub-system D may therefore indicate whether overlapping grants are predicted for each of the set of time resources.
  • the sub-system D may also predict characteristics of the overlapping grants, for example whether the grants are configured grants or a combination of configured and dynamic grants.
  • Sub-system D may predict two or more grants will overlap using the data availabilities at the LCH predicted by sub-system B.
  • Sub-system D may implement a machine learning model.
  • the machine learning model may be a classification model or a regression model.
  • the machine learning model might be a random forest learning model.
  • the models may implement a computational neural network such as a Feed Forward Neural Network or Recurrent Neural Network.
  • the input to the machine learning models can be the system inputs 815 and the data availabilities for each LCH predicted by sub system B.
  • the machine learning model implemented by sub-system D uses the system input values for multiple time resources (e.g. multiple time slots).
  • the machine learning model may predict the grant characteristics for a current time resource using system input values and predicted data availabilities at the LCHs for the current time resource; and a set of input values for a plurality of previous time resources, predicted data availabilities at the LCHs for those previous time resources and predicted grant characteristics made by the sub-system D for those previous time resources.
  • sub-system D The output of sub-system D is input into sub-system C 809.
  • sub-system C 809 predicts the priority associated with each of the grants predicted to overlap at step 709.
  • Sub-system C may make this prediction using as inputs the system inputs 815 and the predicted data availabilities for the LCHs from sub-system B.
  • the priority prediction may be based on the predicted data availabilities for the LCHs from sub-system B.
  • the priority prediction may also take into account any LCH priorities, for example those specified by the network or by a 3GPP standard. Put another way, the priority prediction at step 711 may depend on the predicted data availability for the LCHs and the priorities of those LCHs. These priorities may be MAC layer priorities for the uplink grants mapped to the LCHs.
  • the priority prediction for the overlapping grants may further take into account PHY layer priorities of the grants.
  • the priority prediction at step 711 predicts the priority associated with each of the overlapping grants based on: i) a PHY layer priority for the overlapping grant; and ii) MAC layer priorities for each of the LCH mapped to the overlapping grant.
  • the predicted priorities of the overlapping grants may further depend on (i.e. take account ol) any logical channel prioritization (LCP) restrictions, for example restrictions specified by the network or in accordance with a 3GPP standard such as 3GPP TS 38.321.
  • LCP logical channel prioritization
  • the priority prediction may be based on the predicted traffic data type associated with each grant and/or the services associated with the grant (e.g. whether the service associated with a grant is predicted to be a critical service).
  • the priorities for the overlapping grants may be relative priorities - that is, priorities made relative to each other.
  • a priority may be predicted for each predicted overlapping grant. For example, there may be a set of priority values, and each predicted overlapping grant can be associated with an index to this set of values.
  • Sub-system C may implement a machine learning model.
  • the machine learning model may be a classification model or a regression model.
  • the machine learning model might be a random forest learning model.
  • the models may implement a computational neural network, such as a Feed Forward Neural Network or Recurrent Neural Network.
  • the input to the machine learning models can be the system inputs 815, the prediction of overlapping grants from sub-system D, and the data availabilities for each LCH predicted by the sub-system B.
  • sub-system C The output of sub-system C is input into sub-system E 813.
  • the sub-system E selects the one of the predicted overlapping grants.
  • Sub-system E can select the predicted grant using the priorities of the overlapping grants predicted by sub system C.
  • the sub-system E further receives as an input the predicted data availabilities for the LCHs from sub-system B.
  • sub-system E may select the overlapping grant with predicted available traffic data having the highest predicted priority. This can prevent an overlapping grant from being selected that has no associated predicted traffic available.
  • sub-system E can provide an indication of the selected grant to the processing unit 803.
  • the prediction unit 801 is implemented at the MAC layer and the processing unit is implemented at the PHY layer.
  • an indication of the selected predicted grant is passed from the MAC layer to the PHY layer.
  • the MAC PDU associated with the selected grant is then assembled and also passed to the PHY for transmission.
  • Step 703 of figure 7 can be performed by processing unit 803.
  • processing unit 803 can receive the indication of the selected predicted grant from the prediction unit 801.
  • the processing unit 803 can prepare the UCI to be multiplexed on the selected grant. That is, the processing unit can prepare the UCI to be multiplexed on the uplink channel resource (e.g. PUSCH resource) of the selected grant.
  • the uplink channel resource e.g. PUSCH resource
  • the processing unit 803 can prepare the correct UCI for that selected grant. For example, the processing unit might prepare UCI having a UCI format associated with the selected grant. On preparing the UCI to be multiplexed on the uplink channel resource for the selected grant and receiving the associated MAC PDU, the processing unit 803 can assemble the message for transmission from the wireless device 630.
  • the embodiments herein use a computational prediction process to predict two or more grants will overlap, or conflict, and to predict which of those conflicting grants will be selected, e.g. at the MAC layer.
  • Control information for the predicted selected grant can then be processed (e.g. at the PHY layer) for multiplexing on an uplink channel for the selected grant.
  • the prediction of the overlapping grants and the selection of one of those overlapping grants can happen ahead of time, e.g. a number of slots ahead of the predicted overlap, in other words, a number of slots ahead of the overlapping grants.
  • Each of the prediction steps performed by sub-systems A to D can be performed a number of slots ‘ahead of time’, in other words in advance of the time of the predicted overlapping slots.
  • the embodiments described herein can conveniently overcome the problems associated with the approaches to preparing UCI in the case of overlapping grants illustrated in Figures 3 and 4.
  • the wireless device 630 can prepare the associated UCI in advance of the transmission time, thus overcoming the processing delay associated with sequentially preparing the UCIs for the overlapping grants shown in Figure 3.
  • the UCI preparation is only performed for a selected one of the predicted overlapping grants, processing power is reduced compared to the process shown in Figure 4.
  • the embodiments herein therefore enable the preparation of UCI information in the context of overlapping uplink grants in a time- and computationally-efficient manner.
  • the wireless device 630 may be arranged to perform a ‘baseline’ grant prioritization scheme in the event of a prediction error.
  • the baseline scheme may be one in which a prioritization scheme is applied to resolve a detected grant conflict (as opposed to predicting a grant conflict).
  • the baseline grant prioritization scheme may be one of the schemes described above with reference to Figures 3 and 4. Configuring the wireless device to perform a grant prioritization scheme in this way can provide a level of robustness to errors in the predictions.
  • Example prediction errors within a practical implementation include: i) mis-predicting the arrival of eMBB traffic for a LCH (e.g. at step 707). In this scenario, eMBB traffic becomes available at the wireless device without being predicted in advance.
  • eMBB traffic is falsely predicted to be available at a LCH.
  • eMBB traffic is falsely predicted to be available at a LCH.
  • mis-predicting the arrival of URLLC traffic is false-positive prediction of URLLC traffic.
  • the wireless device would wait for the arrival of the uplink grant for that eMBB traffic and, upon detecting the grant, perform a grant prioritization process based on the LCH priorities (e.g. in accordance with the schemes illustrated in Figures 3 and 4).
  • the wireless would wait for the arrival of the eMBB traffic data and the associated uplink grant.
  • the wireless device Upon the receipt of the uplink grant for the available eMBB traffic data, the wireless device would proceed to perform a grant prioritization scheme based on LCH priorities.
  • the wireless device reselects the grant corresponding to the received URLLC traffic.
  • UCI for the reselected grant is then prepared for multiplexing on the uplink channel resource for that grant.
  • the step of reselecting the grant for the URLLC traffic may be performed at the MAC layer, with the UCI preparation being performed at the PHY layer.
  • This situation may require the wireless device to interrupt UCI preparation for a different grant (if that grant is for lower-priority traffic data), however the time penalty associated with this would be similar to the conventional approach of waiting for the URLLC traffic data availability before preparing the UCI to be multiplexed on the associated grant (e.g. as shown in Figure 3).
  • an error in the prediction would not cause the processing time for the UCI to extend beyond that of the conventional approach utilizing a grant prioritization scheme.
  • the wireless device may instead proceed to send other available traffic data types (e.g. eMBB traffic) over the grant associated with the URLLC traffic data.
  • eMBB traffic eMBB traffic
  • the wireless device may transmit eMBB traffic data over a robust uplink grant associated with predicted URLCC traffic data. Though this may impact spectral efficiency, there would be no latency loss.
  • Each of the sub-systems A-E illustrated in Figure 8 may implement a machine learning model.
  • the machine learning model may be a classification model or a regression model.
  • the machine learning model might be a random forest learning model.
  • the models may implement a computational neural network, such as a Feed Forward Neural Network or Recurrent Neural Network, such as a long short term memory (LSTM) network.
  • LSTM long short term memory
  • the sub-systems may implement neural networks composed of three layers, with each layer containing approximately 100 neurons, This size of network might enable a prediction inference to be made in the order of microseconds. Of course, networks of different structures and size can be used.
  • each of the sub-systems A-E may be implemented as a reinforcement agent.
  • Figure 9 shows an example of a reinforcement agent 901.
  • the reinforcement agent 901 comprises a reinforcement learning (RL) agent 903 and a radio environment monitor 905.
  • Agent 901 can be implemented in software (with agent 903 and monitor 905 in the form of executable software modules) or hardware.
  • the RL Agent 903 operates to learn a mapping function g that maps inputs to the agent, /to a target output, o, of the subsystem in which the agent 901 is implemented. Mathematically, the RL Agent 903 leams the function: g: f ® o (1)
  • the inputs / can be the parameters input into the subsystem described above with reference to Figure 8.
  • the target output o can be the output of the subsystem described above with reference to Figure 8 (for example, predicted data available at the LCHs, if the agent 901 were implemented in sub-system B).
  • the monitor 905 monitors parameters of the radio environment for the target output o used to predict the optimal grant.
  • the parameters could include, for example, the predicted grant’s reliability; the resultant spectral efficiency for the optimal grant and the latency for processing the associated UCI.
  • unit 905 monitors the values of the parameters resulting from the optimal grant predicted using the target output.
  • the monitored parameter values are used to provide a feedback r to the RL Agent 903 to adapt the mapping function g. Put another way, the monitor 905 uses the monitored parameter values to generate a reward, r, to optimize the mapping function.
  • Step 701 (and its associated sub-steps 705 to 713) can be implemented by a prediction unit analogous to the prediction unit 801. Implementing these steps at both the wireless device and network node may conveniently shorten the training phase of the machine learning models by providing a prediction verification.
  • FIG. 10 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 5 and 6. For simplicity of the present disclosure, only drawing references to Figure 10 will be included in this section.
  • the host computer provides user data.
  • the host computer provides the user data by executing a host application.
  • the host computer initiates a transmission carrying the user data to the UE.
  • the base station transmits to the UE the user data which was carried in the transmission that the host computer initiated, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the UE executes a client application associated with the host application executed by the host computer.
  • FIG 11 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 5 and 6. For simplicity of the present disclosure, only drawing references to Figure 11 will be included in this section.
  • the host computer provides user data.
  • the host computer provides the user data by executing a host application.
  • the host computer initiates a transmission carrying the user data to the UE. The transmission may pass via the base station, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the UE receives the user data carried in the transmission.
  • FIG. 12 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 5 and 6. For simplicity of the present disclosure, only drawing references to Figure 12 will be included in this section.
  • the UE receives input data provided by the host computer.
  • the UE provides user data.
  • the UE provides the user data by executing a client application.
  • the UE executes a client application which provides the user data in reaction to the received input data provided by the host computer.
  • the executed client application may further consider user input received from the user.
  • the UE initiates, in an optional third substep 1230, transmission of the user data to the host computer.
  • the host computer receives the user data transmitted from the UE, in accordance with the teachings of the embodiments described throughout this disclosure.
  • FIG. 13 is a flowchart illustrating a method implemented in a communication system, in accordance with one embodiment.
  • the communication system includes a host computer, a base station and a UE which may be those described with reference to Figures 5 and 6. For simplicity of the present disclosure, only drawing references to Figure 13 will be included in this section.
  • the base station receives user data from the UE.
  • the base station initiates transmission of the received user data to the host computer.
  • the host computer receives the user data carried in the transmission initiated by the base station.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
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Abstract

L'invention concerne un procédé mis en œuvre au niveau d'un dispositif sans fil pour traiter des informations de commande pour des attributions de liaison montante (UL) conflictuelles (en provenance d'un nœud de réseau), le procédé consistant à : mettre en œuvre un modèle de prédiction informatique/effectuer un processus de prédiction informatique pour prédire/préempter une attribution UL sélectionnée parmi au moins deux attributions UL dont il aura été déterminé qu'elles se chevauchent dans le temps ; et traiter des informations de commande pour l'attribution sélectionnée préemptée.
PCT/EP2019/086129 2019-12-18 2019-12-18 Informations de commande pour attributions de liaison montante conflictuelles WO2021121590A1 (fr)

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