Non-collocated scell selection for carrier aggregation

Abstract

Methods and apparatuses are disclosed for non-collocated SCell selection for NR CA. In one embodiment, a network node is configured to estimate a first throughput of a WD at a first candidate secondary cell, the estimated first throughput based on a measured inter-network node delay between the network node and a first network node supporting the first candidate secondary cell, the network node supporting a primary cell, and determine whether to select the first candidate secondary cell for the WD based on the estimated first throughput. In another embodiment, a network node is configured to determine an average amount of time resources associated with a hybrid automatic repeat request (HARQ) process experienced by a WD (22) connected to the first candidate secondary cell and a primary cell having an inter-network node delay d, and report the average amount.

Description

Technical Field

[0001] This disclosure relates to wireless communications, and particularly to the selection of non-co-located auxiliary cells (SCells) for 3GPP carrier aggregation (CA) in wireless communication networks such as New Radio (NR) networks. Background Technology

[0002] 3GPP NR (also known as 5G) carrier aggregation (CA) allows users to simultaneously receive data from multiple frequency blocks (from multiple cells), resulting in increased throughput for radio devices (WDs, also known as user equipment or UEs). The radio network selects auxiliary cells (SCells) for WD, considering factors such as which carriers are available in the network and what capabilities the WD supports (in what combinations can it aggregate which carriers, etc.). The carrier selection problem has been extensively studied in the literature, at least for 3GPP Long Term Evolution (LTE), with examples of algorithms considering channel quality, load, etc., to find the optimal carrier for WD selection.

[0003] Carrier aggregation is applicable to both LTE and NR. Due to the diversity of channel characteristics in the NR frequency band, cells can be deployed in different locations optimized for their respective non-CA uses. An example of a non-co-located gNB hosting different cells is... Figure 1 As shown in the diagram. This type of deployment can lead to the creation of gNB-to-gNB links with different characteristics, which may affect the CA gain from adding such SCells. For example, as Figure 1 As shown, SCell_1 and SCell_2 are both candidates for WDs that are initially connected to a special cell (SpCell), which is a primary cell in a primary cell group (MCG) or secondary cell group (SCG).

[0004] The increased latency of the gNB inter-links (Link_1 and Link_2 between SpCell and each SCell) can have the following impacts on CA performance:

[0005] 1) Delayed user data transmission from SpCell to SCell via Link_i; and

[0006] 2) In CA, Hybrid Automatic Repeat Request (HARQ) feedback is transmitted from WD to SpCell and then forwarded to SCell via the gNB inter-link. Delayed HARQ feedback (with long gNB inter-link delays) can eventually cause HARQ process exhaustion and outdated link adaptation information. HARQ process exhaustion occurs when all HARQ procedures for that particular WD (e.g., UE) at the gNB have been used and the gNB is waiting for WD HARQ feedback in order to restart reusing the HARQ procedure (and / or HARQ procedure identifier (ID)) to transmit new WD data.

[0007] As mentioned above, latency depends on many factors, such as the distance between gNBs and the quality of the backhaul (e.g., routers and hubs on this link).

[0008] The existing configuration used for selecting SCell does not take into account how long inter-node latency affects throughput. Summary of the Invention

[0009] Some embodiments advantageously provide methods, systems, and apparatus for non-co-location assisted cell (SCell) selection for 3GPP New Radio (NR, also known as fifth-generation or 5G) carrier aggregation (CA).

[0010] In one embodiment, a network node is configured to: estimate the throughput of the WD at a candidate secondary cell, the estimated throughput being based at least in part on an inter-node delay measured between the network node and another network node, the network node supporting (and / or hosting) a special cell, and the other network node supporting (and / or hosting) a candidate secondary cell; and determine whether to select a candidate secondary cell for the WD based at least in part on the estimated throughput. It should be noted that this disclosure relates to "inter-node" delay. An example of "inter-node" delay within the context of 3GPP could be "gNB" delay. Therefore, the discussion of "inter-node" delay herein should be understood to include "gNB" delay within the context of 3GPP NR implementations.

[0011] In another embodiment, a network node is configured to: determine a duration associated with the exhaustion of a Hybrid Automatic Repeat Request (HARQ) process during inter-network node delays; and report the duration to another network node that supports (and / or hosts) a special cell and a candidate auxiliary cell.

[0012] According to one aspect, a method is provided in a network node configured to communicate with a wireless device (WD). The method includes: estimating a first throughput of the WD at a first candidate secondary cell, the estimated first throughput being based at least in part on an inter-network node delay measured between the network node and a first network node supporting the first candidate secondary cell, the network node supporting the primary cell; and determining whether to select a first candidate secondary cell for the WD based at least in part on the estimated first throughput.

[0013] In some embodiments, the method further includes determining the amount of Hybrid Automatic Repeat Request (HARQ) procedure reused associated with the first candidate secondary cell of the WD, the estimated throughput further being based on the determined amount of HARQ procedure reuse. In some embodiments, the method further includes estimating the throughput reduction associated with HARQ procedure exhaustion at the first candidate secondary cell of the WD, the estimated first throughput further being based on the estimated throughput reduction. In some embodiments, estimating the first throughput of the WD further includes using the same bandwidth, the same radio conditions, and the same load for the first candidate secondary cell and the second candidate secondary cell on which the second throughput of the WD is estimated.

[0014] In some embodiments, the estimated throughput reduction associated with the exhaustion of HARQ procedures at the first candidate auxiliary cell is based on a configuration received from a first network node, the information indicating the maximum number of HARQ procedures associated with the first candidate auxiliary cell of the WD; and the estimation of the first throughput of the WD further includes using different sets of parameter numerologies for the first candidate auxiliary cell and for the second candidate auxiliary cell on which the second throughput of the WD is estimated.

[0015] In some embodiments, the method further includes receiving feedback from a first network node supporting a first candidate secondary cell, the feedback indicating an average amount of time resources associated with the exhaustion of a Hybrid Automatic Repeat Request (HARQ) process experienced by a WD connected to a first candidate secondary cell and a primary cell having an inter-network node delay d, and determining whether to select a first candidate secondary cell for the WD also based on the received feedback.

[0016] In some embodiments, the method further includes: using a machine learning model, the machine learning model including feedback from network nodes supporting candidate auxiliary cells as input; and configuring, deconfiguring and activating multiple candidate auxiliary cells with different inter-network node delays according to the machine learning model, and determining whether to select a first candidate auxiliary cell for WD also based on the output of the machine learning model.

[0017] In some embodiments, the method further includes: estimating multiple throughputs of the WD at a plurality of candidate secondary cells, the plurality of estimated throughputs being at least partially based on inter-node latency measured between the network node and a plurality of network nodes supporting the plurality of candidate secondary cells; selecting at least one of the plurality of candidate secondary cells for the WD based on comparisons between the plurality of estimated throughputs; and configuring the at least one selected of the plurality of candidate secondary cells for the WD. In some embodiments, the method further includes using a predicted throughput Tp based on inter-node latency as input to select at least one of the plurality of candidate secondary cells. In some embodiments, the method further includes using a machine learning model to select at least one of the plurality of candidate secondary cells for the WD, the machine learning model including at least one of the following as input: measured inter-node latency; the amount of automatic repeat request (HARQ) procedure reused; the estimated throughput reduction associated with HARQ procedure exhaustion; and feedback indicating the amount of average time resources associated with HARQ procedure exhaustion for inter-node latency d.

[0018] According to another aspect, a network node configured to communicate with a wireless device WD is provided. The network node includes a processing circuit module. The processing circuit module is configured to: estimate a first throughput of the WD at a first candidate auxiliary cell, the estimated first throughput being at least partially based on a network node inter-node delay measured between the network node and a first network node supporting the first candidate auxiliary cell, the first network node supporting a primary cell; and determine whether to select a first candidate auxiliary cell for the WD based at least partially on the estimated first throughput.

[0019] In some embodiments, the processing circuitry module is further configured to determine the amount of Hybrid Automatic Repeat Request (HARQ) procedure reused associated with the first candidate secondary cell of the WD, and the estimated throughput is also based on the determined amount of HARQ procedure reuse. In some embodiments, the processing circuitry module is further configured to estimate the throughput reduction associated with HARQ procedure exhaustion at the first candidate secondary cell of the WD, and the estimated first throughput is also based on the estimated throughput reduction. In some embodiments, the processing circuitry module is configured to estimate the first throughput of the WD by performing the following operations: using the same bandwidth, the same radio conditions, and the same load for the first candidate secondary cell and the second candidate secondary cell on which the second throughput of the WD is estimated.

[0020] In some embodiments, the estimated throughput reduction associated with the exhaustion of HARQ procedures at the first candidate auxiliary cell of the WD is based on a configuration received from the first network node, the information indicating the maximum number of HARQ procedures associated with the first candidate auxiliary cell of the WD; and the processing circuit module is configured to estimate the first throughput of the WD by employing different sets of parameters for the first candidate auxiliary cell and for the second candidate auxiliary cell on which the second throughput of the WD is estimated.

[0021] In some embodiments, the processing circuit module is further configured to receive feedback from a first network node supporting a first candidate auxiliary cell, the feedback indicating the average amount of time resources associated with the HARQ process exhaustion experienced by a WD connected to a first candidate auxiliary cell and a primary cell having an inter-network node delay d, and the determination of whether to select a first candidate auxiliary cell for the WD is also based on the received feedback.

[0022] In some embodiments, the processing circuit module is further configured to: use a machine learning model, the machine learning model including feedback from network nodes supporting candidate secondary cells as input; and configure, deconfigure, and activate multiple candidate secondary cells with different inter-network node delays according to the machine learning model, and the determination of whether to select a first candidate secondary cell for the WD is also based on the output of the machine learning model. In some embodiments, the processing circuit module is further configured to: estimate multiple throughputs of the WD at multiple candidate secondary cells, the multiple estimated throughputs being at least partially based on inter-network node delays measured between the network node and multiple network nodes supporting the multiple candidate secondary cells; select at least one of the multiple candidate secondary cells for the WD based on comparisons between the multiple estimated throughputs; and configure at least one of the selected multiple candidate secondary cells for the WD.

[0023] In some embodiments, the processing circuit module is further configured to use a predicted throughput Tp based on inter-node latency as input to select at least one of a plurality of candidate secondary cells. In some embodiments, the first candidate secondary cell is not co-located with the primary cell. In some embodiments, the processing circuit module is further configured to use a machine learning model to select at least one of a plurality of candidate secondary cells for WD, the machine learning model including at least one of the following as input: measured inter-node latency; the amount of HARQ procedure reused; the estimated throughput reduction associated with HARQ procedure exhaustion; and feedback indicating the amount of average time resources associated with HARQ procedure exhaustion for inter-node latency d.

[0024] According to another aspect, a method is provided implemented in a network node configured to communicate with a wireless device (WD). The method includes: determining an average amount of time resources associated with exhaustion of a Hybrid Automatic Repeat Request (HARQ) process experienced by a WD connected to a first candidate secondary cell and a primary cell having an inter-network node delay d; and reporting feedback indicating the average amount to another network node, the other network node supporting the primary cell and the network node supporting the candidate secondary cell.

[0025] In some embodiments, the method includes communicating with the WD in the candidate auxiliary cell, the candidate auxiliary cell being selected for carrier aggregation of the WD based at least in part on an estimated WD throughput for the candidate auxiliary cell, the estimated WD throughput being based at least in part on at least one of the following: measured inter-network node delay; the amount of time the Hybrid Automatic Repeat Request (HARQ) procedure is reused; the estimated throughput reduction associated with HARQ procedure exhaustion; a machine learning model; and reported feedback.

[0026] According to another aspect, a network node is provided configured to communicate with a wireless device WD. The network node includes a processing circuit module. The processing circuit module is configured to: determine an average amount of time resources associated with the exhaustion of a Hybrid Automatic Repeat Request (HARQ) process experienced by a WD connected to a first candidate secondary cell and a primary cell having an inter-network node delay d; and report feedback indicating the average amount to another network node, the other network node supporting the primary cell, and the network node supporting the candidate secondary cell.

[0027] In some embodiments, the processing circuit module is further configured to enable the network node to communicate with the WD in the candidate auxiliary cell, the candidate auxiliary cell being selected for carrier aggregation of the WD based at least in part on the estimated WD throughput for the candidate auxiliary cell, the estimated WD throughput being based at least in part on at least one of the following: measured inter-network node delay; the amount of time the Hybrid Automatic Repeat Request (HARQ) procedure is reused; the estimated throughput reduction associated with HARQ procedure exhaustion; a machine learning model; and reported feedback. Attached Figure Description

[0028] A more complete understanding of the presented embodiments and their accompanying advantages and features will be readily apparent when considered in conjunction with the following detailed description and the accompanying drawings, in which:

[0029] Figure 1 An example of non-co-location gNB of SpCell and SCell is shown;

[0030] Figure 2An example is shown of simulating the impact of gNB inter-link latency on CA user throughput of SCell (which has different parameter sets) (for example, comparing throughput and RTT with different HARQ-ACK codebooks, where the comparison is made under different parameter sets rather than comparing throughput between different parameter sets).

[0031] Figure 3 This is a schematic diagram of an exemplary network architecture in accordance with the principles of this disclosure, which illustrates a communication system connected to a host computer via an intermediate network;

[0032] Figure 4 This is a block diagram illustrating how a host computer communicates with a wireless device via a network node through at least a partial wireless connection, according to some embodiments of this disclosure.

[0033] Figure 5 This is a flowchart illustrating an exemplary method for executing a client application at a wireless device, implemented in a communication system according to some embodiments of the present disclosure, the communication system including a host computer, a network node, and a wireless device;

[0034] Figure 6 This is a flowchart illustrating an exemplary method for receiving user data at a wireless device, implemented in a communication system according to some embodiments of the present disclosure, the communication system including a host computer, a network node, and a wireless device;

[0035] Figure 7 This is a flowchart illustrating an exemplary method for receiving user data from a wireless device at a host computer, implemented in a communication system according to some embodiments of the present disclosure, the communication system including a host computer, a network node, and a wireless device;

[0036] Figure 8 This is a flowchart illustrating an exemplary method for receiving user data at a host computer, implemented in a communication system according to some embodiments of the present disclosure, the communication system including a host computer, a network node, and a wireless device;

[0037] Figure 9 This is a flowchart of an exemplary process in a network node according to some embodiments of this disclosure;

[0038] Figure 10 This is a flowchart of an exemplary process in a network node according to some embodiments of this disclosure;

[0039] Figure 11 This is a flowchart of an exemplary process in a network node according to some embodiments of this disclosure;

[0040] Figure 12This is a flowchart of an exemplary process in a network node according to some embodiments of this disclosure;

[0041] Figure 13 This is a flowchart of gNB-to-gNB link-aware SCell selection according to some embodiments of this disclosure;

[0042] Figure 14 Examples of embodiments 1 and 2 according to some embodiments of this disclosure are shown;

[0043] Figure 15 An example of a data set in Embodiment 3, which is an embodiment of some embodiments of the present disclosure, is shown;

[0044] Figure 16 A flowchart illustrating an example of Embodiment 3 according to some embodiments of this disclosure; and

[0045] Figure 17 Examples of knowledge bases according to some embodiments of this disclosure are shown. Detailed Implementation

[0046] Existing solutions do not account for latency between network nodes (e.g., gNB-to-gNB links) during SCell selection. A problem with this is that the network may select an SCell with greater bandwidth that should provide higher throughput, but due to the longer latency, the SCell actually provides lower throughput.

[0047] Even when inter-node link latency is taken into account, simply using latency values ​​may not be sufficient. For example, a cell with high bandwidth but increased latency may still be able to provide greater throughput to the WD compared to a cell with extremely short latency but smaller bandwidth. Additionally, the gNB implementation (such as the type of HARQ codebook) can also affect achievable throughput, as in... Figure 2 What I saw in the video.

[0048] Some embodiments of this disclosure provide a method for estimating the expected user experience (e.g., throughput) of different SCell candidates while taking into account the latency between the network node (NN) where the specific cell (SpCell) resides and the network node (NN) where one or more SCell candidates reside. With more accurate throughput estimation, a better set of SCells can be selected for the WD, and the throughput of the WD can be increased.

[0049] Compared to existing arrangements, some embodiments of this disclosure can provide a more accurate estimate of the potential WD throughput at each SCell before SCell configuration / activation.

[0050] Some embodiments of this disclosure can avoid adding SCells with large inter-network node (e.g., inter-gNB) latency, which may reduce CA gain, especially when there are other SCells with shorter inter-network node latency and less load.

[0051] Some embodiments of this disclosure may increase the use of SCell candidates with long inter-node latency when they actually provide increased throughput due to large bandwidth or less load (such approaches can be considered more mature than excluding all candidates with latency longer than a certain threshold).

[0052] Before describing the exemplary embodiments in detail, it should be noted that the embodiments primarily consist of combinations of device components and processing steps related to non-co-located SCell selection for NR CA. Accordingly, components have been appropriately designated by conventional symbols in the accompanying drawings, thus illustrating only those specific details relevant to understanding the embodiments, such that details readily apparent to those skilled in the art from the description herein do not affect the understanding of this disclosure. Similar reference numerals refer to similar elements throughout the description.

[0053] As used herein, relational terms such as “first” and “second,” “top” and “bottom,” and the like, may be used only to distinguish one entity or element from another, and do not necessarily require or imply 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 limit the concepts described herein. As used herein, the singular forms “a” (a, an) and “described” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It is also understood that, as used herein, the terms “comprising” and / or “including” indicate the presence of the stated feature, integer, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0054] In the embodiments described herein, the combined terms "communicating with" and similar expressions can be used to indicate electrical or data communication, which can be achieved, for example, through physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling, or optical signaling. Those skilled in the art will appreciate that multiple components can interoperate, and that modifications and alterations to the implementation of electrical and data communication are possible.

[0055] In some embodiments described herein, the terms “coupled,” “connected,” and the like may be used herein to indicate a connection (though not necessarily directly) and may include wired and / or wireless connections.

[0056] As used herein, the term "network node" can refer to any type of network node contained in a radio network, and may include any of the following: 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 (e.g., MSR BS), multi-cell / multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, implementer node for controlling repeaters, radio access point (AP), transport point, transport node, remote radio unit (RRU), remote radio headend (RRH), core network node (e.g., mobility management entity (MME), ad hoc network (SON) node, coordination node, location node, MDT node, etc.), external node (e.g., third-party node, node outside the current network), node in distributed antenna system (DAS), spectrum access system (SAS) node, network element management system (EMS), etc. Network nodes may also include test equipment. The term “radio node” as used in this article can also refer to a wireless device (WD) (such as a wireless device (WD)) or a node in a radio network.

[0057] In some embodiments, the non-limiting terms “wireless device” (WD) or “user equipment” (UE) may be used interchangeably. A WD as used herein can be any type of wireless device capable of communicating with a network node or another WD via radio signals, such as a wireless device (WD). A WD can also be a radio communication device, a target device, a device-to-device (D2D) WD, a machine-type WD or a WD capable of machine-to-machine (M2M) communication, a low-cost and / or low-complexity WD, a sensor equipped with a WD, a tablet computer, a mobile terminal, a smartphone, a laptop embedded device (LEE), a laptop mounted device (LME), a USB dongle, a customer premises equipment (CPE), an Internet of Things (IoT) device, or a narrowband IoT (NB-IoT) device, etc.

[0058] Additionally, in some embodiments, the generic term "radio network node" is used. It can be any type of radio network node, including any of the following: 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), IAB node, relay node, access point, radio access point, remote radio unit (RRU), and remote radio headend (RRH).

[0059] Some embodiments may describe steps performed by or in a particular cell (e.g., SpCell, SCell). However, it should be understood that the steps are performed by the NN supporting the particular cell.

[0060] In some embodiments, a non-co-located SCell is an SCell that is not located at the same NN as the supporting (and / or managed) corresponding SpCell, see the example as a general topology. Figure 1 .

[0061] As used herein, the term "signaling" may include any of the following: higher-level signaling (e.g., via Radio Resource Control (RRC) or the like), lower-level signaling (e.g., via physical control channels or broadcast channels), or a combination thereof. Signaling may be implicit or explicit. Signaling may also be unicast, multicast, or broadcast. Signaling may also be directed at another node or via a third node.

[0062] As used herein, the term "radio measurement" can refer to any measurement performed on a radio signal. Radio measurements can be absolute or relative. A radio measurement may be referred to as a signal level, which can be signal quality and / or signal strength. Radio measurements can be, for example, intra-frequency, inter-frequency, RAT (Relative Attack Time) measurements, CA (Cyclic Alternate Measure) measurements, etc. Radio measurements can be unidirectional (e.g., DL or UL) or bidirectional (e.g., Round-Trip Time (RTT), Receive-Transmit (Rx-Tx) measurements, etc.). Some examples of radio measurements include: timing measurements (e.g., Time of Arrival (TOA) timing advance, RTT, Reference Signal Time Difference (RSTD), Rx-Tx, propagation delay, etc.), angle measurements (e.g., angle of arrival), power-based measurements (e.g., received signal power, Reference Received Power (RSRP), received signal quality, Reference Received Quality (RSRQ), Signal-to-Interference-plus-Noise Ratio (SINR), Signal-to-Noise Ratio (SNR), interference power, Total Interference-plus-Noise, Received Signal Strength Indicator (RSSI), noise power, etc.), cell detection or cell identification, Radio Link Monitoring (RLM), System Information (SI) readings, etc. Inter-frequency and inter-RAT measurements can be performed by the WD during measurement gaps, unless the WD is able to perform such measurements without gaps. For example, measurements of configured SCells may not require measurement gaps, as these are considered serving cells.

[0063] The term "parameter set" as used herein may include any one or more of the following, such as: frame duration, subframe or TTI duration, slot or microslot duration, symbol duration, and the number of symbols per slot and subframe, subcarrier spacing, sampling frequency, Fast Fourier Transform (FFT) size, number of subcarriers per resource block (RB) and RB bandwidth, number of RBs within the bandwidth, symbols per subframe, cyclic prefix (CP) length, etc. The parameter set determines the grid of resource elements (REs) in the time and / or frequency domains.

[0064] Signaling generally includes one or more symbols and / or signals and / or messages. Signals may include or represent one or more bits. Indications may represent signaling and / or be implemented as a single signal or multiple signals. One or more signals may be included in and / or represented by a message. Signaling (particularly control signaling) may include multiple signals and / or messages that may be transmitted on different carriers and / or associated with different signaling procedures, such as indicating and / or relating to one or more such procedures and / or corresponding information. Indications may include signaling and / or multiple signals and / or messages, and / or may be included therein, that may be transmitted on different carriers and / or associated with different acknowledgment signaling procedures, such as indicating and / or relating to one or more such procedures. Signaling associated with a channel may be transmitted such that signaling and / or information representing that channel, and / or signaling is interpreted by the transmitter and / or receiver as belonging to that channel. Such signaling generally conforms to the transmission parameters and / or (one or more) formats of said channel.

[0065] An indication can generally be explicit and / or implicit in indicating the information it represents and / or indicates. Implicit indications may be based, for example, on the location and / or resource used for transmission. Explicit indications may be based, for example, on parameterization using one or more parameters and / or one or more indexes corresponding to a table and / or one or more bit patterns representing the information.

[0066] As used herein, the term "time resource" can refer to any type of physical or radio resource expressed in terms of time length. Examples of time resources are: symbols, time slots, subframes, radio frames, TTIs, interleaving times, etc. As used herein, in some embodiments, the terms "time slot" and "time resource" may be used interchangeably.

[0067] A cell can generally be a communication cell provided by a node, such as a cellular or mobile communication network. A serving cell can be a cell on which or via which network nodes (nodes providing or associated with the cell, such as base stations or gNodeBs) transmit and / or transmittable data (which may be data other than broadcast data) to a user equipment, particularly control and / or user or payload data, and / or via or on which the user equipment transmits and / or transmittable data to the node; a serving cell can be a cell for which or on which the user equipment is configured, and / or synchronized to, and / or has undergone an access procedure (e.g., a random access procedure), and / or is in an RRC_connected or RRC_idle state relative to the cell (e.g., where the node and / or user equipment and / or network comply with NR or LTE standards). One or more carriers (e.g., uplink and / or downlink carriers and / or carriers used for uplink and downlink) can be associated with a cell.

[0068] In some embodiments, as used herein, a “set” can be a collection of one or more elements in the set.

[0069] It should be noted that while terms from a particular wireless system (e.g., such as 3GPP LTE and / or New Radio (NR)) may be used in this disclosure, this should not be construed as limiting the scope of this disclosure to the aforementioned systems only. Other wireless systems (including, without limitation, Wideband Code Division Multiple Access (WCDMA), Global Microwave Access Interoperability (WiMax), Ultra Mobile Broadband (UMB), and Global System for Mobile Communications (GSM)) may also benefit from utilizing the ideas covered within this disclosure.

[0070] It should also be noted that the functions described herein, performed by wireless devices or network nodes, can be distributed across multiple wireless devices and / or network nodes. In other words, it is anticipated that the functions of the network nodes and wireless devices described herein are not limited to being performed by a single physical device, but can actually be distributed among several physical devices.

[0071] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It is also understood that terms used herein should be interpreted as having the same meaning as they have in the context of this specification and the relevant field, and not as in an idealized or overly formalized sense, unless expressly defined herein.

[0072] Some embodiments provide non-co-located SCell selection for NR CA. Referring again to the drawings, similar elements are designated by similar reference numerals. Figure 3 The diagram illustrates a communication system 10 (such as a 3GPP-type cellular network supporting standards such as LTE and / or NR (5G)) according to an embodiment, comprising an access network 12 (such as a radio access network) and a core network 14. The access network 12 includes a plurality of network nodes 16a, 16b, 16c (collectively referred to as network nodes 16), such as NBs, eNBs, gNBs, or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (collectively referred to as coverage area 18). Each network node 16a, 16b, 16c can be connected to the core network 14 via a wired or wireless connection 20. A first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to or be paged by the corresponding network node 16a. A second WD 22b in coverage area 18b can wirelessly connect to the corresponding network node 16b. Although multiple WDs 22a, 22b (collectively referred to as wireless devices 22) are shown in this example, the disclosed embodiments are equally applicable to situations where only one WD is in the coverage area or where only one WD is connected to the corresponding network node 16. It should be noted that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include more WDs 22 and network nodes 16.

[0073] It is also anticipated that WD 22 can simultaneously communicate with more than one network node 16 and more than one type of network node 16, and / or be configured to communicate individually with more than one network node 16 and more than one type of network node 16. For example, WD 22 can have dual connectivity with LTE-enabled network nodes 16 and the same or different NR-enabled network nodes 16. As an example, WD 22 can communicate with an eNB of LTE / E-UTRAN and a gNB of NR / NG-RAN.

[0074] The communication system 10 itself may be connected to a host computer 24, which may be implemented as hardware and / or software of a standalone server, a cloud-based server, a distributed server, or as a processing resource in a server farm. The host computer 24 may be owned or controlled by a service provider, or may be operated by or on behalf of the service provider. Connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24, or may extend via an optional intermediate network 30. The intermediate network 30 may be one or more of a public, private, or hosted network. The intermediate network 30 (if any) may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may include two or more subnetworks (not shown).

[0075] Figure 3 The overall communication system enables connectivity between one of the connected WDs 22a and 22b and the host computer 24. This connectivity can be described as an over-the-top (OTT) connection. The host computer 24 and the connected WDs 22a and 22b are configured to transmit data and / or signaling via the OTT connection using access network 12, core network 14, any intermediate network 30, and other possible infrastructure (not shown) acting as intermediaries. The OTT connection can be transparent in the sense that at least some of the participating communication devices are unaware of the routing of uplink and downlink communications. For example, it may not be necessary to inform network node 16 of past routing of incoming downlink communications with data originating from host computer 24 and to be forwarded (e.g., handed over) to the connected WD 22a. Similarly, network node 16 does not need to know the future routing of outgoing uplink communications originating from WD 22a toward host computer 24.

[0076] Network node 16 is configured to include an estimator unit 32, the estimator unit being configured to: estimate the throughput of the WD at a candidate secondary cell, the estimated throughput being at least partially based on a measured inter-node delay between the network node and another network node, the network node supporting the specific cell and the other network node supporting the candidate secondary cell; and determine, at least partially based on the estimated throughput, whether to select the candidate secondary cell for the WD. Network node 16 is configured to include a reporter unit 34, the reporter unit being configured to: determine a duration associated with Hybrid Automatic Repeat Request (HARQ) process exhaustion at the time of inter-node delay; and report the duration to another network node, the other network node supporting the specific cell and the network node supporting the candidate secondary cell.

[0077] Now you can refer to Figure 4The following describes an exemplary implementation of the WD 22, network node 16, and host computer 24 described in the preceding paragraphs according to an embodiment. In the communication system 10, the host computer 24 includes hardware (HW) 38, which includes a communication interface 40 configured to establish and maintain wired or wireless connections with interfaces to different communication devices of the communication system 10. The host computer 24 also includes a processing circuitry module 42, which may have storage and / or processing capabilities. The processing circuitry module 42 may include a processor 44 and a memory 46. In particular, as an addition to or alternative to the processor (such as a central processing unit) and memory, the processing circuitry module 42 may include integrated circuit modules for processing and / or control, such as one or more processors and / or processor cores and / or FPGAs (Field-Programmable Gate Arrays) and / or ASICs (Application-Specific Integrated Circuits) suitable for executing instructions. The processor 44 may be configured to access (e.g., write to and / or read from) memory 46, which may include any kind of volatile and / or non-volatile memory, such as cache memory and / or buffer memory and / or RAM (random access memory) and / or ROM (read-only memory) and / or optical memory and / or EPROM (erasable programmable read-only memory).

[0078] Processing circuit module 42 may be configured to control any methods and / or processes described and / or performed herein, and / or to cause such methods and / or processes to be executed, for example, by host computer 24. Processor 44 corresponds to one or more processors 44 for performing the functions of host computer 24 as described herein. Host computer 24 includes memory 46 configured to store data, programming software code, and / or other information as described herein. In some embodiments, software 48 and / or host application 50 may include instructions that, when executed by processor 44 and / or processing circuit module 42, cause processor 44 and / or processing circuit module 42 to perform the processes described herein with respect to host computer 24. The instructions may be software associated with host computer 24.

[0079] Software 48 may be executable by processing circuitry module 42. Software 48 includes host application 50. Host application 50 may be operable to provide services to remote users, such as WD 22 connected via an OTT connection 52 terminated between WD 22 and host computer 24. In providing services to remote users, host application 50 may provide user data, which is transmitted using OTT connection 52. “User data” may be data and information described herein for implementing the aforementioned functionality. In one embodiment, host computer 24 may be configured to provide control and functionality to a service provider and may be operated by or on behalf of the service provider. Processing circuitry module 42 of host computer 24 enables host computer 24 to observe, monitor, control network node 16 and / or wireless device 22, and to transmit and / or receive data to and / or from network node 16 and / or wireless device 22. The processing circuit module 42 of the host computer 24 may include a monitoring unit 54, which is configured to enable the service provider to observe, monitor, control the network node 16 and / or the wireless device 22, and to transmit and / or receive data to and / or from the network node 16 and / or the wireless device 22.

[0080] The communication system 10 also includes a network node 16, which is provided within the communication system 10 and includes hardware 58 enabling it to communicate with the host computer 24 and with the WD 22. Hardware 58 may include: a communication interface 60 for establishing and maintaining wired or wireless connections to interfaces with different communication devices of the communication system 10; and a radio interface 62 for establishing and maintaining at least a wireless connection 64 with the WD 22 located within the coverage area 18 served by the network node 16. The radio interface 62 may be configured as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host computer 24. The connection 66 may be direct, or it may traverse the core network 14 of the communication system 10 and / or one or more intermediate networks 30 outside the communication system 10.

[0081] In the illustrated embodiment, the hardware 58 of network node 16 further includes a processing circuitry module 68. The processing circuitry module 68 may include a processor 70 and memory 72. Specifically, as an addition to or alternative to the processor (such as a central processing unit) and memory, the processing circuitry module 68 may include integrated circuit modules for processing and / or control, such as one or more processors and / or processor cores suitable for executing instructions and / or FPGAs (Field-Programmable Gate Arrays) and / or ASICs (Application-Specific Integrated Circuits). The processor 70 may be configured to access (e.g., write to and / or read from) memory 72, which may include any kind of volatile and / or non-volatile memory, such as cache memory and / or buffer memory and / or RAM (Random Access Memory) and / or ROM (Read-Only Memory) and / or optical memory and / or EPROM (Erasable Programmable Read-Only Memory).

[0082] Therefore, network node 16 further includes software 74, which is internally stored, for example, in memory 72, or stored in external memory (e.g., a database, storage array, network storage device, etc.) accessible by network node 16 via an external connection. Software 74 may be executable by processing circuitry module 68. Processing circuitry module 68 may be configured to control any methods and / or processes described herein, and / or cause such methods and / or processes to be executed, for example, by network node 16. Processor 70 corresponds to one or more processors 70 for performing the functions of network node 16 as described herein. Memory 72 is configured to store data, programming software code, and / or other information as described herein. In some embodiments, software 74 may include instructions that, when executed by processor 70 and / or processing circuitry module 68, cause processor 70 and / or processing circuitry module 68 to perform the processes described herein for network node 16. For example, processing circuitry module 68 of network node 16 (e.g., an NN supporting specific cells) may include estimator unit 32 configured to perform network node methods as described herein, such as referring to… Figure 9 The methods described in the other accompanying figures.

[0083] In some embodiments, the processing circuitry module 68 of network node 16 (e.g., an NN supporting candidate auxiliary cells) may include a reporter unit 34 configured to perform the methods of network node 16 described herein, such as referring to Figure 10 The methods described in the other accompanying figures.

[0084] The communication system 10 also includes the previously mentioned WD 22. The WD 22 may have hardware 80, which may include a radio interface 82 configured to establish and maintain a wireless connection 64 with a network node 16 serving the coverage area 18 where the WD 22 is currently located. The radio interface 82 may be configured as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers.

[0085] The hardware 80 of the WD 22 also includes processing circuitry 84. Processing circuitry module 84 may include a processor 86 and memory 88. Specifically, as an addition to or alternative to the processor (such as a central processing unit) and memory, processing circuitry module 84 may include integrated circuit modules for processing and / or control, such as one or more processors and / or processor cores suitable for executing instructions and / or FPGAs (Field-Programmable Gate Arrays) and / or ASICs (Application-Specific Integrated Circuits). Processor 86 may be configured to access (e.g., write to and / or read from) memory 88, which may include any kind of volatile and / or non-volatile memory, such as cache memory and / or buffer memory and / or RAM (Random Access Memory) and / or ROM (Read-Only Memory) and / or optical memory and / or EPROM (Erasable Programmable Read-Only Memory).

[0086] Therefore, WD 22 may also include software 90, which is stored, for example, in memory 88 at WD 22 or in external memory accessible by WD 22 (e.g., a database, storage array, network storage device, etc.). Software 90 may be executable by processing circuitry module 84. Software 90 may include a client application 92. Client application 92 may be operable to provide services to human or non-human users via WD 22 with the support of host computer 24. In host computer 24, a executing host application 50 may communicate with the executing client application 92 via an OTT connection 52 terminated between WD 22 and host computer 24. When providing services to a user, client application 92 may receive request data from host application 50 and provide user data in response to the request data. OTT connection 52 may transmit request data and user data. Client application 92 may interact with the user to generate the user data it provides.

[0087] Processing circuit module 84 may be configured to control any of the methods and / or processes described herein, and / or to cause such methods and / or processes to be performed, for example, by WD 22. Processor 86 corresponds to one or more processors 86 for performing the functions of WD 22 described herein. WD 22 includes memory 88 configured to store data, programming software code, and / or other information described herein. In some embodiments, software 90 and / or client application 92 may include instructions that, when executed by processor 86 and / or processing circuit module 84, cause processor 86 and / or processing circuit module 84 to perform the processes described herein with respect to WD 22.

[0088] In some embodiments, the internal operations of network node 16, WD 22, and host computer 24 can be as follows: Figure 4 As shown, and the surrounding network topology can be individually as follows: Figure 3 As shown.

[0089] Figure 4 In the diagram, OTT connection 52 is abstractly depicted to illustrate communication between host computer 24 and wireless device 22 via network node 16, without explicitly mentioning any intermediate devices or the exact routing of messages through these devices. The network infrastructure can determine the routing, which can be configured to be hidden from WD 22, the service provider operating host computer 24, or both. While OTT connection 52 is active, the network infrastructure can further make decisions, dynamically altering the routing (e.g., based on network load balancing considerations or reconfiguration).

[0090] The wireless connection 64 between WD 22 and network node 16 is based on the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments utilize an OTT connection 52 to improve the performance of OTT services provided to WD 22, in which the wireless connection 64 may form a final segment. More precisely, the teachings of parts of these embodiments can improve data rates, latency, and / or power consumption, and thereby provide beneficial effects such as reduced user wait times, relaxed file size limits, better responsiveness, and extended battery life.

[0091] In some embodiments, a measurement process may be provided to facilitate monitoring of data rates, latency, and other factors improved in one or more embodiments. Optional network functionality may also be available for reconfiguring the OTT connection 52 between host computer 24 and WD 22 in response to changes in measurement results. The measurement process and / or the network functionality for reconfiguring the OTT connection 52 may be implemented via software 48 of host computer 24 or software 90 of WD 22, or both. In embodiments, sensors (not shown) may be deployed in or associated with communication devices within the OTT connection 52; the sensors may participate in the measurement process by providing values ​​of the monitored quantities illustrated above or by providing values ​​of other physical quantities from which the software 48, 90 can calculate or estimate the monitored quantities. Reconfiguration of the OTT connection 52 may include message formatting, retransmission settings, preferred routing, etc.; reconfiguration does not affect network node 16 and may be unknown or undetectable to network node 16. Some such processes and functionalities may be known and implemented in the art. In some embodiments, the measurement may involve proprietary WD signaling that facilitates the host computer 24 to measure throughput, propagation time, latency, etc. In some embodiments, the measurement can be implemented because the software 48, 90 enables messages to be transmitted using the OTT connection 52, particularly empty or 'fake' messages, while monitoring propagation time, errors, etc.

[0092] Therefore, in some embodiments, the host computer 24 includes: a processing circuitry module 42 configured to provide user data; and a communication interface 40 configured to forward the user data to the cellular network for transmission to the WD 22. In some embodiments, the cellular network further includes a network node 16 having a radio interface 62. In some embodiments, the network node 16 is configured and / or the processing circuitry module 68 of the network node 16 is configured to perform the functions and / or methods described herein for the following operations:

[0093] Prepare / initiate / maintain / support / end a transmission to WD 22 and / or prepare / terminate / maintain / support / end a reception of a transmission from WD 22.

[0094] In some embodiments, host computer 24 includes processing circuitry module 42 and communication interface 40, the communication interface 40 being configured to receive user data from transmissions from WD 22 to network node 16. In some embodiments, WD 22 is configured to perform the functions and / or methods described herein for the following operations and / or includes a radio interface 82 and / or processing circuitry module 84 configured to perform the functions and / or methods described herein for the following operations: preparing / initiating / maintaining / supporting / terminating transmissions to network node 16 and / or preparing / terminating / maintaining / supporting / terminating reception of transmissions from network node 16.

[0095] Although Figure 3 and Figure 4 Various “units” (such as estimator unit 32 and reporter unit 34) are shown as residing within their respective processors, but it is contemplated that these units can be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry module. In other words, the units can be implemented in hardware or through a combination of hardware and software within the processing circuitry module.

[0096] Figure 5 This illustrates, according to one embodiment, in a communication system (e.g., such as...) Figure 3 and Figure 4 The flowchart illustrates an exemplary method implemented in a communication system. The communication system may include a host computer 24, a network node 16, and a WD 22, which may be referenced... Figure 4 The aforementioned host computers, network nodes, and WDs. In the first step of the method, host computer 24 provides user data (block S100). In an optional sub-step of the first step, host computer 24 provides user data by executing a host application (e.g., such as host application 50) (block S102). In the second step, host computer 24 initiates a transmission carrying user data to WD 22 (block S104). In an optional third step, according to the teachings of the embodiments described throughout this disclosure, network node 16 transmits user data to WD 22 (block S106), the user data being carried in the transmission initiated by host computer 24. In an optional fourth step, WD 22 executes a client application (e.g., such as client application 92) associated with host application 50 executed by host computer 24 (block S108).

[0097] Figure 6 This illustrates, according to one embodiment, in a communication system (e.g., such as...) Figure 3 The flowchart illustrates an exemplary method implemented in a communication system. The communication system may include a host computer 24, a network node 16, and a WD 22, which may be referenced... Figure 3 and Figure 4 The aforementioned host computer, network node, and WD. In the first step of the method, host computer 24 provides user data (block S110). In an optional sub-step (not shown), host computer 24 provides user data by executing a host application (e.g., host application 50). In the second step, host computer 24 initiates a transmission carrying user data to WD 22 (block S112). According to the teachings of the embodiments described throughout this disclosure, the transmission may be carried via network node 16. In an optional third step, WD 22 receives the user data carried in the transmission (block S114).

[0098] Figure 7 This illustrates, according to one embodiment, in a communication system (e.g., such as...) Figure 3 The flowchart illustrates an exemplary method implemented in a communication system. The communication system may include a host computer 24, a network node 16, and a WD 22, which may be referenced... Figure 3 and Figure 4 The aforementioned host computers, network nodes, and WDs. In an optional first step of the method, WD 22 receives input data provided by host computer 24 (block S116). In an optional sub-step of the first step, WD 22 executes client application 92, which responds to the received input data provided by host computer 24 to provide user data (block S118). Additionally or alternatively, in an optional second step, WD 22 provides user data (block S120). In an optional sub-step of the second step, WD provides user data by executing a client application (e.g., such as client application 92) (block S122). In providing user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which user data is provided, WD 22 initiates the transmission of user data to host computer 24 in an optional third sub-step (block S124). According to the teachings of the embodiments described throughout this disclosure, in a fourth step of the method, host computer 24 receives user data transmitted from WD 22.

[0099] Figure 8 This illustrates, according to one embodiment, in a communication system (e.g., such as...) Figure 3 The flowchart illustrates an exemplary method implemented in a communication system. The communication system may include a host computer 24, a network node 16, and a WD 22, which may be referenced... Figure 3 and Figure 4The aforementioned host computer, network node, and WD. In an optional first step of the method, network node 16 receives user data from WD 22 in accordance with the teachings of the embodiments described throughout this disclosure (block S128). In an optional second step, network node 16 initiates a transmission of the received user data to the host computer (block S130). In a third step, host computer 24 receives the user data carried in the transmission initiated by network node 16 (block S132).

[0100] Figure 9 This is a flowchart of an exemplary process in network node 16 according to some embodiments of the present disclosure. According to the example method, one or more blocks and / or functions and / or methods performed by network node 16 may be performed by one or more elements of network node 16 (such as by processing circuitry module 68, estimator unit 32 in processor 70, radio interface 62, etc.). The example method includes estimating (block S134) the throughput of WD at a candidate auxiliary cell, such as via estimator unit 32, processing circuitry module 68, processor 70, communication interface 60, and / or radio interface 62, where the estimated throughput is at least partially based on a measured inter-network node delay between the network node and another network node supporting the specific cell, and the other network node supporting the candidate auxiliary cell. The method includes determining (block S136) whether to select a candidate auxiliary cell for WD, such as via estimator unit 32, processing circuitry module 68, processor 70, communication interface 60, and / or radio interface 62, at least partially based on the estimated throughput.

[0101] In some embodiments, one or more of the following: the candidate auxiliary cell and the special cell are not co-located; and the estimated throughput is based at least in part on at least one of the following: a Hybrid Automatic Repeat Request (HARQ) codebook for the candidate auxiliary cell; the amount of HARQ procedure reused for the candidate auxiliary cell; feedback from the candidate auxiliary cell indicating the amount of time resources associated with HARQ procedure exhaustion for inter-node delay d; a set of parameters used by the auxiliary cell; and a machine learning algorithm. In some embodiments, the method further includes, for example, measuring the inter-node delay between a network node and another network node via estimator unit 32, processing circuit module 68, processor 70, communication interface 60, and / or radio interface 62.

[0102] Figure 10This is a flowchart of an exemplary process in network node 16 according to some embodiments of the present disclosure. According to the example method, one or more blocks and / or functions and / or methods performed by network node 16 may be performed by one or more elements of network node 16, such as by processing circuitry module 68, reporter unit 34 in processor 70, radio interface 62, etc. The example method includes determining (block S138) the duration of time associated with Hybrid Automatic Repeat Request (HARQ) process exhaustion during network node delay, such as via reporter unit 34, processing circuitry module 68, processor 70, communication interface 60, and / or radio interface 62. The method includes reporting (block S140) the duration of time to another network node, such as via reporter unit 34, processing circuitry module 68, processor 70, communication interface 60, and / or radio interface 62, the other network node supporting a specific cell and supporting candidate auxiliary cells.

[0103] In some embodiments, one or more of the following: the duration is based at least in part on the number of time slots that the WD cannot be scheduled due to at least one of the following: unavailability of the HARQ process and other WDs being scheduled; the candidate auxiliary cell and the special cell are not co-located; and the candidate auxiliary cell is selected for carrier aggregation for the WD at least in part based on the estimated WD throughput, the estimated WD throughput being based at least in part on the reported amount.

[0104] Figure 11 This is a flowchart of an exemplary process in network node 16 according to some embodiments of the present disclosure. According to the example method, one or more blocks and / or functions and / or methods performed by network node 16 may be performed by one or more elements of network node 16 (such as by processing circuitry module 68, estimator unit 32 in processor 70, communication interface 60, radio interface 62, etc.). Network node 16 is configured to estimate (block S142) a first throughput of WD at a first candidate auxiliary cell, such as by processing circuitry module 68, estimator unit 32 in processor 70, communication interface 60, and / or radio interface 62, where the estimated first throughput is at least partially based on a measured inter-network node delay between the network node and a first network node supporting the first candidate auxiliary cell, the network node supporting the primary cell. Network node 16 is configured to determine (block S144) whether to select a first candidate auxiliary cell for WD, such as by processing circuitry module 68, estimator unit 32 in processor 70, communication interface 60, and / or radio interface 62, at least partially based on the first estimated throughput.

[0105] In some embodiments, network node 16 is configured to determine, for example, the amount of Hybrid Automatic Repeat Request (HARQ) procedure reused with respect to the first candidate secondary cell of the WD, such as by processing circuit module 68, estimator unit 32 in processor 70, communication interface 60, and / or radio interface 62, wherein the estimated throughput is also based on the determined amount of HARQ procedure reuse. In some embodiments, network node 16 is configured to estimate, for example by processing circuit module 68, estimator unit 32 in processor 70, communication interface 60, and / or radio interface 62, a throughput reduction amount associated with HARQ procedure exhaustion at the first candidate secondary cell of the WD, wherein the estimated first throughput is also based on the estimated throughput reduction amount.

[0106] In some embodiments, network node 16 is configured to estimate the first throughput of WD by means of processing circuit module 68, estimator unit 32 in processor 70, communication interface 60 and / or radio interface 62, by performing the following operations: using the same bandwidth, the same radio conditions and the same load for the first candidate auxiliary cell and the second candidate auxiliary cell on which the second throughput of WD is estimated. In some embodiments, the estimated throughput reduction associated with HARQ process exhaustion at the first candidate auxiliary cell is based on information received from the first network node, the information indicating the maximum number of HARQ processes associated with the first candidate auxiliary cell of WD.

[0107] In some embodiments, network node 16 is configured to estimate the first throughput of the WD by means of processing circuit module 68, estimator unit 32 in processor 70, communication interface 60 and / or radio interface 62, by employing different sets of parameters for a first candidate auxiliary cell and a second candidate auxiliary cell on which the second throughput of the WD is estimated. In some embodiments, network node 16 is configured to receive feedback from a first network node supporting the first candidate auxiliary cell by means of processing circuit module 68, estimator unit 32 in processor 70, communication interface 60 and / or radio interface 62, the feedback indicating the average amount of time resources associated with HARQ process exhaustion experienced by the WD connected to the first candidate auxiliary cell and the primary cell having an inter-network node delay d, and the determination of whether to select the first candidate auxiliary cell for the WD is also based on the received feedback.

[0108] In some embodiments, network node 16 is configured, for example, by processing circuit module 68, estimator unit 32 in processor 70, communication interface 60 and / or radio interface 62: using a machine learning model, which includes feedback from network nodes supporting candidate auxiliary cells as input; and configuring, deconfiguring and activating multiple candidate auxiliary cells with different inter-network node delays according to the machine learning model, and determining whether to select a first candidate auxiliary cell for WD is also based on the output of the machine learning model.

[0109] In some embodiments, network node 16 is configured to, for example by processing circuit module 68, estimator unit 32 in processor 70, communication interface 60, and / or radio interface 62, estimate multiple throughputs of the WD at multiple candidate auxiliary cells, the multiple estimated throughputs being at least partially based on inter-node delays measured between the network node and multiple network nodes supporting the multiple candidate auxiliary cells; select at least one of the multiple candidate auxiliary cells for the WD based on comparisons between the multiple estimated throughputs; and configure the at least one selected from the multiple candidate auxiliary cells for the WD. In some embodiments, network node 16 is configured to, for example by processing circuit module 68, estimator unit 32 in processor 70, communication interface 60, and / or radio interface 62, use a predicted throughput Tp based on inter-node delay as input to select at least one of the multiple candidate auxiliary cells. In some embodiments, the first candidate auxiliary cell is not co-located with the primary cell.

[0110] In some embodiments, network node 16 is configured to use a machine learning model, such as by processing circuit module 68, estimator unit 32 in processor 70, communication interface 60 and / or radio interface 62, to select one of a first and second candidate auxiliary cells for the WD. The machine learning model includes at least one of the following as input: measured inter-node latency; the amount of HARQ procedure reused; the estimated throughput reduction associated with HARQ procedure exhaustion; and feedback indicating the amount of average time resources associated with HARQ procedure exhaustion for inter-node latency d. In some embodiments, the first candidate auxiliary cell is not co-located with the primary cell.

[0111] Figure 12This is a flowchart of an exemplary process in network node 16 according to some embodiments of the present disclosure. According to the example method, one or more blocks and / or functions and / or methods performed by network node 16 may be performed by one or more elements of network node 16 (such as the reporter unit 34, processor 70, radio interface 62, etc. in processing circuitry module 68). Network node 16 is configured to determine (block S146) the amount of average time resources associated with the exhaustion of a Hybrid Automatic Repeat Request (HARQ) process experienced by a WD connected to a first candidate secondary cell and a primary cell having an inter-network node delay d, as determined by the reporter unit 34, processor 70, communication interface 60, and / or radio interface 62 in processing circuitry module 68. Network node 16 is configured to report (block S148) feedback indicating the average amount to another network node, such as the reporter unit, processor 70, communication interface 60, and / or radio interface 62 in processing circuitry module 68, which supports the primary cell and the candidate secondary cell.

[0112] In some embodiments, network node 16 is configured to communicate with the WD in the candidate auxiliary cells, such as via a reporter unit 34, processor 70, communication interface 60 and / or radio interface 62 in the processing circuit module 68, the candidate auxiliary cells being selected for carrier aggregation of the WD based at least in part on the estimated WD throughput for the candidate auxiliary cells, the estimated WD throughput being based at least in part on at least one of the following: measured inter-network node delay; the amount of time the Hybrid Automatic Repeat Request (HARQ) procedure is reused; the estimated throughput reduction associated with HARQ procedure exhaustion; a machine learning model; and reported feedback.

[0113] Having described the general process flow of the arrangements of this disclosure and provided examples of hardware and software arrangements for implementing the processes and functions of this disclosure, the following subsections provide details and examples of arrangements for non-co-location SCell selection of NR CA that can be implemented by network node 16, wireless device 22 and / or host computer 24.

[0114] Some embodiments provide the proposed algorithm, which includes, for example, Figure 13 One or more of the steps shown, Figure 13 This is a flowchart of link-aware SCell selection between network nodes (e.g., between gNBs) according to some embodiments of this disclosure.

[0115] SCell selection can be performed through several steps; see below. Figure 13To describe one such approach. Some embodiments propose new methods for the SCell selection process, and particularly for, for example, step S156, and may depend on the measured or estimated inter-gNB delay value available at NN 16 of each SCell at SpCell.

[0116] Step S150: Measure the inter-node latency in the NR network.

[0117] The NN 16 of SpCell determines the time spent transmitting packets (potentially for HARQ feedback carrying SCell data) between each of the NN 16 of SpCell and the NN 16 of SCell.

[0118] Step S152: Collect candidate SCell information

[0119] The NN 16 of the SpCell collects information related to one or more candidate SCells, such as parameter sets, bandwidth, number of downlink (DL) HARQ procedures, number of connected users (e.g., WD 22), and the configured K1 value used by the SCell to allocate CA WD22 data. In step S154, WD Scell ​​measurements are obtained.

[0120] Step S156: Estimate the WD throughput at the candidate SCell

[0121] The NN 16 combination of SpCells, or otherwise using one or more of the measured channel information, SCell information, and measured inter-network node delay from WD 22, can be used to estimate the potential throughput at each SCell.

[0122] Step S158: Select SCell

[0123] SpCell's NN 16 selects at least one Scell ​​of WD 22 (e.g., a set of one or more Scells, the best Scell, etc.) based on, for example, the estimated throughput (e.g., the estimated throughput from step S156 above, which is based on the inter-node / inter-gNB latency).

[0124] This disclosure describes at least four embodiments as follows:

[0125] 1. Throughput estimation is based on different HARQ codebooks.

[0126] 2. Throughput estimation is based on HARQ process reuse.

[0127] 3. Employ machine learning-based, feedback-driven SCell evaluation.

[0128] 4. SCell selection based on feedback and throughput.

[0129] Example 1 outlines the calculation of throughput estimation for SCell candidates (e.g., based on inter-node / gNB throughput), where throughput depends on which HARQ codebook is used. Example 2 reuses elements from Example 1 but also enhances the estimation to account for, for example, the effects of other enhancements on the SCell, such as the presence of algorithms that reuse HARQ procedures, which compensates for the impact of inter-node / gNB latency on HARQ procedure availability.

[0130] Figure 14 This is a flowchart illustrating example processes of Embodiments 1 and 2. After the initial context establishment in step S160, the NN 16 of SpCell requests SCell information in step S162. The NN 16 of SCell then provides the requested SCell information. In step S164, throughput is calculated for the SCell. In some embodiments, steps S162 and / or S164 may even be performed before the initial context establishment. For example, the SCell information may be static, so that updates are not required when a new WD 22 accesses SpCell and / or SCell. In step S166, SCell throughput is calculated. In step S168, one or more SCells are selected by the NN 16 of SpCell, for example, at least in part based on the calculated / estimated throughput. In step S170, the NN 16 of SpCell configures (one or more) the selected SCells for WD 22.

[0131] The third embodiment introduces a feedback-based approach, in which the number of time slots in which HARQ process exhaustion occurs is recorded and used to evaluate SCell. Figure 15 This is a flowchart illustrating an example process of the third embodiment. In step S172, the NN16 of the SpCell configures one or more selected Scells for WD 22. In step S174, the NN16 of the SpCell performs DL data scheduling. In step S176, the NN16 of the SpCell measures the number of time slots that WD 22 cannot be scheduled on DL due to the unavailability of the DL HARQ process or other WD 22s being scheduled. In step S178, the NN16 of the SpCell calculates the average latency of all WD 22s in the set served by the SpCell at an inter-NB latency d. In step S180, the average latency is reported to the NN16 of the SpCell. In step S182, the NN16 of the SpCell stores the received average latency and can use it for future Scell ​​selection. The third embodiment can also be used in conjunction with embodiments 1 and / or 2.

[0132] Example 1:

[0133] In this embodiment, all SCell candidates can be assumed to have the same bandwidth, radio conditions, and load (achieved by existing load balancing characteristics). Therefore, the WD 22 throughput estimate for each candidate SCell (see step S166, for example) can be primarily a function of the inter-gNB link delay and the SCell parameter set. This embodiment may include one or more of the following:

[0134] -SpCell's NN 16 calculates the time between the scheduler of SCell's NN 16 using a HARQ procedure (and / or HARQ procedure ID) and reusing it on the same WD 22. The time is measured by t reuse To represent, and based on how the system is designed, it is calculated as follows:

[0135] ○Assumption: Using a semi-static HARQ-ACK codebook:

[0136] ■t reuse [ms] = K1 max [ms] + inter-node latency [ms] + t proc [ms];

[0137] ○Among them:

[0138] ■K1: is the time offset between the Physical Downlink Shared Channel (PDSCH) (via SCell) and the Physical Uplink Control Channel / Physical Uplink Shared Channel (PUCCH / PUSCH) (via SpCell) (with HARQ-ACK feedback of WD 22).

[0139] ●K1 can be configured as a set of up to 8 distinct values, where K1 max It is the maximum K1 value applied to the downlink (DL) transmission of SCell.

[0140] ■ Network node delay: This is the one-way delay between NN 16 of SpCell and NN 16 of candidate SCell.

[0141] ■t proc : This refers to the processing time consumed by the physical layer.

[0142] - The NN 16 estimate for SpCell is represented by L, which is the throughput reduction caused by the exhaustion of the HARQ process at SCell, and is calculated as follows:

[0143] ○L=max(0,(t reuse / t slot *r D -N HP ) / (treuse / t slot *r D ));

[0144] ○Among them:

[0145] ■t slot : Duration of the time slot (transmission time interval / TTI), which depends on the parameter set (e.g., 1 / 8 for parameter set 0).

[0146] ■r D :t reuse The ratio of DL slots to the total number of slots in the window (e.g., if there are 15 DL slots and 5 uplink (UL) slots, then = 0.75).

[0147] ■N HP The maximum number of HARQ procedures that can be used to transmit data from the same carrier to the same WD 22 (e.g., = 16).

[0148] -User i connected to SCell candidates (e.g., WD) i 22) throughput through R i,s This is to indicate, and therefore can be updated, to reflect contention with other users (e.g., WD 22) and HARQ process exhaustion, as described below:

[0149] ○R i，s =min(R) N R H )

[0150] ○Among them:

[0151] ■R N This refers to the user / WD 22 throughput implemented at the SCell (assuming, for example, fair sharing of resources with N user / WD 22 users (using this cell as a local user or CA user of the SCell) (e.g., 30% of the connected user / WD 22 users) that have DL data to transmit), and it can be simplified to, for example:

[0152] ●R N = Transport block size (bits) × numberOfDLTtisPersubframe * (1 / N), where numberOfDLTtisPersubframe can be calculated according to the SCell parameter set;

[0153] ■R H This is the throughput calculated after taking into account the HARQ process exhaustion loss calculated in the previous step, and can be simplified to, for example:

[0154] ●R H= Transport block size (bits) × numberOfDLTtisPersubframe × (1-L).

[0155] The NN 16 of SpCell can repeat the above steps for each candidate SCell and select the one with the largest R. i,s SCell or using the estimated R i,s The value is used to select the best set of SCells for WD 22.

[0156] Example 2:

[0157] In this embodiment, SCell has a different set of parameters, and the network can employ some optimization techniques to reuse the HARQ process after WD 22 has already transmitted feedback (but before SCell's NN 16 has received HARQ data).

[0158] The SCell throughput loss from Example 1 can be reused; however, N HP The value can be updated to reflect the amount of internal HARQ process that can be reused. This type of information can be provided from SCell's NN 16 to SpCell's NN 16.

[0159] Example 3: Feedback-based Scell ​​evaluation

[0160] In this embodiment, the NN 16 of each SCell can provide feedback to the NN 16 of the SpCell about the average number of time slots that the currently or previously connected WD 22 has experienced HARQ process exhaustion.

[0161] -In SCells:

[0162] ○ For each served CA WD 22 of NN 16 connected to (currently or previously connected to) SpCell with link latency d:

[0163] ●SCell's NN 16 measures and stores the number of time slots that WD 22 cannot be scheduled on DL due to unavailable DL HARQ procedures (e.g., unavailable DL HARQ procedure IDs, caused by delay feedback via long gNB links) or other WD 22s being scheduled →t s,d,i

[0164] End For;

[0165] The NN 16 of SCell calculates the average for all WD 22 (in set I) served by the NN 16 of SpCell (which has the same gNB inter-delay d as the current SCell).

[0166] ○SCell's NN 16 reports t to each SpCell's NN 16 with delay d. s,d .

[0167] -SpCell's NN 16 can record t s,d And it is stored locally for use in that SCell, and thus can be used for future SCell evaluations.

[0168] In some embodiments, SCell selection may involve machine learning techniques, such as reinforcement learning, where the SpCell's NN 16 attempts to configure and activate multiple SCells with different inter-node latency during the initial phase of deployment, and the measured latency is used by the SCell's NN 16 as a negative reward. The set of actions can be different permutations of the configured / deconfigured candidate SCells. This can be referred to... Figure 16 The following steps are summarized and can be performed at NN 16 of SpCell:

[0169] -Step S184:

[0170] - Define the state as a set S of configured SCells:

[0171] No SCell is configured. S = {·}.

[0172] One SCell is configured. S = {{s1}, {s2}, {s3}, ..., {s... n}}.

[0173] ○N SCells are configured.

[0174] S={{s1,s2},{s1,s3},{s1,s4},...{s n-1 s n}}.

[0175] ○N max One SCell is configured.

[0176] S={{s1, s2, s3, s4},...,{s n-3 s n-2 s n-1 s n If N max It is assumed to be 4.

[0177] - Define the action as:

[0178] Configure SCells; and / or

[0179] ○Unconfigure SCell s.

[0180] - Define the reward as t s,d The function or the throughput based on the latency between network nodes.

[0181] -Step S186:

[0182] Store the reward for each SCell in a knowledge base (e.g., a Q table). SCells that are never evaluated may have very low rewards (e.g., -1).

[0183] ○SCell's NN 16 continuously re-evaluates its latency and sends it back to SpCell's NN16 for updating the knowledge base. Figure 17 An example of a knowledge base in tabular format is shown.

[0184] -Steps S188-S202:

[0185] ○ If no SCell is configured (S188):

[0186] ■ Select and configure the SCell (S190) with the maximum reward;

[0187] ○If WD 22 already has a SCell configured (S192):

[0188] ■ Explore other SCells (S194) by applying the following methods:

[0189] ●Use probability p to configure the SCell with the next highest reward;

[0190] ●Use probability 1-p to configure the SCell that is configured the fewest times (i.e., possibly due to its low reward);

[0191] ○If the WD 22 is already configured with the maximum number of SCells:

[0192] ■ If one of the unconfigured SCells has an updated reward (because the SCell is configured to report a reward for other SpCells with the same inter-node latency as the SpCell currently running the SCell selection algorithm);

[0193] ●If the reward of SCell s is greater than the lowest reward in the configured SCell (S196):

[0194] Then SpCell's NN 16 can deconfigure the SCell with the lowest reward and configure SCell s (S198);

[0195] ●Otherwise

[0196] Otherwise, in step S200, the currently configured SCell is maintained or the SCell with the lowest reward is replaced (i.e., the probe is applied) (using probability 1-p).

[0197] Otherwise

[0198] ● In step S202, the process is repeated from step 2.

[0199] Example 4:

[0200] In some embodiments, the inter-node throughput or scheduling delay calculated in Examples 1, 2, and 3 can be used as input criteria for any other existing SCell selection algorithm.

[0201] One option is to use the predictions as is, replacing Tc (the carrier throughput calculated without considering the inter-node delay that might be used in existing SCell selection algorithms) with Tp (the predicted throughput based on inter-node delay) as the weight for SCell candidates. A preferred option is to use a factor Id (the effect of delay, where Id = Tp / Tc). This makes it possible to perform additional operations on Id, such as limiting the factor to a reasonable range (as there is a risk of algorithmic prediction outliers that might be known to be excluded based on, for example, domain knowledge), and possibly averaging it based on predictions from other WD 22s in the same cell. After this post-processing, Tc can be multiplied by Id to obtain a better estimate of the DL throughput.

[0202] The algorithms executed by NN 16 of SpCell and NN 16 of SCell in Examples 1-4 can be executed in a cloud environment outside the wireless node serving WD 22 by means of carrier aggregation.

[0203] Some embodiments of this disclosure may be provided with respect to one or more of the following aspects:

[0204] 1. An improved choice of SCell that is non-co-located with SpCell NN 16, and is a function of one or more of the following:

[0205] a. Link delay between network nodes;

[0206] b. Configured HARQ-ACK parameters;

[0207] c.SCell parameter set; and

[0208] d. The ability to reuse HARQ procedures.

[0209] 2. Allow SpCell's NN 16 to estimate user (WD22) throughput based on one or more factors (1.a-1.d), which can be combined with other SCell selection criteria to provide further enhanced decision-making.

[0210] 3. Allow the NN 16 of non-co-located SCells to estimate and store the user experience caused by long inter-network node latency, and report it to the NN 16 of SpCell, which can improve future decisions (e.g., non-co-located SCell selection) through machine learning, for example.

[0211] Some embodiments may include one or more of the following:

[0212] Example A1. A network node configured to communicate with a wireless device (WD), the network node being configured and / or including a radio interface and / or including a processing circuit module, the processing circuit module being configured to:

[0213] Estimate the throughput of the WD at the candidate secondary cell, the estimated throughput being at least partially based on the inter-node latency measured between the network node and another network node, the network node supporting the specific cell and the other network node supporting the candidate secondary cell; and

[0214] The selection of the candidate auxiliary cell for WD is determined at least in part based on the estimated throughput.

[0215] Example A2. The network node of Example A1, wherein one or more of the following:

[0216] Candidate auxiliary cells and special cells are not co-located; and

[0217] The estimated throughput is based at least in part on one of the following:

[0218] Hybrid Automatic Repeat Request (HARQ) codebook for candidate auxiliary cells;

[0219] The amount of HARQ procedure reused for candidate auxiliary cells;

[0220] Feedback from candidate auxiliary cells, the feedback indicating the amount of time resources associated with the exhaustion of the HARQ process for the inter-node delay d;

[0221] The parameter set used by the auxiliary cell; and

[0222] Machine learning algorithms.

[0223] Example A3. The network node of Example A1, wherein the network node and / or radio interface and / or processing circuit module are further configured to enable the network node to:

[0224] Measure the inter-node latency between network nodes.

[0225] Example B1. A method implemented in a network node, the method comprising:

[0226] Estimate the throughput of the WD at the candidate secondary cell, the estimated throughput being at least partially based on the inter-node latency measured between the network node and another network node, the network node supporting the specific cell and the other network node supporting the candidate secondary cell; and

[0227] The selection of the candidate auxiliary cell for WD is determined at least in part based on the estimated throughput.

[0228] Example B2. The method of Example B1, wherein one or more of the following:

[0229] Candidate auxiliary cells and special cells are not co-located; and

[0230] The estimated throughput is based at least in part on one of the following:

[0231] Hybrid Automatic Repeat Request (HARQ) codebook for candidate auxiliary cells;

[0232] The amount of HARQ procedure reused for candidate auxiliary cells;

[0233] Feedback from candidate auxiliary cells, the feedback indicating the amount of time resources associated with the exhaustion of the HARQ process for the inter-node delay d;

[0234] The parameter set used by the auxiliary cell; and

[0235] Machine learning algorithms.

[0236] Example B3. The method of Example B1 further includes:

[0237] Measure the inter-node latency between network nodes.

[0238] Example C1. A network node configured to communicate with a wireless device (WD), the network node being configured and / or including a radio interface and / or including a processing circuit module, the processing circuit module being configured to:

[0239] Determine the duration of time associated with the exhaustion of the Hybrid Automatic Repeat Request (HARQ) process in response to inter-node latency; and

[0240] The duration of the time is reported to another network node, which supports special cells and candidate auxiliary cells.

[0241] Example C2. The WD of Example C1, wherein one or more of the following:

[0242] The duration of the time slot is at least in part based on the number of time slots that cannot be scheduled due to at least one of the following: the unavailability of the HARQ process and the scheduling of other time slots;

[0243] Candidate auxiliary cells and special cells are not co-located; and

[0244] The WD selects candidate auxiliary cells for carrier aggregation based at least in part on the estimated WD throughput, and the estimated WD throughput is based at least in part on the reported amount.

[0245] Example D1. A method implemented in a network node, the method comprising:

[0246] Determine the duration of the Hybrid Automatic Repeat Request (HARQ) process exhaustion associated with inter-node latency; and

[0247] The duration of the time is reported to another network node, which supports special cells and candidate auxiliary cells.

[0248] Example D2. The method of Example D1, wherein one or more of the following:

[0249] The duration of the time slot is at least in part based on the number of time slots that cannot be scheduled due to at least one of the following: the unavailability of the HARQ process and the scheduling of other time slots;

[0250] Candidate auxiliary cells and special cells are not co-located; and

[0251] The WD selects candidate auxiliary cells for carrier aggregation based at least in part on the estimated WD throughput, and the estimated WD throughput is based at least in part on the reported amount.

[0252] As will be appreciated by those skilled in the art, the concepts described herein can be implemented as methods, data processing systems, computer program products, and / or computer storage media storing executable computer programs. Accordingly, the concepts described herein can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects of all those generally referred to herein as “circuit” or “module”. Any processes, steps, actions, and / or functionalities described herein can be performed by and / or associated with a corresponding module, which can be implemented by software and / or firmware and / or hardware. Furthermore, this disclosure can take the form of a computer program product on a tangible computer-readable storage medium having computer program code contained in the medium that is executable by a computer. Any suitable tangible computer-readable medium can be used, including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

[0253] Some embodiments are described herein with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer (thereby creating a special-purpose computer), a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create components for implementing the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams.

[0254] These computer program instructions may also be stored in a computer-readable storage medium or storage medium, which are capable of directing a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce manufactured articles, the manufactured articles including instruction components that implement functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0255] Computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0256] It is important to understand that the functions / actions shown in the boxes may not be performed in the order shown in the operation diagram. For example, two boxes shown consecutively may actually be executed substantially concurrently, or the boxes may sometimes be executed in reverse order, depending on the functions / actions involved. Although some diagrams include arrows on the communication path to indicate the main direction of communication, it is important to understand that communication may proceed in the opposite direction to the arrows shown.

[0257] Computer program code used to perform the operations of the concepts described herein can be in the form of, for example... The code may be written in an object-oriented programming language such as C++. However, the computer program code used to perform the operations of this disclosure may also be written in a conventional procedural programming language such as the "C" programming language. The program code may be executed entirely on the user's computer, partially on the user's computer as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer. In the latter case, the remote computer may be connected to the user's computer via a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet provided by an Internet service provider).

[0258] Many different embodiments have been disclosed herein in conjunction with the foregoing description and accompanying drawings. It will be understood that literally describing and illustrating every combination and sub-combination of these embodiments would be excessively repetitive and confusing. Accordingly, all embodiments can be combined in any manner and / or combination, and this specification, including the accompanying drawings, should be understood as a complete written description of all combinations and sub-combinations constituting the embodiments described herein and the ways and processes of making and using them, and should support the claims for any such combinations or sub-combinations.

[0259] The abbreviations that may be used in the preceding description include:

[0260] Explanation of Abbreviations

[0261] NR New Radio

[0262] CA carrier aggregation

[0263] Those skilled in the art will appreciate that the embodiments described herein are not limited to the specific examples and descriptions above. Furthermore, unless otherwise stated above, it should be noted that all drawings are not to scale. Based on the foregoing teachings, various modifications and alterations are possible without departing from the scope of the appended claims.

Claims

1. A method implemented in a network node (16) configured to communicate with a wireless device WD (22), the method comprising: Estimate (S142) the first throughput of the WD (22) at the first candidate auxiliary cell, the estimated first throughput being at least partially based on the network node (16) and a first network node supporting the first candidate auxiliary cell, the network node (16) supporting the primary cell. as well as Whether to select the first candidate auxiliary cell for the WD (22) is determined (S144) at least in part based on the estimated first throughput.

2. The method of claim 1, further comprising: The amount of Hybrid Automatic Repeat Request (HARQ) procedure reused associated with the first candidate auxiliary cell of the WD (22) is determined, and the estimated throughput is also based on the determined amount of HARQ procedure reuse.

3. The method of claim 2, further comprising: Estimate the throughput reduction associated with the HARQ process exhaustion of the WD (22) at the first candidate auxiliary cell, and the estimated first throughput is also based on the estimated throughput reduction.

4. The method of claim 3, wherein, The estimation of the first throughput of the WD (22) further includes using the same bandwidth, the same radio conditions and the same load for the first candidate auxiliary cell and the second candidate auxiliary cell on which the second throughput of the WD (22) is estimated.

5. The method of claim 3, wherein: The estimated throughput reduction associated with the exhaustion of the HARQ procedures of the WD (22) at the first candidate secondary cell is based on a configuration received from the first network node, the configuration indicating the maximum number of HARQ procedures associated with the first candidate secondary cell of the WD (22); and The estimation of the first throughput of the WD (22) further includes using different parameter sets for the first candidate auxiliary cell and the second candidate auxiliary cell on which the second throughput of the WD (22) is estimated.

6. The method of claim 1, further comprising: Feedback is received from the first network node supporting the first candidate auxiliary cell, the feedback indicating the average amount of time resources associated with the exhaustion of the Hybrid Automatic Repeat Request (HARQ) process experienced by the WD (22) connected to the first candidate auxiliary cell and the primary cell with inter-network node delay, and the determination of whether to select the first candidate auxiliary cell for the WD (22) is also based on the received feedback.

7. The method of claim 6, further comprising: A machine learning model is used, which includes feedback from network nodes (16) supporting candidate auxiliary cells as input; as well as The machine learning model is used to configure, deconfigure, and activate multiple candidate auxiliary cells with different inter-network node delays, and the selection of the first candidate auxiliary cell for the WD (22) is also based on the output of the machine learning model.

8. The method of claim 1, further comprising: Estimate multiple throughputs of the WD at multiple candidate auxiliary cells, the multiple estimated throughputs being at least partially based on the inter-network node latency measured between the network node and multiple network nodes supporting the multiple candidate auxiliary cells; At least one of the plurality of candidate auxiliary cells is selected for the WD based on a comparison among the plurality of estimated throughputs; as well as Configure at least one of the multiple candidate auxiliary cells for the WD.

9. The method of claim 8, further comprising: The predicted throughput Tp based on inter-node latency is used as input to select at least one of the plurality of candidate auxiliary cells.

10. The method of claim 8, further comprising: A machine learning model is used to select at least one of the plurality of candidate auxiliary cells for the WD (22), the machine learning model including at least one of the following as input: The measured latency between network nodes; The amount of data reused in the HARQ (Hybrid Automatic Repeat Request) procedure; The estimated throughput reduction associated with HARQ process exhaustion; and Feedback indicates the average amount of time resources associated with the exhaustion of the HARQ process for latency between network nodes.

11. A network node (16) configured to communicate with a wireless device WD (22), the network node (16) including a processing circuit module (68) configured to: Estimate the first throughput of the WD (22) at the first candidate auxiliary cell, the estimated first throughput being at least partially based on the inter-node delay measured between the network node (16) and a first network node supporting the first candidate auxiliary cell, the network node (16) supporting the primary cell; and Whether to select the first candidate auxiliary cell for the WD (22) is determined at least in part based on the estimated first throughput.

12. The network node (16) as described in claim 11, wherein, The processing circuit module (68) is further configured to: The amount of Hybrid Automatic Repeat Request (HARQ) procedure reused associated with the first candidate auxiliary cell of the WD (22) is determined, and the estimated throughput is also based on the determined amount of HARQ procedure reuse.

13. The network node (16) as described in claim 12, wherein, The processing circuit module (68) is further configured to: Estimate the throughput reduction associated with the HARQ process exhaustion of the WD (22) at the first candidate auxiliary cell, and the estimated first throughput is also based on the estimated throughput reduction.

14. The network node (16) as described in claim 13, wherein, The processing circuit module (68) is configured to estimate the first throughput of the WD (22) by being configured to perform the following operations: The first candidate auxiliary cell and the second candidate auxiliary cell on which the second throughput of the WD (22) is estimated use the same bandwidth, the same radio conditions and the same load.

15. The network node (16) as described in claim 13, wherein: The estimated throughput reduction associated with the exhaustion of the HARQ process of the WD (22) at the first candidate auxiliary cell is based on a configuration received from the first network node, which indicates the maximum number of HARQ processes associated with the first candidate auxiliary cell of the WD (22). as well as The processing circuit module (68) is configured to estimate the first throughput of the WD (22) by performing the following operations: using different sets of parameters for the first candidate auxiliary cell and the second candidate auxiliary cell on which the second throughput of the WD (22) is estimated.

16. The network node (16) as claimed in claim 11, wherein, The processing circuit module (68) is further configured to: Feedback is received from the first network node supporting the first candidate auxiliary cell, the feedback indicating the average amount of time resources associated with the exhaustion of HARQ processes experienced by the WD (22) connected to the first candidate auxiliary cell and the primary cell with inter-network node delay, and the determination of whether to select the first candidate auxiliary cell for the WD (22) is also based on the received feedback.

17. The network node (16) as claimed in claim 16, wherein, The processing circuit module (68) is further configured to: A machine learning model is used, which includes feedback from network nodes (16) supporting candidate auxiliary cells as input; and The machine learning model is used to configure, deconfigure and activate multiple candidate auxiliary cells with different inter-network node delays, and the determination of whether to select the first candidate auxiliary cell for the WD (22) is also based on the output of the machine learning model.

18. The network node (16) as claimed in claim 11, wherein, The processing circuit module (68) is further configured to: Estimate multiple throughputs of the WD at multiple candidate auxiliary cells, the multiple estimated throughputs being at least partially based on the inter-network node latency measured between the network node and multiple network nodes supporting the multiple candidate auxiliary cells; At least one of the plurality of candidate auxiliary cells is selected for the WD based on a comparison among the plurality of estimated throughputs; as well as Configure at least one of the multiple candidate auxiliary cells for the WD.

19. The network node (16) as described in claim 18, wherein, The processing circuit module (68) is further configured to: The predicted throughput Tp based on inter-node latency is used as input to select at least one of the plurality of candidate auxiliary cells.

20. The network node (16) as described in claim 18, wherein, The processing circuit module (68) is further configured to: A machine learning model is used to select at least one of the plurality of candidate auxiliary cells for the WD (22), the machine learning model including at least one of the following as input: The measured latency between network nodes; The amount of data reused in the HARQ (Hybrid Automatic Repeat Request) procedure; The estimated throughput reduction associated with HARQ process exhaustion; and Feedback indicates the average amount of time resources associated with the exhaustion of the HARQ process for latency between network nodes.

21. A method implemented in a network node (16) configured to communicate with a wireless device WD (22), the method comprising: Determine (S146) the average time resource amount associated with the exhaustion of the Hybrid Automatic Repeat Request (HARQ) process experienced by the WD (22) connected to the first candidate auxiliary cell and the primary cell with inter-network node delay; as well as Report (S148) to another network node indicating feedback on the average time resource quantity, the other network node supporting the primary cell, and the network node (16) supporting the candidate secondary cell.

22. The method of claim 21, further comprising: The candidate auxiliary cell communicates with the WD (22), the candidate auxiliary cell being selected for carrier aggregation of the WD (22) based at least in part on the estimated throughput of the WD (22) for the candidate auxiliary cell, the estimated throughput of the WD (22) being based at least in part on at least one of the following: The measured latency between network nodes; The amount of data reused in the HARQ (Hybrid Automatic Repeat Request) procedure; The estimated throughput reduction associated with HARQ process exhaustion; Machine learning models; and The reported feedback.

23. A network node (16) configured to communicate with a wireless device WD (22), the network node (16) including a processing circuit module (68) configured to: Determine the average time resource quantity associated with the exhaustion of the Hybrid Automatic Repeat Request (HARQ) process experienced by the WD(22) connected to the first candidate secondary cell and the primary cell with inter-node latency; and The feedback indicating the average time resource quantity is reported to another network node, which supports the primary cell and the network node (16) supports the candidate secondary cell.

24. The network node (16) as described in claim 23, wherein, The processing circuit module (68) is further configured to cause the network node (16): The candidate auxiliary cell communicates with the WD (22), the candidate auxiliary cell being selected for carrier aggregation of the WD (22) based at least in part on the estimated throughput of the WD (22) for the candidate auxiliary cell, the estimated throughput of the WD (22) being based at least in part on at least one of the following: The measured latency between network nodes; The amount of data reused in the HARQ (Hybrid Automatic Repeat Request) procedure; The estimated throughput reduction associated with HARQ process exhaustion; Machine learning models; and The reported feedback.

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