Patent Description:
Mobile networks are rapidly evolving while the industry is struggling to keep up with the rising demand of connectivity, data rates, capacity, and bandwidth. Next Generation mobile networks (e.g., <NUM> New Radio (NR)) are particularly faced with the challenge of providing a quantum-change in capability due to the explosion of mobile device usage, expansion to new use-cases not traditionally associated with cellular networks, and the ever-increasing capabilities of the end-user devices. The requirements for <NUM> are also manifold, as it is envisaged that it will cater for high-bandwidth high-definition streaming and conferencing, to machine interconnectivity and data collection for the Internet-of-Things (IoT), and to ultra-low latency applications such as autonomous vehicles as well as augmented reality (AR), virtual reality (VR) or mixed reality applications, and the like. The evolution toward <NUM> mobile networks is also driven by the diverse requirements of a multitude of new use cases in the areas of enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC) and massive machine-to-machine (M2M) communications, among others. Along with a demand for lower costs, these drivers have led to the development of various radio access network (RAN) architectures to support multiple deployment models.

For <NUM> systems to start delivering value immediately, initial components of the NR technology need to satisfy two urgent market needs, however: assisting <NUM> Long Term Evolution (LTE) deployments where substantial capital expenditures (CAPEX) and operational expenditures (OPEX) have been made; and strategic considerations with respect to the longer-term requirements of <NUM>. In this context, LTE-NR interworking is one of the most important technology components currently being developed. A key scenario for such interworking is widely considered to be LTE-NR dual connectivity (DC), in which user data can be exchanged between a mobile device (also referred to as a user equipment (UE) device) and an NR base station along with the LTE connectivity. Although advances in DC interworking continue to take place on various fronts, several lacunae remain, especially in the context of device handover, thereby requiring further innovation as will be set forth hereinbelow.

A 3GPP technical specification entitled "3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA) and NR; Multi-connectivity; Stage <NUM> (Release <NUM>)" (TS <NUM>, V15. <NUM>, <NUM>-<NUM>) provides an overview of the multi-connectivity operation using E-UTRA and NR radio access technologies.

A paper entitled "<NPL>) discloses a framework for operators to optimally configure EN-DC activation parameters to achieve desired trade-off between maximizing <NUM> sites utility and QoE.

The present patent disclosure is broadly directed a method, a computer-implemented apparatus and associated non-transitory computer-readable media for facilitating adaptive anchor layer mobility in a heterogeneous network implementation configured to support multi-RAT dual connectivity (MR-DC). Aspects of the invention are set out in the independent claims appended hereto. In one arrangement, a handover modulation criterion involving relevant trigger parametrics of an anchor cell node serving a DC-connected UE and a target cell node selected for handover may be compared against a tunable threshold parameter indicative of the effect of the target cell on the anchor cell quality. If the handover modulation criterion does not exceed the tunable threshold value, a quality degradation prediction with respect to the UE may be executed to estimate a likelihood of service failure. Responsive to determining that the likelihood of service failure does not exceed a probability threshold, handover of the UE to the target cell may be suppressed, thereby facilitating a balance between the need to maximize secondary cell connectivity of the DC-connected UE (e.g., connected to an eNB node of the anchor/primary cell in <NUM> LTE and a gNB node of a secondary cell in <NUM> NR) and potential service degradation caused by the stronger target cell.

Disclosed embodiments may provide one or more of the following technical advantages and benefits. For example, embodiments may be configured to maximize NR session time on <NUM> in an LTE-NR interworking network by optimizing the anchor LTE layer handover behavior such that the number of handovers may be minimized without sacrificing service quality. Because the NR session time is maximized, overall user experience with respect to <NUM> sessions/services delivered/consumed via NR legs is improved. By intelligently balancing handover decision-making against service level quality, e.g., on per-session and/or per-UE basis, embodiments herein advantageously reduce unnecessary handovers that place additional demands on the network, e.g., through consumption of radio channels/resources (e.g., Random Access Channels); through additional processing load in admission control, bearer setting and path switching; and have the potential to degrade the Quality of Service (QoS of ongoing connections. Additionally, as a consequence of the overall reduction in handover procedures, UE resources, e.g., power, battery, processing/computing resources, radio resources, etc. may also be better conserved in at least some example embodiments.

These and other advantages will be readily apparent to one of skill in the art in light of the following description and accompanying Figures.

Embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the Figures of the accompanying drawings in which like references indicate similar elements. It should be noted that different references to "an" or "one" embodiment in this disclosure are not necessarily to the same embodiment, and such references may mean at least one.

The accompanying drawings are incorporated into and form a part of the specification to illustrate one or more exemplary embodiments of the present disclosure. Various advantages and features of the disclosure will be understood from the following Detailed Description taken in connection with the appended claims and with reference to the attached drawing Figures in which:.

In the following description, numerous specific details are set forth with respect to one or more embodiments of the present patent disclosure. However, it should be understood that one or more embodiments may be practiced without such specific details. In other instances, well-known circuits, subsystems, components, structures and techniques have not been shown in detail in order not to obscure the understanding of the example embodiments. Accordingly, it will be appreciated by one skilled in the art that the embodiments of the present disclosure may be practiced without such specific components. It should be further recognized that those of ordinary skill in the art, with the aid of the Detailed Description set forth herein and taking reference to the accompanying drawings, will be able to make and use one or more embodiments without undue experimentation.

Additionally, terms such as "coupled" and "connected," along with their derivatives, may be used in the following description, claims, or both. It should be understood that these terms are not necessarily intended as synonyms for each other. "Coupled" may be used to indicate that two or more elements, which may or may not be in direct physical or electrical contact with each other, co-operate or interact with each other. "Connected" may be used to indicate the establishment of communication, i.e., a communicative relationship, between two or more elements that are coupled with each other. Further, in one or more example embodiments set forth herein, generally speaking, an element, component or module may be configured to perform a function if the element is capable of performing or otherwise structurally arranged or programmed under suitable executable code to perform that function.

Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate, mutatis mutandis.

As used herein, a network element, platform or node may be comprised of one or more pieces of service network equipment, including hardware and software that communicatively interconnects other equipment on a network (e.g., other network elements, end stations, etc.), and is adapted to host one or more applications or services with respect to a plurality of subscriber or users, and associated client devices as well as other endpoints, each executing suitable client applications configured to consume various data/voice/media services as well as sense/collect various types of data, information, measurements, etc. As such, some network elements may be disposed in a terrestrial cellular communications network, a non-terrestrial network (NTN) (e.g., a satellite telecommunications network including, inter alia, one or more communications satellites, high-altitude platform stations (HAPS) - which may be tethered or untethered, etc.), or a broadband wireline network, whereas other network elements may be disposed in a public packet-switched network infrastructure (e.g., the Internet or worldwide web, also sometimes referred to as the "cloud"), private packet-switched network infrastructures such as Intranets and enterprise networks, as well as service provider network infrastructures, any of which may span or involve a variety of access networks and core networks in a hierarchical arrangement. In still further arrangements, one or more network elements may be disposed in cloud-based platforms or data centers having suitable equipment running virtualized functions or applications relative to one or more processes set forth hereinbelow.

Example end stations and client devices (broadly referred to as User Equipment or UE devices) may comprise any device configured to consume and/or create any service via one or more suitable access networks or edge network arrangements based on a variety of access technologies, standards and protocols, including a heterogeneous network environment in some embodiments. Example UE devices may therefore comprise various classes of devices, e.g., multi-mode and/or dual-connectivity terminals adapted to communicate using terrestrial cellular communications infrastructure(s) based on different radio access technologies (RATs), WiFi communications infrastructure(s), or NTN communications infrastructure(s), or any combination thereof, which in turn may comprise smartphones, multimedia/video phones, mobile/wireless user equipment, portable media players, Internet appliances, smart wearables such as smart watches, portable laptops, netbooks, palm tops, tablets, phablets, IoT devices, connected vehicles (manual and/or autonomous), unmanned aerial vehicles (UAVs), and the like, as well as portable gaming devices/consoles including augmented reality (AR), virtual reality (VR) or mixed reality devices, etc., each having at least some level of radio network communication functionalities for accessing suitable RAN infrastructures according to some example implementations.

One or more embodiments of the present patent disclosure may be implemented using different combinations of software, firmware, and/or hardware in one or more modules suitably programmed and/or configured. Thus, one or more of the techniques shown in the Figures (e.g., flowcharts) may be implemented using code and data stored and executed on one or more electronic devices or nodes (e.g., a subscriber client device or end station, a network element, etc.). Such electronic devices may store and communicate (internally and/or with other electronic devices over a network) code and data using computer-readable media, such as non-transitory computer-readable storage media (e.g., magnetic disks, optical disks, random access memory, read-only memory, flash memory devices, phase-change memory, etc.), transitory computer-readable transmission media (e.g., electrical, optical, acoustical or other form of propagated signals - such as carrier waves, infrared signals, digital signals), etc. In addition, such network elements may typically include a set of one or more processors coupled to one or more other components, such as one or more storage devices (e.g., non-transitory machine-readable storage media) as well as storage database(s), user input/output devices (e.g., a keyboard, a touch screen, a pointing device, and/or a display), and network connections for effectuating signaling and/or bearer media transmission. The coupling of the set of processors and other components may be typically through one or more buses and bridges (also termed as bus controllers), arranged in any known (e.g., symmetric/shared multiprocessing) or heretofore unknown architectures. Thus, the storage device or component of a given electronic device or network element may be configured to store code and/or data for execution on one or more processors of that element, node or electronic device for purposes of implementing one or more techniques of the present patent disclosure.

Referring to the drawings and more particularly to <FIG>, depicted therein is an example mobile communications network 100A configured to support multi-RAT dual connectivity (MR-DC) wherein one or more embodiments of the present patent disclosure may be practiced in accordance with the teachings herein. A Radio Access Network (RAN) 102A may comprise a plurality of primary cells <NUM>-<NUM> to <NUM>-N supported by respective base stations <NUM>-<NUM> to <NUM>-N operating in a first RAT, wherein each primary cell may include one or more secondary cells effectuated by corresponding base stations operating in a second RAT. By way of illustration, primary cell <NUM>-<NUM> effectuated by base station or node <NUM>-<NUM> includes secondary cells 108B-<NUM> to 108B-K, each effectuated by corresponding base stations or nodes 108A-<NUM> to 108A-K, respectively. Likewise, primary cell <NUM>-N effectuated by base station or node <NUM>-N may include secondary cells 110B-<NUM> to 110B-P, each effectuated by corresponding base stations or nodes 110A-<NUM> to 110A-P. Depending on the particular access technologies implemented with respect to the primary and secondary cellular infrastructures, respectively, RAN 102A may be connected to one or more core networks 102B comprising a plurality of core network elements, e.g., elements <NUM>, <NUM>, via suitable signaling/data interfacing pathways <NUM> (e.g., the control plane or c-plane and user plane or u-plane pathways), that may be configured to effectuate various core network functionalities such as call control/switching, handover/mobility and roaming, charging, gateway access, service invocation, etc., relative to a plurality of subscribers served by respective primary and/or secondary cells of RAN 102A. Further, suitable backhaul network paths may also be provided in an example network arrangement in order to effectuate standards-based signaling/data interfacing between the primary cell nodes and corresponding secondary cell nodes for facilitating, inter alia, expanded coverage, efficient spectrum utilization, MR-DC functionality, etc., with respect to the subscriber UE devices served by the respective primary and/or secondary cells. As illustrated, primary cell node <NUM>-<NUM> is connected via inter-nodal backhaul paths <NUM>-<NUM> to <NUM>-K to secondary cell nodes 108A-<NUM> to 108A-K, respectively, and primary cell node <NUM>-N is connected via inter-nodal backhaul paths <NUM>-<NUM> to <NUM>-P to secondary cell nodes 110A-<NUM> to 110A-P, respectively.

Skilled artisans will recognize upon reference hereto that example network arrangement 102A is illustrative of a heterogeneous wide area cellular communications network wherein multiple RATs and multiple types of access nodes may be implemented using a combination of cellular coverage areas, e.g., macrocells, small cells, microcells, picocells, and femtocells, etc., generally grouped as "macrocells" and "small cells," in order to offer wireless coverage in an environment with a wide variety of wireless coverage zones, ranging from an open outdoor environment to office buildings, homes, and underground areas. Illustratively, primary cells <NUM>-<NUM> to <NUM>-N, comprising a first type of RAT infrastructure, may be operative as a plurality of macrocells, whereas respective secondary cells 108B-<NUM> to 108B-K and 110B-<NUM> to 110B-P, comprising at least a second type of RAT infrastructure, may be operative as corresponding pluralities of small cells, respectively, wherein tightly coordinated complex interoperation between macrocells and small cells may be effectuated to provide a mosaic of radio coverage, with handoff capability between network elements. In some configurations, therefore, example network arrangement 102A may be implemented as a multi-x environment - multi-technology, multi-domain, multi-spectrum, multi-operator and/or multi-vendor infrastructure(s) - with seamless interoperability to deliver assured service quality across the entire network, and having architectural flexibility that is reconfigurable enough to accommodate changing user needs, business goals and subscriber behavior, e.g., including the deployment of inter-generational RATs based on existing RATs and/or future RAT developments. Accordingly, it will be realized that in some embodiments primary cells <NUM>-<NUM> to <NUM>-N may be based on any <NUM>/<NUM>/<NUM>/<NUM>/NextGen (NG) RAT technologies according to applicable 3GPP standards and specifications, with corresponding secondary cells 108B-<NUM> to 108B-K and 110B-<NUM> to 110B-P based on any <NUM>/<NUM>/<NUM>/<NUM>/NextGen RAT technologies different from that of the primary cells.

For purposes of the present patent disclosure, "dual connectivity" may be defined as the capability and functionality of a RAN infrastructure wherein a subscriber station or UE device can be simultaneously connected to two serving base stations, e.g., designated as a master node, MN (also synonymously referred to as an anchor node), and a secondary node, SN, operating in different RAT technologies, for effectuating respective service sessions. In some embodiments, a first service session may be effectuated via a primary cell node e.g., operating as the master node, for consuming and/or providing a first service type, whereas a second service session may be effectuated via a secondary cell node, e.g., operating as the secondary node, for consuming and/or providing a second service type, wherein the first and/or second service types may include but are not limited to services such as data, voice, gaming, streaming, multimedia, etc. As illustrated in <FIG>, a plurality of UE devices <NUM>-<NUM> to <NUM>-L are exemplified with respect to primary cell <NUM>-<NUM>, wherein UE <NUM>-<NUM> is shown as having a radio connection to primary cell node <NUM>-<NUM> as well as a radio connection to secondary cell node 108A-<NUM>. In similar fashion, a plurality of UE devices <NUM>-<NUM> to <NUM>-M are exemplified with respect to primary cell <NUM>-N, wherein UE <NUM>-<NUM> is shown as having a radio connection to primary cell node <NUM>-N as well as a radio connection to secondary cell node 110A-<NUM>. It will be recognized that regardless of which RATs are utilized for the anchor nodes and corresponding secondary nodes in RAN 102A, or regardless of the types of services consumed via respective sessions, it is important that when a DC-connected UE roams from one primary/anchor cell to another, or obtains a better quality signal from a neighbor primary cell due to a change in the ambient radio environment, an optimized handover process is implemented such that neither service/session is unduly disrupted while suitable handoff procedures may be triggered pursuant to applicable standards and protocols relating to mobility management. Example embodiments for facilitating such a scheme will be set forth below in additional detail with particular reference to a communications network having inter-generational DC based on <NUM> LTE and <NUM> NR technologies. Skilled artisans will recognize, however, that the teachings of the present disclosure are not necessarily limited thereto, which may be equally applied in embodiments based on other existing RATs and/or future RATs, mutatis mutandis.

<FIG> depicts an example heterogeneous network environment with MR-DC based on <NUM> LTE and <NUM> NR technologies wherein UE handover (HO) may be managed according to some embodiments. As illustrated, network environment 100B comprises two separate architectural representations of generalized network 100A described above, with <NUM> LTE infrastructure operating as an anchor layer in a first representation 199A and <NUM> NR infrastructure operating as an anchor layer in a second representation 199B. Representation 199A is exemplified with an LTE primary cell coverage area <NUM> effectuated by one or more eNB nodes 162A, 162B, wherein eNB node 162B is operative as an anchor/master node with respect to a <NUM> NR secondary cell <NUM> effectuated by a gNB node <NUM> operative as a secondary node. In analogous fashion, representation 199B is exemplified with a <NUM> primary cell coverage area <NUM> effectuated by one or more gNB nodes 172A, 172B, wherein gNB node 172B is operative as an anchor/master node with respect to an LTE secondary cell <NUM> effectuated by an eNB node <NUM> operative as a secondary node. In each network representation 199A, 199B, respective master nodes and corresponding secondary nodes may be connected via appropriate backhaul paths <NUM>, <NUM>, respectively, which may be configured to facilitate at least one of control signaling and/or user data flows with respect to one or more DC-connected UEs (not specifically shown in this FIG. ) depending on implementation options. Further, appropriate signaling/user data pathways <NUM> may be established between the LTE infrastructure and a core network <NUM>, e.g., Evolved Packet Core (EPC), which may comprise one or more nodes or elements such as, e.g., Mobility Management Entity (MME) nodes, Serving Gateway (SGW) nodes, Packet Data Network (PDN) Gateway (PGW) nodes, Home Subscriber Server (HSS) nodes, etc. Likewise, appropriate signaling/user data pathways 177A may be established between the <NUM> NR infrastructure and a core network <NUM>, e.g., <NUM> Core (5GC), which may comprise one or more nodes or elements such as, e.g., User Plane Function (UPF), Session Management Function (SMF), Access and Mobility Management Function (AMF), etc. In a further arrangement, depending on the level of interworking between <NUM> LTE and <NUM> networks, appropriate signaling/user data pathways 177B may be established between the LTE infrastructure, e.g., LTE nodes 162A/B, and 5GC network <NUM>. Skilled artisans will therefore recognize that a variety of network architectural combinations may be implemented for facilitating MR-DC, for example, depending on which RAT/RAN is configured as the anchor layer and/or which core network services are involved, wherein any of the network architectural combinations may be configured in accordance with applicable standards and/or specifications, e.g., 3GPP Technical Specification (TS) <NUM>. Accordingly, at least in some embodiments involving MR-DC, inter-nodal interfaces <NUM>/<NUM> may be implemented as X2 interfaces, interface <NUM> may be S1-based, and interfaces 177A/B may be implemented as N2/N3 interfaces. Regardless of which particular network architectural combination is implemented based on <NUM> LTE and <NUM> NR infrastructures, example embodiments herein may be configured to address the technical problem of managing triggered handover processes with respect to DC-connected UEs such that connectivity to secondary cells may be optimized in order to maintain expected levels of Key Performance Indicators (KPIs) such as high Quality of Service (QoS) and/or Quality of Experience (QoE) associated with <NUM>/<NUM> services.

One example implementation option involving an LTE anchor layer with respect to <NUM> NR secondary cells, with an EPC core network, is known as Option <NUM> implementation, referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (E-UTRA) NR DC or EN-DC, exemplified in <FIG> as representation 199A. In another implementation option, the anchor layer remains <NUM> LTE and the secondary cells remain <NUM> NR, but the core is a 5GC network (e.g., via interface 177A/B as shown in <FIG>). This implementation is known as Option <NUM>, referred to as NG-RAN E-UTRA-NR DC (NGEN-DC). Yet another implementation option involves the anchor layer being <NUM> NR with <NUM> LTE secondary cells and EPC core, which is known as Option <NUM>, referred to as NR-E-UTRA DC (NE-DC). Depending on how a <NUM> LTE infrastructure is utilized (e.g., for control plane anchoring) and the level of interworking involved, a <NUM> network deployment may be considered a Non-Standalone (NSA) implementation for purposes of some embodiments of the present disclosure. As will be seen below, some example embodiments particularly described in reference to certain EN-DC variations in a <NUM> NSA architecture may also be adapted in respect of other implementation options as well.

<FIG> depict some example EN-DC architectures that may be realized in a <NUM> NSA implementation consistent with at least a portion of the network environment of <FIG> wherein a handover management scheme may be practiced according to some embodiments. As described above, Option <NUM> architectural implementation involves a network having both LTE and NR radio access but using the EPC core of LTE to route control signals. In other words, a UE may be connected to an LTE master eNB node (MeNB) with respect to both user plane and control plane traffic whereas it is connected to a <NUM> secondary gNB node (SgNB) for user plane traffic only. In this arrangement, the UE may be connected to the LTE network first and then connected to the secondary NR infrastructure via a Radio Resource Control (RRC) Connection Configuration process. In one configuration of Option <NUM> exemplified by arrangement 200A of <FIG>, UE <NUM> is connected to MeNB <NUM> and SgNB <NUM>, where X2 interface 208A between MeNB <NUM> and SgNB <NUM> is operative to carry both user plane and control plane traffic. MeNB node <NUM> is operative to interface with EPC infrastructure <NUM> with respect to both user plane traffic (via S1-U interface) and control plane traffic (via S1-C interface). In another variation 200B shown in <FIG>, referred to as Option 3a interworking mode, there is only control plane traffic via X2 interface 208B between MeNB <NUM> and SgNB <NUM>, with the data traffic traversing S1-U interface disposed between SgNB <NUM> and EPC <NUM>, e.g., SGW therein. In a further variation 200C shown in <FIG>, referred to as Option 3x, X2 interface 208C between MeNB <NUM> and SgNB <NUM> is operative to support a portion of LTE user plane traffic, in addition to an S1-U interface supporting user plane traffic directly between SgNB <NUM> and EPC <NUM>. It will be realized that for Option <NUM>, the traffic is split between <NUM> and <NUM> at MeNB <NUM>; for Option 3a, the traffic is split between <NUM> and <NUM> at EPC (i.e., SGW); and for Option 3x, the traffic is split between <NUM> and <NUM> at SgNB <NUM>. Further, although MeNB <NUM> and SgNB <NUM> are shown as separate nodes in <FIG>, some example implementations may integrate both LTE and NR base station functionalities into an integrated or co-located base station entity, with the inter-nodal X2 interfacing being an intra-component pathway in certain configurations.

<FIG> depicts various protocol layers or modules associated with a DC-capable UE device, an anchor node and a secondary node, respectively, in an example architecture <NUM> that may be adapted for facilitating UE handover according to an embodiment. In an example EN-DC arrangement, LTE cells may be configured as a Master Cell Group (MCG) operative as an anchor layer whereas NR cells may be configured as a Secondary Cell Group (SCG). Because the LTE infrastructure is adapted as the anchor layer, a UE may be configured to perform initial registration with an anchor cell in the MCG, which can then add one or more secondary cells of the SCG. Whereas a UE may be communicating with both LTE eNB and NR gNB nodes on the radio side, i.e., the air interface, all the communications (signaling and data) are ultimately transported via the EPC network in example EN-DC implementations, with the eNB node operating as an anchor/master node and the gNB node operating as a secondary node. Accordingly, LTE eNBs and NR gNBs may use their own respective PHY/MAC layers with separate MAC scheduling in some arrangements. To support both the LTE and NR RRC control signaling, various types of signaling radio bearers (SRBs) may be used depending on the type of interworking: Master Cell Group (MCG) SRBs comprising direct SRBs between the master node and the UE device for conveying master node RRC messages that can also embed secondary node RRC configurations; split SRBs comprising SRBs split between the master node and the secondary node at a higher Layer <NUM> component, i.e., at Packet Data Convergence Protocol (PDCP) layer; and Secondary Cell Group (SCG) SRBs comprising direct SRBs between the secondary node and the UE device by which secondary node RRC messages may be sent. With respect to the user plane data traffic, various types of data radio bearers (DRBs) may likewise be used depending on the type of interworking: MCG DRBs comprising bearers terminated at the master node and using only the master node lower layers; MCG split DRBs comprising bearers terminated at the master node but that can use the lower layers of either the master node or secondary node, or both; SCG DRBs comprising bearers terminated at the secondary node and using only the secondary node lower layers; and SCG split DRBs comprising bearers terminated at the secondary node but that can use the lower layers of either the master node or secondary node, or both.

In the example architectural arrangement <NUM> shown in <FIG>, Layer <NUM> protocol stack <NUM> associated with UE <NUM> is operative for supporting various bearers, e.g., MCG bearers <NUM>, Split bearers <NUM> and SCG bearers <NUM>. One or more PDCP, RLC and MAC layer components are provided as part of protocol stack or engine <NUM>, where some of the protocol components are used exclusively for MCG bearers and SCG bearers, respectively, while other components may be shared for supporting Split bearers. As illustrated, E-UTRA PDCP component <NUM>, E-UTRA RLC component <NUM> and E-UTRA MAC component <NUM> of UE protocol stack <NUM> are operative with respect to MCG bearer traffic. Likewise, NR PDCP component <NUM>, NR RLC component <NUM> and NR MAC component <NUM> of UE protocol stack <NUM> are operative with respect to SCG bearer traffic. A shared NR PDCP component <NUM> may interoperate with either E-UTRA RLC <NUM>, NR RLC <NUM>, or both, with respect to Split bearer traffic.

In similar fashion, Layer <NUM> protocol stacks <NUM>, <NUM> associated with a master eNB node and a secondary gNB node, respectively, may each comprise one or more PDCP, RLC and MAC layer components, where some of the protocol components are used exclusively for corresponding MCG bearers and SCG bearers, while other components may be shared for supporting Split bearers. Protocol stack or engine <NUM> associated with MeNB node is operative to support MCG bearers <NUM>, SCG bearers <NUM> and Split bearers <NUM>, and comprises E-UTRA MAC component <NUM>, one or more E-UTRA RLC components <NUM>-<NUM>, E-UTRA PDCP component <NUM>, and one or more NR-PDCP components <NUM> and <NUM>. Protocol stack or engine <NUM> associated with SgNB node is operative to support MCG bearers <NUM>, SCG bearers <NUM> and Split bearers <NUM>, and comprises NR MAC component <NUM>, one or more NR RLC components <NUM>-<NUM>, and one or more NR-PDCP components <NUM>-<NUM>. An X2 interface <NUM> disposed between MeNB and SgNB nodes may be configured to carry inter-nodal Split bearer traffic as well as inter-nodal SCG/MCG traffic, depending on implementation.

As noted elsewhere in the present disclosure, it is important to define a suitable mobility strategy with respect to the anchor LTE bands/layers in an example <NUM> NSA implementation in order to ensure that <NUM> usability within a macrocell coverage zone is maximized. It should be appreciated that this condition or requirement can be challenging as the underlying LTE protocol layers are typically optimized over a period of time so as to provide seamless QoS/QoE on LTE. However, such an arrangement may be suboptimal because <NUM> NR session continuity is largely influenced by anchor LTE mobility. In current LTE/NR implementations, mobility in the anchor LTE layer triggers a release of SN-terminated Split bearer (e.g., the DRB traffic over NR), which causes it to be reconfigured again after completing a handover process in LTE through suitable mechanisms (e.g., based on configuration or responsive to certain LTE measurement reports such as B1 measurement reports). In a typical post-successful EN-DC connection setup scenario, the NR leg release may be triggered by both MeNB and SgNB nodes. For example, SgNB-triggered EN-DC NR leg releases may be caused in response to detecting a Radio Link Failure (RLF) by the gNB node. MeNB-triggered EN-DC NR leg releases may be due to a variety of causes such as degradation in source cell performance caused by, e.g., UE-detected random access failure, RLC Uplink (UL) delivery failure, out of synchronization condition, etc., as well as LTE mobility/handover.

With respect to an example LTE mobility/handover scenario, it is relevant to note that when a neighbor cell with better radio conditions is found, the neighbor cell may be reported by the UE through a suitable measurement report, e.g., RRC Measurement Report, as per fulfillment of applicable respective event criteria. As such, various LTE mobility scenarios are possible within the same carrier and/or different LTE carriers, where inter-frequency mobility events may trigger an evaluation of both source and neighbor cells against static configurable thresholds. On the other hand, intra-frequency mobility events may trigger an evaluation based only on neighbor cell's radio conditions i.e., differences in radio measurements (e.g., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), etc.) between the source cell and neighbor cells are compared against certain parameters such as a3Offset and hysteresis, and if the neighbor cells remain stronger for a duration greater than timetotrigger value, the UE sends an A3 measurement report to the source eNB node requesting handover to a suitable candidate cell (i.e., a target cell). Such a handover, however, may cause NR service disruption because of the latency involved in reestablishing the NR leg in association with the target cell anchor node, which may or may not involve a different NR cell.

<FIG> and <FIG> cumulatively depict a message flow diagram 400A/B associated with a network arrangement comprising an LTE eNB node as a master/anchor node and a <NUM> gNB node as a secondary node, wherein NR service disruption may be caused due to a handover in the LTE anchor layer. In one implementation, message flow diagram 400A/B is illustrative of a procedure that may be executed when a stronger LTE intra-frequency neighbor cell is reported by a UE <NUM> operating in an EN-DC network environment. In response, a source/anchor eNB <NUM> may be configured to evaluate applicable criteria for mobility based on a conditionality defined as follows: <MAT> where M(n) = strength of neighboring cell, M(s) = strength of serving cell, CIO = Cell Individual Offset, and Hyst = Hysteresis.

In one arrangement, the foregoing conditionality may be evaluated based on certain parameters, e.g., timetotrigger, and with respect to applicable trigger quantity, such as RSRP, RSRQ, etc. Responsive to the identified target eNB, e.g., target MeNB <NUM>, source MeNB <NUM> triggers a Handover request to target MeNB <NUM> and initiates an SgNB release request towards associated SgNB <NUM>. After successful LTE handover, UE context is released in both source MeNB <NUM> and associated SgNB <NUM>. In one arrangement, UE <NUM> may be configured with one or more LTE mobility triggering event details (e.g., A1, A2, A3, A5, B1, etc.) in the new MeNB <NUM>. If the new MeNB <NUM> supports EN-DC configuration, UE <NUM> may be configured to measure suitable NR cell(s) for EN-DC setup and report via a B1 measurement report. Responsive to finding appropriate NR cell(s), the B1 measurement report may be sent by UE <NUM> indicating the measured NR cell(s). Responsive thereto, the new anchor MeNB <NUM> may trigger an SgNB addition procedure with the strongest reported NR cell (e.g., subject to the condition that the NR cell is defined as a valid candidate with supporting IP connectivity definitions). Thereafter, the established DRB traffic is suspended with source SgNB <NUM>. Following a successful Random Access Channel (RACH) procedure for the LTE and NR cells, UE <NUM> may establish the Split bearer and resume the NR session, e.g., downlink (DL) data, over the new NR leg with target SgNB <NUM>.

Example message flow diagram, comprising portions 400A and 400B, sets forth the foregoing interactions in additional detail. Responsive to identifying target MeNB <NUM>, a Handover request <NUM> is generated by source MeNB <NUM>, which is propagated to target MeNB <NUM> in accordance with LTE procedures. An Acknowledgement <NUM> may be received by source MeNB <NUM> in response thereto. Responsive to Acknowledgement <NUM>, an SgNB Release Request message <NUM> may be generated by source MeNB <NUM> towards SgNB <NUM> associated therewith, followed by an Acknowledgement message <NUM>. An RRCConnectionReconfiguration message <NUM> may be generated by source MeNB <NUM> towards UE <NUM>. A Secondary RAT data volume report <NUM> may be obtained by source MeNB <NUM> from source SgNB <NUM>. In response, a Secondary RAT report <NUM> may be generated by source MeNB <NUM> towards MME <NUM>. Also, a Secondary Node (SN) Status Transfer message <NUM> may be generated by source MeNB <NUM> towards target MeNB <NUM>. A Data Forwarding message <NUM> may be propagated from SGW <NUM> to target MeNB <NUM> via source MeNB <NUM>. Target MeNB <NUM> then generates a Path Switch Request message <NUM> towards MME <NUM>. In response, a Bearer Modification message <NUM> is generated by MME <NUM> towards SGW <NUM>, which sends a New Path message <NUM> for the MCG bearer to target MeNB <NUM>. Further, MME <NUM> may also generate a Path Switch Acknowledgement message <NUM> towards target MeNB <NUM>. A UE Context Release message <NUM> may be generated by target MeNB <NUM> towards source MeNB <NUM>, which in turn propagates a UE Context Release message <NUM> towards associated SgNB <NUM>. An RRC Connection Reconfiguration message <NUM> with suitable Event Triggers, e.g., A1, A2, A3, A5, B1, etc., as noted above, may be generated by target MeNB <NUM> towards UE <NUM>. Responsive thereto, an RRC Connection Reconfiguration Complete message <NUM> may be generated by UE <NUM> towards target MeNB <NUM>.

A Measurement Report <NUM> (e.g., based B1 Event, which measures or otherwise indicates if a secondary neighbor NR cell is of stronger signal strength/quality by a threshold) may be generated by UE <NUM> towards target MeNB <NUM>. Thereafter, X2-based messaging may take place between target MeNB <NUM> and associated target SgNB <NUM> with respect to requesting to add a new SgNB, associated Acknowledgement, and SN Status Transfer, cumulatively indicated as messaging <NUM>. Target MeNB <NUM> suspends DRBs as indicated at block <NUM>. Target SgNB <NUM> sends an RRC Reconfiguration message <NUM> with appropriate parameters (e.g., NR Physical Cell ID or PCI, Synchronization Signal Block (SSB) position, RACH parameters, etc.) to UE <NUM>, which engages in an LTE RACH procedure <NUM> with target MeNB <NUM>. An Uplink (UL) user data in LTE may be generated by UE <NUM> towards SGW <NUM>, indicated as message flow <NUM>. Thereafter, an RRC Reconfiguration Complete message <NUM> may be generated by UE <NUM> towards target SgNB <NUM>, which in turn generates an SgNB Reconfiguration Complete message <NUM> towards target MeNB <NUM> via X2 interface. An NR RACH procedure <NUM> may take place between UE <NUM> and target SgNB <NUM>. An Evolved-UTRAN Radio Access Bearer (ERAB) Modification Indication <NUM> may be generated by target MeNB <NUM> towards MME <NUM>, responsive to which a Bearer Modification messaging process <NUM> may take place between MME <NUM> and SGW <NUM>. An End Marker Packet may be provided by SGW <NUM> via flow <NUM> to target SgNB <NUM>, which commences/resumes DRB traffic as indicated at block <NUM>. Thereafter, a Downlink (DL) user data in NR may be provided by target SgNB <NUM> via flow <NUM>. A New Path for DL packets may be provided to target SgNB <NUM> by SGW <NUM>, as indicated by flow <NUM>. An ERAB Modification Confirm message <NUM> may be generated by MME <NUM> towards target MeNB <NUM> which is operative as the new anchor node after completion of handover.

It will be appreciated that call flow segment <NUM> indicated in message flow diagram 400A/B is illustrative of a typical NR service interruption that may be caused in response to the anchor LTE handover instigated by source MeNB <NUM>. As can be seen from example message flow diagram 400A/B, the overall impact on user experience may vary significantly in an actual network implementation depending on the latency of several air interface procedures involved in the call flow, e.g., such as LTE and NR RACH procedures. Whereas current <NUM> NSA implementations involving dual connectivity can faithfully execute HO trigger-based handover procedures according to LTE, thereby potentially causing frequent service disruptions and concomitant quality degradation in NR services (e.g., due to the reduction of the overall time that a UE is connected to the NR leg), it has been observed that such handovers are effectuated even in good quality radio conditions (e.g., both serving/source and target LTE cells have good coverage and signal quality, with the target cell being better by an offset). Accordingly, to maximize the time NR sessions remain uninterrupted in spite of the instigation of LTE anchor layer handovers, embodiments herein provide a system and method for facilitating a predictive approach with respect to conditions under which service degradation may be expected to exceed a configured threshold and allowing a handover only when the predicted service degradation in the anchor layer reaches or crosses the degradation threshold (e.g., by a margin). In other words, a technical effect of some embodiments herein is to reduce the number/frequency of anchor layer handovers in an MR-DC network implementation, whereby user experience with respect to <NUM> sessions/services delivered/consumed via NR legs is improved. Further, as a consequence of the overall reduction in HO procedures, UE device resources, e.g., power, battery, processing/computing resources, radio resources, etc. may be better conserved in at least some example embodiments.

Broadly, example embodiments may be effectuated based on what may be referred to as "session level mobility triggering adjustment" where a failure predictor process may be implemented for estimating, determining, or otherwise obtaining a satisfactory operating point of the serving LTE anchor cell for an ongoing EN-DC session of a served UE. As long as the UE is operating in the satisfactory zone, an example embodiment may be configured such that a handover process that would otherwise have been triggered due to a mobility measurement report, e.g., an A3 intra-frequency mobility report, an A5 inter-frequency mobility report, etc., is suppressed so that no handover will be initiated. In some embodiments, subsequent measurement reports may be evaluated against dynamically modified thresholds that may be obtained, calculated, or otherwise estimated in various ways. In some example embodiments, further modulation may be provided such that while suppressing handover, additional interference is not caused in or due to the strongest reported neighboring cell using suitable interference margin thresholds. It will be appreciated that such HO process modulation allows an evaluation of every session to be performed based on each session's merit and determine the most suitable LTE handover threshold based on the current operating point of the serving cell with respect to each served UE. In some example embodiments, a machine learning (ML) process may be implemented for estimating, predicting, or otherwise obtaining quality degradation based on RLF probability that may be used in determining a satisfactory operating zone with respect to an ongoing session. In some aspects relating to ML-based failure prediction, an example embodiment may be implemented based on a deep learning model that may be realized using an artificial neural network (ANN) process. In some further embodiments, an example ANN-based RLF predictor may use a federated architecture to train the process using cellular data obtained from different networks and/or different regions that may undergo data preprocessing. In still further embodiments involving ANN-based RLF prediction, a suitable feature selection process with respect to training, testing and validating an RLF predictor may be employed.

Regardless of whether ML-based RLF prediction and/or other methods for obtaining quality degradation prediction are employed in a handover management process for suppressing anchor layer HO operations while the UE is in a satisfactory operating zone in an anchor cell, example embodiments introduce a handover criterion comprising a tunable parameter that may be applied in addition to existing HO trigger processes. In one arrangement, the tunable parameter may comprise a neighbor cell's interference on a serving/anchor cell, which may be verified against a suitable parametric difference between the anchor cell and the neighbor cell. It will be realized that depending on implementation, such parametric differences may be obtained relative to a variety of cell parameters, e.g., RSRP, RSPQ, etc. Depending on the applicable threshold's verification against the tunable handover criterion, which may also be referred to as a handover modulation criterion, a handover suppression criterion, handover avoidance criterion, or other terms of similar import, RLF/quality degradation prediction may be selectively instigated to determine whether a handover should be initiated. Additional details relating to various aspects of the foregoing embodiments are set forth below.

<FIG> and <FIG> depict flowcharts of various blocks, steps and/or acts that may be (re)combined in one or more arrangements, with or without additional flowcharts of the present disclosure, for effectuating UE handover/mobility modulation according to some embodiments of the present patent disclosure. Process 500A shown in <FIG> is exemplary of a handover management method that may be performed by one or more processors of an anchor node (e.g., <NUM> LTE eNB node) serving one or more dual connectivity (DC) UE devices. As previously discussed, the anchor node may be configured as a master node of a source cell operating in a first RAT with respect to a secondary node operating in a second RAT, wherein the master node and the secondary node may be connected via an inter-nodal interface. In some embodiments, process 500A may be executed on a session-by-session basis and/or per UE basis. At block <NUM>, a measurement report may be received from a UE device, the measurement report containing information relating to one or more neighbor cells operating in the first RAT as an anchor layer. At block <NUM>, responsive to the measurement report, a particular neighbor cell may be selected as a target cell for handing over the UE device. At block <NUM>, a determination may be made that there is traffic with the secondary node over the inter-nodal interface. Responsive to determining that a handover modulation criterion exceeds a threshold value, a handover of the UE device to the target cell may be performed (block <NUM>). Otherwise, responsive to determining that the handover criterion does not exceed the value, a quality degradation prediction (e.g., based on RLF probability) may be performed with respect to the UE device to estimate a likelihood of service failure for the UE device on the serving cell (e.g., due to the effect of the selected neighbor target cell) as set forth at block <NUM>. Responsive to determining that the likelihood of service failure does not exceed a probability threshold, handover of the UE to the target cell may be suppressed (block <NUM>). Otherwise, responsive to determining that the likelihood of service failure exceeds the probability threshold, in some embodiments, a handover of the UE device to the target cell may be initiated (block <NUM>). In one arrangement, if the UE handover is suppressed as set forth at block <NUM>, process 500A may continue to wait for a next measurement report from the UE device (block <NUM>).

In some embodiments, one or more aspects of quality degradation prediction as set forth at block <NUM> may be performed as an ML-based RLF prediction process, wherein a suitable ML process or engine may be trained, tested and validated, as previously noted. Process 500B of <FIG> is exemplary of an ML-based scheme with respect to a handover decision modulation process according to an example embodiment. At block <NUM>, various operations regarding training, testing, and validating an ML-based failure prediction process, e.g., an ANN, may be performed based on a set of features pertaining to the anchor RAT. In one arrangement, such operations may be performed as an offline process that may be executed at the anchor node, at another network node (e.g., a network management node), or at a cloud/data center facility. In one arrangement, one or more ML or artificial intelligence (AI) techniques may be executed in conjunction with the training of the ML process. Skilled artisans will recognize that such techniques may comprise, without limitation, at least one of supervised learning, semi-supervised learning, reinforcement learning, deep learning, and/or federated learning, etc. At block <NUM>, a trained ANN model operative as a quality degradation predictor engine may be generated or otherwise obtained, which is based on RLF probability prediction, to estimate a likelihood of quality degradation. The trained ANN engine may be executed, in response to a set of input variables comprising online and/or real-time data obtained at the anchor node with respect to a particular UE upon receiving a measurement report, to predict RLF probability and determine a handover decision with respect to the UE, e.g., to proceed with handover or suppress handover, as set forth at block <NUM>. Additional details regarding an example ANN engine for predicting RLF probability in an LTE/NR network environment will be set forth further below with respect to certain embodiments of the present patent disclosure.

<FIG> and <FIG> cumulatively depict a flowchart of various blocks, steps and/or acts that may be (re)combined in one or more arrangements, with or without additional flowcharts of the present disclosure, for managing and/or modulating UE handover/mobility according to some embodiments of the present patent disclosure involving EN-DC Option 3x interworking in a <NUM> NSA implementation. In general, an example process comprising a plurality of stages may be described as follows in one operational context having a UE connected to an LTE anchor cell (La), with LTE RRC signaling connection and an established MCG bearer, wherein the source eNB configures the UE with event triggers for mobility (e.g., A1, A2, A3, A5, A6, and B1). For EN-DC, UE may be configured with B1 event and respective b1 threshold, which any measured NR cell must satisfy in order to be added as SCG. As an example, NR Cell (Na) may be considered as meeting the B1 event criteria.

Example process portions 600A and 600B shown in <FIG> and <FIG> taken together illustrate the foregoing stages in a flowchart form with further details according to an embodiment. Upon commencing the process flow (block <NUM>) and where a UE is RRC-connected with successful initial Context Setup (block <NUM>), the UE receives RRC Reconfiguration details for LTE mobility and NR leg addition (e.g., in a B1 report), as set forth at block <NUM>. At block <NUM>, the UE provides the B1 measurement report to the LTE source cell (e.g., La cell), the cell having certain RSRP, RSPQ values, along with one or more NR cells, e.g., Na, Nb, having respective RSRP values. After reception of SgNB Addition Acknowledgement message from a target gNB, La cell's eNB node sends RRC Reconfiguration with details for NR leg addition and PDCP conversion (block <NUM>). Thereafter, RACH procedures may be completed by the UE with La cell and selected NR Na cell over LTE and NR air interfaces, respectively, as set forth at block <NUM>. An ERAB Modification Confirm message is received by La cell eNB from LTE core MME node (block <NUM>). In one arrangement, an iterative loop may be optionally executed to ensure that if there is a VoIP call (Mobile-Originated (MO) or Mobile-Terminated (MT) call) or another GBR bearer service is active, it is completed or released prior to processing any measurement reports from the UE, as set forth at blocks <NUM>, <NUM>. If there is no GBR service and/or the GBR service is no longer active, process 600A/B flows to block <NUM> where an LTE Event (e.g., IntraF HO or InterF HO, etc.) may be reported from the UE and received by La cell. Applicable parameters may comprise, e.g., La cell with RSRP and RSRQ measurements and one or more reported Neighbor (Nbr) cells (e.g., Lb, Lc, Ld cells) with respective RSRQ/RSRP measurements. Depending on implementation, a Trigger quantity may be RSRQ or RSRP, or any other suitable quality/performance indicator, or any combination thereof. Further, depending on applicable conditionalities, e.g., Equation (<NUM>) described above, a target Nbr cell may be selected/identified for potential handover. By way of illustration, Lc is selected as the target Nbr cell, whereupon additional checks and conditionalities may be verified between source La cell and target Lc cell, as set forth at block <NUM>.

In one arrangement, example process 600A/B may involve an optional determination as to whether there is any activity on the X2 interface between source La eNB node and associated NR Na cell. Such activity (e.g., signaling and/or data traffic) may be monitored over a preconfigured or configurable period of time based on suitable timers/counters and notifications, as exemplified at block <NUM>. In one example implementation, if MeNB of La cell receives an inactivity message which is not followed by an activity notification within a time period t_SgNBInactivity (e.g., so as to provide a guard band), i.e., there is no X2 activity for a period of time, a legacy LTE handover procedure (e.g., based on measurement report and current handover thresholds) may be executed for handing the UE over to target Lc cell, wherein source eNB sends a HO Request to target eNB, as set forth at block <NUM>. Thereafter, control may flow to block <NUM> wherein appropriate NR leg addition procedure may be executed by Lc cell, now operating as the anchor cell for EN-DC.

If there is SgNB activity reported/monitored within a configurable time period, example process 600A/B may involve verification of one or more performance/quality related conditionalities as between the La and Lc cells against a tunable handover conditionality parameter that may be set based on ML techniques in some embodiments. As noted previously, RSRPs of La and Lc cells may be compared against the Nbr_Intf threshold parameter, and if the difference is greater than Nbr_Intf threshold, a handover may be initiated towards Lc following legacy handover procedure (shown at block <NUM> and <NUM>). If Lc cell is not stronger than La by a certain threshold (e.g., Nbr_Intf threshold), process 600A/B flows to an ML process for further verification as noted previously (blocks <NUM>, <NUM>). It will be appreciated that in the example embodiment set forth herein, if there is no SgNBInactivity indication received by master eNB (anchor node) within a time period, the intention is to retain the connection on La source cell even if there is a stronger Nbr cell, thereby facilitating a balance between the need to maximize the NR connectivity and potential service degradation caused by the stronger Nbr cell. Accordingly, if the UE reports a target cell Lc that is stronger based on the parameters set forth above, the comparison of difference between Source (La) and Neighbor/target cell (Lc) RSRP with Nbr_Intf threshold identifies the potential target cell as an extremely stronger cell and if the HO is not initiated, the high interference from Lc neighbor cell would continue to impact La source cell performance experienced by the UE. To avoid such scenarios, example embodiments advantageously implement the Nbr_Intf threshold tunable parameter on top of existing CIO and a3offset based conditionalities to further modulate the HO behavior of the anchor network in a <NUM> NSA implementation.

At block <NUM>, an ANN engine is operative responsive to a set of input data <NUM> obtained from, e.g., one or more UE measurement reports, including a latest report, any of which may be differently weighted, for predicting RLF probability and providing a Boolean decision in response. If the handover decision is True (block <NUM>), a legacy handover may be executed (block <NUM>), whereupon the new anchor cell may perform appropriate NR leg addition procedures (block <NUM>). Otherwise, process 600A/B may enter a state of inactivity until a next measurement report is received, as set forth at block <NUM>. Depending on implementation, subject to one or more additional determinations, e.g., no active GBR service, source La cell may continue with another iteration of a modulated HO decision process as set forth herein.

It will be apparent that the foregoing HO decision modulation process may be adapted to various types of architectural implementations described earlier in the present disclosure in reference to <FIG>. For example, with the evolution in radio access technology and higher spectral efficiency achievable in NR, NSA deployment with Option <NUM> involves an anchor node deployed as LTE eNB, secondary node deployed as NR gNB, with EPC most commonly deployed as the core. Accordingly, EN-DC capable UEs within NR coverage experience a gain in throughput and latency performance against legacy LTE service experience. Along the same lines, a technical benefit of the embodiments herein is to reduce the user-plane interruption caused by NR release triggered due to LTE anchor layer HO. Some embodiments comprehended in <FIG>/<FIG> may therefore be suitably configured bearing in mind current NSA NR deployments in different market areas across operators.

Some example embodiments herein are equally applicable to an Option <NUM> network implementation (exemplified in <FIG>), where anchor node remains as LTE eNB, secondary node remains as NR gNB, but core network evolves to <NUM> Core. Due to the similarity of the anchor layer architectures between Option <NUM> and Option <NUM>, it should be appreciated that frequent LTE mobility could impact the user's NR session continuity in Option <NUM> also in a similar fashion. Accordingly, some embodiments of <FIG>/<FIG> may be adapted to an Option <NUM> implementation as well, mutatis mutandis.

For Option <NUM>, anchor node is NR gNB, secondary node is LTE eNB and EPC is deployed as the core. Whereas some embodiments of the present patent disclosure may also be adapted in such an architecture as well, the gain in terms of end user throughput may not be as significant as Option <NUM> or Option <NUM> because anchor node is NR gNB and identification of a satisfactory operating point/range for which RLF probability is low, and only beyond which NR HO should be triggered that may reduce the overall interruption in NR user plane to some extent.

Directing attention to <FIG>, depicted therein is an example generalized artificial neural network (ANN) model <NUM> operative as a machine learning (ML) process for predicting radio link failure for purposes of an embodiment of the present disclosure. As exemplified, ANN model <NUM> is operative in response to a plurality of radio network features (Feature-<NUM> to Feature-N), which may be selected based on their relevance to a network radio link failure, wherein each selected feature is provided to a corresponding input "neuron" or computational node <NUM>-<NUM> to <NUM>-N, that forms part of an input layer <NUM>. Typically, ANN model <NUM> may be configured such that the nodes of the input layer <NUM> are passive, in that they do not modify the data. Rather, they receive a single value on their input, and duplicate the value to their respective multiple outputs, which may depend on the connectivity of the ANN model <NUM>. One or more hidden layers <NUM> may be provided for reducing the dimensionality of the input feature parametric space, wherein each of hidden nodes <NUM>-<NUM> to <NUM>-K and <NUM>-<NUM> to <NUM>-M are active, i.e., they modify the incoming data received from the prior layer nodes and output a value based on a functional computation involving weighted incoming data. In a fully interconnected ANN structure, each value from an input layer may be duplicated and sent to all of the hidden nodes. Regardless of the extent of the interconnectivity, the values entering a hidden node at any given hidden layer are multiplied by weights, which comprise a set of predetermined numbers stored in the engine that may be "learned" in a series of iterative stages involving, e.g., output error back propagation or other methodologies. At each respective hidden node, the weighted inputs are added to produce a single intermediate value, which may be transformed through a suitable mathematical function (e.g., a nonlinear function) to generate an intermediate output within a normalized range (e.g., between <NUM> and <NUM>). Depending on the number of hidden layers, weighted intermediate outputs may be provided to a next layer, and so on, until reaching at least one single active output node <NUM>, which may be configured to generate an output that may be thresholded to provide an indication of a condition (dependent variable) based on the input data.

Whereas neural networks can have any number of layers, and any number of nodes per layer, an example ANN model <NUM> may be configured with a fairly small number of layers, comprising only a portion of the size of the input layer. In the example arrangement shown in <FIG>, two hidden layers and an active output layer are shown, with inputs to first and second hidden layer nodes being modulated by weights {Wa,b} and {Wc,d} respectively, and inputs to the output node being modulated by weights {We,f}. The weights required to make example ANN model <NUM> carry out a particular task, e.g., RLF probability prediction, may be found by a learning algorithm, together with examples of how the system should operate in certain implementations.

<FIG> depicts a block diagram of an apparatus, node, or network element functionality <NUM> associated with a network portion for training, testing and generating a validated ANN process or engine operative in conjunction with a UE handover/mobility scheme for purposes of an example embodiment of the present disclosure. As illustrated, one or more radio access network (RAN) portions <NUM> and one or more core network (CN) portions <NUM> exemplify a representative mobile communications system configured to support an MR-DC architecture implementation described above. A data collection module <NUM> is operative to obtain data relating to a number of performance metrics, parameters or other variables, which may be measured, monitored, obtained, estimated, or otherwise determined for a number of RAN infrastructure elements of RAN <NUM>, wherein the data may be collected periodically, e.g., responsive to a scheduler, or based on occurrence or detection of events, triggers, alarms, etc., or based on operator/management node policies, and the like. Further, the data may be collected via different techniques for different types of variables, RAN infrastructure elements, and the like, e.g., via pull techniques, push techniques, or a combination thereof. A data preprocessing or cleaning module <NUM> is operative to perform data cleaning operations, steps, acts, or functions, which may be guided/unguided or supervised/unsupervised, by human or AI-based experts <NUM> having knowledge and domain expertise relative to the RAN infrastructure, with respect to the input data obtained by the data collection module <NUM>. In some embodiments, different types of data preprocessing may be implemented depending on the types of data collected in RAN networks, wherein the data can comprise categories such as cardinal data, nominal data, ordinal data, rank order data, continuous variable data, discrete variable data, categorical data, Boolean data, and the like. Further, some data cleaning operations may involve imputation of missing values with values determined based on statistical distributions of the data. Accordingly, a modified dataset may therefore be generated by the data preprocessing/cleaning module <NUM>, which may include input by a data input module <NUM> as one or more training datasets <NUM> to a specific ML/ANN model for predicting certain performance or quality related events (e.g., RLF) associated with or relative to one or more infrastructural elements of RAN <NUM> (e.g., eNB nodes). Depending on the particular ML implementation architecture associated with the network, ML model training <NUM> may involve one or more iterations, which in some instances may include (semi)supervised learning based on input from human/AI experts, such that a trained ML model <NUM> that is appropriately fitted is obtained i.e., resulting in a model without under-fitting or over-fitting. In one embodiment, the foregoing operations may be provided as part of the ML training stage or aspect of an example implementation. In a subsequent or separate phase, the fitted/trained ML model <NUM> may be used in conjunction with additional datasets of RAN <NUM> that may also be preprocessed or cleaned by module <NUM>, whereby suitable test datasets or validation datasets <NUM> may be used as input data for generating predictive output relative to one or more performance events in a further example implementation. As one skilled in the art will recognize, at least a portion of the foregoing operations may be performed offline and/or by different network entities depending on implementation. After training, testing and validating the ML/ANN model <NUM>, it may be executed in conjunction with a suitable network element or functionality, e.g., a handover management module <NUM> associated with at least a RAN/eNB element, for providing appropriate handover decisions with respect to modulating a node's handover behavior in accordance with teachings herein.

Some example embodiments of the present patent disclosure may be configured as an added functionality or intelligence in eNB nodes of a <NUM> NSA network operative to support EN-DC. In one arrangement, such added functionality may be activated after an EN-DC session has been established at an anchor eNB node and has active Protocol Data Units (PDUs) over SCG bearer. In one arrangement, responsive to a post-evaluation of potential target LTE cell(s), an embodiment of ANN process may be executed as a probabilistic model applicable on per-session basis (e.g., corresponding to each respective UE that is anchored at the serving eNB node with respect to the UE's <NUM> NR leg), considering not only RSRP and RSRQ of source cell but other metrics such as Channel Quality Indicator (CQI), Block Error Rate (BLER), retransmission events, UL Power Limitation, etc., along with radio link failure events, among others. A computer-implemented ML/ANN engine trained with historical and current measurements as set forth above may be configured to determine whether handover should be triggered towards a reported neighbor LTE cell or the session can be retained with the current source LTE cell with no unsatisfactory degradation in performance. As noted previously, the UE's time on NR session at the anchor/source cell can therefore be extended for a longer period due to avoidance of such handovers.

In one arrangement, an embodiment of the ML/ANN process may be based on a deep learning model that is realized using a federated architecture to train using different networks' data points from different geographical regions, wherein a suitable input feature selection process in combination with data preprocessing may be performed. For example, input measurements or a feature set applicable to known LTE standards may comprise, without limitation, several time-varying session level metrics exemplified below:.

A brief description of the foregoing metrics/measurements is set forth below in Table <NUM>:.

It should be appreciated that the foregoing list is not an exhaustive list, and depending on implementation, more or fewer features may be chosen or added in an ANN model development process for purposes of an example embodiment of the present patent disclosure. Because a selected feature set may comprise metrics having different time series data, a causality test may be used in some embodiments to forecast the occurrence of a negative event (e.g., RLF) based on one or more features. Example causality tests may include, without limitation, Granger causality tests, Chi-squared tests, etc. Based on the causality determinations, a subset of the features may be selected depending on their relevance to the negative event. The selected features may be used by a custom function to predict the probability of occurrence of the negative event (e.g., RLF) in a customizable manner specific to a particular network. Depending on the probability, a decision to execute handover from the serving anchor LTE cell to a target LTE cell may be taken. Further labeling of the probability values to determine an appropriate threshold can be done based on expert supervision in some example embodiments.

<FIG> depicts an example ANN-based failure probability predictor generation scheme <NUM> involving federated and/or supervised learning according to an example embodiment of the present disclosure. A plurality of networks <NUM>-<NUM> to <NUM>-N, operating in different regions and/or by different operators, may be configured to provide, generate, collect, obtain, or otherwise report various data logs, e.g., call/cell trace reports (CTRs) including applicable release codes, to a data preprocessing element <NUM>. In one embodiment, such data collection operations may take place over a configurable period of time. Data preprocessing element <NUM> may be configured to execute one or more data cleaning operations as set forth above and generate session records with calculated metrics for storage in a database <NUM>. A causality test module <NUM> may be applied to at least a portion of the session record data for selecting features causing or otherwise contributing to RLF, thereby generating an input feature set <NUM>. An example custom function evaluation module <NUM> may be configured to provide failure probability at certain radio conditions captured in terms of the features used for training the ML model. In one arrangement, the RLF can be detected explicitly by the specific release cause indicating the event of RLF. In another arrangement, the RLF can be detected implicitly by the extremely poor values of the features representing various radio conditions (e.g., designated as unacceptable according to domain experts). In the latter case, each feature value may be normalized with respect to predefined thresholds (which may be different for each feature) using a rectified linear unit. In one arrangement, the maximum of these values may be considered to drive the failure probability. Set forth below are example code portions associated with custom function evaluation block <NUM>:
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An ANN/ML based failure probability predictor <NUM> is operative responsive to the selected input feature set <NUM> and custom function evaluation <NUM>. An iterative process block may be executed as part of supervised training to generate a trained ANN model or engine <NUM>. As illustrated, if the predicted failure probability is greater than a threshold value (e.g., > <NUM>), an HO decision is returned as True, otherwise an HO decision of False is returned, as set forth at decision module <NUM>. A labeling/verification module <NUM> may be operative in conjunction with input from domain experts, e.g., network engineers, autonomous entities, to determine if the predicted results are acceptable (e.g., within certain thresholds), as set forth at block <NUM>. In one arrangement, appropriate feedback control may be provided to tune the threshold values used in the custom function evaluation <NUM> for training the ML process in response to determining that the predicted results are not satisfactory. Upon achieving successful predictive capability, trained ANN module <NUM> may be obtained for deployment as an added intelligence operative in conjunction with an anchor node for modulating HO decision behavior according to the embodiments set forth in detail hereinabove.

Turning to <FIG>, a block diagram of a computer-implemented apparatus <NUM> is illustrated therein, which may be (re)configured and/or (re)arranged as a platform, (sub)system, server, node or element operative in association with an anchor layer node of a multi-RAT DC-capable network architecture to effectuate HO decision modulation according to an embodiment of the present disclosure. It should be appreciated that apparatus <NUM> may be implemented as part of an integrated network node or platform, including a management node in some embodiments, or as a standalone node depending on implementation. One or more processors <NUM> may be operatively coupled to various modules that may be implemented in persistent memory for executing suitable program instructions or code portions (e.g., code portion <NUM>) with respect to effectuating any of the processes, methods and/or flowcharts set forth hereinabove in association with one or more modules, e.g., HO/mobility module <NUM>, ML/ANN module(s) <NUM> (where ML-based quality degradation or link failure prediction is implemented), other quality degradation predictor modules <NUM> (where non-ML statistical or mathematical techniques are implemented for failure prediction), etc. One or more databases may be provided as part of or in association with apparatus <NUM> for storing various types of data, e.g., ML training data <NUM>, ML validation data <NUM>, session data including CTR data and measurement report data <NUM>, etc., wherein some of the training/validation data may be obtained pursuant to federated learning. A data preprocessor and feature selection module <NUM> may also be provided in some implementations of apparatus <NUM>.

Although not specifically shown herein, one or more Big Data analytics modules may also be interfaced with apparatus <NUM> for providing predictive analytics with respect to HO/mobility behavior of respective UEs and corresponding sessions managed by appropriate anchor layer elements. Depending on the implementation, one or more "upstream" interfaces (I/F) and and/or "downstream" I/Fs, collectively I/F(s) <NUM>, may be provided for interfacing with various network elements (e.g., other eNBs, gNBs, EPC/5GC elements, data center nodes, management nodes (e.g., business support system (BSS) nodes and/or other operations support system (OSS) components, etc.), wherein such interfaces may be referred to as a first interface, a second interface, and so on, depending on configuration, implementation and/or architectural design. Furthermore, in some arrangements of the computer-implemented apparatus <NUM>, various physical resources and services executing thereon may be provided as virtual appliances wherein the resources and service functions are virtualized into suitable virtual network functions (VNFs) via a virtualization layer. Example resources may comprise compute resources, memory resources, and network interface resources, which may be virtualized into corresponding virtual resources, respectively, that may be managed by respective element management systems (EMS) via a virtualization layer (also sometimes referred to as virtual machine monitor (VMM) or "hypervisor").

At least a portion of an example network architecture and associated HO/mobility modulation functionality disclosed herein may also be virtualized as set forth above and architected in a cloud-computing environment comprising a shared pool of configurable virtual resources. Various pieces of hardware/software associated with eNB/gNB nodes, management nodes, etc., may therefore be implemented in a service-oriented architecture, e.g., Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), etc., with multiple entities providing different features of an example embodiment of the present patent disclosure, wherein one or more layers of virtualized environments may be instantiated on commercial off-the-shelf (COTS) hardware. Skilled artisans will also appreciate that such a cloud-computing environment may comprise one or more of private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, multiclouds and interclouds (e.g., "cloud of clouds"), and the like.

Based on the foregoing, skilled artisans will appreciate that at least some example embodiments herein advantageously maximize NR session time on <NUM> in an NR NSA deployment by optimizing the anchor LTE layer handover behavior wherein LTE anchor mobility may be minimized without sacrificing quality. Some example embodiments may be advantageously configured with flexibility to determine mobility thresholds for each EN-DC session (e.g., per session and/or per UE in some arrangements, thereby allowing finer granularity in handover decision management) instead of using current static triggering criteria, which are tunable at cell and QCI level only. Because handover triggering points may be determined per session and optimized based on the operating condition of a given session, better end user experience and improved session integrity may be achieved in an example <NUM> NSA implementation. In some embodiments, the LTE handover threshold may be determined using a federated learning model, which allows a network operator to determine more accurate handover triggering points in a customizable manner for respective regions and/or network portions. As such, example embodiments involving ML/ANN with a learning process may encompass several features (or, metrics) to determine a satisfactory operating point/range for the anchor LTE layer over a broader parametric space. It should be appreciated that such broader range of feature parametric sets provide for handover criteria that are over and above the current handover determination criteria, which may help facilitate a more informed decision-making process regarding handover.

Although example embodiments and their advantages and benefits have been particularly set forth in reference to a <NUM> NSA network architecture involving <NUM>/<NUM> interworking, skilled artisans will recognize that the teachings of the present disclosure are not necessarily limited thereto. Embodiments herein can therefore also be practiced in other network architectures having multi-RAT interworking (e.g., <NUM> networks, <NUM> networks, <NUM> networks, Next Generation Networks, etc.), including mobility anchor layers in either macrocell and/or small cell architectures in a heterogeneous cell densification environment, for supporting MR-DC based mobility/HO management.

In the above-description of various embodiments of the present disclosure, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and may not be interpreted in an idealized or overly formal sense expressly so defined herein.

At least some example embodiments are described herein with reference to block diagrams and/or flowchart illustrations of computer-implemented methods, apparatus (systems and/or devices), computer programs comprising the instructions and/or computer program products. Such computer program instructions may be provided to a processor circuit of a general purpose computer circuit, special purpose computer circuit, and/or other programmable data processing circuit to produce a machine, so that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, transform and control transistors, values stored in memory locations, and other hardware components within such circuitry to implement the functions/acts specified in the block diagrams and/or flowchart block or blocks, and thereby create means (functionality) and/or structure for implementing the functions/acts specified in the block diagrams and/or flowchart block(s). Additionally, the computer program instructions may also be stored in a tangible computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions which implement the functions/acts specified in the block diagrams and/or flowchart block or blocks.

As pointed out previously, tangible, non-transitory computer-readable medium may include an electronic, magnetic, optical, electromagnetic, or semiconductor data storage system, apparatus, or device. More specific examples of the computer-readable medium would include the following: a portable computer diskette, a RAM circuit, a ROM circuit, an erasable programmable read-only memory (EPROM or Flash memory) circuit, a portable compact disc read-only memory (CD-ROM), and a portable digital video disc read-only memory (DVD/Blu-ray). The computer program instructions may also be loaded onto or otherwise downloaded to a computer and/or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer and/or other programmable apparatus to produce a computer-implemented process. Accordingly, embodiments of the present patent disclosure may be embodied in hardware and/or in software (including firmware, resident software, microcode, etc.) that runs on a processor or controller, which may collectively be referred to as "circuitry," "a module" or variants thereof. Further, an example processing unit may include, by way of illustration, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuit (IC), and/or a state machine. As can be appreciated, an example processor unit may employ distributed processing in certain embodiments.

Further, in at least some additional or alternative implementations, the functions/acts described in the blocks may occur out of the order shown in the flowcharts. Also, some blocks in the flowchart(s) can be optionally omitted. Furthermore, although some of the diagrams include arrows on communication paths to show a direction of communication, it is to be understood that communication may occur in the opposite direction relative to the depicted arrows. Finally, other blocks may be added/inserted between the blocks that are illustrated.

It should therefore be clearly understood that the order or sequence of the acts, steps, functions, components or blocks illustrated in any of the flowcharts depicted in the drawing Figures of the present disclosure may be modified, altered, replaced, customized or otherwise rearranged within a particular flowchart, including deletion or omission of a particular act, step, function, component or block. Moreover, the acts, steps, functions, components or blocks illustrated in a particular flowchart may be inter-mixed or otherwise inter-arranged or rearranged with the acts, steps, functions, components or blocks illustrated in another flowchart in order to effectuate additional variations, modifications and configurations with respect to one or more processes for purposes of practicing the teachings of the present patent disclosure.

Claim 1:
A handover, HO, management method (500A) performed by an anchor node serving a dual connectivity, DC, user equipment, UE, the anchor node configured as a master node of a source cell operating in a first radio access technology, RAT, with respect to a secondary node operating in a second RAT, the master node and the secondary node connected via an inter-nodal interface, the method comprising:
receiving (<NUM>) a measurement report from the UE, the measurement report containing information relating to one or more neighbor cells operating in the first RAT;
responsive to the measurement report, selecting (<NUM>) a particular neighbor cell as a target cell for handing over the UE;
determining (<NUM>) that there is traffic with the secondary node over the inter-nodal interface;
responsive to determining that a parametric difference between the source cell and the target cell exceeds a threshold value, performing (<NUM>) a handover of the UE to the target cell;
responsive to determining that the parametric difference is less than or equal to the threshold value, performing (<NUM>) a quality degradation prediction with respect to the UE to estimate a likelihood of service failure for the UE; and
responsive to determining that the likelihood of service failure does not exceed a probability threshold, suppressing (<NUM>) handover of the UE to the target cell.