Dynamic handover criterion for wireless communication networks

Abstract

The present disclosure provides a wireless communication system (100) comprising a user equipment (UE) (102) with a processor (104) and memory (106) that determines optimized handover requirements based on multiple parameters including uplink (UL) data priority flag, downlink (DL) data priority flag, signal strength connectivity priority, real-time application terrestrial network (TN) preference priority, low latency connectivity priority, and battery requirement The UE applies internal biasing to measurement values of cells based on these requirements. The system includes a base station (108) with a processor (110) and transceiver (112) that receives measurement reports from the UE and executes handover decisions. The UE applies positive or negative offsets to measurement values for preferred or non-preferred radio access technology (RAT) cells, enabling optimal handover decisions in mixed RAT deployment environments.

Description

DYNAMIC HANDOVER CRITERION FOR WIRELESS COMMUNICATION NETWORKSFIELD OF INVENTION

[0001] The present invention relates to wireless communication systems, and more particularly to dynamic handover criterion methods for optimizing mobility decisions in mixed radio access technology (RAT) network deployments including 6G networks. This approach provides improved network reliability and reduced latency during user equipment transitions between cells.BACKGROUND OF THE INVENTION

[0002] Wireless communication systems have evolved significantly over the past decades, supporting diverse applications from basic voice calls to high-bandwidth data services, video streaming, and Internet of Things (loT) connectivity. These systems employ various multipleaccess technologies such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), and Long Term Evolution (LTE) to efficiently share system resources among multiple users. Modern networks incorporate advanced features including beamforming, multiple-input multiple-output (MIMO) antenna systems, and carrier aggregation to enhance performance and capacity.

[0003] In contemporary wireless networks, user equipment (UE) mobility management relies primarily on signal strength measurements such as Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) to determine handover decisions between base stations. The Signal-to-Interference-plus-Noise Ratio (SINR) serves as another parameter for assessing channel capacity and connection quality. Base stations configure neighbor cell measurements for user equipment and execute handover algorithms based on received measurement reports, considering factors such as signal strength thresholds and resource availability in target cells.

[0004] Despite these advancements, there remains a challenge in optimizing handover decisions in mixed Radio Access Technology (RAT) deployments where multiple network types coexist, including Sixth Generation (6G), Fifth Generation (5G), Fourth Generation LTE, Terrestrial Networks (TN), and Non-Terrestrial Networks (NTN). Current handover mechanisms often fail toaccount for dynamic user requirements and application-specific connectivity needs, leading to suboptimal network resource utilization and degraded user experience.

[0005] Current approaches to addressing handover optimization include signal strength-based algorithms and load balancing techniques. However, these methods suffer from limitations such as inability to consider real-time application requirements, lack of dynamic adaptation to changing user needs, and insufficient consideration of diverse RAT characteristics in mixed network environments.

[0006] In one patent application WO 2020 / 073757 Al, the invention discloses a mobility management method for non-terrestrial communication networks that utilizes terminal position information and signal measurements to determine handover conditions. The method involves sending configuration messages for neighbor cell signal measurement or position measurement to terminals, enabling handover decisions based on measurement reports and network position information. The invention addresses mobility management in satellite networks, however, the invention does not adequately address the dynamic nature of user requirements and applicationspecific handover criteria in mixed RAT deployments where terrestrial and non-terrestrial networks coexist.

[0007] In another patent application WO 2021 / 159283 Al, the invention discloses a non-terrestrial network switching method that considers both channel measurement parameters and position measurement parameters for handover decisions. The method enables terminals to measure signal strength and location parameters simultaneously, allowing for more informed handover decisions in satellite communication systems. Although the invention provides improvement upon traditional signal-based handover mechanisms, however, the invention does not provide the flexibility to adapt handover criteria based on real-time user application requirements and service priorities in heterogeneous network environments.

[0008] IN one another patent application, where UE estimates the target cell RF capability with rank to provide the better resources for the UE which is not sufficient for dynmic UE requirements beyond just Radio resources need.

[0009] In view of the challenges associated with the above state-of-art, there is a need for dynamic handover criterion methods that can adapt to real-time user requirements and application-specific needs in mixed RAT network deployments. The present invention provides techniques forincorporating user equipment handover requirements as additional criteria in handover decisionmaking processes, enabling optimized mobility management and enhanced user experience across diverse network technologies.OBJECTIVES OF THE IINVENTION

[0010] The primary objective of the present invention is to provide a dynamic handover criterion method that adapts to real-time user requirements and application-specific connectivity needs in mixed radio access technology network deployments.

[0011] Another objective of the present invention is to enhance mobility management by incorporating user equipment handover requirements as additional criteria in handover decisionmaking processes beyond traditional signal strength measurements.

[0012] Another objective of the present invention is to optimize network resource utilization by enabling intelligent handover decisions that consider diverse characteristics of coexisting network technologies including 6G, 5G, LTE, terrestrial networks, and non-terrestrial networks.

[0013] Another objective of the present invention is to reduce handover latency and improve network reliability during user equipment transitions between cells in heterogeneous network environments.

[0014] Yet another objective of the present invention is to provide improved user experience by enabling handover algorithms and dynamically adjust based on service priorities and application requirements.

[0015] Yet another objective of the present invention is to enable seamless connectivity across different radio access technologies while maintaining quality of service for various applications including voice calls, data services, video streaming, and loT connectivity.

[0016] Yet another objective of the present invention is to provide a flexible handover framework accommodating future network technologies and evolving user mobility patterns in nextgeneration wireless communication systems.

[0017] Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.SUMMARY OF THE INVENTION

[0018] According to an aspect of the present invention, a wireless communication system is provided. The system comprises a user equipment (UE) comprising a processor and a memory storing instructions that, when executed by the processor, cause the UE to determine optimized handover requirements based on multiple UE parameters comprising at least one of an uplink (UL) data priority flag, a downlink (DL) data priority flag, a signal strength connectivity priority, a realtime application terrestrial network (TN) preference priority, a low latency connectivity priority, and a battery requirement, and apply internal biasing to measurement values of one or more cells based on the handover requirements. The system further comprises a base station comprising a processor and a transceiver configured to receive standard measurement reports from the UE and execute handover decisions based on the measurement values.

[0019] According to other aspects of the present invention, the processor of the UE is configured to apply a positive or negative offset to measurement value for preferred or non-preferred radio access technology (RAT) cells. The multiple parameters is dynamically determined based on realtime applications running on the UE and current network conditions. The battery optimization requirement enable handover to a radio access technology (RAT) cell and radio frequency cell to save device power without impacting user experience. The radio access technology (RAT) cells comprise at least one of sixth generation network (6G), fifth generation network (5G), long term evolution (4G LTE), terrestrial network (TN), or non-terrestrial network (NTN) cells. The wireless communication system operate in a mixed radio access technology (RAT) deployment environment comprising multiple RATs including at least two of sixth generation network (6G), fifth generation network (5G), long term evolution (4G LTE), terrestrial network (TN), and nonterrestrial network (NTN), and wherein the handover requirements enable optimal cell selection across the multiple RATs based on the UE requirements.BRIEF DESCRIPTION OF FIGURES

[0020] The present invention will be better understood after reading the following detailed description of the presently preferred aspects thereof with reference to the appended drawings, in which the features, other aspects and advantages of certain exemplary embodiments of the invention will be more apparent from the accompanying drawing in which:

[0021] Figure 1 illustrates a block diagram of a wireless communication system for implementing dynamic handover criterion methods;

[0022] Figure 2 illustrates a network deployment scenario illustrating a mobile user equipment in communication with multiple network cells with the help of air interfaces in a mixed radio access technology environment; and

[0023] Figure 3 illustrates a flowchart of a method (300) for handover decision in a wireless communication system.

[0024] Common reference numerals are used throughout the figures to indicate similar features.DETAILED DESCRIPTION OF THE INVENTION

[0025] The following detailed description and embodiments set forth herein below are merely exemplary out of the wide variety and arrangement of instructions which can be employed with the present invention. The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. All the features disclosed in this specification may be replaced by similar other or alternative features performing similar or same or equivalent purposes. Thus, unless expressly stated otherwise, they all are within the scope of the present invention.

[0026] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0027] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention.

[0028] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0029] It should be emphasized that the term “comprises / comprising” when used in this specification is taken to specify the presence of stated features, integers, steps, or components but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.

[0030] The following description sets forth exemplary aspects of the present disclosure. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure. Rather, the description also encompasses combinations and modifications to those exemplary aspects described herein.

[0031] Accordingly, the present disclosure relates to wireless communication systems that implement dynamic handover criterion methods for optimizing mobility decisions in mixed radio access technology network deployments. The above systems operate across various network generations including sixth generation (6G), fifth generation (5G), long term evolution (4G LTE), terrestrial network (TN), and non-terrestrial network (NTN) environments. The disclosed methods address challenges in conventional handover processes where decisions are primarily based on signal strength measurements such as Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), and do not provide sufficient information for optimal cell selection in complex deployment scenarios.

[0032] In mixed RAT deployment environments, multiple radio access technologies are simultaneously available within a given coverage area, creating opportunities for enhanced user experience through intelligent cell selection. The disclosed systems introduce additional handover criteria that consider user equipment requirements and application- specific needs beyond traditional signal strength metrics. The enhanced criteria comprises uplink data priority flags, downlink data priority flags, signal strength connectivity priorities, real-time application terrestrial network preferences, low latency connectivity priorities, and battery optimization requirements. The dynamic nature of these criteria allows for adaptive handover decisions that respond to changing user equipment conditions and application demands.

[0033] The disclosed methods address various use cases where conventional handover algorithms produce suboptimal results. For instance, real-time applications benefit from terrestrial network connectivity to maintain service quality, while other applications tolerate handover to different RAT types without degrading user experience. Broadcasting services such as multimediabroadcast services (MBS) streaming have specific network connectivity requirements that differ from general data transfer applications. The system also accommodate scenarios where user equipment requires enhanced uplink performance rather than downlink optimization, addressing diverse application requirements across different user equipment types.

[0034] The dynamic handover criterion mechanism enable intelligent distribution of network resources among users based on their real-time requirements. This approach help address field- reported issues where users experience connectivity problems or reduced data rates following handover events when using conventional handover criteria. The system also contribute to power efficiency by enabling handover to lower power consumption RAT cells when such transitions do not impact user experience, thereby extending battery life for mobile user equipment while maintaining service quality across various application types.

[0035] In an embodiment, the wireless communication system (100) comprises a user equipment (UE) (102) with a processor (104) and memory (106) that determines handover requirements based on multiple UE parameters including real-time application demands and power consumption considerations, and a base station (108) with a processor (110) and transceiver (112) that receives measurement reports and executes handover decisions. The UE (102) evaluates radio frequency conditions and resource availability of potential target cells, applying internal biasing to measurement values when necessary, while the base station (108) processes these reports alongside additional handover criteria to select optimal target cells across various radio access technologies including 6G, 5G, 4G LTE, terrestrial network (TN), and non-terrestrial network (NTN) communications.

[0036] As illustrated in figure 1, the internal biasing mechanism enables the UE (102) to influence handover decisions through measurement value adjustments that remain transparent to the base station (108). The processor (104) of the UE (102) implement a biasing algorithm that applies calculated offsets to measurement values before generating standard measurement reports. This biasing algorithm evaluate each detected cell against the determined handover requirements and apply positive offsets to measurement values for cells that align with UE (102) preferences or negative offsets for cells that do not meet the specified criteria. The offset calculations is performed using a bias calculation module that processes the handover requirements parameters and generates appropriate adjustment values for each measured cell.

[0037] The bias calculation module determine offset values based on the specific handover requirements identified by the UE (102). In some cases where the UE (102) has determined a terrestrial network preference for real-time applications, the bias calculation module apply positive offsets to terrestrial network cells and negative offsets to non-terrestrial network cells. Similarly, when battery optimization requirements indicate a preference for lower power consumption RAT cells, the bias calculation module generate positive offsets for cells operating on lower power RAT technologies or lower frequency operating cells and negative offsets for cells consume more device power. The magnitude of these offsets are determined by an offset determination component that considers the priority level of each handover requirement parameter.

[0038] The offset determination component utilize predefined offset tables that map different handover requirement combinations to specific bias values. The offset tables contain entries for various scenarios such as uplink data priority requirements, downlink data priority requirements, low latency connectivity needs, and signal strength connectivity priorities. The processor (104) access the offset tables through a table lookup mechanism that retrieves appropriate offset values based on the current handover requirements profile. The table lookup mechanism also support dynamic offset adjustment based on changing application demands or network conditions detected by the UE (102).

[0039] The biasing process is implemented through a measurement adjustment engine that modifies the raw measurement values obtained from the radio frequency measurement circuitry before these values are incorporated into measurement reports. The measurement adjustment engine receive input from both the measurement collection subsystem and the bias calculation module to produce adjusted measurement values that reflect both actual signal conditions and UE (102) preferences. This adjustment process occur within the UE (102) processing pipeline such that the base station (108) receives measurement reports containing biased values without any indication that internal adjustments have been applied. The measurement adjustment engine also maintain original measurement values in a separate storage area for internal UE (102) operations while transmitting only the biased values to the network.

[0040] The dynamic parameter determination process are implemented through a parameter analysis engine within the UE (102) that continuously monitors both real-time applications and scheduled applications to establish appropriate handover requirements. The parameter analysisengine evaluate application characteristics such as data transfer patterns , uplink or downlink data inclination , latency sensitivity, and network connectivity preferences to generate corresponding handover requirement flags. In some cases, the parameter analysis engine interfaces with an application monitoring module that tracks active applications and their resource consumption patterns, enabling the UE (102) to anticipate handover requirements based on application behavior. The application monitoring module maintains an application profile database containing predefined characteristics for various application types, allowing the parameter analysis engine to quickly identify appropriate handover parameters when applications are launched or scheduled for execution.

[0041] Figure 2 illustrates a network deployment scenario showing a mobile user equipment (UE) (102) within a mixed radio access technology environment. The mobile UE (102) communicates with multiple network cells including Cell 1 (1), Cell 2 (2), Cell 3 (3), Cell 4 (4), and Cell n through respective air interfaces (114). Each air interface (114) represents a wireless communication link enabling the mobile UE (102) to exchange measurement reports and handover requirement information with the corresponding network cells. The multiple cells may represent different radio access technology types including 6G, 5G, 4G LTE, terrestrial network (TN), and non-terrestrial network (NTN) cells coexisting within the same coverage area. The mobile UE (102) evaluates each available cell based on traditional signal strength measurements and the dynamic handover requirements disclosed herein to determine optimal connectivity options.

[0042] The scheduled application analysis are performed by a scheduling interface component that communicates with the operating system scheduler to identify upcoming application launches and their anticipated resource requirements. The scheduling interface component access calendar applications, background task schedulers, and system-level application launch predictions to determine future handover requirement changes. In some cases, the scheduling interface component generate predictive handover requirement profiles that enable the UE (102) to prepare for handover decisions before application demands change. The predictive profiles are processed by a requirement prediction module that analyzes historical application usage patterns and generates probability-based handover requirement adjustments for anticipated application scenarios.

[0043] The dynamic parameter determination process also incorporate network condition monitoring through a network condition assessment module that evaluates current network performance metrics and adjusts handover requirements accordingly. The network condition assessment module monitor parameters such as network congestion levels, available bandwidth, and signal quality variations to modify handover requirement priorities. In some cases where network conditions indicate degraded performance on preferred RAT types, the network condition assessment module temporarily adjust handover requirements to prioritize alternative network types that provide better service quality. The assessment results are processed by a condition-based adjustment engine that modifies the handover requirement parameters based on real-time network performance data.

[0044] Field issues resolution are addressed through an issue detection and correction system that identifies connectivity problems and data rate degradation patterns following handover events. The issue detection and correction system are maintain a performance history database that records post-handover performance metrics and correlates these metrics with the handover requirements that were active during each handover decision. In some cases where the performance history database indicates recurring connectivity issues with specific handover requirement combinations, the issue detection and correction system may generate corrective parameter adjustments through a correction algorithm module. The correction algorithm module may analyze patterns of user complaints regarding low data rates or connectivity issues and generate modified handover requirement profiles that avoid problematic cell selections while maintaining service quality for the identified application scenarios.

[0045] The battery optimization requirement enable the wireless communication system (100) to implement power-conscious handover decisions that extend user equipment battery life while maintaining service quality across various application scenarios. The UE (102) incorporate a power consumption analysis module that evaluates the relative power requirements of different radio access technology cells and generates handover requirements that favor lower power consumption options when application demands permit such transitions or UE is operating below a configured threshold battery level. The power consumption analysis module maintain a power consumption database containing measured or estimated power consumption profiles for various RAT types including 6G, 5G, 4G LTE, terrestrial network, and non-terrestrial network communications under different operational conditions.

[0046] The power consumption database store power consumption measurements categorized by factors such as signal strength levels, data transmission rates, and communication protocols to enable accurate power consumption predictions for potential target cells. In some cases, the power consumption analysis module access historical power consumption data collected during previous connections to similar cell types and apply this information to current handover requirement determinations. The power consumption analysis module also interface with a battery monitoring subsystem that provides real-time battery level information and remaining battery life estimates to influence the priority level assigned to power optimization requirements during handover decisions.

[0047] The battery monitoring subsystem generate battery status reports that include current charge levels, discharge rates, and projected battery life under different operational scenarios. The power consumption analysis module process these battery status reports through a power optimization algorithm that calculates the potential battery life extension achievable through handover to lower power consumption RAT cells. In some cases where the battery monitoring subsystem indicates low battery conditions, the power optimization algorithm increase the priority weighting for battery optimization requirements, causing the UE (102) to generate handover requirements that strongly favor power-efficient cell selections even when higher performance cells are available.

[0048] The power optimization algorithm implement a multi-factor analysis process that considers both immediate power consumption differences and long-term battery life implications when generating battery optimization requirements. The algorithm evaluate factors such as the expected duration of connection to potential target cells, the anticipated data transmission requirements during the connection period, and the relative power efficiency of different RAT technologies under the predicted usage conditions. The power optimization algorithm also incorporate application- specific power consumption patterns obtained from an application power profiling module that tracks the power consumption characteristics of different application types during various network connection scenarios.

[0049] The application power profiling module maintain detailed power consumption profiles for applications such as voice calling, video streaming, file downloads, real-time gaming, and background data synchronization across different RAT types. In some cases, the application powerprofiling module determine that certain applications consume significantly less power when connected through specific RAT types, enabling the power optimization algorithm to generate targeted handover requirements that align application demands with power-efficient network connections. The profiling module also track power consumption variations based on signal quality conditions, allowing the power optimization algorithm to account for the relationship between signal strength and power consumption when evaluating potential target cells.

[0050] The power saving handover mechanism are implemented through a power-aware cell selection engine that processes the battery optimization requirements alongside other handover criteria to identify target cells that provide acceptable service quality while minimizing power consumption. The power-aware cell selection engine apply a weighted scoring algorithm that balances power consumption considerations against factors such as signal strength, data rate capabilities, and application-specific requirements. In some cases where multiple target cells provide similar service quality levels, the power-aware cell selection engine generate handover requirements that prioritize the cell with the lowest projected power consumption impact.

[0051] The power-aware cell selection engine interface with a service quality assessment module that evaluates whether potential power-saving handover targets can maintain acceptable user experience levels for current and anticipated application demands. The service quality assessment module analyze factors such as available bandwidth, latency characteristics, and reliability metrics for lower power consumption cells to ensure that power optimization handover decisions do not compromise application performance. In some cases, the service quality assessment module determine minimum acceptable service quality thresholds based on active application requirements and prevent the power- aware cell selection engine from selecting target cells that fall below these thresholds regardless of their power consumption advantages.

[0052] The handover decision process incorporate a power impact prediction component that estimates the battery life extension achievable through different handover scenarios and weighs these benefits against potential service quality trade-offs. The power impact prediction component calculate projected battery life improvements by comparing the estimated power consumption of current cell connections with the predicted power consumption of potential target cells over various time horizons. The component also generate confidence intervals for these predictionsbased on historical accuracy of power consumption estimates and variability in application usage patterns observed by the UE (102).

[0053] The UE optimized mobility information communication mechanism enables the UE (102) to transmit handover requirements directly to the base station (108) through enhanced radio resource control messaging protocols. The processor (104) of the UE (102) encode the determined handover requirements into structured data formats that can be incorporated into one of various RRC message types including RRC Reconfiguration Complete messages, RRC measurement reports, RRC Setup Complete messages, and UE assistance information messages. The memory (106) store encoding templates that define the data structure formats for different handover requirement parameter combinations, enabling the processor (104) to generate consistent and standardized requirement information regardless of the specific RRC message type being utilized for transmission.

[0054] In an embodiment, the base station (108) may configure the feature over the air additional mobility information to one or more UE. Upon enablement of the feature, the UE send the additional mobility information parameter over the air.

[0055] The encoding process are implemented through a requirement encoding module that converts the handover requirement parameters into binary data structures suitable for inclusion in RRC message payloads. The requirement encoding module access parameter mapping tables that define the bit field assignments for different handover requirement types such as uplink data priority flags, downlink data priority flags, signal strength connectivity priorities, terrestrial network preferences, low latency connectivity priorities, and battery optimization requirements. In some cases, the parameter mapping tables support variable-length encoding schemes that allow the UE (102) to include only the handover requirement parameters that are active for the current operational context, thereby minimizing the overhead associated with requirement information transmission.

[0056] The enhanced measurement report generation are performed by a measurement report enhancement engine that combines traditional measurement values with the encoded handover requirement information to create comprehensive reports for transmission to the base station (108). The measurement report enhancement engine interface with both the radio frequency measurement subsystem and the requirement encoding module to produce reports that contain signal strengthmeasurements, signal quality assessments, and UE-specific handover preferences within a single message structure. The enhanced reports include measurement values for multiple detected cells along with corresponding requirement compatibility indicators that specify how well each measured cell aligns with the current handover requirements profile.

[0057] The requirement compatibility indicators are generated by a compatibility assessment component that evaluates each measured cell against the active handover requirements and produces compatibility scores or binary compatibility flags for inclusion in the enhanced measurement reports. The compatibility assessment component access cell characteristic databases that contain information about the capabilities and characteristics of different cell types, enabling the component to determine whether specific cells can satisfy requirements such as low latency connectivity, terrestrial network preferences, or battery optimization needs. In some cases, the compatibility assessment component generate detailed compatibility matrices that specify the degree to which each measured cell satisfies individual handover requirement parameters.

[0058] The RRC message selection process are implemented through a message type selection engine that determines the most appropriate RRC message type for transmitting the UE optimized mobility information based on current network conditions and communication context. The message type selection engine evaluate factors such as the urgency of the handover requirement information, the size of the encoded requirement data, and the availability of different RRC message transmission opportunities to select optimal message types for requirement communication. The engine also maintain a message priority queue that manages multiple pending requirement updates and schedules their transmission through appropriate RRC message opportunities as they become available.

[0059] The message priority queue implement priority-based scheduling algorithms that ensure time-sensitive handover requirement updates are transmitted before less urgent requirement changes. In some cases where the UE (102) detects rapid changes in application demands or network conditions, the message priority queue expedite the transmission of updated requirement information through the next available RRC message opportunity regardless of the specific message type. The queue also support requirement information aggregation, where multiple requirement updates are combined into single RRC messages to reduce signaling overhead while maintaining timely communication of handover preferences to the base station (108).

[0060] The enhanced measurement report structure include dedicated information elements that contain the handover requirement parameters alongside traditional measurement result fields. The dedicated information elements are defined through protocol extensions that maintain backward compatibility with existing RRC message formats while enabling enhanced functionality for base stations (108) that support the UE optimized mobility information processing. The information elements comprises version indicators that specify the format and interpretation rules for the enclosed handover requirement data, enabling the base station (108) to properly decode and process the requirement information regardless of potential future enhancements to the requirement parameter set.

[0061] The protocol extension mechanism are implemented through a protocol adaptation layer that manages the formatting and transmission of enhanced measurement reports across different network deployment scenarios. The protocol adaptation layer detect the capabilities of the serving base station (108) and adjust the format and content of the transmitted requirement information accordingly. In some cases where the base station (108) does not support enhanced measurement report processing, the protocol adaptation layer revert to traditional measurement report formats while maintaining internal requirement processing for potential future handover opportunities with compatible base stations.

[0062] The transmission timing coordination are managed by a transmission scheduling component that synchronizes the delivery of enhanced measurement reports with network measurement reporting cycles and RRC message transmission windows. The transmission scheduling component monitor network-configured measurement reporting intervals and align the transmission of requirement information updates with these scheduled reporting events to minimize additional signaling overhead. The component also support event-triggered requirement transmission, where significant changes in handover requirements trigger immediate enhanced measurement report generation and transmission outside of regular reporting cycles.

[0063] The base station (108) receive the enhanced measurement reports through the transceiver (112) and process the contained requirement information using a requirement extraction module within the processor (110). The requirement extraction module decode the handover requirement parameters from the received RRC messages and store the extracted information in a UE requirement database that maintains current handover preferences for each connected UE (102).The database are organized by UE identifiers and include timestamp information that enables the base station (108) to track the currency and validity of stored requirement information for handover decision processing.

[0064] The UE requirement database support dynamic requirement updates that reflect changing UE (102) conditions and application demands over time. In some cases, the requirement extraction module implement requirement change detection algorithms that identify significant modifications to UE handover preferences and trigger updates to handover decision algorithms accordingly. The database also maintain historical requirement information that enables the base station (108) to identify patterns in UE handover preferences and anticipate future requirement changes based on observed usage patterns and application behaviors.

[0065] The wireless communication system (100) operate within mixed radio access technology deployment environments where multiple RAT types are simultaneously deployed across overlapping coverage areas to provide diverse connectivity options for user equipment. These mixed deployment environments include combinations of sixth generation network (6G), fifth generation network (5G), long term evolution (4G LTE), terrestrial network (TN), and nonterrestrial network (NTN) technologies that coexist within the same geographical region. The base station (108) coordinate with multiple RAT infrastructure elements through a multi-RAT coordination interface that manages handover decisions across different technology types while maintaining service continuity for connected user equipment. The multi-RAT coordination interface communicate with various network elements including 6G base stations, 5G gNodeBs, 4G eNodeBs, terrestrial network access points, and non-terrestrial network satellites or aerial platforms to facilitate seamless mobility across the mixed deployment environment.

[0066] The handover requirements determined by the UE (102) enable the wireless communication system (100) to perform intelligent cell selection across multiple RAT types by providing application-specific and user-specific preferences that guide the selection process beyond traditional signal strength measurements. The processor (110) of the base station (108) implement a multi-RAT handover decision engine that processes the received handover requirements alongside measurement values from different RAT types to identify target cells that provide appropriate service characteristics for the UE (102) operational context. The multi-RAT handover decision engine access a RAT capability database that contains detailed informationabout the service characteristics, coverage patterns, capacity limitations, and technical capabilities of each available RAT type within the deployment area.

[0067] The RAT capability database store information about factors such as maximum data rates, latency characteristics, mobility support capabilities, power consumption profiles, and service quality parameters for different RAT technologies under various operational conditions. In some cases, the multi-RAT handover decision engine query the RAT capability database to determine which available RAT types can satisfy specific handover requirements such as low latency connectivity for real-time applications or terrestrial network preferences for applications that not function properly over non-terrestrial network connections. The database also include information about RAT-specific features such as carrier aggregation capabilities, beam forming support, and advanced antenna technologies that influence handover decisions for user equipment with specific performance requirements.

[0068] The cell selection process across multiple RATs are implemented through a cross-RAT evaluation module that compares potential target cells from different RAT types using a unified scoring algorithm that accounts for both traditional handover criteria and the UE-specific handover requirements. The cross-RAT evaluation module normalize measurement values and capability assessments across different RAT types to enable direct comparison between cells operating on different technologies. The module apply weighting factors that reflect the relative importance of different handover requirements for the current UE (102) operational context, allowing the system to prioritize RAT types that align with active application demands and user preferences.

[0069] The unified scoring algorithm are implemented through a multi-criteria decision matrix that evaluates each potential target cell against multiple factors including signal strength, available bandwidth, latency characteristics, power consumption implications, and compatibility with active handover requirements. The multi-criteria decision matrix assign numerical scores to each evaluation factor and combine these scores using weighted summation or other mathematical aggregation methods to produce overall suitability ratings for each candidate cell. In some cases, the matrix apply threshold filtering to eliminate candidate cells that fail to meet minimum requirements for specific handover criteria before performing the detailed scoring analysis.

[0070] The handover requirements enable the wireless communication system (100) to address specific application scenarios that benefit from particular RAT characteristics or deploymentconfigurations. Broadcasting services such as multimedia broadcast services (MBS) streaming generate handover requirements that prioritize RAT types with enhanced broadcast capabilities, multicast support, or specific quality of service guarantees that align with streaming media delivery requirements. The UE (102) determine MBS-specific handover requirements through a broadcast service analysis module that evaluates active streaming applications and generates corresponding connectivity preferences for transmission to the base station (108).

[0071] The broadcast service analysis module interface with media streaming applications to identify content characteristics such as video resolution, audio quality requirements, streaming duration, and buffer management strategies that influence the selection of appropriate RAT types for MBS delivery. The module generate handover requirements that specify preferences for RAT types with dedicated broadcast channels, enhanced multicast capabilities, or specific bandwidth allocation mechanisms that support high-quality streaming media delivery. In some cases, the broadcast service analysis module may also consider factors such as content caching capabilities and edge computing support available through different RAT types when generating MBS-related handover requirements.

[0072] The MBS streaming support are implemented through a broadcast-aware handover mechanism that processes MBS-specific handover requirements and identifies RAT types and cell configurations that provide enhanced broadcasting capabilities. The broadcast-aware handover mechanism access a broadcast capability registry that contains information about MBS support features available through different RAT deployments including dedicated broadcast channels, multicast group management capabilities, and quality of service provisioning for streaming media applications. The registry also include information about content delivery network integration, edge caching support, and bandwidth allocation policies that affect MBS streaming performance across different RAT types.

[0073] The broadcast capability registry may store detailed information about MBS-related features such as evolved multimedia broadcast multicast service (eMBMS) support in LTE networks, 5G broadcast capabilities, and enhanced broadcast features anticipated in 6G deployments. The registry also include information about terrestrial network broadcast infrastructure and non-terrestrial network broadcast capabilities that may be available through satellite or aerial platform deployments. In some cases, the broadcast-aware handover mechanismmay query the registry to identify RAT types that support specific MBS features such as single frequency network operation, broadcast-multicast service center integration, or advanced error correction mechanisms that enhance streaming media delivery reliability.

[0074] The cross-RAT handover coordination involve communication between the base station (108) and multiple network infrastructure elements through a multi-network interface system that manages handover preparation and execution across different RAT types. The multi-network interface system may implement protocol translation capabilities that enable communication between different RAT control systems and coordinate resource allocation across multiple network types during handover events. The system also manage authentication and security context transfers between different RAT types to maintain secure connectivity during cross-RAT handover operations.

[0075] The multi-network interface system include specialized interface modules for different RAT combinations such as a 5G-to-6G handover interface, an LTE-to-5G handover interface, and a terrestrial-to-non-terrestrial handover interface that handle the specific protocol requirements and coordination procedures for different cross-RAT handover scenarios. Each interface module may implement RAT-specific signaling protocols and coordinate with the appropriate network elements to prepare target cells for incoming handover connections. The interface modules may also manage quality of service mapping between different RAT types to ensure that application performance requirements are maintained during cross-RAT transitions.

[0076] The handover execution across multiple RATs may be coordinated through a cross-RAT handover controller that manages the timing and sequencing of handover operations when transitioning between different RAT types. The cross-RAT handover controller may implement specialized handover procedures that account for the different connection establishment requirements, authentication mechanisms, and service activation procedures associated with different RAT types. The controller may also coordinate with network elements from both source and target RAT types to minimize service interruption during cross-RAT handover events and ensure that application data flows are properly redirected to the new RAT connection.

[0077] The processor (110) of the base station (108) implement a comprehensive handover requirement processing system that evaluates the received UE optimized mobility information alongside traditional measurement data to make enhanced handover decisions. The processor (110)may incorporate a handover requirement analysis engine that extracts and interprets the handover requirement parameters from enhanced measurement reports and applies these parameters as additional criteria in the cell selection process. The handover requirement analysis engine may interface with the UE requirement database to access current and historical handover preference information for each connected UE (102), enabling the processor (110) to maintain context-aware handover decision capabilities that adapt to changing UE operational conditions over time.

[0078] The handover requirement analysis engine process multiple types of handover requirement parameters including uplink data priority flags, downlink data priority flags, signal strength connectivity priorities, real-time application terrestrial network preferences, low latency connectivity priorities, and battery optimization requirements through specialized parameter processing modules. Each parameter type may be handled by dedicated processing components such as an uplink data priority processor, a downlink data priority processor, a connectivity priority processor, a terrestrial network preference processor, a latency requirement processor, and a battery optimization processor. These processing components may evaluate the received requirement parameters against the capabilities and characteristics of available target cells to determine compatibility scores and preference rankings for different handover options.

[0079] The base station (108) may implement a threshold-based cell qualification system through a cell qualification engine that establishes minimum performance criteria for potential target cells before applying UE-specific handover requirements to the selection process. The cell qualification engine may define threshold values for various measurement parameters including Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and signal-to- interference-plus-noise ratio (SINR) measurements that candidate cells must satisfy to be considered eligible for handover. The threshold values may be stored in a threshold configuration database that contains predefined limits for different cell types, RAT technologies, and operational scenarios, enabling the cell qualification engine to apply appropriate qualification criteria based on the specific characteristics of each potential target cell.

[0080] The threshold configuration database may maintain separate threshold sets for different handover scenarios such as intra-RAT handover, inter-RAT handover, emergency handover, and load balancing handover operations. In some cases, the cell qualification engine may apply dynamic threshold adjustment based on network conditions, where threshold values are modifiedin response to factors such as network congestion levels, interference conditions, or resource availability constraints. The dynamic threshold adjustment may be performed by a threshold adaptation module that monitors network performance metrics and adjusts qualification criteria to maintain appropriate handover candidate pool sizes while ensuring minimum service quality levels for connected user equipment.

[0081] The signal-to-interference-plus-noise ratio (SINR) measurements may be processed alongside RSRP and RSRQ measurements through a comprehensive signal quality assessment system implemented within the processor (110). The signal quality assessment system may include a SINR analysis module that evaluates SINR measurements from potential target cells and compares these measurements against SINR threshold values defined in the threshold configuration database. The SINR analysis module may calculate SINR-based qualification scores that reflect the signal quality characteristics of each candidate cell, enabling the handover decision algorithm to consider both signal strength and signal quality factors when evaluating potential handover targets.

[0082] The SINR analysis module may interface with an interference assessment component that evaluates interference conditions at potential target cells and determines how these conditions may affect post-handover service quality for the UE (102). The interference assessment component may analyze interference measurements from multiple sources including co-channel interference, adjacent channel interference, and inter-system interference to generate comprehensive interference profiles for each candidate cell. These interference profiles may be processed by a signal quality prediction engine that estimates the expected signal quality performance that the UE (102) may experience following handover to each qualified target cell.

[0083] The processor (110) may implement a weighting factor algorithm through a multi-criteria weighting engine that assigns relative importance values to different handover criteria based on the received UE handover requirements and current network conditions. The multi-criteria weighting engine may access a weighting factor database that contains predefined weighting values for various combinations of handover requirements and operational scenarios. The weighting values may be applied to different evaluation criteria such as signal strength measurements, signal quality assessments, RAT compatibility factors, power consumptionimplications, and application-specific requirements to generate weighted scores that reflect the relative importance of each criterion for the current handover decision context.

[0084] The weighting factor database may store weighting configurations that correspond to different UE requirement profiles such as high uplink priority configurations, low latency requirement configurations, battery optimization configurations, and terrestrial network preference configurations. In some cases, the multi-criteria weighting engine may combine multiple weighting configurations when the UE (102) has specified multiple active handover requirements, using weighting combination algorithms that balance competing requirements while maintaining coherent handover decision logic. The weighting combination process may be performed by a weight aggregation module that applies mathematical aggregation methods such as weighted averaging, priority-based selection, or multi-objective optimization techniques to generate unified weighting factors for the handover decision algorithm.

[0085] The UE post-handover requirement consideration may be implemented through a posthandover performance prediction system that evaluates how well each qualified target cell may satisfy the UE handover requirements following completion of the handover process. The posthandover performance prediction system may include a requirement satisfaction analyzer that compares the capabilities and characteristics of each candidate cell against the specific handover requirements provided by the UE (102). The requirement satisfaction analyzer may generate requirement satisfaction scores that indicate the degree to which each target cell may fulfill the UE post-handover requirements, enabling the handover decision algorithm to prioritize cells that align with UE operational needs and application demands.

[0086] The requirement satisfaction analyzer may interface with a cell capability assessment module that maintains detailed information about the service capabilities, performance characteristics, and feature support available through different target cells. The cell capability assessment module may access cell capability databases that contain information about factors such as maximum data rates, latency characteristics, quality of service support, carrier aggregation capabilities, and advanced feature availability for each potential target cell. This capability information may be processed alongside the UE handover requirements to determine compatibility levels and generate requirement satisfaction metrics for the handover decision process.

[0087] The intelligent resource distribution may be implemented through a network resource management system that coordinates handover decisions across multiple connected user equipment to optimize overall network performance and resource utilization. The network resource management system may include a resource allocation optimizer that monitors resource usage patterns across different cells and RAT types and influences handover decisions to achieve balanced resource distribution among connected user equipment. The resource allocation optimizer may analyze factors such as cell loading levels, bandwidth utilization, processing capacity consumption, and quality of service resource allocation to identify opportunities for load balancing through strategic handover decisions.

[0088] The resource allocation optimizer may interface with a dynamic load balancing engine that evaluates the resource requirements of different connected user equipment and generates handover recommendations that distribute network load more evenly across available infrastructure resources. The dynamic load balancing engine may consider the handover requirements provided by different user equipment when making load balancing decisions, ensuring that resource distribution optimization does not compromise the service quality requirements of individual users. In some cases, the load balancing engine may identify opportunities to handover user equipment with flexible requirements to less congested cells while maintaining user equipment with strict requirements on cells that provide appropriate service characteristics.

[0089] The real-time requirement processing may be managed through a real-time requirement tracking system that monitors changes in UE handover requirements and adapts handover decision algorithms accordingly. The real-time requirement tracking system may include a requirement change detection module that identifies modifications to UE handover requirements and triggers updates to the handover decision parameters and weighting factors. The requirement change detection module may maintain requirement history records that enable the system to track requirement evolution patterns and anticipate future requirement changes based on observed UE behavior and application usage patterns.

[0090] The requirement change detection module may interface with a dynamic algorithm adaptation engine that modifies handover decision algorithms in response to changing UE requirements and network conditions. The dynamic algorithm adaptation engine may implement algorithm parameter adjustment mechanisms that update weighting factors, threshold values, andevaluation criteria based on real-time requirement changes and network performance feedback. The adaptation process may be performed through machine learning algorithms that analyze the relationship between handover requirements, handover decisions, and post-handover performance outcomes to continuously improve the handover decision process over time.

[0091] The handover decision algorithm may be implemented through a comprehensive decision matrix system that combines traditional handover criteria with UE-specific requirements to generate optimal target cell selections. The decision matrix system may include a multi-factor evaluation engine that processes measurement values, requirement satisfaction scores, weighting factors, and network condition assessments to generate overall suitability ratings for each qualified target cell. The multi-factor evaluation engine may apply mathematical optimization algorithms that balance competing criteria and identify target cells that provide the optimal combination of signal quality, requirement satisfaction, and network resource efficiency for each handover scenario.

[0092] The multi-factor evaluation engine may interface with a handover decision execution module that implements the final target cell selection and initiates the handover preparation and execution procedures. The handover decision execution module may coordinate with network infrastructure elements to prepare the selected target cell for the incoming handover connection and manage the handover signaling procedures that transfer the UE (102) connection from the source cell to the target cell. The execution module may also implement handover success monitoring capabilities that track post-handover performance metrics and provide feedback to the handover decision algorithm for continuous improvement of the decision process.

[0093] The wireless communication system (100) may support additional information parameters that extend beyond the individual uplink and downlink priority flags to include combined parameter configurations that address complex application scenarios requiring coordinated uplink and downlink performance optimization. The UE (102) may determine a combined uplink / downlink data priority flag through a combined priority analysis module that evaluates applications requiring balanced bidirectional data transfer performance such as video conferencing, real-time collaboration applications, and interactive gaming scenarios. The combined priority analysis module may assess the relative importance of uplink and downlink data transfer rates for active applications and generate combined priority flags that indicate when bothcommunication directions require enhanced performance characteristics during handover decisions.

[0094] The combined priority flag generation may be implemented through a bidirectional requirement assessment engine that analyzes application data flow patterns to identify scenarios where uplink and downlink performance requirements are interdependent. The bidirectional requirement assessment engine may monitor application network traffic patterns through a traffic pattern analyzer that tracks data transmission volumes, timing relationships, and quality of service requirements for both uplink and downlink communications. In some cases, the traffic pattern analyzer may identify applications that exhibit symmetric data transfer requirements where degradation in either uplink or downlink performance may compromise overall application functionality, triggering the generation of combined UL / DL priority flags for inclusion in handover requirement communications to the base station (108).

[0095] The traffic pattern analyzer may interface with an application classification system that categorizes different application types based on their bidirectional communication characteristics and generates appropriate combined priority flag configurations for each application category. The application classification system may maintain application profile databases that contain predefined bidirectional communication patterns for various application types including voice over IP applications, video streaming with interactive features, cloud-based productivity applications, and multiplayer gaming platforms. The classification system may also support dynamic application profiling where new or unknown applications are analyzed in real-time to determine their bidirectional communication requirements and generate corresponding combined priority flag recommendations.

[0096] The combined UL / DL priority flag processing may be handled by the base station (108) through a bidirectional requirement processor that evaluates potential target cells based on their ability to support enhanced performance in both uplink and downlink directions simultaneously. The bidirectional requirement processor may access cell capability databases that contain information about the bidirectional performance characteristics of different target cells including uplink capacity limitations, downlink bandwidth availability, and quality of service provisioning capabilities for applications requiring coordinated bidirectional performance. The processor maygenerate bidirectional compatibility scores that reflect how well each candidate target cell may satisfy the combined uplink and downlink performance requirements specified by the UE (102).

[0097] The wireless communication system (100) may address specific use cases where UE (102) applications prioritize uplink speed performance over downlink data transfer capabilities through specialized uplink prioritization mechanisms. The UE (102) may determine uplink speed prioritization requirements through an uplink performance analysis module that identifies applications with high uplink data transmission demands such as content creation applications, live streaming platforms, file upload services, and real-time data collection systems. The uplink performance analysis module may evaluate the uplink bandwidth requirements, latency sensitivity, and reliability needs of active applications to generate uplink priority flags that guide handover decisions toward target cells with enhanced uplink performance capabilities.

[0098] The uplink speed prioritization may be implemented through an uplink requirement specification engine that generates detailed uplink performance requirements for communication to the base station (108) as part of the UE optimized mobility information. The uplink requirement specification engine may interface with application programming interfaces to obtain specific uplink performance requirements from applications including minimum uplink data rates, maximum acceptable uplink latency values, and uplink quality of service parameters. The engine may also monitor real-time uplink performance demands through an uplink traffic monitoring component that tracks uplink data transmission patterns and identifies periods of high uplink activity that may influence handover requirement priorities.

[0099] The uplink traffic monitoring component may analyze uplink data transmission characteristics including burst transmission patterns, sustained data rate requirements, and uplink error rate sensitivity to generate comprehensive uplink performance profiles for different application scenarios. The component may interface with a predictive uplink analysis module that anticipates future uplink performance requirements based on application behavior patterns and scheduled uplink activities such as file uploads, backup operations, or content synchronization processes. The predictive analysis may enable the UE (102) to generate proactive uplink prioritization requirements that prepare for anticipated uplink performance demands before these demands become active.

[0100] The base station (108) may process uplink speed prioritization requirements through an uplink-focused cell evaluation system that assesses potential target cells based on their uplink performance capabilities and capacity availability. The uplink-focused cell evaluation system may access uplink performance databases that contain detailed information about the uplink characteristics of different target cells including uplink channel configurations, uplink power control capabilities, uplink scheduling algorithms, and uplink interference management features. The evaluation system may generate uplink performance scores that reflect the suitability of each candidate target cell for supporting applications with high uplink performance requirements.

[0101] The uplink performance databases may store information about uplink-specific features such as uplink carrier aggregation support, uplink multiple-input multiple-output antenna configurations, and uplink beamforming capabilities that may enhance uplink performance for user equipment with uplink prioritization requirements. The uplink-focused cell evaluation system may also consider uplink loading conditions and resource availability when evaluating target cells, ensuring that handover decisions account for the current uplink capacity utilization and the ability of target cells to accommodate additional uplink traffic from the UE (102). In some cases, the evaluation system may prioritize target cells with dedicated uplink enhancement features or lower uplink loading levels when processing uplink speed prioritization requirements.

[0102] The additional information parameters may support diverse connectivity requirements through a comprehensive parameter framework that addresses various application scenarios and network deployment configurations. The UE (102) may generate connectivity requirement specifications through a connectivity analysis framework that evaluates active applications and network conditions to determine appropriate parameter combinations for different operational contexts. The connectivity analysis framework may interface with application monitoring systems, network condition assessment modules, and user preference settings to generate comprehensive connectivity requirement profiles that encompass multiple parameter types including priority flags, performance requirements, and network type preferences.

[0103] The connectivity requirement profiles may be processed through a parameter optimization engine that balances competing requirements and generates parameter combinations that address multiple application needs while maintaining coherent handover decision logic. The parameter optimization engine may apply multi-objective optimization algorithms that consider trade-offsbetween different connectivity requirements such as power consumption versus performance, latency versus reliability, and uplink versus downlink prioritization. The optimization process may generate parameter weighting schemes that reflect the relative importance of different connectivity requirements for the current UE (102) operational context.

[0104] The parameter optimization engine may interface with a use case adaptation module that adjusts parameter configurations based on specific use case scenarios such as emergency communications, industrial automation applications, autonomous vehicle connectivity, or Internet of Things device communications. The use case adaptation module may maintain use case parameter templates that contain predefined parameter configurations optimized for different application categories and deployment scenarios. The module may also support custom parameter configuration generation for specialized applications that require unique connectivity characteristics not addressed by standard parameter templates.

[0105] In an embodiment, the method for handover decision in a wireless communication system may implement an internal biasing approach that enables the UE (102) to influence handover decisions through measurement value adjustments while maintaining compatibility with existing base station (108) handover algorithms. The method provide a mechanism for incorporating UE- specific handover requirements into the handover decision process without requiring modifications to base station (108) software or network infrastructure elements. The internal biasing approach may enable the UE (102) to apply calculated offsets to measurement values before transmitting standard measurement reports to the base station (108), allowing the UE (102) to guide handover decisions toward target cells that align with current application demands and operational requirements.

[0106] The method include determining, by the UE (102), handover requirements based on multiple parameters including at least one of an uplink data priority flag, a downlink data priority flag, a signal strength connectivity priority, a real-time application terrestrial network preference priority, a low latency connectivity priority, and a battery optimization requirement. The determination process may involve analyzing active applications running on the UE (102) to identify specific connectivity requirements that may influence handover decision outcomes. The UE (102) may evaluate application characteristics such as data transfer patterns, latency sensitivity, network type preferences, and power consumption implications to generate appropriatehandover requirement parameters. In some cases, the determination process may also consider scheduled applications and anticipated application launches that may affect future handover requirements, enabling the UE (102) to prepare for changing connectivity demands before these demands become active.

[0107] The method include applying, by the UE (102), internal biasing to measurement values of one or more measurement cells based on the handover requirements, wherein the internal biasing includes applying a positive or negative offset to preferred or non-preferred radio access technology cells. The biasing application process may involve calculating offset values that reflect the degree of preference or non-preference for different target cells based on their compatibility with the determined handover requirements. The UE (102) may apply positive offsets to measurement values for cells that align with the handover requirements, effectively making these cells appear more attractive to the base station (108) handover decision algorithm. Conversely, the UE (102) may apply negative offsets to measurement values for cells that do not satisfy the handover requirements, reducing the likelihood that these cells may be selected as handover targets. The offset calculation process may consider factors such as the priority level of different handover requirements, the degree of compatibility between target cells and the requirements, and the magnitude of bias needed to influence handover decisions without compromising measurement report validity.

[0108] The method may include sending, by the UE (102), a standard measurement report to the base station (108) containing the biased measurement values without explicit indication that internal adjustments have been applied. The measurement report transmission may utilize existing RRC messaging protocols and measurement reporting procedures, ensuring compatibility with current network infrastructure and base station (108) implementations. The standard measurement report format may contain the adjusted measurement values alongside other measurement information such as cell identifiers, measurement timestamps, and measurement quality indicators. In some cases, the UE (102) may maintain original unbiased measurement values in internal storage for reference purposes while transmitting only the biased values to the base station (108). The measurement report generation process may also include validation procedures that ensure the biased measurement values remain within acceptable ranges and do not introduce measurement anomalies that may trigger error conditions or measurement report rejection by the base station (108).

[0109] The method may include receiving, by the base station (108), the standard measurement report containing the biased measurement values and processing these values through existing handover decision algorithms without awareness of the internal biasing applied by the UE (102). The base station (108) may treat the received measurement values as standard measurement data and apply conventional handover decision logic including threshold comparisons, signal quality assessments, and target cell evaluation procedures. The processing may involve comparing the received measurement values against configured handover thresholds and evaluating potential target cells based on the biased measurement data. In some cases, the base station (108) may also consider additional factors such as network loading conditions, interference levels, and resource availability when processing the measurement report, but these considerations may be applied to the biased measurement values provided by the UE (102).

[0110] The method may include executing, by the base station (108), a legacy handover decision algorithm that processes the biased measurement values alongside other handover criteria to select an appropriate target cell for the UE (102). The legacy algorithm execution may follow established handover decision procedures including measurement evaluation, threshold checking, target cell ranking, and handover trigger determination. The algorithm may apply weighting factors and decision criteria that have been configured for the specific network deployment and cell configuration, processing the biased measurement values as if these values represent actual radio frequency conditions measured by the UE (102). The handover decision algorithm may generate a target cell selection based on the processed measurement data and other network- specific criteria such as load balancing requirements, quality of service considerations, and mobility management policies.

[0111] The method may include initiating, by the base station (108), a handover to a selected cell based on the results of the legacy handover decision algorithm processing. The handover initiation process may involve preparing the target cell for the incoming connection, coordinating resource allocation with the target cell infrastructure, and executing handover signaling procedures that transfer the UE (102) connection from the source cell to the target cell. The handover execution may follow standard handover procedures including handover command transmission, UE (102) synchronization with the target cell, and connection establishment confirmation. In some cases, the handover initiation may also include quality of service parameter transfer, security context migration, and data forwarding setup to ensure service continuity during the handover transition.

[0112] The internal biasing approach implemented by the method may enable the UE (102) to influence handover decisions across mixed radio access technology deployment environments where multiple RAT types are available within overlapping coverage areas. The biasing mechanism may allow the UE (102) to express preferences for specific RAT types such as terrestrial network connections for real-time applications or lower power consumption RAT cells for battery optimization scenarios. The method may support biasing decisions that consider the characteristics of different RAT types including sixth generation network capabilities, fifth generation network features, long term evolution network compatibility, and non-terrestrial network connectivity options. In some cases, the biasing application may involve different offset calculation strategies for different RAT types based on their relative suitability for the determined handover requirements.

[0113] The measurement value adjustment process within the method may implement safeguards that prevent excessive biasing that could compromise the integrity of the measurement reporting system or cause handover decisions that result in poor radio frequency performance. The UE (102) may apply bias limiting algorithms that constrain offset values within acceptable ranges that maintain measurement report validity while providing sufficient influence over handover decisions. The bias limiting process may consider factors such as the actual signal strength conditions, the magnitude of measurement variations typically observed in the deployment environment, and the sensitivity of base station (108) handover algorithms to measurement value changes. In some cases, the UE (102) may also implement bias validation procedures that verify the appropriateness of calculated offset values before applying these offsets to measurement reports.

[0114] The method may support dynamic bias adjustment capabilities that enable the UE (102) to modify biasing parameters in response to changing application demands, network conditions, or handover requirement priorities. The dynamic adjustment process may involve recalculating offset values when handover requirements change due to application state transitions, battery level changes, or network performance variations. The UE (102) may implement bias update algorithms that smoothly transition between different biasing configurations to avoid abrupt changes in measurement reporting that could trigger unexpected handover behavior. In some cases, the dynamic bias adjustment may also include bias removal capabilities that allow the UE (102) torevert to unbiased measurement reporting when handover requirements no longer indicate preferences for specific target cells or RAT types.

[0115] As illustrated in figure 3, the method (300) for handover decision in a wireless communication system may implement an enhanced measurement report approach that enables direct communication of UE handover requirements to the base station (108) through extended RRC messaging protocols. The method (300) may provide a mechanism for transmitting UE- specific connectivity preferences alongside traditional measurement data, allowing the base station (108) to incorporate these preferences as additional handover criteria during target cell selection processes. The enhanced measurement report approach may enable more sophisticated handover decision algorithms that consider both radio frequency conditions and application-specific requirements when evaluating potential target cells across mixed radio access technology deployment environments. The method (300) may support bidirectional communication of handover preferences between the UE (102) and base station (108), enabling network-aware handover optimization that accounts for both UE operational requirements and network resource management considerations.

[0116] The method (300) may include detecting (301) and determining (302), by the UE (102), handover requirements based on multiple UE parameters including at least one of an uplink data priority flag, a downlink data priority flag, a signal strength connectivity priority, a real-time application terrestrial network preference priority, a low latency connectivity priority, and a battery optimization requirement. The determining process may involve comprehensive analysis of active applications, scheduled applications, and current operational conditions to generate appropriate handover requirement parameters that reflect the UE (102) connectivity needs. The UE (102) may evaluate application characteristics through application programming interface interactions, system resource monitoring, and network performance assessment to identify specific handover requirements that may influence target cell selection outcomes. In some cases, the determining process may also incorporate predictive analysis of anticipated application demands based on user behavior patterns, scheduled tasks, and historical application usage data to generate proactive handover requirements that prepare for future connectivity needs.

[0117] The method (300) may include encoding (304), by the UE (102), the handover requirements in a radio resource control message through structured data formatting proceduresthat enable transmission of requirement information within existing RRC message frameworks. The encoding process may involve converting the determined handover requirement parameters into binary data structures that can be incorporated into various RRC message types including RRC Reconfiguration Complete messages, RRC measurement reports, RRC Setup Complete messages, and UE assistance information messages. The UE (102) may apply encoding algorithms that map different handover requirement types to specific bit field assignments within the RRC message payload, enabling standardized transmission of requirement information regardless of the specific RRC message type utilized for communication. In some cases, the encoding process may also include data compression techniques that minimize the overhead associated with handover requirement transmission while maintaining complete requirement information for base station (108) processing.

[0118] The method (300) may include sending (306), by the UE (102), an enhanced measurement report with the encoded handover requirements to the base station (108), wherein the enhanced measurement report contains both measurement values and requirements information within a unified message structure. The sending process may involve coordinating the transmission of enhanced measurement reports with network-configured measurement reporting cycles to minimize additional signaling overhead while ensuring timely delivery of handover requirement updates. The UE (102) may generate enhanced measurement reports that combine traditional measurement data such as RSRP, RSRQ, and SINR values with the encoded handover requirement parameters, creating comprehensive reports that provide both radio frequency assessment data and UE preference information for handover decision processing. In some cases, the sending process may also include message priority management that ensures time-sensitive handover requirement updates are transmitted through expedited RRC message opportunities when rapid changes in application demands or network conditions occur.

[0119] The method (300) may include receiving (308), by the base station (108), the enhanced measurement report containing the measurement values and the handover requirements through RRC message processing procedures that extract both measurement data and requirement information from the received reports. The receiving process may involve parsing the enhanced measurement report structure to separate traditional measurement values from the encoded handover requirement parameters, enabling the base station (108) to process both types of information through appropriate analysis algorithms. The base station (108) may implementenhanced measurement report validation procedures that verify the integrity and consistency of both measurement data and requirement information before incorporating these inputs into handover decision algorithms. In some cases, the receiving process may also include requirement information storage procedures that maintain current handover preferences for each connected UE (102) in requirement databases that support dynamic requirement updates and historical requirement tracking for handover decision optimization.

[0120] The method (300) may include processing (310), by the base station (108), the handover requirements as an additional handover criterion, wherein the processing includes considering UE post-handover requirements and applying an algorithm with factors for each criterion. The processing procedure may involve integrating the received handover requirement parameters with traditional handover decision factors such as signal strength measurements, signal quality assessments, and network loading conditions to generate comprehensive target cell evaluations. The base station (108) may apply multi-criteria decision algorithms that assign weighting factors to different handover criteria based on the specific handover requirements provided by the UE (102), enabling the handover decision process to prioritize target cells that align with UE operational needs and application demands. In some cases, the processing may also include requirement compatibility analysis that evaluates the ability of potential target cells to satisfy the specified handover requirements based on cell capability databases and current network resource availability information.

[0121] The method (300) may include selecting (312), by the base station (108), an optimal neighbor cell based on the measurement values and the handover requirements through comprehensive target cell evaluation procedures that consider both radio frequency performance and requirement satisfaction factors. The selecting process may involve applying scoring algorithms that combine measurement-based assessments with requirement compatibility evaluations to generate overall suitability ratings for each qualified target cell. The base station (108) may implement target cell ranking procedures that prioritize cells based on their ability to satisfy the UE handover requirements while maintaining acceptable radio frequency performance levels and network resource utilization efficiency. In some cases, the selecting process may also include cross-RAT evaluation capabilities that compare potential target cells from different radio access technology types using unified scoring criteria that account for RAT-specific characteristics and capabilities in relation to the specified handover requirements.

[0122] The method (300) may include initiating (314), by the base station (108), a handover to the optimal cell for UE requirements through handover preparation and execution procedures that coordinate the connection transfer from the source cell to the selected target cell. The initiating process may involve communicating with the selected target cell to prepare resources for the incoming UE (102) connection, including bandwidth allocation, quality of service parameter configuration, and security context establishment procedures. The base station (108) may execute handover signaling procedures that notify the UE (102) of the handover decision and provide the necessary information for connection establishment with the target cell, including cell identification parameters, access configuration data, and timing synchronization information. In some cases, the initiating process may also include handover success monitoring procedures that track post-handover performance metrics to validate that the selected target cell provides appropriate service quality for the UE handover requirements and generate feedback for handover decision algorithm optimization.

[0123] The enhanced measurement report structure utilized by the method (300) may support flexible requirement information encoding that accommodates various combinations of handover requirement parameters while maintaining backward compatibility with existing RRC message processing systems. The UE (102) may implement adaptive encoding procedures that adjust the format and content of requirement information based on the capabilities of the serving base station (108) and the specific handover requirements that are active for the current operational context. The encoding process may support variable-length requirement information fields that enable the UE (102) to include only the handover requirement parameters that are relevant for the current application scenario, thereby minimizing signaling overhead while ensuring complete communication of active handover preferences. In some cases, the enhanced measurement report structure may also include version indicators and capability flags that enable future extensions to the requirement information format without compromising compatibility with current network infrastructure implementations.

[0124] The algorithm implementation within the method (300) may incorporate sophisticated weighting factor calculation procedures that balance competing handover requirements and network optimization objectives to generate optimal target cell selections. The base station (108) may apply dynamic weighting algorithms that adjust the relative importance of different handover criteria based on network conditions, resource availability, and the priority levels associated withdifferent UE handover requirements. The algorithm may implement multi -objective optimization techniques that consider trade-offs between requirement satisfaction, network resource efficiency, and radio frequency performance to identify target cells that provide balanced solutions for complex handover scenarios. In some cases, the algorithm implementation may also include machine learning capabilities that analyze historical handover outcomes and requirement satisfaction patterns to continuously improve the weighting factor calculations and target cell selection accuracy over time.

[0125] The method (300) may support comprehensive RRC message type flexibility that enables handover requirement communication through various RRC message opportunities based on network conditions and communication timing requirements. The UE (102) may implement message type selection algorithms that evaluate the availability and suitability of different RRC message types for transmitting handover requirement updates, including consideration of message transmission timing, payload capacity limitations, and network signaling load conditions. The method may support requirement information transmission through RRC Reconfiguration Complete messages when handover requirements change following network configuration updates, RRC measurement reports when requirement updates coincide with regular measurement reporting cycles, and UE assistance information messages when dedicated requirement update transmission is needed outside of regular reporting intervals. In some cases, the method may also support emergency requirement update procedures that utilize any available RRC message opportunity to communicate time-critical handover requirement changes that may affect immediate handover decision outcomes.

[0126] The neighbor cell evaluation process within the method (300) may implement comprehensive assessment procedures that evaluate each potential target cell against multiple handover requirement parameters to generate detailed requirement satisfaction profiles. The base station (108) may apply requirement matching algorithms that compare the capabilities and characteristics of each candidate target cell with the specific handover requirements provided by the UE (102), generating compatibility scores that reflect the degree to which each cell may satisfy the specified requirements. The evaluation process may consider factors such as RAT type compatibility, performance capability alignment, resource availability, and service quality provisioning capabilities when assessing target cell suitability for different handover requirement combinations. In some cases, the neighbor cell evaluation may also include predictive performanceanalysis that estimates the post-handover service quality that the UE (102) may experience at each candidate target cell based on current network conditions and historical performance data for similar handover scenarios.

[0127] The present invention provides the following adavantages:• The present invention provides enhanced mobility management capabilities that enable wireless communication systems to make handover decisions based on comprehensive user equipment requirements rather than relying solely on traditional signal strength measurements.• The present invention improves network resource utilization through intelligent distribution of user equipment connections across multiple radio access technology types based on actual connectivity requirements rather than signal strength alone.• The present invention provides reduced handover latency through proactive handover requirement communication that enables base stations to prepare for handover decisions before traditional measurement-based triggers occur.• The present invention improves user experience through application-aware handover decisions that maintain service quality characteristics across different connectivity scenarios and application types.• The present invention provides seamless connectivity across different radio access technology types through unified handover decision algorithms that evaluate potential target cells from multiple technology generations using consistent criteria and weighting factors.• The present invention improves battery life extension capabilities through powerconscious handover decisions that consider device power consumption implications alongside traditional handover criteria.• The present invention provides enhanced field issue resolution capabilities through dynamic handover criterion mechanisms that address connectivity problems and performance degradation patterns reported in deployed network environments.The present invention improves signaling efficiency through flexible handover requirement communication mechanisms that minimize overhead while ensuring complete transmission of user equipment connectivity preferences to network infrastructure.

[0128] Features of any of the examples or embodiments outlined above may be combined to create additional examples or embodiments without losing the intended effect. It should be understood that the description of an embodiment or example provided above is by way of example only, and various modifications could be made by one skilled in the art. Furthermore, one skilled in the art will recognise that numerous further modifications and combinations of various aspects are possible. Accordingly, the described aspects are intended to encompass all such alterations, modifications, and variations that fall within the scope of the appended claims.

Claims

WE CLAIM:

1. A wireless communication system (100) comprising:• a user equipment (UE) (102) comprising: o a processor (104) and; o a memory (106) storing instructions that, when executed by the processor (104), cause the UE (102) to determine optimized handover requirements based on multiple UE parameters comprising at least one of an uplink (UL) data priority flag, a downlink (DL) data priority flag, a signal strength connectivity priority, a real-time application terrestrial network (TN) preference priority, a low latency connectivity priority, and a battery requirement, and apply internal biasing to measurement values of one or more cells based on the handover requirements,; and• a base station (108) comprising: o a processor (110) and; o a transceiver (112) configured to receive standard measurement reports from the UE and execute handover decisions based on the measurement values.

2. The wireless communication system (100) as claimed in claim 1, wherein the processor (104) of the UE (102) is configured to apply a positive or negative offset to measurement value for preferred or non-preferred radio access technology (RAT) cells.

3. The wireless communication system (100) as claimed in claim 1, wherein the multiple parameters are dynamically determined based on real-time applications running on the UE (102) and current network conditions.

4. The wireless communication system (100) as claimed in claim 1, wherein the battery optimization requirement enables handover to a radio access technology (RAT) cell and radio frequency cell to save device power without impacting user experience.

5. A wireless communication system (100) comprising:• a user equipment (UE) (102) comprising:o a processor (104) and; o a memory (106) storing instructions that, when executed by the processor (104), cause the UE (102) to determine optimized handover requirements based on multiple UE parameters comprising at least one of an uplink (UL) data priority flag, a downlink (DL) data priority flag, a signal strength connectivity priority, a real-time application terrestrial network (TN) preference priority, a low latency connectivity priority, and a battery optimization requirement, wherein the handover requirements is termed as UE optimized mobility information, and communicate the UE optimized mobility information to the base station via a radio resource control (RRC) message comprising a measurement report or other RRC message transmitted over an air interface; and• a base station (108) comprising: o a processor (110) and; o a transceiver (112) configured to receive the UE optimized mobility information via an enhanced measurement report containing user equipment requirements and measurement values, and process the requirements as an additional handover criterion for selecting an optimal cell.

6. The wireless communication system (100) as claimed in claim 5, wherein the UE optimized mobility information is communicated to the base station (108) via a radio resource control (RRC) message or added under a measurement report transmitted over an air interface.

7. The wireless communication system (100) as claimed in claim 4, wherein the radio access technology (RAT) cells comprising at least one of sixth generation network (6G), fifth generation network (5G), long term evolution (4G LTE), terrestrial network (TN), or nonterrestrial network (NTN) cells.

8. The wireless communication system (100) as claimed in claim 1, wherein the wireless communication system operates in a mixed radio access technology (RAT) deployment environment comprising multiple RATs including at least two of sixth generation network (6G), fifth generation network (5G), long term evolution (4G LTE), terrestrial network(TN), and non-terrestrial network (NTN), and wherein the handover requirements enable optimal cell selection across the multiple RATs based on the UE requirements.

9. A method (400) for handover decision in a wireless communication system, comprising:• determining (402), by the user equipment (UE) (102), handover requirements based on multiple parameters including at least one of an uplink (UL) data priority flag, a downlink (DL) data priority flag, a signal strength connectivity priority, a real-time application terrestrial network (TN) preference priority, a low latency connectivity priority, and a battery optimization requirement;• applying (404), by the UE (102), internal biasing to measurement values of one or more measurement cells based on the handover requirements, wherein the internal biasing includes applying a positive or negative offset to preferred or non-preferred radio access technology (RAT) cells, and;• sending (406), by the UE (102), a standard measurement report to the base station (108);• receiving (408), by the base station (108), the standard measurement report;• executing (410), by the base station (108), a legacy handover decision algorithm; and• initiating (412), by the base station (108), a handover to a selected cell.

10. A method (300) for handover decision in a wireless communication system, comprising:• detecting (301) UE (102) handover need or measurement request by UE (102) from base station;• determining (302), by the user equipment (UE) (102), handover requirements based on multiple UE parameters including at least one of an uplink (UL) data priority flag, a downlink (DL) data priority flag, a signal strength connectivity priority, a real-time application terrestrial network (TN) preference priority, a low latency connectivity priority, and a battery optimization requirement;• encoding (304), by the UE (102), the handover requirements in a radio resource control (RRC) message;• sending (306), by the UE (102), an enhanced measurement report with the encoded handover requirements to the base station (108), wherein the enhanced measurement report contains both measurement values and requirements information;• receiving (308), by the base station (108), the enhanced measurement report containing the measurement values and the handover requirements;• processing (310), by the base station (108), the handover requirements as an additional handover criterion, wherein the processing includes considering UE post-handover requirements and applying an algorithm with factors for each criterion;• selecting (312), by the base station (108), an optimal neighbor cell based on the measurement values and the handover requirements; and• initiating (314), by the base station (108), a handover to the optimal cell for UE requirements.

11. The method (300) as claimed in claim 10, wherein the RRC message comprises one of an RRC Reconfiguration Complete message, an RRC measurement report, an RRC Setup Complete message, a UE assistance information message, or other equivalent RRC message.

12. The method (300) as claimed in claim 10, wherein the algorithm applies weighting factors to each of the multiple parameters when selecting the optimal neighbor cell, and wherein the base station (108) evaluates each neighbor cell against the handover requirements to determine the optimal cell for handover.

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