Communication apparatus and method for communication apparatus for ai-based air interface

Abstract

A communication apparatus comprising a controller configured to acquire Idle Mode Measurements Information, IMSI, during idle mode the IMSI comprising a Generalized Synchronization Signals Correlation Profile, GSSCP, and utilize the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications.

Description

[0001] COMMUNICATION APPARATUS AND METHOD FOR COMMUNICATION APPARATUS FOR AI-BASED AIR INTERFACE

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to the field of wireless communication and more specifically to a communication apparatus and a method for communication apparatus for utilizing idle state information to provide enhanced idle mode and connected mode applications.

[0004] BACKGROUND

[0005] In recent years, demand for artificial intelligence (Al) in wireless communication networks has increased due to a growing complexity of network operations, a need for enhancements in managing network resources, and the like. The Al-powered air interfaces process the network information and facilitate resource allocation while processing the signals by analyzing various network parameters and measurements, such as Synchronization Signal Block (SSB) based measurements, Reference Signal Received Power (RSRP), and correlation profiles between synchronization signals, to enhance wireless communication performance. Additionally, the wireless communication network utilizes two operational states (i.e., a connected mode and an idle mode). The connected mode resources, such as Channel State Information Reference Signals (CSI-RS) are used to get information about the channel conditions, enabling applications like beamforming, mobility management, and resource scheduling. However, the connected mode resources depend on additional transmissions for training, which leads to increased overhead on the network, resulting in reduced spectral and energy efficiency. The idle mode resources, such as, synchronization signals are configured to provide periodic network information and enhance wireless communication performance. However, the idle mode resources are not effectively utilized in existing Al-based interfaces, resulting in inefficiencies in reducing network overhead, improving spectral efficiency, optimizing energy consumption, and providing limited overall performance in Al-based air interfaces.

[0006] Certain attempts have been made to utilize idle mode resources efficiently, but such attempts are limited to the utilization of idle mode resources for basic SSB-based RSRP measurements, conventional channel state information, and the like only. In addition, such inefficient utilization of idle mode information creates a dependency on additional reference signal transmissions for Al model training and increases network overhead caused by connected mode measurements. Thus, a technical problem exists of how to enhance wireless communication performance by effectively utilizing idle mode information in Al-based air interfaces while minimizing reliance on additional transmissions in connected mode.

[0007] Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the conventional communication apparatuses and conventional methods for utilizing the idle mode network information.

[0008] SUMMARY

[0009] The present disclosure provides a communication apparatus and a method for the communication apparatus for utilizing idle state information to provide enhanced idle mode and connected mode applications. The present disclosure provides a solution to the existing problem of how to enhance wireless communication performance by effectively utilizing idle mode information in Al-based air interfaces while minimizing reliance on additional transmissions in connected mode. An objective of the present disclosure is to provide a solution that overcomes at least partially the problems encountered in the prior art and provides the communication apparatus and the method for the communication apparatus for utilizing idle state information to provide enhanced idle mode and connected mode applications.

[0010] One or more objectives of the present disclosure are achieved by the solutions provided in the enclosed independent claims. Advantageous implementations of the present disclosure are further defined in the dependent claims.In one aspect, the present disclosure provides a communication apparatus comprising a controller configured to acquire Idle Mode Measurements Information, IMSI, during idle mode the IMSI comprising a Generalized Synchronization Signals Correlation Profile (GSSCP) and utilizes the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications.

[0011] Advantageously, the communication apparatus is configured to acquire Idle Mode measurement information (IMSI) including a Generalized Synchronization Signals Correlation Profile (GSSCP), and utilize the acquired IMSI as an input for an Air Interface Algorithm targeting idle or connected mode applications in order to reduce additional resource requirements. Furthermore, the computing apparatus is configured to provide detailed insights into synchronization signals and environmental characteristics, enabling improved accuracy in tasks, such as beam prediction, interference management, and resource scheduling. Moreover, the computing apparatus is configured to facilitate seamless and enhanced transitions between idle and connected modes while improving network efficiency, reducing congestion, and enhancing the overall user experience.

[0012] In another aspect, the present disclosure provides a method for the communication apparatus comprising acquiring Idle Mode Measurements Information (IMSI) during idle mode the IMSI comprising Generalized Synchronization Signals Correlation Profile (GSSCP) and utilizing the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications.

[0013] The method achieves all the advantages and technical effects of the communication apparatus of the present disclosure.

[0014] It is to be appreciated that all the aforementioned implementation forms can be combined.

[0015] It has to be noted that all devices, elements, circuitry, units, and means described in the present application could be implemented in the software or hardware elements or any kind of combination thereof. All steps which are performed by the various entities described in the present application, as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity which performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements, or any kind of combination thereof. It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

[0016] Additional aspects, advantages, features, and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative implementations construed in conjunction with the appended claims that follow.

[0017] BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[0019] FIG. 1 is a block diagram that illustrates a communication apparatus, in accordance with an embodiment of the present disclosure;

[0020] FIG. 2 is a flowchart of a method for a communication apparatus, in accordance with an embodiment of the present disclosure;

[0021] FIG. 3 is a diagram that illustrates the flow of operations for acquiring and processing Idle Mode measurement information (IMSI) in a computing apparatus, in accordance with an embodiment of the present disclosure;

[0022] FIG. 4 is a diagram that illustrates the flow of operations for acquiring and processing Idle Mode Measurements Information (IMSI) in a computing apparatus, in accordance with an embodiment of the present disclosure; and

[0023] FIG. 5 is a diagram that illustrates a signaling example for acquiring and processing Idle Mode measurement information (IMSI) in a computing apparatus, in accordance with an embodiment of the present disclosure.

[0024] In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

[0025] DETAILED DESCRIPTION OF EMBODIMENTS

[0026] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.

[0027] FIG. 1 is a block diagram that illustrates a communication apparatus, in accordance with an embodiment of the present disclosure. With reference to FIG. 1, there is shown a diagram 100 that depicts a communication apparatus 102 to acquire and use idle mode state information for Al-based air interface as the main input to (Al-based) Air Interface model targets idle mode and connected mode applications. The communication apparatus 102 includes a controller 104, a memory 106, and a network interface 108.

[0028] The communication apparatus 102 is configured to acquire the idle mode measurements information (IMSI) during the idle mode and further utilize the same as an input for the air interface algorithm targeting the idle mode applications or connected mode applications. In an implementation, the communication apparatus 102 is a Terminal Device, and wherein the controller 104 is a Terminal Device controller. As a terminal device, the communication apparatus 102 is configured to handle localized processing of idle mode measurements for efficient device- specific operations. In another implementation, the terminal device is a User Equipment. In yet another implementation, the communication apparatus 102 is a network device and the controller 104 is a network device controller. As a network device, the communication apparatus 102 is configured to manage centralized coordination for network-level optimization. As a result, the communication apparatus 102 is configured to support diverse deployment scenarios, ensuring efficient and scalable operations in wireless networks.

[0029] The controller 104 is configured to acquire idle mode measurement information and further utilize the same for an air interface algorithm targeting idle mode applications. Examples of the controller 104 may include but are not limited to a central data processing device, a microprocessor, a microcontroller, a complex instruction set computing (CISC) processor, an applicationspecific integrated circuit (ASIC) processor, a reduced instruction set (RISC) processor, a very long instruction word (VLIW) processor, a state machine, and other processors or control circuitry.The memory 106 is used to store the idle mode measurement information acquired by the controller 104. Examples of implementation of the memory 106 may include, but are not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Dynamic Random Access Memory (DRAM), Random Access Memory (RAM), Read-Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), and / or CPU cache memory.

[0030] The network interface 108 is used to allow the controller 104 to communicate with the memory 106. Examples of implementation of the network interface 108 may include but are not limited to a network interface, a computer port, a network socket, a network interface controller (NIC), and any other network interface device.

[0031] There is provided the communication apparatus 102 comprising the controller 104, which is configured to acquire Idle Mode Measurements Information (IMSI) during idle mode the IMSI comprises a Generalized Synchronization Signals Correlation Profile (GSSCP) and utilize the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications. The IMSI includes a Generalized Synchronization Signals Correlation Profile (GSSCP), which is acquired by processing synchronization signals (e.g., Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS)) that are periodically transmitted by base stations. Furthermore, the acquired IMSI is utilized as an input to an Air Interface Algorithm that is used to optimize resource allocation. As a result, by utilizing the IMSI, the communication apparatus 102 is used to ensure an improved resource utilization with reduced energy consumption in wireless communication environments.

[0032] In accordance with an embodiment, the controller 104 is further configured to represent the GSSCP as a tensor S ∈ ℂd×d×d×d×dwhere: dt= |픍| is the domain dimension representing the time samples over which the GSSCP is acquired within one SSB time domain period, dssbis the SSB dimension representing the SSB periods over which the GSSCP is acquired and dtis the identities dimension representing the Cell IDs that the UE could detect during GSSCP acquisition and it is denoted by the set I where dt= | / |, dAis the spatial dimension, dFis the frequency dimension. Moreover, such representation is used to provide a comprehensive framework to store and analyze the correlation profiles for various parameters, including time, SSB periods, cell identities, spatial configurations, and frequency ranges that enable the Al-based air interface algorithm to process detailed input data for enhanced predictions or optimizations. As a result, the controller 104 is configured to provide detailed, multi-dimensional insights into the synchronization signals and facilitates an advanced processing, such as spatial-frequency beam prediction or interference mitigation. Additionally, the controller 104 is configured to enhance the overall accuracy and efficiency of both idle mode and connected mode applications are significantly improved, while resource utilization is optimized.

[0033] In accordance with an embodiment, the controller 104 is further configured to refine the IMSI prior to utilizing the IMSI as input for the Air Interface Algorithm by filtering the IMSI to reduce the size of the IMSI. Moreover, such refinement is used to ensure that only relevant and high-quality data is retained from the IMSI, which reduces computational overhead for the Air Interface Algorithm with minimized transmission of unnecessary data. In other words, the controller 104 is configured to identify and select data points from the IMSI. For example, in the case of a Generalized Synchronization Signals Correlation Profile (GSSCP), time-domain samples with average power above a certain threshold are retained, while others are discarded. Further, the filtered IMSI is represented in a reduced format, such as a smaller tensor or a compact data sequence, which retains only the most impactful measurements (e.g., dominant synchronization signals or key cell identities). Moreover, such optimized representation is then passed as input to the Air Interface Algorithm that enhances the overall scalability thereby enabling the communication apparatus 102 to handle a larger number of devices or more complex network scenarios.In accordance with an embodiment, the controller 104 is further configured to refine the GSSCP by filtering out GSSCP tensors with an average power below a threshold power level. In other words, by analyzing the power levels of the GSSCP tensors and selecting only those that meet or exceed the threshold, while discarding the rest, the controller 104 is configured to refine the GSSCP. For example, if the GSSCP tensors represent signal correlations over various time, spatial, and frequency dimensions, tensors with low average power, which are less likely to contribute meaningful data to the Air Interface Algorithm, are excluded. Moreover, such refinement reduces the size of the GSSCP dataset, retaining only the most relevant and impactful information. By focusing on high-power tensors, the controller 104 is configured to enhance the overall computational efficiency, reduce the algorithm's processing load, and accelerate data transmission. The reduced dataset enables the Air Interface Algorithm to operate more effectively while conserving resources such as processing power and bandwidth.

[0034] In accordance with an embodiment, the controller 104 is further configured to represent the GSSCP as a tensor S being a time domain sequence of “reduced tensors” S = [S1, S2,..., Sd] where Si∈ ℂd×d×d×d, and the refined GSSCP as a sequence of d̃T≤ dTselected reduced GSSCP tensors S̃ = [S̃i,..., S̃k] and the corresponding time domain sample vector t̃ = [ti, tj,..., tk]. As a result, the controller 104 is configured to ensure that the GSSCP is systematically organized for efficient processing and analysis, enabling advanced modelling of idle mode information for improved algorithm performance while retaining the most relevant information and ensuring efficient use of computational resources.

[0035] In accordance with an embodiment, the controller 104 is further configured to tokenize the IMSI prior to utilizing the IMSI and after refining the IMSI. The controller (104) is further configured to tokenize the Idle Mode Measurements Information (IMSI) after refining and before utilizing the same as an input for the Air Interface Algorithm. Moreover, such tokenization includes transforming the refined IMSI into a compact and structured format suitable for machine learning models and ensures that the algorithm receives optimized data, improving prediction accuracy and responsiveness of the communication apparatus 102.

[0036] In accordance with an embodiment, the controller 104 is further configured to tokenize the GSSCP after refining the GSSCP by representing the refined GSSCP tensor as S̃ ∈ ℂd̃×d×d×d×dand corresponding time domain samples vectors t, and tokenize the refined GSSCP tensor S over its time domain dimension d. by feeding the refined GSSCP S̃ = [S̃i, S̃j,..., S̃k] to an embedder ei= fe(S̃i) ∈ ℂd×1, feeding the corresponding time domain samples vector t̃ = [ti, tj,..., tk] to a Dynamic Positional Encoder (DPE)., wherein t is used to sample the generic positional encoder sequence during inference pd= p(td),

[0037]

[0038] superimposing the sampled positional encoder sequence with the embedder output x = e + pdand the superimposed result x is given as an input to the air interface algorithm. As a result, the controller 104 is configured to ensure that the refined GSSCP is efficiently structured for Al-based analysis, enhancing the processing of the temporal and spatial data effectively.

[0039] In accordance with an embodiment, the controller 104 is further configured to train a model of the air interface algorithm over a generic positional encoding sequence p = p1, p2, ..., pdwithout any sampling, that covers the overall time domain vector t = [t1, t2,..., td]. As a result, by training the model of the air interface algorithm, the controller 104 is configured to ensure comprehensive coverage of the time domain during training, allowing the model to capture fine-grained temporal patterns, thereby enhancing the accuracy of predictions and resource optimization.

[0040] In accordance with an embodiment, the controller 104 is further configured to acquire the GSSCP based on mapping, one-to-one, a set of identities I = {i1, i2, i3,...} into a set η = {p1, p2, p3,...} of predefined synchronization signals that could be received from a base station and acquiring the GSSCP by processing a received signal using the set r / . The GSSCP is acquired by processing received signals using the set, ensuring accurate mapping of synchronization signals to the corresponding cell identities, thus enabling precise characterization of the radio environment.In accordance with an embodiment, the controller 104 is further configured to acquire the GSSCP by utilizing a bank of sliding window correlators where each sliding window correlator correlates the received signal with one SS signal pj∈ η.

[0041] 1

[0042] sj(t) = ∑y(t - l)pj*(l) where t ∈ 픍

[0043]

[0044] where Lsscorresponds to the length of the considered SS signal, and aggregating outputs of the sliding window correlators to create the GSSCP carrying information regarding a surrounding radio environment. As a result, the outputs of such correlators are aggregated to form the GSSCP, which carries detailed information about the surrounding radio environment, including multipath and interference patterns.

[0045] In accordance with an embodiment, the controller 104 is further configured to reduce the number of sliding window correlators by considering a subset of r / that corresponds to neighbor cells only, wherein t ∈ 픍 is a time sample within the SS period that is represented by the set of time domain samples 픍. The controller 104 is configured to optimize the acquisition of the GSSCP by reducing the number of sliding window correlators, such as by considering only a subset of η corresponding to neighboring cells, processing resources on the most relevant signals while reducing the overall computational complexity while maintaining critical environmental data.

[0046] In accordance with an embodiment, the controller 104 is further configured to receive a remote IMSI from another communication apparatus over an over-the-air interface. The controller 104 is configured to receive a remote IMSI from another communication apparatus over an over-the-air interface in order to allow the sharing of idle mode information across devices (e.g., communication apparatus, network devices, or terminal devices) thereby supporting coordinated processing and network-wide optimization.

[0047] In accordance with an embodiment, the controller 104 is further configured to transmit the IMSI to another communication apparatus over an over-the-air interface. Moreover, such transmission is used to facilitate the exchange of refined idle mode measurements, enabling collaborative air-interface optimization.

[0048] In accordance with an embodiment, the controller 104 is further configured to acquire the GSSCP by processing idle-mode synchronization signal(s) that are received from the serving cell and neighboring cells. In other words, the controller 104 is configured to acquire the GSSCP by processing idle-mode synchronization signals received from the serving cell and neighboring cells, which includes deriving the GSSCP from signals such as the Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS), ensuring comprehensive environmental awareness.

[0049] In accordance with an embodiment, the idle-mode synchronization signal(s) include PSS and / or SSS. In an implementation, the idle-mode synchronization signal includes PSS. In another implementation, the idle-mode synchronization signal includes SSS. In yet another implementation, the idle-mode synchronization signal includes PSS and SSS. As a result, the PSS and / or SSS is used to exploit readily available periodic transmissions instead of requiring additional connected-mode resources, resulting in significant improvements in spectral and energy efficiency by eliminating the need for extra reference signal transmissions with reduced system overhead while maintaining comprehensive signal environment analysis capabilities.

[0050] In accordance with an embodiment, the IMSI further comprises additional measurements performed in idle mode. Moreover, the additional measurements are used to capture multiple dimensions of the radio environment simultaneously, enabling a more complete understanding of network conditions without additional resource consumption that provides an enhanced predictionaccuracy and system performance through multi-dimensional analysis, while maintaining the efficiency benefits of idle-mode operation.

[0051] In accordance with an embodiment, the IMSI further comprises Reference Signal Received Power (RSRP). As a result, the RSRP is used to provide quantitative signal strength information that complements the correlation-based GSSCP data, enabling an accurate assessment of cell coverage and potential handover decisions in order to provide an improved cell selection and reselection performance, along with more reliable mobility management decisions based on comprehensive signal strength information.

[0052] In accordance with an embodiment, the IMSI further comprises Reference Signal Received Quality (RSRQ) and / or Signal-to-Interference-plus-Noise Ratio (SINR). In an implementation, the IMSI includes RSRQ. In another implementation, the IMSI includes SINR. In yet another implementation, the IMSI includes RSRQ and SINR. As a result, the controller 104 is configured to distinguish between high-power but heavily interfered signals and lower-power but cleaner signals, leading to an optimal network selection and resource allocation decisions.

[0053] In accordance with an embodiment, the air-interface algorithm comprises one or more Al-based air-interface models for different idle and / or connected mode applications, such as SSB beam prediction, CSI-RS beam prediction, and CSI prediction. Advantageously, the inclusion of the applications, such as SSB beam prediction, CSI-RS beam prediction, and CSI prediction, the controller 104 is configured to optimize communication performance and efficiency.

[0054] Advantageously, the communication apparatus 102 is configured to acquire Idle Mode Measurements Information (IMSI) including a Generalized Synchronization Signals Correlation Profile (GSSCP), and utilize the acquired IMSI as an input for an Air Interface Algorithm targeting idle or connected mode applications in order to reduce additional resource requirements. Furthermore, the communication apparatus 102 is configured to provide detailed insights into synchronization signals and environmental characteristics, enabling improved accuracy in tasks, such as beam prediction, interference management, and resource scheduling. Moreover, the communication apparatus 102 is configured to facilitate seamless and enhanced transitions between idle and connected modes while improving network efficiency, reducing congestion, and enhancing the overall user experience.

[0055] FIG. 2 is a flowchart of a method for a communication apparatus, in accordance with an embodiment of the present disclosure. FIG. 2 is described in conjunction with elements from FIG. 1. With reference to FIG. 2, there is shown a flowchart of method 200 that includes steps 202 to 204. The controller 104 ( of FIG. 1) is configured to execute the method 200 for the communication apparatus 102 (of FIG. 1) by utilizing idle state information to provide enhanced idle mode and connected mode applications.

[0056] There is provided the method 200 for the communication apparatus 102 by utilizing idle state information to provide enhanced idle mode and connected mode applications.

[0057] At step 202, the method 200 includes acquiring Idle Mode Measurements Information, IMSI, during idle mode the IMSI comprises a Generalized Synchronization Signals Correlation Profile (GSSCP) and at step 204, the method 200 includes utilizing the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications.

[0058] Advantageously, the method 200 is used to acquire Idle Mode measurement information (IMSI) including a Generalized Synchronization Signals Correlation Profile (GSSCP), and utilize the acquired IMSI as an input for an Air Interface Algorithm targeting idle or connected mode applications in order to reduce additional resource requirements. Furthermore, the method200 is used to provide detailed insights into synchronization signals and environmental characteristics, enabling improved accuracy in tasks, such as beam prediction, interference management, and resource scheduling. Moreover, the method 200 is used to facilitate seamless and enhanced transitions between idle and connected modes while improving network efficiency, reducing congestion, and enhancing the overall user experience.

[0059] The steps 202 to 204 are only illustrative, and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the claims herein.

[0060] There is further provided a computer program product comprising program instructions for performing the method 200 when executed by one or more processors in the communication apparatus 102. The computer program product is implemented as an algorithm, embedded in a software stored in a non-transitory computer-readable storage medium. The non-transitory computer-readable storage means may include but are not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. Examples of implementation of computer-readable storage medium, but are not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Random Access Memory (RAM), Read Only Memory (ROM), Hard Disk Drive (HDD), Flash memory, a Secure Digital (SD) card, Solid-State Drive (SSD), a computer-readable storage medium, and / or CPU cache memory.

[0061] FIG. 3 is a diagram that illustrates the flow of operations for acquiring and processing Idle Mode measurement information (IMSI) in a computing apparatus, in accordance with an embodiment of the present disclosure. FIG. 3 is described in conjunction with elements from FIG. 1. With reference to FIG. 3, there is shown a diagram 300 that illustrates an implementation of a new Air Interface in the communication apparatus 102.

[0062] At operation 302, the communication apparatus 102 is configured to acquire the Synchronization Signals Correlation Profile (SSCP) while in idle mode. The SSCP acquisition involves processing the idle-mode synchronization signals, such as Primary Synchronization Signals (PSS) and Secondary Synchronization Signals (SSS), received from the serving cell and neighboring cells. By doing so, the SSCP captures both intra-cell and inter-cell information, offering a comprehensive view of the surrounding radio environment. After the acquisition, at operation 304, the SSCP undergoes refinement to ensure that only relevant information is retained, such as by filtering to reduce the size of the SSCP, thereby enhancing its efficiency and making it suitable for further processing. Moreover, such refinement is used to ensure the elimination of irrelevant or low-power data, which contributes to an optimized and compact representation of the SSCP. Thereafter, the refined SSCP (e.g., at operation 306), or in some cases, the directly acquired SSCP (e.g., at operation 308), is fed into the IMSI tokenizer, such as at operation 310. The tokenizer processes the SSCP alongside other idle mode measurements, such as Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), or Signal-to-Interference-plus-Noise Ratio (SINR). Finally, at operation 312, the received IMSI is used to optimize air-interface algorithms for idle-mode and connected-mode applications, improving network efficiency and adaptability.

[0063] FIG. 4 is a diagram that illustrates the flow of operations for acquiring and processing Idle Mode measurement information (IMSI) in a computing apparatus, in accordance with an embodiment of the present disclosure. FIG. 4 is described in conjunction with elements from FIG. 3. With reference to FIG. 4, there is shown a diagram 400 that illustrates the implementation of a new Air Interface in the communication apparatus 102.

[0064] At operation 402, the communication apparatus 102 is configured to acquire the Synchronization Signals Correlation Profile (SSCP) while in idle mode. The SSCP acquisition involves processing synchronization signals, such as PrimarySynchronization Signals (PSS) and Secondary Synchronization Signals (SSS), received from the serving cell and neighboring cells. The UE processes the received signals using prior knowledge of synchronization signal identities, which map to predefined synchronization signals. Using a bank of sliding window correlators, the SSCP is generated to capture intra-cell and inter-cell information, offering a comprehensive view of the surrounding radio environment. At operation 404, the SSCP undergoes refinement to ensure that only relevant information is retained. This refinement involves filtering to remove irrelevant or low-power data, reducing the size of the SSCP, and creating a compact and optimized representation. The refined SSCP consists of high-value tensors, making it suitable for further processing. Thereafter, at operation 406, the refined SSCP, or in some cases the directly acquired SSCP, is fed into the IMSI tokenizer. The tokenizer processes the SSCP alongside other idle-mode measurements, such as Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), or Signal-to-Interference-plus-Noise Ratio (SINR). The IMSI tokenizer generates tokenized data by extracting critical features from the SSCP. At operation 408, dynamic positional encoding is applied to the tokenized SSCP. This involves a Positional Encoding (PE) generator that creates a positional sequence, embedding temporal and spatial information. During inference, the sequence is dynamically sampled using the time domain samples, while during training, the complete positional sequence is used to ensure a comprehensive temporal representation. At operation 410, the tokenized SSCP and its positional encoding are combined with embeddings generated by an embedder. Finally, at operation 412, the enriched IMSI data is input into Al-based air-interface algorithms including SSB beam prediction, CSI-RS beam prediction, channel state prediction, and resource allocation, thereby optimizing both idle-mode and connected-mode network operations for improved efficiency and adaptability.

[0065] FIG. 5 is a diagram that illustrates a signaling example for acquiring and processing Idle Mode Measurements Information (IMSI) in a computing apparatus, in accordance with an embodiment of the present disclosure. FIG. 5 is described in conjunction with elements from FIG. 3 and FIG. 4. With reference to FIG. 5, there is shown a signaling flow 500 involving a base station 502, a user equipment (UE) 504, a tokenization module 518, and an Al-based inference module 522.

[0066] In an implementation scenario, the training phase includes the transmission of the IMSI measurements configurations (e.g., IMSI measurements such as Synchronization Signals Correlation Profile (SSCP), Reference Signal Received Power (RSRP) or Received Signal Strength Indicator (RSSI) to the UE 504 (e.g., at operation 506) by the base station 502. The UE 504, upon receiving the configuration, starts the IMSI acquisition process (e.g., at operation 510) that includes processing idle-mode synchronization signals and generating an IMSI report that encapsulates the measurements. The IMSI report is transmitted back to the base station 502 (e.g., at operation 514), where it is used as input to train an IMSI-based Al model. The Al model leverages the measurements to optimize idle and connected mode algorithms at the base station. Furthermore, during the inference phase, the base station 502 transfers the trained IMSI-based Al model to the UE 504 (operation 520). Once the Al model is received, the UE 504 initiates the IMSI acquisition process again (operation 516). The tokenization module 518 processes the acquired IMSI measurements to produce tokenized IMSI data, which is utilized by air-interface algorithms targeting idle and connected mode applications, which are executed by the Al-based inference module 522. As a result, the signaling flow exemplifies the iterative process of IMSI measurement acquisition, Al model training, and inference deployment, ensuring that the communication apparatus 102 dynamically adapts to network conditions and user requirements.

[0067] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non-exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural. The word "exemplary" is used herein to mean "serving as an example, instance or illustration". Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or to exclude the incorporation of features from other embodiments. The word"optionally" is used herein to mean "is provided in some embodiments and not provided in other embodiments". It is appreciated that certain features of the present disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination or as suitable in any other described embodiment of the disclosure.

Claims

CLAIMS1. A communication apparatus (102) comprising a controller (104) configured toacquire Idle Mode Measurements Information, IMSI, during idle mode the IMSI comprising a Generalized Synchronization Signals Correlation Profile, GSSCP, andutilize the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications.

2. The communication apparatus (102) according to claim 1, wherein the controller (104) is further configured to represent the GSSCP as a tensor S ∈ ℂdt×dssb×dI×dA×dFwhere dt= |ℑ| is the time domain dimension representing the time samples over which the GSSCP is acquired within one SSB time domain period, dssbis the SSB dimension representing the SSB periods over which the GSSCP is acquired, dtis the identities dimension representing the Cell IDs that the UE could detect during GSSCP acquisition and it is denoted by the set I where dt= | / |, dAis the spatial dimension, dFis the frequency dimension.

3. The communication apparatus (102) according to claims 1 or 2, wherein the controller (104) is further configured to refine the IMSI prior to utilizing the IMSI as input for the Air Interface Algorithm by filtering the IMSI to reduce the size of the IMSI.

4. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to refine the GSSCP by filtering out GSSCP tensors with an average power below a threshold power level.

5. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to represent the GSSCP as a tensor S being a time domain sequence of “reduced tensors” S = [S1;S2,..., Sdt] where Si∈ ℂdssb×dI×dA×dFand the refined GSSCP as a sequence of d̃T≤ dTselected reduced GSSCP tensors S̃ = [S̃i, S̃j,..., S̃k] and the corresponding time domain samples vector t̃ = [ti, tj,..., tk].

6. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to tokenize the IMSI prior to utilizing the IMSI and after refining the IMSI.

7. The communication apparatus (102) according to claims 5 and 6, wherein the controller (104) is further configured to tokenize the GSSCP after refining the GSSCP by representing the refined GSSCP tensor as S̃ ∈ ℂd̃t×dssb×dI×dA×dFandcorresponding time domain samples vectors t̃, and tokenize the refined GSSCP tensor S̃ over its time domain dimension dtby feeding the refined GSSCP S̃ =[S̃i, S̃j,..., S̃k] to an embedder ei=∈ ℂde×1feeding the corresponding time domain samples vector t̃ = [ti, tj,..., tk] to a Dynamic Positional Encoder (DPE)., wherein t̃ is used to sample the generic positional encoder sequence during inference pd= p(td) superimposing the sampled positional encoder sequence with the embedder output x = e + pdand input the superimposed result x is inputs to the air interface algorithm.

8. The communication apparatus (102) according to claim 7, wherein the controller (104) is further configured to train a model of the air interface algorithm over a generic positional encoding sequence p = p1, p2,..., pd, (without any sampling, that covers the overall time domain vector t = [t1, t2,..., td].

9. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to acquire the GSSCP based onmapping, one-to-one, a set of identities I = {i1, i2, i3,...} into a set η = {p1, p2, p3,... } of predefined synchronization signals that could be received from a base station andacquiring the GSSCP by processing a received signal using the set r / .

10. The communication apparatus (102) according to claim 9, wherein the controller (104) is further configured to acquire the GSSCP by utilizing a bank of sliding window correlators where each sliding window correlator correlates the received signal with one SS signal pj∈ η1 = 0where Lsscorresponds to the length of considered SS signal, and aggregating outputs of the sliding window correlators to create the GSSCP carrying information regarding a surrounding radio environment.

11. The communication apparatus (102) according to claim 10, wherein the controller (104) is further configured to reduce the number of sliding window correlators by considering a subset of r / that corresponds to neighbor cells only, wherein t ∈ 픍 is a time sample within the SS period that is represented by the set of time domain samples 픍.

12. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to receive a remote IMSI from another communication apparatus (102) over an over-the-air interface.

13. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to transmit the IMSI to another communication apparatus over an over-the-air interface.

14. The communication apparatus (102) according to any preceding claim, wherein the controller (104) is further configured to acquire the GSSCP by processing idle-mode synchronization signal(s) that are received from the serving cell and neighboring cells.

15. The communication apparatus (102) according to claim 14, wherein the idle-mode synchronization signal(s) include PSS and / or SSS.

16. The communication apparatus (102) according to any preceding claim, wherein the IMSI further comprises additional measurements performed in idle mode.

17. The communication apparatus (102) according to claim 16, wherein the IMSI further comprises Reference Signal Received Power, RSRP.

18. The communication apparatus (102) according to claim 16 or 17, wherein the IMSI further comprises Reference Signal Received Quality, RSRQ, and / or Signal-to-Interference-plus-Noise Ratio, SINR.

19. The communication apparatus (102) according to any preceding claim, wherein the air-interface algorithm comprises one or more Al-based air-interface models for different idle and / or connected mode applications such as SSB beam prediction, CSI-RS beam prediction, CSI prediction.

20. The communication apparatus (102) according to any preceding claim wherein the communication apparatus (102) is a Terminal Device, and wherein the controller (104) is a Terminal Device.

21. The communication apparatus (102) according to claim 20, wherein the terminal device is a User Equipment.

22. The communication apparatus (102) according to any of claims 1 to 19 wherein the communication apparatus (102) is a network device, and wherein the controller is a network device controller.

23. A method (200) for a communication apparatus (102), the method (200) comprisingacquiring Idle Mode Measurements Information, IMSI, during idle mode the IMSI comprising Generalized Synchronization Signals Correlation Profile, GSSCP, andutilizing the IMSI as input for an Air Interface Algorithm targeting idle mode applications or connected mode applications.

24. A computer program product comprising program instructions for performing the method (200) according to claim 23, when executed by one or more processors in a communication apparatus (102).

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