196 results about "Wireless mobile communications" patented technology

The invention relates to an interference power distribution method based on multi-agent deep reinforcement learning, and belongs to the technical field of wireless mobile communication. The method comprises the following steps: S1, in a many-to-many communication confrontation scene, establishing a cooperative interference model of multiple interference devices confronting multiple unmanned aerial vehicle targets; s2, constructing a distributed local observable Markov decision process (De-POMDP), and modeling an interference power distribution problem into a multi-agent complete cooperation model; s3, designing a'decentralized training, distributed decision '(DTDE) multi-agent framework, and reducing the collaborative decision dimension of interference power resources; s4, an extractor is designed, intelligent agent input state information is preprocessed, and the training efficiency is improved; and S5, realizing efficient exploration of a power distribution strategy based on a discrete flexible actor-commentator (DSAC) algorithm. According to the method, the interference power distribution of the multi-interference equipment during confrontation of multiple unmanned aerial vehicle targets can be realized based on multi-agent deep reinforcement learning, the interference resource utilization rate is effectively improved, and the method has good expansibility in a complex communication confrontation scene.

The present disclosure describes methods of data-driven AI / ML model signaling for efficient model switching with machine learning model operation in wireless mobile communication systems including base stations (e.g., gNB) and mobile stations (e.g., UE). The pre-configured model modes with the associated candidate models are used to switch models based on model monitoring.

The invention discloses an AI service connection establishment method and device, a terminal and network side equipment, and belongs to the technical field of communication, and the AI service connection establishment method comprises the steps that the terminal sends a first request message to a network side, and the first request message is used for requesting the network side to allocate network resources for an AI service; and the terminal receives a first request response message returned by the network side, wherein the first request response message comprises at least one of the following items: information of a first network element allocated for the AI service by the network side, information of a second network element allocated for the AI service by the network side, and information of a model. In the technical scheme of the invention, the network side can allocate the communication resource and the AI resource for the AI service according to the request of the terminal, and the problem that a wireless mobile communication system cannot provide the AI service for the terminal is solved.

According to an embodiment of the present disclosure, a method for user equipment in a wireless communication system may comprise the steps of: user equipment receiving RRCReconfiguration from a base station; if MeasGapConfig includes first gap configuration information, the user equipment setting up a first gap, and if the MeasGapConfig includes second gap configuration information, the user equipment setting up a second gap; and the user equipment activating the first gap or the second gap.

A Method and Apparatus for discontinuous reception and discontinuous transmission are provided. The method includes receiving a radio resource control (RRC) reconfiguration message and performing transmission of CSI report on a specific serving cell. In case that the first timer of the specific serving cell is not running, the terminal performs transmission of a first type CSI report on the serving cell, does not perform transmission of a second type CSI report on the serving cell, and does not perform transmission of a third type CSI report on the serving cell. The RRC reconfiguration message comprises channel status information (CSI) report configuration, one or more first DRX configurations and a second DRX configuration.

The method of data-driven AI / ML model signaling for ML status reporting with machine learning model operation in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE) is described, furthermore the mapping relationship is measured for different types of model operation between network and UE so that this measurement can be applied to varying applications with priority index for ML status update based on prioritization.

A system of security that prevents misuse of identity data of an identity data owner in an identity data driven transaction in a global commerce network, that has in the system, a transaction processing entity that after it receives a identity data driven transaction from a transaction initiating entity, puts on hold the processing of the transaction for a period of time and via the identity data owner's wireless mobile communication device, contacts the identity data owner for authorization of the transaction before the transaction processing is completed. The system of security for identity data may be used in the identity data driven transaction is one from a group of (i) credit card payment, (ii) bank account payment, (iii) release of credit profile, (iv) release of financial data, and (v) release of medical data.

The invention relates to a deep reinforcement learning assisted target threat degree evaluation model and an interference beam allocation method, and belongs to the technical field of wireless mobile communication. The method comprises the following steps: S1, in a wartime communication confrontation scene, constructing a cooperative interference model of a multi-interference device confronting enemy unmanned aerial vehicle formation; s2, analyzing constraint conditions of an interference beam distribution process; s3, introducing a target threat degree evaluation link, and constructing an interference beam resource allocation optimization problem; s4, constructing a target threat degree evaluation model based on a tactical importance plotting (TSM) function, and generating a target threat degree evaluation matrix; s5, improving the TSM function by using a discrete flexible actor-commentator (DSAC) algorithm, and reconstructing a target threat degree evaluation matrix; and S6, according to the target threat degree evaluation matrix, executing interference beam allocation. According to the invention, reasonable allocation of interference beams in a many-to-many wartime communication confrontation scene can be realized, so that the utilization rate of interference resources is effectively improved.

The present application describes methods of data-driven AI / ML model signaling for AI / ML based pairable model search signaling, where techniques for pre-configuring and signaling about searching model pairing of machine learning model operations in wireless mobile communication systems including base stations (e.g., gNB) and mobile stations (e.g., UE) are disclosed. The minimum model information set is provided to facilitate the search for pairable model(s) to match through a model pairing search process.

The invention discloses an AI service establishment method and device and a network side, and belongs to the technical field of communication, and the AI service establishment method comprises the steps that a first network element obtains an establishment request for establishing an artificial intelligence AI service for a second network element; the first network element determines an AI policy according to the establishment request; and the first network element returns a response message of the establishment request to the second network element according to the AI policy. According to the embodiment of the invention, the problem that a wireless mobile communication system cannot provide AI service for a third-party server can be solved.

A Method and Apparatus for discontinuous reception and discontinuous transmission are provided. The method includes receiving a radio resource control (RRC) reconfiguration message, receiving a downlink control information, wherein the downlink control information comprises two or more blocks, determining a first block for the first serving cell based on the first parameter for the first serving cell, and deactivating cell discontinuous trans mission (DTX) of the first serving cell in case that a first part of the first block is set to a specific value. Size of the first parameter for the first serving cell is configurable based on a second parameter.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model training modes in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, model training performance can be degraded depending on UE ML condition or capability changes. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring model training capability elements.

According to an embodiment of the present disclosure, a method of a UE in a wireless communication system may comprise: a step in which the UE transmits an uplink message for requesting a first gap or a second gap to a base station; and a step in which the UE receives a downlink message comprising gap configuration information from the base station. A first uplink message may be transmitted to request a first gap. A second uplink message may be transmitted to request a second gap. The first uplink message may include at least one piece of first information. Each of the at least one piece of first information may be related to a predetermined serving cell. Each of the at least one piece of first information may include information indicating whether a first gap is necessary when measuring a synchronization signal / PBCH block (SSB) regarding the predetermined serving cell related thereto. The second uplink message may include one piece of second information. The second information may comprise a value determined on the basis of the length of the second gap and the period of the second gap. During a first time interval of the first gap, the UE may transmit no sounding reference signal (SRS). During a second time interval of the first gap, the UE may transmit an SRS. During a third time interval of the second gap, the UE may transmit no SRS. The first gap may include a first first time interval, a second time interval, and a second first time interval. The third time interval may be a static uplink slot of the second gap.

The utility model discloses an adjustable connecting piece for installation of a wireless mobile communication base station, and relates to the field of wireless mobile communication base stations. The adjustable connecting piece for wireless mobile communication base station installation comprises a wireless communication base station body, a body frame is installed outside the wireless communication base station body, a connecting mechanism is arranged on the outer surface of the body frame, the connecting mechanism comprises an installation plate arranged outside the body frame, and a supporting piece is installed on the back face of the wireless communication base station body. The supporting piece is movably clamped in the mounting plate, and the abutting plate is slidably connected to the outer portion of the main body frame and arranged above the supporting piece. According to the adjustable connecting piece for installing the wireless mobile communication base station, the position of the wireless communication base station body is adjusted by moving the position of the clamping piece, then the position of the clamping piece is fixed through the abutting plate and the installing plate, the using complexity of a connecting structure is reduced, meanwhile, the installing position and number of the wireless communication base station body can be changed at will, and the connecting piece is convenient to use. And the use flexibility of the connecting structure is improved.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model training in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling of model information exchange can be heavily congested based on different ML conditions. Therefore, model operation (e.g., model re-training / updating) can be set up between network and UE by identifying the most applicable selection of parameter combinations.

The present disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a terminal according to the present disclosure may comprise the steps of: receiving, from a base station, a message including configuration information regarding quality of experience (QoE); and transmitting a message for reporting a result of QoE measurement on the basis of the configuration information, wherein the configuration information includes information indicating a first signaling radio bearer (SRB) for an encapsulated QoE measurement report and information indicating a second SRB for a radio access network (RAN) visible QoE measurement report.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based operation modes with ML functionality in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, ML operation using model activation for LCM phases can be degraded due to ML conditions. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring applicable operation modes with ML functionality.

The application describes methods of using the pre-configured AI / ML artificial intelligence / machine learning based ML clustering in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling of model information exchange can be heavily congested. Therefore, model operation e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring ML clustering.

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model operation modes in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling of model information exchange can be heavily congested. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by configuring mapping relation look-up table.

Methods of using pre-configured AI / ML (artificial intelligence / machine learning) based model dataset adaptation in wireless mobile communication system including base stations (e.g., gNB) and mobile stations (e.g., UE). If an AI / ML model is applied to a radio access network, signaling of model dataset information exchange can be heavily congested based on different model execution environments with varying models when a model drift occurs. Therefore, model operation (e.g., model re-training / updating) can be set up between the network and the UE by selecting different subset(s) of features / datasets adaptively.

A method executed by a terminal, according to an embodiment of the present disclosure, comprises: a step in which a terminal receives RRCReconfiguration from a base station, wherein the RRCReconfiguration includes a first information element or second gap configuration information, wherein the first information element includes first gap configuration information, wherein the first gap configuration information includes first information and second information, and the second gap configuration information includes third information, wherein the first information includes a first gap length, and the second information includes a value indicating a UE gap and one of an FR1 gap and an FR2 gap, and the third information includes a second gap length; and a step in which if the RRCReconfiguration includes the first gap configuration information in the first information element, the terminal sets a first gap, and if the RRCReconfiguration includes the second gap configuration information, the terminal sets a second gap.

The invention discloses a real-time wireless personnel identity recognition system constructed based on an array antenna and beam forming, and belongs to the technical field of big data processing. The real-time wireless personnel identity recognition system constructed based on the array antenna and the beam forming comprises the array antenna, a wireless signal adsorption device, a wireless signal processing transmission system and an application system which are electrically connected in sequence, and the array antenna can realize the beam forming technology. The wireless signal adsorption device strongly adsorbs wireless mobile phone communication signals within a certain range through a high-power or preset high-priority networking mode. The technical scheme provides a system which is irrelevant to media, wireless, integrated with multiple communication frequency bands, integrated with software and hardware and capable of identifying personal and crowd identities in real time.

The present disclosure describes a novel method of using the pre-configured AI / ML (artificial intelligence / machine learning) with grouping-based ML element data sharing in wireless mobile communication system including base station (e.g., gNB, TRP, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for frequent signaling exchanges with varying ML element information (e.g., model, dataset, ML configuration parameter, etc.). Therefore, model operation (e.g., model training, inferencing, monitoring, updating, etc.) can be set up between network and UE by configuring groups of ML element data sharing for ML operation.

The present disclosure describes methods for multi-resolution model identification signaling by configuring multiple subsets consisting of models in a wireless communication system by using the pre-configured AI / ML (artificial intelligence / machine learning) based multiple subsets of models in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling overhead can be significantly increased for assessment / monitoring of all identified models at UE side. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be set up between network and UE by using multi-resolution model identification signaling for ML operation.

The present application describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based multi-model combinations in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. In AI / ML model is applied to radio access network, signaling of model information exchange can be heavily congested. Therefore, model operation e.g., model training / inferencing / monitoring / updating can be set up between network and UE by configuring multi-model combinations in association with UE RRC states.

The invention relates to the technical field of wireless mobile communication networks, and discloses a wireless communication network fault diagnosis method based on a generative adversarial network, which comprises the following steps: inputting acquired network fault real data; carrying out data processing, and extracting a key performance indicator (KPI) of the equipment; designing an NS-DCGAN, inputting a key performance indicator (KPI) of the equipment into the NS-DCGAN, and generating virtual fault data in different network states; supplementing external environment characteristic data for the virtual fault data by adopting a clustering algorithm; combining the virtual data set with the real data set, and constructing a wireless communication network fault sample data set D; dividing the data set D into an internal equipment index part D1 and an external environment feature part D2; an EAFD-GhostNet is designed, and the D1 and the D2 are input into the EAFD-GhostNet; and carrying out wireless communication network fault classification diagnosis, and outputting a diagnosis result. According to the method, the problems of insufficient fault samples and unbalanced data distribution can be solved, short-time and efficient multi-dimensional fault diagnosis analysis can be realized, and the accuracy of fault classification diagnosis can be effectively improved.

A Method and Apparatus for head compression is provided. The method includes receiving a RRCRelease, the RRCRelease includes a first information for SDT, determining a one or more radio bearers configured for SDT based on the first information, discarding all stored Packet Data Convergence Protocol Service Data Unit and PDCP Protocol Data Unit in PDCP entity of SRB1, discarding all stored Packet Data Convergence Protocol Service Data Unit and PDCP Protocol Data Unit in PDCP entity of SRB2 if the SRB2 indicator is included in the RRCRelease, storing Ethernet Header Compression information in User Equipment INACTIVE Access Stratum context, receiving a SystemInformationBlock1, the SystemInformationBlock1 includes a second information for SDT, initiating a SDT based on the first information and the second information, reestablishing a PDCP entity for the SRB1 and resuming the SRB1, restoring PDCP configurations of the radio bearers configured for SDT, and resuming the radio bearers.

The present disclosure describes method of advanced condition-based model signaling by of configuring a finite set of multiple model versions for mapping relation using the pre-configured AI / ML (artificial intelligence / machine learning) based model version selection in wireless mobile communication system including base station (e.g., gNB) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, signaling of model condition information exchange can be heavily congested. Therefore, model operation (e.g., model re-training / updating) can be set up between network and UE by configuring model version selection.

The present embodiments relate to a method for a terminal to perform a random access procedure, the method comprising the steps of: receiving random access channel (RACH) configuration information; determining one RACH occasion (RO) among a plurality of ROs configured on the basis of the RACH configuration information; and transmitting a random access preamble in the determined RO, wherein the RACH configuration information includes first RACH configuration information for configuring at least one first RO and second RACH configuration information for configuring at least one second RO for a subband full duplex (SBFD).

The present disclosure describes methods of using sensing mode based AI / ML (artificial intelligence / machine learning) model operation in wireless mobile communication system including base station (e.g., gNB, TN, NTN) and mobile station (e.g., UE). In AI / ML model is applied to radio access network, model performance can be significantly impacted due to the collected sensing data as input to model operation. Therefore, model operation (e.g., model training / inferencing / monitoring / updating) can be adaptively set up between network and UE by using sensing modes.