Wireless communication device and network for task critical transmission and delay admissible transmission

Abstract

A wireless communication device is provided. A wireless communication device includes a set of transceivers and a processor electrically connected to the set of transceivers. The transceiver group is configured to communicate with a first base station over a first channel. The processor is configured to: utilize the first base station to control the transceiver group to transmit and receive critical task data in a first communication mode; collecting data except the key task data from the first base station; controlling, by the first base station, the transceiver group to transmit delay admissible data in the second communication mode, wherein the delay admissible data includes the collected data; and judging whether to switch the transceiver group from the first communication mode to the second communication mode according to the key task data.

Description

[0001] [Cross-references to related applications]

[0002] This application claims priority to U.S. Patent Application No. 18 / 956,086, filed November 22, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to wireless communication devices and methods; more specifically, to wireless communication devices and methods for mission-critical transmission and delay-tolerant transmission. Background Technology

[0004] Wireless transmission technology has gradually evolved from 4G Long-Term Evolution (LTE) to 5G New Radio (NR). In 5G NR networks, a key application scenario is Ultra-Reliable and Low-Latency Communications (URLLC), which implements ultra-low latency and high reliability communication.

[0005] For example, Communication Based Train Control (CBTC) is a high-speed mobile application in signaling systems that uses wireless communication between the train and ground track infrastructure (or trackside facilities) for train operation and control, enabling convenient and precise traffic management. In CBTC applications, high-speed trains require low-latency, highly reliable communication with ground track infrastructure to prevent accidents. However, wireless transmission on high-speed moving trains is susceptible to multiple handover (HO) and radio link failure (RLF) events, leading to degraded transmission quality and increased latency and packet loss in data transmission.

[0006] Therefore, there is a need for a wireless communication device and method with low latency and high reliability in CBTC. Summary of the Invention

[0007] This invention discloses a wireless communication device. The wireless communication device includes a transceiver group and a processor electrically connected to the transceiver group. The transceiver group is configured to communicate with a first base station via a first channel. The processor is configured to: control the transceiver group to transmit and receive mission-critical data using the first base station in a first communication mode; collect data other than mission-critical data from the first base station; control the transceiver group to transmit delay-tolerant data using the first base station in a second communication mode, wherein the delay-tolerant data includes the collected data; and determine whether to switch the transceiver group from the first communication mode to the second communication mode based on the mission-critical data.

[0008] Furthermore, this invention discloses a wireless communication method. The wireless communication method includes: communicating with a first base station via a first channel; transmitting and receiving critical mission data using the first base station in a first communication mode; collecting data other than the critical mission data from the first base station; transmitting delay-tolerant data using the first base station in a second communication mode, wherein the delay-tolerant data includes the collected data; and determining whether to switch from the first communication mode to the second communication mode based on the critical mission data. Attached Figure Description

[0009] The various aspects of this invention best understood from the following detailed description when read in conjunction with the accompanying drawings. It should be noted that, in accordance with industry practice, the features are not drawn to scale. In fact, for clarity of discussion, the dimensions of various features may be arbitrarily increased or decreased.

[0010] Figure 1 It is a wireless communication device according to some embodiments of the present invention.

[0011] Figure 2 This is a schematic diagram showing the input feature sequence and the prediction range corresponding to the collected data according to some embodiments of the present invention.

[0012] Figure 3 This illustrates some embodiments of the present invention. Figure 1 Deployment scenarios for wireless communication devices.

[0013] Figure 4 This is a schematic diagram illustrating an example program between a wireless communication device, a communication-type train control server, and a machine learning training host according to some embodiments of the present invention.

[0014] Figure 5 This describes a wireless communication device and a machine learning training host according to some embodiments of the present invention. Figure 4 A schematic diagram of the process between the data upload task and the model retrieval task.

[0015] Figure 6 This is a wireless communication method performed by a wireless communication device on a vehicle according to some embodiments of the present invention.

[0016] Figure 7A and Figure 7B A scenario illustrating the switching strategy between communication-based train control tasks and data upload tasks according to some embodiments of the present invention is shown. Detailed Implementation

[0017] Some variations of the embodiments are described. In the various views and illustrative embodiments, the same reference numerals are used to indicate the same elements. It should be understood that additional operations may be provided before, during, and / or after the disclosed method, and some of the described operations may be replaced or eliminated for other embodiments of the method.

[0018] This invention provides a wireless communication device and method for mission-critical and latency-tolerant transmission to avoid high latency and packet loss in high-speed network environments (e.g., Communication-Based Train Control (CBTC)). The device and method utilize techniques including machine learning (ML). According to embodiments, the invention is suitable for use in vehicles, where the wireless communication device can perform wireless module configuration for mission-critical and latency-tolerant transmission. Mission-critical transmission requires extremely high reliability to cope with various emergency scenarios on the vehicle. Latency-tolerant transmission needs to guarantee good throughput to ensure smooth return of monitoring data for analysis.

[0019] Figure 1 This is a wireless communication device 100 according to some embodiments of the present invention. The wireless communication device 100 includes a control node 110, a transceiver group 120, and two antenna modules 32 and 34. Figure 1 In some embodiments, transceiver group 120 includes wireless transceivers 22 and 24, which are coupled to antenna modules 32 and 34, respectively. In some embodiments, transceiver group 120 includes one or more wireless transceivers and one or more antenna modules. The number of wireless transceivers and antenna modules is merely exemplary and not intended to limit the invention.

[0020] Each of antenna modules 32 and 34 includes a single antenna or an antenna array. Antenna modules 32 and 34 may have the same or different antenna configurations. In some embodiments, antenna modules 32 and 34 may include a single antenna with an omnidirectional radiation pattern, and the single antenna is capable of communicating with different base stations. In some embodiments, antenna module 32 or 34 may include an antenna array, and the antennas of the antenna array are capable of communicating with at least two base stations.

[0021] Each of wireless transceivers 22 and 24 includes one or more integrated transmitters (not shown) and receivers (not shown), or one or more sets of separate transmitters and separate receivers. Generally, the receiver is capable of down-converting received radio frequency (RF) signals or microwave signals to a baseband frequency, and the transmitter is capable of up-converting received baseband signals to RF signals or microwave frequencies. Furthermore, each of wireless transceivers 22 and 24 is coupled to control node 110 via a fiber optic, wireless, or wired connection.

[0022] Wireless transceiver 22 and antenna module 32 can form a first radio frequency interface, and wireless transceiver 24 and antenna module 34 can form a second radio frequency interface. In some embodiments, the first and second radio frequency interfaces are located at the same position. In some embodiments, the first and second radio frequency interfaces are located at different positions. For example, the wireless communication device 100 is installed on a train, with the first radio frequency interface located at the front of the train car and the second radio frequency interface located in the middle of the train car or other train cars.

[0023] Control node 110 includes processor 12 and memory 14. Processor 12 is electrically connected to transceiver group 120 and is used to control transceiver group 120 to establish communication links with the two base stations according to different frequency band settings. Processor 12 may be a central processing unit (CPU), microprocessor, microcontroller, field-programmable gate array (FPGA) unit, graphics processing unit (GPU), custom integrated circuit (IC), etc.

[0024] In some embodiments, the processor 12 controls the transceiver group 120 to communicate with two base stations employing the same or different generations of communication technologies. For example, a base station may be an evolved Node B (eNB) of a 3GPP Long Term Evolution (LTE) network or a gNode B (gNB) of 5G New Radio (NR). A base station may also be referred to as an access point, access terminal, base station unit, or other terms used in the art. It should be noted that although the inventive concept is described in accordance with 4G and 5G communication protocols or base stations, the invention is not limited to 4G and 5G communication systems and can be extended to other systems.

[0025] In some embodiments, each of the wireless transceivers 22 and 24 may support 3GPP cellular wireless communication standards, such as 4G, 5G, 6G, etc. Wireless transceivers 22 and 24 may support the same or different radio access technologies. Furthermore, the processor 12 is configured to control the wireless transceivers 22 and 24 to use different radio frequency groups. For example, it may control wireless transceiver 22 to use a first group of frequencies and control wireless transceiver 24 to use a second group of frequencies different from the first group of frequencies.

[0026] In some embodiments, processor 12 is configured to control wireless transceivers 22 and 24 to use LTE / 5G dual-mode, such as non-standalone 5G or standalone dual-mode LTE / 5G. For example, wireless transceiver 22 may be controlled to use only LTE, and wireless transceiver 24 may be controlled to use only standalone 5G. Alternatively, wireless transceiver 22 may be controlled to use only LTE, while wireless transceiver 24 may be controlled to use both LTE and 5G capabilities.

[0027] Processor 12 controls the operation of transceiver group 120 according to program instructions and data stored in memory 14. In some embodiments, memory 14 is a memory. Memory 14 is also configured to store a dataset of predictive models (e.g., HO predictive models) and frequency band configurations and decision conditions of policy control models. The predictive models and policy control models are executed by or implemented in processor 12. This dataset includes information about HO / radio link failures (RLF) and signal strength. In some embodiments, wireless communication device 100 is mounted on a vehicle, and the vehicle travels on a fixed or known route. For example, the vehicle is a train or subway moving along a track path. The collected data may include packet information and signal information between wireless communication device 100 and a base station collected along the track path when wireless communication device 100 sends packets to the base station with a consistent processing volume. Wireless communication device 100 is configured to perform model inferences and actions for dynamic network configuration. Furthermore, the collected data is stored in memory 14.

[0028] By analyzing collected data, predictive models are used to identify and classify HO types. In some embodiments, an HO triggering mechanism is provided based on relative measurement results, for example, it can be configured to trigger when the signal quality measurement of a neighboring cell is greater than that of a particular cell. The signal quality measurement may include Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), or Signal to Interference Noise Ratio (SINR).

[0029] The collected data includes information about signal strength (e.g., signal quality measurements) during specific time intervals and the occurrence of certain types of HO events or critical events. In some embodiments, based on the collected data, processor 12 is configured to use a predictive model to predict HO events to determine whether to change the frequency band configuration of transceiver group 120 to prevent multiple HO events, thereby reducing latency and packet loss caused by multiple HO events. Furthermore, based on the collected data, processor 12 is configured to use a predictive model to predict RLF events, the selection of radio technologies (e.g., LTE, 5G NR, 6G, etc.), and frequency configurations (e.g., band locking, carrier aggregation configuration, or dual connectivity configuration).

[0030] Figure 2 This is a schematic diagram illustrating the input feature sequence and the corresponding prediction range based on some embodiments of the present invention. Figure 2In this context, the collected data represents the data scenario from time t0 to time t6. In some embodiments, the time difference between two adjacent time points (e.g., time t1 and time t0) is the time interval TP (e.g., 1 second).

[0031] The collected data is fed into the prediction model (or other pre-trained model) as input features for prediction. Based on the input feature sequence, a prediction range is generated for each collected data point. The classification algorithm in the HO prediction model is then used to predict whether an HO or RLF event will occur, and the regression algorithm in the HO prediction model is used to predict how much time remains before the HO or RLF event. For example, the prediction range 214 between time t2 and time t6 is generated based on the input feature sequence 212 of the collected data obtained from time t0 to time t2. Figure 2 In one embodiment, based on the input feature sequence 212, it is predicted that the HO event 223 will occur within the prediction range 214 and between time t4 and time t5.

[0032] Figure 3 This illustrates some embodiments disclosed in the present invention. Figure 1 Deployment scenarios for wireless communication device 100. In Figure 3 In this embodiment, the wireless communication device 100 is disposed in the moving train 300. Furthermore, it includes... Figure 1 The first radio frequency interface 132 of the wireless transceiver 22 and antenna module 32 is configured to communicate with the base station 310 using a first frequency band setting. Includes Figure 1 The second radio frequency interface 134 of the wireless transceiver 24 and antenna module 34 is configured to communicate with the base station 320 using a second frequency band setting. The first frequency band setting is different from the second frequency band setting. The first radio frequency interface 132 and the second radio frequency interface 134 can be set in the same or different locations in the train 300. In some embodiments, multiple wireless communication devices 100 are provided in the train 300. For example, one wireless communication device 100 is set in the front section of the train 300, while another wireless communication device 100 is set in the rear section of the train 300.

[0033] In some embodiments, when the train 300 moves, a HO procedure is executed between base stations along the route of the train 300. For example, when the train 300 leaves the coverage area of ​​base station 310 and enters the coverage area of ​​base station 312, the HO procedure HO_1 is executed for the first radio frequency interface 132. Similarly, when the train 300 leaves the coverage area of ​​base station 320 and enters the coverage area of ​​base station 322, the HO procedure HO_2 is executed for the second radio frequency interface 134.

[0034] When the train 300 moves, the wireless communication device 100 is set to establish CBTC communication with the CBTC server 360 through the corresponding base station and the network 350, and then collect and transmit information on the position, speed, and direction of the train 300 to control the movement of the train 300. In some embodiments, the network 350 includes the backbone network and / or wireless network of the CBTC system. The backbone network serves as a transmission channel between ground railway devices, such as turnouts that guide the train into different tracks, signal lights that provide instructions to the driver or dispatcher, and track circuits that detect obstacles on the track. In addition, the wireless network is configured to perform data exchange between the wireless communication device 100 and the backbone network through the corresponding base station. For example, when the train 300 moves within the coverage area of the base station 31, the wireless communication device 100 is set to establish CBTC communication with the CBTC server 360 through the base station 310 and the network 350 using the first radio frequency interface 132. Through CBTC communication, the CBTC server 360 can perform Automatic Train Protection (ATP), Automatic Train Operation (ATO), and Automatic Train Supervision (ATS) on the train 300.

[0035] In Figure 3 it, the wireless communication device 100 is set to receive the CBTC signal from the CBTC server 360 and transmit the CBTC signal to the CBTC server 360. The CBTC signal is critical mission data in critical mission applications. In some embodiments, critical mission applications may have requirements for data delivery success rate (e.g., high reliability), delivery within a latency bound (e.g., latency-sensitive applications), and the probability of successful delivery within a latency range. In some embodiments, the probability of successful delivery is greater than the critical value p_threshold, e.g., probability (latency < t_critical value) > p_critical value, where t_t critical value represents a specific time. For example, 100 milliseconds, or in ultra-reliable low-latency communication (URLLC) traffic.

[0036] The wireless communication device 100 is also configured to collect data, including packet information and signal information, from a corresponding base station and provide the collected data to the CBTC server 360. Upon receiving the collected data, the CBTC server 360 is configured to provide the collected data to the ML training host 370. Based on the collected data, the ML training host 370 is configured to train a predictive model and / or a policy control model to be used in the wireless communication device 100, thereby making the trained model more accurate. In response to a request from the wireless communication device 100, the ML training host 370 is configured to provide the updated training model to the wireless communication device 100. The CBTC server 360 is connected to the ML training host 370 via wired or wireless means. In some embodiments, the CBTC server 360 and the ML training host 370 are implemented in a CBTC control center.

[0037] Figure 4 This is a schematic diagram illustrating an exemplary procedure between a wireless communication device 100, a CBTC server 360, and an ML training host 370 according to some embodiments of the present invention. First, the wireless communication device 100 is configured to provide a task registration request 402 to the CBTC server 360. In response to the task registration request 402, the CBTC server 360 is configured to perform a registration operation 403 to register the execution times of various tasks (e.g., CBTC tasks and data upload tasks) in a task scheduler. These tasks are then executed according to the registration schedule. After the registration operation 403 is completed, the CBTC server 360 is configured to provide an acknowledgment (ACK) 404 to the wireless communication device 100. In some embodiments, a start timer and a stop timer are established for each task, and the runtime of each task is controllable, thereby allowing task switching.

[0038] Upon receiving ACK 404, the wireless communication device 100 is configured to execute CBTC task 410. In CBTC task 410, the wireless communication device 100 is configured to perform a prediction operation 412, thereby utilizing the base station to perform model estimation and actions for dynamic network configuration while the train 300 is moving. Based on the result of prediction operation 412, the wireless communication device 100 is configured to provide a CBTC signal 414 containing operational information of the train 300 to the CBTC server 360. The CBTC signal 414 includes key mission data in wireless communication. Based on the CBTC signal 414, the CBTC server 360 is configured to perform a control operation 415 for the train 300 and provides a CBTC signal 416 containing the result of the control operation 415 to the wireless communication device 100. In response to the CBTC signal 416, the wireless communication device 100 is configured to control the operation of the train 300, such as speed. CBTC task 410 is repeated until the registered execution time is reached.

[0039] After CBTC task 410 is completed, data upload task 420 is executed. It should be noted that the reliability of CBTC task 410 is important, therefore it is necessary to ensure that data upload task 420 does not interfere with CBTC task 410.

[0040] In data upload task 420, wireless communication device 100 uploads data 422 to CBTC server 360. Uploaded data 422 includes collected data stored in memory 14, which is latency-tolerant data in wireless communication excluding mission-critical data. As described above, collected data may include packet information and signal information collected along the track path between wireless communication device 100 and the base station, when wireless communication device 100 sends packets to the base station with consistent processing volume, and corresponding signal quality measurements. After obtaining uploaded data 422, CBTC server 360 is configured to perform operation 423 to store uploaded data 422 and provide ACK 424 to wireless communication device 100 upon complete receipt of uploaded data 422. Next, CBTC server 360 is configured to provide uploaded data 426 to ML training host 370. After obtaining uploaded data 426, ML training host 370 is configured to perform operation 427 to store uploaded data 426 and provide ACK 428 to CBTC server 360 upon complete receipt of uploaded data 426. After obtaining the uploaded data 426, which includes the collected data collected by the wireless communication device 100, the ML training host 370 is configured to perform training operations 429 on the model used in the wireless communication device 100.

[0041] In some embodiments, the data upload task 420 is performed periodically by the wireless communication device 100 according to a first time interval, such as daily or every fixed number of days. Furthermore, the wireless communication device 100 is also configured to periodically perform the model retrieval task 430 with the ML training host 370 according to a second time interval, the second time interval being longer than the first time interval. For example, while the data upload task 420 is performed once a day, the model retrieval task 430 may be performed once a week. In the model retrieval task 430, the wireless communication device 100 is configured to provide a model request 432 to the ML training host 370 without going through the CBTC server 360. In response to the model request 432, the ML training host 370 is configured to provide the latest training model 434 to the wireless communication device 100. The wireless communication device 100 is then configured to update the corresponding model based on the obtained latest training model 434.

[0042] Figure 5 This illustrates the wireless communication device 100 and ML training host 370 according to some embodiments of the present invention. Figure 4A schematic diagram of the procedure between the data upload task 420 and the model retrieval task 430. In the wireless communication device 100, collected data is obtained in procedure 502, and the collected data will be used to train the model. For example, in procedure 504, data upload is prepared (e.g., ...). Figure 4 The uploaded data (422 / 426) is then stored as uploaded data in program 506 (e.g., Figure 4 The uploaded data (422) is used to transfer the data to the ML training host 370 in the data upload task 420. Simultaneously, in procedure 508, the collected data is fed into a predictive model for artificial intelligence (AI) / machine learning (ML) inference, so that corresponding actions in procedures 510_1 to 510_n can be executed for the dynamic network configuration. In procedure 508, the AI / ML inference can be latency-sensitive and the execution time can be time-sensitive. Processes 510_1 to 510_n include technologies such as band locking and band switching.

[0043] Band-locking technology involves applying settings, either by the wireless communication device or by the user, such that each wireless interface of the device is configured to connect only to a subset of predetermined frequency bands and is prohibited from connecting to the remaining available frequency bands provided by the base station. By implementing band-locking technology, the number of candidate base station channels to be considered can be reduced, thus avoiding unnecessary hogging procedures.

[0044] exist Figure 5 In the ML training host 370, a model is trained in procedure 512 based on uploaded data 426. Then, in procedure 514, the trained model is stored and managed to provide the latest model to the wireless communication device 100 in response to a model request 432 from the wireless communication device 100. Therefore, the AI / ML inference prediction model in procedure 508 is updated based on the latest trained model 434 from the ML training host 370.

[0045] AI's machine learning (ML) technology can automatically learn from data and past experience to identify features and make predictions. It focuses on using data and algorithms to mimic human learning methods, progressively improving its accuracy. For example, by using statistical methods, algorithms can be trained to classify or predict data and reveal key insights in data mining projects. These techniques can be particularly helpful.

[0046] Such techniques can be particularly useful when users are inside a fast-moving train and wireless communication devices must undergo HO cycles very frequently. Therefore, by implementing these techniques, performance anomalies can be minimized by avoiding overlap of HO cycles between different wireless interfaces.

[0047] In some embodiments, the prediction model of the present invention may be trained first based on multiple training datasets. In some embodiments, each training dataset includes input training data (e.g., RSRP during a time interval) and output training data (e.g., actual HO timing), and then the training datasets are collected. For example, in an HO prediction model, the training datasets are used to train the HO prediction model using a two-stage prediction method for predicting HO events. In the first-stage prediction, the training datasets are used to train the HO prediction model to predict whether an HO event will occur. In the second-stage prediction, the training datasets are used to train the HO prediction model to predict when the HO event will occur.

[0048] In some embodiments, the prediction model is built upon a known machine learning (ML) model, such as a support vector machine (SVM) model, a recurrent neural network (RNN) model, an extreme gradient boosting (XGB) model, a gradient boosting (GB) model, or other algorithms. Those skilled in the art should understand this based on the foregoing disclosure, and therefore, further details are omitted here.

[0049] Figure 6 According to some embodiments of the present invention, the vehicle (e.g., Figure 3 Wireless communication devices on (e.g., 300) Figure 1 The wireless communication method 600 is executed (100). The vehicle travels on a fixed or known route. For ease of explanation, it will be combined with... Figure 3 and Figure 4 To explain Figure 6 The method.

[0050] This wireless communication method is implemented in railway signaling systems used in CBTC (Conditional Rail Traffic Management), which utilizes telecommunications between trains and ground track infrastructure for traffic management and infrastructure control. Compared to traditional signaling systems, CBTC can determine train locations more accurately. This makes railway traffic management safer and more efficient. Metros (and other railway systems) can reduce headway while maintaining or even improving safety.

[0051] In operation S610, the wireless communication device 100 is configured to simultaneously establish communication links with two base stations according to different frequency band settings. For example, in the wireless communication device 100, the first radio frequency interface 132 is configured to communicate with base station 310 through a first channel according to a first frequency band setting, and the second radio frequency interface 134 is used to communicate with base station 320 through a second channel according to a second frequency band setting. Through the communication links, the wireless communication device 100 is configured to send the same or different packets to base stations 310 and 320.

[0052] In operation S620, it is determined whether the signal quality measurements (e.g., RSRP, RSRQ, or SINR) corresponding to base stations 310 and 320 are both greater than a threshold value. If one of the signal quality measurements corresponding to base stations 310 and 320 is less than the threshold value, for example, the radio condition of the first RF interface 132 or the second RF interface 134 has low reliability, the process proceeds to operation S630.

[0053] In operation S630, a first communication mode is executed to transmit mission-critical data (e.g., the CBTC signal of CBTC mission 410) via both the first radio frequency interface 132 and the second radio frequency interface 134. For example, the wireless communication device 100 is configured to send the same packets to base stations 310 and 320 for CBTC mission 410. In some embodiments, the first communication mode is executed via the radio frequency interface with higher reliability of the first radio frequency interface 132 and the second radio frequency interface 134.

[0054] In operation S640, a decision is made on whether to switch to the second communication mode based on the CBTC signal, signal quality measurement, or the train timetable of train 300. For example, if the signal quality measurement of both the first RF interface 132 and the second RF interface 134 is less than a threshold, the wireless communication device 100 is configured to continue operating in the first communication mode, and the process returns to operation S630. In some embodiments, if the CBTC signal indicates that CBTC task 410 has been completed according to the registered timetable or the CBTC signal, or if the timetable shows that train 300 is stationary, for example, train 300 has been stationary at the station for more than a certain time or train 300 has stopped at the parking lot after the working time (or running time), then the wireless communication device 100 is configured to switch from the first communication mode to the second communication mode, and the process proceeds to operation S650.

[0055] In operation S650, a second communication mode is executed to transmit delay-tolerant data (e.g., upload data for data upload task 420) via the first radio frequency interface 132 and the second radio frequency interface 134. For example, the wireless communication device 100 is configured to send the same packets to base stations 310 and 320 for data upload task 420. In some embodiments, the first communication mode is executed via the radio frequency interface with higher reliability, which is the first radio frequency interface 132 and the second radio frequency interface 134. After the transmission of delay-tolerant data is completed, the process returns to operation S620.

[0056] In operation S620, if it is determined that the signal quality measurement values ​​corresponding to base stations 310 and 320 are both greater than the critical value, the process proceeds to operation S660.

[0057] In operation S660, a third communication mode is executed to transmit mission-critical data (e.g., the CBTC signal of CBTC task 410) via one of the first radio frequency interface 132 and the second radio frequency interface 134, and to transmit delay-tolerant data (e.g., upload data of data upload task 420) via the other radio frequency interface. For example, the wireless communication device 100 is configured to send different packets to base stations 310 and 320 respectively for CBTC task 410 and data upload task 420.

[0058] according to Figure 6 Method 600 ensures that a single radio frequency interface does not simultaneously perform the transmission of critical mission data requiring high reliability and the transmission of latency-tolerant data requiring large bandwidth, thereby avoiding interference between CBTC task 410 and data upload task 420.

[0059] According to some embodiments of the present invention, Figure 7A and Figure 7B This illustrates a scenario that demonstrates the switching strategy between CBTC task 410 (i.e., the first communication mode) and data upload task 420 (i.e., the second communication mode).

[0060] exist Figure 7A and 7B In this process, CBTC task 410 and data upload task 420 are executed through the same radio frequency interface. For example, when it is determined in operation S620 that one of the signal quality measurements corresponding to base stations 310 and 320 is less than a threshold value, CBTC task 410 and data upload task 420 are executed through the first radio frequency interface 132 and the second radio frequency interface 134 or a radio frequency interface with higher reliability.

[0061] exist Figure 7ADuring the daily operation of train 300 (i.e., not under power failure 450), CBTC task 410 is executed during 10 to 18 hours of passenger service, while data upload task 420 is executed during maintenance, thus preventing CBTC task 410 and data upload task 420 from interfering with each other.

[0062] exist Figure 7B When train 300 is started (i.e., not in a power outage state 450), CBTC task 410 and data upload task 420 are executed during passenger service hours. However, only one of CBTC task 410 and data upload task 420 is executed at any known time, and the CBTC task has a higher priority than the data upload task 420, thereby preventing interference between CBTC task 410 and data upload task 420. Furthermore, since data upload task 420 is executed during passenger service, real-time network status can be monitored. Therefore, if the network environment changes significantly, the uploaded data can be immediately used to train the model for subsequent updates. In some embodiments, CBTC task 410 and data upload task 420 are dynamically configured to execute based on radio resources, mission-critical application traffic patterns, mission-critical application data delivery requirements, etc. For example, when spare radio resources are available for transmission, more data upload tasks 420 are executed, i.e., delay-tolerant data is sent between two mission-critical transmissions of CBTC task 410.

[0063] While preferred embodiments of the present invention have been described above, they are not intended to limit the invention. Those skilled in the art will be able to make certain changes and modifications without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention is determined by the appended claims.

[0064] Symbol Explanation

[0065] 12: Processor

[0066] 14: Memory

[0067] 22: Wireless transceiver

[0068] 24: Wireless transceiver

[0069] 32: Antenna Module

[0070] 34: Antenna Module

[0071] 100: Wireless communication device

[0072] 110: Control Node

[0073] 120: Transceiver Group

[0074] 132: First radio frequency interface

[0075] 134: Second RF Interface

[0076] 212: Input Feature Sequence

[0077] 214: Prediction Range

[0078] 223: HO Incident

[0079] 300: Moving Train

[0080] 310: Base station

[0081] 312: Base station

[0082] 320: Base station

[0083] 322: Base station

[0084] 350: Network

[0085] 360: CBTC Server

[0086] 370: Machine Learning Training Host

[0087] 402: Task Registration Request

[0088] 403: Register Operation

[0089] 404: Acknowledgement (ACK)

[0090] 410: CBTC Task

[0091] 412: Prediction Operation

[0092] 414: CBTC signal

[0093] 415: Control Operation

[0094] 416: CBTC signal

[0095] 420: Data Upload Task

[0096] 422: Data

[0097] 423: Operation

[0098] 424: Acknowledgement (ACK)

[0099] 426: Uploaded materials

[0100] 427: Operation

[0101] 428: Acknowledgement (ACK)

[0102] 429: Training Operation

[0103] 430: Model Retrieval Task

[0104] 432: Model Request

[0105] 434: Latest trained model

[0106] 450: Power off state

[0107] 502: Program

[0108] 504: Procedure

[0109] 506: Program

[0110] 508: Program

[0111] 510_1: Program

[0112] 510_n: Program

[0113] 512: Program

[0114] 514: Program

[0115] 600: Wireless Communication Methods

[0116] HO_1: HO program

[0117] HO_2: HO program

[0118] S610: Operation

[0119] S620: Operation

[0120] S630: Operation

[0121] S640: Operation

[0122] S650: Operation

[0123] S660: Operation

[0124] t0: Time

[0125] t1: Time

[0126] t2: Time

[0127] t3: Time

[0128] t4: Time

[0129] t5: Time

[0130] t6: Time

[0131] TP: Time interval

Claims

1. A wireless communication device, comprising: A transceiver group configured to communicate with a first base station via a first channel; as well as The processor, electrically connected to the transceiver group, is configured to: In the first communication mode, the transceiver group is controlled to send and receive mission-critical data using the first base station; Collect data from the first base station, in addition to the data related to the critical mission. In the second communication mode, the transceiver group is controlled to transmit delay-tolerance data using the first base station, wherein the delay-tolerance data includes the collected data; and Based on the critical mission data, determine whether to control the transmitter group to switch from the first communication mode to the second communication mode.

2. The wireless communication device of claim 1, wherein the processor is further configured to: Based on the collected data, a predictive model is used to determine whether to control the transceiver group to be handed over to the second base station; or Based on the collected data, the predictive model is used to determine whether to control the transceiver group to communicate with the first base station through the second channel.

3. The wireless communication device of claim 2, wherein the processor is further configured to: The prediction model is updated based on the new model from the training host. Based on the latency tolerance data from the first base station, the training host is configured to train the new model.

4. The wireless communication device of claim 1, wherein the wireless communication device is mounted on the vehicle, and the mission-critical data includes the vehicle's Communication Train Control (CBTC) signals.

5. The wireless communication device of claim 4, wherein the processor is configured to switch the transceiver group from the first communication mode to the second communication mode when the vehicle is stationary.

6. The wireless communication device of claim 5, wherein the processor is further configured to: Based on the critical mission data or vehicle timetable, determine whether the vehicle is stationary.

7. The wireless communication device of claim 1, wherein the transceiver group is further configured to communicate with a second base station via a second channel, wherein the processor is configured to: In the third communication mode, the transceiver group is controlled to send and receive mission-critical data using the first base station and to send latency-tolerant data using the second base station.

8. The wireless communication device of claim 7, wherein in the third communication mode, the first signal quality measurement value corresponding to the first base station and the second signal quality measurement value corresponding to the second base station are greater than a threshold value.

9. The wireless communication device of claim 8, wherein in the first and second communication modes, the first signal quality measurement is greater than the threshold value and the second signal quality measurement is less than the threshold value.

10. The wireless communication device of claim 8, wherein the delay tolerance data includes the first signal quality measurement and the second signal quality measurement.

11. The wireless communication device of claim 7, wherein the transceiver group is configured to communicate with the first base station and the second base station via different generations of communication technologies.

12. A wireless communication method, comprising: Communicating with the first base station via the first channel; In the first communication mode, the first base station is used to send and receive critical mission data; Collect data from the first base station, in addition to the data related to the critical mission. In the second communication mode, the first base station is used to transmit delay tolerance data, wherein the delay tolerance data includes the collected data; and Based on the key mission data, determine whether to switch from the first communication mode to the second communication mode.

13. The wireless communication method of claim 12, further comprising: Based on the collected data, a predictive model is used to determine whether the data should be delivered to the second base station. or Based on the collected data, the predictive model is used to determine whether to communicate with the first base station through the second channel.

14. The wireless communication method of claim 13, further comprising: The prediction model is updated based on the new model from the training host. Based on the latency tolerance data from the first base station, the training host is configured to train the new model.

15. The wireless communication method of claim 12, wherein the mission-critical data includes the vehicle's Communication Train Control (CBTC) signals.

16. The wireless communication method of claim 15, wherein determining whether to switch from the first communication mode to the second communication mode based on the key mission data further comprises: Based on the key mission data or vehicle timetable, determine whether the vehicle is stationary; as well as When the vehicle is stationary, the communication mode is switched from the first communication mode to the second communication mode.

17. The wireless communication method of claim 12, further comprising: Communicating with the second base station via the second channel; as well as In the third communication mode, the first base station is used to send and receive the mission-critical data, and the second base station is used to send the delay tolerance data.

18. The wireless communication method of claim 17, wherein in the third communication mode, the first signal quality measurement value corresponding to the first base station and the second signal quality measurement value corresponding to the second base station are greater than a threshold value.

19. The wireless communication method of claim 18, wherein in the first and second communication modes, the first signal quality measurement value is greater than the threshold value and the second signal quality measurement value is less than the threshold value.

20. The wireless communication method of claim 18, wherein the delay tolerance data includes the first signal quality measurement and the second signal quality measurement.

Images