Wireless communication apparatus and method for mission-critical and delay-tolerant transmissions

Abstract

This invention provides a wireless communication device and method that offers low latency and high reliability in communication-based train control (CBTC). [Solution] The wireless communication device includes a transceiver set 120 and a control node 110 having a processor electrically connected to the transceiver set. The transceiver set communicates with a first base station via a first channel. The processor controls the transceiver set to transmit and receive mission-critical data with the first base station in a first communication mode, collect data other than mission-critical data from the first base station, and control the transceiver set to transmit delay-tolerant data, including the collected data, with the first base station in a second communication mode, and decides whether or not to switch the transceiver set from the first communication mode to the second communication mode based on the mission-critical data.

Description

Technical Field

[0001] [Cross - Reference to Related Applications] This application claims priority to U.S. Patent Application No. 18 / 956,086, filed on November 22, 2024, the entire content of which is hereby incorporated by reference herein.

[0002] This application relates to wireless communication devices and methods, and particularly to wireless communication devices and methods for mission - critical transmission and delay - tolerant transmission.

Background Art

[0003] Wireless transmission technology has been gradually evolving from 4G Long - Term Evolution (LTE) to 5G New Radio (NR). In a 5G NR network, the main application scenario is ultra - reliable low - latency communication (URLLC) for ultra - low latency and high - reliability communication.

[0004] For example, communication - based train control (CBTC) is a high - speed mobile application in a signaling system that uses wireless communication between on - vehicle equipment and ground track equipment (or track - side equipment) for the operation and control of trains to achieve convenient and accurate traffic management. In the application of CBTC, high - speed trains require low - latency and high - reliability communication with ground track equipment to prevent accidents. However, wireless transmission on high - speed mobile trains is affected by multiple handover (HO) and radio link failure (RLF) events, resulting in a decline in transmission quality. As a result, delays and packet losses increase in data transmission.

[0005] Therefore, wireless communication devices and methods with low latency and high reliability in CBTC are desired.

Summary of the Invention

[0006] This application discloses a wireless communication device. The wireless communication device includes a transceiver set and a processor electrically connected to the transceiver set. The transceiver set is configured to communicate with a first base station via a first channel. The processor is configured to control the transceiver set to transmit and receive mission-critical data with the first base station in a first communication mode, to collect data other than mission-critical data from the first base station, to control the transceiver set to transmit delay-tolerant data, including the collected data, with the first base station in a second communication mode, and to determine whether to switch the transceiver set from the first communication mode to the second communication mode based on the mission-critical data.

[0007] Furthermore, the present application discloses a wireless communication method. The wireless communication method includes the steps of: communicating with a first base station via a first channel; transmitting and receiving mission-critical data with the first base station in a first communication mode; collecting data other than mission-critical data from the first base station; transmitting delay-tolerant data, including the collected data, with the first base station in a second communication mode; and determining whether or not to switch from the first communication mode to the second communication mode based on the mission-critical data. [Brief explanation of the drawing]

[0008] The aspects of this disclosure will be best understood from the following detailed description with reference to the accompanying drawings. It should be noted that various features are not depicted to a constant scale, in accordance with common practice in the art. In fact, for clarity in the description, the dimensions of various features may be enlarged or reduced as appropriate.

[0009] [Figure 1] This is a wireless communication device according to some embodiments of the present disclosure. [Figure 2] This figure shows an input feature sequence and prediction range corresponding to collected data according to some embodiments of this disclosure. [Figure 3]The deployment scenarios of the wireless communication device shown in Figure 1, according to some embodiments of this disclosure, are shown. [Figure 4] This figure shows an exemplary procedure between a wireless communication device, a CBTC server, and an ML learning host, according to some embodiments of the present disclosure. [Figure 5] This figure shows the procedure in the wireless communication device and ML learning host between the data upload task and the model search task in Figure 4, according to some embodiments of the present disclosure. [Figure 6] A wireless communication method performed by a wireless communication device on a vehicle, according to some embodiments of this disclosure. [Figure 7A] This disclosure presents scenarios illustrating switching policies for CBTC tasks and data upload tasks according to some embodiments of this disclosure. [Figure 7B] This disclosure presents scenarios illustrating switching policies for CBTC tasks and data upload tasks according to some embodiments of this disclosure. [Modes for carrying out the invention]

[0010] Several variations of the embodiment are described. Throughout the various figures and exemplary embodiments, similar reference numerals are used to represent similar elements. Additional operations may be provided before, during, and / or after the disclosed method, and it should be understood that some of the operations described may be replaced or omitted for other embodiments of the method.

[0011] This disclosure provides wireless communication devices and methods for mission-critical and delay-tolerant transmissions that avoid high latency and packet loss in high-speed network environments (e.g., communication-based train control (CBTC)). The devices and methods utilize techniques including machine learning (ML) techniques. According to embodiments, this disclosure is suitable for use in vehicles in which the wireless communication devices can implement wireless module configurations for mission-critical and delay-tolerant transmissions. Mission-critical transmissions require very high reliability to handle various emergencies in the vehicle. Delay-tolerant transmissions need to ensure good throughput so that monitoring data can be smoothly returned for analysis.

[0012] Figure 1 shows a wireless communication device 100 according to some embodiments of the present disclosure. The wireless communication device 100 includes a control node 110, a transceiver set 120, and two antenna modules 32 and 34. In the embodiment of Figure 1, the transceiver set 120 includes wireless transceivers 22 and 24, which are coupled to antenna modules 32 and 34, respectively. In some embodiments, the transceiver set 120 includes one or more wireless transceivers and one or more antenna modules. The number of wireless transceivers and antenna modules are illustrative and are not intended to limit the present disclosure.

[0013] Each of the 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 having an omnidirectional radiation pattern, and the single antenna can communicate with different base stations. In some embodiments, antenna modules 32 and 34 may include an antenna array, and the antennas in the antenna array can communicate with at least two base stations.

[0014] Each of the 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 receivers can downconvert the received radio frequency (RF) or microwave signals to baseband frequencies, and the transmitters can upconvert the received baseband signals to RF signals or microwave frequencies. Furthermore, each of the wireless transceivers 22 and 24 is coupled to the control node 110 via fiber, wireless, or wired connections.

[0015] The wireless transceiver 22 and antenna module 32 may form a first RF interface, and the wireless transceiver 24 and antenna module 34 may form a second RF interface. In some embodiments, the first RF interface and the second RF interface are located in the same position. In some embodiments, the first RF interface and the second RF interface are located in different positions. For example, the wireless communication device 100 is installed on a train, and the first RF interface is located at the front of the train carriage (bogie), and the second RF interface is located in the center of the train carriage or another train carriage.

[0016] The control node 110 includes a processor 12 and a storage device 14. The processor 12 is electrically connected to the transceiver set 120 and is configured to control the transceiver set 120 to establish communication links with two base stations according to different band settings. The processor 12 may be a central processing unit (CPU), a microprocessor, a microcontroller, a field-programmable gate array (FPGA) unit, a graphics programming unit (GPU), a custom-made integrated circuit (IC), etc.

[0017] In some embodiments, the processor 12 is configured to control the transceiver set 120 to communicate with two base stations using the same or different generations of communication technology. For example, the base stations may be advanced node B (eNB) of a 3GPP Long-Term Evolution (LTE) network or g node B (gNB) of 5G New Radio (NR). The base stations may also be called access points, access terminals, base units, or other terms used in the art. While the concepts of the present invention are described in relation to 4G and 5G communication protocols or base stations, it should be noted that this disclosure is not limited to 4G and 5G communication systems and may extend beyond them.

[0018] In some embodiments, each of the wireless transceivers 22 and 24 may support a 3GPP cellular wireless communication standard such as 4G, 5G, or 6G. The wireless transceivers 22 and 24 may support the same or different wireless access technologies. The processor 12 is also configured to control the wireless transceivers 22 and 24 to use different sets of radio frequencies. For example, wireless transceiver 22 may be controlled to use a first set of frequencies, and wireless transceiver 24 may be controlled to use a second set of frequencies different from the first set.

[0019] In some embodiments, the processor 12 is configured to control the radio transceivers 22 and 24 using LTE / 5G dual mode, for example, non-standalone 5G or standalone dual-mode LTE / 5G. For example, radio transceiver 22 is controlled using LTE only, and radio transceiver 24 is controlled using standalone 5G only. Alternatively, radio transceiver 22 is controlled using LTE only, and radio transceiver 24 is controlled using both LTE and 5G capabilities.

[0020] Processor 12 is configured to control the operation of transceiver set 120 based on program instructions and data stored in storage device 14. In some embodiments, storage device 14 is a memory. Storage device 14 stores a prediction model (e.g., HO prediction model) and a data set for band configuration, and is further configured to determine conditions for a policy control model. The prediction model and the policy control model are executed by processor 12 or implemented within processor 12. The data set includes data related to HO / radio link failure (RLF) and signal strength. In some embodiments, wireless communication device 100 is disposed in a vehicle, and the vehicle moves along a fixed route or a known route. For example, the vehicle is a train or a subway moving along a rail route. The collected data may include packet information and signal messages between wireless communication device 100 and a base station collected along the rail route when wireless communication device 100 transmits packets to the base station with consistent throughput. Wireless communication device 100 is configured to perform model inference and operations for dynamic network configuration. Further, the collected data is stored in storage device 14.

[0021] By analyzing the collected data, the prediction model is used to identify and classify HO types. In some embodiments, the HO trigger mechanism is provided based on relative measurement results. For example, the HO trigger mechanism may be configured to trigger when the signal quality measurement of an adjacent cell is higher than the signal quality measurement of a special cell. The signal quality measurement may include reference signal received power (RSRP), reference signal received quality (RSRQ), or signal-to-interference-plus-noise ratio (SINR).

[0022] The collected data includes signal strength (e.g., signal quality measurement values) during a specific time interval and information regarding the occurrence of a certain type of HO event or critical event. In some embodiments, based on the collected data, the processor 12 is configured to use a prediction model to determine whether to change the band configuration of the transceiver set 120 to prevent a plurality of HO events, thereby reducing the latency and packet loss caused by the plurality of HO events. Further, based on the collected data, the processor 12 is configured to use a prediction model to predict RLF events, the selection of radio technologies (e.g., LTE, 5G NR, 6G, etc.), and frequency configurations (e.g., bank locking, carrier aggregation configuration, or dual connectivity configuration).

[0023] FIG. 2 is a diagram showing an input feature sequence and a prediction range corresponding to collected data according to some embodiments of the present disclosure. In FIG. 2, the collected data represents a data scene 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 a time interval TP (e.g., 1 second).

[0024] The collected data is input into a prediction model (or other pre-trained model) as input features for prediction. In response to the input feature sequence, a prediction range is generated for each collected data to predict whether an HO or RLF event will occur using a classification algorithm in the HO prediction model and to predict the remaining time to the HO or RLF event using a regression algorithm in the HO prediction model. For example, the prediction range 214 between time t2 and time t6 is generated based on the input feature sequence 212 obtained from the collected data from time t0 to time t2. In the embodiment of FIG. 2, based on the input feature sequence 212, it is predicted that an HO event 223 will occur within the prediction range 214 between time t4 and time t5.

[0025] Figure 3 shows a deployment scenario of the radio communication device 100 of Figure 1 according to some embodiments of the present disclosure. In the embodiment of Figure 3, the radio communication device 100 is located inside a moving train 300. Furthermore, a first RF interface 132, including the radio transceiver 22 and antenna module 32 of Figure 1, is configured to communicate with a base station 310 in a first band setting. Furthermore, a second RF interface 134, including the radio transceiver 24 and antenna module 34 of Figure 1, is configured to communicate with a base station 320 in a second band setting. The first band setting is different from the second band setting. The first RF interface 132 and the second RF interface 134 may be located in the same or different locations within the train 300. In some embodiments, multiple radio communication devices 100 are located within the train 300. For example, one radio communication device 100 is located at the front of the train 300, and another radio communication device 100 is located at the rear of the train 300.

[0026] In some embodiments, as train 300 moves, HO procedures are performed between base stations along the route of train 300. For example, if train 300 moves out of the coverage range of base station 310 and into the coverage range of base station 312, HO procedure HO_1 is performed for the first RF interface 132. Similarly, if train 300 moves out of the coverage range of base station 320 and into the coverage range of base station 322, HO procedure HO_2 is performed for the second RF interface 134.

[0027] As train 300 moves, the radio communication device 100 is configured to establish CBTC communication with CBTC server 360 via a corresponding base station and network 350 in order to collect and transmit information on the position, speed, and direction of train 300 for controlling the movement of train 300. In some embodiments, network 350 includes the backbone network and / or radio network of the CBTC system. The backbone network functions as a transmission channel between ground railway equipment such as switches that guide trains onto different tracks, signals that provide commands to drivers or dispatchers, and track circuits that detect obstacles on the track. Furthermore, the radio network is configured to perform data exchange between the radio communication device 100 and the backbone network via a corresponding base station. For example, when train 300 is moving within the coverage area of ​​base station 310, the radio communication device 100 is configured to establish CBTC communication with CBTC server 360 via base station 310 and network 350 using a first RF interface 132. Through CBTC communication, the CBTC server 360 can perform Automatic Train Protection (ATP), Automatic Train Operation (ATO), and Automatic Train Supervision (ATS) for the train 300.

[0028] In Figure 3, the wireless communication device 100 is configured to receive CBTC signals from the CBTC server 360 and transmit CBTC signals to the CBTC server 360. The CBTC signals are mission-critical data in a mission-critical application. In some embodiments, the mission-critical application may have requirements regarding data delivery success rate (e.g., with high reliability), delivery within delay limits (e.g., delay-sensitive applications), and the probability of successful delivery with delay limits. In some embodiments, the probability of successful delivery is greater than a threshold p_threshold, for example, the probability (delay<t_threshold)> p_threshold is the threshold, and t_threshold represents a specific time, for example, 100ms, or indicates being in ultra-high reliability low latency (URLLC) traffic.

[0029] The wireless communication device 100 is further configured to collect data including packet information and signal information together with the corresponding base station and to provide the collected data to the CBTC server 360. After receiving the collected data, the CBTC server 360 is configured to provide the collected data to the ML learning host 370. Based on the collected data, the ML learning host 370 is configured to train the predictive model and / or policy control model 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 learning host 370 is configured to provide the wireless communication device 100 with the latest trained model. The CBTC server 360 is connected to the ML learning host 370 by wired or wireless means. In some embodiments, the CBTC server 360 and the ML learning host 370 are implemented in a CBTC control center.

[0030] Figure 4 shows an exemplary procedure between a wireless communication device 100, a CBTC server 360, and an ML learning host 370, according to some embodiments of the present disclosure. 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 (such as CBTC tasks and data upload tasks) in the 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, start and stop timers are created for each task, and the runtime of each task is controllable, allowing for task switching.

[0031] After receiving ACK404, the radio communication device 100 is configured to perform CBTC task 410. In CBTC task 410, the radio communication device 100 is configured to perform predictive operation 412 to perform model inference and operation for dynamic network configuration with the base station when the train 300 is moving. Based on the results of predictive operation 412, the radio communication device 100 is configured to provide the CBTC server 360 with a CBTC signal 414 containing operational information of the train 300. The CBTC signal 414 contains mission-critical data for radio communication. Based on the CBTC signal 414, the CBTC server 360 is configured to perform control operation 415 for the train 300 and provide the radio communication device 100 with a CBTC signal 416 containing the results of control operation 415. In response to the CBTC signal 416, the radio communication device 100 is configured to control the operation of the train 300, such as speed. The CBTC task 410 is performed repeatedly until the registered execution time is reached.

[0032] After CBTC task 410 is completed, data upload task 420 will be executed. Note that the reliability of CBTC task 410 is important, and therefore, it is necessary to ensure that data upload task 420 does not interfere with CBTC task 410.

[0033] In the data upload task 420, the wireless communication device 100 is configured to upload data 422 to the CBTC server 360, and the uploaded data 422 includes collected data stored in the storage device 14, i.e., delay-tolerant data in wireless communication other than mission-critical data. As described above, the collected data may also include packet information and signal messages between the wireless communication device 100 and the base station, collected along the trajectory path when the wireless communication device 100 transmits packets and corresponding signal quality measurements to the base station at a consistent throughput. After obtaining the uploaded data 422, the CBTC server 360 is configured to perform operation 423 to store the uploaded data 422 and provide an ACK 424 to the wireless communication device 100 if the uploaded data 422 has been fully received. Next, the CBTC server 360 is configured to provide the uploaded data 426 to the ML learning host 370. After obtaining the uploaded data 426, the ML learning host 370 is configured to perform operation 427 to store the uploaded data 426 and provide an ACK 428 to the CBTC server 360 if the uploaded data 426 has been fully received. After obtaining the uploaded data 426, which includes the collected data collected by the wireless communication device 100, the ML learning host 370 is configured to perform a learning operation 429 for the model used in the wireless communication device 100.

[0034] In some embodiments, the data upload task 420 is performed periodically by the wireless communication device 100 based on a first time interval, such as daily or every fixed number of days. Furthermore, the wireless communication device 100 is further configured to periodically perform a model retrieval task 430 with the ML learning host 370 based on 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 learning host 370 without going through the CBTC server 360. In response to the model request 432, the ML learning host 370 is configured to provide the latest trained model 434 to the wireless communication device 100. The wireless communication device 100 is then configured to update the corresponding model based on the latest trained model 434 obtained.

[0035] Figure 5 shows the procedure in the wireless communication device 100 and the ML learning host 370 between the data upload task 420 and the model retrieval task 430 in Figure 4, according to some embodiments of the present disclosure. In the wireless communication device 100, the collected data is acquired in step 502 and used to train a model, for example, the upload data 422 / 426 in Figure 4 is created in step 504 and then stored as upload data (e.g., upload data 422 in Figure 4) in step 506 for transmission to the ML learning host 370 in the data upload task 420. Simultaneously, the collected data is input to a predictive model for AI / ML inference in step 508 to perform the corresponding operations in steps 510_1 to 510_n for dynamic network configuration. In step 508, the AI / ML inference may be latency sensitive, and the execution time may be time sensitive. Steps 510_1 to 510_n include techniques such as band locking and band switching.

[0036] Band locking techniques involve applying settings to the radio communication device or user such that each radio interface of the radio communication device is configured to connect only to a given subset of frequency bands and is prohibited from connecting to the remaining available frequency bands provided by the base station. By implementing band locking techniques, the number of candidate base station channels to consider can be reduced in order to avoid performing unnecessary HO procedures.

[0037] In the ML learning host 370 shown in Figure 5, a model is trained based on the uploaded data 426 in step 512. Next, in step 514, the trained model is stored and managed so that the latest model is provided to the wireless communication device 100 in response to a model request 432 from the wireless communication device 100. Thus, the predictive model for AI / ML inference in step 508 is updated based on the latest model 434 from the ML learning host 370.

[0038] AI's ML (Machine Learning) technology can automatically learn from data and past experience to identify features and make predictions. It focuses on the use of data and algorithms, mimicking human learning methods and gradually improving accuracy. For example, it can learn algorithms to perform classification or prediction through the use of statistical methods, revealing important insights in data mining projects. This technology can be particularly useful.

[0039] Such technologies can be particularly useful when users are located on high-speed moving trains and wireless communication equipment must receive HO (Hardware Interruption) signals very frequently. Therefore, by implementing these technologies, abnormal performance can be minimized by avoiding overlap of HO periods between different wireless interfaces.

[0040] In some embodiments, the predictive model of the present disclosure may first be trained on a plurality of training datasets. In some embodiments, each training dataset includes input training data (e.g., RSRPs between time intervals) and output training data (e.g., actual HO timings), and the training datasets are then collected. For example, in an HO predictive model, the training datasets are used to train an HO predictive model by using a two-stage prediction method to predict HO events. In the first stage of prediction, the training datasets are used to train the HO predictive model to predict whether or not an HO event will occur. In the second stage of prediction, the training datasets are used to train the HO predictive model to predict the time of occurrence of an HO event.

[0041] In some embodiments, the predictive model is established based on known ML models such as support vector machine (SVM) models, recurrent neural network (RNN) models, eXtreme gradient boosting (XGB) models, gradient boosting (GB) models, or other algorithms understood by those skilled in the art based on the above disclosures, and is therefore not further described herein.

[0042] Figure 6 shows a wireless communication method 600 performed by a wireless communication device (e.g., 100 in Figure 1) on a vehicle (e.g., 300 in Figure 3), according to some embodiments of the present disclosure. The vehicle travels along a fixed or known route. For convenience of explanation, the method of Figure 6 will be described in relation to Figures 3 and 4.

[0043] Wireless communication methods are implemented in railway signaling systems for CBTC, which use telecommunications between trains and ground track equipment for traffic management and infrastructure control. CBTC allows for more accurate determination of train positions than conventional signaling systems. This makes railway traffic management safer and more efficient. Subways (and other railway systems) can reduce operating intervals while maintaining or even improving safety.

[0044] In operation S610, the wireless communication device 100 is configured to simultaneously establish communication links with two base stations based on different band settings. For example, in the wireless communication device 100, the first RF interface 132 is configured to communicate with base station 310 via a first channel based on a first band setting, and the second RF interface 134 is configured to communicate with base station 320 via a second channel based on a second band setting. Over the communication links, the wireless communication device 100 is configured to transmit the same or different packets to base stations 310 and 320.

[0045] In operation S620, it is determined whether both signal quality measurements (e.g., RSRP, RSRQ, or SINR) corresponding to base stations 310 and 320 are greater than the threshold. If one of the signal quality measurements corresponding to base stations 310 and 320 is less than the threshold, for example, if the radio state of the first RF interface 132 or the second RF interface 134 is unreliable, the flow proceeds to operation S630.

[0046] In operation S630, a first communication mode is performed to transmit mission-critical data (e.g., CBTC signals for CBTC task 410) via both the first RF interface 132 and the second RF interface 134. For example, the radio communication device 100 is configured to transmit the same packets to base stations 310 and 320 for CBTC task 410. In some embodiments, the first communication mode is performed via the RF interface of the first RF interface 132 and the second RF interface 134 that has higher reliability.

[0047] In operation S640, a decision is made whether to switch to the second communication mode based on the CBTC signal, signal quality measurement, or train 300's vehicle schedule. For example, if the signal quality measurement for both the first RF interface 132 and the second RF interface 134 is less than that threshold, the radio communication device 100 is configured to continue operating in the first communication mode, and the flow returns to operation S630. In some embodiments, if the CBTC signal indicates that the CBTC task 410 is completed based on the registration schedule or the CBTC signal, or if the vehicle schedule indicates that train 300 is stationary, for example, that train 300 will remain at the station for a certain amount of time or that train 300 will park in a parking garage after a set operating time (or running time), the radio communication device 100 is configured to switch from the first communication mode to the second communication mode, and the flow proceeds to operation S650.

[0048] In operation S650, the second communication mode is executed to transmit delay-tolerant data (e.g., upload data for data upload task 420) via both the first RF interface 132 and the second RF 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 RF interface with higher reliability among the first RF interface 132 and the second RF interface 134. After the transmission of delay-tolerant data is complete, the flow returns to operation S620.

[0049] In operation S620, if it is determined that both signal quality measurements corresponding to base stations 310 and 320 are greater than the threshold, the flow proceeds to operation S660.

[0050] In operation S660, a third communication mode is performed to transmit mission-critical data (e.g., CBTC signals for CBTC task 410) via one of the first RF interface 132 and the second RF interface 134, and delay-tolerant data (e.g., upload data for data upload task 420) via the other RF interface. For example, the radio communication device 100 is configured to transmit different packets to base stations 310 and 320 for CBTC task 410 and data upload task 420, respectively.

[0051] According to method 600 in Figure 6, the transmission of mission-critical data requiring high reliability and the transmission of delay-tolerant data requiring a large amount of bandwidth are not performed simultaneously for a single RF interface, thereby avoiding interference between the CBTC task 410 and the data upload task 420.

[0052] Figures 7A and 7B illustrate scenarios showing a switching policy for CBTC task 410 (i.e., first communication mode) and data upload task 420 (i.e., second communication mode) according to some embodiments of the present disclosure.

[0053] In Figures 7A and 7B, the CBTC task 410 and the data upload task 420 are performed via the same RF interface. For example, in operation S620, if it is determined that one of the signal quality measurements corresponding to base stations 310 and 320 is below a threshold, the CBTC task 410 and the data upload task 420 are performed via both the first RF interface 132 and the second RF interface 134, or via the RF interface with higher reliability.

[0054] In Figure 7A, the CBTC task 410 is executed during the 10-18 hour passenger service period each day when train 300 is started (i.e., not in the power-off state 450), and the data upload task 420 is executed during the maintenance period, thereby preventing interference between the CBTC task 410 and the data upload task 420.

[0055] In Figure 7B, when train 300 is started (i.e., not in power-off state 450), the CBTC task 410 and the data upload task 420 are executed during the passenger service period. However, only one of the CBTC task 410 and the data upload task 420 is executed at any given time, and the CBTC task has a higher priority than the data upload task 420, thereby preventing the CBTC task 410 and the data upload task 420 from interfering with each other. Furthermore, because the data upload task 420 is executed during the passenger service period, the real-time network condition can be monitored. Therefore, if the network environment changes significantly, the uploaded data can be used to immediately train the model for subsequent updates. In some embodiments, the CBTC task 410 and the data upload task 420 are dynamically configured to be executed based on radio resources, traffic patterns of mission-critical applications, data delivery requirements of mission-critical applications, etc. For example, when there are spare radio resources available for transmission, more data upload tasks 420 are executed, i.e., delay-tolerant data is transmitted between two mission-critical transmissions of the CBTC task 410.

[0056] While preferred embodiments of the Disclosure have been described above, they are not intended to limit the Disclosure, and those skilled in the art can make several changes and modifications without departing from the spirit and scope of the Disclosure; therefore, the scope of protection of the Disclosure is defined by the appended claims.

Claims

1. A transceiver set configured to communicate with a first base station via a first channel, The transceiver set is electrically connected, Control the transceiver set to transmit and receive mission-critical data with the first base station in first communication mode. Collect data other than the mission-critical data from the first base station, In the second communication mode, the transceiver set is controlled to transmit delay-tolerant data including collected data to the first base station. A wireless communication device including a processor configured to determine whether or not to control the transceiver set to switch from the first communication mode to the second communication mode based on the mission-critical data.

2. The aforementioned processor, Based on the collected data, a predictive model is used to determine whether or not to control the transceiver set to hand over to the second base station, or The wireless communication device according to claim 1, further configured to determine whether to control the transceiver set to communicate with the first base station via a second channel using the predictive model based on the collected data.

3. The processor is further configured to update the prediction model based on a new model from the training host. The wireless communication device according to claim 2, wherein the learning host is configured to learn the new model based on the delay-tolerant data from the first base station.

4. The wireless communication device according to claim 1, wherein the device is installed in a vehicle and the mission-critical data includes communication-based train control (CBTC) signals for the vehicle.

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

6. The wireless communication device according to claim 5, wherein the processor is further configured to determine whether or not the vehicle is stationary based on the mission-critical data or vehicle schedule.

7. The transceiver set is further configured to communicate with a second base station via a second channel. The wireless communication device according to claim 1, wherein the processor is configured to control the transceiver set to transmit and receive the mission-critical data with the first base station and transmit the delay-tolerant data with the second base station in a third communication mode.

8. The wireless communication device according to 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.

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

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

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

12. The steps include communicating with the first base station via the first channel, The steps include transmitting and receiving mission-critical data with the first base station in a first communication mode, The steps include: collecting data other than the mission-critical data from the first base station; The second communication mode involves the first base station and the step of transmitting delay-tolerant data including collected data, A wireless communication method comprising the step of determining whether or not to switch from the first communication mode to the second communication mode based on the mission-critical data.

13. A step of determining whether or not to hand over to a second base station using a predictive model based on the collected data, or The wireless communication method according to claim 12, further comprising the step of determining whether or not to communicate with the first base station via a second channel using the predictive model based on the collected data.

14. The process further includes the step of updating the predictive model based on a new model from a training host, The wireless communication method according to claim 13, wherein the learning host is configured to learn the new model based on the delay-tolerant data from the first base station.

15. The wireless communication method according to claim 12, wherein the mission-critical data includes communication-based train control (CBTC) signals for the vehicle.

16. The step of deciding whether or not to switch from the first communication mode to the second communication mode based on the mission-critical data is: A step of determining whether the vehicle will come to a standstill based on the mission-critical data or vehicle schedule, The wireless communication method according to claim 15, further comprising the step of switching from the first communication mode to the second communication mode when the vehicle is stationary.

17. The steps include communicating with the second base station via the second channel, The wireless communication method according to claim 12, further comprising the steps of transmitting and receiving the mission-critical data with the first base station and transmitting the delay-tolerant data with the second base station in a third communication mode.

18. The wireless communication method according to 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.

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

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

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