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

Abstract

A wireless communication device and method with low latency and high reliability for communications-based train control (CBTC) is provided. The wireless communication device includes a transceiver set and a control node having a processor electrically connected to the transceiver set. The transceiver set communicates with a first base station over 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, collects data other than the mission-critical data from the first base station, controls the transceiver set to transmit delay-tolerant data including the collected data with the first base station in a second communication mode, and determines whether 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 November 22, 2024, which is incorporated herein by reference in its entirety.

[0002] The present application relates to wireless communication devices and methods, and more particularly to wireless communication devices and methods for mission-critical and delay-tolerant transmissions. [Background technology]

[0003] Radio transmission technology is gradually evolving from 4G Long Term Evolution (LTE) to 5G New Radio (NR). In 5G NR networks, the main application scenario is Ultra-Reliable Low Latency Communications (URLLC) for ultra-low latency and high reliability communications.

[0004] For example, communications-based train control (CBTC) is a high-speed mobile application in signaling systems that uses wireless communications between onboard equipment and ground track equipment (or trackside equipment) for train operation and control to achieve convenient and accurate traffic management. In CBTC applications, high-speed trains require low-latency and highly reliable communications with ground track equipment to prevent accidents. However, wireless transmissions on high-speed mobile trains are subject to multiple handover (HO) and radio link failure (RLF) events, resulting in degradation of transmission quality. This results in increased latency and packet loss in data transmission.

[0005] Therefore, a wireless communication apparatus and method having low latency and high reliability in CBTC is desired. Summary of the Invention

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

[0007] The present application further discloses a wireless communication method, including: communicating with a first base station over a first channel, transmitting and receiving mission-critical data with the first base station in a first communication mode, collecting data other than the mission-critical data from the first base station, transmitting delay-tolerant data including the collected data to the first base station in a second communication mode, and determining whether to switch from the first communication mode to a second communication mode based on the mission-critical data. [Brief explanation of the drawings]

[0008] Aspects of the present disclosure will be best understood from the following detailed description when taken in conjunction with the accompanying drawings, in which: It should be noted that, in accordance with common practice in the industry, various features have not been drawn to scale. In fact, the dimensions of various features may be arbitrarily increased or decreased for clarity of illustration.

[0009] [Figure 1] 1 is a wireless communication device according to some embodiments of the present disclosure. [Figure 2] FIG. 1 illustrates an input feature sequence and prediction range corresponding to collected data, according to some embodiments of the present disclosure. [Figure 3]2 illustrates a deployment scenario for the wireless communication device of FIG. 1 according to some embodiments of the present disclosure. [Figure 4] 1 illustrates an example procedure between a wireless communication device, a CBTC server, and an ML learning host according to some embodiments of the present disclosure. [Figure 5] 5 illustrates a procedure in the wireless communication device and the ML learning host between the data upload task and the model retrieval task of FIG. 4 according to some embodiments of the present disclosure. [Figure 6] 1 is a wireless communication method performed by a wireless communication device on a vehicle, according to some embodiments of the present disclosure. [Figure 7A] 1 illustrates a scenario illustrating a switching policy for a CBTC task and a data upload task, according to some embodiments of the present disclosure. [Figure 7B] 1 illustrates a scenario illustrating a switching policy for a CBTC task and a data upload task, according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0010] Several variations of the embodiments are described. Like reference numerals are used to denote like elements throughout the various figures and exemplary embodiments. It should be understood that additional operations may be provided before, during, and / or after the disclosed methods, and that some of the described operations may be substituted or eliminated for other embodiments of the methods.

[0011] The present disclosure provides a wireless communication apparatus and method for mission-critical and delay-tolerant transmissions that avoid high latency and packet loss in networks in high-speed environments (e.g., communications-based train control (CBTC)). The apparatus and method use techniques including machine learning (ML) techniques. According to an embodiment, the present disclosure is suitable for use in a vehicle in which the wireless communication apparatus can implement radio module configurations for mission-critical and delay-tolerant transmissions. Mission-critical transmissions require very high reliability to handle various vehicle emergencies. Delay-tolerant transmissions must guarantee good throughput so that monitoring data can be smoothly returned for analysis.

[0012] FIG. 1 illustrates 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 FIG. 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 the number of antenna modules are merely exemplary 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. The antenna modules 32 and 34 may have the same or different antenna configurations. In some embodiments, the antenna modules 32 and 34 may include a single antenna having an omnidirectional radiation pattern, where the single antenna is capable of communicating with different base stations. In some embodiments, the antenna modules 32 and 34 may include an antenna array, where the antennas in the antenna array are capable of communicating 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 received radio frequency (RF) or microwave signals to baseband frequencies, and the transmitters can upconvert received baseband signals to RF or microwave frequencies. Furthermore, each of the wireless transceivers 22 and 24 is coupled to the control node 110 via a fiber, wireless, or wired connection.

[0015] The wireless transceiver 22 and the antenna module 32 may form a first RF interface, and the wireless transceiver 24 and the antenna module 34 may form a second RF interface. In some embodiments, the first RF interface and the second RF interface are located at the same location. In some embodiments, the first RF interface and the second RF interface are located at different locations. For example, the wireless communication device 100 may be mounted on a train, with the first RF interface located at the front of a train carriage and the second RF interface located in the center of the train carriage or other train carriage.

[0016] The control node 110 includes a processor 12 and a storage device 14. The processor 12 is electrically connected to a transceiver set 120 and configured to control the transceiver set 120 to establish communication links with two base stations according to different band configurations. 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 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 generation communication technologies. For example, the base stations may be evolved Node Bs (eNBs) in a 3GPP Long Term Evolution (LTE) network or gNode Bs (gNBs) in a 5G New Radio (NR) network. The base stations may also be referred to as access points, access terminals, base units, or other terms used in the art. It should be noted that while the concepts of the present invention are described with respect to 4G and 5G communication protocols or base stations, the present disclosure is not limited to 4G and 5G communication systems and may extend beyond.

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

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

[0020] The processor 12 is configured to control the operation of the transceiver set 120 based on program instructions and data stored in the storage device 14. In some embodiments, the storage device 14 is a memory. The storage device 14 is further configured to store a dataset for a prediction model (e.g., an HO prediction model) and band configuration, and to determine conditions for a policy control model. The prediction model and the policy control model are executed by the processor 12 or implemented within the processor 12. The dataset includes data related to HO / Radio Link Failure (RLF) and signal strength. In some embodiments, the wireless communication device 100 is disposed in a vehicle, and the vehicle travels a fixed or known route. For example, the vehicle is a train or subway traveling along a track path. The collected data may include packet information and signal messages between the wireless communication device 100 and a base station collected along a track path when the wireless communication device 100 transmits packets to the base station at a consistent throughput. The wireless communication device 100 is configured to perform model inference and operation for dynamic network configuration. The collected data is further stored in the storage device 14.

[0021] By analyzing the collected data, a predictive model is used to identify and classify the HO type. 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 value of the neighboring cell is higher than the signal quality measurement value of the special cell. The signal quality measurement value may include Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), or Signal-to-Interference-and-Noise Ratio (SINR).

[0022] The collected data includes information about signal strength (e.g., signal quality measurements) during a particular time interval 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 and determine whether to change the band configuration of transceiver set 120 to prevent multiple HO events, thereby reducing delay 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, selection of a radio technology (e.g., LTE, 5G NR, 6G, etc.), and frequency configuration (e.g., bank locking, carrier aggregation configuration, or dual connectivity configuration).

[0023] 2 is a diagram illustrating an input feature sequence and a prediction horizon 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 are input to 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 until the HO or RLF event using a regression algorithm in the HO prediction model. For example, a prediction range 214 between time t2 and time t6 is generated based on the input feature sequence 212 of the collected data acquired from time t0 to time t2. In the embodiment of FIG. 2, based on the input feature sequence 212, an HO event 223 is predicted to occur between time t4 and time t5 within the prediction range 214.

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

[0026] In some embodiments, as train 300 moves, it performs an HO procedure between base stations along the route of train 300. For example, when train 300 moves out of the coverage area of ​​base station 310 and into the coverage area of ​​base station 312, it performs an HO procedure HO_1 for first RF interface 132. Similarly, when train 300 moves out of the coverage area of ​​base station 320 and into the coverage area of ​​base station 322, it performs an HO procedure HO_2 for second RF interface 134.

[0027] As the train 300 moves, the wireless communication device 100 is configured to establish CBTC communications with the CBTC server 360 via a corresponding base station and network 350 to collect and transmit information about the train 300's position, speed, and direction for controlling the movement of the train 300. In some embodiments, the network 350 includes a backbone network and / or a wireless network of a CBTC system. The backbone network serves as a transmission channel between ground-based railway equipment, such as switches that direct trains onto different tracks, signals that provide instructions to train drivers or dispatchers, and track circuits that detect obstacles on the tracks. Furthermore, the wireless network is configured to perform data exchange between the wireless communication device 100 and the backbone network via a corresponding base station. For example, when the train 300 moves within the coverage area of ​​the base station 310, the wireless communication device 100 is configured to establish CBTC communications with the CBTC server 360 via the base station 310 and network 350 using the first RF interface 132. Through CBTC communications, 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 FIG. 3 , wireless communication device 100 is configured to receive CBTC signals from CBTC server 360 and transmit CBTC signals to 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 a data delivery success rate (e.g., with high reliability), delivery within a delay bound (e.g., a delay-sensitive application), and a probability of successful delivery with a delay bound. In some embodiments, the probability of successful delivery is greater than a threshold p_threshold, e.g., a probability (delay<t_threshold)> p_threshold and t_threshold represents a specific time, for example 100 ms, or represents Ultra Reliable Low Latency Communications (URLLC) traffic.

[0029] Wireless communication device 100 is further configured to collect data including packet information and signal information with a corresponding base station and provide the collected data to CBTC server 360. After receiving the collected data, CBTC server 360 is configured to provide the collected data to ML learning host 370. Based on the collected data, ML learning host 370 is configured to train a prediction model and / or a policy control model used in wireless communication device 100, so that the trained model may be more accurate. In response to a request from wireless communication device 100, ML learning host 370 is configured to provide the latest trained model to wireless communication device 100. CBTC server 360 is connected to ML learning host 370 in a wired or wireless manner. In some embodiments, CBTC server 360 and ML learning host 370 are implemented in a CBTC control center.

[0030] FIG. 4 illustrates an example 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. Initially, 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., a CBTC task and a data upload task) in a task scheduler. These tasks are then executed based on the registration schedule. After the registration operation 403 is completed, the CBTC server 360 is configured to provide an acknowledgement (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 the ACK 404, the wireless communication device 100 is configured to execute a CBTC task 410. In the CBTC task 410, the wireless communication device 100 is configured to perform a prediction operation 412 to perform model inference and action for dynamic network configuration with base stations when the train 300 is moving. Based on the result of the prediction operation 412, the wireless communication device 100 is configured to provide a CBTC signal 414 to the CBTC server 360, the CBTC signal 414 including operation information of the train 300. The CBTC signal 414 includes mission-critical data in wireless communication. Based on the CBTC signal 414, the CBTC server 360 is configured to execute a control action 415 for the train 300 and provide a CBTC signal 416 to the wireless communication device 100, the CBTC signal 414 including the result of the control action 415. In response to the CBTC signal 416, the wireless communication device 100 is configured to control the operation of the train 300, such as its speed. The CBTC task 410 is repeatedly executed until the registered execution time is reached.

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

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

[0034] In some embodiments, the data upload task 420 is periodically performed 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, the data upload task 420 may be performed once a day, while 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 involving the CBTC server 360. In response to the model request 432, the ML learning host 370 is configured to provide the wireless communication device 100 with an updated trained model 434. The wireless communication device 100 is then configured to update the corresponding model based on the retrieved updated trained model 434.

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

[0036] The band locking technique involves applying a setting by the wireless communication device or a user so that each wireless interface of the wireless communication device is configured to connect only to a predetermined subset of frequency bands and is prohibited from connecting to the remaining available frequency bands provided by the base station. By performing the band locking technique, the number of candidate channels of the base station that are considered can be reduced in order to avoid performing unnecessary HO procedures.

[0037] 5, in step 512, a model is trained based on the uploaded data 426. Next, in step 514, the trained model is stored and managed so as to provide the latest model 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 training host 370.

[0038] AI ML techniques can automatically learn from data and past experience to identify features and make predictions. This focuses on the use of data and algorithms to mimic human learning methods and gradually improve their accuracy. For example, through the use of statistical methods, algorithms can be trained to make classifications or predictions and reveal key insights in data mining projects. This technology can be particularly useful.

[0039] Such techniques can be particularly useful when a user is located on a fast-moving train and the wireless communication device must undergo frequent handovers. Therefore, by implementing these techniques, abnormal performance can be minimized by avoiding overlapping handover periods between different wireless interfaces.

[0040] In some embodiments, the prediction model of the present disclosure may be first trained 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 the training datasets are then collected. For example, in an HO prediction model, the training datasets are utilized to train the HO prediction model by using a two-stage prediction approach to predict HO events. In the first stage of prediction, the training dataset is used to train the HO prediction model to predict whether an HO event will occur. In the second stage of prediction, the training dataset is used to train the HO prediction 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 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 that will be understood by those skilled in the art based on the above disclosure, and therefore will not be further described herein.

[0042] 6 is a wireless communication method 600 performed by a wireless communication device (e.g., 100, FIG. 1) on a vehicle (e.g., 300, FIG. 3) according to some embodiments of the present disclosure. The vehicle travels along a fixed or known route. For ease of explanation, the method of FIG. 6 will be described with reference to FIGS. 3 and 4.

[0043] The wireless communication method is implemented in railway signaling systems for CBTC, which uses electrical communications between trains and surface track equipment for traffic management and infrastructure control. CBTC allows train locations to be known more accurately than traditional signaling systems. This makes rail traffic management safer and more efficient. Subways (and other rail systems) can reduce headway 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 the base station 310 via a first channel based on a first band setting, and the second RF interface 134 is configured to communicate with the base station 320 via a second channel based on a second band setting. Via the communication links, the wireless communication device 100 is configured to transmit the same or different packets to the base stations 310 and 320.

[0045] 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. If one of the signal quality measurements corresponding to base stations 310 and 320 is less than a threshold, for example, if the radio conditions of first RF interface 132 or second RF interface 134 have low reliability, flow proceeds to operation S630.

[0046] In operation S630, a first communication mode is executed to transmit mission-critical data (e.g., CBTC signals for CBTC task 410) over both first RF interface 132 and second RF interface 134. For example, wireless 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 executed over the RF interface having the higher reliability of first RF interface 132 or second RF interface 134.

[0047] In operation S640, a decision is made as to whether to switch to the second communication mode based on the CBTC signal, the signal quality measurements, or the vehicle schedule of the train 300. For example, if the signal quality measurements of both the first RF interface 132 and the second RF interface 134 are less than a threshold, the wireless communication device 100 is configured to continue operating in the first communication mode, and 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 the train 300 is stationary, for example, the train 300 will remain at a station for more than a certain time or the train 300 will stop at a parking garage after an operating time (or running time), the wireless communication device 100 is configured to switch from the first communication mode to the second communication mode, and flow proceeds to operation S650.

[0048] At operation S650, the second communication mode is executed to transmit delay-tolerant data (e.g., upload data for the 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 transmit the same packet to the base stations 310 and 320 for the data upload task 420. In some embodiments, the first communication mode is executed via the RF interface having higher reliability, either the first RF interface 132 or the second RF interface 134. After the transmission of the delay-tolerant data is completed, flow returns to operation S620.

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

[0050] In operation S660, the third communication mode is executed to transmit mission-critical data (e.g., CBTC signals for the 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 the data upload task 420) via the other RF interface. For example, the wireless communication device 100 is configured to transmit different packets for the CBTC task 410 and the data upload task 420 to the base stations 310 and 320, respectively.

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

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

[0053] 7A and 7B, the CBTC task 410 and the data upload task 420 are performed over 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 less than a threshold, then the CBTC task 410 and the data upload task 420 are performed over both the first RF interface 132 and the second RF interface 134, or over the RF interface with higher reliability.

[0054] In FIG. 7A , each day that train 300 is activated (i.e., not in power-off state 450), CBTC task 410 is executed during the 10-18 hour passenger service period, and data upload task 420 is executed during the maintenance period, thereby preventing CBTC task 410 and data upload task 420 from interfering with each other.

[0055] In FIG. 7B , when the train 300 is powered up (i.e., not in the power-off state 450), the CBTC task 410 and the data upload task 420 execute during passenger service periods. However, only one of the CBTC task 410 and the data upload task 420 executes 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 executes during passenger service periods, real-time network conditions can be monitored. Thus, if the network environment changes significantly, the uploaded data can be used to immediately train models for subsequent updates. In some embodiments, the CBTC task 410 and the data upload task 420 are dynamically configured to execute 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 execute, i.e., delay-tolerant data is transmitted between two mission-critical transmissions of the CBTC task 410.

[0056] Although the preferred embodiments of the present disclosure have been described above, they are not intended to limit the present disclosure, and a person skilled in the art can make some changes and modifications without departing from the spirit and scope of the present disclosure, and therefore the protective scope of the present disclosure is defined by the appended claims.

Claims

1. a transceiver set configured to communicate with a first base station over a first channel; electrically connected to the transceiver set; controlling the transceiver set to operate in a first communication mode for transmitting and receiving mission-critical data with the first base station; collecting data other than the mission-critical data from the first base station; controlling the transceiver set to operate in a second communication mode to transmit delay tolerant data, including collected data, with the first base station; and a processor configured to determine whether to control the transceiver set to switch from the first communication mode to the second communication mode when the mission-critical data indicates that a task is completed.

2. The processor: determining whether to control the transceiver set to handover to a second base station using a predictive model based on the collected data; or 10. The wireless communications device of claim 1, further configured to use the predictive model based on the collected data to determine whether to control the transceiver set to communicate with the first base station over a second channel.

3. the processor is further configured to update the predictive model based on a new model from a training host; The wireless communication device of claim 2 , wherein the training host is configured to train the new model based on the delay tolerant data from the first base station.

4. The wireless communication device of claim 1 , wherein the wireless communication device is located in a vehicle and the mission-critical data comprises a communications-based train control (CBTC) signal for the vehicle.

5. The wireless communication device of 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 communications device of claim 5 , wherein the processor is further configured to determine whether the vehicle is stationary based on the mission-critical data or a vehicle schedule.

7. the transceiver set is further configured to communicate with a second base station over a second channel; 10. The wireless communication device of 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 to transmit the delay-tolerant data with the second base station in a third communication mode.

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

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

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

11. The wireless communication device of 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. communicating with a first base station over a first channel; operating in a first communication mode and transmitting and receiving mission-critical data with the first base station in the first communication mode; collecting data other than the mission-critical data from the first base station; operating in a second communication mode and transmitting delay tolerant data including the collected data to the first base station in the second communication mode; determining whether to switch from the first communication mode to the second communication mode when the mission-critical data indicates that a task is completed.

13. determining whether to hand over to a second base station using a prediction model based on the collected data; or The wireless communication method of claim 12 , further comprising: determining whether to communicate with the first base station over a second channel using the predictive model based on the collected data.

14. updating the predictive model based on new models from a training host; The wireless communication method of claim 13 , wherein the training host is configured to train the new model based on the delay tolerant data from the first base station.

15. The wireless communication method of claim 12 , wherein the mission-critical data includes communications-based train control (CBTC) signals for rolling stock.

16. determining whether to switch from the first communication mode to the second communication mode based on the mission-critical data, determining whether the vehicle is stationary based on the mission critical data or a vehicle schedule; 16. The wireless communication method of claim 15, further comprising: switching from the first communication mode to the second communication mode when the vehicle is stationary.

17. communicating with a second base station over a second channel; The wireless communication method of claim 12 , further comprising: 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. 18. The wireless communication method of claim 17, wherein in the third communication mode, a first signal quality measure corresponding to the first base station and a second signal quality measure corresponding to the second base station are greater than a threshold.

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

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

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