Delay reduction techniques for control channel decoding

Abstract

This document introduces techniques for reducing latency at a terminal device. In particular, techniques for reducing latency in detecting downlink control information (DCI) in a downlink signal such as a physical downlink control channel (PDCCH) signal. The techniques include determining a probability that each aggregation level of a control channel element (CCE) in the signal carries DCI. The probability can be based on a packet error rate (PER) of the signal. The PER can vary based on factors such as a signal-to-noise ratio (SNR) and / or a fading measurement. Further, the probability can depend on a reception history of the terminal device. Based on these factors, the terminal device can decode the CCEs in an order that indicates the probability that the respective aggregation level carries DCI.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 060,465, filed August 3, 2020, entitled “METHOD FOR BLIND DECODING OF CONTROL CHANNEL”, which is incorporated herein by reference in its entirety. Technical Field

[0003] The disclosed technology relates to latency reduction techniques for network devices, and more specifically, to latency reduction techniques for decoding control channels at network devices. Background Technology

[0004] In wireless communication systems, information is typically encoded before transmission. Data with an encoded format, including header information, helps the receiver decode the data upon reception. The receiver (e.g., a terminal device) must then know the location of the information needed to decode the encoded value within the signal. This information, called Downlink Control Information (DCI), is transmitted on the Physical Downlink Control Channel (PDCCH). Furthermore, DCI can be transmitted on multiple Control Channel Elements (CCEs) within the signal. The receiver needs to parse the CCEs to determine the location of the DCI before decoding them.

[0005] Traditionally, the methods used to locate information are laborious. More specifically, the receiver may search sequentially on a first-come, first-served basis. In other words, the receiver decodes and reads each resource block linearly until the DCI is found. Attached Figure Description

[0006] This embodiment is shown by way of example and is not intended to be limited to the figures in the accompanying drawings.

[0007] Figure 1 A high-level block diagram of the receiver components is shown.

[0008] Figure 2A An example of a CCE received during a time period is shown.

[0009] Figure 2B An example of the order in which CCEs are decoded is shown.

[0010] Figure 2CAnother example of the order in which CCEs are decoded is shown.

[0011] Figure 3 This is a flowchart illustrating a method for decoding CCE.

[0012] Figure 4 It is a block diagram illustrating an example form of a machine that is operable to perform aspects of the disclosed technology. Detailed Implementation

[0013] Telecommunications systems require signal receivers to decode values ​​within signals. Each signal can carry multiple types of data, such as control data and user data. Therefore, the signal receiver must decode each signal to determine the appropriate data for a particular situation. For example, some data might provide instructions on how to decode the remaining data. Other data might provide details about the order in which the receiver should read the data. Thus, decoding signals is the receiver's primary task.

[0014] Specifically, in the downlink direction, the terminal device needs to decode the received signal. To do this, the terminal device needs to parse the signal through the network to find the DCI required for decoding. The DCI can be stored in multiple resource blocks aggregated into CCEs. The aggregation level is the number of CCEs used to transmit control information (e.g., DCI). Aggregation levels typically have values ​​of 1, 2, 4, and 8.

[0015] Networks can also use DCIs to, for example, instruct terminals to perform uplink transmissions or adjust the timing or power of communications. Therefore, due to the variability of instructions, the bit length of the information in the DCI is also variable. Another factor is the signal-to-noise ratio (SNR). The SNR of the signal received at the terminal can be low or high. When the SNR of the received signal at the terminal is high, the network requires fewer resources (e.g., a smaller CCE aggregation level) to transmit the DCI. When the SNR is low, the network requires more resources (e.g., a larger CCE aggregation level) to transmit the DCI.

[0016] Typically, the range of resources used for network transmission of DCI is large. Although the terminal usually knows this DCI range, it does not know whether the network is transmitting DCI, in which specific resource the DCI is transmitted, or the DCI format (if any) the network is using. As mentioned above, a common approach is to lock the reception of the CCE and linearly search for CCEs of all possible sizes. In other words, the terminal device searches one by one until it finds a CCE with DCI. This linear search leads to several problems. Some of these problems are described below.

[0017] First, linear search techniques consume more power than necessary. Due to the nature of linear search, there are cases where a DCI is found in the first decoded hypothesis (with a certain combination of CCEs). However, there are also cases where a DCI is found in the last decoded hypothesis (with another combination of CCEs). It's conceivable that the inherent nature of linear search leads to the assumption that, on average, a DCI is found in a hypothesis between the first and last hypotheses. In any case, power consumption varies depending on where the DCI is found. Considering that power consumption is a major factor in terminal device design, the power consumption for finding the DCI must also be optimized.

[0018] Second, complementary to the power usage problem is the use of computational resources for finding the DCI. Terminal devices have limited capacity and computing power, therefore, they need to use these limited resources efficiently. In this case, the linear search for the DCI utilizes limited capacity that could be applied to other tasks. In other words, the terminal device may need to allocate computational power for finding the DCI regardless of whether the DCI is found in the first or last hypothesis, because the terminal device needs to allocate resources for the worst-case scenario. Therefore, the allocated computational resources can be used efficiently.

[0019] Third, linear search introduces latency, particularly DCI detection latency. This is especially problematic when the terminal device communicates on multiple carriers. High DCI detection latency leads to other consequences, such as large buffer sizes. Large buffer sizes, in turn, require more hardware space. Furthermore, DCI detection latency results in a poor user experience. For example, in autonomous vehicles, the car is expected to process information and orientation in near real-time. If DCI latency exists, the car may not be able to process information at the appropriate speed, potentially leading to emergencies. Therefore, linear search can cause problems in the design and application of terminal devices.

[0020] Therefore, at least one technique for more effectively detecting DCIs is introduced here. Specifically, the technique involves determining the probability of transmitting a DCI at a given aggregation level. This probability depends on the Packet Error Rate (PER). PER can depend on factors such as SNR and fading measurements (e.g., fading channel). In addition to PER, the probability can also depend on the receiving history of the terminal device. For example, the logic behind this technique can be based on the principle that distance from the base station affects PER, and thus the probability of transmitting a DCI at each aggregation level. If the terminal device is close to the base station, the SNR is likely to be high. Therefore, a DCI is more likely to be transmitted at a lower aggregation level. This is because, at a low SNR value, the base station can transmit the DCI without having to transmit multiple copies to accommodate noise. Similar logic can be applied to fading measurements. Fading measurements are affected by factors such as Doppler shift and frequency selectivity. For example, if a PDCCH is transmitted at a high error rate due to Doppler shift, the terminal can determine that it is more likely to transmit a DCI at a higher aggregation level. Finally, past receiving history can help determine the probability that the base station will transmit a DCI at a particular aggregation level. For example, reception history can indicate that most DCIs were previously transmitted at aggregation level 4. Therefore, a terminal can determine that a CCE with aggregation level 4 is more likely to carry a DCI than other CCEs. In this way, the technology addresses the aforementioned and other issues by efficiently determining the location of the DCI.

[0021] In the following description, examples of terminal devices (e.g., mobile devices) and receivers are used for illustrative purposes only to explain various aspects of the technology. For example, a cellular phone can utilize the technology for locating DCI. However, it should be noted that the technology disclosed herein is not limited to applicability to terminal devices, receivers, or any other particular type of device. Other devices, such as electronic devices or systems (e.g., base stations), can be adapted to these technologies in a similar manner.

[0022] Furthermore, references are made to the downlink direction and DCI. These references are for illustrative purposes only. Therefore, it should be noted that the techniques described herein can be applied to other directions (e.g., uplink) and are used to locate additional information within the signal.

[0023] Overview of Control Channel Decoding

[0024] Figure 1A high-level block diagram 100 of the receiver components is shown. Figure 100 includes a log-likelihood ratio (LLR) buffer 102, a de-rate matcher 104, a decoder 106, an error detector 108, a blind decoding controller 110, and an aggregation level predictor 112. The outputs from these components and the techniques applied between these components result in a DCI output to another component of the receiver (e.g., a terminal device).

[0025] In some embodiments, the LLR buffer 102 can be programmed to store LLRs. For example, the LLR buffer 102 can store the LLR value for each aggregation level. Alternatively or additionally, the LLR buffer 102 can receive data from the processor (…). Figure 1 Input (not shown in the image).

[0026] The rate matching de-ratio matching unit 104 receives input from the LLR buffer 102 based on the location of the CCEs. Additionally, the rate matching de-ratio matching unit 104 receives input from the blind decoding controller 110 (described below). Typically, the rate matching de-ratio matching unit 104 may receive instructions from the blind decoding controller 110 regarding which CCEs to decode. Based on these instructions, the rate matching de-ratio matching unit 104 can extract the LLR information for the CCEs from the LLR buffer 102. For example, the blind decoding controller 110 may indicate that a given set of CCEs can be decoded. In response, the rate matching de-ratio matching unit 104 can extract the LLR of that CCE set.

[0027] Decoder 106 can decode CCE to retrieve DCI. Decoder 106 can receive input from derate rate matcher 104 and blind decoding controller 110. Depending on the technology used by the receiver, decoder 106 can be applied to various decoding techniques. For example, decoder 106 can use Viterbi decoding for Long Term Evolution (LTE) and polar coordinate decoding for New Radio (NR). In some embodiments, decoder 106 may include multiple sub-decoders, each applying a different decoding technique. Decoder 106 may also include a decision module configured to stream CCE to the appropriate sub-decoder. For example, decoder 106 may include a sub-decoder applying only Viterbi technology and another sub-decoder applying only polar coordinate decoding technology. The decision module can then, based on the technology, direct the data to the appropriate sub-decoder.

[0028] Decoder 106 can output to error detector 108. Error detector 108 can determine whether a decoded DCI is a valid DCI. To do this, error detector 108 can perform error detection on each result from decoder 106. Error detector 108 can apply known error checks, such as cyclic redundancy check (CRC), decoding metric check, and / or DCI field validity check. If error detector 108 determines that a decoded DCI does indeed contain an error, the decoded DCI is discarded. Upon discarding, error detector 108 can indicate to blind decoding controller 120 that further decoding is required. In some cases, error detector 108 can move to the next decoded DCI. If the decoded DCI is valid, error detector 108 can output the DCI for reception by another component of the receiver. In some embodiments, a valid DCI can also be output to blind decoding controller 110, as described below.

[0029] The blind decoding controller 110 can determine the order in which DCI hypotheses (with a specific combination of CCEs) are decoded. In some embodiments, the blind decoding controller 110 can receive information about valid DCIs from the error detector 108. The blind decoding controller 110 can use this information to skip other hypotheses with CCEs that overlap with the decoded DCIs to avoid some overhead. For example, the error detector 108 can inform the blind decoding controller 110 that a DCI from a given CCE is valid. Subsequently, the blind decoding controller 110 can determine that another hypothesis overlaps with the detected DCI. Based on this determination, the blind decoding controller 110 can instruct the decoder 106 to skip decoding the other hypothesis.

[0030] Furthermore, the blind decoding controller 110 can receive input from the aggregation level predictor 112. The aggregation level predictor 112 can perform techniques for determining the probability of each aggregation level of the DCI to be detected. Based on this probability, the aggregation level predictor 112 can instruct the blind decoding controller 110 to provide instructions to the de-rate matcher 104 and the decoder 106 to decode the CCE based on this probability. For example, the decoding can be performed in descending order of probability. The CCE with the highest probability corresponding to the aggregation level with the DCI can be decoded first. Subsequently, the CCE with the second highest probability corresponding to the aggregation level can be decoded. The decoding can be performed in this descending order until the DCI is found.

[0031] The probability of a DCI being transmitted at the aggregation level can depend on the PER (Percentage Error Rate). PER can be affected by factors such as SNR (Sum of Noise) and / or fading measurements. In addition to PER, this probability is also influenced by reception history. Determining the probability based on SNR can rely on the principle that PER and SNR are inversely proportional. A transmitter (e.g., a base station) can know the terminal's SNR from the terminal's channel state information feedback. When the terminal's SNR is low, the transmitter (e.g., the base station) transmits multiple copies of the DCI. The transmitter does this to better ensure that the receiver can piece together the entire signal, even if some packets are lost during transmission or some packets contain incorrect bits.

[0032] Furthermore, the SNR is typically high when the transmitter and receiver are close. This is because the likelihood of noise interference is lower when the transmission distance is shorter. Thus, based on the SNR, the receiver can determine whether the PER is high or low. Subsequently, based on the PER, the receiver can also determine whether multiple copies of the DCI may have been transmitted.

[0033] For example, if the transmitter must transmit multiple copies, it can use a higher aggregation level. As mentioned above, because each CCE contains 72 resource elements, the transmitter can increase the aggregation level when transmitting multiple copies (e.g., more symbols). For example, the base station can be far enough from the receiver that it can determine to transmit three copies of a 90-bit DCI information. Therefore, the transmission includes 270 bits of DCI information. This transmission can be performed using Quadrature Phase Shift Keying (QPSK), which allows each resource element to include two bits. Therefore, for 270 bits, based on the capacity of CCEs on QPSK, at least 135 resource elements are required, meaning at least two CCEs are needed to transmit the three copies of this DCI. Furthermore, because at least two CCEs are required, the aggregation level is at least 2.

[0034] On the receiver side, the receiver may not be aware of the details mentioned above. However, the receiver can determine, for example, based on the distance to the base station, that the SNR may be low, which indicates that the PER may be high. Furthermore, the receiver may include the ability to measure the SNR of a given signal, rather than deriving the SNR from other values ​​(e.g., distance to the base station). For example, during reception, the receiver may measure the SNR of the signal and information from the aggregation level predictor 112.

[0035] Based on the determination and / or SNR measurement, the aggregation level predictor 112 can predict the probability of each aggregation level carrying a DCI. In the example above, the aggregation level predictor 112 can determine that aggregation level 4 has the highest probability, and aggregation levels 8, 2, and 1 sequentially begin the next highest probable candidate. The aggregation level predictor 112 can instruct the blind decoding controller 110 to first decode the CCE with aggregation level 4, then decode the CCE with aggregation level 8, then decode the CCE with aggregation level 2, and finally decode the CCE with aggregation level 1.

[0036] For example, the probability of carrying DCI at the aggregation level based on PER can be calculated as follows. The target PER can be represented as: per 目标 The SNR-to-PER relationship at each aggregation level can be based on curve p. al (SNR) is used to determine this. Once the receiver measures the SNR value, it maps the measured SNR value onto a curve to determine the possible aggregation level of the signal corresponding to the measured SNR value. This possible aggregation level can be represented as per al =p al (snr). In this way, the aggregation level with the highest probability can be determined as Furthermore, by descending the probability, the aggregation levels can be sorted as al1, al1*2, ..., maxAL, al1 / 2, ..., 1.

[0037] Another factor in determining the probability can be fading measurements. Fading measurements are affected by factors such as Doppler shift or delay spread. Similar to SNR, the relationship between PER and fading measurements helps determine the probability of carrying DCI at each aggregation level. For example, PER may increase due to the occurrence of Doppler shift. Therefore, aggregation level predictor 112 can determine higher probabilities for higher aggregation levels.

[0038] In some embodiments, the aggregation-level predictor 112 can use both fading measurements and SNR measurements to determine the probability. For example, the aggregation-level predictor 112 can determine that the fading measurement can shift the SNR by an increment Δ. Taking the increment into account, an SNR-PER curve p can be used. al (snr) maps the measured SNR to al per al =p al (snr-Δ). Subsequently, the aggregation level with the highest probability was determined as Furthermore, the aggregation levels can be sorted based on probability as al1, al1*2, ..., maxAL, al1 / 2, ..., 1.

[0039] Another factor that can help determine the probability is the receiver's reception history. Reception history can be based on communication between the receiver and a specific transmitter, on all communication the receiver has received, on the distance between the receiver and transmitter, or other such categories. In any case, the general principle is that the aggregation level predictor 112 can determine the probability based on the terminal device's previous reception history. For example, if the receiver has previously received 60% of the received DCI at aggregation level 4, 20% at aggregation level 2, and another 20% at aggregation level 8, then the probability can reflect that reception history. In another example, the aggregation level predictor 112 can determine the current at which the signal is received between the receiver and transmitter, where the receiver previously received DCI most of the time at aggregation level 4. Therefore, the probability can reflect that reception history.

[0040] In some embodiments, reception history can be categorized based on SNR. For example, the SNR range can be divided into M+1 intervals (bins), such as: (-∞, SNR1], (SNR1, SNR2], ..., (SNR... M-1 SNR M ],(SNR M-1 The signal's SNR can be measured as "snr". The "snr" value can then be placed within the correct interval, where SNR... m-1 <snr≤SNR m Based on this placement, the aggregation level predictor 112 can determine the probability.

[0041] In some embodiments, after the “SNR” is placed in the interval, the aggregation level predictor 112 can determine whether the number of valid DCIs collected with an SNR within the range of the interval exceeds a predetermined threshold. If the number of valid DCIs does exceed the predetermined threshold, the aggregation level predictor 112 can determine a probability based on reception history. If the number of valid DCIs is below the predetermined threshold, the aggregation level predictor 112 can apply one of the other techniques mentioned above (e.g., fading measurement).

[0042] In this way, the receiver can decode the CCE in order based on the probability that the DCI is transmitted at a given aggregation level. This probability can be based on PER, which is affected by factors such as SNR, fading measurements, and reception history.

[0043] Example of sorting CCE

[0044] Figure 2AExample 200 of CCEs received over a period of time is shown. Example 200 includes CCE groups 206, 208, 210, 212, 214, and 216 using corresponding aggregation levels (ALs). In Example 200, the receiver may first receive the AL2 hypothesis with CCE 7 and CCE 8, then receive the AL2 hypothesis with CCE 5 and CCE 6, then receive the AL2 hypothesis with CCE 3 and CCE 4, then receive the AL2 hypothesis with CCE 1 and CCE 2, then receive the AL4 hypothesis with CCE 9-CCE 12, then receive the AL4 hypothesis with CCE 1-CCE 4, and so on, until all ALs and corresponding CCEs have been received. In some embodiments, such as in 206, CCEs may be used for different hypotheses with different ALs. For example, the receiver can receive AL 2 hypotheses with CCE 1-CCE 4, such as 210 and 212, and can also receive AL 4 hypotheses with CCE 1-CCE 4, such as 206.

[0045] The reception time can be T0 and T N between. Figures 2A-2C This is merely an example illustrating the technology described in this application. Specifically, AL can indicate the number of CCEs assigned to each PDCCH. For example, AL 4 indicates that the number of CCEs assigned to a set of PDCCHs is 4, as shown below. Figure 2A As shown in Table 1 below, the relationship between the number of AL and CCE is as follows:

[0046] Table 1

[0047] 1 1 2 2 4 4 8 8 16 16

[0048] Figure 2B An example of the order 202 in which the CCE received in Example 200 is decoded is shown. In order 202, the system (e.g., aggregation level predictor 112) may have determined the probability for each aggregation level and sorted the aggregation levels in descending order. The system can then instruct decoder 218 to decode the CCE based on this aggregation level order. Figure 2B In this context, AL2 is determined to have a higher probability of carrying DCI than AL4. Therefore, decoder 218 first decodes the CCE using AL2.

[0049] exist Figure 2BIn this context, 210, 212, 214, and 216 use AL2. When multiple CCEs use the same aggregation level, decoder 218 and / or other systems (e.g., aggregation level predictor 112) can determine that the multiple CCEs can be decoded in the order they were received. Alternatively or additionally, the system can perform additional checks on the CCEs using the same aggregation level. For example, the system can determine the fading channel for 214 and 216. Based on the fading channel, the order in which they are decoded can be determined. For example, in... Figure 2B In this case, CCE 216 may have already been received first, and therefore can be decoded before CCE 214.

[0050] After using CCEs 210, 212, 214, and 216 with AL2, the next AL with the highest probability is AL4. Figure 2B In this example, CCE9-CCE12 and CCE1-CCE4 (208 and 206 respectively) use AL4. Therefore, the logic described above can be applied to determine which CCE can be decoded first.

[0051] In some embodiments, once the DCI is found, the remaining CCEs may not need to be decoded. For example, in Figure 2B If a DCI is found in CCE 216, the remaining CCEs may not need to be decoded. In some embodiments, the determination to continue decoding may depend on the aggregation level. For example, in AL 2, the system may know that the DCI is segmented on at least two CCEs. Therefore, multiple CCEs using AL2 can be decoded. Figure 2B For example, if a DCI is found in CCE 216, the system can continue decoding the CCE using AL2 until all hypotheses have been decoded and obtained.

[0052] Figure 2C Another example 204 shows the order of decoding CCEs. Figure 2C In this context, AL4 has a higher probability of carrying a DCI than AL2. Therefore, decoder 218 decodes the CCE based on this probability. Similarly, when multiple CCEs use the same aggregation level, the system can determine, for example, which CCE is received first. Based on this determination, CCEs using the same aggregation level can be decoded.

[0053] Method Example

[0054] Figure 3A flowchart of a method 300 for decoding a CCE is shown. Method 300 includes blocks 302, 304, and 306. Additionally, method 300 may optionally include blocks 308 and 310. Method 300 can be applied to a terminal device such as a mobile device (e.g., an iPhone) operating in NR or LTE technologies. The terminal device may include a receiver for receiving signals from network nodes such as base stations. The terminal device may also include a processor for performing at least some of the techniques described herein. Furthermore, at least some of the techniques described herein can be applied when signals are received in the downlink plane of a telecommunications system such as a PDCCH. In some embodiments, method 300 can be applied to a network node such as a base station. In this case, method 300 includes having a receiver of the PDCCH determine probabilities and decoding in an order indicated by the determined probabilities.

[0055] In box 302, the receiver (e.g., a terminal device) can receive a PDCCH signal comprising multiple sets of CCEs. Each set of CCEs can be associated with one of multiple aggregation levels. The aggregation level can be the number of CCEs used to transmit the DCI. In box 304, the receiver can determine the probability of a DCI occurring at each aggregation level based on PER. PER can be based on either SNR and / or fading measurements. Typically, PER and SNR are negatively correlated, while PER and fading measurements can be directly correlated. Fading measurements can be based on channel state information, such as antenna correlation, Doppler shift, and other such measurements. In addition to PER, the probability can be based on the terminal's reception history and / or the transmissions of network nodes. The reception history can indicate, for example, the aggregation level of previously received DCIs.

[0056] Based on the determined probabilities, in box 306, the terminal can decode multiple sets of CCEs sequentially. This order can reflect the determined probability of a DCI occurring at each aggregation level. For example, decoding can proceed from the aggregation level with the highest probability to the aggregation level with the lowest probability. In some cases, there may be more than one CCE with the same aggregation level. In this case, the terminal can decode multiple CCEs in the order they are received. In some cases, when the receiver determines that the probability of one or more aggregation levels is zero, the receiver can skip decoding that aggregation level / these aggregation levels.

[0057] Furthermore, based on this technology, decoding can include applying different decoding schemes. For example, if the technology is LTE, the terminal can apply the Viterbi decoding algorithm. Optionally, in NR, the terminal can apply the Polar decoding algorithm. In either case, after decoding the CCE, the terminal can determine in box 308 whether the DCI is valid. To do this, the terminal can run an error check, such as CRC. If the DCI is invalid, then in box 310, based on the error check, the terminal can discard the DCI and continue decoding other CCEs with different aggregation levels or the same CCE.

[0058] Example of a computing system

[0059] Figure 4 A block diagram illustrating an example form of a computer system operable to perform aspects of the disclosed techniques is shown. For example, processing system 400 may be an example implementation of a network node or terminal device capable of implementing the techniques described above. At least a portion of processing system 400 may be included in an electronic device (e.g., a computer server) supporting one or more CPNs and / or one or more UPNs. Processing system 400 may include one or more processors 402, main memory 406, non-volatile memory 410, network adapter 412 (e.g., a network interface), display 418, input / output device 420, control device 422 (e.g., a keyboard and pointing device), drive unit 424 including storage medium 426, and signal generation device 430 communicatively connected to bus 416. Bus 416 represents any one or more individual physical buses, point-to-point connections, or any combination thereof connected by appropriate bridges, adapters, or controllers. Therefore, bus 416 can include, for example, a system bus, a Peripheral Component Interconnect (PCI) bus or PCI-Express bus, a HyperTransport or Industry Standard Architecture (ISA) bus, a Small Computer System Interface (SCSI) bus, any version of the Universal Serial Bus (USB), an IIC (I2C) bus, or the Institute of Electrical and Electronics Engineers (IEEE) standard 1394 bus, also known as "FireWire". The bus can also be responsible for relaying data packets between components of network service tools such as switching engines, network ports, and tool ports (e.g., via full-duplex or half-duplex lines).

[0060] In various embodiments, the processing system 400 operates as a standalone device, although it may be connected (e.g., wired or wirelessly) to other devices. For example, the processing system 400 may include a terminal directly coupled to a network application computer. As another example, the processing system 400 may be wirelessly coupled to a network service tool.

[0061] In various embodiments, the processing system 400 may be a server computer, client computer, personal computer (PC), user equipment, tablet PC, laptop computer, personal digital assistant (PDA), cellular phone, iPhone, iPad, Blackberry, processor, telephone, network service tool, network router, switch or bridge, console, handheld console, (handheld) gaming device, music player, any portable, mobile, handheld device, or any machine capable of executing a set of instructions (sequence or otherwise) specifying actions to be performed by the processing system 400.

[0062] Although main memory 406, non-volatile memory 410, and storage medium 426 (also referred to as "machine-readable medium") are shown as a single medium, the terms "machine-readable medium" and "storage medium" should be understood to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) storing one or more sets of instructions 428. The terms "machine-readable medium" and "storage medium" should also be understood to include any medium capable of storing, encoding, or carrying a set of instructions for execution by processing system 400, and causing processing system 400 to perform any or more of the methods of the currently disclosed embodiments.

[0063] Typically, routines executed to implement the techniques disclosed above can be implemented as part of an operating system or application, component, program, object, module, or sequence of instructions (collectively, a "computer program"). A computer program typically includes one or more instructions (e.g., instructions 404, 408, and 428) that are stored in different memories and storage devices in the computer at different times, and when the instructions are read and executed by one or more processing units or processors 402, cause the processing system 400 to operate to perform elements relating to the various aspects of the above disclosure.

[0064] Furthermore, although embodiments have been described in the context of full-featured computers, computer systems and / or other devices, those skilled in the art will understand that various embodiments can be distributed as program products in various forms, and this disclosure applies equally to any particular type of machine or computer-readable medium used for the actual implementation of the distribution.

[0065] Further examples of machine-readable storage media, machine-readable media, or computer-readable (storage) media include recordable media, such as volatile and non-volatile memory devices 410, floppy disks and other removable disks, hard disk drives, optical disks (e.g., compact disk read-only memory (CD ROM), digital versatile optical disks (DVD)), and transmission media, such as digital communication links and analog communication links.

[0066] Network adapter 412 enables processing system 400 to transmit data within network 414 with external entities (e.g., network service tools) using any known and / or convenient communication protocols supported by processing system 400 and external entities. Network adapter 412 may include one or more of the following: network adapter card, wireless network interface card, router, access point, wireless router, switch, multilayer switch, protocol converter, gateway, bridge, bridge router, hub, digital media receiver, and / or repeater.

[0067] Network adapter 412 may include a firewall, which in some embodiments may govern and / or manage permissions to access / proximize data in a computer network and track different trust levels between different machines and / or applications. The firewall may be any number of modules having any combination of hardware and / or software components capable of enforcing a predetermined set of access permissions between a specific group of machines and applications, between machines, and / or between applications, for example, to regulate traffic and resource sharing between these varying entities. The firewall may also additionally manage and / or access access control lists that detail permissions, including, for example, access and manipulation permissions for objects by individuals, machines, and / or applications, and describe the context in which the permissions are situated.

[0068] Other network security functions can be implemented or included in the firewall's functionality, including intrusion prevention, intrusion detection, next-generation firewalls, personal firewalls, etc.

[0069] As described above, the techniques described herein are implemented by, for example, programmable circuits (e.g., one or more microprocessors), programmed with software and / or firmware, implemented entirely as dedicated hardwired (i.e., non-programmable) circuits, or a combination thereof. The dedicated circuits can take the form of, for example, one or more application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc.

[0070] It should be noted that, unless otherwise specified, or if the above embodiments may be mutually exclusive in function and / or structure, any of the embodiments described above can be combined with another embodiment.

[0071] in conclusion

[0072] The embodiments described herein illustrate the necessary information to enable those skilled in the art to practice the embodiments and demonstrate the best mode for practicing the embodiments. Upon reading the specification with reference to the accompanying drawings, those skilled in the art will understand the concepts of this disclosure and will recognize that the application of these concepts is not particularly addressed herein. These concepts and applications fall within the scope of this disclosure and the appended claims.

[0073] The above description and accompanying drawings are illustrative and should not be construed as limiting. Many specific details have been described to provide a thorough understanding of this disclosure. However, in some cases, well-known details have not been described to avoid obscuring the description. Furthermore, various modifications may be made without departing from the scope of the embodiments.

[0074] As used herein, unless otherwise specified, terms such as “processing,” “computing,” “operation,” “determining,” and “generating” refer to the actions and processes of a computer or similar electronic computing device that process and convert data represented as physical (electronic) quantities in computer memory or registers into other data represented as physical quantities in computer memory, registers, or other such storage media, transmission, or display devices.

[0075] The reference to "an embodiment" or "embodiment" in this document means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of this disclosure. The phrase "in an embodiment" appearing in different places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. Furthermore, various features that may be shown by some embodiments but not by others are described. Similarly, various requirements are described, which may be requirements of some embodiments but not of others.

[0076] The terms used in this specification generally have their common meaning in the art, in the context of this disclosure, and in the specific context in which each term is used. Certain terms used to describe this disclosure are discussed above or elsewhere in the specification to provide additional guidance to practitioners regarding the description of this disclosure. For convenience, certain terms are highlighted, for example, using italics and / or quotation marks. The use of highlighting does not affect the scope and meaning of the terms; in the same context, a term has the same scope and meaning regardless of whether it is highlighted. It is understood that the same thing can be expressed in more than one way.

[0077] Therefore, alternative languages ​​and synonyms may be used for any one or more terms discussed herein, and have no particular significance in relation to whether a term is elaborated or discussed herein. Synonyms for certain terms are provided. Listing one or more synonyms does not preclude the use of other synonyms. Examples used anywhere in this specification, including examples of any terms discussed herein, are merely illustrative and are not intended to further limit the scope and meaning of this disclosure or any exemplary terms. Similarly, this disclosure is not limited to the various embodiments given in this specification.

[0078] Without intending to further limit the scope of this disclosure, examples of instruments, apparatus, methods, and related results according to embodiments of this disclosure have been given above. It should be noted that headings or subheadings have been used in the examples for the reader's convenience, but these should in no way limit the scope of this disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the event of any conflict, this document, including the definitions, shall prevail.

[0079] In summary, it should be understood that specific embodiments of the invention have been described herein for illustrative purposes, but various modifications may be made without departing from the scope of the invention. Therefore, the invention is not limited to what is defined in the appended claims.

Claims

1. A method for reducing latency, comprising: At the terminal, a physical downlink control channel (PDCCH) signal is received, the PDCCH signal comprising multiple sets of control channel elements (CCEs), wherein each set of CCEs is associated with one of multiple aggregation levels; The terminal determines the probability of the presence of downlink control information (DCI) at each of the plurality of aggregation levels based on the packet error rate (PER); and The terminal decodes the multiple sets of CCEs sequentially, wherein the order is based on the probability of the DCI being present at each aggregation level, and the order is from the highest probability to the lowest probability. The aggregation level with the highest probability was determined as follows: Furthermore, the aggregation levels are sorted by probability descending order. ,in, For the target PER, The PER for each corresponding aggregation level among the plurality of aggregation levels.

2. The method according to claim 1, wherein, The PER is based on any one of the following: the signal-to-noise ratio (SNR) of the PDCCH signal, the fading measurement of the PDCCH signal, and any combination thereof.

3. The method according to claim 2, wherein, The PER and the SNR are negatively correlated, and the PER is directly correlated with the fading measurement.

4. The method according to claim 2, wherein, The fading measurement is based on any one of the following: delay caused by Doppler shift, frequency selectivity, and any combination thereof.

5. The method according to any one of claims 1 to 4, wherein, One or more CCEs are associated with the same aggregation level, and the method further includes: The one or more CCEs are decoded based on the order in which the terminal receives them.

6. The method according to any one of claims 1 to 4, wherein, The aggregation level indicates the number of CCEs used to send the DCI.

7. The method according to any one of claims 1 to 4, wherein, The terminal operates in Long Term Evolution (LTE) technology, and decoding the plurality of CCEs further includes: The Viterbi decoding algorithm is applied.

8. The method according to any one of claims 1 to 4, wherein, The terminal operates in New Radio (NR) technology, and decoding the plurality of CCEs further includes: Apply polar coordinate decoding algorithm.

9. The method according to any one of claims 1 to 4, further comprising: In response to decoding a given CCE including the DCI, the validity of the DCI is determined based on the result of an error check.

10. The method according to claim 9, wherein, Performing the error check includes: Perform a cyclic redundancy check (CRC) to detect changes in the DCI.

11. The method of claim 9, further comprising: In response to determining that the DCI is invalid, the DCI is discarded and the remaining CCEs among the plurality of CCEs are decoded, or a given CCE with a different aggregation level is decoded.

12. A system for reducing latency, comprising: A receiver, through which it receives a Physical Downlink Control Channel (PDCCH) signal, the PDCCH signal comprising multiple sets of Control Channel Elements (CCEs), wherein each CCE is associated with one of multiple aggregation levels; and The processor is configured as follows: The probability of downlink control information (DCI) being present at each of the multiple aggregation levels is determined based on the packet error rate (PER); and The multiple sets of CCEs are decoded sequentially, wherein the order is based on the probability of the DCI being present at each aggregation level, and the order is from the highest probability to the lowest probability. The aggregation level with the highest probability was determined as follows: Furthermore, the aggregation levels are sorted by probability descending order. ,in, For the target PER, The PER for each corresponding aggregation level among the plurality of aggregation levels.

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