TW202415034A - Adaptive modulation and coding method and network device - Google Patents

Abstract

The disclosure provides an adaptive modulation and coding method and a network device. The method includes: obtaining a channel quality measurement value, a transmission error rate and a historical channel quality correction value of a transmission channel between the network device and a communication device; calculating a channel quality prediction value based on the transmission error rate and the historical channel quality correction value; calculating a gain factor of a prediction model based on a dispersion degree of the channel quality measurement value; generating a channel quality correction value based on the channel quality prediction value, the gain factor and the channel quality measurement value; and determining a modulation coding mechanism used by the network device and the communication device for transmission based on the channel quality correction value.

Description

Adaptive modulation and coding method and network device

The present disclosure relates to a communication mechanism, and more particularly to an adaptive modulation and coding method and a network device.

In modern society, wireless communication devices such as mobile phones have become an indispensable part of people's lives. However, since there are often unpredictable problems in the wireless environment, how to maintain stable transmission quality during wireless transmission has always been an important research issue.

In all wireless communication systems, whether static or dynamic user equipment, they often face the problem of unstable communication quality. For example, wireless communication quality may be affected by external environmental interference (such as random channel attenuation, interference between base stations or user equipment, etc.) or self-transceiver noise, thus bringing many challenges.

In the communication model, signal to noise ratio (SNR), channel quality indicator (CQI) and modulation scheme are the factors that most directly affect communication quality. Moreover, as the communication service requirements for data quality become higher and higher, link adaptation technology can improve spectrum efficiency and transmission rate without increasing system bandwidth. Therefore, it is crucial to establish the logical relationship between these factors.

In view of this, the present disclosure provides an adaptive modulation and encoding method and a network device, which can be used to solve the above technical problems.

The present disclosure provides an adaptive modulation and coding method, which is suitable for a network device, including: obtaining a channel quality measurement value, a transmission error rate and a historical channel quality correction value of a transmission channel between the network device and a communication device; calculating a channel quality prediction value based on the transmission error rate and the historical channel quality correction value; calculating a gain factor of a prediction model based on the dispersion of the channel quality measurement value; generating a channel quality correction value based on the channel quality prediction value, the gain factor and the channel quality measurement value; and determining a modulation and coding mechanism used for transmission between the network device and the communication device based on the channel quality correction value.

The embodiment of the present disclosure provides a network device, including a storage circuit and a processor. The storage circuit stores a program code. The processor is coupled to the storage circuit and accesses the program code to execute: obtaining a channel quality measurement value, a transmission error rate, and a historical channel quality correction value of a transmission channel between the network device and the communication device; calculating a channel quality prediction value based on the transmission error rate and the historical channel quality correction value; calculating a gain factor of a prediction model based on the dispersion of the channel quality measurement value; generating a channel quality correction value based on the channel quality prediction value, the gain factor, and the channel quality measurement value; and determining a modulation coding mechanism used for transmission between the network device and the communication device based on the channel quality correction value.

Therefore, the present disclosure can obtain a more suitable modulation coding scheme, thereby achieving better transmission performance.

Please refer to FIG. 1 , which is a schematic diagram of a network device according to one embodiment of the present disclosure. In different embodiments, the network device 100 can be applied to various communication systems, such as: Global System of Mobile communication (GSM) system, Code Division Multiple Access (CDMA) system, General Packet Radio Service (GPRS), LTE system, Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) communication system or 5G system, etc. In some embodiments, the 5G system or 5G network can also be called a new radio (NR) system or NR network.

In some embodiments, the network device 100 may be a base transceiver station (BTS) in a GSM system or a CDMA system, a base station (NodeB, NB) in a WCDMA system, an evolutionary Node B (eNB) in an LTE system, or a wireless controller in a cloud radio access network (CRAN). Alternatively, the network device 100 may be a mobile switching center, a relay station, an access point, a vehicle-mounted device, a wearable device, a hub, a switch, a bridge, a router, a network-side device in a 5G network, or a network device 100 in a future-evolved public land mobile network (PLMN), etc.

In some embodiments, the network device 100 may also be a gNB in a 5G system and may be used as a distributed unit (DU) and/or a radio unit (RU), but is not limited thereto.

In FIG1 , the network device 100 includes a storage circuit 102 and a processor 104. The storage circuit 102 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk or other similar devices or a combination of these devices, and can be used to record multiple program codes or modules.

The processor 104 is coupled to the storage circuit 102 and can be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), any other type of integrated circuit, a state machine, an Advanced RISC Machine (ARM) based processor, and the like.

In some embodiments, the communication system to which the network device 100 belongs may also include other communication devices. In different embodiments, the communication device may refer to an access terminal, a user equipment (UE), a user unit, a user station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent or a user device. The access terminal may be a cellular phone, a wireless phone, a Session Initiation Protocol (SIP) phone, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a 5G network or a terminal device in a future evolved PLMN, etc.

For the sake of explanation, it is assumed below that the network device 100 and the communication device it corresponds to/serves are both operated in a 5G system. In this case, the network device 100 can be understood as a gNB, but the present disclosure is not limited thereto.

In the embodiment of the present disclosure, the processor 104 can access the modules and program codes recorded in the storage circuit 102 to implement the adaptive modulation and encoding method proposed in the present disclosure, the details of which are described as follows.

Please refer to FIG. 2, which is a flowchart of an adaptive modulation and coding method according to an embodiment of the present disclosure. The method of this embodiment can be executed by the network device 100 of FIG. 1. The following is a description of the details of each step of FIG. 2 in conjunction with the components shown in FIG. 1.

In step S210, the processor 104 obtains a channel quality measurement value, a transmission error rate, and a historical channel quality correction value of the transmission channel between the network device 100 and the communication device.

In one embodiment, the processor 104 obtains the kth transmission verification result of the transmission channel between the network device 100 and the communication device to represent the transmission error rate, wherein k is an index value.

In the embodiment of the present disclosure, there may be a transmission channel between the network device 100 and the communication device, and the channel quality measurement value of the transmission channel may be represented as SNR or CQI according to the state of the transmission channel. In one embodiment, if the transmission channel under consideration is an uplink, the processor 104 may represent the channel quality measurement value as SNR. In another embodiment, if the transmission channel under consideration is a downlink, the processor 104 may represent the channel quality measurement value as CQI, but is not limited thereto.

In the embodiment disclosed herein, different k values correspond to different discrete time points. For example, the kth transmission verification result (hereinafter referred to as denoted by ) can be understood as the transmission verification result at the kth time point. Similarly, the kth channel quality prediction value (hereinafter referred to as ), the kth gain factor (hereinafter referred to as ), the kth channel quality measurement value (hereinafter referred to as ) and the k-th channel quality correction value (hereinafter referred to as The terms such as ) can be understood as the channel quality prediction value, gain factor, channel quality measurement value and channel quality correction value at the kth time point respectively.

In different embodiments, the time change corresponding to the change in the k value may vary depending on the communication system under consideration and its related configuration parameters. For example, when the network device 100 is assumed to be a gNB in a 5G system, each increase of 1 in the k value may represent the passage of a transmission time interval (TTI). That is, the time difference between the k+1th time point and the kth time point is one TTI. For example, assuming that the subcarrier used in the 5G system under consideration is 30kHz, the time length of one TTI is, for example, 0.5ms. In this case, the time difference between the k+1th time point and the kth time point is 0.5ms, but is not limited to this.

For ease of explanation, the historical channel quality correction value considered in the following assumption is the channel quality correction value of the k-1th time point (i.e., the channel quality correction value of the previous time point (k-1th time point)), and can be expressed as ). In other embodiments, the processor 104 may also select any channel quality correction value before the kth time point as the historical channel quality correction value considered in step S210.

In one embodiment, the k-th transmission check result corresponds to a plurality of cyclic redundancy check (CRC) results from the k-1-th time point to the k-W-th time point, where W is a preset window length.

In different embodiments, the value of W can be selected as any positive integer value according to the needs of the designer. In one embodiment, it is assumed that the network device 100 operates in the TDD mode of the 5G system and the slot pattern used is DDDSU. This means that there are 4 downlink signals (corresponding to D) and 1 uplink signal (corresponding to U) in every 2.5ms.

Based on this, assuming that the considered W is 8 (which corresponds to 20ms, i.e., 2.5ms*8), the processor 104 may determine the kth transmission verification result based on the CRC results from the k-1th time point to the k-8th time point ( ).

For example, assuming that the CRC results from the k-1th time point to the k-8th time point are represented as [0, 0, 0, 0, 1, 0, 0, 0] (where 0 represents CRC failure and 1 represents CRC pass), the processor 104 can know that there are 7 failed CRCs and 1 successful CRC in the past 8 CRCs. In this case, the processor 104 can determine the kth transmission verification result ( ) is -7/8, but is not limited to this.

In step S220, the processor 104 calculates a channel quality prediction value based on the transmission error rate and the historical channel quality correction value.

In one embodiment, the processor 104 generates a channel quality prediction value associated with the channel quality based on the state transition characteristic, the control input characteristic, the transmission error rate, and the historical channel quality correction value.

In one embodiment, the channel quality prediction value is, for example, the kth channel quality prediction value ( ), and the processor 104 may be based on the state transfer characteristics, the control input characteristics, the kth transmission verification result ( ) and the historical channel quality correction value (e.g. ) generates the k-th channel quality prediction value ( ).

In one embodiment, the state transfer characteristics and the control input characteristics can be represented by a state transfer matrix A and a control input matrix B, respectively. In different embodiments, the contents of the state transfer matrix A and the control input matrix B can be adjusted according to the needs of the designer. In one embodiment, the contents of the state transfer matrix A and the control input matrix B can be set to fixed values.

In one embodiment, the k-th channel quality prediction value ( ) can be represented as: " (Formula 1), but is not limited to this.

In one embodiment, since the transmission channel may be affected by the ambient temperature and/or the machine temperature, the state transfer matrix A may accordingly include the relevant temperature prediction parameter (represented by w). In this case, the state transfer matrix A is, for example, , but is not limited to this.

In step S230, the processor 104 calculates a gain factor of the prediction model M1 based on the dispersion of the channel quality measurement values.

In different embodiments, the processor 104 may select an appropriate model as the prediction model M1 according to the needs of the designer. For ease of explanation, the following embodiments will use a Kalman filter as an example of the prediction model M1, but the present disclosure is not limited thereto.

In one embodiment, the processor 104 generates a model prediction error characteristic of the prediction model M1 based on the state transition characteristic, the state estimation characteristic, and the model measurement error characteristic.

In one embodiment, the state transfer characteristic can be represented by a state transfer matrix A. When the prediction model M1 is assumed to be a Kalman filter, the model prediction error characteristic is, for example, the kth model prediction error characteristic, and the kth model prediction error characteristic can be represented by the kth state estimation covariance matrix The state estimation characteristic is, for example, the k-1th state estimation characteristic, and the k-1th state estimation characteristic can be represented by a covariance matrix The model measurement error characteristics can be represented by a model measurement error matrix Q.

In one embodiment, the k-th model prediction error characteristic (e.g. ) can be represented as: " (Formula 2), but is not limited to this.

Thereafter, the processor 104 estimates the gain factor of the prediction model M1 based on the dispersion of the channel quality measurement value, the model prediction error of the prediction model M1, and the state observation characteristic.

In one embodiment, the dispersion of the channel quality measurement value is, for example, the standard deviation R of SNR or CQI (depending on whether the transmission channel is uplink or downlink). In addition, when the prediction model M1 is assumed to be a Kalman filter, the state observation characteristic is, for example, the state observation matrix H.

In one embodiment, the gain factor is, for example, the k-th gain factor, and the k-th gain factor can be represented as: " (Formula 3), but is not limited to this.

In step S240, the processor 104 generates a signal based on the channel quality prediction value (e.g. ), gain factors (e.g. ) and channel quality measures (e.g. ) to generate channel quality correction values (e.g. ).

In one embodiment, the k-th channel quality measurement value ( ) may be an actual channel quality measurement value of the transmission channel at the kth time point, but in other embodiments, the kth channel quality measurement value ( ) can also be represented as an average channel quality measurement value of the transmission channel, but is not limited to this.

In one embodiment, the processor 104 may, for example, calculate the kth gain factor based on the kth gain factor ( ), the kth channel quality measurement value ( ), state observation characteristics and the k-th channel quality prediction value ( ) determines the correction factor (expressed as C).

When the prediction model M1 is assumed to be a Kalman filter, the state observation characteristic is, for example, the state observation matrix H. Based on this, the correction factor can be represented as: “C= ", but it is not limited to this.

Afterwards, the processor 104 may convert the k-th channel quality prediction value ( ) is corrected to the k-th channel quality correction value ( ).

In one embodiment, the k-th channel quality correction value ( ) can be represented as: " (Formula 4), but is not limited to this.

In one embodiment, the processor 104 may update the state estimation characteristic based on the gain factor, the state observation characteristic, and the model prediction error characteristic. For example, the processor 104 may update the state estimation characteristic based on the kth gain factor ( ), state observation characteristics (e.g. H) and the k-th model prediction error characteristics (e.g. ) to estimate the k-th state estimation characteristics (e.g., the covariance matrix corresponding to the k-th time point ) as the updated state estimation feature. In one embodiment, the kth state estimation feature (e.g. ) can be represented as: " (Formula 5), where I is a unit matrix, but is not limited to this.

In step S250, the processor 104 determines the modulation and coding scheme used for transmission between the network device 100 and the communication device based on the channel quality correction value.

In one embodiment, the processor 104 calculates the kth channel quality correction value ( ) determines the modulation and coding scheme used for the transmission channel between the network device 100 and the communication device.

In the embodiment of the present disclosure, the k-th channel quality correction value ( ) is, for example, a certain SNR or CQI value (depending on whether the transmission channel is an uplink or a downlink), and the processor 104 may determine a corresponding modulation and coding scheme based on any known adaptive modulation and coding (AMC) literature.

Please refer to FIG. 3, which is a schematic diagram of an AMC scenario according to an embodiment of the present disclosure. In this embodiment, FIG. 3 is, for example, a reproduction of part of the content in "JC Ikuno, System level modeling and optimization of the LTE downlink. PhD thesis, E389, TU Wien (2013).". In one embodiment, if the transmission channel under consideration is the downlink between the network device 100 and the communication device, the obtained k-th channel quality correction value ( ) is, for example, a CQI value. In this case, the processor 104 may find the corresponding modulation and coding scheme in FIG3 based on the CQI value. For example, assuming that the k-th channel quality correction value ( ) corresponds to CQI 7, the processor 104 may select 16-quadrature amplitude modulation (QAM) as the corresponding modulation coding scheme. For another example, assuming that the k-th channel quality correction value ( ) corresponds to CQI 15, the processor 104 may, for example, select 64-QAM as the corresponding modulation coding scheme, but is not limited to this.

In one embodiment, if the transmission channel under consideration is an uplink between the network device 100 and the communication device, the obtained k-th channel quality correction value ( ) is, for example, a certain SNR value. In this case, the processor 104 can find the corresponding CQI value in FIG. 3 based on the SNR value, and then determine the modulation coding scheme accordingly. For example, assuming that the k-th channel quality correction value ( ) is 10dB, the processor 104 can know that the corresponding CQI value is CQI 10 based on the "SNR to CQI mapping" in FIG. 3, and accordingly selects 64-QAM as the corresponding modulation coding scheme. For another example, assuming that the kth channel quality correction value ( ) is 0dB, the processor 104 can know that the corresponding CQI value is CQI 5 based on the “SNR to CQI mapping” in FIG. 3, and accordingly select 4-QAM as the corresponding modulation coding scheme, but is not limited thereto.

In one embodiment, when the prediction model M1 is assumed to be a Kalman filter, the kth gain factor can be understood as a Kalman gain. When is 0, it means the prediction error is 0. In this case, will be exactly equal to , that is, fully trust the channel quality prediction value provided by the prediction model M1. On the other hand, when When is 1, it means the measurement error is 0. In this case, will be exactly equal to , that is, the channel quality measurement value of full trust in the channel quality.

In addition, it can be seen from equations 2 and 3 that when the R value is fixed, the larger the Q value, The larger the Q value is, the more trust we have in the channel quality measurement. If the Q value is infinite, then On the other hand, if the Q value is smaller or the R value is larger, The smaller the value, the more trust is placed on the channel quality prediction value provided by the prediction model M1. If the Q value is zero or the R value is infinite, then is 0.

From another point of view, the larger the R value, the greater the change in channel quality (SNR or CQI), that is, the communication environment between the network device 100 and the communication device is less stable. In this case, the obtained Therefore, if we directly use the knowledge method and directly based on To determine the corresponding modulation and coding scheme, it may reduce the transmission performance (for example, increase the BLER).

However, unlike direct channel quality measurements such as ) determines the modulation coding scheme. The method of the disclosed embodiment can be based on the model characteristics of the prediction model M1 and the communication environment between the network device 100 and the communication device, and the channel quality prediction value (for example ) and channel quality measures (e.g. ) are appropriately combined to obtain channel quality correction values (e.g. ). Due to channel quality correction values (e.g. ) can better reflect the actual communication environment, so the disclosed embodiment can obtain a more suitable modulation coding scheme, thereby achieving better transmission performance (such as a lower BLER).

Furthermore, since the method of the disclosed embodiment can select an appropriate modulation coding scheme at each time point under consideration, the transmission mode can be adjusted more flexibly in response to the current communication environment. For example, when the TTI is 0.5 ms, the method of the disclosed embodiment can determine an appropriate modulation coding scheme every 0.5 ms, thereby achieving the effect of flexibly adjusting the modulation coding scheme.

Please refer to FIG. 4, which is an application scenario diagram according to an embodiment of the present disclosure. In FIG. 4, it is assumed that the network device 100 is a gNB in a 5G system, but it is not limited to this. As shown in FIG. 4, the prediction model M1 may belong to the DU of the gNB, and in the prediction model M1, , and R, then the corresponding Afterwards, the network device 100 may be based on The corresponding MCS is determined and notified to the gNB RU through the medium access control (MAC) layer of Layer 1 (L1), which in turn controls the physical layer (PHY) of the RU Layer 1 to perform the corresponding transmission.

In other embodiments, the operation of the prediction model M1 may also consider other parameters, such as temperature prediction parameters, according to the needs of the designer, but is not limited thereto.

In summary, the method of the disclosed embodiment can appropriately combine the channel quality prediction value of the prediction model for the channel quality with the channel quality measurement value of the channel quality to obtain the channel quality correction value. Since the channel quality correction value can better reflect the actual communication environment, the disclosed embodiment can obtain a more suitable modulation coding scheme based on it, thereby achieving better transmission performance.

Although the present disclosure has been disclosed as above by way of embodiments, it is not intended to limit the present disclosure. Any person having ordinary knowledge in the relevant technical field may make some changes and modifications without departing from the spirit and scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the definition of the attached patent application scope.

100: Network device 102: Storage circuit 104: Processor S210~S250: Step :kth transmission verification result : kth channel quality measurement value : kth channel quality correction value R: standard deviation

FIG. 1 is a schematic diagram of a network device according to an embodiment of the present disclosure. FIG. 2 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present disclosure. FIG. 3 is a schematic diagram of an AMC scenario according to an embodiment of the present disclosure. FIG. 4 is an application scenario diagram according to an embodiment of the present disclosure.

S210~S250: Steps

Claims (18)

An adaptive modulation and coding method, suitable for a network device, includes: Obtaining a channel quality measurement value, a transmission error rate and a historical channel quality correction value of a transmission channel between the network device and a communication device; Calculating a channel quality prediction value based on the transmission error rate and the historical channel quality correction value; Calculating a gain factor of a prediction model based on a dispersion degree of the channel quality measurement value; Generating a channel quality correction value based on the channel quality prediction value, the gain factor and the channel quality measurement value; and Determining a modulation and coding mechanism used for transmission between the network device and the communication device based on the channel quality correction value. A method as described in claim 1, wherein the historical channel quality correction value is the channel quality correction value at a previous time point. The method as claimed in claim 1, wherein the transmission channel between the network device and the communication device is an uplink, and the channel quality is characterized by a signal-to-noise ratio. The method as claimed in claim 1, wherein the transmission channel between the network device and the communication device is a downlink, and the channel quality is characterized by a channel quality indicator. The method as described in claim 1 further includes: characterizing the transmission error rate based on a transmission verification result. As described in claim 1, the step of calculating the channel quality prediction value based on the transmission error rate and the historical channel quality correction value includes: Generating the channel quality prediction value based on a state transition characteristic, a control input characteristic, the transmission error rate and the historical channel quality correction value. The method as claimed in claim 1, wherein the step of calculating the gain factor of the prediction model based on the dispersion of the channel quality measurement value comprises: generating a model prediction error characteristic of the prediction model based on a state transition characteristic, a state estimation characteristic and a model measurement error characteristic; estimating the gain factor of the prediction model based on the dispersion of the channel quality measurement value, the model prediction error characteristic and a state observation characteristic. The method as described in claim 7 further includes: Updating the state estimation characteristic based on the gain factor, the state observation characteristic and the model prediction error characteristic. A method as described in claim 7, wherein the dispersion of the channel quality measurement value is characterized by a standard deviation of the channel quality measurement value. A network device comprises: a storage circuit storing a program code; and a processor coupled to the storage circuit and accessing the program code to execute: obtaining a channel quality measurement value, a transmission error rate and a historical channel quality correction value of a transmission channel between the network device and a communication device; calculating a channel quality prediction value based on the transmission error rate and the historical channel quality correction value; calculating a gain factor of a prediction model based on a dispersion degree of the channel quality measurement value; generating a channel quality correction value based on the channel quality prediction value, the gain factor and the channel quality measurement value; and determining a modulation coding mechanism used for transmission between the network device and the communication device based on the channel quality correction value. A network device as described in claim 10, wherein the historical channel quality correction value is the channel quality correction value at a previous time point. The network device as described in claim 10, wherein the transmission channel between the network device and the communication device is an uplink, and the channel quality is characterized by a signal-to-noise ratio. The network device as described in claim 10, wherein the transmission channel between the network device and the communication device is a downstream link, and the channel quality is characterized by a channel quality indicator. The network device of claim 10, wherein the processor is further configured to: characterize the transmission error rate based on a transmission verification result. A network device as described in claim 10, wherein the processor is configured to: Generate the channel quality prediction value based on a state transition characteristic, a control input characteristic, the transmission error rate and the historical channel quality correction value. A network device as described in claim 10, wherein the processor is configured to: generate a model prediction error characteristic of the prediction model based on a state transition characteristic, a state estimation characteristic and a model measurement error characteristic; estimate the gain factor of the prediction model based on the dispersion of the channel quality measurement value, the model prediction error of the prediction model and a state observation characteristic. In the network device of claim 16, the processor is further configured to: update the state estimation characteristic based on the gain factor, the state observation characteristic, and the model prediction error characteristic. A network device as described in claim 16, wherein the dispersion of the channel quality measurement value is characterized by a standard deviation of the channel quality measurement value.