Adaptive modulation coding method and device, and storage medium

Abstract

The embodiment of the invention provides an adaptive modulation and coding method and device, and a storage medium, and the method comprises the steps: obtaining the initial probability distribution of a signal to interference and noise ratio (SINR) of a wireless channel, carrying out the resource scheduling according to the initial probability distribution, and obtaining the HARQ feedback; and updating the initial probability distribution according to HARQ feedback to obtain real-time probability distribution so as to realize adaptive modulation coding. According to the method and the device, the problem of waste of channel measurement resources during adaptive modulation and coding in the prior art is solved, and the effect of saving the waste of the channel measurement resources is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of communications, and in particular, to an adaptive modulation and coding method and device, and a storage medium. Background Art

[0002] Compared with wired channels, wireless channels have narrower coherence bandwidth and shorter coherence time, so wireless channels change quickly, which is a significant feature of wireless channels. If a fixed modulation and coding method is used, it is difficult to fully utilize spectrum resources. Therefore, adaptive coding and modulation technology (Adaptive Modulation and Coding, AMC) is introduced in wireless communication technology to improve spectrum efficiency.

[0003] In order to adapt the modulation and coding strategy to the channel conditions of the user equipment (UE), the traditional adaptive modulation and coding technology AMC needs to combine channel measurement information and hybrid automatic repeat request (HARQ) feedback acknowledgement / negative acknowledgement (ACK / NACK) to learn the channel quality, that is, the signal to interference plus noise ratio (SINR). Channel measurement consumes a lot of resources, resulting in a waste of channel measurement resources. At the same time, the learning time based on HARQ feedback is strongly correlated with the difference between the channel measurement SINR and the actual channel SINR. The greater the difference, the longer the learning time, which often requires dozens or even hundreds of transmission opportunities. Summary of the invention

[0004] The embodiments of the present invention provide an adaptive modulation and coding method and device, and a storage medium, so as to at least solve the problem of waste of channel measurement resources when performing adaptive modulation and coding in the related art.

[0005] According to one embodiment of the present invention, an adaptive modulation and coding method is provided, comprising: obtaining an initial probability distribution of a signal to interference plus noise ratio (SINR) of a wireless channel, performing resource scheduling according to the initial probability distribution, and obtaining hybrid automatic repeat request (HARQ) feedback; updating the initial probability distribution according to the HARQ feedback, and obtaining a real-time probability distribution to implement adaptive modulation and coding.

[0006] According to another embodiment of the present invention, an adaptive modulation and coding device is provided, including: a scheduling feedback module, used to obtain an initial probability distribution of a signal-to-interference-and-noise ratio (SINR) of a wireless channel, perform resource scheduling according to the initial probability distribution, and obtain a hybrid automatic repeat request (HARQ) feedback; an updating module, used to update the initial probability distribution according to the HARQ feedback, and obtain a real-time probability distribution to implement adaptive modulation and coding.

[0007] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when run.

[0008] The present invention provides an adaptive modulation and coding method, which obtains the initial probability distribution of the signal to interference and noise ratio (SINR) of the wireless channel, performs resource scheduling according to the initial probability distribution, obtains hybrid automatic repeat request (HARQ) feedback, updates the initial probability distribution according to the HARQ feedback, obtains the real-time probability distribution, and realizes adaptive modulation and coding. The problem of channel measurement resource waste in the related art when performing adaptive modulation and coding is solved, and the effect of saving channel measurement resource waste is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a hardware structure block diagram of a computer terminal of an adaptive modulation and coding method according to an embodiment of the present invention;

[0010] Figure 2 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present invention;

[0011] Figure 3 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present invention;

[0012] Figure 4 is a structural block diagram of an adaptive modulation and coding device according to an embodiment of the present invention;

[0013] Figure 5 is a structural block diagram of an adaptive modulation and coding device according to an embodiment of the present invention;

[0014] Figure 6 It is a flowchart of an adaptive modulation and coding method according to a scenario embodiment of the present invention. DETAILED DESCRIPTION

[0015] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0017] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal of an adaptive modulation and coding method according to an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0018] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the adaptive modulation and coding method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0019] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0020] In this embodiment, an adaptive modulation and coding method running on the above-mentioned computer terminal is provided. Figure 2 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0021] Step S202, obtaining an initial probability distribution of a signal to interference plus noise ratio (SINR) of a wireless channel, performing resource scheduling according to the initial probability distribution, and obtaining a hybrid automatic repeat request (HARQ) feedback;

[0022] In actual implementation, any probability distribution model may be used to obtain the initial probability distribution of SINR, including but not limited to Beta distribution and Gaussian distribution.

[0023] In an exemplary embodiment, resource scheduling is performed according to an initial probability distribution to obtain hybrid automatic repeat request HARQ feedback, including: obtaining a modulation and coding scheme (MCS) of a modulation and coding scheme for resource scheduling according to the initial probability distribution; performing resource scheduling according to the MCS to obtain hybrid automatic repeat request HARQ feedback.

[0024] In an exemplary embodiment, obtaining a modulation and coding scheme MCS for resource scheduling according to an initial probability distribution includes: obtaining an SINR reference value at each scheduling moment according to the initial probability distribution; and obtaining the MCS according to the SINR reference value.

[0025] In an exemplary embodiment, obtaining an SINR reference value at each scheduling moment according to an initial probability distribution includes one of the following: obtaining a random number that satisfies the initial probability distribution, and determining an SINR reference value according to the random number; obtaining an SINR value corresponding to a highest probability value in the initial probability distribution as an SINR reference value; obtaining an average of the SINR values ​​of the initial probability distribution as an SINR reference value.

[0026] In an exemplary embodiment, obtaining the MCS according to the SINR reference value includes one of the following: selecting a fixed bit error rate (Block Error Rate, BLER) according to a demodulation curve to determine the MCS according to the SINR reference value; calculating the spectral efficiency (Spectral Efficiency, SE) corresponding to different BLERs according to the demodulation curve, and selecting the MCS corresponding to the maximum SE according to the SINR reference value.

[0027] Step S204: Update the initial probability distribution according to the HARQ feedback to obtain the real-time probability distribution to implement adaptive modulation and coding.

[0028] In an exemplary embodiment, updating the initial probability distribution according to HARQ feedback to obtain the real-time probability distribution includes: constructing a likelihood function according to the HARQ feedback, and obtaining the real-time probability distribution according to the likelihood function and the initial probability distribution.

[0029] In the actual implementation process, a likelihood function is constructed based on HARQ feedback, and the real-time probability distribution is obtained by performing calculations based on the likelihood function, the initial probability distribution, and the evidence factor:

[0030]

[0031] Wherein, t represents the scheduling time, P[sinr[t+1]] represents the posterior probability, i.e., the real-time probability distribution, P[sinr[t]] represents the prior probability, i.e., the initial probability distribution, P[c[t]|sinr[t]] is the likelihood function, which represents the probability of observing c[t] under the assumed sinr[t], c[t] represents the HARQ feedback, and P[c[t]] represents the evidence factor, i.e., the probability of observing c[t] occurring.

[0032] In an exemplary embodiment, after the initial probability distribution is updated according to the HARQ feedback to obtain the real-time probability distribution, the method further includes: performing relaxation processing on the real-time probability distribution to expand the SINR selectable range of the real-time probability distribution. Figure 3 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0033] Step S302, obtaining an initial probability distribution of a signal to interference plus noise ratio (SINR) of a wireless channel, performing resource scheduling according to the initial probability distribution, and obtaining a hybrid automatic repeat request (HARQ) feedback;

[0034] Step S304, updating the initial probability distribution according to the HARQ feedback to obtain the real-time probability distribution to implement adaptive modulation and coding;

[0035] Step S306: Perform relaxation processing on the real-time probability distribution to expand the SINR selectable range of the real-time probability distribution.

[0036] In an exemplary embodiment, a new probability distribution model is constructed according to the real-time probability distribution to perform relaxation processing on the real-time probability distribution.

[0037] In the actual implementation process, there are different ways to relax the real-time probability distribution, which may include: the probability distribution model of the real-time probability distribution is a Beta distribution, and a new Beta distribution model is constructed according to the mean of the real-time probability distribution to obtain the real-time probability distribution after relaxation; the probability distribution model of the real-time probability distribution is a Gaussian distribution, the mean of the real-time probability distribution is calculated, a new Gaussian distribution model is constructed according to the mean and the first variance, and the real-time probability distribution and the new Gaussian distribution model are convolved to obtain the real-time probability distribution after relaxation. Among them, the first variance can be any number determined according to the actual situation, and there is no specific limitation.

[0038] In the actual implementation process, the relaxation method has nothing to do with the type of probability distribution model initially adopted. For different types of probability distributions, a new probability distribution can be directly generated or a new probability distribution model can be obtained by convolving the probability distribution with the Gaussian distribution. In addition, the timing of the relaxation process can be determined according to the actual situation, which will not be described here.

[0039] In the actual implementation process, the real-time probability model is generally relaxed before the MCS is determined, and then resource scheduling is performed. The various steps of the adaptive modulation and coding method in the embodiment of the present invention are a cyclic process for a fixed period of time. At the initial starting moment, the historical probability distribution constructed based on historical data can be regarded as the initial probability distribution. For each subsequent moment, the real-time probability distribution is updated and obtained at the current moment. The probability distribution at the moment before the current moment can be called the historical probability distribution.

[0040] Through the above steps, an adaptive modulation and coding method is provided, which obtains the initial probability distribution of the signal to interference and noise ratio SINR of the wireless channel, performs resource scheduling according to the initial probability distribution, obtains hybrid automatic repeat request HARQ feedback; updates the initial probability distribution according to the HARQ feedback, obtains the real-time probability distribution, and realizes adaptive modulation and coding. The problem of channel measurement resource waste in the related art when performing adaptive modulation and coding is solved, and the effect of saving channel measurement resource waste is achieved.

[0041] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0042] In this embodiment, an adaptive modulation and coding device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0043] Figure 4 is a structural block diagram of an adaptive modulation and coding device according to an embodiment of the present invention. Figure 4 As shown, the modulation and coding device 40 includes: a scheduling feedback module 410, which is used to obtain the initial probability distribution of the signal to interference and noise ratio SINR of the wireless channel, perform resource scheduling according to the initial probability distribution, and obtain hybrid automatic repeat request HARQ feedback; an updating module 420, which is used to update the initial probability distribution according to the HARQ feedback, obtain the real-time probability distribution, so as to realize adaptive modulation and coding.

[0044] In an exemplary embodiment, the scheduling feedback module 410 may include an acquisition unit and a scheduling unit, wherein the acquisition unit is used to acquire the modulation and coding scheme MCS for resource scheduling according to the initial probability distribution, and the scheduling unit is used to perform resource scheduling according to the MCS and acquire hybrid automatic repeat request HARQ feedback.

[0045] In an exemplary embodiment, the acquisition unit acquires an SINR reference value at each scheduling moment according to an initial probability distribution; and acquires the MCS according to the SINR reference value.

[0046] In an exemplary embodiment, the updating module 420 constructs a likelihood function according to the HARQ feedback, and obtains the real-time probability distribution according to the likelihood function and the initial probability distribution.

[0047] Figure 5 is a structural block diagram of an adaptive modulation and coding device according to an embodiment of the present invention. Figure 5 As shown, the modulation and coding device 50 includes Figure 4In addition to the various modules shown, the system also includes: a relaxation processing module 510, which is used to perform relaxation processing on the real-time probability distribution to expand the SINR optional range of the real-time probability distribution.

[0048] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0049] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0050] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0051] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0052] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0053] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0054] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0055] In order to enable those skilled in the art to better understand the technical solution of the present invention, it is described below in conjunction with specific scenario embodiments.

[0056] Scenario Example 1

[0057] In a scenario embodiment of the present invention, an adaptive modulation and coding method based on SINR distribution is provided, by modeling SINR as a distribution rather than a fixed value; only using HARQ feedback information (ACK / NACK) to learn the probability distribution of SINR, so that the distribution of SINR converges quickly to the range of actual channel SINR.

[0058] For each UE, or for each transmission mode of each UE, the base station needs to learn the channel quality SINR that matches the channel conditions, so that the scheduling MCS can be determined more accurately. The AMC scheme based on channel quality SINR distribution proposed in the present invention is mainly divided into four main steps: initializing SINR probability distribution, determining scheduling MCS according to SINR probability distribution, updating SINR probability distribution according to actual HARQ feedback, and relaxing SINR probability distribution. Figure 6 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present invention. Figure 6 As shown, the process includes the following steps:

[0059] Step S602, obtaining an initial probability distribution of a signal to interference plus noise ratio (SINR) of a wireless channel;

[0060] (1) Use any probability distribution model to represent the initial probability distribution of SINR, denoted as P[sinr[1]], where P represents the probability density function.

[0061] (2) It does not rely on channel measurement information, that is, no prior information related to SINR is required.

[0062] (3) If there is prior information about SINR, the initial distribution can be adjusted to make the probability density of the prior SINR information higher.

[0063] Step S604, performing resource scheduling according to the initial probability distribution and obtaining HARQ feedback;

[0064] First, the scheduling MCS is determined according to the SINR probability distribution;

[0065] (1) At each scheduling time t (t: 1 to T), a SINR reference value (denoted as ), including but not limited to the following methods: generating a random number that satisfies the SINR probability distribution, thereby determining the SINR reference value; selecting the SINR reference value as the SINR with the highest probability in the SINR probability distribution; selecting the SINR reference value as the mean of the SINR probability distribution.

[0066] (2) Determine the scheduling MCS (denoted as ), including but not limited to the following:

[0067] 1. Refer to the SINR-MCS-BLER curve (or table) (denoted as bler(mcs, sinr)). Under a certain fixed BLER, determine the scheduling MCS based on the selected SINR. Generally, a conservative MCS is selected.

[0068] 2. Refer to the SINR-MCS-BLER curve (or table), consider the different spectrum efficiency losses caused by different BLERs, and determine an MCS with optimal spectrum efficiency based on the selected SINR.

[0069] Step S606, updating the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution;

[0070] (1) According to the above scheduling Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), denoted as c[t].

[0071] (2) Update the SINR probability distribution, denoted as P[sinr[t+1]]:

[0072]

[0073] Among them, P[sinr[t+1]] is the posterior probability that needs to be updated, that is, the real-time probability distribution in the above embodiment. The update of the posterior probability requires the prior probability, likelihood function, and evidence. P[sinr[t]] is the prior probability, that is, the initial probability distribution in the above embodiment; P[c[t]|sinr[t]] is the probability of observing c[t](ACK / NACK) under the assumed sinr[t], which is the likelihood function; P[c[t]] is the probability of the observed c[t](ACK / NACK) occurring, which is the evidence.

[0074] Step S608: Perform relaxation processing on the real-time probability distribution to expand the SINR selectable range of the real-time probability distribution.

[0075] Afterwards, the real-time probability distribution that has completed the relaxation process is returned to step S604, and resource scheduling is performed according to the real-time probability distribution that has completed the relaxation process to obtain HARQ feedback.

[0076] In step S608, the SINR probability distribution P[sinr[t+1]] is relaxed. In a specific embodiment, the SINR probability distribution P[sinr[t+1]] is relaxed. Relaxation can widen the SINR optional range of the SINR probability distribution, so that the SINR probability distribution can quickly converge to different SINRs, which is suitable for scenarios where the SINR changes. The faster the SINR changes, the stronger the need for relaxation.

[0077] The choice of relaxation timing is generally: never relax; applicable to scenarios where SINR changes extremely slowly. Alternatively, relaxation is performed after each SINR probability distribution update. Alternatively, relaxation is performed when the SINR probability distribution P[sinr[t+1]] meets the specified conditions. In general, relaxation is performed when the variance of the optional SINR probability distribution P[sinr[t+1]] is lower than the threshold; the faster the SINR changes, the larger the threshold.

[0078] Relaxation methods include but are not limited to the following:

[0079] The SINR probability distribution P[sinr[t+1]] has a mean of P[sinr[t+1]] and a variance of σ 2 The faster the SINR changes, the greater the variance σ of the Gaussian distribution. 2 The bigger.

[0080] Use an arbitrary probability distribution model to re-express the probability distribution of SINR P[sinr[t+1]], while making the probability density of the mean of P[sinr[t+1]] higher to utilize the existing learning results. Generally, use the same probability distribution model as the initial probability distribution.

[0081] Scenario Example 2

[0082] For each UE, or further for each transmission mode of each UE, the base station needs to learn the channel quality SINR that matches the channel condition, and learn according to the following steps. In this scenario embodiment, the SINR probability distribution model adopts the BETA model. The steps for performing adaptive modulation coding are as follows:

[0083] Step 1: Use BEAT distribution to initialize the SINR probability distribution. The initial probability density function is denoted as P[sinr[1]].

[0084] The SINR range is limited to [-15dB, 45dB], corresponding to the definition domain of BEAT distribution between [0, 1].

[0085] Without any prior information about SINR, the shape parameter α of the BEAT distribution can be set to β = 20, so that the mean of the initial SINR distribution is in the middle of the SINR range [-15dB, 45dB], that is, 15dB.

[0086] Step 2: At each scheduling time t (t: 1 to T), a SINR is selected according to the SINR probability density function P[sinr[t]].

[0087] (1) Obtain the cumulative probability density function of P[sinr[t]]: F[sinr[t]] = ∫P[sinr[t]]d(sinr[t]);

[0088] (2) Obtain the inverse function of the cumulative probability density F[sinr[t]]: sinr[t] = F -1 [u[t]];

[0089] (3) Generate a random number that satisfies a uniform distribution between 0 and 1 as u[t], and obtain the selected SINR according to (2), which is denoted as

[0090] Step 3: At each scheduling time t (t: 1~T), the scheduling MCS is determined according to the selected SINR, denoted as

[0091] Refer to the SINR-MCS-BLER table bler(mcs,sinr), the fixed BLER is 10%, according to Select the scheduling MCS. The SINR-MCS-BLER table bler(mcs,sinr) can be obtained in advance through simulation or actual scheduling data statistics, which is generally called a demodulation curve.

[0092] Generally, a conservative MCS is used. For example, the SINR corresponding to BLER 10% and MCS20 is 15dB, and the SINR corresponding to BLER 10% and MCS21 is 16dB. is 15.6dB, then select the scheduling MCS as 20.

[0093] Step 4: At each scheduling time t (t: 1~T), update the SINR probability distribution according to the actual HARQ feedback.

[0094] According to the above schedule Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), denoted as c[t].

[0095] Update the SINR probability distribution to obtain P[sinr[t+1]]:

[0096]

[0097] Among them, P[c[t]] can be expressed as:

[0098]

[0099] Step 5: At each scheduling time t (t: 1~T), when the variance of P[sinr[t+1]] is less than 1, relax P[sinr[t+1]]. Relaxation method:

[0100] Keep the shape parameter α=20, or set the shape parameter α to another value, such as 30;

[0101] Calculate the mean of P[sinr[t+1]], denoted as μ; let μ = α / (α+β), and calculate the shape parameter β;

[0102] Generate a new BEAT distribution based on α and β, denoted as P[sinr[t+1]].

[0103] Step 6: Repeat steps 2, 3, 4, and 5. In step 6, the above steps are repeated, and the latest real-time probability distribution is continuously used for MCS determination and actual scheduling, and then the last (last moment) real-time probability distribution is updated to obtain the latest real-time probability distribution at the current moment, wherein, for the current moment, the real-time probability distribution at the last moment can be regarded as the historical probability distribution.

[0104] Scenario Example 3

[0105] For each UE, or further for each transmission mode of each UE, the base station needs to learn the channel quality SINR that matches the channel condition, and learn according to the following steps. In this scenario embodiment, the SINR probability distribution model adopts the BEAT beta model. The steps for performing adaptive modulation and coding are as follows:

[0106] Step 1: Use BEAT distribution to initialize the SINR probability distribution. The initial probability density function is denoted as P[sinr[1]].

[0107] (1) The SINR range is limited to [-15dB, 45dB], which corresponds to the definition domain of the BEAT distribution between [0, 1].

[0108] (2) Assuming that the SINR can be inferred to be near X based on PL or RSRP or other information, combined with the BEAT distribution characteristic "mean = α / (α+β)", let the mean of the SINR initial distribution (i.e., α / (α+β)) = X, the shape parameter α = 20, and the shape parameter β be obtained, and then the SINR initial distribution is obtained. In this way, the mean of the SINR initial distribution is near the SINR prior information X.

[0109] Step 2: At each scheduling time t (t: 1 to T), a SINR is selected according to the SINR probability density function P[sinr[t]].

[0110] (1) Obtain the cumulative probability density function of P[sinr[t]]: F[sinr[t]] = ∫P[sinr[t]]d(sinr[t]);

[0111] (2) Obtain the inverse function of the cumulative probability density F[sinr[t]]: sinr[t] = F -1 [u[t]];

[0112] (3) Generate a random number that satisfies a uniform distribution between 0 and 1 as u[t], and obtain the selected SINR according to (2), which is denoted as

[0113] Step 3: At each scheduling time t (t: 1~T), the scheduling MCS is determined according to the selected SINR, denoted as

[0114] (1) Refer to the SINR-MCS-BLER table bler(mcs,sinr), according to Find all possible MCS and the corresponding BLER, recorded as (MCS 1 ,BLER 1 )、(MCS 2 ,BLER 2 )、......、(MCS n ,BLER n );

[0115] (2) Look up the MCS-SE (spectral efficiency) correspondence table and find the SE corresponding to the above possible MCS, recorded as (MCS 1 , S.E. 1 )、(MCS 2 , S.E. 2 )、......、(MCS n , S.E. n );

[0116] (3) Calculate the spectrum efficiency of all possible MCSs after considering BLER, SE1×(1-BLER 1 )、SE 2 ×(1-BLER 2 ),......,SE n ×(1-BLER n );

[0117] (4) Select the MCS that corresponds to the maximum spectrum efficiency after considering BLER as the scheduling MCS.

[0118] Step 4: At each scheduling time t (t: 1~T), update the SINR probability distribution according to the actual HARQ feedback.

[0119] (1) According to the above scheduling Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), denoted as c[t].

[0120] (2) Update the SINR probability distribution to obtain P[sinr[t+1]]:

[0121]

[0122] Where P[c[t]] can be expressed as:

[0123]

[0124] Step 5: At each scheduling time t (t: 1~T), relax P[sinr[t+1]]. Relaxation method:

[0125] (1) Calculate the mean of P[sinr[t+1]], denoted by μ;

[0126] (2)P[sinr[t+1]]=P[sinr[t+1]]*N(μ,σ μ 2 ), where * represents convolution, N(μ,σ 2 ) means the mean is μ and the variance is σ μ 2 Gaussian distribution.

[0127] Step 6: Repeat steps 2, 3, 4, and 5.

[0128] Scenario Example 4

[0129] For each UE, or further for each transmission mode of each UE, the base station needs to learn the channel quality SINR that matches the channel condition, and learn according to the following steps. In this scenario embodiment, the SINR probability distribution model adopts a Gaussian distribution model. The steps for performing adaptive modulation and coding are as follows:

[0130] Step 1: Use Gaussian distribution to initialize SINR probability distribution. The initial probability density function is denoted as P[sinr[1]].

[0131] (1) Assume that the SINR can be inferred to be around X based on PL or RSRP or other information, and let the mean of the initial Gaussian distribution be X and the variance be σx 2 , σ x The value 3 can be taken to obtain the initial distribution of SINR, so that the mean of the initial distribution of SINR is close to the SINR prior information X.

[0132] Step 2: At each scheduling time t (t: 1 to T), a SINR is selected according to the SINR probability density function P[sinr[t]].

[0133] (1) Obtain the cumulative probability density function of P[sinr[t]]: F[sinr[t]] = ∫P[sinr[t]]d(sinr[t]);

[0134] (2) Obtain the inverse function of the cumulative probability density F[sinr[t]]: sinr[t] = F -1 [u[t]];

[0135] (3) Generate a random number that satisfies a uniform distribution between 0 and 1 as u[t], and obtain the selected SINR according to (2), which is denoted as

[0136] Step 3: At each scheduling time t (t: 1~T), the scheduling MCS is determined according to the selected SINR, denoted as

[0137] (1) Refer to the SINR-MCS-BLER table bler(mcs,sinr), according to Find all possible MCS and the corresponding BLER, recorded as (MCS 1 ,BLER 1 )、(MCS 2 ,BLER 2 )、......、(MCS n ,BLER n );

[0138] (2) Look up the MCS-SE (spectral efficiency) correspondence table and find the SE corresponding to the above possible MCS, recorded as (MCS 1 , S.E. 1 )、(MCS 2 , S.E. 2 )、......、(MCS n , S.E. n );

[0139] (3) Calculate the spectrum efficiency of all possible MCSs after considering BLER, SE 1 ×(1-BLER 1 )、SE2 ×

[0140] (1-BLER 2 ),......,SE n ×(1-BLER n );

[0141] (4) Select the MCS that corresponds to the maximum spectrum efficiency after considering BLER as the scheduling MCS.

[0142] Step 4: At each scheduling time t (t: 1~T), update the SINR probability distribution according to the actual HARQ feedback.

[0143] (1) According to the above scheduling Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), denoted as c[t].

[0144] (2) Update the SINR probability distribution to obtain P[sinr[t+1]]:

[0145]

[0146] Where P[c[t]] can be expressed as:

[0147]

[0148] Step 5: At each scheduling time t (t: 1~T), relax P[sinr[t+1]]. Relaxation method:

[0149] (1) Calculate the mean of P[sinr[t+1]], denoted by μ;

[0150] (2) Regenerate P[sinr[t+1]] with mean μ and variance σ μ 2 Gaussian distribution; where σ μ Can be compared with σ x Equal, other values ​​are also possible.

[0151] Step 6: Repeat steps 2, 3, 4, and 5.

[0152] In summary, the adaptive modulation and coding method based on SINR distribution provided in the embodiment of the present invention models SINR as a distribution rather than a fixed value; HARQ feedback information (ACK / NACK) is used to learn the probability distribution of SINR, so that the distribution of SINR converges quickly to the range of actual channel SINR. The SINR convergence speed of the traditional method is strongly correlated with the difference between the channel measurement SINR and the actual channel SINR. The greater the difference, the slower the convergence speed. The method based on SINR distribution proposed in the present invention does not rely on channel measurement information, nor does it rely on any prior information of the actual channel SINR. The correlation with the difference between the initial distribution and the actual channel SINR is very weak, so the convergence speed is stable and the convergence speed is fast. At the same time, the method proposed in the present invention does not rely on channel measurement information, so it also saves channel measurement resources. The technical solution of the embodiment of the present invention does not rely on channel measurement information, and only uses HARQ feedback information to realize the learning of channel quality SINR. The initial probability distribution of SINR is represented by an arbitrary probability distribution model, and the probability distribution of SINR can be updated by using HARQ feedback, so as to learn the possible situations of channel quality SINR.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive modulation and coding method, characterized in that: include: Obtaining an initial probability distribution of a signal to interference plus noise ratio (SINR) of a wireless channel; Perform resource scheduling according to the initial probability distribution and obtain hybrid automatic repeat request HARQ feedback; The initial probability distribution is updated according to the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding.

2. The method according to claim 1, characterized in that Performing resource scheduling according to the initial probability distribution and obtaining hybrid automatic repeat request HARQ feedback includes: Acquire a modulation and coding scheme MCS for resource scheduling according to the initial probability distribution; Resource scheduling is performed according to the MCS to obtain hybrid automatic repeat request HARQ feedback.

3. The method according to claim 2, characterized in that Acquiring a modulation and coding scheme MCS for resource scheduling according to the initial probability distribution includes: Obtaining a SINR reference value at each scheduling moment according to the initial probability distribution; The MCS is acquired according to the SINR reference value.

4. The method according to claim 3, characterized in that Obtaining a SINR reference value at each scheduling moment according to the initial probability distribution includes one of the following: Obtaining a random number that satisfies the initial probability distribution, and determining the SINR reference value according to the random number; Obtaining an SINR value corresponding to a highest probability value in the initial probability distribution as the SINR reference value; A mean value of the SINR values ​​of the initial probability distribution is obtained as the SINR reference value.

5. The method according to claim 3, characterized in that: Acquiring the MCS according to the SINR reference value includes one of the following: According to the demodulation curve, a fixed bit error rate BLER is selected to determine the MCS according to the SINR reference value; The spectrum efficiency SE corresponding to different BLERs is calculated according to the demodulation curve, and the MCS corresponding to the maximum value of the SE is selected according to the SINR reference value.

6. The method according to claim 1, characterized in that Updating the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution includes: A likelihood function is constructed according to the HARQ feedback, and the real-time probability distribution is obtained according to the likelihood function and the initial probability distribution.

7. The method according to claim 1, characterized in that After updating the initial probability distribution according to the HARQ feedback to obtain the real-time probability distribution, the method further includes: The real-time probability distribution is relaxed to expand the SINR selectable range of the real-time probability distribution.

8. The method according to claim 7, characterized in that Performing relaxation processing on the real-time probability distribution includes: A new probability distribution model is constructed according to the real-time probability distribution to perform relaxation processing on the real-time probability distribution.

9. An adaptive modulation and coding device, characterized in that: include: A scheduling feedback module, used to obtain an initial probability distribution of a signal to interference plus noise ratio (SINR) of a wireless channel, perform resource scheduling according to the initial probability distribution, and obtain a hybrid automatic repeat request (HARQ) feedback; An updating module is used to update the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the method described in any one of claims 1 to 8 when executed by a processor.

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