Adaptive modulation and coding method and apparatus, and storage medium

Abstract

An adaptive modulation and coding method and apparatus, and a storage medium. The method comprises: obtaining an initial probability distribution of a signal-to-interference-to-noise ratio (SINR) of a wireless channel, performing resource scheduling on the basis of the initial probability distribution, and obtaining a hybrid automatic repeat request (HARQ) feedback; and updating the initial probability distribution on the basis of the HARQ feedback to obtain a real-time probability distribution, so as to implement adaptive modulation and coding. The problem in the related art of channel measurement resource waste during adaptive modulation and coding is solved.

Description

Adaptive modulation coding method, device, and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on Chinese patent application CN202311015332.2, filed on August 11, 2023, entitled “Adaptive modulation and coding method, device, and storage medium,” and claims the priority of the patent application. All of the disclosed contents are incorporated into this disclosure by reference. Technical Field

[0003] The embodiments of the present disclosure relate to the field of communications, and in particular, to an adaptive modulation and coding method and apparatus, and a storage medium. Background Art

[0004] Compared to wired channels, wireless channels have narrower coherence bandwidths and shorter coherence times, resulting in rapid changes. This is a notable characteristic of wireless channels. Using fixed modulation and coding schemes makes it difficult to fully utilize spectrum resources. Therefore, adaptive modulation and coding (AMC) has been introduced into wireless communication technology to improve spectrum efficiency.

[0005] To adapt the modulation and coding strategy to the channel conditions of user equipment (UE), traditional adaptive modulation and coding (AMC) technology relies on combining channel measurement information with Hybrid Automatic Repeat Request (HARQ) feedback (Acknowledgement / Negative Acknowledgement, ACK / NACK) to learn channel quality, specifically the Signal to Interference plus Noise Ratio (SINR). Channel measurement consumes a significant amount of resources, resulting in a waste of these resources. Furthermore, the learning time associated with HARQ feedback is strongly correlated with the difference between the measured and actual SINRs. A larger difference results in longer learning times, often requiring dozens or even hundreds of transmissions.

[0006] Summary of the Invention

[0007] The embodiments of the present disclosure provide an adaptive modulation and coding method and apparatus, and a storage medium to at least solve the problem of waste of channel measurement resources when performing adaptive modulation and coding in related technologies.

[0008] According to one embodiment of the present disclosure, an adaptive modulation and coding method is provided, including: obtaining an initial probability distribution of a signal-to-interference-and-noise ratio (SINR) of a wireless channel, performing resource scheduling based on the initial probability distribution, and obtaining hybrid automatic repeat request (HARQ) feedback; and updating the initial probability distribution based on the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding.

[0009] According to another embodiment of the present disclosure, an adaptive modulation and coding apparatus is provided, comprising: a scheduling feedback module, configured to obtain an initial probability distribution of a signal-to-interference-and-noise ratio (SINR) of a wireless channel, perform resource scheduling based on the initial probability distribution, and obtain hybrid automatic repeat request (HARQ) feedback; and an update module, configured to update the initial probability distribution based on the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding.

[0010] According to another embodiment of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG1 is a hardware structure block diagram of a computer terminal for an adaptive modulation and coding method according to an embodiment of the present disclosure;

[0012] FIG2 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present disclosure;

[0013] FIG3 is a flow chart of an adaptive modulation and coding method according to an embodiment of the present disclosure;

[0014] FIG4 is a structural block diagram of an adaptive modulation and coding apparatus according to an embodiment of the present disclosure;

[0015] FIG5 is a structural block diagram of an adaptive modulation and coding apparatus according to an embodiment of the present disclosure;

[0016] FIG6 is a flowchart of an adaptive modulation and coding method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0019] The method embodiments provided in the embodiments of the present disclosure can be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking operation on a computer terminal as an example, FIG1 is a hardware structure block diagram of a computer terminal for an adaptive modulation and coding method according to an embodiment of the present disclosure. As shown in FIG1 , the computer terminal may include one or more (only one is shown in FIG1 ) processors 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 will be understood by those skilled in the art that the structure shown in FIG1 is only for illustration and does not limit the structure of the above-mentioned mobile terminal. For example, the mobile terminal may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .

[0020] Memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the adaptive modulation and coding method in the embodiments of the present disclosure. Processor 102 executes the computer programs stored in memory 104 to perform various functional applications and data processing, thereby implementing the aforementioned method. Memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 may further include memory remotely located from processor 102, and such remote memory may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0021] The transmission device 106 is used to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by a communications provider of a computer terminal. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0022] In this embodiment, an adaptive modulation and coding method running on the above-mentioned computer terminal is provided. FIG2 is a flow chart of the adaptive modulation and coding method according to an embodiment of the present disclosure. As shown in FIG2 , the flow chart includes the following steps:

[0023] Step S202, obtaining an initial probability distribution of a signal-to-interference-and-noise ratio (SINR) of a wireless channel, performing resource scheduling according to the initial probability distribution, and obtaining hybrid automatic repeat request (HARQ) feedback;

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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 the SINR reference value; obtaining an average of the SINR values ​​of the initial probability distribution as the SINR reference value.

[0028] In an exemplary embodiment, obtaining the MCS according to the SINR reference value includes one of the following: selecting a fixed bit error rate (BLER) according to a demodulation curve to determine the MCS according to the SINR reference value; calculating the 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.

[0029] Step S204: Update the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding.

[0030] 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.

[0031] In actual implementation, 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:

[0032] 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.

[0033] In an exemplary embodiment, after updating the initial probability distribution based on 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 the adaptive modulation and coding method according to an embodiment of the present disclosure. As shown in Figure 3, the process includes the following steps:

[0034] Step S302: obtaining an initial probability distribution of a signal-to-interference-and-noise ratio (SINR) of a wireless channel, performing resource scheduling according to the initial probability distribution, and obtaining hybrid automatic repeat request (HARQ) feedback;

[0035] Step S304: updating the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding;

[0036] Step S306: Perform relaxation processing on the real-time probability distribution to expand the SINR selectable range of the real-time probability distribution.

[0037] 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.

[0038] In actual implementation, different methods of relaxing the real-time probability distribution may include: if the probability distribution model of the real-time probability distribution is a Beta distribution, a new Beta distribution model is constructed based on the mean of the real-time probability distribution to obtain the relaxed real-time probability distribution; if 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 based on the mean and the first variance, and the real-time probability distribution and the new Gaussian distribution model are convolved to obtain the relaxed real-time probability distribution. The first variance can be any number determined based on actual conditions and is not specifically limited.

[0039] In practice, the relaxation method is independent of the type of probability distribution model initially employed. For different probability distributions, a new probability distribution can be generated directly or by convolving the probability distribution with a Gaussian distribution. Furthermore, the timing of the relaxation can be determined based on practical circumstances and will not be further described here.

[0040] 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 of 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.

[0041] Through the above steps, an adaptive modulation and coding method is provided. This method obtains an initial probability distribution of the wireless channel's signal-to-interference-and-noise ratio (SINR), performs resource scheduling based on the initial probability distribution, and obtains hybrid automatic repeat request (HARQ) feedback. The initial probability distribution is then updated based on the HARQ feedback to obtain a real-time probability distribution, thereby implementing adaptive modulation and coding. This method addresses the issue of channel measurement resource waste during adaptive modulation and coding in related technologies, thereby reducing the waste of channel measurement resources.

[0042] 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 the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure is essentially 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, magnetic disk, optical disk), including a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0043] This embodiment also provides an adaptive modulation and coding apparatus for implementing the above-mentioned embodiments and preferred implementations. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0044] Figure 4 is a structural block diagram of an adaptive modulation and coding device according to an embodiment of the present disclosure. As shown in Figure 4, the modulation and coding device 40 includes: a scheduling feedback module 410, which is configured to obtain an initial probability distribution of the signal-to-interference-and-noise ratio (SINR) of a wireless channel, perform resource scheduling based on the initial probability distribution, and obtain hybrid automatic repeat request (HARQ) feedback; and an update module 420, which is configured to update the initial probability distribution based on the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation and coding.

[0045] In an exemplary embodiment, the scheduling feedback module 410 may include an acquisition unit and a scheduling unit, wherein the acquisition unit is configured to acquire a modulation and coding scheme MCS for resource scheduling based on an initial probability distribution, and the scheduling unit is configured to perform resource scheduling based on the MCS and acquire hybrid automatic repeat request HARQ feedback.

[0046] In an exemplary embodiment, the acquiring unit acquires an SINR reference value at each scheduling moment according to the initial probability distribution; and acquires the MCS according to the SINR reference value.

[0047] 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.

[0048] Figure 5 is a structural block diagram of an adaptive modulation and coding device according to an embodiment of the present disclosure. As shown in Figure 5, in addition to the various modules shown in Figure 4, the modulation and coding device 50 also includes: a relaxation processing module 510, which is configured to perform relaxation processing on the real-time probability distribution to expand the SINR optional range of the real-time probability distribution.

[0049] It should be noted that the above modules can be implemented through 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.

[0050] An embodiment of the present disclosure further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when run.

[0051] 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.

[0052] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments.

[0053] 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.

[0054] 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 here.

[0055] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using a general-purpose computing device, they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices, they can be implemented using program code executable by the computing device, and thus, 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 performed in a different order than herein, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present disclosure is not limited to any particular combination of hardware and software.

[0056] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, they are described below in conjunction with specific scenario embodiments.

[0057] Scenario Example 1

[0058] In an embodiment of the disclosed scenario, an adaptive modulation and coding method based on SINR distribution is provided. By modeling SINR as a distribution rather than a fixed value and using only HARQ feedback information (ACK / NACK) to learn the probability distribution of SINR, the distribution of SINR can quickly converge to the range of actual channel SINR.

[0059] 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 the channel quality SINR distribution proposed in this disclosure is mainly divided into four main steps: initializing the SINR probability distribution, determining the scheduling MCS according to the SINR probability distribution, updating the SINR probability distribution according to the actual HARQ feedback, and relaxing the SINR probability distribution. Figure 6 is a flow chart of the adaptive modulation and coding method according to the embodiment of the scenario of this disclosure. As shown in Figure 6, the process includes the following steps:

[0060] Step S602: obtaining an initial probability distribution of a signal-to-interference-and-noise ratio (SINR) of a wireless channel;

[0061] (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.

[0062] (2) It does not rely on channel measurement information, that is, it does not require SINR related prior information.

[0063] (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.

[0064] Step S604: Perform resource scheduling according to the initial probability distribution and obtain HARQ feedback;

[0065] First, the scheduling MCS is determined based on the SINR probability distribution;

[0066] (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.

[0067] (2) Determine the scheduling MCS (denoted as ), including but not limited to the following:

[0068] 1. Refer to the SINR-MCS-BLER curve (or table) (denoted as bler(mcs, sinr)). Under a fixed BLER, determine the scheduling MCS based on the selected SINR. Generally, a conservative MCS is used.

[0069] 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.

[0070] Step S606: Update the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution;

[0071] (1) According to the above scheduling Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), which is recorded as c[t].

[0072] (2) Update the SINR probability distribution, denoted as P[sinr[t+1]]:

[0073] Here, P[sinr[t+1]] is the posterior probability that needs to be updated, i.e., the real-time probability distribution in the above embodiment. Updating the posterior probability requires a prior probability, a likelihood function, and evidence. P[sinr[t]] is the prior probability, i.e., 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], i.e., the likelihood function; and P[c[t]] is the observed probability of c[t] (ACK / NACK) occurring, i.e., 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 one specific embodiment, the SINR probability distribution P[sinr[t+1]] is relaxed. Relaxation can broaden the SINR range of the SINR probability distribution, enabling the SINR probability distribution to quickly converge to different SINRs, making it suitable for scenarios with varying SINRs. The faster the SINR changes, the greater the need for relaxation.

[0077] The relaxation timing is generally chosen as: never; this is suitable for scenarios where SINR changes very 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. Generally, relaxation is performed when the variance of the SINR probability distribution P[sinr[t+1]] falls below a 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] Re-express the probability distribution of SINR P[sinr[t+1]] using an arbitrary probability distribution model, while making the probability density of the mean of P[sinr[t+1]] higher to take advantage of existing learning results. Generally, use the same probability distribution model as the initial probability distribution.

[0081] Scenario Example 2

[0082] For each UE, or more specifically, for each transmission mode of each UE, the base station needs to learn the channel quality SINR that matches the channel conditions. This learning is performed according to the following steps. In this scenario embodiment, the SINR probability distribution model adopts the BETA model. The steps for performing adaptive modulation and 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], which corresponds to the BEAT distribution domain [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 recorded as

[0090] Step 3: At each scheduling time t (t: 1~T), the scheduling MCS is determined according to the selected SINR, which is recorded 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, and 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. If the bandwidth is 15.6dB, select the scheduling MCS as 20.

[0093] Step 4: At each scheduling time t (t: 1 to 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), which is recorded as c[t].

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

[0096] Among them, P[c[t]] can be expressed as: P[c[t]]=∫P[c[t]|sinr[t]]×P[sinr[t]]d(sinr[t]),

[0097] express bler(mcs,sinr).

[0098] 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:

[0099] Keep the shape parameter α = 20, or set the shape parameter α to another value, such as 30;

[0100] Calculate the mean of P[sinr[t+1]], denoted by μ; let μ = α / (α+β), and calculate the shape parameter β;

[0101] Generate a new BEAT distribution based on α and β, denoted as P[sinr[t+1]].

[0102] 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. Then, the previous real-time probability distribution (the previous moment) is updated to obtain the latest real-time probability distribution at the current moment. For the current moment, the real-time probability distribution at the previous moment can be regarded as the historical probability distribution.

[0103] Scenario Example 3

[0104] For each UE, or more specifically, for each transmission mode of each UE, the base station needs to learn the channel quality SINR that matches the channel conditions. This learning is performed according to the following steps. In this scenario embodiment, the SINR probability distribution model adopts the BEAT model. The steps for performing adaptive modulation and coding are as follows:

[0105] Step 1: Use BEAT distribution to initialize the SINR probability distribution. The initial probability density function is denoted as P[sinr[1]].

[0106] (1) The SINR range is limited to [-15dB, 45dB], which corresponds to the domain of the BEAT distribution between [0, 1].

[0107] (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 initial SINR distribution (i.e., α / (α+β)) = X, the shape parameter α = 20, and the shape parameter β be obtained, and then the initial SINR distribution is obtained. In this way, the mean of the initial SINR distribution is near the SINR prior information X.

[0108] 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]].

[0109] (1) Obtain the cumulative probability density function of P[sinr[t]]: F[sinr[t]] = ∫P[sinr[t]]d(sinr[t]);

[0110] (2) Obtain the inverse function of the cumulative probability density F[sinr[t]] sinr[t] = F -1 [u[t]];

[0111] (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 recorded as

[0112] Step 3: At each scheduling time t (t: 1~T), the scheduling MCS is determined according to the selected SINR, which is recorded as

[0113] (1) Refer to the SINR-MCS-BLER table bler(mcs,sinr), according to Find all possible MCSs and their corresponding BLERs, which are recorded as (MCS1, BLER1), (MCS2, BLER2), ..., (MCS n , BLER n );

[0114] (2) Look up the MCS-SE (spectral efficiency) correspondence table and find the SE corresponding to the above possible MCSs, which are recorded as (MCS1, SE1), (MCS2, SE2), ..., (MCS n , SE n );

[0115] (3) Calculate the spectrum efficiency of all possible MCSs after considering BLER, SE1×(1-BLER1), SE2×(1-BLER2), ..., SE n ×(1-BLER n );

[0116] (4) Select the MCS that corresponds to the maximum spectrum efficiency after considering BLER as the scheduling MCS.

[0117] Step 4: At each scheduling time t (t: 1 to T), update the SINR probability distribution according to the actual HARQ feedback.

[0118] (1) According to the above scheduling Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), which is recorded as c[t].

[0119] (2) Update the SINR probability distribution to obtain P[sinr[t+1]]:

[0120] Where P[c[t]] can be expressed as: P[c[t]]=∫P[c[t] | sinr[t]]×P[sinr[t]]d(sinr [ t]),

[0121] express bler(mcs,sinr).

[0122] Step 5: At each scheduling time t (t: 1~T), relax P[sinr[t+1]]. Relaxation method:

[0123] (1) Calculate the mean of P[sinr[t+1]], denoted as μ;

[0124] (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.

[0125] Step 6: Repeat steps 2, 3, 4, and 5.

[0126] Scenario Example 4

[0127] 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 conditions, 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:

[0128] Step 1: Use Gaussian distribution to initialize the SINR probability distribution. The initial probability density function is denoted as P[sinr[1]].

[0129] (1) Assuming that the SINR can be inferred to be around X based on PL or RSRP or other information, let the mean of the initial Gaussian distribution be X and the variance be σ x 2 ,σ x The value 3 can be taken to obtain the initial distribution of SINR, so that the mean of the initial SINR distribution is close to the SINR prior information X.

[0130] 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]].

[0131] (1) Obtain the cumulative probability density function of P[sinr[t]]: F[sinr[t]] = ∫P[sinr[t]]d(sinr[t]);

[0132] (2) Obtain the inverse function of the cumulative probability density F[sinr[t]] sinr[t] = F -1 [u[t]];

[0133] (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 recorded as

[0134] Step 3: At each scheduling time t (t: 1~T), the scheduling MCS is determined according to the selected SINR, which is recorded as

[0135] (1) Refer to the SINR-MCS-BLER table bler(mcs,sinr), according to Find all possible MCSs and their corresponding BLERs, which are recorded as (MCS1, BLER1), (MCS2, BLER2), ..., (MCS n , BLER n );

[0136] (2) Look up the MCS-SE (spectral efficiency) correspondence table and find the SE corresponding to the above possible MCSs, which are recorded as (MCS1, SE1), (MCS2, SE2), ..., (MCS n , SE n );

[0137] (3) Calculate the spectrum efficiency of all possible MCSs after considering BLER, SE1×(1-BLER1), SE2×(1-BLER2), ..., SE n ×(1-BLER n );

[0138] (4) Select the MCS that corresponds to the maximum spectrum efficiency after considering BLER as the scheduling MCS.

[0139] Step 4: At each scheduling time t (t: 1 to T), update the SINR probability distribution according to the actual HARQ feedback.

[0140] (1) According to the above scheduling Actual scheduling will generate corresponding HARQ feedback (ACK / NACK), which is recorded as c[t].

[0141] (2) Update the SINR probability distribution to obtain P[sinr[t+1]]:

[0142] Where P[c[t]] can be expressed as: P[c[t]]=∫P[c[t]|sinr[t]]×P[sinr[t]]d(sinr[t]),

[0143] express bler(mcs,sinr).

[0144] Step 5: At each scheduling time t (t: 1~T), relax P[sinr[t+1]]. Relaxation method:

[0145] (1) Calculate the mean of P[sinr[t+1]], denoted as μ;

[0146] (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.

[0147] Step 6: Repeat steps 2, 3, 4, and 5.

[0148] In summary, the SINR distribution-based adaptive modulation and coding method provided in the embodiments of the present disclosure models SINR as a distribution rather than a fixed value. It utilizes HARQ feedback information (ACK / NACK) to learn the SINR probability distribution, allowing the SINR distribution to quickly converge to the range of the actual channel SINR. The SINR convergence speed of conventional methods is strongly correlated with the difference between the measured channel SINR and the actual channel SINR; the larger the difference, the slower the convergence speed. However, the SINR distribution-based method proposed in the present disclosure does not rely on channel measurement information or any prior information about the actual channel SINR. Its correlation with the difference between the initial distribution and the actual channel SINR is very weak, resulting in a stable and rapid convergence speed. Furthermore, the method proposed in the present disclosure does not rely on channel measurement information, thus conserving channel measurement resources. The technical solution of the embodiments of the present disclosure does not rely on channel measurement information, but only utilizes HARQ feedback information to learn the channel quality SINR. By using an arbitrary probability distribution model to represent the initial SINR probability distribution, the SINR probability distribution can be updated using HARQ feedback, thereby learning possible channel quality SINR scenarios.

[0149] The above description is merely a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Those skilled in the art will readily appreciate that the present disclosure is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the principles of the present disclosure shall be within the scope of protection of the present disclosure.

Claims

1. An adaptive modulation and coding method, comprising: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, comprising: A scheduling feedback module, configured 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; The updating module is configured to update the initial probability distribution according to the HARQ feedback to obtain a real-time probability distribution to implement adaptive modulation coding.

10. A computer-readable storage medium having a computer program stored therein, wherein: When the computer program is executed by a processor, the method described in any one of claims 1 to 8 is implemented.

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