CN108667564B - On-line learning adaptive link MCS switching control method - Google Patents
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Abstract
The invention belongs to the technical field of mobile communication, and discloses an online learning adaptive link MCS switching control method, which selects parameters; the transmitting end circularly transmits different MCS schemes, and the receiving end processes and stores related data; classifying the stored data according to different MCS schemes and screening effective data; respectively calculating corresponding related parameters of each MCS according to the effective data; the receiving end selects and feeds back a corresponding MCS according to the current channel state and an updating criterion, the sending end sends data according to the fed-back MCS, and the receiving end processes and stores the data; after multiple updates, the final corresponding parameters of each MCS are obtained, and the receiving end feeds back the MCS according to the corresponding criteria; and respectively counting the system throughput of each method under different signal-to-noise ratios according to the switching criterion, the EESM switching criterion and the KNN algorithm switching criterion. The invention does not need to rely on a channel model in a specific environment and does not need to obtain accurate classification data of each MCS in advance.
Description
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to an online learning adaptive link MCS switching control method.
Background
Currently, the current state of the art commonly used in the industry is such that:with the development and evolution of mobile communication technology, the communications are made to be mobile, broadband, and IP. However, in the wireless mobile communication system, the time-varying characteristics due to the path loss, fading, interference, and the like of the wireless channel significantly affect the system performance. The time-varying property of the wireless channel makes the quality of the received signal a random variable, so that the transmission performance of the system will also vary with the variation of the quality of the received signal under the condition that the transmission mode and the transmission parameters are fixed, and the requirement of the communication service cannot be met. These poor characteristics of the wireless channel, the interference inside and outside the complex and variable wireless system, and the trend of green communication, pose a serious challenge to the research of high-frequency-band utilization and high-power-utilization information transmission technology in the current and future wireless communication systems. In order to meet the increasing demand of people for future wireless communication, how to improve the transmission rate to the maximum extent on limited resources so as to improve the spectrum utilization efficiency to the maximum extent gradually becomes a research hotspot in the field of mobile communication. Wherein the link adaptation technique is improving transmissionThe method has excellent advantages in terms of transmission rate and spectrum utilization, and thus is gradually becoming one of the popular techniques for wireless communication research. The link adaptation techniques mainly include a power control technique and a rate control technique. The power control technology is a technology for ensuring the transmission efficiency of a communication system by dynamically adjusting the power of a transmitting end so that the SNR of a receiving end meets certain requirements. Rate control techniques, also commonly referred to as AMC techniques. Specifically, the communication system adopts multiple Modulation and Coding Schemes (MCS), and the MCS can be dynamically selected according to the change of the channel condition, so as to realize the maximum throughput of the system. The basic idea of link adaptation is: dynamically tracking channel variations and determining current channel characteristics based on actual channel transmission conditions, and further adjusting adaptive parameters of the system: the method comprises the steps of coding, coding rate, modulation and the like, thereby meeting the requirements of different services, weakening the influence of the time-varying characteristic of a wireless channel on the system performance, and achieving the aims of achieving lower error rate, improving the system transmission efficiency, ensuring the system transmission reliability and improving the overall system throughput. In short, when the channel quality is poor, the combination of low-order modulation and low-rate coding is adopted; when the channel condition is good, a combination of high order modulation and high rate coding is used. The link adaptation technique achieves the best balance between BLER and spectrum utilization efficiency, and the obtained gain of the system channel capacity is obvious, so that the research on link adaptation is necessary to improve the overall performance of the system. Generally, the index for measuring the channel quality refers to the snr of the received signal at the receiving end, which is high in snr, and considers that the channel state is good and the snr is low, which is considered as bad. Since the accuracy of SNR estimation and the correctness of MCS selection are very important for the improvement of system performance, how to switch between various MCSs becomes a critical issue of AMC technology. Existing handover control schemes are mainly classified into two categories: the first category is a look-up table based scheme. The method carries out system performance simulation through a relevant channel model, and maps the signal-to-noise ratio of the system into an effective signal-to-noise ratio to approximate a performance curve under the AWGN channel through some nonlinear mapping methods (such as EESM, MMIB and the like)So as to obtain the corresponding table between each MCS and the effective signal-to-noise ratio, and directly look up the corresponding table during switching. However, the performance of this scheme depends to a large extent on the specific channel model, and cannot be updated in an adjusted manner according to the actual system environment, thereby reducing the flexibility of its use. The second category is some machine learning algorithms. Such algorithms are represented by some supervised learning methods, such as KNN algorithm, SVM algorithm, etc. The main idea is classification, and the classifier is trained by some labeled training data to obtain the optimal parameters for classification. The method can better distinguish the use ranges of different MCS by selecting a plurality of characteristic parameters, and the switching control is more accurate. But the requirement on training data is high, and accurate classification data is required. In practical situations, these data that have been classified accurately are often unknown, and the computational complexity of such algorithms is high.
In summary, the problems of the prior art are as follows:
(1) the performance of the look-up table based scheme depends to a large extent on the specific channel model and cannot be updated adaptively to the actual system environment, reducing its flexibility of use.
(2) The machine learning algorithm has high requirements on training data and must be accurate classification data. In practical situations, these data that have been classified accurately are often unknown, and the computational complexity of such algorithms is high.
The difficulty and significance for solving the technical problems are as follows:for a common lookup table algorithm, an accurate channel model is difficult to obtain, some empirical models are often used, but the accuracy of the empirical models is not high, and similarly, an accurate classification sample must be obtained by the current machine learning method, but the accurate classification sample is also difficult to obtain, and a good solution for how to obtain the accurate channel model or the accurate classification sample does not exist at present. The method does not need channel model information and accurate classification samples, and can obtain each type of signal without some simple tests in the transmitting and receiving processThe accurate use condition of the modulation coding mode is a completely online learning method, and is simple and effective compared with the existing method.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an online learning adaptive link MCS switching control method.
The invention is realized in this way, a method for controlling the switching of online learning adaptive link MCS, which comprises: selecting parameters; the transmitting end circularly transmits different MCS schemes, and the receiving end processes and stores related data; classifying the stored data according to different MCS schemes and screening effective data; respectively calculating corresponding related parameters of each MCS according to the effective data; the receiving end selects and feeds back a corresponding MCS according to the current channel state and an updating criterion, the sending end sends data according to the fed-back MCS, and the receiving end processes and stores the data; after multiple updates, the final corresponding parameters of each MCS are obtained, and the receiving end feeds back the MCS according to the corresponding criteria; and respectively counting the system throughput of each method under different signal-to-noise ratios according to the switching criterion, the EESM switching criterion and the KNN algorithm switching criterion.
Further, the online learning adaptive link MCS switching control method comprises the following steps:
(1) selecting a parameter updating time interval T, selecting an exploration coefficient alpha and a parameter updating frequency K;
(2) the transmitting end continuously and sequentially transmits MCS with different numbers in a circulating mode within the time that T is more than 0 and less than T, the receiving end processes each data frame, and estimates and stores the MCS number and the signal-to-noise ratio of each frame and a corresponding transmission check result ack, wherein 1 is wrong and 0 is right;
(3) classifying the stored data according to different MCS numbers, sorting the classified data from low to high according to the signal-to-noise ratio, and screening out effective data;
(4) the valid data for each MCS is according to the formula:
calculating the corresponding parameter thetaaIn the above formula Da＝[snr1，snr2，...，snrN；b，b，...，b]，Ca＝[1-ack1，1-ack2，...，1-ackN]α denotes the number of each MCS;
(5) in the next time interval T, the receiving end follows the following criteria according to the current channel state:
selecting MCS number to feed back to the sending end, and updating the MCS number, the signal-to-noise ratio and the sending check result ack of each frame in storage at the same time, wherein the MCS number is selected to feed back to the sending end, and the MCS number, the signal-to-noise ratio and the sending check result ack are stored in the formula
blertRefers to the target frame error rate, and alpha refers to the number of each MCS;
(6) making K-1 and K-0, and executing step (7); otherwise, returning to the step (3), and sequentially executing the step (3) to the step (6);
(7) after updating the parameters for K times, the MCS numbers are according to the final parameters
Calculating and selecting;
(8) and (5) simulating the switching rule in the step (7) under different signal-to-noise ratios with the switching rule based on the EESM and KNN algorithms, and counting the system throughput.
Further, the stored data in the step (3) are classified according to different MCS numbers, and the classified data are sorted from low to high according to the signal-to-noise ratio, so as to screen out effective data.
Further, the effective data representation method and the handover related parameter calculation method in the step (4).
Further, the channel quality representation method in the step (5)
Another object of the present invention is to provide a mobile communication system applying the online-learned adaptive link MCS switching control method.
In summary, the advantages and positive effects of the invention are:compared with the traditional algorithm based on the lookup table, the method does not need to depend on a channel model in a specific environment, is an online learning method, can obtain the proper switching interval of each MCS according to the actual receiving result, does not need to obtain the accurate classification data of each MCS in advance compared with some supervision learning algorithms, and can still obtain better switching effect when the channel changes.
The above table shows that under the condition that the channel model is inaccurate and the provided classification samples have some deviations, the algorithm can enable the system to obtain higher throughput under the condition of the same average signal-to-noise ratio, and the switching result is obviously better than the other two types.
Drawings
Fig. 1 is a flowchart of an online learning adaptive link MCS switching control method according to an embodiment of the present invention.
Fig. 2 is a flowchart of an implementation of an online learning adaptive link MCS switching control method according to an embodiment of the present invention.
Fig. 3 is a graph comparing system throughput at different signal-to-noise ratios with the switching criterion based on the EESM and KNN algorithms provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention particularly relates to an online learning adaptive link MCS switching control method, which can be used for switching an MCS of a link adaptive system under a scattering channel of which a real channel model is unknown. The method is used for solving the technical problem that the lookup table scheme excessively depends on a channel model and a supervised learning algorithm depends on the known definite classification.
As shown in fig. 1, the method for controlling MCS switching of an online learning adaptive link according to an embodiment of the present invention includes the following steps:
s101: selecting parameters; within a certain time, the transmitting end circularly transmits different MCS schemes, and the receiving end processes and stores related data; classifying the stored data according to different MCS schemes and screening effective data;
s102: respectively calculating corresponding related parameters of each MCS according to the effective data; in a certain time, the receiving end selects and feeds back a corresponding MCS according to the current channel state and an updating criterion, the sending end sends data according to the fed-back MCS, and the receiving end processes and stores the data;
s103: after multiple updates, the final corresponding parameters of each MCS are obtained, and the receiving end feeds back the MCS according to the corresponding criteria; and respectively counting the system throughput of each method under different signal-to-noise ratios according to the switching criterion, the EESM switching criterion and the KNN algorithm switching criterion.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 2, the method for controlling MCS switching of an online learning adaptive link according to an embodiment of the present invention specifically includes the following steps:
step 1, selecting a parameter updating time interval T, selecting an exploration coefficient alpha, and selecting a parameter updating frequency K:
the signal-to-noise ratio of the actually measured signal receiving end changes rapidly, so the parameter updating time T is selected to be 30 minutes; the exploration coefficient alpha represents the exploration degree, in brief, the receiving end can feedback the adjacent MCS numbers in an exploratory manner except that the receiving end always feeds back the MCS number which is considered to be optimal under the parameter, and the larger the alpha value is, the larger the detection degree is, and the actually selected alpha value is 0.2; generally, in a certain channel environment, after multiple parameter updates, a relatively stable state may be achieved, which is related to different usage scenarios, and theoretically, the larger the value of the parameter update times K is, the more accurate the finally calculated switching parameter is, and the actually selected parameter update times K is 5.
the first is a probing phase, which sends MCS number selection in two ways: the first cyclic transmission, namely continuously and cyclically transmitting different MCS numbers within the time of 0 < T < T; the second type is random transmission, namely, MCS numbers are randomly transmitted within the time of 0 < T < T. In this period, the receiving end processes each frame of data and stores the corresponding parameters of each frame, including MCS number, signal-to-noise ratio (SNR) and corresponding transmission check result ack (1 is wrong and 0 is right).
Step 3, classifying the stored data according to different MCS numbers, sorting the classified data from low to high according to the signal-to-noise ratio, and screening out effective data:
for the stored data, firstly, the data are classified according to different MCS numbers, then the classified data are respectively sequenced according to the sequence of the SNR from low to high, and the corresponding sending check result ack is also changed along with the change of the SNR position. In order to accelerate the convergence speed, the average check value (ack) calculation can be performed on the signal-to-noise ratio according to the 0.5dB increase interval, all the original data with the average value within the (0, 1) interval are regarded as valid data, and each MCS valid data is expressed as:
Da＝[snr1，snr2，...，snrN；b，b，...，b]；
the corresponding verification result is expressed as:
Ca＝[1-ack1，1-ack2，...，1-ackN]；
a refers to the number of each MCS, N refers to the number of valid data, and b is an unknown parameter.
Step 4, according to the formula, the effective data of each MCS is:
calculating the corresponding parameter thetaaIn the above formula Da＝[snr1，snr2，...，snrN；b，b，...，b]，Ca＝[1-ack1，1-ack2，...，1-ackN]And a refers to the number of each MCS:
numbering according to each MCS
Calculating parameters
selecting MCS number to feed back to the sending end, and updating the MCS number, signal-to-noise ratio (SNR) and corresponding Frame Error Rate (FER) of each frame in storage at the same time, wherein
blertRefers to the target frame error rate, a refers to the number of each MCS:
in the next time interval T, the receiving end can write as the current channel state (i.e. the current SNR) according to the current channel state
Separately calculate for each MCS
Based on target frame error rate blert(actual selection: 10%), selection
Corresponding maximum MCS number. If there is no MCS number satisfying the condition, the minimum MCS number is selected at this time. Note that the stored data in storage needs to be updated at this time.
And step 6, enabling K to be K-1. If K is equal to 0, executing step 7; otherwise, returning to the step 3, and sequentially executing the steps 3, 4, 5 and 6:
after the last transmission time interval is over, the parameter is considered to have been updated once, so K is K-1. Then judging whether the parameter updating times reach a set value, namely whether K is equal to 0, and if so, executing the next step; if not, returning to the step 3, and sequentially executing the steps 3, 4, 5 and 6 to continuously update the parameters until the updating times are completed.
Step 7, after updating the parameters for K times, the MCS numbers are according to the final parameters
And (3) calculation and selection:
at this time, the number of updates has reached the preset value, and thus the finally obtained handover parameter can be considered
Has been substantially close to the optimum value, and thereafter, the MCS switching is performed according to the final parameters
And (4) calculating.
And 8, simulating the switching criterion in the step 7 and the switching criterion based on EESM and KNN algorithms under different signal-to-noise ratios, and counting the system throughput.
The following will describe the effects of the present invention in detail.
In order to compare the performance of the method with that of other methods, the performance measurement parameter is selected as the throughput, so that under the condition of different signal to noise ratios, a system adopts different MCS switching methods for simulation (mainly comprising the final switching parameter of the method, an EESM switching algorithm and a KNN switching algorithm), and the throughputs of different methods are respectively counted and compared.
1 simulation conditions and contents:
MATLAB R2011b software is used for carrying out comparison simulation on the method and the existing method based on a lookup table (EESM) and supervised learning (KNN algorithm), wherein when the EESM algorithm selects the optimal parameter value, a channel model is slightly different from an actual model, two samples of each known MCS classification given by the KNN algorithm are error samples, and the result is shown in figure 3.
2, simulation result analysis:
referring to fig. 3, the horizontal axis represents the signal-to-noise ratio in dB, the vertical axis represents the system throughput in bps, the channel of this embodiment is the measured scattering channel, the parameter update time interval T of the present invention is 30 minutes, the search coefficient α is selected to be 0.2, the parameter update frequency K is selected to be 5, and the K value in the KNN algorithm is selected to be 10. As can be seen from the figure, under the condition of low signal-to-noise ratio, namely below-10 dB, the performance of a plurality of algorithms is not greatly different, and the algorithm of the invention is slightly superior to other two algorithms; under the condition of high signal-to-noise ratio, the KNN algorithm and the EESM algorithm have similar performance, curves are crossed, sometimes the EESM is better, sometimes the KNN algorithm is better, the performance of the algorithm is obviously better than that of the other two algorithms, and the throughput difference is larger along with the improvement of the signal-to-noise ratio. The algorithm of the present invention is generally superior to the other two algorithms.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. An online-learning adaptive link MCS switching control method, characterized in that the online-learning adaptive link MCS switching control method comprises: selecting parameters; the transmitting end circularly transmits different MCS schemes, and the receiving end processes and stores related data; classifying the stored data according to different MCS schemes and screening effective data; respectively calculating corresponding related parameters of each MCS according to the effective data; the receiving end selects and feeds back a corresponding MCS according to the current channel state and an updating criterion, the sending end sends data according to the fed-back MCS, and the receiving end processes and stores the data; after multiple updates, the final corresponding parameters of each MCS are obtained, and the receiving end feeds back the MCS according to the corresponding criteria; respectively counting the system throughput of each method under different signal-to-noise ratios according to the switching criterion, the EESM switching criterion and the KNN algorithm switching criterion;
the online learning adaptive link MCS switching control method comprises the following steps:
(1) selecting a parameter updating time interval T, selecting an exploration coefficient alpha and a parameter updating frequency K;
(2) the transmitting end continuously and sequentially transmits MCS with different numbers in a circulating mode within the time that T is more than 0 and less than T, the receiving end processes each data frame, and estimates and stores the MCS number and the signal-to-noise ratio of each frame and a corresponding transmission check result ack, wherein 1 is wrong and 0 is right;
(3) classifying the stored data according to different MCS numbers, sorting the classified data from low to high according to the signal-to-noise ratio, and screening out effective data;
(4) the valid data for each MCS is according to the formula:
calculating the corresponding parameter thetaaIn the above formula Da＝[snr1，snr2，...，snrN；b，b，...，b]，Ca＝[1-ack1，1-ack2，...，1-ackN]A indicates the number of each MCS;
(5) in the next time interval T, the receiving end follows the following criteria according to the current channel state:
selecting MCS number to feed back to the sending end, and updating the MCS number, the signal-to-noise ratio and the sending check result ack of each frame in storage at the same time, wherein the MCS number is selected to feed back to the sending end, and the MCS number, the signal-to-noise ratio and the sending check result ack are stored in the formula
blertRefers to the target frame error rate, a refers to the number of each MCS;
(6) making K equal to K-1, and if K equal to 0, executing step (7); otherwise, returning to the step (3), and sequentially executing the step (3) to the step (6);
(7) after updating the parameters K times, the MCS numbers are according toAccording to final parameters
Calculating and selecting;
(8) and (5) simulating the switching rule in the step (7) under different signal-to-noise ratios with the switching rule based on the EESM and KNN algorithms, and counting the system throughput.
2. The on-line learning adaptive link MCS switching control method according to claim 1, wherein the channel quality representation method in step (5)
3. A mobile communication system applying the online learning adaptive link MCS switching control method of any claim 1-2.
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