CN110380794B - Data-driven radio frequency transmission power calibration method and device for wireless communication system - Google Patents

Data-driven radio frequency transmission power calibration method and device for wireless communication system Download PDF

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CN110380794B
CN110380794B CN201910630033.7A CN201910630033A CN110380794B CN 110380794 B CN110380794 B CN 110380794B CN 201910630033 A CN201910630033 A CN 201910630033A CN 110380794 B CN110380794 B CN 110380794B
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CN110380794A (en
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全智
谢良辉
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Shenzhen Zhongcheng Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/10—Monitoring; Testing of transmitters
    • H04B17/11—Monitoring; Testing of transmitters for calibration
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a method and a device for adaptively calibrating transmitting power in a radio frequency front-end module based on a data-driven algorithm, which are used for a wireless communication system. By converting the transmit power calibration problem into a dynamic linearization model, the control system design is simplified. Compared with other power calibration methods, the calibration algorithm provided by the invention bypasses the modeling step, does not need to establish an accurate system model, and only needs input/output (I/O) measurement data of the system. The related experimental results of the invention show that the error of the emission power of the device can be reduced from 1.5dB to within 0.25dB by using the calibration algorithm and the device based on the data driving.

Description

Data-driven radio frequency transmission power calibration method and device for wireless communication system
Technical Field
The present invention relates to the field of wireless communications, and in particular, to a method and apparatus for calibrating data-driven Radio Frequency (RF) transmit power for a wireless communication system.
Background
Wireless communication systems are generally adapted to have a transceiver for transmitting and receiving wireless signals, which refers to long-distance transmission and communication among a plurality of nodes without propagation via conductors or cables, and includes various fixed, mobile and portable applications, such as two-way radios, cellular phones, personal digital assistants and wireless networks. Other examples of wireless radio communication are GPS, garage door remote control, wireless mouse, etc.
Most wireless communication technologies use radio, including Wi-Fi, which is only a few meters away, and also deep space networks. In a wireless communication system, various devices generally have an associated transmitting component such as an antenna to transmit a wireless signal to a receiving end or a relay end, and some common device types are a wireless communication cellular device, a bluetooth device, a ZigBee device, a Wi-Fi device, a wireless local area network device and the like. RF communication systems such as cellular devices, bluetooth, Zigbee and Wi-Fi require accurate RF transmit power to function properly. Accurate RF transmit power can improve spectral efficiency, device energy efficiency, and simultaneously improve network throughput.
Radio Frequency (RF) means electromagnetic frequencies that can radiate into space, ranging from 300kHz to 300 GHz. In the theory of electronics, current flows through a conductor, and a magnetic field is formed around the conductor; an alternating current passes through a conductor, around which an alternating electromagnetic field, called an electromagnetic wave, is formed. When the frequency of the electromagnetic waves is lower than 100kHz, the electromagnetic waves can be absorbed by the ground surface and cannot form effective transmission, but when the frequency of the electromagnetic waves is higher than 100kHz, the electromagnetic waves can be transmitted in the air and reflected by an ionosphere at the outer edge of the atmosphere to form long-distance transmission capability. High frequency electromagnetic waves with long distance transmission capabilities are commonly referred to as radio frequencies. Radio frequency technology is widely used in the field of wireless communication, and systems such as cable television, Wi-Fi, WLAN, etc. use radio frequency transmission.
In real application scenarios, such as CDMA and LTE systems, techniques such as Automatic Power Control (APC) are typically used to adjust the RF transmit power. The method can effectively reduce the influence of signal attenuation caused by the distance change between the user equipment and the base station. Considering that in today with frequent wireless communication evolution, system functions of some mobile terminals, especially 5G and later mobile terminals, are further expanded, and the amount of transmitted data is significantly increased, it is also necessary to adjust own transmission power to prolong battery life for Wi-Fi devices, especially for battery-powered mobile terminals. Therefore, each wireless device must accurately adjust its own transmission power according to the communication environment.
In wireless communication devices, the RF front-end module is usually composed of analog components, and minor manufacturing errors in the production of the device and components therein have a high impact on the output transmission power of the communication system, while reducing link quality and increasing the defect rate in the production line. Therefore, even if the same type of communication chips are designed and produced in the same factory, they have different transmission power levels. For the above reasons, in addition to various interference factors, such as nonlinearity of the power amplifier, environmental changes such as impedance matching and temperature, and path loss of the circuit, the output RF power is different from the desired RF target power. In large scale manufacturing, these factors result in a difference of 3-5dB between the output power and the target power. Therefore, it is necessary to calibrate the transmission power of the device before shipment. The existing radio frequency transmission power calibration method mainly depends on adjusting PA parameters. An analog on-line gain calibration loop is introduced, for example, for the PA to calibrate the system transmit power. This, however, significantly increases design and development costs. The transmit power is calibrated by establishing a linear model of the Transmit Signal Strength Indication (TSSI) and PA parameters as a two-point calibration method. However, there is typically a non-linear relationship between the TSSI and PA parameters, which leads to poor calibration accuracy and slow convergence speed when using a two-point calibration method. Another way is to model the relationship between the TSSI and PA parameters as a finite response filter FIR system and then transform the transmit power calibration problem into a parameter estimation problem that can be solved adaptively using least mean square LMS filtering. However, in the prior art, all prior work required a mathematical model of the system process first. Even if an accurate mathematical model is designed, its high order, complex structure or non-linearity makes it unsuitable for practical implementation.
The present invention proposes a data-driven based method and apparatus for calibrating RF transmit power levels without prior knowledge of the system model. Compared with the prior art, the method and the device can improve the calibration precision and shorten the calibration time. The proposed data-driven based method and apparatus is based on a dynamically linearized data model, in which time-varying parameters called pseudo-jacobian matrices (PJM) are estimated for each power measurement. The PJM may be used to determine the optimal values of PA parameters via online I/O data estimation of the system.
Disclosure of Invention
The present invention is directed to a data-driven RF transmit power calibration method and apparatus for a wireless communication system.
In order to achieve the purpose, the technical scheme of the invention is as follows: a data-driven RF transmit power calibration method for a wireless communication system, the method comprising the steps of: establishing an equivalent dynamic linearization model; and searching for a transmission power calibration parameter based on the equivalent dynamic linearization model.
Preferably, PA is stored in NVRAM with three different parameters u ═ u0,u1,u2]TControl, PA parameters u andthe mean square error y between the measured power and the target power has a non-linear relationship:
y(k)＝f(y(k-1),…,u(k-ny),u(k),u(k-1),…,u(k-nu),
wherein the integer ny，nuIs the order, f (-) is a non-linear function, the error y of the system at the k-th time is from k-nuInput state u from the next to the k-th, and from k-nyThe error value y from the beginning to k-1 times.
Preferably, the present invention can simplify the nonlinear relationship as:
y(k)＝f(y(k-1),u(k),u(k-1))。
preferably, a time-varying parameter is present in the invention
Figure GDA0002591151710000031
The system was converted to the following equivalent dynamic linearized data model:
Figure GDA0002591151710000032
wherein
Figure GDA0002591151710000033
Figure GDA0002591151710000034
For all k are bounded, b is a normal number.
As another preferred approach, the present invention uses a modified projection algorithm to estimate the PJM parameters
Figure GDA0002591151710000035
The criteria function for the PJM estimation is defined as:
Figure GDA0002591151710000036
wherein mu>0 is a weighting factor used to avoid excessive changes in the PJM estimate,
Figure GDA0002591151710000037
is that
Figure GDA0002591151710000038
An estimate of (d).
As another preferred solution, the method of the present invention can solve the criterion function optimal condition based on the above process:
Figure GDA0002591151710000039
obtaining:
Figure GDA00025911517100000310
where η ∈ (0,2] is the step-size constant;
to make the system have better time-varying tracking characteristics and to ensure Δ u ≠ 0, the following reset scheme is used:
Figure GDA00025911517100000311
Figure GDA0002591151710000041
wherein
Figure GDA0002591151710000042
Is the initial value of PJM, a small normal number;
for a given target error y*Should be zero, search the corresponding PA parameter u (k) so that the corresponding y (k) can obtain the target value y*＝0；
The following criteria function is used:
J(u(k))＝‖y*-y(k)‖2+λ||u(k)-u(k-1)||2(11)
wherein λ >0 is a weighting factor for preventing excessive variation of the u (k) estimate;
solving the optimal conditions:
Figure GDA0002591151710000043
can obtain the product
Figure GDA0002591151710000044
Where ρ ∈ (0, 1) is the step-size constant;
the data-driven based algorithm adjusts the input PA parameters u (k) according to (9) and (13) successive iterations such that the output power error of the system is as close to y as possible*。
As another preferred embodiment, the method of the present invention further comprises the steps of:
1, inputting:
desired power error: y is*＝0
Target power: pd＝[9:18]dBm
Threshold value: pth＝0.25dB
2 initializing the parameters η, mu, p, y (1),
Figure GDA0002591151710000045
u(1)，Pm
and 3, outputting: u ═ u1，u2，u3]
4, repeating the steps 5-12:
5.
Figure GDA0002591151710000051
6:
Figure GDA0002591151710000052
7:
Figure GDA0002591151710000053
8:
Figure GDA0002591151710000054
9：endif
10:
Figure GDA0002591151710000055
11 storing u (k) in NVRAM;
measuring the transmission power P of a wireless communication devicem；
13 to max (| P)m-Pd|)＜Pth。
In addition, the present invention also provides a data-driven RF transmit power calibration apparatus for a wireless communication system, comprising: the equivalent dynamic linearization model module is used for establishing the equivalent dynamic linearization model step; and the transmitting power calibration parameter searching module is used for searching the transmitting power calibration parameter based on the equivalent dynamic linearization model.
As another preferred solution, the claimed apparatus of the present invention further comprises NVRAM; PA parameter control unit for controlling the NVRAM by three different parameters u ═ u0,u1,u2]TControlling the PA; and a nonlinear relation control part, wherein a PA parameter u has a nonlinear relation with a mean square error y between the measured power and the target power:
y(k)＝f(y(k-1),…,y(k-ny),u(k),u(k-1),…,u(k-nu) Wherein the integer ny，nyIs the order, f (-) is a non-linear function, the error y of the system at the k-th time is from k-nuInput state u from the next to the k-th, and from k-nyThe error value y from the beginning to k-1 times.
As another preferable aspect, the apparatus claimed in the present invention further comprises a nonlinear relation simplification means for simplifying the nonlinear relation to:
y(k)＝f(y(k-1),u(k),u(k-1))。
as another preferred solution, the claimed device of the present invention further comprises an equivalent dynamic linearization conversion module for setting a time-varying parameter
Figure GDA0002591151710000061
Convert the system intoThe following equivalent dynamic linearized data model:
Figure GDA0002591151710000062
wherein
Figure GDA0002591151710000063
Figure GDA0002591151710000064
For all k are bounded, b is a normal number.
As another preferred solution, the claimed apparatus of the present invention further comprises an estimation module: method for estimating PJM parameters using a modified projection algorithm
Figure GDA0002591151710000065
The criteria function for the PJM estimation is defined as:
Figure GDA0002591151710000066
where μ >0 is a weighting factor used to avoid excessive variation in the PJM estimate,
Figure GDA0002591151710000067
is that
Figure GDA0002591151710000068
An estimate of (d).
As another preferred solution, in the apparatus claimed in the present invention, the transmission power calibration parameter searching module further includes:
the optimal condition solving module is used for solving the optimal condition of the criterion function:
Figure GDA0002591151710000069
obtaining:
Figure GDA00025911517100000610
where η ∈ (0,2] is the step-size constant;
a reset module for using the following reset scheme for better time-varying tracking characteristics of the system and for ensuring Δ u ≠ 0:
Figure GDA00025911517100000611
wherein
Figure GDA00025911517100000612
Is the initial value of PJM, a small normal number;
a calibration parameter search module for searching for a given target error y*Should be zero, search the corresponding PA parameter u (k) so that the corresponding y (k) can obtain the target value y*＝0；
The following criteria function is used:
J(u(k))＝‖y*-y(k)‖2+λ‖u(k)-u(k-1)‖2, (11)
wherein λ >0 is a weighting factor for preventing excessive variation in the u (k) estimate;
solving the optimal conditions:
Figure GDA0002591151710000071
can obtain the product
Figure GDA0002591151710000072
Where ρ ∈ (0, 1) is the step-size constant;
an iteration module for adjusting the input PA parameters u (k) according to (9) and (13) successive iterations based on a data-driven algorithm such that the output power error of the system is as close as possible to y*。
As another preferred aspect, the claimed apparatus of the present invention further comprises: the execution module is used for specifically executing the following steps:
1, inputting:
desired power error:y*＝0
target power: pd＝[9:18]dBm
Threshold value: pth＝0.25dB
2 initializing the parameters η, mu, p, y (1),
Figure GDA0002591151710000073
u(1)，Pm
and 3, outputting: u ═ u1，u2，u3]
4, repeating the steps 5-12:
5.
Figure GDA0002591151710000074
6:
Figure GDA0002591151710000081
7：
Figure GDA0002591151710000082
8：
Figure GDA0002591151710000083
9：endif
10：
Figure GDA0002591151710000084
11 storing u (k) in NVRAM;
measuring the transmission power P of a wireless communication devicem；
13 to max (| P)m-Pd|)＜Pth。
The data-driven RF transmitting power calibration method and device for the wireless communication system convert the power calibration system into a dynamic linear model between an input power parameter and an output power error, and compared with the calibration model in the prior art, the data-driven calibration method does not need an accurate system model and only relies on system I/O data to calibrate the system power. The analysis of actually processed objective experimental data shows that the error of the transmitting power of the device can be obviously reduced after a plurality of iterations.
Drawings
FIG. 1 is one type of RF front end power control architecture of the present invention;
FIG. 2 is a graph of the output power of two devices of the present invention at the same PA parameters;
FIG. 3 is an arrangement of the transmit power calibration method and apparatus of the present invention;
FIG. 4 is a diagram of one embodiment of measured transmit power and predetermined transmit power in different iterations using the proposed method and apparatus;
fig. 5 indicates the error of the transmit power from the preset transmit power in different method iterations of the invention.
Detailed Description
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that although the terms first, second, etc. may be used in embodiments of the invention to describe methods and corresponding apparatus, these keywords should not be limited to these terms. These terms are only used to distinguish keywords from each other. For example, a first beam tracking method and corresponding apparatus may also be referred to as a second beam tracking method and corresponding apparatus, and similarly, a second beam tracking method and corresponding apparatus may also be referred to as a first beam tracking method and corresponding apparatus, without departing from the scope of embodiments of the present invention.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
As shown in fig. 1, most RF front-end modules are mainly composed of mixers, filters, PAs and other components. In an RF transmitter, a mixer up-converts the modulated signal to a higher frequency and a filter is used to improve the signal SNR. The PA amplifies the modulated signal to high power before transmission through the antenna. All of these analog devices have some variable parameters that can cause RF transmit power level deviations. Since the PA is the last stage of the RF transmitter, all analog device-induced errors, including the PA, will be amplified by the PA. It follows that PA is the main source of power error in the output signal. Most RF transmit power calibration methods minimize RF transmit power errors by adjusting PA parameters.
Generally, power control in the RF front-end module has two ways: open loop and closed loop. Description figure 1 shows a closed loop power control architecture in an RF transmitter that includes a directional coupler, a power detector, an analog-to-digital converter (ADC), a digital-to-analog converter (DAC), and a logic control unit. After the PA amplifies the RF signal, a small portion of the signal is fed back to the power detector through the directional coupler. The ADC then converts the output of the detector into a digital signal, the TSSI value, which is used to capture the actual output power. The logic control unit uses the difference between the TSSI value and the target power level to adjust PA parameters and store them in non-volatile random access memory (NVRAM).
As shown in the description accompanying fig. 2, the description accompanying fig. 2 shows output power curves of two Wi-Fi devices of the same type. The present invention uses power meters to measure their actual power at the same preset power. Ideally, the preset power should be equal to the measured power. However, as is clear from fig. 2, the actual output power curve of each device differs from the target power curve by about 1.5dBm, and the output powers of the two devices are also different from each other in comparison result.
In the present invention, a method and apparatus for data-driven RF transmit power calibration is presented to find the best setting of the transmit power level. The method comprises an equivalent dynamic linearization model step and a transmitting power calibration step.
Equivalent dynamic linearization model: in the present invention, consider the case where PA is controlled by three different parameters stored in NVRAM0,u1,u2]T. The non-linear relationship between the PA parameter u and the mean squared error y between the measured power and the target power can be described as:
y(k)＝f(y(k-1),…,y(k-ny,u(k),u(k-1),…,u(k-nu)) (1)
wherein the integer ny，nuIs the unknown order, and f (-) is the unknown nonlinear function. As can be seen from (1), the error y of the system at the k-th time is equal to the error from k-nuInput state u from the next to the k-th, and from k-nyThe error value y from the beginning to k-1 times. To reduce the complexity of the model, the model can be simplified as:
y(k)＝f(y(k-1),u(k),u(k-1)) (2)
the nonlinear system is represented as a dynamic linearization system based on two preconditions: precondition 1: the partial derivative of the unknown function f (-) with respect to the PA parameter u (k) is continuous. Precondition 2: system (2) is generalized Lipschitz, i.e.:
Figure GDA00025911517100001112
and is
Δy(k)＝y(k)-y(k-1), (4)
Δu(k)＝u(k)-u(k-1),‖Δu(k)‖≠0, (5)
Where b is a normal number.
If the non-linear system (2) satisfies the preconditions 1 and 2, then there must be a time-varying parameter
Figure GDA0002591151710000111
In this way the system (2) can be converted into the following equivalent dynamic linearized data model:
Figure GDA0002591151710000112
wherein
Figure GDA0002591151710000113
This is called Pseudo Jacobian Matrix (PJM) by the present invention, and
Figure GDA0002591151710000114
bounded for all k.
Power calibration parameter search due to
Figure GDA0002591151710000115
Is a time-varying parameter, and the traditional projection algorithm and the least square algorithm can not track well
Figure GDA0002591151710000116
A change in (c). The present invention estimates the PJM parameter using a modified projection algorithm
Figure GDA0002591151710000117
The criterion function of the PJM estimation may be definedComprises the following steps:
Figure GDA0002591151710000118
wherein mu>0 is a weighting factor used to avoid excessive variation of the PJM estimate, and
Figure GDA0002591151710000119
is that
Figure GDA00025911517100001110
An estimate of (d). By solving the optimal conditions:
Figure GDA00025911517100001111
the following can be obtained:
Figure GDA0002591151710000121
in order for the system to have better time-varying tracking characteristics and to ensure Δ u ≠ 0, the following reset scheme is used:
Figure GDA0002591151710000122
Figure GDA0002591151710000123
wherein
Figure GDA0002591151710000124
Is the initial value of PJM, a small positive constant. For a given target error y*Should be zero, the corresponding PA parameter u (k) needs to be found so that the corresponding y (k) can obtain the target value y*Therefore, consider the following criteria function:
J(u(k))＝‖y*-y(k)‖2+λ‖u(k)-u(k-1)‖2, (11)
where λ >0 is a weighting factor used to prevent excessive variation in the u (k) estimate. Solving the optimal conditions:
Figure GDA0002591151710000125
can obtain the product
Figure GDA0002591151710000126
Where ρ ∈ (0, 1)]Is the step constant. In the invention, the data-driven based algorithm can continuously adjust the input PA parameters u (k) according to (9) and (13) so that the output power error of the system is as close to y as possible*。
Unlike the power calibration method in the prior art, in the data-driven-based method of the present invention, there is no need to build an accurate system model, and only I/O data of the system is needed. As in equation (3), the system can be represented as a dynamic linearized model, where the input data are the three PA parameters u (k) or u ═ u0,u1,u2]TAnd the output is the measured actual power PmAnd a target power PdThe mean square error y (k) of (a), wherein the dynamic linearization information is the PJM matrix. Once the dynamic linearized model of the system is built using a data-driven approach, it can be used to optimize PA parameters so that the transmit power curve can converge to the target power through iterative calibration.
As can be seen from FIG. 1 of the specification, the default PA parameter u may be used as the initial input value, as noted earlier in the specification, depending on the power control problem, it may be necessary to set some other variables reasonably, including: η, μ, ρ, λ, etc., to improve power calibration iteratively in the proposed method and apparatus of the invention, the data-driven algorithm calculates a new set of PA parameters u from (9) and (13) at each iteration, which will store the same initial PA parameter u stored in NVRAMIn NVRAM. The output power is then measured and fed back to the algorithm to obtain a new set of PA parameters. This process is repeated until the maximum error value of the output power compared to the target power is less than a certain threshold value Pth. The main steps of the proposed method are described in detail with reference to the description of the invention below.
In addition, as one of the preferred embodiments, the present invention proposes experimental results to verify the performance of the proposed data-driven based RF transmit power calibration algorithm. As shown in fig. 3 of the specification, the present invention uses an 802.11n communication module as an RF front-end module to be calibrated. It operates with 20MHz bandwidth in the 2.4GHz band with a transmit power of 9 to 18 dBm. Table 1 shows the setup parameters of the wireless communication module in the experiment.
As one of the preferred embodiments, the corresponding method and apparatus of the present invention is set forth in FIG. 3 of the specification. In the power calibration process, the control center, i.e., PC, first stores the initial value of PA parameter u (1) in NVRAM. Thereafter, the wireless device loads the stored PA parameters and transmits a signal according to the target power value. The PC reads the actual transmit power through a power meter and calculates the mean square error y. The PC updates the PA parameters by (9) and (13) and repeats the process until the maximum error between the measured transmit power and the target transmit power is less than 0.25 dB. The detailed calibration procedure is shown in algorithm 1.
Table 1 wireless communication device configuration
Figure GDA0002591151710000141
The specific procedure of the data-driven RF transmit power calibration control algorithm 1 is as follows:
algorithm 1 data-driven RF transmit power calibration control
1, inputting:
desired power error: y is*＝0.
Target power: pd＝[9:18]dBm.
Threshold value: pth＝0.25dB.
2 initializing the parameters η, mu, p, y (1),
Figure GDA0002591151710000142
u(1)，Pm.
and 3, outputting: u ═ u1，u2，u3]
4, repeating the steps 5-12:
5.
Figure GDA0002591151710000143
6:
Figure GDA0002591151710000144
7:
Figure GDA0002591151710000145
8:
Figure GDA0002591151710000146
9:endif
10:
Figure GDA0002591151710000147
11. storing u (k) in NVRAM;
12. measuring transmission power P of wireless communication devicem；
13. Up to max (| P)m-Pd|)＜Pth。
Description figure 4 shows the relation between the measured transmission power and the preset power by using the data-driven-based method proposed by the present invention, and description figure 5 shows the error of the transmission power relative to the target transmission power. As can be seen from the results, by continuously adjusting the PA parameters, the transmission power error of the wireless communication device is significantly reduced. For example, after four calibrations, the transmit power error of the device can be reduced from 1.5dB to an acceptable 0.25 dB.
The invention provides a data-driven radio frequency emission power calibration method and device for a wireless communication system, which convert a power calibration system into a dynamic linear model between an input power parameter and an output power error. Unlike previous calibration models, the proposed data-driven calibration method does not require an accurate system model, relying only on system I/O data to calibrate system power. After a plurality of iterations, the emission power error of the device can be obviously reduced.
In all the above embodiments, in order to meet the requirements of some special data transmission and read/write functions, the above method and its corresponding devices may add devices, modules, devices, hardware, pin connections or memory and processor differences to expand the functions during the operation process.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described method, apparatus and unit may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the method steps into only one logical or functional division may be implemented in practice in another manner, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as individual steps of the method, apparatus separation parts may or may not be logically or physically separate, or may not be physical units, and may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, the method steps, the implementation thereof, and the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The above-described method and apparatus may be implemented as an integrated unit in the form of a software functional unit, which may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), an NVRAM, a magnetic disk, or an optical disk, and various media capable of storing program codes.
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, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
It should be noted that: the above embodiments are only used to explain and illustrate the technical solution of the present invention more clearly, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A data-driven radio frequency transmit power calibration method for a wireless communication system, the method comprising the steps of:
establishing an equivalent dynamic linearization model;
based on the equivalent dynamic linearization model, searching for a transmission power calibration parameter;
estimating PJM parameters using a modified projection algorithm
Figure FDA0002591151700000011
The criteria function for the PJM estimation is defined as:
Figure FDA0002591151700000012
where μ >0 is a weighting factor used to avoid excessive variation in the PJM estimate,
Figure FDA0002591151700000013
is that
Figure FDA0002591151700000014
An estimated value of (d);
to make the system have better time-varying tracking characteristics and to ensure Δ u ≠ 0, the following reset scheme is used:
Figure FDA0002591151700000015
Figure FDA0002591151700000016
wherein
Figure FDA0002591151700000017
Is the initial value of PJM, a small normal number;
the above steps are implemented using the following specific algorithm:
1. inputting:
desired power error: y is*＝0
Target power: pd＝[9：18]dBm
Threshold value: pth＝0.25dB
2. Initializing parameters：η，μ，ρ，，y(1)，
Figure FDA0002591151700000018
u(1)，Pm
3. And (3) outputting: u ═ u1，u2，u3]
4. And (5) repeating the steps of 5-12:
5.
Figure FDA0002591151700000019
6.
Figure FDA00025911517000000110
7.
Figure FDA00025911517000000111
8.
Figure FDA00025911517000000112
9.endif
10.
Figure FDA0002591151700000021
11. storing u (k) in NVRAM;
12. measuring transmission power P of wireless communication devicem；
13. Up to max (| P)m-Pd|)＜Pth。
2. The data-driven radio frequency transmit power calibration method of claim 1, wherein the PA is calibrated by three different parameters u-u stored in the NVRAM0，u1，u2]TAnd controlling, wherein a nonlinear relation exists between the PA parameter u and the mean square error y between the actual output power and the target power:
y(k)＝f(y(k-1)，...，y(k-ny)，u(k)，u(k-1)，...，u(k-nu) Therein is integral withNumber ny，nuIs the order, f (-) is a non-linear function, the error y of the system at the k-th time is from k-nuInput state u from the next to the k-th, and from k-nuThe error value y from the beginning to k-1 times.
3. The data-driven radio frequency transmit power calibration method of claim 2, wherein the nonlinear relationship is simplified to:
y(k)＝f(y(k-1)，u(k)，u(k-1))。
4. the data-driven radio frequency transmit power calibration method of claim 3, wherein a time-varying parameter φ (k) is present, converting the system into the following equivalent dynamic linearized data model:
Figure FDA0002591151700000022
wherein
Figure FDA0002591151700000023
Is a pseudo-jacobian matrix (PJM),
Figure FDA0002591151700000024
for all k are bounded, b is a normal number.
5. The data-driven radio frequency transmit power calibration method of claim 4, wherein solving the criterion function optimal condition:
Figure FDA0002591151700000025
obtaining:
Figure FDA0002591151700000026
where η ∈ (0,2] is the step-size constant;
to make the system have better time-varying tracking characteristics and to ensure Δ u ≠ 0, the following reset scheme is used:
Figure FDA0002591151700000031
Figure FDA0002591151700000032
wherein
Figure FDA0002591151700000033
Is the initial value of PJM, a small normal number;
for a given target error y*Should be zero, search the corresponding PA parameter u (k) so that the corresponding y (k) can obtain the target value y*＝0；
The following control input criteria functions are used:
J(u(k))＝||y*-y(k)||2+λ||u(k)-u(k-1)||2， (11)
wherein λ >0 is a weighting factor for preventing excessive variation in the u (k) estimate;
solving the optimal conditions:
Figure FDA0002591151700000034
can obtain the product
Figure FDA0002591151700000035
Where ρ ∈ (0, 1) is the step-size constant;
the data-driven based algorithm adjusts the input PA parameters u (k) according to (9) and (13) successive iterations such that the output power error of the system is as close to y as possible*。
6. A data driven radio frequency transmit power calibration apparatus for a wireless communication system, the apparatus comprising:
the equivalent dynamic linearization model module is used for establishing the equivalent dynamic linearization model step;
the transmission power calibration parameter searching module is used for searching the transmission power calibration parameter based on the equivalent dynamic linearization model;
the device further comprises: an estimation module: method for estimating PJM parameters using a modified projection algorithm
Figure FDA0002591151700000036
The criteria function for the PJM estimation is defined as:
Figure FDA0002591151700000037
where μ >0 is a weighting factor used to avoid excessive variation in the PJM estimate,
Figure FDA0002591151700000038
is that
Figure FDA0002591151700000039
An estimated value of (d);
a reset module for using the following reset scheme for better time-varying tracking characteristics of the system and for ensuring Δ u ≠ 0:
Figure FDA0002591151700000041
Figure FDA0002591151700000042
wherein
Figure FDA0002591151700000043
Is the initial value of PJM, a small normal number;
the device further comprises:
the execution module is used for realizing the transmission power calibration function by specifically executing the following algorithm:
1. inputting:
desired power error: y is*＝0
Target power: pd＝[9：18]dBm
Threshold value: pth＝0.25dB
2.η, mu, p, y (1),
Figure FDA0002591151700000044
u(1)，Pm
3. and (3) outputting: u ═ u1，u2，u3]
4. And (5) repeating the steps of 5-12:
5.
Figure FDA0002591151700000045
6.
Figure FDA0002591151700000046
7.
Figure FDA0002591151700000047
8.
Figure FDA0002591151700000048
9.endif
10.
Figure FDA0002591151700000049
11. storing u (k) in NVRAM;
12. measuring transmission power P of wireless communication devicem；
13. Up to max (| P)m-Pd|)＜Pth。
7. The data driven radio frequency transmit power calibration apparatus of claim 6, wherein the apparatus further comprises:
NVRAM；
PA parameter control unit for controlling the NVRAM by three different parameters u ═ u0，u1，u2]TControlling the PA;
and a nonlinear relation control part, wherein a PA parameter u has a nonlinear relation with a mean square error y between the measured power and the target power:
y(k)＝f(y(k-1)，...，y(k-ny)，u(k)，u(k-1)，...，u(k-nu)，
wherein the integer ny，nuIs the order, f (-) is a non-linear function, the error y of the system at the k-th time is from k-nuInput state u from the next to the k-th, and from k-nyThe error value y from the beginning to k-1 times.
8. The data driven radio frequency transmit power calibration apparatus of claim 7, wherein the apparatus further comprises: a nonlinear relationship simplification component for simplifying the nonlinear relationship to:
y(k)＝f(y(k-1)，u(k)，u(k-1))。
9. the data driven radio frequency transmit power calibration apparatus of claim 8, wherein the apparatus further comprises:
the equivalent dynamic linearization conversion module is used for setting a time-varying parameter phi (k) and converting the system into the following equivalent dynamic linearization data model:
Figure FDA0002591151700000051
wherein
Figure FDA0002591151700000052
Figure FDA0002591151700000053
For all k are bounded, b is a normal number.
10. The data-driven radio frequency transmit power calibration apparatus of claim 9, wherein the transmit power calibration parameter search module further comprises:
the optimal condition solving module is used for solving the optimal condition of the criterion function:
Figure FDA0002591151700000054
obtaining:
Figure FDA0002591151700000055
where η ∈ (0,2] is the step-size constant;
a reset module for using the following reset scheme for better time-varying tracking characteristics of the system and for ensuring Δ u ≠ 0:
Figure FDA0002591151700000056
Figure FDA0002591151700000057
wherein
Figure FDA0002591151700000061
Is the initial value of PJM, a small normal number;
a calibration parameter search module for searching for a given target error y*Should be zero, search the corresponding PA parameter u (k) so that the corresponding y (k) can obtain the target value y*＝0；
The following criteria function is used:
J(u(k))＝||y*-y(k)||2+λ||u(k)-u(k-1)||2， (11)
wherein λ >0 is a weighting factor for preventing excessive variation in the u (k) estimate;
solving the optimal conditions:
Figure FDA0002591151700000062
can obtain the product
Figure FDA0002591151700000063
Where ρ ∈ (0, 1) is the step-size constant;
an iteration module for adjusting the input PA parameters u (k) according to (9) and (13) successive iterations based on a data-driven algorithm such that the output power error of the system is as close as possible to y*。
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