EP4690462A1

EP4690462A1 - Method and apparatus of linearizaing power amplifier

Info

Publication number: EP4690462A1
Application number: EP23931238.2A
Authority: EP
Inventors: Sener DIKMESE; Ang FENG; Pablo PASCUAL; Mats GAN KLINGBERG; Hao GAO
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-04-03
Filing date: 2023-04-03
Publication date: 2026-02-11
Also published as: WO2024207162A1

Abstract

A method, apparatus and computer readable storage medium for power amplifier linearization are disclosed. In a method, a plurality of parameters is determined for a predistortion process of the at least one power amplifier to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one power amplifier. The predistortion process is caused to be performed using at least the plurality of parameters to linearize the at least one power amplifier.

Description

METHOD AND APPARATUS OF LINEARIZAING POWER AMPLIFIER

TECHNICAL FIELD

The non-limiting and example embodiments of the present disclosure generally relate to the technical field of telecommunications, and specifically to a method, an apparatus, and a medium for power amplifier linearization.

BACKGROUND

This section introduces aspects that may facilitate a better understanding of the disclosure. Accordingly, the statements of this section are to be read in this light and are not to be understood as admissions about what is in the prior art or what is not in the prior art.
In wireless communication devices, such as base stations (BSs) , linearization may be used to compensate for nonlinearity of a radio frequency (RF) circuit. For instance, a digital predistortion (DPD) technique may be an effective way to mitigate spectrum regrowth for a power amplifier (PA) . Due to potential advantages over other techniques in terms of size and cost reduction, the DPD technique has become an important technique in BSs.
Polynomial based and look-up table (LUT) based DPD models may be two popular models to compensate nonlinearity effects of a PA. Some search strategies may be used in DPD model optimization, which may sweep an interested area in a parameter space. These search strategies may be applied in a trial-and-error approach, thereby increasing the number of parameters and searching various parameter combinations. To fulfill the requirements of both Federal Commission Committee (FCC) and 3rd Generation Partnership Project (3GPP) , it may be needed to optimize different DPD models for different test cases of a radio product. As the requested application cases from customers for next generation radio systems increase, two or more carrier configurations, together with wideband and multiband scenarios, may require a DPD cost function to be defined in a more complex form.
SUMMARY
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
To overcome or mitigate at least one of the above-mentioned problems or other problems or provide a useful solution, embodiments of the present disclosure propose a method, an apparatus and a medium for PA linearization.
In a first aspect of the present disclosure, there is provided a method of linearizing at least one PA. In the method, a plurality of parameters is determined for a predistortion process of the at least one PA to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one PA. The predistortion process is caused to be performed using at least the plurality of parameters to linearize the at least one PA.
In an embodiment, the plurality of parameters may comprise a plurality of basis functions and/or a plurality of coefficients associated with the plurality of basis functions.
In an embodiment, the plurality of basis functions may be determined to reduce the difference between the reference signal and the feedback signal and to enhance the efficiency of the at least one PA.
In an embodiment, the plurality of basis functions may be determined from a plurality of candidate basis functions.
In an embodiment, the plurality of coefficients may be determined based on the plurality of basis functions to reduce the difference between the reference signal and the feedback signal and to enhance the efficiency of the at least one PA.
In an embodiment, the plurality of parameters may be determined using a cost function associated with the difference and the efficiency.
In an embodiment, the plurality of parameters may be determined using a gradient boosting process.
In an embodiment where the plurality of parameters comprises a plurality of basis functions, a boosting step may be determined for the plurality of basis functions to accelerate the reduction of the difference between the reference signal and the feedback signal and the enhancement of the efficiency of the at least one PA.
In an embodiment, the at least one PA may comprise two or more PAs. An output signal of the predistortion process may be split into two or more signals, the two or more signals being input to the two or more PAs.
In an embodiment, the predistortion process may be performed based on a memory polynomial (MP) model or a look-up table (LUT) model.
In a second aspect of the present disclosure, there is provided an apparatus of linearizing at least one PA. The apparatus comprises a processor and a memory coupled to the processor, the memory containing instructions executable by the processor, whereby the apparatus is operative to perform the method according to the first aspect.
In a third aspect of the present disclosure, there is provided an apparatus of linearizing at least one PA. The apparatus comprises means for performing the method according to the first aspect.
In a fourth aspect of the disclosure, there is provided a computer-readable storage medium having instructions stored thereon, the instructions, which, when executed by at least one processor of a device, causes the device to perform the method according to the first aspect.
With the present disclosure, linearization performances of PAs may be improved, and the efficiency of the PAs may be optimized as well.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of some embodiments of the present disclosure in the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become more apparent, wherein the same reference generally refers to the same components in the embodiments of the present disclosure.
FIG. 1A is a diagram showing an example architecture of predistortion optimization in accordance with some embodiments.
FIG. 1B is a diagram showing an example dual-input structure of PA linearization according to some embodiments.
FIG. 2 is a flow chart showing an example method of PA linearization in accordance with some embodiments.
FIG. 3A is a flow chart showing an example process for function and coefficient adaptation in accordance with some embodiments of the present disclosure.
FIG. 3B is a diagram showing an example process of simultaneous adaptation in both function and coefficient spaces in accordance with some embodiments.
FIG. 4A is a diagram showing performance comparison of the proposed approach and a conventional approach in accordance with some embodiments.
FIG. 4B is a diagram showing performance comparison of the proposed approach and another conventional approach in accordance with some embodiments.
FIG. 4C is a diagram showing performance of the proposed approach in accordance with some embodiments.
FIG. 5A is a diagram showing an example structure of simultaneous adaptation in both function and coefficient spaces for a direct-learning architecture (DLA) in accordance with some embodiments.
FIG. 5B is a diagram showing an example architecture of predistortion optimization for an indirect-learning architecture (ILA) according to some embodiments.
FIG. 5C is a diagram showing an example structure of simultaneous adaptation in both function and coefficient spaces for ILA in accordance with some embodiments.
FIG. 6 is a block diagram showing an apparatus for PA linearization in accordance with some embodiments.
FIG. 7 is a block diagram showing a computer readable storage medium in accordance with some embodiments.
FIG. 8 is a block diagram showing function units of an apparatus for PA linearization in accordance with some embodiments.
FIG. 9 is a block diagram showing an example of a communication system in accordance with some embodiments.
FIG. 10 is a block diagram showing a user equipment in accordance with some embodiments.
FIG. 11 is a block diagram showing a network node in accordance with some embodiments.
FIG. 12 is a block diagram showing a host in accordance with some embodiments.
FIG. 13 is a block diagram showing a virtualization environment in accordance with some embodiments.

DETAILED DESCRIPTION

Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.
Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.
Reference throughout this specification to features, advantages, or similar language does not imply that all of the features and advantages that may be realized with the present disclosure should be or are in any single embodiment of the disclosure. Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Furthermore, the described features, advantages, and characteristics of the disclosure may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize that the disclosure may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments of the disclosure.
As used herein, the term "network" , or "communication network/system" refers to a network/system following any suitable communication standards, such as new radio (NR) , long term evolution (LTE) , LTE-Advanced (LTE-A) , wideband code division multiple access (WCDMA) , high-speed packet access (HSPA) , and so on. Furthermore, the communications between a terminal device and a network node in the communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G) , the second generation (2G) , 2.5G, 2.75G, the third generation (3G) , 4G, 4.5G, 5G communication protocols, and/or any other protocols either currently known or to be developed in the future such as 6G.
The term "network node" refers to a network device with accessing function in a communication network via which a terminal device accesses to the network and receives services therefrom. The network node may include a base station (BS) , an access point (AP) , a multi-cell/multicast coordination entity (MCE) , a controller or any other suitable device in a wireless communication network. The BS may be, for example, a node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a next generation NodeB (gNodeB or gNB) , a remote radio unit (RRU) , a radio header (RH) , a remote radio head (RRH) , a relay, a low power node such as a femto, a pico, and so forth.
Yet further examples of the network node comprise multi-standard radio (MSR) radio equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs) , base transceiver stations (BTSs) , transmission points, transmission nodes, positioning nodes and/or the like. More generally, however, the network node may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to a wireless communication network or to provide some service to a terminal device that has accessed to the wireless communication network.
The term "terminal device" refers to any end device that can access a communication network and receive services therefrom. By way of example and not limitation, the terminal device may refer to a user equipment (UE) , or other suitable devices. The UE may be, for example, a subscriber station, a portable subscriber station, a mobile station (MS) or an access terminal (AT) . The terminal device may include, but not limited to, portable computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, a mobile phone, a cellular phone, a smart phone, a tablet, a wearable device, a personal digital assistant (PDA) , a vehicle, and the like.
As yet another specific example, in an Internet of things (IoT) scenario, a terminal device may also be called an IoT device and represent a machine or other device that performs monitoring, sensing and/or measurements etc., and transmits the results of such monitoring, sensing and/or measurements etc. to another terminal device and/or a network equipment. The terminal device may in this case be a machine-to-machine (M2M) device, which may in a 3rd generation partnership project (3GPP) context be referred to as a machine-type communication (MTC) device.
As one particular example, the terminal device may be a UE implementing the 3GPP narrow band Internet of things (NB-IoT) standard. Particular examples of such machines or devices are sensors, metering devices such as power meters, industrial machinery, or home or personal appliances, e.g., refrigerators, televisions, personal wearables such as watches etc. In other scenarios, a terminal device may represent a vehicle or other equipment, for example, a medical instrument that is capable of monitoring, sensing and/or reporting etc. on its operational status or other functions associated with its operation.
As used herein, the terms "first" , "second" and so forth refer to different elements. The singular forms "a" and "an" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" , "comprising" , "has" , "having" , "includes" and/or "including" as used herein, specify the presence of stated features, elements, and/or components and the like, but do not preclude the presence or addition of one or more other features, elements, components and/or combinations thereof. The term "based on" is to be read as "based at least in part on" . The term "one embodiment" and "an embodiment" are to be read as "at least one embodiment" . The term "another embodiment" is to be read as "at least one other embodiment" . Other definitions, explicit and implicit, may be included below.
As mentioned above, the DPD technique has become an important technique to compensate for nonlinearity of a RF circuit such as a PA. The effects of a memory (or nonlinearity effects) , which may correspond to memory delays or taps such as data and address taps in both PA and DPD modeling, may be unavoidable in practical systems, as an output of the PA may not only depend on current input values, but also on previous input values. Polynomial based, and LUT based DPD models may be two popular models to compensate such nonlinearity effects of a PA. The polynomial based models may encompass different variants, such as MP and generalized MP (GMP) models. Herein, the term “MP model” may be used to encompass both MP and GMP models.
The MP and LUT based models may be expressed as equations (1) and (2) , respectively:
y (n) =∑_m, _l, _ka_m, _l, _kx (n-m) ·|x (n-l) |^k, (1)
y (n) =∑_m, _lx (n-m) ·f_m, _l (|x (n-l) |) , (2)
where x (n) and y (n) represent a model input and a model output, respectively, n represents a discrete time index, n=0, 1, 2, …, N-1, where N represents an integer. m and l represent memory taps, also known as a data delay and an address delay, respectively, k in equation (1) represent a nonlinear order, also known as a polynomial order, and a_m, _l, _k represents a model coefficient. f_m, _l in equation (2) may denote LUT values corresponding to the data delay m and the address delay l. Both the models may use several basis functions to characterize relationship between an input and an output of a DPD block.
More basis functions used in the DPD may have better performance, but an extra cost of computational complexity and resource utilization. To reduce the computational complexity and improve the resource utilization, the DPD model may need to be pruned, also known as DPD model optimization with limited memory taps.
Pruning strategies may be classified in two groups: a priori pruning strategy, and a posteriori pruning strategy. A priori pruning strategy may be applied without knowledge of an internal structure of a PA, while a posteriori pruning strategy may use signal processing techniques based on sparsity assumption. Moreover, some search strategies may be used in the DPD model optimization, which may sweep an interested area in a parameter space. With such search strategies, DPD parameters may be optimized to achieve a desired performance and stability of radio products. However, these search strategies may be applied in a trial-and-error approach, which may increase the number of parameters to be searched and need to search various parameter combinations.
Gradient boosting decision tree (GBDT) is one of widely used machine learning (ML) algorithms due to its efficiency, accuracy and interpretability. It may also achieve good performance in many ML use cases. Boosting can be regarded as a class of learning algorithms that may fit models by combining several different simple models. These simple models may be referred to as basis models or learners. These basis models may have limited fitting ability, but after combining these basis models as an ensemble model, they can form a relatively more accurate model. GBDT may fit a basis function, i.e., a tree, in each iteration, and then update the model accordingly.
To fulfill the requirements of both FCC and 3GPP, different DPD models may need to be optimized for different test cases of a radio product. Test cases with significant differences of instantaneous bandwidths (IBWs) , occupied bandwidths (OcBWs) or frequency positions may be very likely resulted in different optimized DPD models. There may be hundreds of uses cases defined, especially for wideband and multiband products, considering 5G NR, but a reasonable small number of DPD models may need to be optimized in practice. However, several specialized DPD models may be inevitably optimized which may target troublesome test cases and be required to make some decisions manually.
For example, in an optimization process, if optimized results of some test cases (also referred to as failed test cases) are not good as expected, one of the failed test cases may be manually selected to re-perform optimization of model parameters. However, the manually selected test case may hardly lead to good coverage for the failed test cases. Therefore, this manual decision-making process may be treated as a trial-and-error process. Even with huge experiences from experts, optimality may be still unguaranteed, or it may be difficult for optimality analysis and automated implementation. Moreover, it may require a large amount of man hours to finish the optimization of model parameters for many test cases. It is very common that it takes an approximate two-week time period to find a suitable result of DPD model parameters for various test cases of one specific product. This procedure may require huge computational complexity and prolong time-to-market for a product development.
Some techniques have been recently proposed to speed up the optimization process. There is a class of matching pursuit-based approaches (or algorithms) for sparsity representation in the signal processing field, including, for example, orthogonal matching pursuit (OMP) , subspace pursuit (SP) , and/or the like. Matching or correlation may generally be used in the matching pursuit based algorithms. Some of these approaches may introduce a greedy pursuit framework into a LUT model. Some of the approaches may be extended into more general use cases by using the block OMP (B-OMP) . These approaches may demonstrate importance of studying and finding better solutions for this optimization problem. A target of this class of approaches or algorithms may be to minimize an error between a desired signal and a recovered signal using sparse basis functions. However, as the requested application cases from customers for next generation radio systems increase, two or more carrier configurations, together with wideband and multiband scenarios, may require a DPD cost function to be defined in a more complex form.
In addition, conventional approaches may require a static database to store different settings for different test cases, which may degrade the DPD performance in a dynamic environment, such as dynamic traffic, temperature, hardware aging, and/or the like. Moreover, compared with a huge number of carrier configurations, there may be a quite limited number of models stored in a product database. In this case, the carrier configurations may be divided into the limited number of clusters. In the same cluster, the carrier configurations may share the same model, which means that this shared model may be optimal for one typical carrier configuration, but just suboptimal for the others. Thus, it may be valuable to try filling a performance gap for the carrier configurations that have not reached an optimal behavior.
Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Some embodiments of the present disclosure propose a predistortion optimization scheme which considers not only a difference between a reference signal (such as a desired signal) and a feedback signal (such as a recovered signal) , but also enhance efficiency of PA (s) . For instance, in the DPD optimization problem, a norm of the DPD parameters may be added into a cost function as a regularization term to stabilize the DPD convergence, and an energy efficiency term may also be added into the cost function to improve the energy efficiency of a PA.
Some embodiments of the present disclosure will be discussed by taking MP and LUT models as examples of DPD models for performing a predistortion process of a PA. The proposed scheme may be used in some other DPD models as well, including Volterra series, pruned Volterra series, and dynamic deviation reduction, and/or the like.
Certain embodiments may provide one or more of the following technical advantage (s) . The proposed predistortion optimization scheme may allow parameters of a predistortion process to be adapted dynamically considering both the reduction of the difference and the enhancement of the efficiency. In this way, PA linearization may be improved while PA efficiency may be improved.
Moreover, there may be no need to find the optimal data and address delays. Further, the DPD model optimization may be solved for various test cases in a very efficient way without a need of a pre-defined static DPD model database, thereby reducing design costs and shortening the time-to-market for radio products. Furthermore, dynamical update of the parameters may reduce power consumption by using a simple DPD model while fulfilling the linearization performance requirements.
In addition, as mentioned above, the static database used to store DPD models for different test cases may degrade the DPD performance when it comes to the dynamic environment, such as dynamic traffic, temperature, hardware aging, and/or the like. The proposed predistortion optimization scheme can dynamically update the parameters such as the basis functions and/or the corresponding coefficients of the DPD models, thereby improving the DPD performance. Furthermore, as mentioned above, a traditional clustering approach that shares one model for several different carrier configurations may degrade the DPD performance, as the suboptimal model may be used in several cases. Due to the dynamic parameter adaptation, the proposed scheme may fill the performance gap for various carrier configurations.
Some example implementations will be described in detail below with reference to the accompanying drawings.
FIG. 1A shows an example architecture 100 of predistortion optimization in accordance with some embodiments. The architecture 100 may be a direct-learning architecture (DLA) which may be deployed at a radio unit of a network node such as a BS or a terminal device such as a UE or any other devices provided with one or more PAs.
As shown in FIG. 1A, the architecture 100 may comprise a predistortion process 105, which may be implemented by a DPD model, for example. By the predistortion process 105, a PA 110 may be linearized. The predistortion process may be optimized in both function and coefficient spaces to improve the PA linearization.
The architecture 100 may further comprise a parameter adaptation process 115 for the optimization of the predistortion process 105. In the parameter adaptation process 115, a plurality of parameters for the predistortion process is determined to both reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the PA 110.
An example algorithm used in the parameter adaptation process 115 will be described below. The MP model may be used for the algorithm illustration. In this example, equation (1) may be rewritten into a compact form as:
where f_i and a_i denote the i-th MP basis function and the corresponding coefficient, respectively, S denotes the pre-defined total number of basis functions, and the function represents the relationship between y and x. The basis function f_i and the corresponding coefficient a_i, as parameters, may be provided to the predistortion process 105.
The proposed algorithm hereinafter may be simplified and described via a DPD model regression problem. It is to be understood that the proposed algorithm can be in general deployed in both DLA and ILA as well. In an example, the target of the DPD model regression is to solve the following equation:
r and C represent a reference signal and a cost function, respectively. In this example, an output signal y, instead of z from a feedback path as shown in FIG. 1A, may act as a feedback signal. In an example, a squared error err (. ) of the reference signal and the feedback signal may be used as a term (also referred to an error term) in the cost function to reduce a difference between the reference signal and the feedback signal, which may be formulated as:
err (r (n) , y (n) ) =|r (n) -y (n) |² (5)
Moreover, an efficiency term I may be added in the cost function to enhance the efficiency of the PA 110. An example implementation of the efficiency term I will be discussed below with reference to FIG. 1B.
FIG. 1B shows an example dual-input structure 120 of PA linearization in accordance with some embodiments. In the structure 120, a DPD model 125 may be used to perform a predistortion process for two PAs, referred to as a main PA 130 and an auxiliary PA 135, respectively, to further improve the PA efficiency.
As shown in FIG. 1B, an output signal y of the DPD model 125 may be split into two paths, referred to as p₁ (y) and p₂ (y) , as inputs to the main and auxiliary PAs 130 and 135, respectively. The efficiency term may be written as:
I=I (p₁ (y) , p₂ (y) ) =I (h (y) ) (6)
where I may be defined based on a pre-trained model during a PA design. p₁ and p₂ may be defined as non-linear functions in a digital domain and calculated based on an optimal dual-input combination corresponding to the highest PA efficiency. I (p₁ (y) , p₂ (y) ) may be rewritten into a compact form I (h (y) ) , where the function h (. ) combines the contributions from the two paths. The efficiency term I may be obtained as a function of the outputs y of the DPD model and regarded as a part of the cost function. Thus, the cost function may be formulated as:
C (r (n) , y (n) ) =err (r (n) , y (n) ) +γI (h (y) ) (7)
where γ represents a weight or factor. Based on equation (3) as above, the cost function may be reformulated as:
With this cost function, the PA efficiency can be optimized as well.
It is to be understood that the proposed predistortion optimization scheme may be implemented in either of or a combination of software (SW) and hardware (HW) . In an example, the proposed scheme may be implemented in a legacy radio product with limited HW modifications.
FIG. 2 shows a flowchart of an example method 200 of PA linearization in accordance with some embodiments. The method 200 may be applied in both DLA as shown in FIG. 1A and ILA as shown in FIG. 5B, and may be implemented at either or both of a radio unit and a cloud.
As shown in FIG. 2, at block 210, a plurality of parameters is determined for a predistortion process (such as the predistortion process 105 as shown in FIG. 1A) of at least one PA (such as the PA 110 as shown in FIG. 1A) to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one PA. The determination of the parameters may involve two-phase learning. In the first phase, the parameters may be added to an ensemble model for the predistortion process, which may be called as an ensemble phase (EP) . In the second phase, the parameters may be dynamically adjusted to track the dynamic signal, which may be called as a tracking phase (TP) . In both above-mentioned phases, the parameters may be determined to achieve both the reduction of the difference and the enhancement of the efficiency. In an example, a cost function associated with the difference and the efficiency may be used to determine the parameters to achieve both the reduction and the enhancement.
In some embodiments, a structure with two or more PAs, such as the dual-input structure 120 as shown in FIG. 1B, may be used where an output signal of the predistortion process may be split into two or more signals and the two or more signals may be input to the two or more PAs. Accordingly, the efficiency term I formulated by equation (6) and the cost function formulated by equation (7) may be used to further improve the PA efficiency.
In some embodiments, the predistortion process may be performed based on a MP model or a LUT model, which may be easily deployed in the radio products with affordable efforts. Compared to GBDT, MP and LUT may be more efficient in terms of both linearization performance and computational complexity.
In some example embodiments, the plurality of parameters may be determined using a gradient boosting (GB) process. In an example, the GB process together with MP or LUT may be used in PA and DPD modeling. Herein, the GB based MP or LUT may be referred to as GB-MP and GB-LUT. Thus, the MP and LUT algorithm may be introduced into a GBDT framework, but the DT may not be used. The GB-MP and/or GB-LUT may just define a form of the optimization problem, and thus any optimization solution may be applied. In this way, the proposed GB-MP and/or GB-LUT process may increase degrees of freedom in many applications and use cases.
In some embodiments, the plurality of parameters to be determined may comprise a plurality of basis functions and/or a plurality of coefficients associated with the plurality of basis functions. As discussed above, the parameter adaptation may be implemented at either or both of a radio unit and a cloud. Accordingly, a plurality of basis functions and/or a plurality of associated coefficients may be determined at either a radio unit or a cloud. As such, the parameter adaptation may be implemented in a more flexible and efficient way.
In some embodiments, both the plurality of basis functions and the associated coefficients may be determined by considering both the reduction of the difference between the reference signal and the feedback signal and the enhancement of the efficiency of the at least one PA. As such, the simultaneous adaptation may be performed in both function and coefficient space. By introducing the joint optimization of basis functions and the corresponding function coefficients, the linearization performance may be further improved.
The basis functions and the associated coefficients may be determined by either or both of the radio unit or the cloud, for example, depending on the deployed architecture such as the DLA and the ILA. In some embodiments, one of the basis functions and the associated coefficients may be determined at the radio unit, and the other thereof may be determined at the cloud. In these embodiments, at one of the radio unit and the cloud, the plurality of basis functions may be determined to reduce the difference between the reference signal and the feedback signal and to enhance the efficiency of the at least one PA. At the other of the radio unit and the cloud, the plurality of coefficients may be determined based on the basis functions to reduce the difference and to enhance the efficiency.
In some other embodiments, both the basis functions and the associated coefficients may be determined by either the radio unit or the cloud. In these embodiments, at the determining entity which may be either the radio unit or the cloud, after the basis functions is determined by considering both the reduction of the difference between the reference signal and the feedback signal and the enhancement of the efficiency of the at least one PA, the associated coefficients may be determined to reduce the difference as well as enhance the efficiency.
An example process based on GB-MP for the function and coefficient adaptation will be described below with reference to FIG. 3A. This example process may involve two-phase learning, e.g., EP and TP. In both the two phases, the basis functions and the corresponding coefficients may be updated at the same time.
As shown in FIG. 3A, a GB-MP process 300 may comprise two for-loops where the first for-loop performs EP, and the second for-loop performs TP. The first phase, e.g., EP, may be started by defining the expected number of basis functions: S, and initializing at block 302. Other constants may also be used to initializeAt block 304, based on the cost function formulated by equation (8) , a negative gradient at the data point x(n) may be calculated as:
where an estimate of the model at the i-1-th iteration may be denoted byandrepresents the conjugate of
At block 306, at the i-th iteration, a new basis function f_i may be learnt, which may satisfy the following:
where argmin is argument of a minimum, which means that f_i (x (n) ) is determined to be most highly correlated with the negative gradient. μ represents a boosting step.
In some embodiments, the basis function f_i may be learnt from a set of candidate basis functions F to further reduce the computational complexity:
f_i∈F. (11)
The selected basis function set may be written as
Based on the basis function seta basis matrix may be generated.
Then, at block 308, the corresponding coefficient a_i may be calculated by using line search:
In an example, a learning rate factor ρ (0<ρ≤1) may be introduced. Accordingly, at block 310, the model at the i-th iteration may be updated as:
By iteratively running the above steps (blocks 304, 306, 308 and 310) for the pre-defined S iterations, at block 312, the output of EP may be obtained, including the basis function set as shown in equation (12) and the resulting model as follows:
Next, the second phase, i.e., TP, may track the changes of the models. In the TP, blocks 314 and 316 may follow the steps (blocks 304 and 306) as shown in equations (9) and (10) to seek the basis function candidate f_k at the k-th iteration.
The selected candidate f_k may be combined with the basis function setfrom the (k-1) -th iteration to form a new setat block 318:
The cardinality ofmay be:
To track the signals in the TP, at block 320, the S basis functions may be selected (or determined) fromto obtain a subsetT ^(k) , i.e., Among all the possible combinations for T ^(k) , the optimal one, denoted bymay be obtained by solving:
where θ_j represents the corresponding coefficient of the j-th basis function f_j.
At block 322, the output signal at the k-th iteration of the TP may be obtained as:
where
Some details of the GB-MP algorithm may be found in Algorithm 1 below, where EP is implemented from the 1st step to the 8th step, and TP is implemented from the 9th step to the 15th step.
It is noted that in the 11^th step, f_k is selected as one basis function candidate for further optimization. It is still possible to select more than one candidate in this step. For instance, p candidates may be selected which may correspond to the p smallest values of Then, in the 12^th step, the cardinality of the setis p+S.
FIG. 3B shows an example process 330 of simultaneous adaptation in both function and coefficient spaces in accordance with some embodiments.
As shown in FIG. 3B, in the process 330, at block 332, basis function candidate tracking may be performed. At block 334, basis function determination may be performed based on the basis function candidates. At block 336, basis matrix generation may be performed based on the determined basis functions. At block 338, coefficient adaptation may be performed based on the generated basis matrix. At block 340, the DPD model for the predistortion process may be updated based on the basis functions and the corresponding coefficients.
The basis function adaptation loop at blocks 332, 334 and 336 and the coefficient adaptation loop at block 338 may be independent loops. The frequencies of the basis function adaptation and the traditional coefficient adaptation may be configured in a flexible way. For instance, the basis function adaptation frequency may be much lower than the coefficient adaptation frequency.
In an example, the simultaneous adaptation in both function and coefficient spaces may be implemented in a distributed manner that the basis function adaptation may be placed on a cloud, while the coefficient adaptation may be placed in a radio unit. As shown in FIG. 3B, the basis function candidate tracking at block 332, the basis function determination at block 334 and the basis matrix generation at block 336 may be deployed on the cloud.
To further improve the convergence speed, in some embodiments, a boosting step may be determined for the plurality of basis functions to accelerate the reduction of the difference between the reference signal and the feedback signal and the enhancement of the efficiency of the at least one PA. For example, considering more complex DPD cost functions and some potential adaptation stability issues, the classical GBDT framework may be further extended by adding the second order gradient. In an example, the Hessian boosting (HB) based algorithm may be used to speed up the adaptation convergence.
In an example, the second-order gradient as the extension version of the proposed approach may be added in the GB-MP and GB-LUT to further improve the convergence. Herein, HB embedded MP and LUT may be referred to as HB-MP and HB-LUT. As an example, the second order gradient based on HB-MP added in the above framework may be written as:
Then, at the i-th iteration, the learnt basis function f_i may satisfy:
The second order gradient may be used in both EP and TP. The operations and processes based on HB-MP are similar to those based on GB-MP, and the detailed thereof will be omitted.
It is to be understood that only for the purpose of illustration, some embodiments have been discussed above based on the GB-MP and HB-MP algorithms. The proposed predistortion optimization scheme may in general work together with other DPD behavior models such as LUT.
Simulation experiments, where GB-LUT is used, show that the proposed scheme may have better performance than convention solutions. As shown in FIGS. 4A and 4B, compared with the exhaustive search, which may search all the tap combinations within a pre-defined search space, the normalized mean square error (NMSE) performances of GB-LUT are better. This search space may be defined based on experience from intensive experiments, where some policies may constrain a shape of the search space, for example, the search may be more focus on a diagonal part of a data-address map.
In FIGS. 4A and 4B, a line 410 with cross markers represents the proposed GB-LUT, and a line 420 with circle markers in FIG. 4A and a line 430 with the square markers in FIG. 4B represent two exhaustive search approaches, i.e., exhaustive search 1 and 2, respectively. Exhaustive search 1 and 2 represent the approaches by using a large pre-defined search space and a small one, respectively. It may be concluded from FIG. 4A that in terms of the NMSE performance, the proposed GB-LUT may reach similar performance with the exhaustive search 1, but with much less elapsed time compared with exhaustive search 1. It may be concluded from FIG. 4B that the GB-LUT significantly outperforms exhaustive search 2, and the elapsed time of the proposed GB-LUT and exhaustive search 2 is on the same level.
Here, it is to be noted that in FIG. 4B, maximum 10 basis functions are shown, as it is a relatively realistic number of basis functions for products. As the number of basis functions grows, exhaustive search 2 can approximately reach -40 dB, since it may provide strong fitting capability with such a large number of suboptimal basis functions. Moreover, since different approaches implement different operations, to simplify the computational complexity analysis, the elapsed time for 50 basis functions is used for comparison.
Table 1 shows the performance comparison between the proposed GB-LUT and the exhaustive search.
Table 1
The purpose of the TP is to track the model updates, and the number of basis function candidates in the TP can be configured. Therefore, the proposed approach outperforms the traditional static models, when it comes to the dynamic environment, such as dynamic traffic, temperature, hardware aging, and/or the like. Moreover, even with the static environment, such as the model regression in the simulation, it may be seen from FIG. 4C that as the number of basis function candidates increases, the performance may be improved until the specific number of basis function candidates. As shown in FIG. 4C, 3 or 4 candidates can be sufficient to obtain reasonable acceptable NMSE performance, which may mean that the significantly increased number of basis function candidates may not be needed in the proposed approach, thereby further reducing computational complexity.
The conventional approaches such as a Widrow-Hoff least mean square approach, which may be one of fundamental adaptive filter approaches and have been widely used for DPD adaptation, cannot co-optimize the DPD basis functions and the associated coefficients. To implement the simultaneous adaptation of the basis functions and the corresponding coefficients, a general coefficient adaptation framework may be modified by adding another loop for the basis function adaptation. Example framework modifications for DLA and ILA will be discussed with reference to FIGS. 5A to 5C.
FIG. 5A shows a structure 500 of simultaneous adaptation in both function and coefficient spaces for DLA in accordance with some embodiments.
In the structure 500 for DLA, the basis function adaptation may be embedded into a DPD coefficient adaptation framework for the co-optimization of the basis functions and the corresponding coefficients. Thus, the DPD adaptation may be reformulated to embed a new loop for the basis function adaptation. As shown in FIG. 5A, this embedded loop may comprise basis function candidate tracking at block 502, basis function determination at block 504, and basis matrix generation at block 506 before coefficient adaption at block 508. The structure 500 may allow simultaneous implementation of the newly added basis function adaptation together with the traditional DPD coefficient adaptation. The update frequency of the basis function and the coefficient may be flexibly configured.
In addition to the scenario of the DLA as shown FIG. 1A, the proposed predistortion optimization scheme may be applied in an indirect-learning architecture (ILA) .
FIG. 5B shows an example architecture 510 of predistortion optimization for ILA according to some embodiments.
As shown in FIG. 5B, the architecture 510 may comprise two DPD models where one DPD model 512 for PA linearization may be deployed in a radio unit and the other DPD model 514 for predistortion optimization may be deployed on a cloud. A parameter adaptation process 516 in the architecture 510 may be also deployed on the cloud partially or even as a whole. In an example, basis function adaptation may be implemented on the cloud, and coefficient adaptation may be implemented in the radio unit; and vice versa. In another example, both the basis function adaptation and the coefficient adaptation may be implemented on the cloud. The parameters such as basis function f_i and/or the corresponding coefficient a_i derived at the cloud may be provided to the DPD model 512 to linearize the PA 110.
Likewise, some or even all of the operations of parameter adaptation (for example, at blocks 502, 504, 506, and/or 508 as shown in FIG. 5A) in DLA may be deployed on a cloud. Thus, the classical DPD learning frameworks of both DLA and ILA may work with the proposed predistortion optimization scheme.
FIG. 5C shows a structure 520 of simultaneous adaptation in both function and coefficient spaces for ILA in accordance with some embodiments.
In the structure 520 for ILA, both the basis function adaptation and the coefficient adaptation may be implemented on a cloud. As shown in FIG. 5C, after basis function candidate tracking at block 522, basis function determination at block 524, and basis matrix generation at block 526, and coefficient adaption at block 528, the basis functions and the corresponding coefficients may be provided to the DPD model 514 for DPD learning. The learning result may be provided to the DPD model 512 to perform the predistortion process on the PA 110.
Next, still with reference to FIG. 2, at block 220, the predistortion process is caused to be performed using at least the plurality of parameters to linearize the at least one PA. As an example, as shown in FIG. 1A, the result of the parameter adaption process 115 may be provided to the predistortion process 105. Then, the predistortion process 105 may be performed using the resulting parameters. As another example, in the embodiments where the parameters are derived on a cloud as shown in FIG. 5B, the derived parameters may be provided to the radio unit to run the DPD model 512 using these parameters to perform the predistortion process for the PA 110.
FIG. 6 shows an apparatus 600 for PA linearization in accordance with some embodiments. The apparatus 600 may be implemented at a network node such as a BS or a terminal device such as a UE or any other devices provided with one or more PAs.
As shown in FIG. 6, the apparatus 600 may comprise a processor 605 and a memory 610. The memory 610 may contain instructions 615 executable by the processor 605, whereby the apparatus 600 may be operative to: determine a plurality of parameters for a predistortion process of the at least one PA to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one PA; and cause the predistortion process to be performed using at least the plurality of parameters to linearize the at least one PA.
In an embodiment, the apparatus 600 may be further operative to implement actions or operations according to any of the above-mentioned embodiments described with reference to FIGS. 1A to 5C.
The processor 605 may be any kind of processing component, such as one or more microprocessor or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs) , special-purpose digital logic, and the like. The memory 610 may be any kind of storage component, such as read-only memory (ROM) , random-access memory, cache memory, flash memory devices, optical storage devices, etc.
FIG. 7 shows a computer readable storage medium in accordance with some embodiments.
As shown in FIG. 7, the computer readable storage medium 700 comprising instructions 615 which when executed by a processor of a device, cause the device to perform any above-mentioned embodiments described with reference to FIGS. 1A to 5C.
The computer readable storage medium 700 may be configured to include memory such as RAM, ROM, programmable read-only memory (PROM) , erasable programmable read-only memory (EPROM) , electrically erasable programmable read-only memory (EEPROM) , magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, or flash drives.
FIG. 8 shows function units of an apparatus 800 for PA linearization in accordance with some embodiments.
As shown in FIG. 8, the apparatus 800 may comprise: a determination unit 805 configured to determine a plurality of parameters for a predistortion process of the at least one PA to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one PA; and a causing unit 810 configured to cause the predistortion process to be performed using at least the plurality of parameters to linearize the at least one PA.
In an embodiment, the apparatus 800 may be further operative to implement actions or operations according to any of the above-mentioned embodiments described with reference to FIGS. 1A to 5C.
The term unit may have conventional meaning in the field of electronics, electrical devices and/or electronic devices and may include, for example, electrical and/or electronic circuitry, devices, modules, processors, memories, logic solid state and/or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and/or displaying functions, and so on, as such as those that are described herein.
In some embodiments, an apparatus capable of performing the method 200 may comprise means for performing the respective operations of the method 200. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module. The apparatus may comprise means for determining a plurality of parameters for a predistortion process of the at least one PA to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one PA; and means for causing the predistortion process to be performed using at least the plurality of parameters to linearize the at least one PA.
In an embodiment, the apparatus may further comprise means for implementing actions or operations according to any of the above-mentioned embodiments described with reference to FIGS. 1A to 5C.
FIG. 9 shows an example of a communication system 900 in accordance with some embodiments.
In the example, the communication system 900 includes a telecommunication network 902 that includes an access network 904, such as a radio access network (RAN) , and a core network 906, which includes one or more core network nodes 908. The access network 904 includes one or more access network nodes, such as network nodes 910A and 910B (one or more of which may be generally referred to as network nodes 910) , or any other similar 3^rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 910 facilitate direct or indirect connection of user equipment (UE) , such as by connecting UEs 912A, 912B, 912C, and 912D (one or more of which may be generally referred to as UEs 912) to the core network 906 over one or more wireless connections.
Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 900 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 900 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.
The UEs 912 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 910 and other communication devices. Similarly, the network nodes 910 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 912 and/or with other network nodes or equipment in the telecommunication network 902 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 902.
In the depicted example, the core network 906 connects the network nodes 910 to one or more hosts, such as host 916. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 906 includes one more core network nodes (e.g., core network node 908) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 908. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC) , Mobility Management Entity (MME) , Home Subscriber Server (HSS) , Access and Mobility Management Function (AMF) , Session Management Function (SMF) , Authentication Server Function (AUSF) , Subscription Identifier De-concealing function (SIDF) , Unified Data Management (UDM) , Security Edge Protection Proxy (SEPP) , Network Exposure Function (NEF) , and/or a User Plane Function (UPF) .
The host 916 may be under the ownership or control of a service provider other than an operator or provider of the access network 904 and/or the telecommunication network 902, and may be operated by the service provider or on behalf of the service provider. The host 916 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio/video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.
As a whole, the communication system 900 of FIG. 9 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM) ; Universal Mobile Telecommunications System (UMTS) ; Long Term Evolution (LTE) , and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G) ; wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi) ; and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax) , Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.
In some examples, the telecommunication network 902 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 902 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 902. For example, the telecommunications network 902 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC) /Massive IoT services to yet further UEs.
In some examples, the UEs 912 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 904 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 904. Additionally, a UE may be configured for operating in single-or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC) , such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio –Dual Connectivity (EN-DC) .
In the example, the hub 914 communicates with the access network 904 to facilitate indirect communication between one or more UEs (e.g., UE 912c and/or 912d) and network nodes (e.g., network node 910b) . In some examples, the hub 914 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 914 may be a broadband router enabling access to the core network 906 for the UEs. As another example, the hub 914 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 910, or by executable code, script, process, or other instructions in the hub 914. As another example, the hub 914 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 914 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 914 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 914 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 914 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices.
The hub 914 may have a constant/persistent or intermittent connection to the network node 910b. The hub 914 may also allow for a different communication scheme and/or schedule between the hub 914 and UEs (e.g., UE 912c and/or 912d) , and between the hub 914 and the core network 906. In other examples, the hub 914 is connected to the core network 906 and/or one or more UEs via a wired connection. Moreover, the hub 914 may be configured to connect to an M2M service provider over the access network 904 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 910 while still connected via the hub 914 via a wired or wireless connection. In some embodiments, the hub 914 may be a dedicated hub –that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 910b. In other embodiments, the hub 914 may be a non-dedicated hub –that is, a device which is capable of operating to route communications between the UEs and network node 910b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.
FIG. 10 shows a UE 1000 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA) , wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , smart device, wireless customer-premise equipment (CPE) , vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP) , including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.
A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC) , vehicle-to-vehicle (V2V) , vehicle-to-infrastructure (V2I) , or vehicle-to-everything (V2X) . In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller) . Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter) .
The UE 1000 includes processing circuitry 1002 that is operatively coupled via a bus 1004 to an input/output interface 1006, a power source 1008, a memory 1010, a communication interface 1012, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIG. 10. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.
The processing circuitry 1002 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1010. The processing circuitry 1002 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs) , application specific integrated circuits (ASICs) , etc. ) ; programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP) , together with appropriate software; or any combination of the above. For example, the processing circuitry 1002 may include multiple central processing units (CPUs) .
In the example, the input/output interface 1006 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1000. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc. ) , a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.
In some embodiments, the power source 1008 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet) , photovoltaic device, or power cell, may be used. The power source 1008 may further include power circuitry for delivering power from the power source 1008 itself, and/or an external power source, to the various parts of the UE 1000 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1008. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1008 to make the power suitable for the respective components of the UE 1000 to which power is supplied.
The memory 1010 may be or be configured to include memory such as random access memory (RAM) , read-only memory (ROM) , programmable read-only memory (PROM) , erasable programmable read-only memory (EPROM) , electrically erasable programmable read-only memory (EEPROM) , magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1010 includes one or more application programs 1014, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1016. The memory 1010 may store, for use by the UE 1000, any of a variety of various operating systems or combinations of operating systems.
The memory 1010 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID) , flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM) , synchronous dynamic random access memory (SDRAM) , external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs) , such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC) , integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card. ’ The memory 1010 may allow the UE 1000 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1010, which may be or comprise a device-readable storage medium.
The processing circuitry 1002 may be configured to communicate with an access network or other network using the communication interface 1012. The communication interface 1012 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1022. The communication interface 1012 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network) . Each transceiver may include a transmitter 1018 and/or a receiver 1020 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth) . Moreover, the transmitter 1018 and receiver 1020 may be coupled to one or more antennas (e.g., antenna 1022) and may share circuit components, software or firmware, or alternatively be implemented separately.
In the illustrated embodiment, communication functions of the communication interface 1012 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA) , Wideband Code Division Multiple Access (WCDMA) , GSM, LTE, New Radio (NR) , UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP) , synchronous optical networking (SONET) , Asynchronous Transfer Mode (ATM) , QUIC, Hypertext Transfer Protocol (HTTP) , and so forth.
Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1012, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature) , random (e.g., to even out the load from reporting from several sensors) , in response to a triggering event (e.g., when moisture is detected an alert is sent) , in response to a request (e.g., a user initiated request) , or a continuous stream (e.g., a live video feed of a patient) .
As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.
A UE, when in the form of an Internet of Things (IoT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR) , a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal-or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV) , and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and/or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 1000 shown in FIG. 10.
As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.
In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.
FIG. 11 shows a network node 1100 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points) , base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs) ) .
Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs) , sometimes referred to as Remote Radio Heads (RRHs) . Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS) .
Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs) , base transceiver stations (BTSs) , transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs) , Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs) ) , and/or Minimization of Drive Tests (MDTs) .
The network node 1100 includes a processing circuitry 1102, a memory 1104, a communication interface 1106, and a power source 1108. The network node 1100 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc. ) , which may each have their own respective components. In certain scenarios in which the network node 1100 comprises multiple separate components (e.g., BTS and BSC components) , one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1100 may be configured to support multiple radio access technologies (RATs) . In such embodiments, some components may be duplicated (e.g., separate memory 1104 for different RATs) and some components may be reused (e.g., a same antenna 1110 may be shared by different RATs) . The network node 1100 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1100, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1100.
The processing circuitry 1102 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node 1100 components, such as the memory 1104, to provide network node 1100 functionality.
In some embodiments, the processing circuitry 1102 includes a system on a chip (SOC) . In some embodiments, the processing circuitry 1102 includes one or more of radio frequency (RF) transceiver circuitry 1112 and baseband processing circuitry 1114. In some embodiments, the radio frequency (RF) transceiver circuitry 1112 and the baseband processing circuitry 1114 may be on separate chips (or sets of chips) , boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1112 and baseband processing circuitry 1114 may be on the same chip or set of chips, boards, or units.
The memory 1104 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM) , read-only memory (ROM) , mass storage media (for example, a hard disk) , removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD) ) , and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry 1102. The memory 1104 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 1102 and utilized by the network node 1100. The memory 1104 may be used to store any calculations made by the processing circuitry 1102 and/or any data received via the communication interface 1106. In some embodiments, the processing circuitry 1102 and memory 1104 is integrated.
The communication interface 1106 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 1106 comprises port (s) /terminal (s) 1116 to send and receive data, for example to and from a network over a wired connection. The communication interface 1106 also includes radio front-end circuitry 1118 that may be coupled to, or in certain embodiments a part of, the antenna 1110. Radio front-end circuitry 1118 comprises filters 1120 and amplifiers 1122. The radio front-end circuitry 1118 may be connected to an antenna 1110 and processing circuitry 1102. The radio front-end circuitry may be configured to condition signals communicated between antenna 1110 and processing circuitry 1102. The radio front-end circuitry 1118 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1118 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1120 and/or amplifiers 1122. The radio signal may then be transmitted via the antenna 1110. Similarly, when receiving data, the antenna 1110 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1118. The digital data may be passed to the processing circuitry 1102. In other embodiments, the communication interface may comprise different components and/or different combinations of components.
In certain alternative embodiments, the network node 1100 does not include separate radio front-end circuitry 1118, instead, the processing circuitry 1102 includes radio front-end circuitry and is connected to the antenna 1110. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1112 is part of the communication interface 1106. In still other embodiments, the communication interface 1106 includes one or more ports or terminals 1116, the radio front-end circuitry 1118, and the RF transceiver circuitry 1112, as part of a radio unit (not shown) , and the communication interface 1106 communicates with the baseband processing circuitry 1114, which is part of a digital unit (not shown) .
The antenna 1110 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 1110 may be coupled to the radio front-end circuitry 1118 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 1110 is separate from the network node 1100 and connectable to the network node 1100 through an interface or port.
The antenna 1110, communication interface 1106, and/or the processing circuitry 1102 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 1110, the communication interface 1106, and/or the processing circuitry 1102 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.
The power source 1108 provides power to the various components of network node 1100 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component) . The power source 1108 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1100 with power for performing the functionality described herein. For example, the network node 1100 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1108. As a further example, the power source 1108 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.
Embodiments of the network node 1100 may include additional components beyond those shown in FIG. 11 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 1100 may include user interface equipment to allow input of information into the network node 1100 and to allow output of information from the network node 1100. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1100.
FIG. 12 is a block diagram of a host 1200, which may be an embodiment of the host 916 of FIG. 9, in accordance with various aspects described herein. As used herein, the host 1200 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1200 may provide one or more services to one or more UEs.
The host 1200 includes processing circuitry 1202 that is operatively coupled via a bus 1204 to an input/output interface 1206, a network interface 1208, a power source 1210, and a memory 1212. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as FIGS. 10 and 11, such that the descriptions thereof are generally applicable to the corresponding components of host 1200.
The memory 1212 may include one or more computer programs including one or more host application programs 1214 and data 1216, which may include user data, e.g., data generated by a UE for the host 1200 or data generated by the host 1200 for a UE. Embodiments of the host 1200 may utilize only a subset or all of the components shown. The host application programs 1214 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC) , High Efficiency Video Coding (HEVC) , Advanced Video Coding (AVC) , MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC) , MPEG, G. 711) , including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems) . The host application programs 1214 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 1200 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 1214 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP) , Real-Time Streaming Protocol (RTSP) , Dynamic Adaptive Streaming over HTTP (MPEG-DASH) , etc.
FIG. 13 is a block diagram illustrating a virtualization environment 1300 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1300 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host) , then the node may be entirely virtualized.
Applications 1302 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc. ) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
Hardware 1304 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1306 (also referred to as hypervisors or virtual machine monitors (VMMs) ) , provide VMs 1308A and 1308B (one or more of which may be generally referred to as VMs 1308) , and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 1306 may present a virtual operating platform that appears like networking hardware to the VMs 1308.
The VMs 1308 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1306. Different embodiments of the instance of a virtual appliance 1302 may be implemented on one or more of VMs 1308, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV) . NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.
In the context of NFV, a VM 1308 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1308, and that part of hardware 1304 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1308 on top of the hardware 1304 and corresponds to the application 1302.
Hardware 1304 may be implemented in a standalone network node with generic or specific components. Hardware 1304 may implement some functions via virtualization. Alternatively, hardware 1304 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1310, which, among others, oversees lifecycle management of applications 1302. In some embodiments, hardware 1304 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1312 which may alternatively be used for communication between hardware nodes and radio units.
Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.
In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and/or by end users and a wireless network generally.
Abbreviation Explanation
3GPP 3rd Generation Partnership Project
ACLR Adjacent Channel Leakage Ratio
B-OMP Block Orthogonal Matching Pursuit
BS Base Station
DLA Direct-Learning Architecture
DPD Digital PreDistortion
DT Decision Tree
EP Ensemble Phase
FCC Federal Commission Committee
GBDT Gradient Boosting Decision Tree
GB-HB Gradient Boosting Hessian Boosting
GB-MP Gradient Boosting Memory Polynomial
GB-LUT Gradient Boosting Look Up Table
GMP General Memory Polynomial
HB Hessian Boosting
IBW Instantaneous Bandwidth
ILA Indirect-Learning Architecture
LUT Look-Up Table
MP Memory Polynomial
NMSE Normalized Mean Squared Error
NR New Radio
OBUE Operating Band Unwanted Emissions
OcBW Occupied Bandwidth
PA Power Amplifier
TP Tracking Phase

Claims

A method (200) of linearizing at least one power amplifier (110, 130, 135) , comprising:

determining (210) a plurality of parameters for a predistortion process (105) of the at least one power amplifier (110, 130, 135) to reduce a difference between a reference signal and a feedback signal and to enhance efficiency of the at least one power amplifier (110, 130, 135) ; and

causing (220) the predistortion process (105) to be performed using at least the plurality of parameters to linearize the at least one power amplifier (110, 130, 135) .
The method (200) of claim 1, wherein the plurality of parameters comprises a plurality of basis functions and/or a plurality of coefficients associated with the plurality of basis functions.
The method (200) of claim 2, wherein determining (210) the plurality of parameters comprises:

determining the plurality of basis functions to reduce the difference between the reference signal and the feedback signal and to enhance the efficiency of the at least one power amplifier (110, 130, 135) .
The method (200) of claim 3, wherein the plurality of basis functions is determined from a plurality of candidate basis functions.
The method (200) of any of claims 2-4, wherein determining (210) the plurality of parameters comprises:

determining, based on the plurality of basis functions, the plurality of coefficients to reduce the difference between the reference signal and the feedback signal and to enhance the efficiency of the at least one power amplifier (110, 130, 135) .
The method (200) of any of claims 1-5, wherein the plurality of parameters is determined using a cost function associated with the difference and the efficiency.
The method (200) of any of claims 1-6, wherein the plurality of parameters is determined using a gradient boosting process.
The method (200) of claim 7, wherein the plurality of parameters comprises a plurality of basis functions, and the method (200) further comprises:

determining a boosting step for the plurality of basis functions to accelerate the reduction of the difference between the reference signal and the feedback signal and the enhancement of the efficiency of the at least one power amplifier (110, 130, 135) .
The method (200) of any of claims 1-8, wherein the at least one power amplifier (110, 130, 135) comprises two or more power amplifiers (130, 135) , and an output signal of the predistortion process (105) is split into two or more signals, the two or more signals being input to the two or more power amplifiers (130, 135) .
The method (200) of any of claims 1-9, wherein the predistortion process (105) is performed based on a memory polynomial model or a look-up table model.
An apparatus (600) of linearizing at least one power amplifier, comprising:

a processor (605) ; and

a memory (610) coupled to the processor (605) , the memory (610) containing instructions (615) executable by the processor (605) , whereby the apparatus (600) is operative to perform the method (200) according to any of claims 1-10.
A computer-readable storage medium (700) having instructions (615) stored thereon, the instructions (615) , which, when executed by at least one processor of a device, cause the device to perform the method (200) according to any of claims 1-10.