Interference mitigation with multi-band digital pre-distortion

A method comprising determining a plurality of digital pre-distortion engines, determining signals for the pre-distortion engines, determining terms for a matrix and filter the matrix, based on the filtered matrix, determining correlation matrixes, obtaining pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combining the pre-distorted signals to a combined pre-distorted signal.

FIELD

The following exemplary embodiments relate to wireless communication and operating over multiple frequency bands.

BACKGROUND

An access node may enable wireless network connections to terminal devices. A part of the access node is a power amplifier which may introduce nonlinearity which may lead to undesired broadening of frequency spectrum being used. Therefore, it is desirable to be able to anticipate the kind of nonlinearity that may be introduced such that measures can be taken to mitigate or prevent the nonlinearity from occurring.

BRIEF DESCRIPTION

According to another aspect there is provided an apparatus comprising means for determining a plurality of digital pre-distortion engines, determining signals for the pre-distortion engines, determining terms for a matrix and filter the matrix, based on the filtered matrix, determining correlation matrixes, obtaining pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combining the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided an apparatus comprising at least one processor, and at least one memory including a computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to: determine a plurality of digital pre-distortion engines, determine signals for the pre-distortion engines, determine terms for a matrix and filter the matrix, based on the filtered matrix, determine correlation matrixes, obtain pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combine the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a method comprising determining a plurality of digital pre-distortion engines, determining signals for the pre-distortion engines, determining terms for a matrix and filter the matrix, based on the filtered matrix, determining correlation matrixes, obtaining pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combining the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a computer program product readable by a computer and, when executed by the computer, configured to cause the computer to execute a computer process comprising determining a plurality of digital pre-distortion engines, determining signals for the pre-distortion engines, determining terms for a matrix and filter the matrix, based on the filtered matrix, determining correlation matrixes, obtaining pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combining the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a computer program product comprising computer-readable medium bearing computer program code embodied therein for use with a computer, the computer program code comprising code for performing determining a plurality of digital pre-distortion engines, determining signals for the pre-distortion engines, determining terms for a matrix and filter the matrix, based on the filtered matrix, determining correlation matrixes, obtaining pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combining the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a computer program product comprising instructions for causing an apparatus to perform at least the following: determine a plurality of digital pre-distortion engines, determine signals for the pre-distortion engines, determine terms for a matrix and filter the matrix, based on the filtered matrix, determine correlation matrixes, obtain pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combine the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a computer program comprising instructions for causing an apparatus to perform at least the following: determine a plurality of digital pre-distortion engines, determine signals for the pre-distortion engines, determine terms for a matrix and filter the matrix, based on the filtered matrix, determine correlation matrixes, obtain pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combine the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a computer readable medium comprising program instructions for causing an apparatus to perform at least the following: determine a plurality of digital pre-distortion engines, determine signals for the pre-distortion engines, determine terms for a matrix and filter the matrix, based on the filtered matrix, determine correlation matrixes, obtain pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combine the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the following: determine a plurality of digital pre-distortion engines, determine signals for the pre-distortion engines, determine terms for a matrix and filter the matrix, based on the filtered matrix, determine correlation matrixes, obtain pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combine the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a system comprising a transmitter, a power amplifier and an apparatus that comprises at least one processor, and at least one memory including a computer program code, wherein the system is caused to: determine a plurality of digital pre-distortion engines, determine signals for the pre-distortion engines, determine terms for a matrix and filter the matrix, based on the filtered matrix, determine correlation matrixes, obtain pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combine the pre-distorted signals to a combined pre-distorted signal.

According to another aspect there is provided a system comprising means for: determining a plurality of digital pre-distortion engines, determining signals for the pre-distortion engines, determining terms for a matrix and filter the matrix, based on the filtered matrix, determining correlation matrixes, obtaining pre-distorted signals from the digital pre-distortion engines, wherein the pre-distorted signals are pre-distorted based on the determined correlation matrixes, and combining the pre-distorted signals to a combined pre-distorted signal.

DESCRIPTION OF EMBODIMENTS

Embodiments described herein may be implemented in a communication system, such as in at least one of the following: Global System for Mobile Communications (GSM) or any other second generation cellular communication system, Universal Mobile Telecommunication System (UMTS, 3G) based on basic wideband-code division multiple access (W-CDMA), high-speed packet access (HSPA), Long Term Evolution (LTE), LTE-Advanced, a system based on IEEE 802.11 specifications, a system based on IEEE 802.15 specifications, and/or a fifth generation (5G) mobile or cellular communication system. The embodiments are not, however, restricted to the system given as an example but a person skilled in the art may apply the solution to other communication systems provided with necessary properties.

FIG.1depicts examples of simplified system architectures showing some elements and functional entities, all being logical units, whose implementation may differ from what is shown. The connections shown inFIG.1are logical connections; the actual physical connections may be different. It is apparent to a person skilled in the art that the system may comprise also other functions and structures than those shown inFIG.1. The example ofFIG.1shows a part of an exemplifying radio access network.

FIG.1shows terminal devices100and102configured to be in a wireless connection on one or more communication channels in a cell with an access node (such as (e/g)NodeB)104providing the cell. The access node104may also be referred to as a node. The physical link from a terminal device to a (e/g)NodeB is called uplink or reverse link and the physical link from the (e/g)NodeB to the terminal device is called downlink or forward link. It should be appreciated that (e/g)NodeBs or their functionalities may be implemented by using any node, host, server or access point etc. entity suitable for such a usage. It is to be noted that although one cell is discussed in this exemplary embodiment, for the sake of simplicity of explanation, multiple cells may be provided by one access node in some exemplary embodiments.

The terminal device (which may also be called as UE, user equipment, user terminal, user device, etc.) illustrates one type of an apparatus to which resources on the air interface are allocated and assigned, and thus any feature described herein with a terminal device may be implemented with a corresponding apparatus, such as a relay node. An example of such a relay node is a layer 3 relay (self-backhauling relay) towards the base station. Another example of such a relay node is a layer 2 relay. Such a relay node may contain a terminal device part and a Distributed Unit (DU) part. A CU (centralized unit) may coordinate the DU operation via F1AP-interface for example.

The terminal device may refer to a portable computing device that includes wireless mobile communication devices operating with or without a subscriber identification module (SIM), or an embedded SIM, eSIM, including, but not limited to, the following types of devices: a mobile station (mobile phone), smartphone, personal digital assistant (PDA), handset, device using a wireless modem (alarm or measurement device, etc.), laptop and/or touch screen computer, tablet, game console, notebook, and multimedia device. It should be appreciated that a terminal device may also be an exclusive or a nearly exclusive uplink only device, of which an example is a camera or video camera loading images or video clips to a network. A terminal device may also be a device having capability to operate in Internet of Things (IoT) network which is a scenario in which objects are provided with the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. The terminal device may also utilise cloud. In some applications, a terminal device may comprise a small portable device with radio parts (such as a watch, earphones or eyeglasses) and the computation is carried out in the cloud. The terminal device (or in some embodiments a layer 3 relay node) is configured to perform one or more of user equipment functionalities.

5G may also utilize satellite communication to enhance or complement the coverage of 5G service, for example by providing backhauling. Possible use cases comprise providing service continuity for machine-to-machine (M2M) or Internet of Things (IoT) devices or for passengers on board of vehicles, and/or ensuring service availability for critical communications, and/or future railway/maritime/aeronautical communications. Satellite communication may utilise geostationary earth orbit (GEO) satellite systems, but also low earth orbit (LEO) satellite systems, for example, mega-constellations (systems in which hundreds of (nano)satellites are deployed). Each satellite106in the mega-constellation may cover several satellite-enabled network entities that create on-ground cells. The on-ground cells may be created through an on-ground relay node104or by a gNB located on-ground or in a satellite or part of the gNB may be on a satellite, the DU for example, and part of the gNB may be on the ground, the CU for example. Additionally, or alternatively, high-altitude platform station, HAPS, systems may be utilized. HAPS may be understood as radio stations located on an object at an altitude of 20-50 kilometres and at a fixed point relative to the Earth. For example, broadband access may be delivered via HAPS using lightweight, solar-powered aircraft and airships at an altitude of 20-25 kilometres operating continually for several months for example.

For fulfilling the need for improving the deployment and performance of communication systems, the concept of “plug-and-play” (e/g)NodeBs has been introduced. A network which is able to use “plug-and-play” (e/g)NodeBs, may include, in addition to Home (e/g)NodeBs (H(e/g)nodeBs), a home node B gateway, or HNB-GW (not shown inFIG.1). A HNB Gateway (HNB-GW), which may be installed within an operator's network may aggregate traffic from a large number of HNBs back to a core network.

In a wireless network operating on multiple frequency bands is beneficial as it enables flexibility that is not achieved when operating on single frequency band. However, if the frequency bands are close to each other, the likelihood of intermodulation interference is increased. Intermodulation may be understood as amplitude modulation of signals containing two or more different frequencies, frequency components, and it may be caused by nonlinearities in a system. In an access node, a source for non-linearity may be a power amplifier, PA, due to limitations of its electronic components or may also be caused by algorithms used. The intermodulation between frequencies components may form additional components at frequencies that are not just at integer multiples of either frequency components, but also at a sum and difference frequencies of the original frequencies and at sums and differences of multiples of those frequencies. If intermodulation interference occurs, it is possible that it cannot be filtered out. This may be due to a finite roll-off response of filters used. For example, in digital filtering, hardware or software implementations may comprise a limited number of multipliers, thereby forcing to use a finite number of taps per filter. For example, some designs allocate either 32, 64, 96 filter taps per digital filter. Due to the limited amount of filter taps, a filter roll-off may occur slowly in a frequency thereby causing signal interference in bands that are adjacent.

In order to mitigate the nonlinearity caused by a PA, a digital pre-distortion, DPD, may be utilized. DPD may enable a PA to function at, or close to, its saturation point. The DPD may utilize algorithms such as direct learning algorithm, DLA, indirect learning algorithm, ILA, or fixed point-based algorithms. In ILA, for example, an output signal is used as an input of a post-distorter to extract the parameters of a pre-distorter. The DLA, on the other hand, may be based on identifying a PA model, whose inverse PA model is a DPD function to be calculated, i.e. determined. The DLA may model the PA as a memory polynomial with odd terms, for example only odd terms, and may then calculate a reverse function of the memory polynomial by means of a recursive algorithm.

FIG.2illustrates an exemplary embodiment of how DPD with direct learning could be used with a power amplifier to mitigate nonlinearity. A signal is first input to a digital pre-distorter210that then performs a DPD function and the pre-distorted signal is then converted by a digital to analog converter, DAC,220to an analog signal that is then amplified by the power amplifier230. The amplified analog signal is transmitted by the transmitter240. Also, the output amplified analog signal is used as an input to a feedback loop250. In the feedback loop250the amplified analog signal is input to an analog to digital converter260to obtain a digital signal. The obtained digital signal is then input to an identifying unit270which filters and normalizes the signal. The identifying unit may also receive the pre-distorted signal output by the digital pre-distorter210as an input. The identifying unit270may then, based on the input signals, determine, by for example extracting, characteristics of the PA230. These characteristics may then be used by the function unit280to determine a DPD function that may be used as an output from the feedback loop250that is then provided as an input to the digital pre-distorter210. It is to be noted that the digital pre-distorter210also receives as an input a signal that has not bee pre-distorted and thus uses the feedback pre-distorted signal and a signal that has not been pre-distorted to produce a new pre-distorted signal.

An example of modelling a nonlinear component such as a PA230is a generalized memory polynomial, GMP, model. Other models that may be used to model a nonlinear electronic component are for example radial pruning Volterra model and simplified radial pruning Volterra model. In GMP model linear and multiple non-linear parts may be expressed in terms of the input signal x(n). Input signal may be shifted by q (i.e. x(n−q)) to represent memory of the nonlinear devices. Thus, a non-linear output of a component y(n) may be expressed with linear and nonlinear terms. These terms may also be understood as characteristics of the component.

A DPD engine may be understood as component, that may be a logical component that performs pre-distortion of an input signal in order to mitigate nonlinearities introduced by a PA when amplifying the signal. Thus, the DPD engine may dynamically, thanks to a feedback loop, produce inverse of PA characteristics. The DPD engine is implemented, in some exemplary embodiments, as a composite of one linear filter and N−1 high order term linear filters, though this implementation may vary. In this exemplary embodiment, the input to the linear filter may be the input signal. Each high order term filter may have as an input some power of the amplitude of the input and each may have a different number of taps as well. The filtering may be used to pre-distort the input signal and mitigate the PA distortion such that the baseband equivalent of the output of the PA is close to being the same as the input signal.

When multiple bands are used, there may be a DPD engine for each carrier or for each band or there may be a combination of engines dedicated to a band and engine dedicated to a carrier. Thus, for example, if there are 3 frequency bands used, then there may be three DPD engines correspondingly, one for each band. Yet, if the frequency bands, that are radio frequency, RF, bands, are close to each other causing intermodulation, IM, interference amongst each other, it may be that multiple DPD engines are not able to mitigate that interference. Thus, it would be desirable to further enhance DPD implementation such that the self-interference may be mitigated thereby allowing multiband operations even if the bands are close to each other.

FIG.3illustrates a flow chart according to an exemplary embodiment in which multiple DPD engines may be used even though there are multiple bands that are close to each other. In other words, self-interference mitigation for multi-band DPD, SIM MDPD, is illustrated in this exemplary embodiment. First in S1 it is determined how many DPD engines are required. It may be possible to allocate one DPD engine per carrier or on a DPD engine per band or a combination of both. Next, in S2, frequencies of numerical control oscillators, NCOs, are obtained such that there are NCOs corresponding to the DPD engines. For example, one NCO for each DPD engine. The NCO frequency may be for example the centre of a collection of carriers. An NCO may be understood as a digital signal generator that creates a synchronous, discrete-time, discrete-valued representation of a waveform. NCOs may be used in conjunction with a DAC. Some benefits of an NCO may comprise agility, accuracy, stability and reliability.

Next, in S3, filters are selected for the DPD engines. There may be for example one filter for each DPD engine. In this exemplary embodiment the filters are finite impulse response, FIR, filters although in some other exemplary embodiments other filters may be used. FIR is a filter that has an impulse response of finite duration. In some other exemplary embodiments, one FIR filter may be used for multiple, even for all, DPD engines. If a real FIR filter, a FIR filter with no complex coefficients, is used, then the center of the DPD engine may be set to 0 Hz. If a complex FIR filter, a FIR filter with complex coefficients, is used, then the FIR filter may be specifically designed for its corresponding DPD engine. The center of the complex filter may be coincident with the corresponding DPD engine. In S4 signals are then determined for the DPD engines. For example, shifted terms for 0 Hz may be determined for a real FIR filter and represented as txb1, x1b, x2b and x3b. Alternatively, if a complex FIR filter is used, then the signals do not need to be shifted and the terms may be represented as tx, x1, x2, x3. In S5 then terms are selected for a matrix and thereby also a memory depth and a nonlinear order are selected.

In S6 a basis function filtering is performed to the matrix to generate a filtered matrix. The basis function filtering may be understood as filtering non-linear terms in time domain. It is to be noted that the configuration selected may be used irrespective of whether the terms are shifted to 0 Hz or center, or not. It is also to be noted that signals are not shifted if a complex FIR is used. In S7 then filtered correlation matrixes are determined. In this exemplary embodiment, the filtered matrixes are auto and cross correlation matrixes. Based on the matrixes, coefficients may be obtained in accordance with a DPD algorithm used. Then in S8 pre-distorted digital signals are generated by the DPD engines. Each DPD engine may generate a pre-distorted digital signal. If a real FIR filter is used, then the pre-distorted signals are centred at 0 Hz if the signals are shifted to 0 Hz. In S9 then the pre-distorted signals are shifted back to carrier or band center. Alternatively, if a complex FIR filter is used, the shifting back is not required as signals were not shifted to 0 Hz before. Then in S10 the pre-distorted signals from the DPD engines are combined to be a total pre-distorted signal. This may be described for example as: total pre-distorted signal=Pre-distorted signal 1+Pre-distorted signal 2+Pre-distorted signal 3. The total pre-distorted signal may then be used as part of the feedback loops of the engines thereby allowing determination regarding which DPD interferes with which one.

FIG.4illustrates simulation results of an exemplary embodiment such as the one illustrated inFIG.3. In the simulated exemplary embodiment, there are three carries that are placed at frequencies, that may be considered as baseband frequencies. The frequencies are −45 MHz for carrier 1, +21 MHz for carrier 2 and +45 MHz for carrier 3 and therefore three DPD engines are used. In an alternative exemplary embodiment, carrier 1 may belong to RF band 1 and carriers 2 and 3 may belong to RF band 2 in which case two DPD engines would be used. The black part410illustrates IM interference between carriers 2 and 3 and the white part420below that illustrates the effect the linearization has in the exemplary embodiment. In this exemplary embodiment an area that will be suppressed by for example cavity filters is illustrated by the square430.

The sample rate of the DPD engines in this exemplary embodiment is 491.52 MSPS. Higher sampling rates may also be used if the specific multi-band configuring is wider to meet Nyquist criterion. The signals in this this exemplary embodiment may be represented as tx=x1+x2+x3, in which tx is the un-predistorted signal composite in which all the three signals ofFIG.1are included. x1 is the first carrier located at −45 MHz, which is band1, x2 is the second carrier located at 15 MHz, which is band 2 and x3 is the third carrier located at +45 MHz, which is band 3. Correspondingly,

x1b is the first carrier shifted to 0 Hz. Associated NCO1 shift is +45 MHz,

x2b is the 2ndcarrier shifted to 0 Hz. Associated NCO2 shift is −15 MHz, and

x3b is the 3rdcarrier shifted to 0 Hz. Associated NCO3 shift is −45 MHz.

Fb1 is the feedback of the first carrier shifted to 0 Hz. Associated NCO1 shift of +45 MHz is used along with filtering to extract it. txb1 is the composite tx shifted with NCO1 as in x1b, which is +45 MHz. It is to be noted that the components x1b, txb1 and Fb1, associated with the first DPD engine are shifted to 0 Hz with NCO1.

Equations associated with the first DPD engine are discussed below. It is to be noted that the equations associated with the second DPD and the third DPD may be the same or similar. The results illustrated inFIG.4were obtained from the 3 DPD engines used in the exemplary embodiment. Thus, the matrix YMat was obtained:
YMat=[x1bx1b*|x1b|2x1b*|x2b|2x1b*|x3b|2txb1*|txb1|2x1b*|x1b|4x1b*|x2b|4x1b*|x3b|4txb1*|txb1|4]

The vertical columns of the matrix YMat are time-based shifts such as n, n+1, n+2 etc. Thus, YMat is an n×9 matrix. Yet, it is to be noted that the terms illustrated above may not be not fixed but the selection of the terms may be based on the complexity of the DPD model as well as the complexity of the PA and the carrier configuration. It is to be noted that memory terms are excluded from the discussion for simplicity. Examples of some additional terms that may be used in some exemplary embodiments are:
x1b*(|x1b|2+|x2b|2+|x3b|2)k,
x1b*(|x1b|2+|x2b|2)k,
x1b*(|x1b|2+|x3b|2)k,
x1b*(|x2b|2+|x3b|2)k
x1b*|x1b|2*k1*|x2b|2*k2*|x3b|2*k3
x1b*|x1b|2*k1*|x2b|2*k2
x1b*|x1b|2*k1*|x3b|2*k3
x1b*|x2b|2*k2*|x3b|2*k3

The matrix is derived without basis function filtering so the basis function filtering is applied next. The basis function filtering may be performed either in time domain or in frequency domain. An example of basis function filtering applied to a third order non-linearity is:
f(x1b*|x1b|2)
YMatf=[f(x1b)f(x1b*|x1b|2)f(x1b*|x2b|2)f(x1b*|x3b|2)f(txb1*|txb1|2)f(x1b*|x1b|4)f(x1b*|x2b|4) . . .f(x1b*|x3b|4)f(txb1*|txb1|4)]

It is to be noted that the non-linear terms in YMatf are filtered, thus not exhibiting frequency components outside that of the filter f. This is beneficial for the SIM MDPD in which adjacent carrier IMs may be interfering with each other. Hence, the auto and cross correlation functions generated for DPD engines 2 and 3 may limit the frequency content to be within the filter response bandwidth. This allows the adjacent DPD engines to obtain an accurate estimate of the interfering IMs. Because of the basis function filtering, the least squares solution may obtain the frequency content that is applicable to a certain DPD engine. Although the adjacent DPD engines are overlapping in frequency domain identifying its own IMs may be achieved over a plurality of iterations.

If the auto- and the cross-correlation functions had not been filtered, the non-linear basis functions would exhibit unrestricted frequency components. Thus, an ineffective IM estimation would be resulted when such IMs are interfering between the adjacent DPD engines. Such a solution may not mitigate the self-interfering components correctly as the least squares solutions used by the DPD engines may not have the correct frequency exposure for the interfering IMs. Below are examples of the auto- and cross-correlation functions.

Basis function filtered auto correlation function: Auto1=(YMatfH)(YMatf)

Basis function filtered cross correlation function: Cross1=(YMatfH)(f(Fb1))

Once the identification is complete, the pre-distortion wave form may be generated in baseband that is 0 Hz. Pre-distorted waveform, Pred1b(n), may then be shifted to the correct frequency with a conjugate of NCO1. The following equations illustrate how the pre-distortion waveform may be generated for the first DPD engine corresponding to carrier 1. Pre-distorted waveforms for other carriers may be generated in a similar way.
Pred1b(n)=x1b(n)+filter(TotalError(n))
TotalError(n)=ymatn*wn1

ymatnis the row vector of the matrix YMat corresponding to time n. TotalError is filtered before it is added to the desired signal x1b. It to be noted that the Pred1b is in this example at the baseband 0 Hz. Pred1b(n) will then be shifted back to its original frequency −45 MHz before being added to other pre-distorted signals.

FIG.5illustrates simulation results when a 65-tap digital filter is used in the exemplary embodiment ofFIG.4. Each carrier may be cantered to the middle of the filter. The filter may be a real FIR or a complex FIR filter for example. If a real FIR filter is used the carriers are shifted to 0 Hz. If a complex filter is used the shift may not be needed. IMs within this filter are corrected and the correction may be controlled by either widening or narrowing the filter without outnumbering the dedicated filter taps.

FIG.6illustrates simulation results when real FIR filter is applied to an undistorted, clean, signal in band 2. Since the filters are real and therefore symmetric around 0 Hz the carriers are shifted to place the centre of carrier 2, or band 2, to be near 0 Hz which is the centre of the filter. As illustrated, the filter is unable to fully suppress carrier 3. Hence any inter carrier IMs410illustrated inFIG.4do not experience any attenuation. This may be corrected by applying the exemplary embodiment illustrated inFIG.3.

Advantages of the exemplary embodiments described above comprise the ability to linearize PA in a multi-carrier and a multi-band situation. The SIM MDPD described above may be used with any carrier separation. Hence, in some exemplary embodiments, a contiguous carrier such as NR-100 may be split into multiple bands using the SIM MDPD concept described above.

The apparatus700ofFIG.7illustrates an example embodiment of an apparatus that may be an access node or be comprised in an access node. The apparatus may be, for example, a circuitry or a chipset applicable to an access node to realize the described embodiments. The apparatus700may be an electronic device comprising one or more electronic circuitries. The apparatus700may comprise a communication control circuitry710such as at least one processor, and at least one memory720including a computer program code (software)722wherein the at least one memory and the computer program code (software)722are configured, with the at least one processor, to cause the apparatus700to carry out any one of the example embodiments of the access node described above.

The memory720may be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory. The memory may comprise a configuration database for storing configuration data. For example, the configuration database may store current neighbour cell list, and, in some example embodiments, structures of the frames used in the detected neighbour cells.

The apparatus700may further comprise a communication interface730comprising hardware and/or software for realizing communication connectivity according to one or more communication protocols.

The communication interface730may provide the apparatus with radio communication capabilities to communicate in the cellular communication system. The communication interface may, for example, provide a radio interface to terminal devices. The apparatus700may further comprise another interface towards a core network such as the network coordinator apparatus and/or to the access nodes of the cellular communication system. The apparatus700may further comprise a scheduler740that is configured to allocate resources.

The processor710interprets computer program instructions and processes data. The processor710may comprise one or more programmable processors. The processor710may comprise programmable hardware with embedded firmware and may, alternatively or additionally, comprise one or more application specific integrated circuits, ASICs.

The processor710is coupled to a memory720. The processor is configured to read and write data to and from the memory720. The memory720may comprise one or more memory units. The memory units may be volatile or non-volatile. It is to be noted that in some example embodiments there may be one or more units of non-volatile memory and one or more units of volatile memory or, alternatively, one or more units of non-volatile memory, or, alternatively, one or more units of volatile memory. Volatile memory may be for example RAM, DRAM or SDRAM. Non-volatile memory may be for example ROM, PROM, EEPROM, flash memory, optical storage or magnetic storage. In general, memories may be referred to as non-transitory computer readable media. The memory720stores computer readable instructions that are execute by the processor710. For example, non-volatile memory stores the computer readable instructions and the processor710executes the instructions using volatile memory for temporary storage of data and/or instructions.

The computer readable instructions may have been pre-stored to the memory720or, alternatively or additionally, they may be received, by the apparatus, via electromagnetic carrier signal and/or may be copied from a physical entity such as computer program product. Execution of the computer readable instructions causes the apparatus700to perform functionality described above.

In the context of this document, a “memory” or “computer-readable media” may be any non-transitory media or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer.

It is to be noted that the apparatus700may further comprise various component not illustrated in theFIG.7. The various components may be hardware component and/or software components.