WO2018026316A1

WO2018026316A1 - Pim suppression

Info

Publication number: WO2018026316A1
Application number: PCT/SE2016/050735
Authority: WO
Inventors: Haiying CAO; Spendim Dalipi
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2016-08-01
Filing date: 2016-08-01
Publication date: 2018-02-08

Abstract

A PIM signal is estimated by summing multiple input signals with different polarizations. Each input signal is input to a transmitter (10, 20) of multiple transmitters (10, 20) to output a respective RF transmit signal. PIM distortion in an output signal output from a receiver (50) in response to a RF receive signal received by the receiver (50) is suppressed based on the PIM signal. The output signal comprises PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitter (10, 20). A reduction in the complexity of the PIM suppression and modelling is achieved by summing input signals with different polarizations, in particular for a wireless communication device (1) comprising multiple transmitters (10, 20).

Description

PIM SUPPRESSION

TECHNICAL FIELD

The present embodiments generally relate to passive intermodulation (PIM), and in particular to methods, devices and computer programs for suppressing and modelling PIM distortion.

BACKGROUND

PIM is a form of interference or distortion that occurs in wireless communication devices that simultaneously transmit signals at multiple frequencies through passive devices. Such passive devices may include cables, antennas and connectors included in the transmit path of the wireless communication devices.

PIM distortion is the result of two or more high power tones mixing at device non-linearities. The higher the signal amplitudes, the more pronounced the effect of the non-linearities, and the more prominent PIM distortion occurs. The non-linearities could be in metal-to-metal contacts, such as imperfect metal contacts, oxidized or contaminated contact surface, junctions of dissimilar metals, etc. Non-linearities may also be due to material non-linearities, including magnetic materials in the signal path, temperature variation, etc. The existence of PIM will severely impact the sensitivity of a receiver and, thus, the network performance of the wireless communication devices. PIM suppression has been studied and several solutions have been proposed. For instance, U.S. patent nos. 8,855,172, 8,890,619 and 9,026,064 disclose different PIM suppression solutions. U.S. patent no. 8,855,175 describes a digital PIM compensator at the receiver. In this solution, the PIM compensator uses a digital input signal of the transmitter to generate an estimated PIM signal, which is then subtracted from the digital output signal of a main receiver. U.S. patent no. 8,890,619 discloses a tunable non-linear circuit that generates an intermodulation products (IMP) signal. An auxiliary receiver receives this IMP signal and outputs an auxiliary receiver output signal that only includes a subset of the IMPs that fall within a passband of a main receiver. This auxiliary receiver output signal is used to derive a PIM estimate signal. U.S. patent no. 9,026,064 takes a composite signal from the transmitter path and estimates and dynamically cancels PIM distortion.

The complexity of prior art PIM suppression solutions increases significantly when the number of transmitters in the wireless communication device increases and/or when there are multiple PIM sources. There is therefore a need for a PIM suppression solution that has sufficient low complexity to be used in, for instance, multi-transmitter implementations.

SUMMARY

It is a general objective to provide a computationally efficient passive intermodulation (PIM) suppression and modelling.

This and other objectives are met by embodiments described herein. An embodiment relates to a PIM suppression method comprising estimating a PIM signal by summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective radio frequency (RF) transmit signal. The method also comprises suppressing, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a non- linear function of the multiple RF transmit signals output by the multiple transmitters.

Another embodiment relates to a PIM modelling method comprising summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The method also comprises estimating a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The method further comprises adjusting the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

A further embodiment relates to a PIM suppression device. The device is configured to estimate a PIM signal by summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device is also configured to suppress, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

Another embodiment relates to a PIM suppression device comprising a PIM signal estimator for estimating a PIM signal by summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device also comprises a suppressing unit for suppressing, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

Yet another embodiment relates to a PIM modelling device. The device is configured to sum multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device is also configured to estimate a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The device is further configured to adjust the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

Another embodiment relates to a PIM modelling device comprising a summing unit for summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device also comprises a PIM signal estimator for estimating a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The device further comprises an adjusting unit for adjusting the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

A further embodiments relates to a computer program comprising instructions, which when executed by at least one processor, cause the at least one processor to estimate a PIM signal by summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The at least one processor is also caused to suppress, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters. Yet another embodiment relates to a computer program comprising instructions, which when executed by at least one processor, cause the at least one processor to sum multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The at least one processor is also caused to estimate a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The at least one processor is further caused to adjust the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

A related embodiment defines a carrier comprising a computer program according to above. The carrier is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

The PIM suppression and modelling of the embodiments significantly reduce the complexity of estimating PIM signals and PIM model coefficients as compared to prior art solutions, in particular for multi- transmitter implementations. BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments, together with further objects and advantages thereof, may best be understood by making reference to the following description taken together with the accompanying drawings, in which:

Fig. 1 schematically illustrates a frequency spectrum showing PIM distortion in a multi-transmitter configuration;

Fig. 2 is a flow chart illustrating a PIM suppression method according to an embodiment; Fig. 3 is flow chart illustrating an embodiment of the estimating step in Figs. 2 and 7;

Fig. 4 is a flow chart illustrating an additional optional step of the method shown in Fig. 2; Fig. 5 is a flow chart illustrating an embodiment of the determining step in Fig. 4; Fig. 6 is a flow chart illustrating a PIM suppressing method according to another embodiment;

Fig. 7 is a flow chart illustrating a PIM modelling method according to an embodiment;

Fig. 8 is flow chart illustrating an embodiment of the adjusting step in Fig. 7;

Fig. 9 is a flow chart illustrating a PIM modelling method according to another embodiment;

Fig. 10 schematically illustrates a wireless communication device according to an embodiment;

Fig. 11 schematically illustrates a wireless communication device according to another embodiment; Fig. 12 schematically illustrates an implementation example of a low complexity PIM model;

Fig. 13 schematically illustrates another implementation example of a low complexity PIM model;

Fig. 14 schematically illustrates a further implementation example of a low complexity PIM model; Fig. 15 schematically illustrates yet another implementation example of a low complexity PIM model;

Fig. 16 schematically illustrates a PIM suppression/modelling device according to an embodiment; Fig. 17 schematically illustrates a PIM suppression/modelling device according to another embodiment; Fig. 18 schematically illustrates a PIM suppression/modelling device according to a further embodiment; Fig. 19 is a schematic block diagram of a computer-program-based implementation of an embodiment; Fig. 20 schematically illustrates a PIM suppression device according to yet another embodiment;

Fig. 21 schematically illustrates a PIM modelling device according to yet another embodiment;

Fig. 22 schematically illustrates a distributed implementation among multiple network devices; and Fig. 23 is a schematic illustration of an example of a wireless communication system with one or more cloud-based network devices according to an embodiment.

DETAILED DESCRIPTION

Throughout the drawings, the same reference numbers are used for similar or corresponding elements.

The present embodiments generally relate to PIM, and in particular to methods, devices and computer programs for suppressing and modelling PIM distortion. Fig. 1 schematically illustrates a frequency spectrum showing PIM distortion in a multi-transmitter configuration. In this figure, two transmitters of a wireless communication device transmit two respective signals indicated by bold lines and of frequencies fi, h for the first transmitter and of frequencies f3, U for the second transmitter. PIM distortion occurs if these signals are transmitted through a passive device with a non-linear response. This means that even if the signals are transmitted in the transmit bands TX1, TX2 of the respective transmitter, the transmission of the signals through the passive device with nonlinear response generates PIM that spread over the frequency spectrum. In the figure, PIM components that leak or couple to the receive band RX of the receiver of the wireless communication device are indicated by the 3^rd order PIM component 2fi-f2, 2fi-f₃, 2fi-f4, 2f2-f3 and 2f2-f4. These PIM components may therefore appear as interference, i.e. PIM distortion or interference, to the receiver.

In Fig. 1 only some of the 3^rd order PIM components are shown in order to simplify the figure. There are also other 3^rd order and higher order PIM components. However, the 3^rd order PIM components have the highest possibility of coupling into the receive band RX, especially if the frequencies of the

transmitted signals are separated by a large frequency gap. Furthermore, such 3^rd order PIM components generally have powers that are much higher than that of higher order PIM components.

PIM distortion is of particular concern in wide bandwidth communication systems, such as Long Term Evolution (LTE) and LTE Advanced systems or wireless communication systems involving multiple frequency bands, such as Multi-Standard Radio in Non-Continuous spectrum (MSR-NC) and LTE Carrier Aggregation (CA) systems.

PIM suppression solutions have been proposed in the art, such as exemplified by the previously mentioned U.S. patent nos. 8,855,172, 8,890,619 and 9,026,064. However, the complexity of prior art PIM suppression solutions increases significantly when the number of transmitters in the wireless communication device increases and/or when there are multiple PIM sources in and/or in the vicinity of the wireless communication device.

For instance, the PIM suppression model proposed in U.S. patent nos. 8,855,172 and 8,890,619 can, assuming two transmitters (2T) in a wireless communication device as shown in Fig. 1 , be written as:

In the PIM suppression model above,

represents weighting factors, also referred to as PIM model coefficients herein, represents different polarizations, represents different carriers, x_Pik

represents an input signal with polarization and carrier represents discrete time delays, n

represents discrete time and "*" represents conjugate operation. Thus, the PIM suppression model needs to determine or estimate 20 PIM model coefficients already for a wireless communication device having two transmitters (2T) with two polarizations (2P), i.e. an 2T2P implementation. If the wireless communication device instead has four transmitters (4T) with two polarizations (4T2P), 144 PIM model coefficients need to be determined or estimated.

The present embodiments reduce the complexity when estimating PIM and in particular for the case with a wireless communication device having multiple, i.e. at least two, transmitters and/or multiple PIM sources, i.e. multiple passive devices with non-linearities that may cause PIM Fig. 2 is a flow chart illustrating a PIM suppression method according to an embodiment. The method starts in step S1 , which comprises estimating a PIM signal by summing multiple input signals with different polarizations. Each input signal of the multiple input signals is input to a transmitter of multiple transmitters configured to output a respective radio frequency (RF) transmit signal. A next step S2 comprises suppressing, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver. The output signal comprises PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

Thus, the PIM suppression method as shown in Fig. 2 involves estimating a PIM signal that can be used to suppress, such as cancel or at least reduce, PIM distortion in an output signal from the receiver and where the PIM distortion is a non-linear function of the RF transmit signals output by multiple transmitters. The PIM suppression could be in the form of a complete or near complete cancellation of the PIM distortion in the output signal. PIM suppression as used herein also encompass a reduction of the PIM distortion in the output signal, i.e. not necessarily a complete cancellation of the PIM distortion. In clear contrast to prior art PIM suppression solutions, the present embodiments generate the PIM signal by summing multiple input signals with different polarizations. This approach results in a computationally more efficient PIM signal estimation, requiring estimating fewer PIM model coefficients as compared to the prior art solutions. Hence, input signals with different polarizations can be summed and handled together in order to reduce the number of PIM model coefficients that need to be determined in order to estimate the PIM signal.

The PIM suppression method of the embodiments is in particular suitable for usage in a wireless communication device comprising multiple transmitters. As shown in the foregoing, having a wireless communication device with more than one transmitter will significantly increase the complexity in estimating PIM signals according to the prior art solutions, such as requiring determining 20 PIM model coefficients for a 2T2P scenario and 144 PIM model coefficients for a 4T2P scenario. This should be compared to the present embodiments, which could handle the 2T2P scenario by determining merely up to four PIM model coefficients. This is possible due to the summation of input signals with different polarizations.

In an embodiment, suppressing PIM distortion in step S2 comprises subtracting the PIM signal from the output signal. The result is a compensated signal, i.e. a PIM compensated signal, in which the PIM distortion has been suppressed, i.e. cancelled or at least reduced, as compared to the uncompensated signal, i.e. the output signal.

Fig. 3 is a flow chart illustrating an embodiment of the estimating step S1 in Fig. 2. In this embodiment, step S12 comprises summing the multiple input signals with different polarizations. A next step S13 comprises filtering the summed multiple input signals with different polarizations with at least one PIM model coefficient. The following step S14 comprises generating n^th order PIM components falling within a receive band of the receiver based on the filtered summed multiple input signals with different polarizations to form the PIM signal.

Thus, the multiple input signals with different polarizations are, in this embodiment, first summed to obtain one or more summed signals in step S12. Each such summed signal is filtered with a respective PIM model coefficient in step S13 to obtain one or more filtered summed signals. The PIM signal is then generated based on the filtered summed signals based on the n^th order PIM components that fall within the receive band of the receiver. In Fig. 1 , the 3^rd order PIM components 2fi-f4, 2fi-f₃, 2f2-f4, 2f2-f3, 2fi-f2 falls within the receive band (RX) of the receiver.

The PIM model coefficient(s) is(are) determined in order to obtain the n^th order PIM components falling within the receive band to form the PIM signal that can be used to suppress the PIM distortion. In a particular embodiment, step S14 comprises generating 3^rd order PIM components falling within the receive band to from the PIM signal, which is further described herein.

The method then continues, in this embodiment, to step S2 in Fig. 2, where the PIM distortion is suppressed using the formed PIM signal. Fig. 3 also illustrates two additional, optional steps of the method. Step S10 comprises delaying the multiple input signals. This step S10, thereby involves delaying the different input signals to align the input signals with each other and with the RF receive signal and output signal. The delays thereby compensate for time differences between the input signals and the output signal and any time differences in between different input signals, such as due to different paths of the input signals at the transmission side of the wireless communication device.

Correspondingly, step S11 comprises adjusting gain and/or phase of the multiple input signals with coupling factors. In this optional step, differences in gains, differences in phases or differences in gains and phases between the multiple input signals can be adjusted using coupling factors.

The coupling factors are preferably complex coupling factors that, in an embodiment, can be implemented as finite impulse response (FIR) filters. These coupling factors can be seen as pre-filtering operators and can be determined in an offline setting, such as by sweeping each coupling factor and the ones that minimize PIM at the output. Alternatively, a non-linear optimization can be done where the coupling factors are optimized together with the PIM model coefficients.

The adjustments in steps S10 and S11 can be performed serially in any order, or at least at least partly in parallel. For instance, the input signals could be filtered with FIR filters to both adjust for delay, gain and phase in a single step.

The adjustments in steps S10 and S11 are preferably performed prior to summing the delay-adjusted input signals, the gain-adjusted input signals, the phase-adjusted input signals, the delay- and gain- adjusted input signals, the delay- and phase-adjusted input signals, the gain- and phase-adjusted input signals, or the delay-, gain- and phase-adjusted input signals, depending on whether steps S10 and S11 are performed or not.

In an embodiment, step S1 of Fig. 2 comprises estimating the PIM signal by summing multiple input signals with different polarizations but a same carrier. In this embodiment, input signals of a same carrier but of different polarizations are summed together to thereby reduce the number of terms, i.e. PIM model coefficients, that need to be determined in order to derive the PIM signal.

For instance, an example with two transmitters (2T) and two polarizations (2P) would result, as shown above, in 20 terms according to prior art PIM suppression solutions. However, this embodiment summing input signals with different polarizations but a same carrier merely needs to determine four PIM model coefficients in a 2T2P implementation.

In a particular embodiment, step S12 of Fig. 3 comprises summing the multiple input signals with different polarizations but a same carrier by calculating

factors adjusting gain and/or phase of the multiple input signals, represents different polarizations,

represents different carriers, represents an input signal with polarization and carrier

represents discrete time delays and n represents discrete time.

The optional, but preferred, coupling factors are used to adjust the gain and/or phase of the multiple

input signals as previously described in connection with step S11. Correspondingly, step S10 comprises adjusting the delays of the multiple input signals, which is represented by the parameters above.

In this particular embodiment, the input signals with different polarizations but a same carrier, i.e. a same frequency band, are summed and treated as a single, optionally delay-, gain- and/or phase-adjusted, signal. This is shown above by summing input signals with a same k but different β. In a particular

embodiment, the input signals are transmitted by a first transmitter of the wireless

communication device, whereas the input signals x are transmitted by a second transmitter

of the wireless communication device.

Step S13 then comprises filtering the summed multiple input signals with different polarizations but a

summed signal from step S12 is thereby filtered with a respective PIM model coefficient in step S13.

In this embodiment, step S14 of Fig. 3 comprises generating

order PIM components falling within a receive band of the receiver to form the PIM signal

Hence, in this embodiment the PIM signal is formed by optionally first delay-, gain- and/or phase-adjusting the multiple input signals using the delay parameters and the coupling factors The delay-,

gain- and/or phase-adjusted input signals with different polarizations but a same carrier are then summed

respective PIM model coefficients w_1→ and the 3^rd order PIM components falling within the receive band of the receiver are generated to form the above presented PIM signal.

Thus, the basic idea of this PIM suppression embodiment is to sum the input signals with different polarizations together and optionally introduce a coupling factor to adjust the gain and/or phase of the different input signals.

In another PIM suppression embodiment, all the carriers are treated as a whole signal. In such an embodiment, step S1 of Fig. 2 comprises estimating the PIM signal by summing multiple input signals with different polarizations and different carriers.

This embodiment optionally comprises steps S10 and S11 as shown in Fig. 3 in order to adjust for delay, gain and/or phase of the different input signals. The method then continues to step S12, which comprises summing the multiple input signals with different polarizations and different carriers by calculating

In this case, a_1→ represents coupling factors adjusting gain and/or phase of the multiple input signals, represents different polarizations, represents different carriers, epresents an input

signal with polarization β and carrier represents discrete time delays and n represents discrete

time. The next step S13 comprises filtering the summed multiple input signals with different polarizations and different carriers with a PIM model coefficient w by calculating

Finally, step S14 comprises

generating 3^rd order PIM components falling within a receive band of the receiver to form the PIM signal

The advantage of this second embodiment is that the complexity of the PIM suppression can be significantly reduced, in particular when there are many carriers or frequency combinations in a wideband signal. As shown above, in a 2T2P implementation, the second embodiment has only a single PIM model coefficient to determine. This reduction in complexity might, at a first glance, imply a reduction in the degree of freedom as compared to the prior art solutions having 20 individual PIM model coefficients. However, by introducing the optional coupling factors, the degree of freedom can be improved and achieves similar performance but with a lower computational complexity. A limitation with this second embodiment is that the sampling frequency, f_s, needs to be three times the whole signal, including several carriers, bandwidth instead of a single carrier bandwidth. On the other hand, if a multi-carrier wideband signal is used or several transmitters are involved, the complexity reduction of the second embodiment will overweight this limitation. The second embodiment has only one term, i.e. PIM model coefficient, to adapt and, thus, the complexity is greatly reduced also. The required sampling frequency for this embodiment is, however, three times of the instantaneous bandwidth (IBW). This should be compared to the first embodiment, which only requires three times the maximum bandwidth of a certain carrier. Therefore, if the signal bandwidth is small and the IBW is wide, the first embodiment has lower complexity for implementation, while if the signal bandwidth is wide or more transmitters are involved, the second embodiment might have even lower complexity than the first embodiment. This can be seen in Table 1 and 2, which show the complexity comparison for the PIM suppression in U.S. patent nos. 8,855,172 and 8,890,619 (prior art) and the first and second embodiments (model I and II) mentioned above.

Thus, as shown in Table 1 and 2, the first embodiment significantly reduces complexity as compared to the prior art as measured in number of multipliers and adders per ps. Correspondingly, if the signal bandwidth is wide (20 MHz per carrier) and/or the number of transmitters increase, both the first embodiment and in particular the second embodiment significantly reduce the complexity as compared to the prior art solution. In the first and second embodiments, the PIM signal is generated based on the 3^rd order PIM components that fall within the receive band of the receiver. It is generally sufficient from PIM distortion suppression point of view to suppress such 3^rd order PIM components. Higher order PIM components generally have significantly lower power as compared to the 3^rd order PIM components. This together with the increased complexity of including higher order PIM components in the estimation of the PIM signal implies that in a preferred embodiment 3^rd order PIM components are generated in step S14 of Fig. 3.

In a particular embodiment, step S1 of Fig. 2 comprises estimating a PIM signal

represents optional coupling factors adjusting gain and/or phase of multiple input signals with different polarizations input to a transmitter of two transmitters configured to output a respective RF transmit signal, represents PIM model coefficients, represents different polarizations, /c₁_₂ represents

different carriers, represents an input signal with polarization β and carrier k, m_1→ represents discrete time delays and n represents discrete time.

In another particular embodiment, step S1 of Fig. 2 comprises estimating a PIM signal

wherein represents optional coupling factors adjusting gain and/or phase of multiple input

signals with different polarizations input to a transmitter of two transmitters configured to output a respective RF transmit signal, w represents a PIM model coefficient, represents different

polarizations, represents different carriers, represents an input signal with polarization

β and

carrier represents discrete time delays and n represents discrete time.

Fig. 4 is a flow chart illustrating an additional, optional step of the method shown in Fig. 2 or 3. This flow chart comprises determining, in step S20, the at least one PIM model coefficient based on reducing energy of a difference between the output signal and the PIM signal.

This energy reduction of the difference could be seen as an optimization procedure using a least mean squares (LMS) algorithm or another optimization algorithm. LMS algorithms are a class of adaptive filters used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal, i.e. difference between the desired signal (output signal without any PIM distortion) and the actual signal (output signal with PIM distortion).

Fig. 5 is a flow chart illustrating a particular embodiment of step S20 in Fig. 4. In this particular embodiment, step S21 comprises summing the multiple input signals with different polarizations. This step S21 basically corresponds to step S12 in Fig. 3. A next step S22 comprises filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. N^th order PIM components falling within a receive band of the receiver is generated in step S23 based on the filtered summed multiple input signals with different polarizations to form a PIM signal. Steps S22 and S23 basically correspond to steps S13 and S14 in Fig. 3 but with the difference that at least one candidate PIM model coefficient is used in step S22, whereas at least one already determined PIM model coefficient is used in step S12. The energy of the difference between the output signal and the PIM signal is measured in step S24. If the measured energy is lower than a stored energy measured when using at least one stored PIM model coefficient to filter summed multiple input signals the at least one stored PIM model coefficient is replaced in step S25 by the at least one candidate PIM model coefficient. If the measured energy is not lower than the stored energy the at least one candidate PIM model coefficient is adjusted to form at least one updated candidate PIM model coefficient. The method is then continues to step S21 where the method steps S21 to S25 are repeated but using the updated candidate PIM model coefficient(s).

Thus, the loop illustrated by the line L1 in Fig. 5 is preferably repeated until a reduction in the measured energy is achieved. In such a case, new PIM model coefficient(s) corresponding to one(s) used in step S22 is(are) saved and thereby replace(s) the previously stored PIM model coefficient(s). This loop could be performed a predetermined number of times and then using the stored PIM model coefficient(s) when filtering the summed input signals in step S13 in Fig. 3. Alternatively, the loop is performed until the measured energy is smaller than a set threshold value. In a further alternative, the update of the PIM model coefficient(s) could be performed at scheduled time instance or so-called update periods. In such a case, a new round of the loop is performed at the start of each new update period.

Fig. 6 if a flow chart illustrating a PIM suppression method according to another embodiment. In a first step, the baseband signal, i.e. input signals, and the received transmit signal from the receiver, i.e. output signal, are provided. A next step comprises integer and fractional delay compensation of the input signals. The baseband signal with different polarizations or carriers is summed in a next step. Third order intermodulation (IM3) components falling into the victim uplink (UL) band, i.e. receive band of the receiver, is generated in the next step. These IM3 components are filtered using at least one PIM model coefficient to generate the estimated PIM signal. The estimated PIM signal is subtracted from the output signal of the receiver to thereby obtain a PIM compensated or suppressed output signal. The PIM model coefficient(s) is(are) optionally adapted to minimize the energy of the subtraction of the estimated PIM signal and the output signal of the receiver.

As is shown by comparing Figs. 3 and 6, the filtering and the generation of the n^th/3^rd order PIM components could be performed serially in any order, i.e. first filtering and then generation of the PIM components or first generation of the PIM components and then filtering. It is also possible to perform these to method steps at least partly in parallel, i.e. in a single or combined method step. Fig. 7 is a flow chart illustrating a PIM modelling method according to an embodiment. The method comprises summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. A next step S31 comprises estimating a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The at least one candidate PIM model coefficient is then adjusted in step S32 based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

The PIM modelling method shown in Fig. 7 is thereby a method for modelling PIM in a wireless communication device having access to multiple transmitters and a receiver. The PIM modelling method further adjusts PIM model coefficient(s) to generate a PIM signal that will suppress, cancel or reduce PIM distortion in the output signal.

The method steps S30 and S32 could be performed more than once in order to optimize the at least one candidate PIM model coefficient in the PIM modelling method, which is indicated by the line L2

Fig. 8 is a flow chart illustrating additional, optional steps of the method shown in Fig. 7. The method continues from step S31 in Fig. 7. A next step S40 comprises measuring energy of a difference between the PIM signal and the output signal. In this embodiment, step S32 of Fig. 7 comprises replacing, as shown in step S41 , at least one stored PIM model coefficient by the at least one candidate PIM model coefficient if the measured energy is lower than a stored energy measured when using the at least one stored PIM model coefficient to filter summed multiple input signals. Step S41 also comprises, otherwise adjusting the at least one candidate PIM model coefficient to form at least one updated candidate PIM model coefficient. The method then ends or continues to step S30 in Fig. 7 to start a new run of the loop.

In a first embodiment, the PIM modelling method operates according to the previously described first embodiment. In such a case, step S30 preferably comprises summing multiple input signals with different polarizations but a same carrier.

In a particular embodiment, step S30 is performed as previously described in connection with step S12 in Fig. 3, i.e. comprises summing the multiple input signals with different polarizations but a same carrier by calculating

In a second embodiment, the PIM modelling method operates according to the previously described second embodiment. In such a case, step S30 preferably comprises summing multiple input signals with different polarizations and different carriers.

In a particular embodiment, step S30 is performed as previously described in connection with step S12 in Fig. 3, i.e. comprises summing the multiple input signals with different polarizations and different

The at least one PIM model coefficient used in the PIM suppression method and determined in the PIM modelling method is, in an embodiment, selected from a group consisting of i) at least one complex FIR filter and ii) at least one cascade of a real FIR filter and a complex scalar. Thus, in an embodiment the PIM model coefficient(s) may be implemented in the form of a complex FIR filter or a cascade of a real FIR filter and a complex scalar depending on the actual implementation cost in different cases. They can be controlled such that dedicated PIM can be cancelled, e.g. compensate for internal PIM only. In some embodiments, using a real FIR filter and a complex scalar instead of a complex FIR filter may simplify the estimation of PIM signal and PIM model coefficients and the implementation.

Fig. 9 is a flow chart illustrating a PIM modelling method according to another embodiment. The method comprises providing a baseband signal, i.e. the multiple input signals. The baseband signal is filtered with weighting coefficients, i.e. the candidate PIM model coefficients. A next step comprises summing the baseband signal with different polarizations or carriers. IM3 components falling into the victim UL band is then generated to form the PIM signal. The estimated PIM signal is subtracted from the output signal of the receiver and the energy of this subtraction is measured. If the energy is minimized the candidate PIM model coefficient(s) is(are) saved to be used during PIM suppression. If the energy is not minimized the weighting coefficients are adjusted to start a new round.

As is shown by comparing Figs. 9 and 3, the filtering of the baseband signal can be performed prior to or following summing the baseband signal with different polarizations or carriers. These two method steps can alternatively be performed at least partly in parallel in a single or combined method step.

Fig. 10 schematically illustrates a wireless communication device 1 according to an embodiment. The wireless communication device 1 comprises multiple transmitters 10, 20. Each transmitter 10, 20 includes conventional transmitter components, such as, for example, up-conversion circuitry (not shown) and a power amplifier (PA) 11 , 21. The transmitters 10, 20 operate to process, e.g. up-convert and amplify, baseband or downlink (DL) input signals to output respective RF transmit signals. The RF transmit signals then pass through a duplexer 30 to an antenna 40. The RF transmit signals are thereby transmitted by the wireless communication device 1. The RF transmit signals pass, after being output from the transmitters 10, 20, one or more PIM sources. The PIM source(s) could, for instance, be an antenna port of the duplexer 30, or any other passive component between outputs of the transmitters 10, 20 and the antenna 40 that includes some non- linearity. The PIM source may also, or alternatively, be an external device, i.e. external of the wireless communication device 1 but generally arranged in the vicinity thereof, such as a fence. The non-linearity of the at least one PIM source introduces PIM distortion into a RF receive signal received at the antenna 40.

The wireless communication device 1 also comprises a receiver 50, which includes conventional receiver components, such as, for example, a low noise amplifier (LNA) 51 , filters, down-conversion circuitry (not shown), etc. The receiver 50 operates to process, e.g. amplify, filter and down-convert, the RF receive signal received from the antenna 40 via the duplexer 30 to output an output signal. The PIM component of the RF transmit signals produced by the at least one PIM source that fall within the receive band of the receiver 50 result in PIM distortion in the output signal.

The wireless communication device 1 also comprises a PIM suppression device and/or a PIM modelling device 100 according to the embodiments in order to estimate a PIM signal from the input signals. The PIM modelling device 100 models the at least one PIM source to estimate the PIM signal from the multiple input signals. A subtraction circuitry 60 operates to subtract the PIM signal from the output signal to thereby provide a compensated signal.

In an embodiment, the wireless communication device 1 comprises the PIM suppression device 100 having access to PIM model coefficient(s) to be used when estimating a PIM signal in order to suppress distortion in the output signal from the receiver 50. In another embodiment, the wireless communication device 1 comprises the PIM modelling device 100 to determine the PIM model coefficient(s). In a preferred embodiment, the functionalities of both the PIM suppression device and the PIM modelling device are implemented in the wireless communication device 1 to thereby both determine PIM model coefficient(s) and use the PIM model coefficient(s) in suppressing PIM distortion. The wireless communication device 1 may then comprise a PIM suppression device and a PIM modelling device or a combined PIM suppression and modelling device.

Fig. 11 schematically illustrates a wireless communication device 1 according to another embodiment. In this figure, only a single transmitter 10 has been illustrated in order to simplify the figure. A downlink (DL) or baseband signal is input to a crest factor reduction (CFR) circuitry 12. The transmitter 10 comprises, in this embodiment, a digital pre-distortion (DPD) circuitry 13, a power amplifier 11 and a bandpass filter 14. Correspondingly, the receiver 50 comprises, in this embodiment, a bandpass filter 54, a low noise amplifier 51 and an automatic gain control (AGC) circuitry 52. The figure also illustrates an integer and fraction delay compensation circuitry 70 that operates for delaying the input signals according to the previously mentioned delay parameters m_1→.

The wireless communication device 1 as shown in Figs. 10 and 11 may be a network node of a wireless communication network. As used herein, the non-limiting term "network node" may refer to base stations, access points, network control nodes such as network controllers, radio network controllers, base station controllers, access controllers, and the like. In particular, the term "base station" may encompass different types of radio base stations including standardized base station functions such as Node Bs, or evolved Node Bs (eNBs), and also macro/micro/pico radio base stations, home base stations, also known as femto base stations, relay nodes, repeaters, radio access points, Base Transceiver Stations (BTSs), and even radio control nodes controlling one or more Remote Radio Units (RRUs), or the like.

The wireless communication device 1 may alternatively be a wireless user device or equipment (UE), such as in the form of a mobile phone, a cellular phone, a smart phone, a personal digital assistant (PDA) equipped with radio communication capabilities, a laptop or a personal computer (PC) equipped with an internal or external mobile broadband modem, a tablet with radio communication capabilities, a target device, a device to device UE, a machine type UE or UE capable of machine to machine communication, customer premises equipment (CPE), laptop embedded equipment (LEE), laptop mounted equipment (LME), USB dongle, a portable electronic radio communication device, a sensor device equipped with radio communication capabilities or the like. In particular, the term "wireless communication device" should be interpreted as non-limiting terms comprising any type of wireless user device or UE communicating with a network node in a wireless communication system and/or possibly communicating directly with another wireless communication device. In other words, a wireless communication device may be any device equipped with circuitry for wireless communication according to any relevant standard for communication.

Fig. 12 schematically illustrates an implementation example of a low complexity PIM model. In this example, a 2T2P scenario is assumed. The different input signals are preferably integer and fractional delay compensated in a delay adjustment circuitry using the delay parameters m_1→. The delay-adjusted input signals are then gain and/or phase adjusted using the coupling factors a_1→. Thereafter the delay- , gain- and/or phase-adjusted input signals with different polarizations but a same carrier are summed. In this case, two summers are used since the input signals are of two carriers. Thereafter, the 3^rd order PIM components falling within the receive band of the receiver are generated in intermodulation (IM) generators and are summed to form the PIM signal. The PIM signal is subtracted from the output signal. The figure also illustrates an adaptor that is configured to adjust the PIM model coefficients used by the IM generators in order to minimize the energy of the difference between the output signal and the PIM signal.

The adaptor may also adapt the delay parameters and/or the coupling factors as indicated in the figure.

Fig. 13 schematically illustrates another implementation example of a low complexity PIM model. In this embodiment, the delay adjustment circuitry and the coupling factor circuitries are replaced by filters, such as FIRs. In such a case, the adaptor preferably operates to adjust the filter coefficients of the FIRs. Fig. 14 schematically illustrates a further implementation example of a low complexity PIM model. The different input signals are preferably integer and fractional delay compensated in a delay adjustment circuitry using the delay parameters m The delay-adjusted input signals are then gain and/or phase

adjusted using the coupling factors Thereafter the delay-, gain- and/or phase-adjusted input

signals with different polarizations and different carriers are summed in a single summer. Thereafter, the PIM signal is generated in an IM generator based on the 3^rd order PIM components falling within the receive band of the receiver. The PIM signal is subtracted from the output signal. The figure also illustrates an adaptor that is configured to adjust the PIM model coefficient used by the IM generator in order to minimize the energy of the difference between the output signal and the PIM signal. The adaptor may also adapt the delay parameters and/or the coupling factors as indicated in the figure.

Fig. 15 schematically illustrates yet another implementation example of a low complexity PIM model. In this embodiment, the delay adjustment circuitry and the coupling factor circuitries are replaced by filters, such as FIRs. In such a case, the adaptor preferably operates to adjust the filter coefficients of the FIRs.

Another aspect of the embodiments relates to a PIM suppression device. The device is configured to estimate a PIM signal by summing multiple input signals with different polarizations, Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device is also configured to suppress, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

In an embodiment, the device is configured to subtract the PIM signal from the output signal.

In an embodiment, the device is configured to sum the multiple input signals with different polarizations. The device is also configured to filter the summed multiple input signals with different polarizations with at least one PIM model coefficient. The device is further configured to generate n^th order PIM components falling within a receive band of the receiver based on the filtered summed multiple input signals with different polarizations to form the PIM signal.

In an embodiment, the device is configured to adjust delay between the multiple input signals.

In an embodiment, the device is configured to adjust gain and/or phase of the multiple input signals with coupling factors.

In an embodiment, the device is configured to estimate the PIM signal by summing multiple input signals with different polarizations but a same carrier. In a particular embodiment, the device is configured to sum the multiple input signals with different polarizations but a same carrier by calculating

summed multiple input signals with different polarizations but a same carrier with PIM model coefficients

In another embodiment, the device is configured to estimate the PIM signal by summing multiple input signals with different polarizations and different carriers.

In another particular embodiment, the device is configured to sum the multiple input signals with different polarizations and different carriers by calculating

represents optional coupling factors adjusting gain and/or phase of multiple input signals with different polarizations input to a transmitter of two transmitters configured to output a respective RF transmit signal, represents PIM model coefficients, represents different polarizations, represents

different carriers, represents an input signal with polarization and carrier represents

discrete time delays and n represents discrete time.

signals with different polarizations input to a transmitter of two transmitters configured to output a respective RF transmit signal, w represents a PIM model coefficient, β^₂ represents different polarizations, /c₁_₂ represents different carriers, represents an input signal with polarization β and carrier k, m_1→ represents discrete time delays and n represents discrete time.

In an embodiment, the device is configured to determine the at least one PIM model coefficient based on reducing energy of a difference between the output signal and the PIM signal.

In a particular embodiment, the device is configured to sum the multiple input signals with different polarizations. The device is also configured to filter the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The device is further configured to generate n^th order PIM components falling within a receive band of the receiver based on the filtered summed multiple input signals with different polarizations to form a PIM signal. The device is additionally configured to measure energy of the difference between the output signal and the PIM signal. The device is also configured to replace at least one stored PIM model coefficient by the at least one candidate PIM model coefficient if the measured energy is lower than a stored energy measured when using the at least one stored PIM model coefficient to filter summed multiple input signals and otherwise adjust at least one candidate PIM model coefficient to form at least one updated candidate PIM model coefficient. A further aspect of the embodiments relates to a PIM modelling device. The device is configured to sum multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device is also configured to estimate a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The device is further configured to adjust the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters. In an embodiment, the device is configured to measure energy of a difference between the output signal and the PIM signal. The device is also configured to replace at least one stored PIM model coefficient by the at least one candidate PIM model coefficient if the measured energy is lower than a stored energy measured when using the at least one stored PIM model coefficient to filter summed multiple input signals and otherwise adjust at least one candidate PIM model coefficients to form at least one updated candidate PIM model coefficients.

In an embodiment, the device is configured to sum multiple input signals with different polarizations but a same carrier.

In another embodiment, the device is configured to sum multiple input signals with different polarizations and different carriers. In another particular embodiment, the device is configured to sum the multiple input signals with different polarizations and different carriers by calculating

The device is configured to filter the summed multiple

input signals with different polarizations and different carriers with a PIM model coefficient w by calculating

. The device is further configured to generate 3^rd order PIM components falling within a receive band of the receiver to form the PIM signal

Yet another aspect of the embodiments relates to a wireless communication device comprising multiple transmitters configured to multiple input signals and output multiple RF transmit signals. The wireless communication device also comprises a receiver configured to receive a RF receive signal and output an output signal. The RF receive signal comprises PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters. The wireless communication device further comprises a PIM suppression device according to the embodiments and/or a PIM modelling device according to the embodiments.

It will be appreciated that the methods and arrangements described herein can be implemented, combined and re-arranged in a variety of ways. For example, embodiments may be implemented in hardware, or in software for execution by suitable processing circuitry, or a combination thereof.

The steps, functions, procedures, modules and/or blocks described herein may be implemented in hardware using any conventional technology, such as discrete circuit or integrated circuit technology, including both general-purpose electronic circuitry and application-specific circuitry.

Alternatively, or as a complement, at least some of the steps, functions, procedures, modules and/or blocks described herein may be implemented in software such as a computer program for execution by suitable processing circuitry such as one or more processors or processing units.

Examples of processing circuitry includes, but is not limited to, one or more microprocessors, one or more Digital Signal Processors (DSPs), one or more Central Processing Units (CPUs), video acceleration hardware, and/or any suitable programmable logic circuitry such as one or more Field Programmable Gate Arrays (FPGAs), or one or more Programmable Logic Controllers (PLCs).

It should also be understood that it may be possible to re-use the general processing capabilities of any conventional device or unit in which the proposed technology is implemented. It may also be possible to re-use existing software, e.g. by reprogramming of the existing software or by adding new software components. Fig. 16 is a schematic block diagram illustrating an example of a PIM suppression or modelling device 100, based on a processor-memory implementation according to an embodiment. In this particular example, the device 100 comprises a processor 101 and a memory 102. The memory 102 comprises instructions executable by the processor 101.

In an embodiment, the processor 101 is operative to estimate the PIM signal by summing the multiple input signals with different polarizations. The processor 101 is also operative to suppress PIM distortion in the output signal based on the PIM signal.

In another embodiment, the processor 101 is operative to sum the multiple input signals with different polarizations. The processor 101 is also operative to estimate the PIM signal by filtering the summed multiple input signals with different polarizations. The processor 101 is further operative to adjust the at least one candidate PIM model coefficient based on reducing the energy of the difference between the PIM signal and the receive signal.

Optionally, the device 100 may also include a communication circuit 103. The communication circuit 103 may include functions for wired and/or wireless communication with other devices and/or network nodes in the wireless communication network. In a particular example, the communication circuit 103 may be based on radio circuitry for communication with one or more other nodes, including transmitting and/or receiving information. The communication circuit 103 may be interconnected to the processor 101 and/or memory 102. By way of example, the communication circuit 103 may include any of the following: a receiver, a transmitter, a transceiver, input/output (I/O) circuitry, input port(s) and/or output port(s). Fig. 17 is a schematic block diagram illustrating another example of a PIM suppression or modelling device 110, based on a hardware circuitry implementation according to an embodiment. Particular examples of suitable hardware circuitry include one or more suitably configured or possibly reconfigurable electronic circuitry, e.g. Application Specific Integrated Circuits (ASICs), FPGAs, or any other hardware logic such as circuits based on discrete logic gates and/or flip-flops interconnected to perform specialized functions in connection with suitable registers (REG), and/or memory units (MEM).

Fig. 18 is a schematic block diagram illustrating yet another example of a PIM suppression or modelling device 120, based on combination of both processor(s) 122, 123 and hardware circuitry 124, 125 in connection with suitable memory unit(s) 121. The device 120 comprises one or more processors 122, 123, memory 121 including storage for software (SW) and data, and one or more units of hardware circuitry 124, 125. The overall functionality is thus partitioned between programmed software for execution on one or more processors 122, 123, and one or more pre-configured or possibly reconfigurable hardware circuits 124, 125. The actual hardware-software partitioning can be decided by a system designer based on a number of factors including processing speed, cost of implementation and other requirements.

Fig. 19 is a schematic diagram illustrating an example of a computer-implementation of 200 according to an embodiment. In this particular example, at least some of the steps, functions, procedures, modules and/or blocks described herein are implemented in a computer program 240, which is loaded into the memory 220 for execution by processing circuitry including one or more processors 210. The processor(s) 210 and memory 220 are interconnected to each other to enable normal software execution. An optional input/output device 230 may also be interconnected to the processor(s) 210 and/or the memory 220 to enable input and/or output of relevant data such as input signals, PIM signal, PIM model coefficients, etc.

The term 'processor' should be interpreted in a general sense as any system or device capable of executing program code or computer program instructions to perform a particular processing, determining or computing task.

The processing circuitry including one or more processors 210 is thus configured to perform, when executing the computer program 240, well-defined processing tasks such as those described herein.

The processing circuitry does not have to be dedicated to only execute the above-described steps, functions, procedure and/or blocks, but may also execute other tasks.

In a particular embodiment, the computer program 240 comprises instructions, which when executed by at least one processor 210, cause the at least one processor 210 to estimate a PIM signal by summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The at least one processor 210 is also caused to suppress, based on said PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a nonlinear function of the multiple RF transmit signals output by the multiple transmitters. In another particular embodiment, the computer program 240 comprises instructions, which when executed by at least one processor 210, cause the at least one processor 210 to sum multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The at least one processor 210 is also caused to estimate a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The at least one processor 210 is further caused to adjust the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

The proposed technology also provides a carrier 250 comprising the computer program 240. The carrier 250 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.

By way of example, the software or computer program 240 may be realized as a computer program product, which is normally carried or stored on a computer-readable medium 250, in particular a non-volatile medium. The computer-readable medium may include one or more removable or non-removable memory devices including, but not limited to a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc (CD), a Digital Versatile Disc (DVD), a Blu-ray disc, a Universal Serial Bus (USB) memory, a Hard Disk Drive (HDD) storage device, a flash memory, a magnetic tape, or any other conventional memory device. The computer program 240 may thus be loaded into the operating memory 220 of a computer or equivalent processing device 200 for execution by the processing circuitry 210 thereof. The flow diagram or diagrams presented herein may be regarded as a computer flow diagram or diagrams, when performed by one or more processors. A corresponding device may be defined as a group of function modules, where each step performed by the processor corresponds to a function module. In this case, the function modules are implemented as a computer program running on the processor.

The computer program residing in memory may thus be organized as appropriate function modules configured to perform, when executed by the processor, at least part of the steps and/or tasks described herein. Fig. 20 is a schematic diagram illustrating an example of a PIM suppression device 130 comprising a PIM signal estimator 131 for estimating a PIM signal by summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device 130 also comprises a suppressing unit 132 for suppressing, based on the PIM signal, PIM distortion in an output signal output from a receiver in response to a RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

Fig. 21 is a schematic diagram illustrating an example of a PIM modelling device 140 comprising a summing unit 141 for summing multiple input signals with different polarizations. Each input signal is input to a transmitter of multiple transmitters configured to output a respective RF transmit signal. The device 140 also comprises a PIM signal estimator 142 for estimating a PIM signal by filtering the summed multiple input signals with different polarizations with at least one candidate PIM model coefficient. The device 140 further comprises an adjusting unit 143 for adjusting the at least one candidate PIM model coefficient based on reducing energy of a difference between the PIM signal and an output signal output from a receiver in response to an RF receive signal received by the receiver and comprising PIM distortion that is a non-linear function of the multiple RF transmit signals output by the multiple transmitters.

It is becoming increasingly popular to provide computing services (hardware and/or software) in network devices, such as network nodes and/or servers, where the resources are delivered as a service to remote locations over a network. By way of example, this means that functionality, as described herein, can be distributed or re-located to one or more separate physical nodes or servers. The functionality may be relocated or distributed to one or more jointly acting physical and/or virtual machines that can be positioned in separate physical node(s), i.e. in the so-called cloud. This is sometimes also referred to as cloud computing, which is a model for enabling ubiquitous on-demand network access to a pool of configurable computing resources such as networks, servers, storage, applications and general or customized services.

Fig. 22 is a schematic diagram illustrating an example of how functionality can be distributed or partitioned between different network devices in a general case. In this example, there are at least two individual, but interconnected network devices 300, 301 , which may have different functionalities, or parts of the same functionality, partitioned between the network devices 300, 301. There may be additional network devices 302 being part of such a distributed implementation. The network devices 300, 301, 302 may be part of the same wireless communication system, or one or more of the network devices may be so- called cloud-based network devices located outside of the wireless communication system.

Fig. 23 is a schematic diagram illustrating an example of a wireless communication network or system, including an access network 2 and/or a core network 3 and/or an operations and support system (OSS), 4 in cooperation with one or more cloud-based network devices 300. The figure also illustrates a user device 5 connected to the access network 2 and capable of conducting wireless communication with a base station representing an embodiment of a wireless communication device 1. The embodiments described above are to be understood as a few illustrative examples of the present invention. It will be understood by those skilled in the art that various modifications, combinations and changes may be made to the embodiments without departing from the scope of the present invention. In particular, different part solutions in the different embodiments can be combined in other configurations, where technically possible. The scope of the present invention is, however, defined by the appended claims.

Claims

1. A passive intermodulation, PIM, suppression method comprising:

estimating (S1) a PIM signal by summing multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal; and

suppressing (S2), based on said PIM signal, PIM distortion in an output signal output from a receiver (50) in response to a RF receive signal received by said receiver (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

2. The method according to claim 1 , wherein suppressing (S2) said PIM distortion comprises subtracting (S2) said PIM signal from said output signal.

3. The method according to claim 1 or 2, wherein estimating (S1) said PIM signal comprises:

summing (S12) said multiple input signals with different polarizations;

filtering (S13) said summed multiple input signals with different polarizations with at least one PIM model coefficient; and

generating (S14) n^th order PIM components falling within a receive band of said receiver (50) based on said filtered summed multiple input signals with different polarizations to form said PIM signal.

4. The method according to any of the claims 1 to 3, further comprising:

adjusting (S10) delay between said multiple input signals.

5. The method according to any of the claims 1 to 4, further comprising:

adjusting (S11) gain and/or phase of said multiple input signals with coupling factors.

6. The method according to any of the claims 1 to 5, wherein estimating (S1) said PIM signal comprises estimating (S1) said PIM signal by summing multiple input signals with different polarizations but a same carrier.

7. The method according to claim 6, wherein estimating (S1) said PIM signal comprises:

summing (S12) said multiple input signals with different polarizations but a same carrier by

wherein represents coupling factors adjusting gain and/or phase of said

multiple input signals, represents different polarizations, represents different carriers,

represents an input signal with polarization β and carrier represents discrete time delays and

n represents discrete time;

filtering (S13) said summed multiple input signals with different polarizations but a same carrier with PIM model coefficients by calculating

generating (S14

) 3 order PIM components falling within a receive band of said receiver (50) to form said PIM signal

8. The method according to any of the claims 1 to 5, wherein estimating (S1) said PIM signal comprises estimating (S1) said PIM signal by summing multiple input signals with different polarizations and different carriers.

9. The method according to claim 8, wherein estimating (S1) said PIM signal comprises:

summing (S12) said multiple input signals with different polarizations and different carriers by calculating

wherein represents coupling factors adjusting gain and/or phase of said multiple

input signals, epresents different polarizations, k represents different carriers represents

an input signal with polarization β and carrier represents discrete time delays and represents

discrete time;

filtering (S13) said summed multiple input signals with different polarizations and different carriers

5

10. The method according to any of the claims 3 to 9, further comprising determining (S20) said at least one PIM model coefficient based on reducing energy of a difference between said output signal and said PIM signal.

10 11. The method according to claim 10, wherein determining (S20) said at least one PIM model coefficient comprises:

summing (S21) said multiple input signals with different polarizations;

filtering (S22) said summed multiple input signals with different polarizations with at least one candidate PIM model coefficient;

15 generating (S23) n^th order PIM components falling within a receive band of said receiver (50) based on said filtered summed multiple input signals with different polarizations to form a PIM signal;

measuring (S24) energy of said difference between said output signal and said PIM signal; and replacing (S25) at least one stored PIM model coefficient by said at least one candidate PIM model coefficient if said measured energy is lower than a stored energy measured when using said at least one 20 stored PIM model coefficient to filter summed multiple input signals and otherwise adjusting at least one candidate PIM model coefficient to form at least one updated candidate PIM model coefficient.

12. A passive intermodulation, PIM, modelling method comprising:

summing (S30) multiple input signals with different polarizations, each input signal being input to 25 a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal;

estimating (S31) a PIM signal by filtering said summed multiple input signals with different polarizations with at least one candidate PIM model coefficient; and

adjusting (S32) said at least one candidate PIM model coefficient based on reducing energy of a 30 difference between said PIM signal and an output signal output from a receiver (50) in response to an RF receive signal received by said receiver (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

13. The method according to claim 12, further comprising: measuring (S40) energy of a difference between said PIM signal and said output signal, wherein adjusting (S32) said at least one candidate PIM model coefficients comprises:

replacing (S41 ) at least one stored PIM model coefficient by said at least one candidate PIM model coefficient if said measured energy is lower than a stored energy measured when using said at least one stored PIM model coefficient to filter summed multiple input signals and otherwise adjusting at least one candidate PIM model coefficients to form at least one updated candidate PIM model coefficient.

14. The method according to claim 12 or 13, wherein summing (S30) said multiple input signals comprises summing (S30) multiple input signals with different polarizations but a same carrier.

15. The method according to claim 14, wherein

summing (S30) said multiple input signals comprises summing (S12) said multiple input signals with different polarizations but a same carrier by calculating

coupling factors adjusting gain and/or phase of said multiple input signals, represents different

β and

carrier

represents discrete time delays and n represents discrete time;

estimating (S31) said PIM signal comprises:

filtering (S13) said summed multiple input signals with different polarizations but a same carrier with candidate PIM model coefficients by calculating

generating (S14) 3^rd order PIM components falling within a receive band of said receiver (50) to form said PIM signal

16. The method according to claim 12 or 13, wherein summing (S30) said multiple input signals comprises summing (S30) multiple input signals with different polarizations and different carriers.

17. The method according to claim 16, wherein

5 summing (S30) said multiple input signals comprises summing (S12) said multiple input signals with different polarizations and different carriers by calculating

represents coupling factors adjusting gain and/or phase of said multiple input signals, β

represents different polarizations,

represents different carriers, represents an input signal with polarization β and

10 carrier represents discrete time delays and n represents discrete time;

estimating (S31) said PIM signal comprises:

filtering (S13) said summed multiple input signals with different polarizations and different carriers with a PIM model coefficient w by calculating

15

20 18. The method according to any of the claims 3-5, 7, 9-17, wherein said at least one PIM model coefficient is selected from a group consisting of i) at least one complex finite impulse response, FIR, filter and ii) at least one cascade of a real FIR filter and a complex scalar.

19. A passive intermodulation, PIM, suppression device (100, 110, 120), wherein

25 said device (100, 110, 120) is configured to estimate a PIM signal by summing multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal; and

said device (100, 110, 120) is configured to suppress, based on said PIM signal, PIM distortion in an output signal output from a receiver (50) in response to a RF receive signal received by said receiver 30 (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

20. The device according to claim 19, wherein said device (100, 110, 120) is configured to subtract said PIM signal from said output signal.

21. The device according to claim 19 or 20, wherein

said device (100, 110, 120) is configured to sum said multiple input signals with different polarizations;

said device (100, 110, 120) is configured to filter said summed multiple input signals with different polarizations with at least one PIM model coefficient; and

said device (100, 110, 120) is configured to generate n^th order PIM components falling within a receive band of said receiver (50) based on said filtered summed multiple input signals with different polarizations to form said PIM signal.

22. The device according to any of the claims 19 to 21 , wherein said device (100, 110, 120) is configured to adjust delay between said multiple input signals.

23. The device according to any of the claims 19 to 22, wherein said device (100, 110, 120) is configured to adjust gain and/or phase of said multiple input signals with coupling factors.

24. The device according to any of the claims 19 to 23, wherein said device (100, 110, 120) is configured to estimate said PIM signal by summing multiple input signals with different polarizations but a same carrier.

25. The device according to claim 24, wherein

said device (100, 110, 120) is configured to sum said multiple input signals with different polarizations but a same carrier by calculating

adjusting gain and/or phase of said multiple input signals, represents different polarizations,

represents discrete time delays and n represents discrete time;

said device (100, 110, 120) is configured to filter said summed multiple input signals with different polarizations but a same carrier with PIM model coefficients by calculating

26. The device according to any of the claims 19 to 23, wherein said device (100, 110, 120) is configured to estimate said PIM signal by summing multiple input signals with different polarizations and different carriers.

27. The device according to claim 26, wherein

said device (100, 110, 120) is configured to sum said multiple input signals with different polarizations and different carriers by calculating

, wherein represents coupling factors adjusting gain and/or phase of said multiple input signals, ₂ represents different polarizations, represents different carriers, represents an input signal with polarization β and carrier represents discrete time delays and n represents discrete time;

said device (100, 110, 120) is configured to filter said summed multiple input signals with different polarizations and different carriers with a PIM model coefficient by calculating

28. The device according to any of the claims 21 to 27, said device (100, 110, 120) is configured to determine said at least one PIM model coefficient based on reducing energy of a difference between said output signal and said PIM signal.

29. The device according to claim 28, wherein

said device (100, 110, 120) is configured to filter said summed multiple input signals with different polarizations with at least one candidate PIM model coefficient;

said device (100, 110, 120) is configured to generate n^th order PIM components falling within a receive band of said receiver (50) based on said filtered summed multiple input signals with different polarizations to form a PIM signal;

said device (100, 110, 120) is configured to measure energy of said difference between said output signal and said PIM signal; and

said device (100, 110, 120) is configured to replace at least one stored PIM model coefficient by said at least one candidate PIM model coefficient if said measured energy is lower than a stored energy measured when using said at least one stored PIM model coefficient to filter summed multiple input signals and otherwise adjust at least one candidate PIM model coefficient to form at least one updated candidate PIM model coefficient.

30. The device according to any of the claims 19 to 29, further comprising:

a processor (101); and

a memory (102) comprising instructions executable by said processor (101), wherein said processor (101) is operative to estimate said PIM signal by summing said multiple input signals with different polarizations; and

said processor (101) is operative to suppress PIM distortion in said output signal based on said PIM signal.

31. A passive intermodulation, PIM, suppression device (130) comprising

a PIM signal estimator (131) for estimating a PIM signal by summing multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal; and

a suppressing unit (132) for suppressing, based on said PIM signal, PIM distortion in an output signal output from a receiver (50) in response to a RF receive signal received by said receiver (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

32. A passive intermodulation, PIM, modelling device (100, 110, 120), wherein

said device (100, 110, 120) is configured to sum multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal;

5 said device (100, 110, 120) is configured to estimate a PIM signal by filtering said summed multiple input signals with different polarizations with at least one candidate PIM model coefficient; and

said device (100, 110, 120) is configured to adjust said at least one candidate PIM model coefficient based on reducing energy of a difference between said PIM signal and an output signal output from a receiver (50) in response to an RF receive signal received by said receiver (50) and comprising 10 PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

33. The device according to claim 32, wherein

said device (100, 110, 120) is configured to measure energy of a difference between said PIM 15 signal and said output signal; and

said device (100, 110, 120) is configured to replace at least one stored PIM model coefficient by said at least one candidate PIM model coefficient if said measured energy is lower than a stored energy measured when using said at least one stored PIM model coefficient to filter summed multiple input signals and otherwise adjust at least one candidate PIM model coefficients to form at least one updated 20 candidate PIM model coefficients.

34. The device according to claim 32 or 33, wherein said device (100, 110, 120) is configured to sum multiple input signals with different polarizations but a same carrier.

25 35. The device according to claim 34, wherein

30 adjusting gain and/or phase of said multiple input signals, represents different polarizations,

represents different carriers, Χβ_Χ represents an input signal with polarization and carrier

represents discrete time delays and n represents discrete time; said device (100, 110, 120) is configured to filter said summed multiple input signals with different polarizations but a same carrier with candidate PIM model coefficients w_1→ by calculating

said device (100, 110, 120) is configured to generate 3^rd order PIM components falling within a receive band of said receiver (50) to form said PIM signal

36. The device according to claim 32 or 33, wherein said device (100, 110, 120) is configured to sum multiple input signals with different polarizations and different carriers.

37. The device according to claim 36, wherein

said device (100, 110, 120) is configured to sum said multiple input signals with different

discrete time delays and n represents discrete time;

The device according to any of the claims 32 to 37, further comprising: a processor (101); and

a memory (102) comprising instructions executable by said processor (101), wherein said processor (101 ) is operative to sum said multiple input signals with different polarizations; said processor (101) is operative to estimate said PIM signal by filtering said summed multiple 5 input signals with different polarizations; and

said processor (101 ) is operative to adjust said at least one candidate PIM model coefficient based on reducing said energy of said difference between said PIM signal and said receive signal.

39. A passive intermodulation, PIM, modelling device (140) comprising:

10 a summing unit (141) for summing multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal;

a PIM signal estimator (142) for estimating a PIM signal by filtering said summed multiple input signals with different polarizations with at least one candidate PIM model coefficient; and 15 an adjusting unit (143) for adjusting said at least one candidate PIM model coefficient based on reducing energy of a difference between said PIM signal and an output signal output from a receiver (50) in response to an RF receive signal received by said receiver (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

20 40. A wireless communication device (1 ) comprising:

multiple transmitters (10, 20) configured to receive multiple input signals and output multiple radio frequency, RF, transmit signals;

a receiver (50) configured to receive a RF receive signal and output an output signal, said RF receive signal comprises PIM distortion that is a non-linear function of said multiple RF transmit signals 25 output by said multiple transmitters (10, 20); and

a PIM suppression device (100, 1 10, 120, 130) according to any of the claims 19 to 31 and/or a PIM modelling device (100, 1 10, 120, 140) according to any of the claims 32 to 39.

41. A computer program (240) comprising instructions, which when executed by at least one processor 30 (210), cause said at least one processor (210) to

estimate a passive intermodulation, PIM, signal by summing multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal; and suppress, based on said PIM signal, PIM distortion in an output signal output from a receiver (50) in response to a RF receive signal received by said receiver (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

42. A computer program (240) comprising instructions, which when executed by at least one processor (210), cause said at least one processor (210) to

sum multiple input signals with different polarizations, each input signal being input to a transmitter (10, 20) of multiple transmitters (10, 20) configured to output a respective radio frequency, RF, transmit signal;

estimate a passive intermodulation, PIM, signal by filtering said summed multiple input signals with different polarizations with at least one candidate PIM model coefficient; and

adjust said at least one candidate PIM model coefficient based on reducing energy of a difference between said PIM signal and an output signal output from a receiver (50) in response to an RF receive signal received by said receiver (50) and comprising PIM distortion that is a non-linear function of said multiple RF transmit signals output by said multiple transmitters (10, 20).

43. A carrier (250) comprising a computer program (240) according to claim 41 or 42, wherein said carrier (250) is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.