EP3827522A1 - Filter modelling for pim cancellation - Google Patents
Filter modelling for pim cancellationInfo
- Publication number
- EP3827522A1 EP3827522A1 EP18830026.3A EP18830026A EP3827522A1 EP 3827522 A1 EP3827522 A1 EP 3827522A1 EP 18830026 A EP18830026 A EP 18830026A EP 3827522 A1 EP3827522 A1 EP 3827522A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- filter
- cancellation
- coefficients
- model
- frequency
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
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Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/20—Monitoring; Testing of receivers
- H04B17/29—Performance testing
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/38—Transceivers, i.e. devices in which transmitter and receiver form a structural unit and in which at least one part is used for functions of transmitting and receiving
- H04B1/40—Circuits
- H04B1/50—Circuits using different frequencies for the two directions of communication
- H04B1/52—Hybrid arrangements, i.e. arrangements for transition from single-path two-direction transmission to single-direction transmission on each of two paths or vice versa
- H04B1/525—Hybrid arrangements, i.e. arrangements for transition from single-path two-direction transmission to single-direction transmission on each of two paths or vice versa with means for reducing leakage of transmitter signal into the receiver
Definitions
- This specification relates to an apparatus, method and computer program product relating to filter modelling, for example for passive intermodulation (PIM) cancellation.
- PIM passive intermodulation
- PIM Passive InterModulation
- intermodulation products occur at frequencies/ corresponding to k a f a + k b ft, + k c f c + ..., wherein f a , fi, f ,... are the frequencies of the plural signals, and k a , k b , kc,... are integer coefficients (positive, negative, or o).
- the sum k a +k b +k c + ,... is denoted as the order of the intermodulation product, denoted as IMP3,
- IMP5 IMP7 etc. for IMP of 3 rd , 5 th , and 7 th order, respectively.
- the amplitude of the IMPs decreases with increasing order of the IMPs.
- IMP3 is typically most relevant because it is located close to the input signal and has relatively high amplitude. Issues may occur when PIM products line up with received signals in a receiver, as if the PIM power is greater than the received signal itself, the receiver decoding process may fail due to negative signal to noise ratio.
- an apparatus comprising: means for sampling a frequency domain response of a receiver duplex filter to provide a window of samples inside and outside of the filter passband; means for transforming the frequency samples into a plurality of time domain complex coefficients, representing an impulse response of the filter; and means for providing at least some of the time domain complex coefficients as a filter model representing the filter for a subsequent means for passive intermodulation cancellation.
- the means for providing the time domain complex coefficients may comprise means for transforming or solving an equation that includes frequency domain samples.
- the means for sampling the frequency domain response may be configured to sample S21 forward complex gain parameters produced by the filter.
- the means for sampling the frequency domain response may be configured to sample at a resolution of substantially too kHz or less.
- the apparatus may further comprise means for over-sampling the filter model after transformation to match the sample frequency (fe ) of the subsequent passive intermodulation cancellation means.
- the means for transforming the samples may comprise means for taking the inverse discrete Fourier transform (IDFT) of the frequency domain samples.
- IDFT inverse discrete Fourier transform
- the apparatus may further comprise means for inputting noise to the provided filter model and means for performing linear regression to fit the input noise to the output of the filter model to produce an updated filter model comprising updated time domain complex coefficients for replacing the provided filter model and for use in subsequent passive intermodulation cancellation.
- the noise may be additive white Gaussian noise (AWGN).
- AWGN additive white Gaussian noise
- the means for providing the filter model may be configured to use only a subset of the time domain complex coefficients for the model.
- the subset of time domain complex coefficients may comprises substantially 35 coefficients or fewer.
- the apparatus may further comprising means for performing passive intermodulation cancellation on a received signal based on the rate-matched filter model incorporated into non-linear terms.
- the apparatus may further comprise means for filtering the derived PIM model prior to performing passive intermodulation to reject duplicate frequency components outside of the filter passband.
- the cancellation means maybe configured to remove, at a sample frequency (fs), passive intermodulation components from the received signal by subtracting from the received signal a cancelling signal determined using the filter model incorporated into non-linear terms.
- fs sample frequency
- the cancellation means may provide the cancelling signal by:
- v is the nonlinear vector
- H is the Hermitian operation
- Rx is the received signal.
- the apparatus may be provided in a cellular base station.
- a method comprising: sampling a frequency domain response of a receiver duplex filter to provide a window of samples inside and outside of the filter passband; transforming the frequency samples into a plurality of time domain complex coefficients, representing an impulse response of the filter; and providing at least some of the time domain complex coefficients as a filter model representing the filter for a subsequent means for passive intermodulation cancellation.
- Sampling the frequency domain response may comprise sampling S21 forward complex gain parameters produced by the filter.
- Sampling the frequency domain response may be at a resolution of substantially too kHz or less.
- the method may further comprise over-sampling the filter model after transformation to match the sample frequency (fs) of the subsequent passive intermodulation cancellation operation.
- Transforming the samples may comprise taking the inverse discrete Fourier transform (IDFT) of the frequency domain samples.
- IDFT inverse discrete Fourier transform
- the method may further comprise inputting noise to the provided filter model and performing linear regression to fit the input noise to the output of the filter model to produce an updated filter model comprising updated time domain complex coefficients for replacing the provided filter model and for use in a subsequent passive
- the noise may be additive white Gaussian noise (AWGN).
- AWGN additive white Gaussian noise
- Providing the filter model may use only a subset of the time domain complex coefficients for the model.
- the subset of time domain complex coefficients may comprise substantially 35
- the method may further comprise performing passive intermodulation cancellation on a received signal based on the rate-matched filter model incorporated into non-linear terms.
- the method may further comprise filtering the derived PIM model prior to performing passive intermodulation to reject duplicate frequency components outside of the filter passband.
- the cancellation operation may remove, at a sample frequency (fs), passive
- the cancellation operation may provide the cancelling signal by:
- v is the nonlinear vector
- H is the Hermitian operation
- Rx is the received signal.
- an apparatus comprising at least one processor, at least one memory directly connected to the at least one processor, the at least one memory including computer program code, and the at least one processor, with the at least one memory and the computer program code being arranged to perform the method of any preceding method definition.
- a computer program product comprising a set of instructions which, when executed on an apparatus, is configured to cause the apparatus to carry out the method of any preceding method definition.
- a non-transitory computer readable medium comprising program instructions stored thereon for performing a method, comprising: sampling a frequency domain response of a receiver duplex filter to provide a window of samples inside and outside of the filter passband; transforming the frequency samples into a plurality of time domain complex coefficients, representing an impulse response of the filter; and providing at least some of the time domain complex coefficients as a filter model representing the filter for a subsequent means for passive intermodulation cancellation.
- an apparatus comprising: at least one processor; and at least one memory including computer program code which, when executed by the at least one processor, causes the apparatus: to sample a frequency domain response of a receiver duplex filter to provide a window of samples inside and outside of the filter passband; to transform the frequency samples into a plurality of time domain complex coefficients, representing an impulse response of the filter; and to provide at least some of the time domain complex coefficients as a filter model representing the filter for a subsequent means for passive intermodulation cancellation.
- FIG. 1 is a schematic circuit diagram of a radio system comprising a duplexer and a passive intermodulation (PIM) cancellation system according to example
- FIG. 2 is a flow diagram indicating processing operations performed in generating a filter model according to example embodiments
- FIG. 3 is a flow diagram indicating processing operations performed in generating a filter model according to other example embodiments;
- FIG. 4 is a graph indicative of a receive duplex filter impulse response, providing an example model as produced in accordance with example embodiments;
- FIG. 5 is a flow diagram indicating processing operations performed in passive intermodulation (PIM) cancellation, using the filter model, according to example embodiments;
- PIM passive intermodulation
- FIG. 6 is a graph indicative of a filter model when oversampled, in accordance with example embodiments.
- FIG. 7 is a graph indicative of the frequency responses from a known passive intermodulation (PIM) cancellation operation
- FIG. 8 is a graph indicative of the frequency responses from a passive intermodulation (PIM) cancellation operation, in accordance with example embodiments;
- Fig. 9 shows hardware modules according to some embodiments.
- Fig. to shows a non-volatile media according to some embodiments.
- PIM Passive InterModulation
- PIM products may be generated at very low power levels, for example due to the aging of antennas, corroded or loose connectors and duplex filters that are passive. Imperfections of cables, combiners and attenuators may also generate PIM. PIM generation with transmit signals is generally harmless due to its low level.
- PIM signals can be higher than the receive signals.
- the receiver decoding process will fail due to negative signal to noise ratio. This may cause a significant throughput loss in the uplink direction (mobile to base station).
- PIM cancellation may also mean mitigate.
- a PIM cancellation algorithm estimates PIM by comparing, or correlating, the transmit (Tx) and the receive (Rx) signal path. The PIM cancellation algorithm may then build up a model that attempts to cancel, or at least reduce, the PIM products on the receiver (Rx) bandwidth.
- Example embodiments relate to PIM cancellation which can cater for situations where one or more PIM signals are close to, span, or outside of the band edge of the receiver (Rx). In such cases, conventional PIM cancellations algorithms may be sub-optimal.
- Example embodiments herein enable to estimate accurately a PIM model which is used as part of a PIM cancellation algorithm.
- the PIM model takes into account the impact of a radio duplexer component which may be provided in part of a telecommunication system, for example in a cellular base station or eNB, for any existing or future cellular system (e.g. 2G/3G/4G/5G).
- a duplexer may be considered as a three-port filtering device which allows transmitters (Tx) and receivers (Rx) operating at different frequencies to share the same antenna.
- a duplexer typically comprises two bandpass filters in parallel, one providing the path between the transmitter (Tx) and the antenna, and the other providing the path between the antenna and the receiver (Rx).
- the group delay characteristics of a receiver (Rx) duplexer filter (RDF) may cause a non-linear spreading effect on PIM signals such that PIM signals may exist close to or outside of the band edge of the RDF.
- PIM corrections may degrade by as much as 3 dB, depending on the group delay characteristic, this being due to inherent characteristics of PIM signals.
- normal radios consist of two passive duplexer filters (i.e. one for the transmitter (Tx) and one for the receiver (Rx)) that remove the outer band spectrum leakage. They both exhibit frequency attenuation just outside the band edge frequencies. Most duplex filters begin this attenuation process even 2MHz inwards from the band edge.
- a typical 5MHz transmitter (Tx) signal combination will create a non-linear spreading effect on PIM signals to cause a threefold bandwidth enhancement up to 15MHz.
- a 20MHz transmitter signal combination will generate a 60MHz wide PIM signal.
- the PIM signal will be fivefold larger when compared to the transmitter (Tx) signals.
- a non-linear regression model will attempt to find the optimum point to accurately model PIM with: a) amplitude b) phase and a c) single suitable delay. Since the actual delay is distributive, the mathematical model will find this environment to be insurmountable, resulting in degradation in PIM cancellation performance.
- FIG. 1 is a schematic diagram of an example transceiver system 10 that involves PIM cancellation according to example embodiments.
- the transceiver system 10 comprises a duplexer 12 connected to a common antenna 14; the duplexer 12 comprises first and second duplexer filters, a first being a transmitter (Tx) duplexer filter having a passband providing a signal path between a transmitter (Tx) 16 and the antenna, and the second being a receiver (Rx) duplexer filter providing a signal path between the antenna and a receiver (Rx) 18. No path between the transmitter 16 and the receiver 18 should exist.
- Tx transmitter
- Rx receiver
- a PIM cancellation module 20 may be provided between the input and output paths of the transmitter 16 and receiver 18 respectively.
- the PIM cancellation module 20 may be implemented in hardware, software or a combination thereof.
- the PIM cancellation module 20 operates using an algorithm which is similar to known PIM cancellation algorithms. For example, a filter model 29 corresponding to the receive duplexer filter may be determined in accordance with embodiments to be explained below. The net effect of the algorithm employed by the PIM cancellation module is to provide improved or optimal PIM cancellation at the received signal.
- FIG. 1 also indicates example spectra including a transmission band 24, transmission and receive bands 24, 26 including PIM components 28 at the duplexer 12, the receive band 26 and PIM components 28 at the receiver 18, and the“cleaned” receive band 26A after PIM cancellation by the PIM cancellation module 20.
- the distributive nature of the receiver duplexer filter’s group delay is negated by removing the characteristic from the PIM cancellation performed at the PIM cancellation module 20.
- a PIM model is generated, for subtraction from the signal received at the receiver 18 (which includes PIM), the PIM modelling being restricted to amplitude, phase and a single, suitably determined delay.
- the PIM model takes account of the receiver duplexer filter’s characteristics to provide a set of non-linear coefficients that negate or exclude said characteristics. This is found to provide an improved, if not optimal, cancellation solution.
- receiver duplexer filter modelling hereafter simply“filter modelling” and PIM cancellation.
- Modelling may take place at a reduced sampling rate of/s/n, e.g.fi /2, fs /3,fc/4 or /s/6 etc., where fs is the sampling rate of the PIM cancellation algorithm to be described later on.
- the reduced sampling rates discussed above are for illustrative purposes only. However, it should be appreciated that the reduced sampling rate relative to the PIM cancellation algorithm sampling rate is not essential to the broad concept.
- FIG. 2 shows processing operations that may be performed by a processing means, for example as shown in FIG. 9, which may or may not be at the PIM cancellation module 20, whether by means of hardware, software or a combination thereof. Additional or less operations maybe performed in some embodiments.
- the processing comprises sampling a frequency domain response of a receiver duplex filter to provide a window of samples inside and outside of the filter passband, transforming the frequency samples into a plurality of time domain complex coefficients, representing an impulse response of the filter, and providing at least some of the time domain complex coefficients as a filter model representing the filter for a subsequent means for passive intermodulation cancellation.
- a first operation 2.1 may comprise frequency domain sampling of the receiver duplexer filter response to provide a window of samples which cover the passband of the filter and also part of the stopband, i.e. a part of the stopband adjacent the passband.
- a second operation 2.2 may comprise applying an inverse discrete Fourier transform (IDFT) to the window of samples.
- a third operation 2.3 may comprise getting a plurality of time-domain complex coefficients as a result of the second operation 2.2, representing a filter model.
- IDFT inverse discrete Fourier transform
- a fourth operation 2.4 may comprise performing PIM cancellation using the filter model.
- FIG. 3 is a more detailed flow diagram shows processing operations that may be performed by a processing means, which may or may not be at the PIM cancellation module 20, whether by means of hardware, software or a combination thereof.
- a first operation 3.1 may comprise frequency domain sampling of the receiver duplexer filter response to provide a window of samples which cover the passband of the filter and also part of the stopband, i.e. a part of the stopband adjacent the passband.
- the response may be generated by using a vector network analyser (VNA) applied to the duplexer, and sampling of the produced forward complex gain scatter parameters (S21).
- VNA vector network analyser
- S21 forward complex gain scatter parameters
- a second operation 3.2 may comprise preparing the samples obtained from the first operation 3.1 to match a selected sample rate, less than the sample rate that will be used by the PIM cancellation algorithm subsequently. For example, reduced sampling rate of/s/3,/s/4 or/s/6 etc. maybe used, where fs is the sampling rate of the PIM cancellation algorithm.
- fs is the sampling rate of the PIM cancellation algorithm.
- W_s3 The window maybe referred to as W_s3.
- a third operation 3.3 may comprise applying an ID FT to the window W_s3 to generate time-domain samples, referred to as Tap_s3.
- a fourth operation 3.4 may comprise getting a plurality of time-domain complex coefficients as a result of the third operation 3.3, representing a filter model.
- a fifth operation 3.5 may comprise selecting the first N complex coefficients to represent the model, as opposed to all. For example, it has been found that the first 35 complex coefficients gives a very good representation.
- a sixth operation 3.6 may comprise performing PIM cancellation using the filter model.
- a seventh operation 3.7 may comprise inputting noise, such as additive white Gaussian noise (N_awgn) to the complete filter model (Tap_s3) from the fourth operation 3.4 (e.g. 2048 complex coefficients) to generate an output referred to as Filt_out_s3.
- An eighth operation 3.8 may comprise performing linear regression to best-fit the input noise (N_awgn) to the output (Filt_out_s3) from the seventh operation 3.7 to produce, in a ninth operation 3.9, modified time domain complex coefficients representing a more optimised filter, referred to as W_opt.
- the process may return to the fifth operation 3.5 whereby the selected first N coefficients (which can be 30 or 35 in the current example) represent W_opt for subsequent PIM cancellation.
- An alternative to the FIG. 3 example is to determine a number of complex coefficients that are needed to dedicate to a duplexer model, e.g. a frequency domain vector describing the coefficients, which vector is referred to as Wf.
- Another operation may comprise generating an IDFT matrix suitable for Fs/ N, which we will call IDFT m .
- Another operation may be to solve the above linear equation to maximise the number of S21 points, and then taking the IFFT of Wf to generate time domain complex coefficients Wt.
- FIG. 4 is a graph showing in this case the first 30 time domain complex coefficients, which may model the impulse response of the receiver duplexer filter for subsequent PIM cancellation.
- a duplexer model W_opt is obtained at a sampling rate oifs/N, with N being 3 in the example.
- PIM cancellation generally takes place at a higher sampling rate (i.e./s) than the low sampled duplexer filter model.
- the low rate duplexer model may be oversampled in a first operation 5.1, or, broadly speaking, rate matching the filter model to the correction sample rate. If the model is obtained using fs/3, then the oversampled model at fs can be generated by inserting two zeros after every complex coefficient (as in [coef 0 o o coefi o o... etc]).
- a further, optional operation 5.2 may comprise applying a mask filter. More specifically, in some embodiments, PIM cancellation techniques are related to operations on duplicate spectrums, and hence the duplicate spectrums of the duplexer may be removed by a mask filter, or anti-aliasing filter. With mask filtering, all spectrum duplicates will be removed, thus equating it to the actual bandwidth of the receiver duplexer filter. Without the mask filter, all frequency duplicates will be visible in model extraction. PIM cancellation without the mask filter can be pursued as a less complex solution, for the reason that the duplicate frequencies outside the bandwidth of interest may absorb interference into the model domain.
- a further operation 5.3 may comprise performing PIM cancellation using the filter model.
- Linear regression yields sub optimum results due to the fact that delay is distributive when the PIM signal is located at receiver band edge.
- the duplexer introduces a non-flat magnitude response on the PIM signal.
- the complex receive duplexer filter model described above is incorporated into the linear regression solution. It can be described by an equation as shown in (2). Note that the above-mentioned mask filter is ignored in this equation (2), as is any memory of the non-linear terms, which are ignored for simplicity. An actual implementation may comprise memory taps.
- x is a reference signal used to compute the PIM model.
- each of the non-linear terms are filtered by the rate matched duplexer model f duplex In this way, the non-linear terms do not have to care for the magnitude and the distributive delay of the Rx duplexer. Hence, the PIM cancellation model is free to model the PIM only, giving an improved result.
- the above-mentioned mask filter may be included on top of t e f duplex filter model to remove any duplicate spectrum.
- the cancellation technique, including the mask filter, can be illustrated as in equation (3). No transmission leakage or outer band noise will interfere in this case.
- Equation (4) shows this this formulation:
- Observations from table 1 include that a complex filter model gives better performance over a real filter, that the use of 35 coefficients gives better performance over 30 coefficients, in both cases, especially at the band edges.
- FIG. 9 shows an apparatus according to an embodiment.
- the apparatus maybe configured to perform the operations described herein, for example operations described with reference to any of FIGS. 2, 3 and 5.
- the apparatus comprises at least one processor 420 and at least one memory 410 directly or closely connected to the processor.
- the memory 410 includes at least one random access memory (RAM) 410b and at least one read-only memory (ROM) 410a.
- Computer program code (software) 415 is stored in the ROM 410a.
- the apparatus may be connected to a TX path and a RX path of a base station in order to obtain respective signals. However, in some embodiments, the TX signals and RX signals are input as data streams into the apparatus.
- the apparatus may be connected with a user interface UI for instructing the apparatus and/or for outputting results.
- the instructions maybe input e.g. from a batch file, and the output maybe stored in a non-volatile memory.
- the at least one processor 420, with the at least one memory 410 and the computer program code 415 are arranged to cause the apparatus to at least perform at least the method according to FIGS. 2, 3 and 5
- Fig. 10 shows a non-transitory media 130 according to some embodiments.
- the non- transitory media 130 is a computer readable storage medium. It may be e.g. a CD, a DVD, a USB stick, a blue ray disk, etc.
- the non-transitory media 130 stores computer program code causing an apparatus to perform the method of any of FIGS. 2, 3 and 5, when executed by a processor such as processor 420 of FIG. 9.
- Names of network elements, protocols, and methods are based on current standards. In other versions or other technologies, the names of these network elements and/ or protocols and/ or methods may be different, as long as they provide a corresponding functionality.
- a base station may be a BTS, a NodeB, a eNodeB, a WiFi access point etc.
- a memory may be volatile or non-volatile. It may be e.g. a RAM, a sram, a flash memory, a FPGA block ram, a DCD, a CD, a USB stick, and a blue ray disk.
- each of the entities described in the present description may be based on a different hardware, or some or all of the entities may be based on the same hardware. It does not necessarily mean that they are based on different software. That is, each of the entities described in the present description maybe based on different software, or some or all of the entities may be based on the same software.
- Each of the entities described in the present description may be embodied in the cloud.
- embodiments provide, for example, a delay estimation device for PIM cancellation, or a component thereof, an apparatus embodying the same, a method for controlling and/or operating the same, and computer program(s) controlling and/or operating the same as well as mediums carrying such computer program(s) and forming computer program product(s).
- a delay estimation device for PIM cancellation maybe incorporated e.g. in a Nokia Airframe expandable base station.
- Implementations of any of the above described blocks, apparatuses, systems, techniques or methods include, as non-limiting examples, implementations as hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof. Some embodiments may be implemented in the cloud. It is to be understood that what is described above is what is presently considered the preferred embodiments. However, it should be noted that the description of the preferred embodiments is given by way of example only and that various modifications may be made without departing from the scope as defined by the appended claims.
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Abstract
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PCT/US2018/038262 WO2019245533A1 (en) | 2018-06-19 | 2018-06-19 | Estmating a delay |
PCT/US2018/062443 WO2019245598A1 (en) | 2018-06-19 | 2018-11-26 | Filter modelling for pim cancellation |
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EP4122106A4 (en) * | 2020-03-18 | 2023-12-27 | Telefonaktiebolaget LM Ericsson (publ.) | Removal of passive intermodulation in antenna systems |
CN112311483B (en) * | 2020-09-22 | 2022-09-02 | 中国空间技术研究院 | Passive intermodulation test evaluation method for satellite navigation signals |
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DE69028782T2 (en) * | 1989-07-25 | 1997-02-13 | Seiko Comm Holding Nv | DIGITAL FILTER AND DEVELOPMENT METHOD |
KR100278019B1 (en) * | 1998-03-28 | 2001-01-15 | 윤종용 | A method for optimizing forward link coverage in cdma network |
EP1583255B1 (en) * | 2004-04-02 | 2012-10-31 | Sony Deutschland GmbH | Spectral distance optimised multi-band UWB transmission |
US20070217490A1 (en) * | 2005-03-15 | 2007-09-20 | Bae Systems Plc | Modem |
US8428164B2 (en) * | 2007-09-25 | 2013-04-23 | Telefonaktiebolaget Lm Ericsson (Publ) | Interference randomization of control channel elements |
EP2263332A2 (en) * | 2008-03-12 | 2010-12-22 | Hypres Inc. | Digital radio-frequency tranceiver system and method |
WO2013133673A1 (en) * | 2012-03-08 | 2013-09-12 | 엘지전자 주식회사 | Method and user equipment for receiving reference signals, and method and base station for transmitting reference signals |
GB2502279B (en) * | 2012-05-21 | 2014-07-09 | Aceaxis Ltd | Reduction of intermodulation products |
US8891603B2 (en) * | 2012-06-25 | 2014-11-18 | Tektronix, Inc. | Re-sampling S-parameters for serial data link analysis |
FR2999364A1 (en) * | 2012-12-10 | 2014-06-13 | Centre Nat Etd Spatiales | METHOD FOR PREDICTIVE EVALUATION OF THE INTERMODULATION POWER IN AN ELECTRONIC DEVICE |
GB2511865B (en) * | 2013-03-15 | 2015-07-01 | Aceaxis Ltd | Processing interference in a wireless network |
WO2015043673A1 (en) * | 2013-09-30 | 2015-04-02 | Nokia Solutions And Networks Oy | Mechanism for improving receiver sensitivity |
EP2884668B1 (en) * | 2013-12-12 | 2020-11-18 | Nokia Solutions and Networks Oy | Improvement of receiver sensitivity |
EP3076558B1 (en) * | 2013-12-17 | 2019-05-08 | Huawei Technologies Co., Ltd. | Method and device for reducing intermodulation interference |
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2018
- 2018-06-19 WO PCT/US2018/038262 patent/WO2019245533A1/en active Application Filing
- 2018-11-26 EP EP18830026.3A patent/EP3827522A1/en active Pending
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