WO1998040970A1 - An improved cdma receiver - Google Patents
An improved cdma receiver Download PDFInfo
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- WO1998040970A1 WO1998040970A1 PCT/AU1998/000159 AU9800159W WO9840970A1 WO 1998040970 A1 WO1998040970 A1 WO 1998040970A1 AU 9800159 W AU9800159 W AU 9800159W WO 9840970 A1 WO9840970 A1 WO 9840970A1
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- 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
- H04B1/69—Spread spectrum techniques
- H04B1/707—Spread spectrum techniques using direct sequence modulation
- H04B1/7097—Interference-related aspects
Definitions
- the present invention relates to code-division, multiple access (CDMA) communication systems and, in particular, to an improved CDMA receiver.
- CDMA code-division, multiple access
- One of the major problems associated with using CDMA systems is the low rate of channel utilisation.
- the utilisation is typically between 10 - 20% of the theoretical channel capacity.
- This low level of utilisation results from multiple access interference (MAI) between competing system users and effectively limits the total performance. It possible to show that, as the number of users increases in the system, the ratio of bit- energy to noise power spectral density (Ej,/N 0 ) required to support a given bit error rate (BER), rapidly tends to infinity.
- MAI multiple access interference
- a class of suboptimal, single-user receivers has been investigated which offer improved capacity in terms of the achievable number of simultaneous users.
- This class of receiver requires no exact knowledge of user timing, phase or the sequences of interfering users' signals, and no exact power control. These features make synchronisation and system design simpler, and minimise the signalling overhead in the system.
- Such a receiver termed a linear adaptive, fractionally-spaced filter (AFSF) is based on a minimum mean-square error (MMSE) criteria and seeks to minimise the interference by adapting to the cyclo-stationary nature of the MAI in a training session. Once training is complete, the receiver can either operate with fixed filter coefficients, or operate in an adaptive, decision feedback mode.
- AFSF linear adaptive, fractionally-spaced filter
- MMSE minimum mean-square error
- AWGN additive white Gaussian noise
- the LMS algorithm is a member of the family of stochastic gradient based 5 algorithms. It uses an estimate of the gradient of the error function and as such, does not require measurement of the pertinent correlation functions, nor does it require matrix inversion.
- the LMS algorithm is simple and robust and performs well under a wide range of channel conditions and input signal powers. More sophisticated, computationally intensive algorithms such as recursive least squares (RLS) and Kalman ⁇ o filtering offer significantly greater convergence rates, but suffer from sensitivity to noise power and synchronisation conditions.
- RLS recursive least squares
- Kalman ⁇ o filtering offer significantly greater convergence rates, but suffer from sensitivity to noise power and synchronisation conditions.
- apparatus for processing a received spread spectrum signal comprising: input means for sampling the received signal and dividing the samples into 20 groups of consecutive samples; a plurality of adaptive step-size filter receivers arranged to process each group of samples to each provide a filtered output for the group and an error value, each filter receiver having a unique step size used in combination with the corresponding error value to adapt a characteristic of the filter receiver; and 25 selection means for processing the error values to select one of the filtered outputs as an output of the apparatus for the group of samples.
- the filter receivers operate using a least mean squared (LMS) process to modify a filter characteristic thereof and generally comprise tapped delay line (transversal) filters and the LMS process updates filter taps of the filters according to 30 the corresponding step size.
- LMS least mean squared
- the filter receivers are arranged in parallel to process the group of samples simultaneously.
- the filter receivers may be configured to iterate over each group of samples a plurality (MAXTRAIN) of times until at least one of the error values falls below a predetermined error value at which time a filtered 35 output from the corresponding filter receiver is output from the apparatus.
- the selection means examines the error values to determine a best error value therefrom to thereby apply filter characteristics of the corresponding filter receiver to each of the filter receivers for the next iteration.
- the filter characteristics comprise tap values for a tapped delay line
- each of the error values is processed to derive a corresponding mean squared error (MSE) value for the group of samples and the lowest
- the apparatus further comprises training means for determining an initial filter characteristic for each filter receiver prior to commencement of iterations over the group of samples.
- the means for determining includes means for iterating over a training sequence until an associated error falls below a predetermined value whereupon an initial filter characteristic is set on each of the filter receivers.
- each group of samples substantially corresponds at a coherence time of the communication channel from which the spread spectrum signal is received, thereby establishing a series of quasi-stationary environments thus permitting the suppression of multiple access interference by the apparatus.
- the number of spread user symbols in each group is between 10 and 1000, and most preferably between 20 and 200.
- each adaptation step size is a multiple of the estimate of the received signal power.
- the filter receivers may be arranged in cascade.
- a method for receiving a spread spectrum signal comprising the steps of: dividing a sampled received signal into groups of samples; applying a plurality of least mean squares filter processes to each group, each of the processes including a unique step size and forming a corresponding filtered group signal and an error signal; and processing the error signals to select a best one of the filtered group signals.
- each adaptation step size is a multiple of the inverse of the estimate of the received signal power
- (b) provide for the division of the received signal into short, quasi- stationary regions of length P ⁇ f symbols and adjusting block size as necessary during periods when channel changes rapidly. Such division is unnecessary for systems using an AWGN channel;
- (g) disclose preventing a given set of tap values from being used as initial is conditions (above) if the error signal produced for the given adaptation step size for the current data region contains high instantaneous values of squared error (large singe error value). In this case, the next best tap values are used. If none are available, tap values are not swapped.
- the data can be approximated as independent, and the LMS
- the length of these regions is lower bounded by the requirement to retain a valid estimate of the stochastic gradient of the error function.
- the present invention may be implemented with groups of more than 10 symbols, generally in the 10's of symbol, and in some cases between 100 and 200 symbols.
- the length is upper bounded by the time delay associated with processing and the extent to which time delays may be
- FIG. 1 is a schematic block diagram representation of a CDMA system according to a preferred embodiment
- Fig. 2 is a schematic block diagram representation of a receiver used in Fig. 1 ;
- Fig. 3 is a flow diagram for the decision feedback stage of the receiver of Fig. 6;
- Fig. 4 is a flow diagram for the training stage of the receiver of Fig. 6;
- Fig. 5 is an example of a filter useful in the receiver of Fig. 2;
- Fig. 6 is a detailed diagram of a receiver according to the preferred embodiment;
- Fig. 7 is a diagram illustrating the packet structure used by the preferred embodiment including the training period and the data sub-blocks;
- Fig. 8 is schematic diagram of another embodiment;
- Fig. 9 is a schematic diagram of a further embodiment; and
- Fig. 10 is a schematic representation of a still further embodiment..
- Fig. 1 shows a CDMA system 10 including a number of transmitter circuits 20 and a number of receiver circuits 30.
- Each of the transmitter circuits 20 includes a data source 21 which provides input data (d) to a quadrature phase-shift keying (QPSK) modulator 22.
- the modulator 22 outputs to a mixer 23 also input with a spreading signal(s) to thus create a spread-spectrum signal (p) that is typically filtered in a low- pass-filter 24 prior to transmission.
- QPSK quadrature phase-shift keying
- QPSK modulation/demodulation is a preferment.
- Other modulation constellation mapping techniques can be practiced, including: phase shift keying - BPSK, QPSK, and 8-PSK (in general, M-PSK); amplitude-phase shift keying - 16- QAM, 32-QAM (in general, APSK); and amplitude shift keying - as above, but only using real valued samples.
- M phase shift keying
- 32-QAM in general, APSK
- amplitude shift keying - as above, but only using real valued samples.
- Each subscriber in the system 10 is assigned a unique signature sequence.
- the total number of subscribers is equal to the number of sequences available.
- the number of sequences available depends upon the sequence family chosen and the length J of the sequences.
- the system users are those subscribers who are actively engaged in transmission and the number of users in the system 10 is K .
- Each user symbol a j (i) is spread by the unique sequence assigned to the k'th user, s ⁇ .
- the modulator output signals at time t are pj(t), ..., pk(t), ... p (t).
- the users of the system 10 transmit over a common channel (e.g. free space), which is depicted in Fig. 1 by a fading channel mixer 40, and a convergence of transmitted signals onto an adder 45, also input with AWGN 47, and which precede each of the receiver circuits 30.
- Fig. 1 only shows one such convergence, the convergence of the transmitted signals on the other receiver circuits 30 being omitted from Fig. 1 for the sake of clarity.
- the mixer 40 and the adder 45 shown in Fig. 1 are not physical devices but result from the nature of common channel transmission and reception.
- the received signals, r ⁇ (t),..., ⁇ (t),..., r ⁇ (t) are applied to independent linear adaptive fractionally spaced receivers 50, which together with a QPSK demodulator 35, form the receiving circuit 30.
- the system structure is not dependent on the type of modulation used.
- the symbols are then spread by the signature sequence allocated to the given user.
- the output filter 24 suppresses the signal spectrum outside the specified bandwidth B c and an up-converter (not illustrated but known in the art) shifts the signal spectrum from baseband by the carrier frequency f c .
- the signal is input to a module 52 where it is downshifted to base-band by a down-converter and filtered by an antialiasing filter (AAF), having a bandwidth B s .
- AAF antialiasing filter
- the sampler 54 outputs to a receiver filter 56, an example of which can be a tapped delay line (transversal) filter shown in Fig. 5, and which has filter coefficient inputs (taps) cq(t), ⁇ 2(t)...and so on.
- the filter 56 could alternatively be implemented with a lattice structure.
- An output b of the receiver 50 is then demodulated by the demodulator 35 to produce the estimated bit stream, d' ⁇ .
- the output b of the receiver 50 corresponding to that output from the receiver filter 56, is used in a feedback network 58 to adjust the tap coefficients ⁇ input to the receiver filter 56.
- the network 58 includes a switch 60 which directs the output b to either a training sequence 62, or a limiter 64 for normal operation subsequent to a training sequence.
- the training sequence allows the receiver to minimise the MAI. It also allows phase tracking and equalisation of channel distortion.
- the output from the limiter 64 or training sequence 62 is then subtracted from the output signal b in a subtractor 66 to provide an error signal which is input to an LMS update processor 68.
- the processor 68 is also input with a step size, which determines the convergence rate of the LMS update process. Recursive least squares (RLS) or some other gradient technique may optionally be practiced.
- the processor 68 outputs the tap values ⁇ , mentioned above.
- the error function produced by the difference between the receiver output symbol estimate sequence b ⁇ , and the reference symbol sequence, is used in the update process for the adaptive tap coefficients.
- the reference sequence is generated by a training sequence generator.
- the update process used for the receiver structure is preferably a least mean squares (LMS) process which is the simplest member of a family of MMSE processes and is very robust in the sense of convergence under a range of MAI conditions .
- LMS least mean squares
- the feedback network 58 ensures that the LMS receiver 50 has an adaptive structure.
- a mis-adjustment between the LMS receiver coefficients ⁇ and those of an optimal receiver in any given situation is proportional to the adaptation step-size chosen. So for small step-sizes, the mis-adjustment is small, however the rate of convergence is low.
- the mean square error (MSE) produced from each set of receiver taps approaches convergence in decreasing order of step-size. Therefore, it is possible to approach a small MSE by decreasing the lower limit of possible step-size values, and increasing the number of training or decision feedback iterations provided the signal region considered is stationary. Alternatively, it is possible to improve the rate of convergence by increasing the upper limit on step-size values.
- MSE short term MSE
- the short term MSE is defined as the average of squared error (SE) values over some region at end of the training or adaptation of the region.
- the region typically includes a number of symbols, represented by a larger number of samples of the detected signal.
- the threshold value can be set according to the required BER quality of the received signal.
- the square error for any symbol should also not exceed some fixed fraction of the maximum error (100 %).
- a range of step-sizes are considered for both training and decision feedback stages.
- the range of step-sizes is preferably based on multiples of the optimal step-size for an AWGN channel. This is equivalent to training with S receivers simultaneously.
- step-size values can be chosen in many ways.
- the number of step-sizes S being considered can be chosen depending on the desired complexity of the system. More generally, S is the number of parallel AFSFs being used.
- the best tap values for the receiver are chosen based on the final short-term average value of the squared error (i.e. : the short term MSE).
- the short term MSE the final short-term average value of the squared error
- the received signal is split into small quasi-stationary regions and both data and training signals are processed F ⁇ f and F tra j n times, respectively.
- the receiver 50 is effectively given more symbols with constant or very similar MAI characteristics to converge over. This allows the receiver 50 to adapt to the MAI without having to contend with the non-stationary nature of the channel. If the channel is changing rapidly in the region of interest however, it will be necessary to reduce the size of the region further to obtain a quasi-stationary region. This is particularly true in the event of the channel undergoing a deep fade simultaneously with a change of phase. A lower limit on the data region length must be imposed however in order to preserve the gradient vector estimate required for the LMS algorithm to operate.
- the short-term MSE is above a certain threshold or some value of the square error is greater than a fixed value of the maximum symbol error, then that small section of data is "blocked" or marked as being in poor condition. If two (or more) such receivers are used in an diversity situation, then the received values of the short term MSE in each receiver may be used as a metric to determine from which antenna the data decisions are to be taken for a given data section. An example of this is seen in Fig. 8 where a number of LMS adaptive step size receivers *l-*w substantially simultaneously receive the same transmission and their corresponding short term MSE's are compared and used to select for output the received signal having the least error.
- the relative weighting of the contributions of each antenna may be used, as seen in Fig. 9 where the short term MSE's are used to combine the received signals according to the inverse proportions of the short term MSE's.
- the short term MSE's may be raised to a power (n, being a real number) in order to provide relative weighting to the smaller short term MSE's.
- the data region is abandoned if the threshold is not reached. If the region is abandoned, the transmitter can be requested to resend the packet.
- the degree of convergence depends significantly on the number of iterations of training, so this provides a parameter which may be used to trade computational complexity for convergence rate in terms of number of symbols ⁇ o required.
- the short-term MSE threshold can be used to specify a level of quality in terms of expected bit error rate (BER). A low threshold leads to more data regions being blocked while ensuring a lower average error rate for those which are not blocked.
- Fig. 6 is a detailed block diagram of the receiver 630 according to the preferred embodiment, which is described hereinafter with reference to the flow
- the receiver 630 comprises downshifting and anti-aliasing filtering module 652, a sampler 654, W receiver filters 656A to 656C, tap-update modules 668 A to 668C, output buffers 666A to 666C, and a select module 668.
- the output b of receiver 650 is coupled to a demodulator device 635.
- the received signal is provided as input to the downshifting and anti-aliasing filtering module 652 which operates in the manner described above with reference to module 52 of Fig. 2.
- the output of this module is provided as input to the sampler 654, which provides sampled output r to a signal buffer module 660.
- the sampled signal includes a plurality of symbols which are required to be decoded.
- 30 signal is divided into groups or blocks of samples where a number of samples are used to represent a symbol.
- the number of spread user symbols in each group is between 10 and 1000, and most preferably between 20 and 200.
- the output of signal buffer 660 is provided to a train buffer 662. As shown in Fig. 6, the train buffer 662 has two inputs: a load-train-buffer signal 670 and a MAXTRAIN signal 664.
- 35 train-buffer signal 670 is output by the select module 668.
- the train buffer 662 tracks its output, which is provided in a feedback manner.
- MAXTRAIN is the maximum number of times each data section is trained over. This is a predetermined number based on the allowable complexity of the receiver 630. The larger MAXTRAIN is the more computationally intensive the receiver 630 becomes.
- MAXTRAIN may be changed at any time, and can be allowed to decrease as more data or "sub-packets" are processed. The confidence in the convergence of the receiver 630 decreases as the number of data blocks processed increases.
- the output of the train buffer 662 is provided to each of W receiver filters 656A to 656C. The train buffer 662 holds the signal which each of the receiver stages is iteratively processing.
- the train buffer 662 loads a length of sampled signal and holds it until the filter stages have completed processing. When this occurs, the best result is selected and the train buffer is loaded with the next block of input signal.
- the signal buffer 660 is used before the train buffer 662 to hold the continually arriving signal at the front end of the receiver 630.
- the data structure comprising the sequence of transmitted/received symbols is shown in Fig. 7, including the packet structure including the training period and the data sub-blocks. The length of each data block is variable (depending on the channel fade rate). As noted before, each data block is iteratively processed.
- the receiver filters 656A to 656C are each provided with new tap values 672 from the output of the select module 668.
- receiver filters 656A to 656C are each provided with respective tap update inputs from the corresponding tap-update module 668A to 668C.
- the tap-updates module 668A to 668C are also each provided with a respective step-size input and an error signal 678A to 678C from the respective receiver filter 656A to 656C.
- the error signals 678 A to 678C are generated by each receiver filter 656A- 656C in the same manner as those signals are generated in Fig. 2.
- Each receiver filter 656A-656C has a step size that is some multiple of the "optimum" step size.
- the optimum step size is related to the inverse of the received signal power.
- Each filter 656A-656C uses a fraction of this optimal step-size (e.g., 2x, lx, 0.5x, 0.25x, 0.125x) as its step-size.
- Each filter 656A-656C therefore performs a calculation to determine the step size once the "optimum" step size has been calculated or estimated. The stage where the estimation of the optimal step size is not shown.
- Each of the receiver filters 656A to 656C provides two further outputs.
- the tap values 674A to 674C of the respective receiver filters 656A to 656C are provided as inputs to the select module 668.
- the filtered output signals 676 A to 676C of receiver filters 656A to 656C are provided to respective output buffers 666A to 666C.
- the output buffers 666A to 666C are coupled to the select module 668.
- the select module 668 provides the load train buffer signal 670 and the new tap values 672.
- the output signal b from the select module 668 is provided to the demodulation device 635.
- the select module 668 provides the load train buffer signal 670 to select between either the training sequence or normal operation subsequent to a training sequence.
- the select module 668 selects the best set of receiver coefficients from the tap values 674A-674C after each processing iteration has been completed for a given data block.
- the select module 668 uses the filter coefficients of the best filter, as the initial values for all filters for the next iteration. Again, such data blocks or sub-blocks are shown in Fig. 7.
- the best coefficients are determined to belong to the filter that has the lowest short term MSE for the iteration just completed for the block.
- This short term MSE as noted above is taken as being the sum of the square differences between the guesses of the received symbols (expected symbols) and the symbol estimates produced as the filter output. In the preferred embodiment, the short term MSE is taken over half the block length.
- a set of filter coefficients is not used as initial values for the next iteration if they have resulted in a squared error value for any symbol (in the current data block) which is greater than a fixed fraction of the maximum symbol error. Preferably, this value is 90% of the symbol error.
- the block is marked as "bad” and may be used as a trigger for a re-transmission attempt.
- the short term MSE is used to indicate whether any filter output has produced a sufficiently good output to have confidence in the "guesses" (expected values) of the received symbols. If the short term MSE is large, the block is marked as being "bad".
- the size of a data block depends on the fading rate of the channel. If the fade rate is high, the block size decreases.
- each of the receiver filters 656A-656C receives new tap values 672 from the select module 668, and this occurs after each iteration. During the iteration, each receiver filter 656A-656C is provided with respective updated tap values from the corresponding tap update modules 668A to 668C.
- the receiver filters 656A to 656C comprise means for generating the corresponding error signals. Again, the error signals 678A to 678C correspond to those generated in Fig. 2.
- the output b of the filter 650 is selected by the select module 668 and corresponds to that in the buffer 66A-66C for the receiver filter 656A-656C having the best set of tap values. The output b may be taken as it becomes available, but generally is taken after MAXTRAIN iterations after the whole block of data has been processed.
- step 410 in which training data is loaded.
- the select module 668 generates the load train buffer signal 670 to do so.
- the data is provided at the output of the train buffer 662.
- step 412 multi-step LMS processing is carried out.
- decision step 414 a check is made to determine if the maximum number of loops has been reached in respect of each of the short term MSE values. If decision step 414 returns false (no), processing continues at step 412. Otherwise, if decision step 414, returns true (yes), processing continues at decision step 416.
- decision step 416 the short term MSE values are checked to determine if they are acceptable. If decision step 416 returns false (no), processing continues at
- decision step 418 a check is made to determine if the maximum number of loops is to be increased. If decision step 418 returns true (yes), processing continues at step 412, where the maximum number of loops is increased. Otherwise, if decision step 418 returns false (no), processing continues at decision step 420. ⁇ o In decision step 420, a check is made to determine if the training period is to be decreased. If decision step 420 returns true (yes), processing continues at step 412, in which the training period is decreased. Otherwise, if decision step 420 returns false (no), processing continues at step 422. In step 422, a failure has occurred and all regions are blocked.
- step 416 determines whether decision step 416 returns true (yes). If decision step 416 returns true (yes), processing continues at step 424.
- step 424 the initial tap state is set.
- the select module 668 provides new taps 672 to each of the receiver filters 656 A to 656C.
- Fig. 3 is a flow diagram illustrating the decision feedback process implemented
- Processing commences at step 310, in which the received signal is loaded from the signal buffer 660 and provided as input via the training buffer 662 to the receiver filter 656A to 656C.
- step 312 multi-step LMS processing is applied to the data to generate short term MSEs.
- decision step 314 a check is made to determine if the maximum
- decision step 314 returns false (no)
- processing continues at step 312. Otherwise, if decision step 314 returns true (yes), processing continues at decision step 316.
- decision step 316 a check is made of the short term MSEs provided from step 312 to determine if each of them is sufficient. If decision step 316 returns false (no), processing continues at
- decision step 318 a check is made to determine if the maximum number of loops is to be increased. If decision step 318 returns true (yes), processing continues at step 312 in which the maximum number of loops is increased. Otherwise, if decision step 318 returns false (no), processing continues at decision step 320.
- decision step 320 a check is made to determine if the region size is to be decreased. If decision step 320 returns true (yes), processing continues at step 312, in which the region size is decrease. Otherwise, if decision step 320 returns false (no), processing continues at step 322. In step 322, a failure has occurred and the region is blocked. Otherwise, if decision step 316 returns true (yes), processing continues at step 324. In step 324, the tap values are set to be the best short term MSEs, which is provided by the output of the select module as new taps 672 to the receiver filters 656A to 656C. Processing then continues with the next data region.
- the embodiment of Figs. 3, 4 and 6 may be applied in each of the embodiments of Figs. 8 and 9.
- Figs. 3, 4 and 6 processes over one section of data a plurality of times, it is thus possible to have a corresponding plurality of cascaded processes each performing a single iteration for each block of samples.
- Such an arrangement requires more hardware but is faster and thus suited where reception delays are undesired.
- An example of such a cascaded arrangement is seen in Fig. 10 where MAXTRAIN multi-step LMS receiver stages, corresponding to the receiver filter module and select module of Fig. 6 are cascaded. Each stage performs a single iteration, and passes its results, being the selected filter output having the smallest short term MSE' and the corresponding new tap values, to the next stage.
Abstract
Description
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Application Number | Priority Date | Filing Date | Title |
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EP98906759A EP0966794A1 (en) | 1997-03-13 | 1998-03-13 | An improved cdma receiver |
AU62856/98A AU6285698A (en) | 1997-03-13 | 1998-03-13 | An improved cdma receiver |
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AUPO5630A AUPO563097A0 (en) | 1997-03-13 | 1997-03-13 | An improved CDMA receiver |
AUPO5630 | 1997-03-13 |
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PCT/AU1998/000159 WO1998040970A1 (en) | 1997-03-13 | 1998-03-13 | An improved cdma receiver |
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WO2000076079A1 (en) * | 1999-06-04 | 2000-12-14 | Atlantic Aerospace Electronics Corporation | System and method for applying and removing gaussian covering functions |
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US8891605B2 (en) | 2013-03-13 | 2014-11-18 | Qualcomm Incorporated | Variable line cycle adaptation for powerline communications |
DE102014111716B4 (en) * | 2013-08-17 | 2016-12-08 | Avago Technologies General Ip (Singapore) Pte. Ltd. | Adaptive equalizer |
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WO2000076079A1 (en) * | 1999-06-04 | 2000-12-14 | Atlantic Aerospace Electronics Corporation | System and method for applying and removing gaussian covering functions |
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EP1569358A3 (en) * | 2004-02-26 | 2005-10-12 | Intellon Corporation | Channel adaptation synchronized to periodically varying channel |
US8891605B2 (en) | 2013-03-13 | 2014-11-18 | Qualcomm Incorporated | Variable line cycle adaptation for powerline communications |
DE102014111716B4 (en) * | 2013-08-17 | 2016-12-08 | Avago Technologies General Ip (Singapore) Pte. Ltd. | Adaptive equalizer |
Also Published As
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EP0966794A1 (en) | 1999-12-29 |
AUPO563097A0 (en) | 1997-04-10 |
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