This is the U.S. national phase of PCT/EP2016/081541, filed Dec. 16, 2016, which claims the benefit of European Patent Application No. 16154802.9, filed Feb. 9, 2016.
The disclosure relates to filter banks and methods for operating filter banks.
Filter banks (FBs) play an important role in a number of digital signal processing (DSP) applications. They are usually employed in two basic configurations: First, a broadband signal is decomposed into its narrowband components, with the so called Analysis Filter Bank (AFB), for further processing and then the broadband signal is reconstructed back with the help of the Synthesis Filter Bank (SFB). In the second configuration, many narrowband signals are combined into one broadband signal with the SFB and then are reconstructed again with the AFB. The first configuration is usually called Sub-band Coding (SBC) and involves applications like, for example, signal compression, adaptive sub-band processing, channel equalization and spectrum sensing. The second configuration is the so called transmultiplexer (TMUX) and it is mainly applied to wired and wireless communications systems, where a number of data streams of one or more users are combined into one broadband signal to be transmitted via some medium and in the receiver, the different streams are then recovered. Modulated Filter Banks (MFB) are between the most popular FB classes because they have implementation structures with very low computational complexity.
The most prominent use of MFBs is in multicarrier modulation schemes. Those schemes are frequently called Filter Bank based Multicarrier (FBMC). The most trivial FBMC system is the so called Orthogonal Frequency Division Multiplexing (OFDM). OFDM utilizes a trivial prototype filter (all ones) and does not achieve maximum spectral efficiency due to the cyclic prefix. OFDM has been used in different wired and wireless standards, like ADSL, VDSL, WiFi and LTE, the 4th Generation of cellular systems. In the discussions of future mobile communications standards, more specifically the 5th Generation (5G), more general implementations of FBMC systems are under consideration. One of the candidate schemes is FBMC with the symbols in each subcarrier using Offset Quadrature Amplitude Modulation (OQAM) mapping.
It is object of the disclosure to provide improved technologies for filter banks, in particular the processing or computational efficiency shall be improved.
A synthesis filter bank according to claim 1, an analysis filter bank according to claim 7, a filter bank according to claim 12, a method for operating a synthesis filter bank according to claim 13 and a method for operating an analysis filter bank according to claim 14 are provided. Further embodiments are subject matter of dependent claims.
In one aspect, a synthesis filter bank is disclosed, the synthesis filter bank comprises a transform module which is configured to receive a plurality of input signals, transform the plurality of input signals, and output a plurality of transformed signals, a plurality of filter modules, which are coupled to the transform module, and wherein each filter module of the plurality of filter modules is configured to receive two transformed signals from the transform module, process the two received transformed signals and output two processed signals, and a parallel-to-serial module which is coupled to the plurality of filter modules and which is configured to receive the processed signals from the plurality of filter modules, combine the received processed signals and output a combined signal.
In another aspect, an analysis filter bank is disclosed. The analysis filter bank comprises a serial-to-parallel module which is configured to receive an input signal and divide the received input signal into a plurality of output signals, a plurality of filter modules which are coupled to the serial-to-parallel module, wherein each filter module of the plurality of filter modules is configured to receive two output signals from the serial-to-parallel module, process the two received output signals and output two processed signals, and a transform module which is coupled to the plurality of filter modules and which is configured to receive the processed signals, transform the plurality of processed signals, and output a plurality of transformed signals.
Further, a filter bank comprising a synthesis filter and an analysis filter bank is disclosed. The analysis filter bank and the synthesis filter bank can be arranged in any order in the filter bank. For example, a synthesis filter bank may receive a plurality of input signals. An output of the synthesis filter bank can be provided as input to the analysis filter bank which outputs a plurality of signals. Alternatively, an input may be first received by the analysis filter bank, and the outputs of the analysis filter bank may be provided as inputs to the synthesis filter bank in order to output a signal.
In a further aspect, a method for operating a synthesis filter bank is provided, comprising: receiving a plurality of input signals by a transform module, transforming, by the transform module, the plurality of input signals, outputting, by the transform module, a plurality of transformed signals, receiving, by a plurality of filter modules which are coupled to the transform module, the plurality of transformed signals, wherein each filter module of the plurality of filter modules is configured to receive two transformed signals from the transform module, processing, by each of the plurality of filter modules, the two received transformed signals, outputting, by each of the plurality of filter modules, two processed signals, receiving, by a parallel-to-serial module which is coupled to the plurality of filter modules, the processed signals, combining, by the parallel-to-serial module, the received processed signals, and outputting, by the parallel-to-serial module, a combined signal.
In yet another aspect, a method for operating an analysis filter bank is disclosed, comprising: receiving, by a serial-to-parallel module, an input signal, dividing, by the serial-to-parallel module, the received input signal into a plurality of output signals, receiving, by a plurality of filter modules which are coupled to the serial-to-parallel module, the plurality of output signals, wherein each filter module of the plurality of filter modules is configured to receive two output signals from the serial-to-parallel module, processing, by each of the plurality of filter modules, the two received output signals, outputting, by each of the plurality of filter modules, two processed signals, receiving, by a transform module which is coupled to the plurality of filter modules, the processed signals, transforming, by the transform module, the plurality of processed signals, and outputting, by the transform module, a plurality of transformed signals.
The plurality of filter modules may be designed from an orthogonal prototype filter, wherein each filter module comprises a first filter and a second filter, wherein the second filter is a power complementary filter of the first filter such that the two received transformed signals are processed simultaneously. The filter modules may be implemented as a finite impulse response filter. In this case a single finite impulse response (FIR) low-pass filter, the so-called prototype filter, needs to be designed and the other filters of the plurality of filter modules can be obtained by decomposing it in its polyphase components, which can be from types 1, 2 or 3. The prototype filter may be a prototype filter with linear phase, i.e. with symmetrical impulse response.
For the synthesis filter bank and/or for the analysis filter band, the transform module may be configured to provide a linear transform of the received signals. The transform module may be a Fourier transform module configured to perform a discrete Fourier transform (DFT) of received signals in a computationally efficient manner, for example by employing a fast Fourier transform (FFT) algorithm. Alternatively, the transform module may be an inverse Fourier transform module configured to perform an inverse discrete Fourier transform (IDFT) of received signals in a computationally efficient manner, for example by employing an inverse fast Fourier transform (IFFT) algorithm. In case the polyphase decomposition is of type 1, an inverse Fourier transform module may be employed in the synthesis and/or analysis filter bank. If the polyphase decomposition is of type 2 or 3, a Fourier transform module may be employed in the synthesis and/or analysis filter bank.
The synthesis filter bank and/or the analysis filter bank may be configured to receive a digital signal which may be represented by a complex number. The digital signal may contain samples which may be real numbers, like voice, audio, images or videos. In those cases, a real valued prototype filter may be modulated either with sines or cosines. It is worth noting that a prototype filter may be real valued and a modulation of it may be complex valued.
For example, in wired and wireless communications, the information to be transmitted usually modulates the amplitude and the phase of a carrier wave. The digital signal may be represented by complex numbers to convey both amplitude and phase information.
For each of the plurality of filter modules of the synthesis filter bank, one of the two processed signals may be complex conjugated before it is received by the parallel-to-serial module.
For each of the plurality of filter modules of the synthesis filter bank, for each even time index a negative sign may applied to one of the two processed signals before it is received by the parallel-to-serial module, e.g. by multiplying the signal with a factor (−1)m+1, wherein m is the time index. A sign of the processed signal which is complex conjugated may be turned negative before it is received by the parallel-to-serial module.
A number of the transformed signals which are received by the plurality of filter modules of the synthesis filter bank may be half the number of input signals received by the transform module.
For each of the plurality of filter modules of the synthesis filter bank, one of the two transformed signals may be delayed in time before being received by the respective filter module, e.g. by T/2, wherein 1/T is the sampling rate.
For each of the plurality of filter modules of the analysis filter bank, an output signal may be complex conjugated before it is received by the respective filter module.
For each of the plurality of filter modules of the analysis filter bank, for each even time index a negative sign may be applied to one of the two output signals before it is received by the respective filter module, e.g. by multiplying the signal with a factor (−1)m+1, wherein m is the time index.
For each of the plurality of filter modules of the analysis filter bank, one of the two processed signals may be delayed in time before being received by the serial-to-parallel module, e.g. by T/2, wherein 1/T is the sampling rate.
Feature disclosed with reference to the different filter banks also apply to the methods for operating the filter banks and vice versa.
Following, exemplary embodiments are disclosed with reference to figures. Here show:
Following, we consider uniform exponentially MFBs. In uniform FBs, all subchannels or sub-band signals have the same sampling rate 1/T. The filters Fk(z) in the SFB, as depicted in
where s=σ+jω and ω=2πf is the angular frequency and f the technical frequency. In MFBs, both Hk(z) and Fk(z) are obtained by exponentially modulating HP(z):
with
We also assume here, without loss of generality, that the length of the prototype is P=KM, where K is the time overlapping factor and determines not only the complexity of the filter bank, but also its memory. The block ↑
performs an upsampling by a factor
that means
samples with value zero are inserted between each original sample at a rate
higher than the input rate.
The block Ok in the SFB performs a
staggering or of the real and imaginary parts of the low-rate signals xk[] and is frequently called OQAM-staggering, -modulation or -mapping.
The filtered signals at the AFB are downsampled by the block ↓
that removes
samples and lowers the rate by a factor
The block Ok′ represents the OQAM or real and imaginary de-staggering.
Another structure for the SFB is shown in
k=0, . . . , M−1 is the k-th type-1 polyphase component of HP(z) upsampled by a factor of 2 and given by the relation
with Gk(zM)=Σm=0K-1hP[mM+k]zM−m and since we have assumed that the prototype has P=KM coefficients, each polyphase component has K non-zero coefficients.
The subfilters are given by
We define the vectors
h(z)=[H0(z)H1(z) . . . HM−1(z)]T,
f(z)=[F0(z)F1(z) . . . FM−1(z)]T,
a(z)=[1z−1 . . . z−(M−1)]T,
x(z)=[{tilde over (X)}1(zM){tilde over (X)}2(zM) . . . {tilde over (x)}M−1(zM)]T,
y(z)=[{tilde over (Y)}1(zM){tilde over (Y)}2(zM) . . . {tilde over (Y)}M−1(zM)]T,
and get the following input output relations for the AFB and SFB
x(z)=h(z)S(z),
S(z)=fT(z)y(z),
where
h(z)=DWMHG(zM)α(z),
fT(z)=αT(z)G(zM)WMD
with D=diag(1, d, . . . , dM-1), G(zM)=diag(G0(zM), G1(zM), . . . , GM−1(zM), WM and WMH are the M size unitary DFT and IDFT matrix. The DFT or IDFT and can be efficiently implemented using Fast Fourier Transform (FFT) algorithms. The equations can be further developed with the application of the Nobble identities and combined with the OQAM staggering to come up with the structure in
where now k=0, . . . ,
It is worth noting that because of the symmetry of the prototype impulse response the following holds
where the right hand side is called the causal para-conjugate of the transfer function
For the case that the prototype filter has a lengths of P=M or P=2M (K=1 and K=2), there exists closed form expressions of the filter's coefficients hP[l], l=0, . . . , P−1 the so called Extended Lapped Transform (ELT). Those closed form expressions are obtained from the assumption that the polyphase components can be realized with a lattice structure as will be explained later. For K=1 the ELT prototype is given by
and for K=2 is given by
For the cases where K>2 there is no closed form solution to directly obtain the prototype coefficients. One possibility is to obtain the prototype by numerical optimization methods. Given a certain design criteria for the optimization of HP (z), e.g. minimization of the stopband energy, the
equations can be employed in a constrained optimization method. It is worth noting that the constraints are non-linear relations on the prototype filter coefficients. The typical objective function will usually also be a non-linear function of the prototype coefficients. It is worth noting that although the prototype has a symmetrical impulse response, necessary to achieve PR, one multiplier per prototype filter coefficient has to be realized in the polyphase network.
Regarding
as well as their para-conjugates GM−1−k(z2) and
possess this property and thus can be grouped as shown in
We can see in
with
The lattice structure for each
is shown in
by a successive polynomial degree reduction. In
that has many similarities to the one in
are para-conjugate of the ones in
Another important detail is that the last rotor in both cases has only one branch, to generate the single output. Now, if a full rotor is implemented in the last lattice stage, we would obtain a lattice structure with two inputs and two outputs. This 2×2 lattice structure is depicted in
Here two pairs of power complementary polyphase components are implemented in one nonrecursive 2×2 lattice. The 2×2 lattice structure is one of the best known implementation of an orthogonal filter.
By considering again the structures in
Now the lattice rotors with only two multipliers reduce the complexity almost to the same as the direct form. We could say that structure for exponentially modulated SFBs based on the prototype polyphase decomposition of type 1 is the one in
One alternative to the lattice structure of
We have shown until now structures of the SFB and only for the prototype polyphase decomposition of type 1. Similar structures can be derived based on polyphase decompositions of type 2 shown in
and for polyphase decompositions of type 3, as shown in
Further embodiments are depicted in
The structures are based on the further development of the polyphase networks from
Issues related on how to connect the DFTs and IDFTs to the polyphase filters and to the polyphase matrix contents are discussed. We will focus here on the SFBs. First, the negative sign in one of the transfer functions in the matrix that is obtained automatically from the lattice structure. This is solved with the periodic sign inversion for even time instants represented by the multiplier −1m+1. It can be shown that the impulse responses in the second row are applied in a time interleaving fashion and by this alternation of the samples sign, only the impulse response with the inversed sign is reverted to its original value.
The second issue is that the DFT or IFDT provide M outputs, but now only M/2 inputs exist in the polyphase network. It can be demonstrated that because of the special characteristics of the signals at the input of the DFT or IDFT, the outputs actually show a big redundancy. More specifically, half of the outputs are complex conjugated versions of the other half With this in mind, one can see that all the necessary signals are generated by half of the DFT or IDFT outputs. We just need to take the complex conjugate signal from one of the outputs of each polyphase sets. Since all coefficients of the polyphase components are real valued, this is equivalent to take the complex conjugate of the two input signals. This means that half of the outputs of the DFT or IDFT do not need to be calculated, reducing even more the total computational complexity. Similar issues exist for the AFB but in reversed order and the corresponding solutions can be seen in the structures proposed.
As we mentioned before, structures with similar complexity to the efficient lattice were developed for CMFBs and SMFBs and received the name fast extended lapped transforms. They were originally not derived as an extension of the polyphase decomposition of the prototype, but it is worth mention that they can be derived in similar way as shown here.
In some applications of exponentially MFBs, for example, wired and wireless communications, an additional filter, an equalizer or a precoder, has to be included before the OQAM de-staggering for each subchannel in the AFB or after the staggering in the SFB. One good example is the channel equalization in multicarrier modulation schemes, where one multitap filter is included before the OQAM demapping for each subcarrier in the AFB in order to compensate for the frequency selectivity of the channel. In
In addition to the new polyphase network for AFB and SFB structures, we also can improve the subchannel processing, by making use of the real and imaginary staggering. Since there are operations involving real and imaginary part removal and downsampling, we can make use of some multirate processing identities to reduce the complexity.
If the subchannel filter is a single complex coefficient ωk, it can be jointly implemented with the OQAM de-staggering with the structure shown in
However, the subchannel equalizers will be multitap in general, so we can decompose the into their polyphase components also. For each subchannel k we decompose the equalizer in two components as follows
with V0,k(zM)=V0,k(R)(zM)+jV0,k(I)(zM), where V0,k(R)(zM) and V0,k(I)(zM) contain the real and imaginary parts of the coefficients of V0,k(zM).
Now we are able to jointly realize the subchannel equalizers and the OQAM de-staggering as shown in
As mentioned before it is trivial to show the corresponding structures for even k and for precoders in the SFB.
Furthermore, the proposed structures can be used for the following applications:
In order to numerically compare the different polyphase filtering structures for exponentially MFBs, we need first to determine the complexity as function of K and M. As a figure of merit we consider the total number of multiplications, where in each of them one factor is purely real number, the coefficient, and the other is a complex signal.
The total number of multiplications of the polyphase filtering for the state-of-the-art structures by considering the three different possibilities for the realization of the polyphase components pairs is given by:
Direct form: KM
Low complexity lattice: (2K+1)M/2=(K+0.5)M
Lifting steps: (3(K−1)+2)M/2=(3K−1)M/2
For the proposed structures the two possibilities are given by:
Lattice (2K+2)M/4=(K+1)M/2
Lifting steps: 3KM/4
It is worth noting that a direct form implementation of the polyphase components in the proposed architecture ends up with the same number of multipliers as in the state-of-the-art.
In Tables 1, 2, 3 and 4 the complexity achieved by the new proposed structures is shown in numbers for different number of subchannels M and different lengths of the prototype coefficients.
We can see that for very short prototypes (K=2) a reduction of 25% in the number of multiplications can be achieved. For very long prototypes (K=10 or K=20) a reduction of almost 50% can be achieved. For typical multicarrier applications (K=4) a reduction of almost 40% can be achieved.
The features disclosed in the application, the claims and the figures can be relevant for the implementation of embodiments in any combination with each other.
Number | Date | Country | Kind |
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16154802 | Feb 2016 | EP | regional |
Filing Document | Filing Date | Country | Kind |
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PCT/EP2016/081541 | 12/16/2016 | WO | 00 |
Publishing Document | Publishing Date | Country | Kind |
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WO2017/137126 | 8/17/2017 | WO | A |
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