pith. sign in

arxiv: 2505.05388 · v3 · submitted 2025-05-08 · 📡 eess.SP

On Multiangle Discrete Fractional Periodic Transforms

Pith reviewed 2026-05-22 15:47 UTC · model grok-4.3

classification 📡 eess.SP
keywords multiangle discrete fractional Fourier transformM-periodic transformsdiscrete Fourier transformcomputational efficiencysymmetry exploitationtime-frequency analysissignal processingfractional transforms
0
0 comments X

The pith

The multiangle centered discrete fractional Fourier transform generalizes to arbitrary M-periodic cases, covering standard discrete transforms while halving the FFT count through symmetry exploitation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper extends the multiangle centered discrete fractional Fourier transform to general M-periodic transforms. The generalization includes the discrete Fourier, sine, cosine, Hadamard, and Hartley transforms among others. It also introduces a multiangle standard discrete fractional Fourier transform and uses symmetries present in both versions to reduce the number of required FFT operations by half. The resulting efficiency supports applications where computational resources are limited.

Core claim

The MA-CDFRFT generalizes to M-periodic transforms that encompass standard discrete Fourier, sine, cosine, Hadamard, and Hartley transforms, and the symmetries inherent to the MA-CDFRFT and the new MA-DFRFT halve the number of FFTs needed to compute them.

What carries the argument

Generalization of the multiangle centered discrete fractional Fourier transform to M-periodic transforms, together with symmetry-based reduction of FFT operations in both the centered and standard variants.

If this is right

  • Standard discrete transforms including DFT, DST, DCT, Hadamard, and Hartley become computable within the same efficient framework.
  • The halved FFT requirement applies equally to the newly defined multiangle standard discrete fractional Fourier transform.
  • Resource-constrained devices gain practical access to these time-frequency tools without increased computational overhead.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Real-time signal processing pipelines could adopt the reduced-FFT versions for faster execution on embedded hardware.
  • The symmetry reduction technique may extend to other fractional or periodic operators beyond those treated here.
  • Parameter choices for the generalized transforms could be tuned to optimize specific applications like filtering or feature extraction.

Load-bearing premise

The symmetries that halve the FFT count for the original MA-CDFRFT continue to apply without change or loss of correctness when the method is extended to arbitrary M-periodic transforms.

What would settle it

A direct implementation count for the generalized transform on a non-Fourier M-periodic case such as the Hadamard transform that shows the number of FFT calls remains unchanged from a baseline method.

read the original abstract

The efficient multiangle centered discrete fractional Fourier transform (MA-CDFRFT) [1] has proven to be a useful tool for time-frequency analysis; in this paper, we generalize the MA-CDFRFT to general M -periodic transforms, which, among others, include the standard discrete Fourier, discrete sine, discrete cosine, Hadamard and discrete Hartley transform. Furthermore, we exploit the symmetries inherent to the MA-CDFRFT and our novel multiangle standard discrete fractional Fourier transform (MA-DFRFT) to halve the number of FFTs needed to compute these transforms, which paves the way for applications in resource-constrained environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper generalizes the multiangle centered discrete fractional Fourier transform (MA-CDFRFT) to general M-periodic transforms, which include the standard discrete Fourier, discrete sine, discrete cosine, Hadamard, and discrete Hartley transforms. It introduces the multiangle standard discrete fractional Fourier transform (MA-DFRFT) and exploits symmetries from both the MA-CDFRFT and MA-DFRFT to halve the number of FFTs needed for computation.

Significance. If the generalization is rigorously derived and the symmetry-based FFT reduction holds uniformly, the work offers a unified efficient framework for fractional versions of multiple classical orthogonal transforms. This could be valuable for time-frequency analysis in resource-constrained signal processing applications, building directly on prior MA-CDFRFT results with potential for reproducible implementations.

major comments (2)
  1. [Generalization to M-periodic transforms and symmetry exploitation sections] The central claim that symmetries enabling FFT halving in the MA-CDFRFT transfer without modification to arbitrary M-periodic cases (including DCT, DST, Hadamard, and Hartley) is load-bearing for the efficiency result. The eigenstructures of these transforms differ in sign patterns and periodicity from the centered fractional Fourier kernel, so explicit verification or re-derivation of the conjugation/sign-flip identities is needed for each family rather than assuming uniform applicability.
  2. [MA-DFRFT definition and complexity analysis] The manuscript states that the MA-DFRFT symmetries also contribute to halving the FFT count across the listed transforms, but without a concrete breakdown (e.g., via equations showing how the fractional angle parameters map under each basis), it is unclear whether the reduction is parameter-free or requires case-specific adjustments.
minor comments (2)
  1. [Abstract] The abstract lists specific transforms after 'among others'; clarify whether the M-periodic generalization applies to all M-periodic orthogonal bases or is demonstrated only for the enumerated ones.
  2. Ensure consistent notation for the fractional angle parameters across the MA-CDFRFT, MA-DFRFT, and generalized cases to avoid ambiguity in the symmetry relations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough review and constructive feedback on our generalization of the MA-CDFRFT to M-periodic transforms and the associated efficiency improvements. We address each major comment below and will incorporate clarifications in the revised manuscript.

read point-by-point responses
  1. Referee: [Generalization to M-periodic transforms and symmetry exploitation sections] The central claim that symmetries enabling FFT halving in the MA-CDFRFT transfer without modification to arbitrary M-periodic cases (including DCT, DST, Hadamard, and Hartley) is load-bearing for the efficiency result. The eigenstructures of these transforms differ in sign patterns and periodicity from the centered fractional Fourier kernel, so explicit verification or re-derivation of the conjugation/sign-flip identities is needed for each family rather than assuming uniform applicability.

    Authors: We agree that eigenstructures vary across M-periodic families. Our generalization defines the multiangle transforms at the level of the general M-periodic kernel, deriving the conjugation and sign-flip identities directly from the M-periodicity condition. This framework ensures the FFT-halving symmetries hold uniformly for the listed transforms without separate re-derivations. To improve clarity and address the referee's concern, we will add a new subsection with explicit specialization of the general identities to DCT, DST, Hadamard, and Hartley cases. revision: yes

  2. Referee: [MA-DFRFT definition and complexity analysis] The manuscript states that the MA-DFRFT symmetries also contribute to halving the FFT count across the listed transforms, but without a concrete breakdown (e.g., via equations showing how the fractional angle parameters map under each basis), it is unclear whether the reduction is parameter-free or requires case-specific adjustments.

    Authors: The MA-DFRFT symmetries are derived analogously to the centered case within the same M-periodic framework, with fractional angle parameters mapping uniformly under the basis for all listed transforms; the FFT reduction is therefore parameter-free once the general form is fixed. We will revise the complexity analysis to include explicit equations illustrating the angle mappings for representative bases (e.g., DFT and DCT) and confirm the uniform applicability. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation builds on external prior result

full rationale

The paper cites the MA-CDFRFT from reference [1] as the foundation and states that it generalizes this to M-periodic transforms while exploiting symmetries from both the original and the new MA-DFRFT. No equations or claims in the provided text reduce a derived quantity to a fitted parameter or self-defined input by construction. The central claims (generalization and FFT halving) rest on explicit extension of prior work rather than re-deriving the base result from the paper's own outputs. This is a standard, non-circular citation of independent prior results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed from abstract only; no explicit free parameters, axioms or invented entities are identifiable. Full manuscript would be required to audit any implicit assumptions about periodicity or symmetry preservation.

pith-pipeline@v0.9.0 · 5626 in / 1120 out tokens · 46803 ms · 2026-05-22T15:47:04.811156+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

35 extracted references · 35 canonical work pages · 1 internal anchor

  1. [1]

    INTRODUCTION Fractional transforms such as the discrete fractional Fourier trans- form (DFRFT) are important tools in digital signal processing, as they are used for linearly frequency modulated (LFM) chirp es- timation, compression and mitigation [2, 3, 4], cryptography [5], improved spectrograms [6], processing optical systems [7], among other applicati...

  2. [2]

    PRELIMINARIES 2.1.M-periodic matrices LetW∈C N×N be anM-periodic matrix, i.e.,W M =I,M∈N, whereIis the identity matrix.WisM-periodic if and only if

  3. [3]

    it is diagonizable overCand

  4. [4]

    On Multiangle Discrete Fractional Periodic Transforms

    all its eigenvalues are theM th roots of unity. We write the eigenvalues ofWas a diagonal matrix Λ=diag(ω l M ), whereω M =e −j 2π M is a primitiveM th root of unity andl∈N N 0 . We can compute fractional powersW a, wherea∈Ris the so-called fractional order through its eigendecompositionW a = VΛ aV −1, whereVare the eigenvectors ofW. Noninteger pow- ers o...

  5. [5]

    ,4(N−1)/N}in parallel

    EFFICIENT MULTIANGLE FRACTIONAL M−PERIODIC TRANSFORMS In [1], the authors have used the eigenvalue multiplicities of the CDFT to construct an algorithm which efficiently computes the multiangle CDFRFT (MA-CDFRFT), i.e., the CDFRFTs of frac- tional orders{0,4/N, . . . ,4(N−1)/N}in parallel. A naive approach has complexityO(N 3)due to theNmatrix-vector mult...

  6. [6]

    Note that these symme- tries are present in all admissible eigenvector sets of the (C)DFT

    HALVING THE COMPUTATIONAL COMPLEXITY OF THE MA-(C)DFRFT In this section, we utilize the symmetries within the (C)DFT eigen- vectors to halve the number of FFTs needed to compute the chirp fractional definition of the MA-(C)DFRFT. Note that these symme- tries are present in all admissible eigenvector sets of the (C)DFT. We leave optimizations of other mult...

  7. [7]

    toN/2. This result is not surprising, as for evenN, the fractional ordersa 4 evaluated by the MA-(C)DFRFT are such that a4[r] = (a 4[r+N/2] + 2)mod4.(12) Due to angle-additivity we know thatW a+2 =W aW 2 while W 2 =Pfor the (C)DFT, i.e., rows0throughN/2−1of the even- length MA-(C)DFRFT are reversed versions of rowsN/2through N−1. In analogy to the even-le...

  8. [8]

    APPLICATIONS While potential applications are manifold (see Sec. 1 for some ex- amples), one possible use case of this reduced complexity is the de- cryption of fractional transform-based cryptographic schemes such as [5], which utilize the decorrelation between DFRFTs (or related transforms) of different fractional orders. More concretely, our gen- erali...

  9. [9]

    CONCLUSION In this paper, we reduce the computational complexity of all multian- gle fractionalM-periodic transforms fromO(N 3)toO(N 2 logN). We do this by generalizing the method in [1] to arbitrary period- lengthsM, eigenvalue exponentsland eigenvectorsV; our gener- alization now includes the fractional extensions of important trans- forms such as discr...

  10. [10]

    On the multiangle centered discrete fractional Fourier transform,

    J. G. Vargas-Rubio and B. Santhanam, “On the multiangle centered discrete fractional Fourier transform,”IEEE Signal Processing Letters, vol. 12, no. 4, pp. 273–276, 2005

  11. [11]

    SAR- based vibration estimation using the discrete fractional Fourier transform,

    Q. Wang, M. Pepin, R. J. Beach, R. Dunkel, T. Atwood, B. San- thanam, W. Gerstle, A. W. Doerry, and M. M. Hayat, “SAR- based vibration estimation using the discrete fractional Fourier transform,”IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 4145–4156, 2012

  12. [12]

    FMCW radar interference miti- gation based on the fractional Fourier transform,

    C. Oswald and F. Pernkopf, “FMCW radar interference miti- gation based on the fractional Fourier transform,”IEEE Trans- actions on Radar Systems, vol. 4, pp. 549–563, 2026

  13. [13]

    On the fractional Fourier transform for FMCW radar interference mitigation,

    C. Oswald, J. Kulmer, and F. Pernkopf, “On the fractional Fourier transform for FMCW radar interference mitigation,” in2025 IEEE Radar Conference (RadarConf25). IEEE, 2025, pp. 781–786

  14. [14]

    A blind watermarking algorithm based on fractional Fourier transform and visual cryptogra- phy,

    S. Rawat and B. Raman, “A blind watermarking algorithm based on fractional Fourier transform and visual cryptogra- phy,”Signal Processing, vol. 92, no. 6, pp. 1480–1491, 2012

  15. [15]

    An improved spectro- gram using the multiangle centered discrete fractional Fourier transform,

    J. G. Vargas-Rubio and B. Santhanam, “An improved spectro- gram using the multiangle centered discrete fractional Fourier transform,” inProceedings.(ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.IEEE, 2005, vol. 4, pp. iv–505

  16. [16]

    The fractional Fourier transform and some of its applications to optics,

    A. Torre, “The fractional Fourier transform and some of its applications to optics,” inProgress in Optics, vol. 43, pp. 531–

  17. [17]

    Fractional Fourier transform as a signal processing tool: An overview of recent developments,

    E. Sejdi ´c, I. Djurovi ´c, and L. Stankovi ´c, “Fractional Fourier transform as a signal processing tool: An overview of recent developments,”Signal Processing, vol. 91, no. 6, pp. 1351– 1369, 2011

  18. [18]

    The discrete fractional cosine and sine transforms,

    S.C. Pei and M.H. Yeh, “The discrete fractional cosine and sine transforms,”IEEE Transactions on Signal Processing, vol. 49, no. 6, pp. 1198–1207, 2001

  19. [19]

    Discrete fractional Hadamard trans- form,

    S.C. Pei and M.H. Yeh, “Discrete fractional Hadamard trans- form,” in1999 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 1999, vol. 3, pp. 179–182

  20. [20]

    Eigenvalues and eigenvectors of generalized DFT, generalized DHT, DCT-IV and DST-IV matrices,

    C.C. Tseng, “Eigenvalues and eigenvectors of generalized DFT, generalized DHT, DCT-IV and DST-IV matrices,”IEEE Transactions on Signal Processing, vol. 50, no. 4, pp. 866–877, 2002

  21. [21]

    Multiplicity of fractional Fourier transforms and their rela- tionships,

    G. Cariolaro, T. Erseghe, P. Kraniauskas, and N. Laurenti, “Multiplicity of fractional Fourier transforms and their rela- tionships,”IEEE Transactions on Signal Processing, vol. 48, no. 1, pp. 227–241, 2002

  22. [22]

    The discrete fractional Fourier transform,

    C. Candan, M. A. Kutay, and H. M. Ozaktas, “The discrete fractional Fourier transform,”IEEE Transactions on Signal Processing, vol. 48, no. 5, pp. 1329–1337, 2000

  23. [23]

    The discrete fractional Fourier transform based on the DFT matrix,

    A. Serbes and L. Durak-Ata, “The discrete fractional Fourier transform based on the DFT matrix,”Signal Processing, vol. 91, no. 3, pp. 571–581, 2011

  24. [24]

    Discrete fractional Fourier transforms based on closed-form Hermite–Gaussian- like DFT eigenvectors,

    J. R. de Oliveira Neto and J. B. Lima, “Discrete fractional Fourier transforms based on closed-form Hermite–Gaussian- like DFT eigenvectors,”IEEE Transactions on Signal Process- ing, vol. 65, no. 23, pp. 6171–6184, 2017

  25. [25]

    An introduction to the angular Fourier trans- form,

    L. B. Almeida, “An introduction to the angular Fourier trans- form,” in1993 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1993, vol. 3, pp. 257– 260

  26. [26]

    Comparison of centered discrete fractional Fourier transforms for chirp parameter esti- mation,

    D. J. Peacock and B. Santhanam, “Comparison of centered discrete fractional Fourier transforms for chirp parameter esti- mation,” in2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE). IEEE, 2013, pp. 65–68

  27. [27]

    A comparative study of commut- ing matrix approaches for the discrete fractional Fourier trans- form,

    I. Bhatta and B. Santhanam, “A comparative study of commut- ing matrix approaches for the discrete fractional Fourier trans- form,” in2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE). IEEE, 2015, pp. 1–6

  28. [28]

    Computation of an eigendecomposition- based discrete fractional Fourier transform with reduced arith- metic complexity,

    J. R. de Oliveira Neto, J. B. Lima, G. J. da Silva Jr, and R. M. C. de Souza, “Computation of an eigendecomposition- based discrete fractional Fourier transform with reduced arith- metic complexity,”Signal Processing, vol. 165, pp. 72–82, 2019

  29. [29]

    An orthonormal class of exact and simple DFT eigenvectors with a high degree of symmetry,

    T. Erseghe and G. Cariolaro, “An orthonormal class of exact and simple DFT eigenvectors with a high degree of symmetry,” IEEE transactions on Signal Processing, vol. 51, no. 10, pp. 2527–2539, 2003

  30. [30]

    Closed-form orthogonal DFT eigenvectors generated by complete generalized legendre sequence,

    S.C. Pei, C.C. Wen, and J.J. Ding, “Closed-form orthogonal DFT eigenvectors generated by complete generalized legendre sequence,”IEEE Transactions on Circuits and Systems I: Reg- ular Papers, vol. 55, no. 11, pp. 3469–3479, 2008

  31. [31]

    Analysis and comparison of discrete fractional Fourier transforms,

    X. Su, R. Tao, and X. Kang, “Analysis and comparison of discrete fractional Fourier transforms,”Signal Processing, vol. 160, pp. 284–298, 2019

  32. [32]

    Eigenvalue and eigenvector de- composition of the discrete Fourier transform,

    J. McClellan and T. Parks, “Eigenvalue and eigenvector de- composition of the discrete Fourier transform,”IEEE Transac- tions on Audio and Electroacoustics, vol. 20, no. 1, pp. 66–74, 1972

  33. [33]

    A low-complexity ap- proach to computation of the discrete fractional Fourier trans- form,

    D. Majorkowska-Mech and A. Cariow, “A low-complexity ap- proach to computation of the discrete fractional Fourier trans- form,”Circuits, Systems, and Signal Processing, vol. 36, pp. 4118–4144, 2017

  34. [34]

    Efficient DFT architectures based upon symmetries,

    T. Erseghe and G. Cariolaro, “Efficient DFT architectures based upon symmetries,”IEEE transactions on Signal Pro- cessing, vol. 54, no. 10, pp. 3829–3838, 2006

  35. [35]

    Mitigating the time-varying doppler shift in high-mobility wireless communi- cations using multi-angle centered discrete fractional Fourier transform,

    Amir Raeisi Nafchi, Mona Esmaeili, Alireza Ghasempour, Eric Hamke, Balu Santhanam, and Ramiro Jordan, “Mitigating the time-varying doppler shift in high-mobility wireless communi- cations using multi-angle centered discrete fractional Fourier transform,” in2021 IEEE 12th Annual Ubiquitous Comput- ing, Electronics & Mobile Communication Conference (UEM- CO...