Bonjour, ceci est un commentaire. Pour supprimer un commentaire, connectez-vous et affichez les commentaires de cet article. Vous pourrez alors…
Minglu Jin, Sooyoung Kim, Doseob Ahn, Deock-GilOh, and Jae Moung Kim, “A Fast LUT Predistorter for Power Amplifier in OFDM Systems”, The 14th IEEE Intemational Sysmposium on personal Indoor and Mobile Radio Communication Proceedings , Beijing, China, Sep. 2003, pp. 1894-1897
- Listed: 24 May 2026 23 h 49 min
Description
Minglu Jin, Sooyoung Kim, Doseob Ahn, Deock-GilOh, and Jae Moung Kim, “A Fast LUT Predistorter for Power Amplifier in OFDM Systems”, The 14th IEEE Intemational Sysmposium on personal Indoor and Mobile Radio Communication Proceedings , Beijing, China, Sep. 2003, pp. 1894-1897
**Minglu Jin, Sooyoung Kim, Doseob Ahn, Deock-GilOh, and Jae Moung Kim, “A Fast LUT Predistorter for Power Amplifier in OFDM Systems”, The 14th IEEE International Symposium on Personal Indoor and Mobile Radio Communication Proceedings , Beijing, China, Sep. 2003, pp. 1894-1897**
In 2003, the IEEE International Symposium on Personal Indoor and Mobile Radio Communication highlighted a breakthrough in radio‑frequency engineering: a *fast LUT predistorter* designed specifically for power amplifiers in OFDM systems. This paper, authored by Minglu Jin, Sooyoung Kim, Doseob Ahn, Deock‑Gil Oh, and Jae Moung Kim, addressed a critical bottleneck in modern wireless communication—non‑linear distortion introduced by power amplifiers when handling the complex, high‑peak‑to‑average‑power‑ratio (PAPR) signals that characterize Orthogonal Frequency Division Multiplexing (OFDM).
OFDM, the backbone of LTE, 5G, and Wi‑Fi standards, spreads data across many subcarriers to combat multipath fading. However, its inherently high PAPR strains power amplifiers, causing signal clipping and spectral regrowth that violate spectral mask regulations. Traditional linearization methods, such as digital predistortion (DPD), often require intensive computations or large lookup tables (LUTs), which can slow down real‑time processing and increase power consumption.
The authors tackled this challenge by proposing a *fast LUT predistorter* that cleverly reduces memory footprint while preserving high‑order accuracy. By segmenting the input dynamic range and optimizing LUT entries through adaptive compression techniques, the predistorter achieves rapid lookup times without sacrificing linearization quality. This innovation enabled OFDM transmitters to operate closer to the saturation point of power amplifiers, boosting energy efficiency—a vital consideration for battery‑powered mobile devices and IoT nodes.
Beyond the technical details, the paper’s impact lies in its practical applicability. Mobile radio engineers could implement the fast LUT predistorter in FPGA or ASIC designs with minimal resource overhead, leading to more compact and cost‑effective baseband units. Moreover, the methodology has since informed subsequent research on hybrid DPD-LUT architectures and machine‑learning‑enhanced linearization, illustrating its foundational role in the evolution of power amplifier technology.
Today’s 5G NR deployments still grapple with PAPR‑induced linearization challenges, especially for massive MIMO and millimeter‑wave bands. Revisiting Jin et al.’s fast LUT approach offers a low‑latency, low‑complexity alternative that complements modern DSP pipelines. For practitioners and researchers alike, the 2003 IEEE symposium paper remains a valuable reference point for designing next‑generation OFDM transceivers that marry high spectral efficiency with robust amplifier linearity.
In conclusion, the “Fast LUT Predistorter for Power Amplifier in OFDM Systems” paper exemplifies how thoughtful algorithmic optimization can resolve deep‑rooted hardware constraints. Its legacy persists in contemporary RF design, reminding us that sometimes the key to unlocking better performance lies in rethinking how we map signals to memory, not just in adding more computational power.
5 total views, 4 today
Sponsored Links
W. Ching, E. Fung and M. Ng. A multivariate Markov Chain Model for Categori...
W. Ching, E. Fung and M. Ng. A multivariate Markov Chain Model for Categorical Data Sequences and Its Applications in Demand Predictions. IMA Journal of […]
No views yet
J. Bower. Computational Modeling of Genetic and Biochemical Networks. MIT P...
J. Bower. Computational Modeling of Genetic and Biochemical Networks. MIT Press, Cambridge, M.A. 2001. **J. Bower. Computational Modeling of Genetic and Biochemical Networks. MIT Press, […]
3 total views, 3 today
T. Akutsu, S. Miyano and S. Kuhara. Inferring Qualitative Relations in Gene...
T. Akutsu, S. Miyano and S. Kuhara. Inferring Qualitative Relations in Genetic Networks and Metabolic Arrays. Bioinformatics, 16: 727-734, 2000. **T. Akutsu, S. Miyano and […]
No views yet
Rui Y, Chen Y., 揃etter proposal distributions: Object tracking using unscen...
Rui Y, Chen Y., 揃etter proposal distributions: Object tracking using unscented particle filter? IEEE Conf. on Computer Vision and Pattern Recognition, 2001, pp. 786−793 **”Rui […]
1 total views, 1 today
N.J. Gordon, D.J. Salmond, A.F.M. Smith, “Novel approach to nonlinear/non-G...
N.J. Gordon, D.J. Salmond, A.F.M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation”, IEE. proceedings-F, vol.140, no.2, 1993, pp.107-113 None
2 total views, 2 today
J. H. Kotecha and P. M. Djuric, “Gaussian Particle Filtering”, IEEE Transac...
J. H. Kotecha and P. M. Djuric, “Gaussian Particle Filtering”, IEEE Transactions on signal processing. vol.51, no.10, 2003, pp. 2592-2601. **J. H. Kotecha and P. […]
No views yet
Tanya Bertozzi, Didier Le Ruyet, Gilles Rigal and Han Vu-Thien, “On Particl...
Tanya Bertozzi, Didier Le Ruyet, Gilles Rigal and Han Vu-Thien, “On Particle Filtering for Digital Communications”, Proc. of 4th IEEE Workshop on Signal Processing Advances […]
3 total views, 3 today
Arnaud Doucet, “On Sequential Simulation-Based Methods for Bayesian Filteri...
Arnaud Doucet, “On Sequential Simulation-Based Methods for Bayesian Filtering”, Technical report. Signal Processing Group, Department of Engineering, University of Cambridge, 1998 None
3 total views, 3 today
M. Sanjeev Arulampalam, Simon Maskell, N. Gordon and T. Clapp, “A tutorial ...
M. Sanjeev Arulampalam, Simon Maskell, N. Gordon and T. Clapp, “A tutorial on particle filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking.” IEEE Transactions on signal processing, […]
2 total views, 2 today
Michael Isard, Andrew Blake, “Condensation – conditional density propagatio...
Michael Isard, Andrew Blake, “Condensation – conditional density propagation for visual tracking”, International Journal of Computer Vision, 1998. pp. 5~28 None
2 total views, 2 today
W. Ching, E. Fung and M. Ng. A multivariate Markov Chain Model for Categori...
W. Ching, E. Fung and M. Ng. A multivariate Markov Chain Model for Categorical Data Sequences and Its Applications in Demand Predictions. IMA Journal of […]
No views yet
J. Bower. Computational Modeling of Genetic and Biochemical Networks. MIT P...
J. Bower. Computational Modeling of Genetic and Biochemical Networks. MIT Press, Cambridge, M.A. 2001. **J. Bower. Computational Modeling of Genetic and Biochemical Networks. MIT Press, […]
3 total views, 3 today
T. Akutsu, S. Miyano and S. Kuhara. Inferring Qualitative Relations in Gene...
T. Akutsu, S. Miyano and S. Kuhara. Inferring Qualitative Relations in Genetic Networks and Metabolic Arrays. Bioinformatics, 16: 727-734, 2000. **T. Akutsu, S. Miyano and […]
No views yet
Rui Y, Chen Y., 揃etter proposal distributions: Object tracking using unscen...
Rui Y, Chen Y., 揃etter proposal distributions: Object tracking using unscented particle filter? IEEE Conf. on Computer Vision and Pattern Recognition, 2001, pp. 786−793 **”Rui […]
1 total views, 1 today
N.J. Gordon, D.J. Salmond, A.F.M. Smith, “Novel approach to nonlinear/non-G...
N.J. Gordon, D.J. Salmond, A.F.M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation”, IEE. proceedings-F, vol.140, no.2, 1993, pp.107-113 None
2 total views, 2 today
J. H. Kotecha and P. M. Djuric, “Gaussian Particle Filtering”, IEEE Transac...
J. H. Kotecha and P. M. Djuric, “Gaussian Particle Filtering”, IEEE Transactions on signal processing. vol.51, no.10, 2003, pp. 2592-2601. **J. H. Kotecha and P. […]
No views yet
Tanya Bertozzi, Didier Le Ruyet, Gilles Rigal and Han Vu-Thien, “On Particl...
Tanya Bertozzi, Didier Le Ruyet, Gilles Rigal and Han Vu-Thien, “On Particle Filtering for Digital Communications”, Proc. of 4th IEEE Workshop on Signal Processing Advances […]
3 total views, 3 today
Arnaud Doucet, “On Sequential Simulation-Based Methods for Bayesian Filteri...
Arnaud Doucet, “On Sequential Simulation-Based Methods for Bayesian Filtering”, Technical report. Signal Processing Group, Department of Engineering, University of Cambridge, 1998 None
3 total views, 3 today
M. Sanjeev Arulampalam, Simon Maskell, N. Gordon and T. Clapp, “A tutorial ...
M. Sanjeev Arulampalam, Simon Maskell, N. Gordon and T. Clapp, “A tutorial on particle filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking.” IEEE Transactions on signal processing, […]
2 total views, 2 today
Michael Isard, Andrew Blake, “Condensation – conditional density propagatio...
Michael Isard, Andrew Blake, “Condensation – conditional density propagation for visual tracking”, International Journal of Computer Vision, 1998. pp. 5~28 None
2 total views, 2 today
Recent Comments