TR2019-137
Optimal Power Encoding of OFDM Signals in All-Digital Transmitters
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- , "Optimal Power Encoding of OFDM Signals in All-Digital Transmitters", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GLOBECOM38437.2019.9014289, December 2019, pp. 1-6.BibTeX TR2019-137 PDF
- @inproceedings{Tanovic2019dec,
- author = {Tanovic, Omer and Ma, Rui and Orlik, Philip V. and Megretski, Aleksandre},
- title = {Optimal Power Encoding of OFDM Signals in All-Digital Transmitters},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2019,
- pages = {1--6},
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/GLOBECOM38437.2019.9014289},
- issn = {2576-6813},
- isbn = {978-1-7281-0962-6},
- url = {https://www.merl.com/publications/TR2019-137}
- }
- , "Optimal Power Encoding of OFDM Signals in All-Digital Transmitters", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GLOBECOM38437.2019.9014289, December 2019, pp. 1-6.
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MERL Contact:
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Research Areas:
Communications, Electronic and Photonic Devices, Signal Processing
Abstract:
In this paper, we propose a power encoding method for converting a high-resolution OFDM baseband signal into a signal that assumes only a finite number of values. This is achieved by the series interconnection of, optimally (co)designed, delta-sigma and digital pulse-width modulators (DPWM). Given an input signal class with a known amplitude distribution (e.g., OFDM), parameters of the multilevel DPWM are designed such that the mean squared error of the inherent DPWM quantization noise is minimized. Parameters of the delta-sigma are then chosen with respect to the designed DPWM, so to optimally shape the inherent quantization noise out of the spectral band of interest. The superior performance of the proposed novel power encoding scheme is demonstrated by Matlab simulations on several standardized LTE test signals.
Related News & Events
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NEWS MERL Scientists Presenting 11 Papers at IEEE Global Communications Conference (GLOBECOM) 2019 Date: December 9, 2019 - December 13, 2019
Where: Waikoloa, Hawaii, USA
MERL Contacts: Jianlin Guo; Toshiaki Koike-Akino; Philip V. Orlik; Pu (Perry) Wang
Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information SecurityBrief- MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.
GLOBECOM is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 3000 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. Themed “Revolutionizing Communications,” GLOBECOM2019 will feature a comprehensive high-quality technical program including 13 symposia and a variety of tutorials and workshops to share visions and ideas, obtain updates on latest technologies and expand professional and social networking.
- MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.