Spectral Compensation for Multicarrier Communication
(2007) In IEEE Transactions on Signal Processing 55(7). p.3366-3379- Abstract
- Spectral compensation is an information-processing technique applied in the transmitter to improve the spectral efficiency of multicarrier modulation under a given power spectral density constraint. A set of carefully chosen tones, the so-called information tones, carries data. The. remaining tones, referred to as compensation tones, are modulated with a linear combination of the data, such that the spectral characteristic of the transmit signal and thus the throughput are improved. This paper investigates strategies to find the set of compensation tones and presents the optimal solution for the linear combination of the data given the tone-set split. The optimality criterion is maximization of throughput for a time-dispersive Gaussian... (More)
- Spectral compensation is an information-processing technique applied in the transmitter to improve the spectral efficiency of multicarrier modulation under a given power spectral density constraint. A set of carefully chosen tones, the so-called information tones, carries data. The. remaining tones, referred to as compensation tones, are modulated with a linear combination of the data, such that the spectral characteristic of the transmit signal and thus the throughput are improved. This paper investigates strategies to find the set of compensation tones and presents the optimal solution for the linear combination of the data given the tone-set split. The optimality criterion is maximization of throughput for a time-dispersive Gaussian channel known to the transmitter. Furthermore, a. suboptimal design is proposed, which has low runtime-complexity and achieves near-optimal performance. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/602741
- author
- Magesacher, Thomas LU
- organization
- publishing date
- 2007
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- multicarrier modulation, (PSD) constraint, power spectral density, spectral shaping
- in
- IEEE Transactions on Signal Processing
- volume
- 55
- issue
- 7
- pages
- 3366 - 3379
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000247488700020
- scopus:34347385626
- ISSN
- 1053-587X
- DOI
- 10.1109/TSP.2007.894390
- language
- English
- LU publication?
- yes
- id
- 4576bce7-20c7-4c59-8631-9b65ddfd2624 (old id 602741)
- date added to LUP
- 2016-04-01 16:15:22
- date last changed
- 2022-01-28 18:23:11
@article{4576bce7-20c7-4c59-8631-9b65ddfd2624,
abstract = {{Spectral compensation is an information-processing technique applied in the transmitter to improve the spectral efficiency of multicarrier modulation under a given power spectral density constraint. A set of carefully chosen tones, the so-called information tones, carries data. The. remaining tones, referred to as compensation tones, are modulated with a linear combination of the data, such that the spectral characteristic of the transmit signal and thus the throughput are improved. This paper investigates strategies to find the set of compensation tones and presents the optimal solution for the linear combination of the data given the tone-set split. The optimality criterion is maximization of throughput for a time-dispersive Gaussian channel known to the transmitter. Furthermore, a. suboptimal design is proposed, which has low runtime-complexity and achieves near-optimal performance.}},
author = {{Magesacher, Thomas}},
issn = {{1053-587X}},
keywords = {{multicarrier modulation; (PSD) constraint; power spectral density; spectral shaping}},
language = {{eng}},
number = {{7}},
pages = {{3366--3379}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Signal Processing}},
title = {{Spectral Compensation for Multicarrier Communication}},
url = {{http://dx.doi.org/10.1109/TSP.2007.894390}},
doi = {{10.1109/TSP.2007.894390}},
volume = {{55}},
year = {{2007}},
}