NDLI: Clustered OFDM with adaptive antenna arrays for interference suppression

Content Provider	IEEE Xplore Digital Library
Author	Hua Zhang Ye Li
Copyright Year	2002
Abstract	We investigate clustered orthogonal frequency-division multiplexing (OFDM) with adaptive antenna arrays for interference suppression. To calculate weights for interference suppression, instantaneous correlations of the received signals and channel responses corresponding to the desired signals have to be estimated. However, due to smaller size of each cluster for clustered OFDM than for classical OFDM, the discrete Fourier transform (DFT)-based estimator has large leakage and results in severe performance degradation. Therefore, a polynomial-based parameter estimator is proposed to combat the severe leakage of the DFT based estimator. We study the impact of the polynomial order and window size on the estimation error. An approximately optimal window size for the polynomial-based estimator is obtained and an adaptive algorithm for the optimal window size is developed. With the adaptive algorithm, the polynomial-based estimator has no leakage and does not require channel statistics. Simulation results show that the developed algorithm improves the performance significantly.
Starting Page	2189
Ending Page	2197
Page Count	9
File Size	435995
File Format	PDF
ISSN	15361276
Volume Number	3
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive arrays OFDM Antenna arrays Interference suppression Polynomials Discrete Fourier transforms Adaptive algorithm Frequency division multiplexing Degradation Parameter estimation polynomial-based parameter estimation Adaptive antenna arrays clustered orthogonal frequency-division multiplexing (OFDM) interference suppression
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering Computer Science Applications

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