EM-Based Joint Channel Estimation and IQ Imbalances for OFDM Systems

The imbalances between the In-phase (I) and Quadrature-phase (Q) branches represent a significant source of impairment in the orthogonal frequency division multiplexing (OFDM) systems. Recently, it has been shown that the unwanted IQ imbalances can be actually exploited to achieve a diversity gain....

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Vydáno v:IEEE transactions on broadcasting Ročník 58; číslo 1; s. 106 - 113
Hlavní autoři: Marey, M., Samir, M., Dobre, O. A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.03.2012
Institute of Electrical and Electronics Engineers
Témata:
Applied sciences
Bit error rate
Channel estimation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
IQ imbalances
Maximum likelihood estimation
ML and EM algorithms
Multiplexing
OFDM
Radiocommunications
Receivers
Signal and communications theory
Signal, noise
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
Transmitters
Transmitters. Receivers
Performance evaluation
ML and EM algorithms
Parameter estimation
IQ imbalances
OFDM
Bit error rate
Transmitter
Iterative method
Joint detection
Pulse response
Channel estimation
Orthogonal frequency division multiplexing
Maximum likelihood
Optimal planning
EM algorithm
Queue
ISSN:0018-9316, 1557-9611
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Popis
Shrnutí:The imbalances between the In-phase (I) and Quadrature-phase (Q) branches represent a significant source of impairment in the orthogonal frequency division multiplexing (OFDM) systems. Recently, it has been shown that the unwanted IQ imbalances can be actually exploited to achieve a diversity gain. In this contribution, by taking into account the diversity gain resulting from the IQ imbalances, we develop a novel algorithm to jointly estimate the channel impulse response and IQ imbalances occurring at both the transmitter and receiver. Starting from the Maximum Likelihood (ML) principle, we derive an estimation algorithm based on the expectation maximization (EM) algorithm, which exploits information from the pilot symbols and detected data symbols in a systematic fashion. To reduce the complexity of the estimation algorithm, a sub-optimal scheme is also introduced. The results indicate that the proposed algorithms achieve a significant improvement in the bit error rate (BER) performance after three iterations as compared to conventional data-aided algorithms.
ISSN:0018-9316
1557-9611
DOI:10.1109/TBC.2011.2179730