Channel Estimation Using Implicit Training

Orozco-Lugo, A.G.; Lara, M.; McLernon, Des

doi:10.1109/tsp.2003.819993

Cited by 199 publications

(158 citation statements)

References 26 publications

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“…A tarefa basilar do igualizador é compensar as influências do canal [22]. Esta compensação exige, no entanto, que uma estimação da resposta do canal esteja disponível.…”

Section: Levantamento Bibliográficounclassified

“…Esta situação apresenta como consequência uma redução do ritmo de bit útil, diminuindo por sua vez a eficiência espectral do sistema. Um método considerado promissor para resolver este problema realiza a estimação de canal utilizando pilotos implícitos, que são adicionados aos blocos de dados em vez de serem multiplexados com os mesmos [22]. Isto significa que se pode aumentar significativamente o número de pilotos a transmitir, mantendo a capacidade e eficiência espectral do sistema.…”

Section: Método Dos Pilotos Implícitosunclassified

“…Um método alternativo foi estudado em [22] e [60] e baseia-se na ideia de agrupar uma sequência de pilotos e somar directamente com uma sequência de dados transmitida em simultâneo. Esta abordagem é geralmente designada por transmissão de pilotos implícitos ou pilotos sobrepostos ou pilotos incorporados e exige que alguma da potência seja utilizada na 47 sequência de pilotos.…”

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Robust channel estimation for broadband satellite communications

Balseiro

Souto

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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ISCTE Outubro, 2010 i AgradecimentosEm primeiro lugar quero expressar o meu profundo apreço e sincera gratidão pelo meu orientador da dissertação Dr. Nuno Souto pelo seu esforço constante, orientação e inúmeros momentos de atenção que ele dedicou ao longo deste trabalho de investigação. As suas preciosas sugestões tornaram este trabalho interessante e desafiante para mim.Devo um agradecimento aos meus amigos pela sua ajuda, motivação e apoio.O apoio da família desempenha um papel fundamental no sucesso de uma pessoa.Gostaria de agradecer aos meus pais, irmão e outros membros da família incluindo todos os meus tios, tias e primos; do fundo do meu coração. O seu apoio incondicional sempre me ajudou a seguir os caminhos certos na vida.Um agradecimento especial para a minha namorada por me ter ajudado a ultrapassar os obstáculos, ter acreditado sempre em mim e na minha capacidade para desenvolver este trabalho, por me ter motivado e apoiado nos momentos mais difíceis.ii Resumo da DissertaçãoCom o rápido crescimento das comunicações digitais nos últimos anos, a necessidade de transmissões de dados a alta velocidade aumentou significativamente. Além disso, espera-se que os futuros sistemas sem fios sejam capazes de suportar uma ampla gama de serviços que Multiplexing (OFDM) is a promising candidate for achieving high data rates in mobile environment, due to its resistance Inter Symbol Interference (ISI), which is a common problem found in high speed data communication. In OFDM, modulation may be differential or coherent. When using differential modulation there is no need for a channel estimate but its performance is inferior than the coherent system. Coherent modulation requires the channel estimation which gives better performance but with relatively more complex receiver structure. In this thesis two channel estimation techniques will be studied: Pilot Symbol Assisted Modulation (PSAM) and implicit pilots. In the first case the pilots are multiplexed with the transmitted data, while in the second situation pilots are placed in positions along with the data. In this thesis, we consider different estimation techniques in terms of Bit Error Rate (BER) and Mean Square Error (MSE). In order to do this analysis a Land Mobile Satellite (LMS) channel model was proposed which will be the basis for the study of the robustness and efficiency of the estimation methods presented.

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“…A tarefa basilar do igualizador é compensar as influências do canal [22]. Esta compensação exige, no entanto, que uma estimação da resposta do canal esteja disponível.…”

Section: Levantamento Bibliográficounclassified

Section: Método Dos Pilotos Implícitosunclassified

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Robust channel estimation for broadband satellite communications

Balseiro

Souto

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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“…In this paper, we adopt the so-called superimposed training approach [25], i.e., the pilot symbols are added to the data symbols, which only incurs power loss (and no spectral loss). In this case, the transmitted signal can be represented as x = t + c, where…”

Section: (And Tmentioning

confidence: 99%

A Low-Complexity Iterative Channel Estimation and Detection Technique for Doubly Selective Channels

Guo

Ping

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

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In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) Abstract-In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-preſxing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a lowcomplexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform intertap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

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“…Superimposed training has been recently considered by several authors [3][4][5], where the training (a periodic non random) sequence is arithmetically added to the transmitted sequence. In this way no bandwidth is lost when sending the training data.…”

Section: Introductionmentioning

confidence: 99%

Source Separation of Asynchronous OFDM Signals using Superimposed Training

Venkateswaran

Veen

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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Abstract-In multi-user orthogonal frequency division multiplexing (OFDM) systems, an asynchronous interferer is defined as a signal whose cyclic prefix (CP) does not align with the desired user CP. Such ad-hoc multi-user OFDM systems are considered and an algorithm to perform blind space-time source separation is presented. In this paper we consider a scheme where a periodic sequence is added to the transmitted OFDM signal to be able to identify it. The periodic sequence has a cyclostationary property and can be used to estimate the channel and to separate the interfering users from the desired user signals.

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Channel Estimation Using Implicit Training

Cited by 199 publications

References 26 publications

Robust channel estimation for broadband satellite communications

Robust channel estimation for broadband satellite communications

A Low-Complexity Iterative Channel Estimation and Detection Technique for Doubly Selective Channels

Source Separation of Asynchronous OFDM Signals using Superimposed Training

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