Multiuser channel estimation for detection of cochannel signals

Grant, S.J.; Cavers, J.K.

doi:10.1109/icc.1999.767885

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Then, (3) can be written as

SN R_{p, s} = X_{s} Y_{p, s} γ .

For the perfect FB (PFB) case, the loss factor Y p , s = 1, whereas for a codebook FB system Y p , s < 1. The system rate is given as

R = {log}_{2} ()(, 1 + SN R_{p, s}) .

The imperfect CSI is modelled as [23]

{\hat{h}}_{k}^{s} = η h_{k}^{s} + \sqrt{1 - η^{2}} ϵ,

where

{\hat{h}}_{k}^{s}

is imperfect CSI, ε is an independent zero mean complex Gaussian variable with variance 0.5 per dimension and η is the correlation between

{\hat{h}}_{k}^{s}

and

h_{k}^{s}

.…”

Section: System Modelmentioning

confidence: 99%

Reducing codebook loss with reconfigurable receive antennas

2016

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In this paper, a reconfigurable antenna codebook feedback scheme is investigated to reduce precoding codebook loss without increasing the number of feedback bits. Analysis and simulations show that increasing the number of receive antenna states (S) has the same effect as codebook size expansion when the codeword and state selection is based on minimizing the distance between channel and codeword.In addition, we propose an optimum selection approach. The SNR and rates achieved with perfect feedback in a traditional non-reconfigurable antenna system, can be exceeded with only S = 2, using the proposed optimum selection. In fact doubling S gives an approximate SNR gain of 40 % compared to S = 1. We also consider the effects of channel estimation errors and correlated antenna states. We show that the performance losses due to correlation can be quantified with a simple closed form result. Index TermsReconfigurable antenna, limited feedback, precoding, codebook, MISO, beamforming. P. J. Smith was with the University of Canterbury † during this research.

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“…Then, (3) can be written as

SN R_{p, s} = X_{s} Y_{p, s} γ .

For the perfect FB (PFB) case, the loss factor Y p , s = 1, whereas for a codebook FB system Y p , s < 1. The system rate is given as

R = {log}_{2} ()(, 1 + SN R_{p, s}) .

The imperfect CSI is modelled as [23]

{\hat{h}}_{k}^{s} = η h_{k}^{s} + \sqrt{1 - η^{2}} ϵ,

where

{\hat{h}}_{k}^{s}

is imperfect CSI, ε is an independent zero mean complex Gaussian variable with variance 0.5 per dimension and η is the correlation between

{\hat{h}}_{k}^{s}

and

h_{k}^{s}

.…”

Section: System Modelmentioning

confidence: 99%

Reducing codebook loss with reconfigurable receive antennas

2016

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“…. , n T } onde h k,jé o coeficiente de canal, ρé a potência do coeficiente de correlação [14] entre o coeficiente verdadeiro e o estimado, istoé, h 2 k,j e h 2 k,j , e ξé uma variável aleatória Gaussiana complexa independente de média zero com variância 1 2 por dimensão.…”

Section: A Degradação De Desempenho Com Estimativas Imperfeitas Do Canalunclassified

Decodificação de Sistemas QO-STBC Baseada em Máxima Verossimilhança e Agrupamento Reduzido

Jacob¹,

Suzuki²,

Ciriaco³

et al. 2009

Anais Do XXVII Simpósio Brasileiro De Telecomunicações

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Resumo-Este trabalho analisa uma extensão do esquema de decodificação de máxima verossimilhança (ML -Maximum Likelihood) para códigos espaço-temporais (STBC -Space-Time Block Codes) quase-ortogonais (QO -Quasi-Orthogonal) proposto em [1], objetivando a otimização do compromisso capacidade × desempenho × complexidade em sistemas com múltiplas antenas de entrada e saída (MIMO -Multiple Input/Multiple Output). O desempenho do sistema foi quantificado para cenários comíndice de modulação de alta ordem 16− e 64−QAM, 4 × 4 antenas e codificação por checagem de paridade de baixa densidade (LDPC -Low Density Parity Check), decodificador de ML de reduzida complexidade baseado em busca por grupos (RCS -Reduced Cluster Search), agregando ao sistema entrelaçadores aleatórios e LDPCs com distintos comprimentos de blocos. Sob tais condições, resultados numéricos indicaram melhoria substancial de desempenho, mantendo-se ainda ganhos crescentes na razão de complexidade (RCS-ML por ML convencional) quando a ordem de modulação aumenta.

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Iterative near–far resistant channel estimation by using a linear minimum mean squared error detector

Rızaner

Amca

Hacıoğlu

et al. 2007

Wireless Pers Commun

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Channel estimation techniques for CDMA system need to combat multiple access interference (MAI) to improve the estimation performance. The linear MMSE detector has certain advantages with respect to the near-far problem and can be used to develop a channel estimation algorithm. In this paper, an efficient iterative method for near-far resistant single-user mobile radio channel estimation in slow fading multipath direct sequence code division multiple access (DS-CDMA) channels is presented. Computer simulation results demonstrate that a significant performance improvement can be achieved with the proposed method especially under extreme near-far conditions.

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Multiuser channel estimation for detection of cochannel signals

Cited by 11 publications

References 19 publications

Reducing codebook loss with reconfigurable receive antennas

Reducing codebook loss with reconfigurable receive antennas

Decodificação de Sistemas QO-STBC Baseada em Máxima Verossimilhança e Agrupamento Reduzido

Iterative near–far resistant channel estimation by using a linear minimum mean squared error detector

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