Iterative estimation and decoding for FH-SS with slow Rayleigh fading

Kang, Jieun; Stark, W.E.

doi:10.1109/26.891212

Cited by 14 publications

(27 citation statements)

References 13 publications

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“…The channel interleaver is a simple matrix interleaver whose size is made equal to the coded turbo frame size as in Reference [4]. For comparisons with the MAP-based method of Reference [2], the number of regions M in the Lloyd-Max quantiser is set to 8, as in Reference [2]. We denote the information bit energy by E b where E b = 2E c since a rate-half coding is used.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…We denote the information bit energy by E b where E b = 2E c since a rate-half coding is used. In Figure 1, the required E b /N I to achieve a bit-error-rate (BER) of 2 × 10 −4 is plotted against ρ for various SINR estimation techniques; namely our proposed MMSE estimation technique, the EM estimation technique in Reference [4], and the MAP estimation technique of A in Reference [2] with B known perfectly (genie-aided) in each hop. For reference, we also include the performance in the case of perfect knowledge of both A and B.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…However, the integral complexity will grow exponentially with the hop size. Hence, we follow the method in Reference [2] and use Lloyd-Max quantiser [8] to quantise the fading amplitudes into M regions, R 1 , R 2 , . .…”

Section: Iterative Mmse Estimation Of Sinrmentioning

confidence: 99%

“…We again use an iterative technique in which the turbo decoder extrinsic information is fed back to the SINR estimator. We compare the performance of the proposed MMSE-based method with [2] in which we provide perfect knowledge of the noise variance that varies from hop to hop. This is because a MAP-based approach that estimates both the channel and interference variance is not available.…”

Section: Introductionmentioning

confidence: 99%

“…In the absence of training symbols, a few estimation techniques [2--4] have been proposed for obtaining the LLR that differ in their performance, complexity, underlying assumptions, and the amount of side information available at the receiver. In Reference [2], a maximum a posteriori (MAP)-based approach is used that depends on quantizing the probability density function of the fading magnitude. This method gives performance close to that of perfect SNR knowledge for interference-free channels.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Iterative MMSE estimation of SINR for turbo‐coded FH‐SS systems in partial‐band interference and fading channels

Gomaa

Elezabi

2010

Trans Emerging Tel Tech

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The minimum mean square error (MMSE) estimate of the signal-to-interference-and-noise ratio (SINR) is derived for turbo-coded slow frequency hopping systems in the presence of partial-band interference and Rayleigh fading. The MMSE estimator operates without training symbols and iteratively uses the turbo decoder extrinsic information to improve the accuracy of the SINR estimate. Performance comparisons are made with a genie-aided MAP-based estimator and nearoptimal performance is obtained for all but impractically small hop sizes.

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Section: Performance Comparisonsmentioning

confidence: 99%

Section: Performance Comparisonsmentioning

confidence: 99%

Section: Iterative Mmse Estimation Of Sinrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Iterative MMSE estimation of SINR for turbo‐coded FH‐SS systems in partial‐band interference and fading channels

Gomaa

Elezabi

2010

Trans Emerging Tel Tech

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Iterative Equalization and Decoding for SFH Spread-Spectrum Communications using Reed–Solomon Codes

Ramchandran

Noneaker

2007

Int J Wireless Inf Networks

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A low-complexity, packet-level iterative detection technique is considered for slow-frequency-hop (SFH) spread-spectrum communications in intersymbolinterference (ISI) channels. Maximum-likelihood sequence estimation (MLSE) equalization with state pinning and bounded-distance errors-and-erasures decoding of ReedSolomon code words are employed within each iteration of equalization and decoding. The design of a bit interleaver is examined for use with the iterative detection technique. The effect of state pinning on the equalizer performance is characterized, and the probability of packet error and the detection complexity of the SFH system are evaluated for a range of static and fading ISI channels. The use of an earlytermination criterion is also considered as a way to achieve a tradeoff between performance and detection complexity with the iterative detection technique.keywords Frequency-hop spread spectrum Á Packet-radio communications Á Reed-Solomon codes Á Iterative decoding Á Intersymbol-interference channels Á Equalization

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Diversity Combining and SNR Estimation for Turbo-Coded Frequency-Hopped Spread-Spectrum in Partial-Band Interference and Fading Channels

Elezabi

Gomaa

2008

Wireless Pers Commun

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We compare different combinations of the repetition diversity order L and code rate R for turbo-coded Frequency-Hopped Spread-Spectrum (FH/SS) communication systems in the presence of fading and partial-band Gaussian interference. For a fixed overall channel code rate R/L we show that using the lowest code rate and no repetition diversity always performs better than using a higher code rate and some repetition for both coherent and non-coherent schemes. We then propose a simple maximum-likelihood-based method for signal-to-noise-ratio (SNR) estimation in Non-Coherent Binary Frequency Shift Keying (NCBFSK) without training symbols. Except for impractically small hop sizes of 8 bits or less we obtain performance virtually equal to that of perfect SNR knowledge but with much less complexity than iterative schemes previously proposed. For the case of Coherent Binary Phase Shift Keying (CBPSK) we derive the Expectation Maximization (EM) estimate of the SNR without training symbols and iteratively feed the estimator with the extrinsic information from the turbo decoder. The performance for CBPSK is near that of perfect SNR knowledge for hop sizes of 64 bits or more. Unlike previously proposed methods for CBPSK the EM estimate of SNR does not require knowledge of the noise and interference variance, received bit energy, or the fading channel model.

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Iterative estimation and decoding for FH-SS with slow Rayleigh fading

Cited by 14 publications

References 13 publications

Iterative MMSE estimation of SINR for turbo‐coded FH‐SS systems in partial‐band interference and fading channels

Iterative MMSE estimation of SINR for turbo‐coded FH‐SS systems in partial‐band interference and fading channels

Iterative Equalization and Decoding for SFH Spread-Spectrum Communications using Reed–Solomon Codes

Diversity Combining and SNR Estimation for Turbo-Coded Frequency-Hopped Spread-Spectrum in Partial-Band Interference and Fading Channels

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