Periodic Sequences with Optimal Properties for Channel Estimation and Fast Start-Up Equalization

Milewski, A.

doi:10.1147/rd.275.0426

Cited by 217 publications

(98 citation statements)

References 8 publications

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“…The testbench is based on blocks of 3 PN sequences [30] called training blocks. Two types of PN sequences were tested: m-sequences [28,29] and CAZAC [31]. For each type of sequence, four chip lengths were used: 255, 511, 1023 and 2047.…”

Section: Synchronization Analysismentioning

confidence: 99%

Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link

et al. 2016

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La Salle and the Observatori de l'Ebre (OE) have been involved in a remote sensing project in Antarctica for the last 11 years. The OE has been monitoring the geomagnetic activity for more than twenty years and also the ionospheric activity of the last ten years in the Spanish Antarctic Station Juan Carlos I (ASJI) (62.7 • S, 299.6 • E). La Salle is finishing the design and testing of a low-power communication system between the ASJI and Cambrils (41.0 • N, 1.0 • E) with a double goal: (i) the transmission of data from the sensors located at the ASJI and (ii) the performance of an oblique ionospheric sounding of a 12,760 km HF link. Previously, La Salle has already performed sounding and modulation tests to describe the channel performance in terms of availability, Signal-to-Noise Ratio (SNR), Doppler spread and delay spread. This paper closes the design of the physical layer, by means of the channel error study and the synchronization performance, and concludes with a new physical layer proposal for the Oblique Ionosphere Sounder. Narrowband and wideband frames have been defined to be used when the oblique sounder performs as an ionospheric sensor. Finally, two transmission modes have been defined for the modem performance: the High Robustness Mode (HRM) for low SNR hours and the High Throughput Mode (HTM) for the high SNR hours.

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Section: Synchronization Analysismentioning

confidence: 99%

Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link

et al. 2016

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“…An appealing feature has been shown in [17] that a sequence has CAZAC if and only if its discrete Fourier-transform (DFT) has CAZAC and vice versa. Therefore, the uniformly spaced pilot tones generated from Chu sequence according to (5) also have constant amplitude.…”

Section: Training Sequence Constructionmentioning

confidence: 99%

Frequency Offset Estimation for OFDM Systems with a Novel Frequency Domain Training Sequence

Jiang¹

2006

IEICE Transactions on Communications

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SUMMARY A novel frequency domain training sequence and the corresponding carrier frequency offset (CFO) estimator are proposed for orthogonal frequency division multiplexing (OFDM) systems over frequency-selective fading channels. The proposed frequency domain training sequence comprises two types of pilot tones, namely distinctively spaced pilot tones with high energies and uniformly spaced ones with low energies. Based on the distinctively spaced pilot tones, integer CFO estimation is accomplished. After the subcarriers occupied by the distinctively spaced pilot tones and their adjacent subcarriers are nulled for the sake of interference cancellation, fractional CFO estimation is executed according to the uniformly spaced pilot tones. By exploiting a predefined lookup table making the best of the structure of the distinctively spaced pilot tones, computational complexity of the proposed CFO estimator can be decreased considerably. With the aid of the uniformly spaced pilot tones generated from Chu sequence with cyclically orthogonal property, the ability of the proposed estimator to combat multipath effect is enhanced to a great extent. Simulation results illustrate the good performance of the proposed CFO estimator.

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“…Perfect periodic sequences (PPSs) [9], [10] have been proposed for linear [11], [12] and nonlinear [8], [13]- [17] system identification as an alternative to stochastic inputs. A PPS ensures that the cross-correlation between any two different basis functions of the system, estimated over a period, is zero.…”

Section: Introductionmentioning

confidence: 99%

Multivariance nonlinear system identification using Wiener basis functions and perfect sequences

Orcioni

Cecchi

Carini

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Multivariance identification methods exploit input signals with multiple variances for estimating the Volterra kernels of nonlinear systems. They overcome the problem of the locality of the solution, i.e., the fact that the estimated model well approximates the system only at the same input signal variance of the measurement. The estimation of a kernel for a certain input signal variance requires recomputing all lower order kernels. In this paper, a novel multivariance identification method based on Wiener basis functions is proposed to avoid recomputing the lower order kernels with computational saving. Formulas are provided for evaluating the Volterra kernels from the Wiener multivariance kernels. In order to further improve the nonlinear filter estimation, perfect periodic sequences that guarantee the orthogonality of the Wiener basis functions are used for Wiener kernel identification. Simulations and real measurements show that the proposed approach can accurately model nonlinear devices on a wide range of input signal variances.

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Periodic Sequences with Optimal Properties for Channel Estimation and Fast Start-Up Equalization

Cited by 217 publications

References 8 publications

Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link

Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link

Frequency Offset Estimation for OFDM Systems with a Novel Frequency Domain Training Sequence

Multivariance nonlinear system identification using Wiener basis functions and perfect sequences

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