A simplification of the Log-map algorithm for use in turbo decoders and turbo equalizers is presented. The simplified algorithm is based on the MacLaurin Series expansion of the logarithmic term in the Jacobian logari...
详细信息
ISBN:
(纸本)0780391810
A simplification of the Log-map algorithm for use in turbo decoders and turbo equalizers is presented. The simplified algorithm is based on the MacLaurin Series expansion of the logarithmic term in the Jacobian logarithmic function used in the Log-map algorithm. In terms of complexity the proposed algorithm can easily be implemented using adders and comparators as this is the case for the Max-Log-map algorithm. Also, simulation results show that the proposed algorithm has performance very close to the Log-map algorithm for both turbo decoding and turbo equalization, even in a high loss intersymbol interference (ISI) channel where there is a wide gap between the performance of the Log-map and Max Log-map turbo equalizers.
We describe an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify these landmarks and measure their bearings relative to each other. Given such noisy...
详细信息
We describe an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify these landmarks and measure their bearings relative to each other. Given such noisy input, the algorithm estimates the robot's position and orientation with respect to the map of the environment. The algorithm makes efficient use of our representation of the landmarks by complex numbers. The algorithm runs in time linear in the number of landmarks. We present results of simulations and propose how to use our method for robot navigation.
This paper presents a framework of maximum-a-posteriori (map) algorithms for the automatic modulation classification (AMC) in orthogonal frequency division multiplexing (OFDM) based communication systems with adaptive...
详细信息
ISBN:
(纸本)9781424483273
This paper presents a framework of maximum-a-posteriori (map) algorithms for the automatic modulation classification (AMC) in orthogonal frequency division multiplexing (OFDM) based communication systems with adaptive modulation (AM). The proposed classifiers intensively utilize side information typically available in wireless time-division duplex (TDD) systems that is channel reciprocity, the known frame structure and the knowledge about the total transmission data rate. As the computational complexity of the optimal algorithms is rather high, a metric approximation is used whose accuracy increases with rising signal-to-noise power ratio (SNR). Numerical results show that the system performance degradation in terms of the packet-error ratio (PER) due to erroneously detected modulation schemes is small in typical wireless communication scenarios if the proposed AMC algorithm is applied.
Speech is decomposed into three different components. based on the idea of Daudet and Torresani (Signal Processing, vol. 82, no. 11, pp. 1595, 2002), as signal = tonal + transient + residual. The tonal and transient c...
详细信息
ISBN:
(纸本)0780391543
Speech is decomposed into three different components. based on the idea of Daudet and Torresani (Signal Processing, vol. 82, no. 11, pp. 1595, 2002), as signal = tonal + transient + residual. The tonal and transient components identified using a small number of coefficients of the modified discrete cosine transform (.MDCT.) and the wavelet transform, respectively. Determinations of the significant MDCT and wavelet coefficients in the algorithm of Daudet and Torresani, referred as the D&T algorithm, ire achieved by thresholds. All MDCT coefficients are assumed to be independent as well as wavelet coefficients. However, the MDCT coefficients probably have statistical dependencies, namely the clustering, and persistence properties, and so do the wavelet coefficients. We propose a modification to the D&T algorithm, that can capture statistical dependencies by utilizing the hidden Markov model. The Viterbi and the Maximum a Posteriori (map) algorithms, used to find file optimal state distribution, are applied to determine the significant MDCT and wavelet coefficients automatically. The modified algorithm was used to encode 43 monosyllabic Consonant-Vowel-Consonant (CVC) words and 3 sentences. Results showed that the modified algorithm improves the coding efficiency by 37% compared with the threshold method of D&T algorithm when equal numbers of significant coefficients;are used.
暂无评论