Blind Carrier Phase Recovery for General 2{\pi}/M-rotationally Symmetric Constellations

This paper introduces a novel blind carrier phase recovery estimator for general 2{\Pi}/M-rotationally symmetric constellations. This estimation method is a generalization of the non-data-aided (NDA) nonlinear Phase Metric Method (PMM) estimator already designed for general quadrature amplitude constellations. This unbiased estimator is seen here as a fourth order PMM then generalized to Mth order (Mth PMM) in such manner that it covers general 2{\Pi}/M-rotationally symmetric constellations such as PAM, QAM, PSK. Simulation results demonstrate the good performance of this Mth PMM estimation algorithm against competitive blind phase estimators already published for various modulation systems of practical interest.Comment: 14 pages, 12 figures, International Journal of Wireless & Mobile Networks (IJWMN

Quantile forecast discrimination ability and value

While probabilistic forecast verification for categorical forecasts is well established, some of the existing concepts and methods have not found their equivalent for the case of continuous variables. New tools dedicated to the assessment of forecast discrimination ability and forecast value are introduced here, based on quantile forecasts being the base product for the continuous case (hence in a nonparametric framework). The relative user characteristic (RUC) curve and the quantile value plot allow analysing the performance of a forecast for a specific user in a decision-making framework. The RUC curve is designed as a user-based discrimination tool and the quantile value plot translates forecast discrimination ability in terms of economic value. The relationship between the overall value of a quantile forecast and the respective quantile skill score is also discussed. The application of these new verification approaches and tools is illustrated based on synthetic datasets, as well as for the case of global radiation forecasts from the high resolution ensemble COSMO-DE-EPS of the German Weather Service

Analysis of Joint Source Channel LDPC Coding for Correlated Sources Transmission over Noisy Channels

In this paper, a Joint Source Channel coding scheme based on LDPC codes is investigated. We consider two concatenated LDPC codes, one allows to compress a correlated source and the second to protect it against channel degradations. The original information can be reconstructed at the receiver by a joint decoder, where the source decoder and the channel decoder run in parallel by transferring extrinsic information. We investigate the performance of the JSC LDPC code in terms of Bit-Error Rate (BER) in the case of transmission over an Additive White Gaussian Noise (AWGN) channel, and for different source and channel rate parameters. We emphasize how JSC LDPC presents a performance tradeoff depending on the channel state and on the source correlation. We show that, the JSC LDPC is an efficient solution for a relatively low Signal-to-Noise Ratio (SNR) channel, especially with highly correlated sources. Finally, a source-channel rate optimization has to be applied to guarantee the best JSC LDPC system performance for a given channel

Statistical post-processing of heat index ensemble forecasts: is there a royal road?

We investigate the effect of statistical post-processing on the probabilistic skill of discomfort index (DI) and indoor wet-bulb globe temperature (WBGTid) ensemble forecasts, both calculated from the corresponding forecasts of temperature and dew point temperature. Two different methodological approaches to calibration are compared. In the first case, we start with joint post-processing of the temperature and dew point forecasts and then create calibrated samples of DI and WBGTid using samples from the obtained bivariate predictive distributions. This approach is compared with direct post-processing of the heat index ensemble forecasts. For this purpose, a novel ensemble model output statistics model based on a generalized extreme value distribution is proposed. The predictive performance of both methods is tested on the operational temperature and dew point ensemble forecasts of the European Centre for Medium-Range Weather Forecasts and the corresponding forecasts of DI and WBGTid. For short lead times (up to day 6), both approaches significantly improve the forecast skill. Among the competing post-processing methods, direct calibration of heat indices exhibits the best predictive performance, very closely followed by the more general approach based on joint calibration of temperature and dew point temperature. Additionally, a machine learning approach is tested and shows comparable performance for the case when one is interested only in forecasting heat index warning level categories.Comment: 29 pages, 12 figure

Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach, called d-ECC, is applied to wind forecasts from the high resolution ensemble system COSMO-DE-EPS run operationally at the German weather service. Scenarios generated by ECC and d-ECC are compared and assessed in the form of time series by means of multivariate verification tools and in a product oriented framework. Verification results over a 3 month period show that the innovative method d-ECC outperforms or performs as well as ECC in all investigated aspects

Stratified rank histograms for ensemble forecast verification under serial dependence

Rank histograms are a popular way to assess the reliability of ensemble forecasting systems. If the ensemble forecasting system is reliable, the rank histogram should be flat, ``up to statistical fluctuations''. There are two long noted challenges to this approach. Firstly, uniformity of the overall distribution is implied by but does not imply reliability; ideally the distribution of the ranks should be uniform even conditionally on different forecast scenarios. Secondly, the ranks are serially dependent in general, precluding the use of standard goodness--of--fit tests to assess the uniformity of rank distributions without any further precautions. The present paper deals with both these issues by drawing together the concept of stratified rank histograms, which have been developed to deal with the first issue, with ideas that exploit the reliability condition to manage the serial correlations, thus dealing with the second issue. As a result, tests for uniformity of stratified rank histograms are presented that are valid under serial correlations

Efficient Hardware Design for Computing Pairings Using Few FPGA In-built DSPs

This paper is devoted to the design of a 258-bit multiplier for computing pairings over Barreto-Naehrig (BN) curves at 128-bit security level. The proposed design is optimized for Xilinx field programmable gate array (FPGA). Each 258-bit integer is represented as a polynomial with five, 65 bit signed integer, coefficients. Exploiting this splitting we designed a pipelined 65-bit multiplier based on new Karatsuba- Ofman variant using non-standard splitting to fit to the Xilinx embedded digital signal processor (DSP) blocks. We prototype the coprocessor in two architectures pipelined and serial on a Xilinx Virtex-6 FPGA using around 17000 slices and 11 DSPs in the pipelined design and 7 DSPs in the serial. The pipelined 128-bit pairing is computed in 1. 8 ms running at 225MHz and the serial is performed in 2.2 ms running at 185MHz. To the best of our knowledge, this implementation outperforms all reported hardware designs in term of DSP use. Keywords

Assessing spatial precipitation uncertainties in a convective-scale ensemble

New techniques have recently been developed to quantify the location-dependent spatial agreement between ensemble members, and the spatial spread-skill relationship. In this paper a summer of convection permitting ensemble forecasts are analysed to better understand the factors influencing location-dependent spatial agreement of precipitation fields and the spatial spread-skill relationship over the UK. The aim is to further investigate the agreement scale method, and to highlight the information that could be extracted for a more long-term routine model evaluation. Overall, for summer 2013, the UK 2.2km-resolution ensemble system was found to be reasonably well spread spatially, although there was a tendency for the ensemble to be over confident in the location of precipitation. For the forecast lead times considered (up to 36 hrs) a diurnal cycle was seen in the spatial agreement and in the spatial spread-skill relationship: the forecast spread and error did not increase noticeably with forecast lead time. Both the spatial agreement, and the spatial spread-skill, were dependent on the fractional coverage and average intensity of precipitation. A poor spread-skill relationship was associated with a low fractional coverage of rain and low average rain rates. The times with a smaller fractional coverage, or lower intensity, of precipitation were found to have lower spatial agreement. The spatial agreement was found to be location dependant, with higher confidence in the location of precipitation to the northwest of the UK