Modeling of the Land Mobile Satellite Channel considering the Terminal’s Driving Direction

A precise characterization of the Land Mobile Satellite (LMS) channel, that is, the channel between a satellite and a mobile terminal, is of crucial importance while designing a satellite-based communication system. State-of-the-art statistical LMS channel models offer the advantage of requiring only a few input parameters, which include the environment type and the elevation angle of the satellite. However, the azimuth angle relative to the driving direction of the mobile terminal is usually ignored, as its proper modeling requires either an extensive measurement campaign or a significant effort from the user, as a precise geometrical description of the scenario is required. In this contribution we show that the impact of the driving direction on the channel statistics is not negligible and requires to be modeled explicitly. Moreover, we propose a statistical LMS channel model whose parameters are obtained via an image-based state estimation method. The image-based method is verified by a comparison with measured radio frequency signal levels. The proposed method allows obtaining a complete statistical description of the channel for arbitrary elevation and azimuth angles

Stochastic Analysis of the LMS Algorithm for System Identification with Subspace Inputs

This paper studies the behavior of the low rank LMS adaptive algorithm for the general case in which the input transformation may not capture the exact input subspace. It is shown that the Independence Theory and the independent additive noise model are not applicable to this case. A new theoretical model for the weight mean and fluctuation behaviors is developed which incorporates the correlation between successive data vectors (as opposed to the Independence Theory model). The new theory is applied to a network echo cancellation scheme which uses partial-Haar input vector transformations. Comparison of the new model predictions with Monte Carlo simulations shows good-to-excellent agreement, certainly much better than predicted by the Independence Theory based model available in the literature

Prediction of performance of the DVB-SH system relying on mutual information

DVB-SH (Digital Video Broadcasting-Satellite Handled) is a broadcasting standard dedicated to hybrid broadcasting systems combining a satellite and a terrestrial part. On the satellite part, dedicated interleaving and time slicing mechanisms are proposed to mitigate the effects of Land Mobile Satellite (LMS) channel, based on a convolutional interleaver. Depending on the parameters of this interleaver, this mechanism enables to split in time a codeword on duration from 100 ms to about 30s. This mechanism signi?cantly improves the error recovery performance of the code but in literature, exact evaluation at system level of this improvement is missing. The objective of this paper is to propose a prediction method compatible with fast simulations, to quantitatively evaluate the system performance in terms of Packet Error Rate (PER). The main dif?culty is to evaluate the decoding probability of a codeword submitted to several levels of attenuation. The method we propose consists in using as metric the Mutual Information (MI) between coded bit at the emitter side and the received symbol. It is shown that, by averaging the MI over the codeword and by using the decoding performance function g such that PER=g(MI)determined on the Gaussian channel, we can signi?cantly improve the precision of the prediction compared to the two other methods based on SNR and Bit Error Rate (BER). We evaluated these methods on three arti?cial channels where each codeword is transmitted with three or four different levels of attenuations. The prediction error of the SNR-based (resp. the input BER-based) method varies from 0.5 to 1.7 dB (resp. from 0.7 to 1.2 dB) instead of the MI-based method achieves a precision in the order of 0.1 dB in the three cases. We then evaluate this method on real LMS channels with various DVB-SH interleavers and show that the instantaneous PER can also be predicted with high accuracy

A Unifying Approach to Quaternion Adaptive Filtering: Addressing the Gradient and Convergence

A novel framework for a unifying treatment of quaternion valued adaptive filtering algorithms is introduced. This is achieved based on a rigorous account of quaternion differentiability, the proposed I-gradient, and the use of augmented quaternion statistics to account for real world data with noncircular probability distributions. We first provide an elegant solution for the calculation of the gradient of real functions of quaternion variables (typical cost function), an issue that has so far prevented systematic development of quaternion adaptive filters. This makes it possible to unify the class of existing and proposed quaternion least mean square (QLMS) algorithms, and to illuminate their structural similarity. Next, in order to cater for both circular and noncircular data, the class of widely linear QLMS (WL-QLMS) algorithms is introduced and the subsequent convergence analysis unifies the treatment of strictly linear and widely linear filters, for both proper and improper sources. It is also shown that the proposed class of HR gradients allows us to resolve the uncertainty owing to the noncommutativity of quaternion products, while the involution gradient (I-gradient) provides generic extensions of the corresponding real- and complex-valued adaptive algorithms, at a reduced computational cost. Simulations in both the strictly linear and widely linear setting support the approach

Learning neural trans-dimensional random field language models with noise-contrastive estimation

Trans-dimensional random field language models (TRF LMs) where sentences are modeled as a collection of random fields, have shown close performance with LSTM LMs in speech recognition and are computationally more efficient in inference. However, the training efficiency of neural TRF LMs is not satisfactory, which limits the scalability of TRF LMs on large training corpus. In this paper, several techniques on both model formulation and parameter estimation are proposed to improve the training efficiency and the performance of neural TRF LMs. First, TRFs are reformulated in the form of exponential tilting of a reference distribution. Second, noise-contrastive estimation (NCE) is introduced to jointly estimate the model parameters and normalization constants. Third, we extend the neural TRF LMs by marrying the deep convolutional neural network (CNN) and the bidirectional LSTM into the potential function to extract the deep hierarchical features and bidirectionally sequential features. Utilizing all the above techniques enables the successful and efficient training of neural TRF LMs on a 40x larger training set with only 1/3 training time and further reduces the WER with relative reduction of 4.7% on top of a strong LSTM LM baseline.Comment: 5 pages and 2 figure

Adaptive Transmission Techniques for Mobile Satellite Links

Adapting the transmission rate in an LMS channel is a challenging task because of the relatively fast time variations, of the long delays involved, and of the difficulty in mapping the parameters of a time-varying channel into communication performance. In this paper, we propose two strategies for dealing with these impairments, namely, multi-layer coding (MLC) in the forward link, and open-loop adaptation in the return link. Both strategies rely on physical-layer abstraction tools for predicting the link performance. We will show that, in both cases, it is possible to increase the average spectral efficiency while at the same time keeping the outage probability under a given threshold. To do so, the forward link strategy will rely on introducing some latency in the data stream by using retransmissions. The return link, on the other hand, will rely on a statistical characterization of a physical-layer abstraction measure.Comment: Presented at the 30th AIAA International Communications Satellite Systems Conference (ICSSC), Ottawa, Canada, 2012. Best Professional Paper Awar

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling