Recent Advances in Design and Implementation of Satellite Gateways for Massive Uncoordinated Access Networks

This paper provides an overview of recent results of a design, development and performance evaluation study of satellite gateways to receive and manage the traffic from a large population of uncoordinated user terminals. In particular, direct satellite access scenarios for machine-to-machine communications and the Internet of Things have been targeted. Tests were carried out in a representative laboratory environment emulating realistic system scenarios. Performance results, as presented in this paper indicate that the proposed gateway architecture, based on an efficient access protocol, is capable of managing a very high number of uncoordinated terminals transmitting short messages with a low duty cycle. The applicability of the proposed solution to both geostationary and non-geostationary satellite systems has also been examined. The key concept of the gateway is based on a novel receiver architecture that implements the linear minimum mean square error (MMSE) spread spectrum signal detection and successive interference cancellation techniques. The receiver uses features such as a multi-stage detector together with a robust preamble detection. The end-to-end solution includes also the use of a new waveform with a quasi-constant envelope at the terminal to modulate and transmit data packets to be received and detected by the gateway via a satellite link.

Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter

This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements’ distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA’s loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (C/N0)-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver’s tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity.

FAST ARRAY ALGORITHMS FOR FILTERING OF MARKOVIAN JUMP LINEAR SYSTEMS WITH STRUCTURED TIME-VARIANT PARAMETERS

In this paper were developed fast array algorithms for the linear minimum mean square error estimator for a class of Markovian jump linear systems with structured time-variant parameters. The fast array algorithms for systems with structured time-variant parameters arises as an alternative to calculate this type algorithm for some variation in the time of the parameters. Numerical example to show the advantage of using fast array algorithm to filter this class of systems are provided.

A New Speech Enhancement Technique Based on Stationary Bionic Wavelet Transform and MMSE Estimate of Spectral Amplitude

Speech enhancement has gained considerable attention in the employment of speech transmission via the communication channel, speaker identification, speech-based biometric systems, video conference, hearing aids, mobile phones, voice conversion, microphones, and so on. The background noise processing is needed for designing a successful speech enhancement system. In this work, a new speech enhancement technique based on Stationary Bionic Wavelet Transform (SBWT) and Minimum Mean Square Error (MMSE) Estimate of Spectral Amplitude is proposed. This technique consists at the first step in applying the SBWT to the noisy speech signal, in order to obtain eight noisy wavelet coefficients. The denoising of each of those coefficients is performed through the application of the denoising method based on MMSE Estimate of Spectral Amplitude. The SBWT inverse, S B W T − 1 , is applied to the obtained denoised stationary wavelet coefficients for finally obtaining the enhanced speech signal. The proposed technique’s performance is proved by the calculation of the Signal to Noise Ratio (SNR), the Segmental SNR (SSNR), and the Perceptual Evaluation of Speech Quality (PESQ).

A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

This study suggests a new optimal family of exponential-type estimators for estimating population mean in stratified random sampling. These estimators are based on the traditional and nontraditional measures of auxiliary information. Expressions for the bias, mean square error, and minimum mean square error of the proposed estimators are derived up to first order of approximation. It is observed that proposed estimators perform better than the traditional estimators (unbiased, combined ratio, and combined regression) and other recent estimators. A real dataset is used to highlight the applicability of proposed estimators. In addition, a simulation study is carried out to assess the performance of new family as compared to other estimators.

Iterative Interference Cancellation for Multi-Carrier Modulation in MIMO-DWT Downlink Transmission

The Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) scheme represents the dominant radio interface for broadband multicarrier communication systems. However, with insufficient Cyclic Prefixes (CP), Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI) occur due to the time-varying multipath channel. This means that the performance of the system will be degraded. In this paper, we investigate the interference problem for a MIMO Discrete Wavelet Transform (MIMO-DWT) system under the effect of the downlink LTE channel. A Low-Density Parity-Check (LDPC) decoder is used to estimate the decoded signal. The proposed iterative algorithm uses the estimated decoded signal to compute the components required for ICI/ISI interference reduction. In this paper, Iterative Interference Cancellation (IIC) is employed to mitigate the effects of interference that contaminates the received signal due to multiple antenna transmission and a multipath channel. An equalizer with minimum mean square error is considered. We compare the performance of our proposed algorithm with the traditional MIMO-OFDM scheme in terms of bit error probability under insufficient CP. Simulation results verify that significant improvements are achieved by using IIC and MIMO-IIC for both systems.

On Smoothed MWSD Estimation of Mixing Proportion

The problem considered in the present paper is estimation of mixing proportions of mixtures of two (known) distributions by using the minimum weighted square distance (MWSD) method. The two classes of smoothed and unsmoothed parametric estimators of mixing proportion proposed in a sense of MWSD due to Wolfowitz(1953) in a mixture model F(x)=p (x)+(1-p) (x) based on three independent and identically distributed random samples of sizes n and , =1,2 from the mixture and two component populations. Comparisons are made based on their derived mean square errors (MSE). The superiority of smoothed estimator over unsmoothed one is established theoretically and also conducting Monte-Carlo study in sense of minimum mean square error criterion. Large sample properties such as rates of a.s. convergence and asymptotic normality of these estimators are also established. The results thus established here are completely new in the literature.

Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.

Results of Large-Scale Propagation Models in Campus Corridor at 3.7 and 28 GHz

The indoor application of wave propagation in the 5G network is essential to fulfill the increasing demands of network access in an indoor environment. This study investigated the wave propagation properties of line-of-sight (LOS) links at two long corridors of Chosun University (CU). We chose wave propagation measurements at 3.7 and 28 GHz, since 3.7 GHz is the closest to the roll-out frequency band of 3.5 GHz in South Korea and 28 GHz is next allocated frequency band for Korean telcos. In addition, 28 GHz is the promising millimeter band adopted by the Federal Communications Commission (FCC) for the 5G network. Thus, the 5G network can use 3.7 and 28 GHz frequencies to achieve the spectrum required for its roll-out frequency band. The results observed were applied to simulate the path loss of the LOS links at extended indoor corridor environments. The minimum mean square error (MMSE) approach was used to evaluate the distance and frequency-dependent optimized coefficients of the close-in (CI) model with a frequency-weighted path loss exponent (CIF), floating-intercept (FI), and alpha–beta–gamma (ABG) models. The outcome shows that the large-scale FI and CI models fitted the measured results at 3.7 and 28 GHz.

An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box

Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.