Leonardo Betancur

WiMAX channel: PHY model in network simulator 2

The IEEE 802.16 standard, also known as WiMAX is oriented to fixed broadband wireless metropolitan access. This work presents a novel proposal for the PHY layer and propagation model that allows a faster and more detailed the link level simulations execution. This paper describes the development process for the model and the implementation details in NS2 (Network Simulator NS2 v 2.8) for PMP (point multi point) topologies. We show the parameters used, the channel model, the PHY layer model and the SER (Symbol Error Rate) calculation method. We also present typical simulation scenarios and the results obtained.

A statistical channel model for on body Area networks in Ultra Wide Band Communications

This paper presents a novel statistical channel model for Ultra Wide Band Communications when On Body Area Networks Environments are considered. Results and Analysis of frequency measurements from 1GHz to 12GHz for body area networks are described. We propose a modified Saleh-Valenzuela statistical channel model considering different assumptions for small scale statistics in the model. Results show that the small scale amplitude follows a log normal distribution, the inter arrival times between multi path components are poisson process; the decay factor, the number of paths and clusters follow exponential distributions. The proposed model can be used for realistic simulations for on body communications in Ultra Wide Band systems.

A novel SNR estimation algorithm for MB OFDM ultra wide band communications

In this paper we propose a novel signal to noise ratio algorithm based on the moments of the received signal. The novel algorithm has been designed for Multi Band Orthogonal Frequency Division Multiplexing (MB-OFDM) communication systems in ultra wide band channels. The proposed algorithm is a switched estimator that uses two equation models, one is for large SNR scale and the other for low SNR scale. The proposed model can be used for SNR estimation in adaptive modulation and channel estimation and link quality control in MB OFDM systems. This algorithm can be implemented in receivers with simple structures. The performance of the proposed SNR estimation algorithm and its comparison with other SNR estimators was achieved through simulations.

Performance of spatially multiplexed systems based on numerical simulations of OFDM-MIMO in a two-core fiber with low coupling, and a few mode fiber with high DGD

In this paper, we show numerical simulations for analyzing spatially multiplexed systems based on orthogonal frequency division multiplexing and multiple-input multiple-output Alamouti code in order to compare the performance of the transmission in a two-core fiber with low coupling, and in few-mode fiber with high differential group delay. These new fibers are the most recent alternatives used for improving the throughput of long-haul and high-speed optical fiber networks. We compare the bit-error-rate of two orthogonal frequency division multiplexing signals, after 100 km of optical fiber, considering only its linear effects. The first one uses an 8 phase-shift keying signal operating at 130 Gbps, and the second one uses a 16 quadrature amplitude modulated signal operating at 170 Gbps. Our results show a better bit error rate performance using a few-mode fiber, compared with a two-core fiber. Additionally, Alamouti code shows a better effectiveness for compensation of linear impairments in a two-core fiber in comparison with a few-mode fiber.

Compressive multispectral model for spectrum sensing in cognitive radio networks

Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.

From theory to practice: Implementations issues for techniques based on AoA and TDoA

This paper presents a bridge between theory and hardware implementations of radio direction finding (RDF) techniques particularly Angle of Arrival (AoA) and radiolocalization technique namely Time Difference of Arrival (TDoA) in non-collaborative scenarios. It presents aspects that must be considered in the system design and performance tests, including antenna array features in AoA. Minimum required distance between target node (TN) and reference nodes (RN), sampling frequency, etc., in TDoA. Furthermore, the paper presents a method to calculate the experimental estimation range value in TDoA algorithms.

Compression Ratio Design in Compressive Spectral Imaging

In this work, a compression strategy for Focal Plane Array Measurements (FPA_M) in Compressive Spectral Imaging (CSI) is presented. The strategy is applied to four CSI architectures. The implementation consists of two stages: transformation and coding. The transformation stage is performed according to the FPA_M structure. Finally, an arithmetic coding is applied.

Algoritmos de Radiolocalización basados en ToA, TDoA y AoA

Based on the geometric principles of Triangulation and Trilateration some location estimation techniques such as ToA (Time of Arrival), TDoA (Time Difference of Arrival) and AoA (Angle of Arrival) are presented. For these estimation techniques different algorithms are analyzed including the Analytical method, Least Squares method and Taylor Series method for ToA and TDoA, and Capon, Barttlet and MUSIC for AoA. This article pretends to present survey of location estimation techniques and their mathematical description.