Adaptive modulation for cognitive radios

This thesis examines the benefits of using adaptive modulation in terms of spectral efficiency and probability of bit error for cognitive radio networks. In channels that fluctuate dynamically over time, systems that are based upon the conventional methods of fixed modulation formats do not perform well. Adaptive modulation provides many parameters that can be adjusted relative to the channel fading, including data rate, transmit power, instantaneous BER, symbol rate, and channel code rate or scheme. In this thesis, a systematic study on the increase in spectral efficiency obtained by optimally varying combinations of the modulation formats for a cognitive radio is provided...Simulations show how adaptively changing the modulation schemes improves the performance of the system by meeting a BER constraint over a range of SNR --Abstract, page iii

Spectrum Sensing Algorithms for Cognitive Radio Based on Statistical Covariances

Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and associated threshold are found based on statistical theory. The methods do not need any information of the signal, the channel and noise power a priori. Also, no synchronization is needed. Simulations based on narrowband signals, captured digital television (DTV) signals and multiple antenna signals are presented to verify the methods

Survey on Wireless Sensor Networks for Reliable Life Services and Other Advanced Applications

Wireless is an old technology; however with the advancement of science and technology it has enabled us to send signals to one or more devices without tangible wire connections and reduced complexity. The increasing human needs have lead to the integration of different technologies into a single unit and Wireless Sensor Network (WSN) is one such integration where sensors are integrated with wireless system to form a network. This network is used for a plethora of applications in sectors like defence, agriculture, medical, environment and industry

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

An Autonomous Channel Selection Algorithm for WLANs

IEEE 802.11 wireless devices need to select a channel in order to transmit their packets. However, as a result of the contention-based nature of the IEEE 802.11 CSMA/CA MAC mechanism, the capacity experienced by a station is not fixed. When a station cannot win a sufficient number of transmission opportunities to satisfy its traffic load, it will become saturated. If the saturation condition persists, more and more packets are stored in the transmit queue and congestion occurs. Congestion leads to high packet delay and may ultimately result in catastrophic packet loss when the transmit queue’s capacity is exceeded. In this thesis, we propose an autonomous channel selection algorithm with neighbour forcing (NF) to minimize the incidence of congestion on all stations using the channels. All stations reassign the channels based on the local monitoring information. This station will change the channel once it finds a channel that has sufficient available bandwidth to satisfy its traffic load requirement or it will force its neighbour stations into saturation by reducing its PHY transmission rate if there exists at least one successful channel assignment according to a predicting module which checks all the possible channel assignments. The results from a simple C++ simulator show that the NF algorithm has a higher probability than the dynamic channel assignment without neighbour forcing (NONF) to successfully reassign the channel once stations have become congested. In an experimental testbed, the Madwifi open source wireless driver has been modified to incorporate the channel selection mechanism. The results demonstrate that the NF algorithm also has a better performance than the NONF algorithm in reducing the congestion time of the network where at least one station has become congested

Two-scale envelope-domain analysis of injected chirped oscillators

The response of chirped oscillators under the injection of independent signals, for spectrum sensing in cognitive radio, and under self-injection, for radio frequency identification, is analyzed in detail. The investigation is performed by means of a semianalytical formulation, based on a realistic modeling of the free-running oscillator, extracted from harmonic-balance simulations or from experimental measurements, through a new characterization technique. In the new formulation, the oscillator is linearized about a free-running solution that varies with the control voltage. This enables its application to oscillators having a frequency characteristic that deviates from the linear one. In the case of injection by independent signals, the two-scale envelope-domain formulation will enable an efficient handling of the difference between the slow chirp frequency and the beat frequency. The input carriers can be detected from their dynamic synchronization intervals or, at lower input-power levels, from the dynamics of the beat frequency. Noise perturbations are introduced into the formulation, which enables an estimation of the minimum detectable signal. In the case of a self-injected oscillator for radio frequency identification, an insightful formulation is derived to predict the propagation and tag-resonance effects on the instantaneous oscillation frequency. The tag-resonance signature gives rise to a distinct modulation of the oscillation frequency during the chirp period, which can be detected from the variation of the oscillator bias current. The analysis methods are illustrated through their application to a chirped oscillator, operating in the band 2-3 GHz.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF/FEDER) under Project TEC2014-60283-C3-1-R and Project TEC2017-88242-C3-1-

Internet of Things in Water Management and Treatment

The goal of the water security IoT chapter is to present a comprehensive and integrated IoT based approach to environmental quality and monitoring by generating new knowledge and innovative approaches that focus on sustainable resource management. Mainly, this chapter focuses on IoT applications in wastewater and stormwater, and the human and environmental consequences of water contaminants and their treatment. The IoT applications using sensors for sewer and stormwater monitoring across networked landscapes, water quality assessment, treatment, and sustainable management are introduced. The studies of rate limitations in biophysical and geochemical processes that support the ecosystem services related to water quality are presented. The applications of IoT solutions based on these discoveries are also discussed

Unmanned aerial vehicle-to-wearables (UAV2W) indoor radio propagation channel measurements and modeling

In this paper, off-body ultra-wide band (UWB) channel characterization and modeling are presented between an unmanned aerial vehicle (UAV) and a human subject. The wearable antenna was patched at nine different body locations on a human subject during the experiment campaign. The prime objective of this work was to study and evaluate the distance and frequency dependent path loss factors for different bandwidths corresponding to various carrier frequencies, and also look into the time dispersion properties of such unmanned aerial vehicle-to-wearables (UAV2W) system. The environment under consideration was an indoor warehouse with highly conductive metallic walls and roof. Best fit statistical analysis using Akaike Information Criteria revealed that the Log-normal distribution is the best fit distribution to model the UWB fading statistics. The study in this paper will set up a road map for future UAV2W studies to develop enhanced retail and remote health-care monitoring/diagnostic systems