Agent-Based Model of the Spectrum Auctions with Sensing Imperfections in Dynamic Spectrum Access Networks

Cognitive radio (CR) is the underlying platform for the application of dynamic spectrum access (DSA) networks. Although the auction theory and spectrum trading mechanisms have been discussed in the CR related works, their joint techno-economic impact on the efficiency of distributed CR networks has not been researched yet. In this paper we assume heterogeneous primary channels with network availability statistics unknown to each secondary user (SU) terminal. In order to detect the idle primary user (PU) network channels, the SU terminals trigger regularly the spectrum sensing mechanism and make the cooperative decision regarding the channel status at the fusion center. The imperfections of the spectrum mechanism create the possibility of the channel collision, resulting in the existence of the risk (in terms of user collision) in the network. The spectrum trading within SU network is governed by the application of the sealed-bid first-price auction, which takes into account the channel valuation as well as the statistical probability of the risk existence. In order to maximize the long-term payoff, the SU terminals take an advantage of the reinforcement comparison strategy. The results demonstrate that in the investigated model, total revenue and total payoff of the SU operator (auctioneer) and SU terminals (bidders) are characterized by the existence of the global optimum, thus there exists the optimal sensing time guaranteeing the optimum economic factors for both SU operator and SU terminals

Compact automatic modulation recognition using over-the-air signals and FOS features

The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second- generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideratio

Assessing The Biophysical Naturalness Of Grassland In Eastern North Dakota With Hyperspectral Imagery

Over the past two decades, non-native species within grassland communities have quickly developed due to human migration and commerce. Invasive species like Smooth Brome grass (Bromus inermis) and Kentucky Blue Grass (Poa pratensis), seriously threaten conservation of native grasslands. This study aims to discriminate between native grasslands and planted hayfields and conservation areas dominated by introduced grasses using hyperspectral imagery. Hyperspectral imageries from the Hyperion sensor on EO-1 were acquired in late spring and late summer on 2009 and 2010. Field spectra for widely distributed species as well as smooth brome grass and Kentucky blue grass were collected from the study sites throughout the growing season. Imagery was processed with an unmixing algorithm to estimate fractional cover of green and dry vegetation and bare soil. As the spectrum is significantly different through growing season, spectral libraries for the most common species are then built for both the early growing season and late growing season. After testing multiple methods, the Adaptive Coherence Estimator (ACE) was used for spectral matching analysis between the imagery and spectral libraries. Due in part to spectral similarity among key species, the results of spectral matching analysis were not definitive. Additional indexes, Level of Dominance and Band variance , were calculated to measure the predominance of spectral signatures in any area. A Texture co-occurrence analysis was also performed on both Level of Dominance and Band variance indexes to extract spatial characteristics. The results suggest that compared with disturbed area, native prairie tend to have generally lower Level of Dominance and Band variance as well as lower spatial dissimilarity. A final decision tree model was created to predict presence of native or introduced grassland. The model was more effective for identification of Mixed Native Grassland than for grassland dominated by a single species. The discrimination of native and introduced grassland was limited by the similarity of spectral signatures between forb-dominated native grasslands and brome-grass stands. However, saline native grasslands were distinguishable from brome grass

PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level

In industrial process automation, sensors (pressure, temperature, etc.), controllers, and actuators (solenoid valves, electro-mechanical relays, circuit breakers, motors, etc.) make sure that production lines are working under the pre-defined conditions. When these systems malfunction or sometimes completely fail, alerts have to be generated in real-time to make sure not only production quality is not compromised but also safety of humans and equipment is assured. In this work, we describe the construction of a smart and real-time edge-based electronic product called PreMa, which is basically a sensor for monitoring the health of a Solenoid Valve (SV). PreMa is compact, low power, easy to install, and cost effective. It has data fidelity and measurement accuracy comparable to signals captured using high end equipment. The smart solenoid sensor runs TinyML, a compact version of TensorFlow (a.k.a. TFLite) machine learning framework. While fault detection inferencing is in-situ, model training uses mobile phones to accomplish the `on-device' training. Our product evaluation shows that the sensor is able to differentiate between the distinct types of faults. These faults include: (a) Spool stuck (b) Spring failure and (c) Under voltage. Furthermore, the product provides maintenance personnel, the remaining useful life (RUL) of the SV. The RUL provides assistance to decide valve replacement or otherwise. We perform an extensive evaluation on optimizing metrics related to performance of the entire system (i.e. embedded platform and the neural network model). The proposed implementation is such that, given any electro-mechanical actuator with similar transient response to that of the SV, the system is capable of condition monitoring, hence presenting a first of its kind generic infrastructure

Scheduling based optimization in software defined radio and wireless networks

The objective of this work is to enable dynamic sharing of software-defined radio (SDR) transceivers through the concepts of hardware virtualization and real-time resource management. SDR is a way to build a digital radio that consists of a software back-end for digital signal processing (DSP) and an analog front-end transceiver for waveform generation and reception. This work proposes the use of a virtualization layer to decouple back-end SDR software from front-end transceivers. With this arrangement, front-ends are said to be virtualized, and it becomes possible to share a limited number of front-ends among many SDR back-ends through different multiplexing techniques. In the first work, the hardware/software infrastructure needed for such a system is explored. An intelligent resource management algorithm is presented that demonstrates a potential increase in the number of supported SDR back-ends. The second work presents an exploration of this system\u27s application to aircraft telemetry systems and the potential improvements to reliability. The work includes a reliability model for virtualized SDR aircraft telemetry systems as well as simulations demonstrating changes in performance as hardware fails. In the final work, an improved resource management algorithm based on Markov decision process (MDP) is proposed. This approach addresses concerns wireless regulatory agencies and standards bodies may raise regarding performance degradation caused by sharing transceivers. The process of sharing transceivers causes service disruptions to occur whenever the instantaneous demand for front-ends exceeds capacity. This MDP approach provides a feasibility test and a guarantee that all SDRs can stay within their respective wireless specifications. The proposed technique guarantees Pareto efficient distribution of resources. To make this approach possible, a connection is established between dynamic transceiver sharing and equivalent interference --Abstract, page iv

Spectrum Sharing: Quantifying the Benefits of Different Enforcement Scenarios

Recent studies have forecasted major growth in mobile broadband traffic. Due to the predicted high growth rate of mobile broadband traffic over the coming years (demand), there is a need for more wireless network capacity (supply). One of the major approaches to expand mobile wireless capacity is to add more spectrum to the market by enabling “spectrum sharing”. The FCC has issued many reports indicating that the US is dangerously close to running out of capacity for mobile data, which is why the FCC and the NTIA have been working continually to enable spectrum sharing. The spectrum usage rights granted by the Federal government to spectrum users/licensees come with the expectation of protection from harmful interference. As a consequence of the growth of wireless demand and services of all types, technical progress enabling smart agile radio networks, and on-going spectrum management reform, there is both a need and opportunity to use and share spectrum more intensively. This dissertation is written on the premise that spectrum sharing will be a major factor in increasing the capacity supply in the near future. The focus of this dissertation is to examine and quantify the benefits of spectrum sharing through different enforcement scenarios. Enabling spectrum sharing regimes on a non-opportunistic basis means that sharing agreements must be implemented. To have meaning, those agreements must be enforceable. This dissertation will examine the spectrum sharing between government and commercial users and try to generalize some finding, which can be implemented, in different spectrum sharing cases. This analysis is valuable because it will help regulators/governments prepare for possible future scenarios in addressing the potential capacity crunch. In addition, it can give the incumbents more insight into expected future sharing as well as into how to optimize mitigation of possible harmful interference that may result. It is also of value to commercial users and operators in that they can use the results of this work to make more informed decisions about the economic benefits of different spectrum sharing market and opportunities

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

Nonlinear Distortion in Wideband Radio Receivers and Analog-to-Digital Converters: Modeling and Digital Suppression

Emerging wireless communications systems aim to flexible and efficient usage of radio spectrum in order to increase data rates. The ultimate goal in this field is a cognitive radio. It employs spectrum sensing in order to locate spatially and temporally vacant spectrum chunks that can be used for communications. In order to achieve that, flexible and reconfigurable transceivers are needed. A software-defined radio can provide these features by having a highly-integrated wideband transceiver with minimum analog components and mostly relying on digital signal processing. This is also desired from size, cost, and power consumption point of view. However, several challenges arise, from which dynamic range is one of the most important. This is especially true on receiver side where several signals can be received simultaneously through a single receiver chain. In extreme cases the weakest signal can be almost 100 dB weaker than the strongest one. Due to the limited dynamic range of the receiver, the strongest signals may cause nonlinear distortion which deteriorates spectrum sensing capabilities and also reception of the weakest signals. The nonlinearities are stemming from the analog receiver components and also from analog-to-digital converters (ADCs). This is a performance bottleneck in many wideband communications and also radar receivers. The dynamic range challenges are already encountered in current devices, such as in wideband multi-operator receiver scenarios in mobile networks, and the challenges will have even more essential role in the future.This thesis focuses on aforementioned receiver scenarios and contributes to modeling and digital suppression of nonlinear distortion. A behavioral model for direct-conversion receiver nonlinearities is derived and it jointly takes into account RF, mixer, and baseband nonlinearities together with I/Q imbalance. The model is then exploited in suppression of receiver nonlinearities. The considered method is based on adaptive digital post-processing and does not require any analog hardware modification. It is able to extract all the necessary information directly from the received waveform in order to suppress the nonlinear distortion caused by the strongest blocker signals inside the reception band.In addition, the nonlinearities of ADCs are considered. Even if the dynamic range of the analog receiver components is not limiting the performance, ADCs may cause considerable amount of nonlinear distortion. It can originate, e.g., from undeliberate variations of quantization levels. Furthermore, the received waveform may exceed the nominal voltage range of the ADC due to signal power variations. This causes unintentional signal clipping which creates severe nonlinear distortion. In this thesis, a Fourier series based model is derived for the signal clipping caused by ADCs. Furthermore, four different methods are considered for suppressing ADC nonlinearities, especially unintentional signal clipping. The methods exploit polynomial modeling, interpolation, or symbol decisions for suppressing the distortion. The common factor is that all the methods are based on digital post-processing and are able to continuously adapt to variations in the received waveform and in the receiver itself. This is a very important aspect in wideband receivers, especially in cognitive radios, when the flexibility and state-of-the-art performance is required