Automatic Modulation Classification (AMC) is a key enabling technology in Cognitive Radio (CR) Networks. The ability of CR transceivers to detect and classify unknown wireless signals has various applications in civilian and military domains. Performance of AMC degrades severely under low Signal-to-Noise Ratio (SNR) and variable channel conditions. Cooperative classification has been presented as a means to overcome the detrimental channel effects by combining the results from physically scattered CR nodes. In this work, Maximum Likelihood (ML) combining of classification features is presented as a data fusion algorithm that provides better classification accuracy compared to hard decision combining algorithms without high network overhead. The performance of a cumulants-based modulation classifier under Additive White Gaussian Noise (AWGN) is analyzed. The enhancement in classification performance when applying ML combining of more than one classifier is presented. Theoretical analysis as well as various simulations are presented for ML combining of CR nodes with equal SNR. In addition, analysis is extended to the case where CR nodes have different SNRs. Theory and simulations show that applying ML combining will result in a better classification accuracy, even when one of the nodes has a much lower SNR.

Keeping up to date with the most current technologies in the field is essential for all effective electrical and computer engineers. The updated 7th edition of Principles of Communications presents the reader with more in-chapter examples, providing for a more supportive framework for learning. Readers are exposed to digital data transmission techniques earlier in the book, so they can appreciate the characteristics of digital communication systems prior to learning about probability and stochastic processes. They will also find expanded forward error correction code examples, and additional MATLAB problems.

In this paper we consider spectrum sharing between a communication system, modeled as MIMO interference channel (MIMO IFC), and MIMO radar. We first derive a zero-forcing precoder to eliminate the interference at the communication receivers. We show that this precoder is equivalent to orthogonal projection matrix onto the null space of an effective interference channel and show that this choice of precoder will degrade the radar target direction estimation performance. We show that the radar performance can be improved at the cost of non-zero interference at communication users by projecting the radar signal onto a subspace which is an expanded version of the aforementioned null space. We propose two approaches to smoothly expand the projected subspace and study the tradeoff between radar performance and interference at the communication users through simulation.

In this paper, we consider spectrum sharing between multiple-input multiple-output (MIMO) radar system and a communication system modeled as MIMO interference channel. We derive a zero-forcing precoder for radar transmitter which completely eliminates the radar interference to communication users. Obtaining the precoder requires the knowledge of an effective interference channel matrix composed of the channel matrices to all of the communication receivers and the postprocessing matrices employed by them. We propose a channel estimation phase in which all of the communication receivers coordinate in their choice of training symbols and power transmission and the radar transmitter can estimate the effective interference channel. We investigate the effect of radar precoder and channel estimation error on the performance of radar and interference to communication receivers. Our results indicate that while the precoder null steers the radar interference to communication users it degrades the radar performance by introducing correlation to the probing signals. We show that this performance loss can be compensated for by increasing the number of radar antennas.

Wireless Personal Communications: Channel Modeling and Systems Engineering presents a broad range of topics in wireless communications, including perspectives from both industry and academia. This book serves as a reflection of emerging technologies in wireless communications and features papers from world-renowned authors on the subject. Wireless Personal Communications: Channel Modeling and Systems Engineering is divided into six sections. The first five of these cover the following topics: Propagation and Channel Modeling (4 papers); Antennas (6 papers); Multiuser Detection (3 papers); Radio Systems and Technology (4 papers); and Wireless Data (3 papers). The last section contains invited papers on areas of significant interest. Wireless Personal Communications: Channel Modeling and Systems Engineering serves as an excellent reference source and may be used as a text for advanced courses on the subject. It is an essential tool for graduate students, postgraduate researchers, academics, and anyone working in the research aspect of the wireless communications industry.

Wireless Personal Communications: Bluetooth Tutorial and Other Technologies presents a broad range of topics in wireless communications, including perspectives from both industry and academia. The book serves as a reflection of emerging technologies in wireless communications and features papers from world-renowned authors on the subject. A new tutorial on the emerging Bluetooth technology is also presented. Wireless Personal Communications: Bluetooth Tutorial and Other Technologies serves as an excellent reference and may be used as a text for advanced courses on the subject. It is an essential tool for graduate students, postgraduate researchers, academics, and anyone working in the research aspect of the wireless communications industry.

In this chapter we examine in some detail the Erlang-B (probability of blocking) and Erlang-C (probability of delay) results developed in the previous chapter. In addition, we examine the time delay given that an arrival is queued for later service in an input buffer.

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 × 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

In this paper, a global optimization technique is applied to solve the optimal transmitter placement problem for indoor wireless systems. An efficient pattern search algorithm-DIRECT (Dividing RECTangles) of Jones, Perttunen, and Stuckman (1993)-has been connected to a parallel 3D radio propagation ray tracing modeler running on a 200node Beowulf cluster of Linux workstations. Surrogate functions for a parallel WCDMA (wideband code division multiple access) simulator were used to estimate the system performance for the global optimization algorithm. Power coverage and BER (bit error rate) are considered as two different criteria for optimizing locations of a specified number of transmitters across the feasible region of the design space. This paper briefly describes the underlying radio propagation and WCDMA simulations and focuses on the design issues of the optimization loop. Inde:v Terms-bit error rate, DIRECT algorithm, global optimization, power coverage, ray tracing, surrogate function, transmitter placement, wideband code division multiple access.

Cognitive radio (CR) is an enabling technology for numerous new capabilities such as dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this diverse set of applications, CR researchers leverage a variety of artificial intelligence (AI) techniques. To help researchers better understand the practical implications of AI to their CR designs, this paper reviews several CR implementations that used the following AI techniques: artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). Factors that influence the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed. To provide readers with a more concrete understanding, these factors are illustrated in an extended discussion of two CR designs.

Energy consumption for mobile and wireless communication device, such as cell phones, has long been an important aspect for both designers and customers. This paper shows how a cognitive radio (CR) framework can help to reduce system energy consumption of a mobile and wireless communication device based on the application quality of service requirement, the channel condition, and the radio capabilities and characteristics. The CR framework enables not only adaptation of modulation, coding rate, coding gain, and radiated power as conventional adaptive modulation (AM) scheme, but also joint adjustment of radio component characteristics (e.g., power amplifier (PA) characteristics) to achieve high energy efficiency. A unified PA efficiency model characterizing theoretical Class A, Class B, and practical PAs is adopted and enables the analysis of the impact of different radio configurations and channel conditions on energy efficiency. Significant energy savings (up to 90%) using the proposed CR framework for systems with theoretical PAs and with a realistic PA can be achieved compared with the conventional AM approach in simulation. This framework can also be used to manage other radio resources.

The purpose of this chapter is to demonstrate several of the simulation programs developed in the previous chapter. Not all, or even most, of the simulation programs are discussed here as the user of the simulations is encouraged to experiment, make changes and in general expand upon the basic capabilities illustrated here. Specifically, the second-order PLL and the QPSK demodulation loop are discussed in some details in the following pages. First, however, we pause to examine the structure of the simulation programs and the assumed loop inputs.

In this chapter1, we first consider the basic theory of the PLL and explore some of its operating characteristics. No attempt is made to present a complete or unified treatment since this is more than adequately accomplished in the literature. There are a number of excellent textbooks that treat the PLL in considerable detail as listed in Bibliography. Our aim, however, is to develop the theory to a point that allows us to develop and understand a number of simple models for these interesting and useful devices. We then turn our attention in the following chapter to several important structures for demodulation and tracking that are derived from the basic PLL. These include the Costas PLL, the QPSK tracking loop, and the N-phase tracking loop. MATLAB simulations based on these models are then developed as described in Chapter 4 and 5.

In this chapter, we develop several of the structures that are useful in communications applications and based upon the basic PLL. While there are a number of candidate devices that we could explore in this chapter, we restrict our attention to the most common devices that form the basic building blocks for more complex systems. These include the Costas PLL, the QPSK tracking loop and the N-Phase tracking loop.

The Phase-Locked Loop (PLL), and many of the devices used for frequency and phase tracking, carrier and symbol synchronization, demodulation, and frequency synthesis, are fundamental building blocks in today's complex communications systems. It is therefore essential for both students and practicing communications engineers interested in the design and implementation of modern communication systems to understand and have insight into the behavior of these important and ubiquitous devices. Since the PLL behaves as a nonlinear device (at least during acquisition), computer simulation can be used to great advantage in gaining insight into the behavior of the PLL and the devices derived from the PLL. The purpose of this Synthesis Lecture is to provide basic theoretical analyses of the PLL and devices derived from the PLL and simulation models suitable for supplementing undergraduate and graduate courses in communications. The Synthesis Lecture is also suitable for self study by practicing engineers. A significant component of this book is a set of basic MATLAB-based simulations that illustrate the operating characteristics of PLL-based devices and enable the reader to investigate the impact of varying system parameters. Rather than providing a comprehensive treatment of the underlying theory of phase-locked loops, theoretical analyses are provided in sufficient detail in order to explain how simulations are developed. The references point to sources currently available that treat this subject in considerable technical depth and are suitable for additional study.

In this chapter, we will study PLL in the presence of noise. No attempt is made to present a complete discussion since this is adequately accomplished in the literature. Our purpose is to provide basic understanding of PLL performance in the presence of noise. The PLL with additive Gaussian noise is analyzed. The discussion of nonlinear analysis is limited to the first-order PLL with additive noise. We also briefly talk about the effect of phase noise. The MATLAB simulation for the first-order PLL with additive noise is given in the last section.