Introduction to theoretical considerations in wireless systems

This chapter is intended to provide a short summary of the relevant portions of each of the theories that are considered to be fundamental to the development of cognitive wireless systems. Some introductory material on radio-frequency (RF) propagation and device operating constraints is also provided for those not familiar with the relevant device and communications physics. Only those aspects of each area that are utilized in the analysis in the latter chapters are included, so this material should not be considered as even an introduction to any of the four areas. In most cases, the results are stated, and the reader is referred to the references, or further reading, for their derivation, or for a more comprehensive and complete development of each field of study.

Decision theory in cognitive wireless systems

Role of decision theory

One of the principles that we wish to introduce to wireless networking is that nodes have no absolute or mandatory requirement to perform any sensing, protocol reporting, or content-storage requirements. Instead of performing these operations automatically, they are considered to be performed only if the node can justify performing the operation on the basis of the likely performance benefits it achieves, or the performance risks it avoids. Information theory expresses the probabilistic considerations.

Decision-theory literature generally partitions into “normative,” which addresses how rational decisions should be made, and “descriptive,” which describes how humans actually make decisions.

Introduction to wireless system challenges

In this chapter, we will explore some of the fundamental challenges that are common to most, if not all, wireless networking systems and architectures. The intent is to approach the problem in a general framework that can derive meaningful insights into the broad categories of wireless architectures, as well as specific issues associated with specific architectures and designs. Although many of the current wireless architectures are highly specialized and homogeneous, it will be shown that the necessity for increased capability and cost-effective performance, within increasing spectrum constraints, is driving architectures to become more expansive and heterogeneous in their structure. These structures introduce opportunities for optimization across a range of heterogeneous techniques, technologies, and architectures, as well as a requirement for unique optimization methods within each of the homogeneous architectures. This chapter will also introduce some of the fundamental metrics that will be the basis for subsequent analysis and for the development of decision criteria.

Evolution of wireless and mobile architectures

Although cellular and mobile communications are a special case of a wide number of wireless architectures, their impact on popular usage, and society in general, is profound. It is important that their specific trends, and design considerations, be reflected in even the most general treatment of wireless networking.

Not only is spectrum a highly constrained resource, but also energy consumption, real-estate for towers, visual obstruction, and other aspects of the wireless ecosystem are significant considerations in the evolution of wireless technology.

Overview

This chapter provides an overview of the growth of wireless architectures that are challenged in terms of both capacity and density spectrum density, and outlines general options for addressing these challenges. One of the premises of this book is that the challenge of operating in dense spectrum will fundamentally change the nature of wireless operation for all spectrum-dependent systems, and that these changes will both require new technology (control, coordination, and decision making as examples) and create new opportunities to fundamentally extend the capabilities of wireless systems, ultimately to the point of comparability with wired delivery modes.

Although this book is not specifically about implementation of dynamic spectrum access (DSA), many of the techniques it describes assume DSA features will be present in future wireless systems. This is not an unreasonable assumption, insofar as both regulatory acceptance and technical necessity would appear to support this spectrum-access modality, for all emerging spectrum applications. DSA has many advantages in terms of enabling increased use of the spectrum, but it is also enabling for even more significant changes in wireless network operation, including the ability of nodes to mitigate interference to themselves from other nodes, locate frequencies with optimal propagation to achieve network density, and manage modulation and bandwidth.

Much of the DSA research literature focuses on achieving interference-free operation. Here, we will use the flexibility provided by DSA to manage a node's own environment.

Introduction

This chapter provides an overview of future wireless networking research needs, and the author's perception of the challenges and some of the obstacles that it faces. The field of cognitive wireless has existed in some form for well over a decade, at the time of writing, and yet no commercial or widely deployed implementations of it have even been seriously offered for deployment. Conference attendance primarily consists of academic researchers, with limited participation by several major corporate research organizations. There is little participation by the product-development community or venture-capital investors. The wireless technologies that dominate wireless operation had to leave the research community and be adopted as commercially and economically viable with cost and engineering feasibility. It is clear that current wireless research has not created the perception that is an essential element in the investment or product strategy for wireless advancement, at least in commercial practice.

This is not an argument against theoretical research. Quite the opposite; it is the basic theoretical research that will establish the argument that the benefits of this technology are universal and globally applicable to a wide range of architectures, not just those used as examples in a particular implementation. Showing cognitive features in a wireless fidelity (Wi-Fi) network will not be meaningful to cellular designers unless coupled with an argument that the reported benefits are much more universal and inherent than just to Wi-Fi.

For many decades, wireless designers have focused on range and link performance. In an emerging wireless-based economy, the need for high device and bandwidth density has begun to drive communications in an opposite direction, stressing spectrum reuse through short-range, highly localized communication architectures. Some of these adaptions are apparent now: low-cost femtocells fill a service gap between taller and higher-power cellular towers and the lower-power wireless local area networks; backhaul bandwidth to the infrastructure is as much of a constraint as the wireless up- and down-links; and wireless architectures have evolved to use a diversity of technologies, beyond cellular. Future communications architectures are unlikely to be a choice among cellular, wireless local area network, peer-to-peer, and fixed modalities, but will instead constitute an integrated framework whose optimizing process will be so dynamic that these modalities will be invisible to users.

Wireless technology is at a fundamental tipping point. The goal of new technology is not to “build out” and achieve spatial coverage, but to “in build” to achieve device and information density. Many of the fundamental principles that were appropriate to achieve network coverage are reversed when the objective of the network transitions to density and scalability. Additionally, future wireless environments will become much more heterogeneous, as they integrate multiple technologies; opportunistic, as dedicated spectrum is no longer available, and spectrum must be dynamically shared; and adaptive, as the technology must recognize the local challenges of coverage and density and adapt in situ.