It is not so long ago that links in a wired network were not able to cooperate freely but were subject to central control by ISDN and ATM protocols. We now live in a world where Internet protocols have made it possible for networks to grow like weeds. We take it for granted that links should regulate their own use by generating prices that reflect congestion and that users adjust rates in response to the cost of traversing the network.

The IP revolution that has transformed the wireline world is coming to wireless. Migration from cellphones to smartphones has created demand for capacity that simply cannot be met by circuit switched networks engineered to provide worst case coverage at the cell boundary. This is a monograph written by revolutionaries that maps the new world of what is possible when wireless resources are properly shared.

The monograph is remarkable for starting with services, with medium access, and then asking how to engineer the physical layer that the higher layers want to see. The authors answered this question themselves by making a journey from concept to working system and then staging field trials. This monograph is the result of a virtuous cycle where engineering challenges led to theoretical insights and new theory was proved out in working systems.

Why we wrote this book

Back in the late 1990s, when CDMA was widely considered the dominant technology for cellular 3G, two of the authors and a few colleagues in Bell Laboratories designed an alternative technology called Flash-OFDM with two basic yet fundamental ideas: OFDMA-based airlink and all IP-based network architecture. In early 2000, we founded a startup company, Flarion Technologies, to prove Flash-OFDM in the market by building terminals and base stations, and testing and deploying the networks in a wide variety of locations, configurations, and frequency bands. As arguably the first commercially deployed OFDMA/IP-based cellular system, Flash-OFDM helped make those two ideas the key enabling features in 4G mobile broadband LTE.

From the remarkable journey of designing, developing, and deploying Flash-OFDM, we have learned, and in some cases “unlearned,” a few important lessons:

1. • While early cellular wireless communications design focuses predominantly on the physical layer, mobile broadband requires more system-level thinking across different protocol layers than just the physical layer. For example, OFDMA, in comparison with CDMA, more readily facilitates a simplified IP-based network architecture design, where air interface specific technology functions and processing are collapsed into a base station and IP layer protocols are used for handoff.

2. • Conventional wisdom developed in early cellular wireless communications needs to be reexamined from first principles. For example, the wireless channel is conventionally modeled with additive noise and multiplicative channel response; we found that selfnoise should also be included when multiplexing users with large signal dynamic range in OFDMA. As another example, universal frequency reuse is conventionally considered the most spectrally efficient; we found that for data, fractional frequency reuse improves both cell edge and cell average performance.

This publication was conceived as a textbook for a first-year graduate course in the Signals and Systems Area of the Electrical Engineering Department at UCLA to introduce basic statistical concepts of detection and estimation and their applications to engineering problems to students in communication, telecommunication, control, and signal processing. Students majoring in electromagnetics and antenna design often take this course as well. It is not the intention of this book to cover as many topics as possible, but to treat each topic with enough detail so a motivated student can duplicate independently some of the thinking processes of the originators of these concepts. Whenever possible, examples with some numerical values are provided to help the reader understand the theories and concepts. For most engineering students, overly formal and rigorous mathematical methods are probably neither appreciated nor desirable. However, in recent years, more advanced analytical tools have proved useful even in practical applications. For example, tools involving eigenvalue–eigenvector expansions for colored noise communication and radar detection; non-convex optimization methods for signal classification; non-quadratic estimation criteria for robust estimation; non-Gaussian statistics for fading channel modeling; and compressive sensing methodology for signal representation, are all introduced in the book.

In general, the use of multiple antennas provides three types of gains: diversity gain, power/SINR gain (including interference suppression), and multiplexing (bandwidth or degree of freedom) gain. In Chapter 4, we studied the first two and learned that

1. • Antenna diversity changes the channel statistics and reduces the probability of the channel being in a deep fade, thereby improving the link reliability. Receive diversity is relatively easy to obtain. With space-time coding, transmit diversity can be achieved without the transmitter knowing the channel.

2. • Beamforming achieves power/SINR gain by increasing signal power and/or reducing interference. Maximal ratio combining is commonly used for receive beamforming and is applicable for transmit beamforming if the channel is known at the transmitter. Without precise channel knowledge, transmit beamforming is not possible in a single user link, but can be opportunistically achieved in a multiuser system with multiple receivers and limited SINR feedback. Power/SINR gain is mostly beneficial in the low SINR regime.

In general, the three types of gains can be simultaneously obtained, but there is a tradeoff of how much of each type a given communication scheme can achieve. A well-known example is the fundamental tradeoff of diversity and multiplexing studied in [186]. For the sake of simplicity, we focus on multiplexing gain in this chapter, and do not address the tradeoff.

Hypothesis testing is a concept originated in statistics by Fisher [1] and Neyman–Pearson [2] and forms the basis of detection of signals in noises in communication and radar systems.

Simple hypothesis testing

Suppose we measure the outcome of a real-valued r.v. X. This r.v. can come from two pdf's associated with the hypotheses, H0 or H1. Under H0, the conditional probability of X is denoted by p0(x) = p(x∣H0), −∞ < x < ∞, and under H1, the conditional probability of X is denoted by p1(x) = p(x|H1), − ∞ < x < ∞. This hypothesis is called “simple” if the two conditional pdf's are fully known (i.e., there are no unknown parameters in these two functions). From the observed x value (which is a realization of the r.v. X), we want to find a strategy to decide on H0 or H1 in some optimum statistical manner.

Example 3.1 The binary hypothesis problem in deciding between H0 or H1 is ideally suited to model the radar problem in which the hypothesis H0 is associated with the absence of a target and the hypothesis H1 is associated with the presence of a target. In a binary hypothesis problem, there are four possible states, whether H0 or H1 is true and whether the decision is to declare H0 or to declare H1. Table 3.1 summarizes these four states and the associated names and probabilities.

In this chapter we consider various analytical and simulation tools for system performance analysis of communication and radar receiver problems. In Section 8.1, we treat the analysis of receiver performance with Gaussian noise, first using the closure property of Gaussian vectors under linear operations. We then address this issue without using this closure property. Section 8.2 deals with the analysis of receiver performance with Gaussian noise and other random interferences caused by intersymbol interferences (ISI) due to bandlimitation of the transmission channel. Section 8.2.1 introduces the evaluation of the average probability of error based on the moment bounding method. Section 8.3 considers the analysis of receiver performance with non-Gaussian noises including the spherically invariant random processes (SIRP). By exploiting some basic properties of SIRP, Section 8.3.1 obtains a closed form expression for the receiver. We determine the average probability of error for the binary detection problem with additive multivariate t-distributed noise (which is a member of SIRP). Section 8.3.2 again uses some properties of SIRP to model wireless fading channels with various fading envelope statistics. By using Fox H-function representations of these pdfs, novel average probability of error expressions under fading conditions can be obtained. Section 8.3.3 treats the probabilities of a false alarm and detection of a radar problem with a robustness constraint. Section 8.4 first shows a generic practical communication/radar system, which may have various complex operations, making analytical evaluation of system performance in many cases difficult.

The central design idea of mobile broadband is to adapt wireless to the Internet, not vice-versa. Compared with its wireline counterpart, mobile broadband faces two major technical challenges: fading and interference, which make the wireless link less reliable, and mobility, which requires handoff from one cell to another as a user moves. The previous chapters describe the physical and MAC layer approaches of dealing with fading and interference and improving link reliability and system capacity. In this chapter, we will expand our scope to view the airlink as part of an end-to-end network system and address the handoff issue.

Network architecture describes the necessary functions of the network system, partitions them to a set of logical nodes, and defines the interfaces between the nodes. An end-to-end network system is usually quite complex. To handle the complexity in a scalable manner, a good design practice is to adopt a layered structure. For example, the famous open system interconnection (OSI) model defines a networking framework of implementing protocols in seven layers, namely the application, presentation, session, transport, networking, data link, and physical layers. When two nodes communicate with each other, control is passed down from a higher layer to a lower one in one node, all the way to the bottom physical layer, then over the physical channel to the other node, and finally moving up the hierarchy in that node. The TCP/IP model of the Internet simplifies the layering model to four layers, namely the application, transport, Internet, and network access layers.

Strictly speaking, OFDMA is merely a multiple access principle of sharing spectrum. To build a full-fledged mobile broadband communication system, one needs to design a whole set of system operations. We highlight a few important elements of the required system operations in Figure A.1 and provide a high level overview in this section.

Cell search, synchronization, and identification

After power-up, the user first detects the existence of a base station in the area by searching for some signal signature in the downlink. Below are some examples of commonly used signal signature:

1. • Correlation between cyclic prefix and the last portion of the OFDM symbol (see Section B.2 for cyclic prefix correlation).

2. • Synchronization channel, which consists of pre-defined signal waveforms with special properties, such as autocorrelation function close to a delta function (see Section 9.5 for an example of synchronization channel).

3. • Common pilot channel, which consists of a subset of high power tone-symbols distributed according to pre-defined patterns (see Section B.3 for an example of pilot channel).

4. • Beacon channel, which consists of a special OFDM symbol in which most of signal power is concentrated on one tone-symbol (see Section 9.5 for the use of beacon channel).

Once the user has discovered a base station, it synchronizes its carrier frequency, clock, and symbol time with the received downlink signal and then retrieves from the broadcast channel certain system information, such as system time, base station identifier, operator identifier.