Cognitive radio technology, a revolutionary communication paradigm that can utilize the existing wireless spectrum resources more efficiently, has been receiving growing attention in recent years. Now that network users need to adapt their operating parameters to the dynamic environment, and may pursue different goals, traditional spectrum-sharing approaches based on a fully cooperative, static, and centralized network environment are no longer applicable. Instead, game theory has been recognized as an important tool in studying, modeling, and analyzing the cognitive interaction process. In this chapter, we introduce the most fundamental concepts of game theory, and explain in detail how these concepts can be leveraged in designing spectrum-sharing protocols, with an emphasis on state-of-the-art research contributions in cognitive radio networking. This chapter provides a comprehensive treatment of game theory with important applications in cognitive radio networks, and will aid the design of efficient, self-enforcing, and distributed spectrum-sharing schemes in future wireless networks.

Introduction

Cognitive radio technology has emerged in recent years as a revolutionary communication paradigm, which can provide faster and more reliable wireless services by utilizing the existing spectrum band more efficiently. A notable difference of a cognitive radio network from traditional wireless networks is that users need to be aware of the dynamic environment and adaptively adjust their operating parameters on the basis of interactions with the environment and other users in the network. Traditional spectrum-sharing and management approaches, however, generally assume that all network users cooperate unconditionally in a static environment, and thus they are not applicable to a cognitive radio network.

Introduction

In this chapter we lay the foundation for the analysis and design of communication systems, and digital communication systems in particular. We employ deterministic signals to carry information from the transmitter to the receiver. These deterministic signals contain certain a priori features sufficiently adequate for the receiver to retrieve the information. Note that the information always appears random to the receiver, that is, it does not know which data it will receive; otherwise, communications would not be needed. Deterministic signals form a very broad class of signals; therefore, the first step is to categorize them so that their characterization can be fully exploited. The categorization leads to the labels continuous-time, discrete-time, periodic, aperiodic, analog, digital, energy, and power signals. Further study leads us to orthogonal signals and the use of signal space to represent digital signals as vectors. We also review linear time-invariant (LTI) systems and the important convolution operation that relates the inputs and outputs of an LTI system.

We then investigate Fourier series representation of continuous-time periodic signals, and Fourier transform of continuous-time aperiodic signals. The Fourier transform is indispensable in the analysis and design of LTI systems. The energy spectral density of an energy signal and the power spectral density of a power signal are studied. From here the autocorrelation functions of both energy and power signals are examined.

This chapter considers the problem of average throughput maximization relative to the total energy consumed in packetized sensor communications. A near-optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and channel condition is presented. Many approaches require the state transition probability, which may be hard to obtain in a practical situation. Therefore, we are motivated to utilize a class of learning algorithms, called reinforcement learning (RL), to obtain the near-optimal policy in point-to-point communication and a good transmission strategy in multi-node scenarios. For comparison purposes, stochastic models are developed to obtain the optimal strategy in point-to-point communication. We show that the learned policy is close to the optimal policy. We further extend the algorithm to solve the optimization problem in a multi-node scenario by independent learning. We compare the learned policy with a simple policy, whereby the agent chooses the highest possible modulation and selects the transmit power that achieves a predefined signal-to-interference ratio (SIR) given one particular modulation. The learning algorithm achieves more than twice the throughput per energy of the simple policy, particularly in the high-packet-arrival-rate regime. Besides the good performance, the RL algorithm results in a simple, systematic, self-organized, and distributed way to decide the transmission strategy.

Introduction

Recent advances in micro-electro-mechanical-system (MEMS) technology and wireless communications have made possible the large-scale deployment of wireless sensor networks (WSNs), which consist of small, low-cost sensors with powerful processing and networking capabilities.

In this chapter, we consider a class of energy-aware routing algorithms that explicitly take into account the connectivity of the remaining sensor network for lifetime improvement. In typical sensor-network deployments, some nodes may be more important than other nodes because the failure of these nodes causes network disintegration, which results in early termination of information delivery. To mitigate this problem, we consider a class of routing algorithms called keep-connect algorithms, which use computable measures of network connectivity in determining how to route packets. Such algorithms embed the importance of the nodes in the routing cost/metric. The importance of a node is characterized by the algebraic connectivity of the remaining graph when that node fails. We prove several properties of the routing algorithm, including the energy-consumption upper bound. Using extensive simulations, we demonstrate that the algorithm achieves significant performance improvement compared with the existing routing algorithms. More importantly, we show that it is more robust in terms of algebraic network connectivity for lifetime improvement than the existing algorithms. Finally, we present a distributed implementation of the algorithm.

Introduction

Advances in low-power integrated-circuit devices and communications technologies have enabled the deployment of low-cost, low-power sensors that can be integrated to form a sensor network. This type of network has vastly important applications, i.e., from battlefield surveillance systems to modern highway and industry monitoring systems; from emergency rescue systems to early forest-fire detection and very sophisticated earthquake early-detection systems, etc. Having such a broad range of applications, the sensor network is becoming an integral part of human lives. Moreover, it has been identified as one of the most important technologies nowadays.

In this chapter we address the problem of network maintenance, in which we aim to maximize the lifetime of a sensor network by adding a set of relays to it. The network lifetime is defined as the time until the network becomes disconnected. The Fiedler value, which is the algebraic connectivity of a graph, is used as an indicator of the network's health. The network-maintenance problem is formulated as a semi-definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. First, we present a network maintenance algorithm that obtains the SDP-based locations for a given set of relays. Second, we study a routing algorithm, namely the weighted minimum-power routing (WMPR) algorithm, that significantly increases the network lifetime due to the efficient utilization of the deployed relays. Third, we consider an adaptive network maintenance algorithm that relocates the deployed relays on the basis of the network health indicator. Further, we study the effect of two different transmission scenarios, with and without interference, on the network maintenance algorithm. Finally, we consider the network repair problem, in which we find the minimum number of relays together with their SDP-based locations needed in order to reconnect a disconnected network. We then present an iterative network repair algorithm that utilizes the network maintenance algorithm.

Introduction

There has been much interest in wireless sensor networks due to their various areas of application such as battlefield surveillance systems, target tracking, and industrial monitoring systems.

In this chapter, we introduce a set of mechanisms to protect mobile ad hoc networks against routing-disruption attacks launched by inside attackers. First, each node launches a route-traffic observer to monitor the behavior of each valid route in its route cache, and to collect the packet-forwarding statistics submitted by the nodes on this route. Since malicious nodes may submit false reports, each node also keeps cheating records for other nodes. If a node is detected as dishonest, this node will be excluded from future routes, and the other nodes will stop forwarding packets for it. Third, each node will try to build friendship with other nodes to speed up malicious-node detection. Route diversity will be explored by each node in order to discover multiple routes to the destination, which can increase the chance of defeating malicious nodes that aim to prevent good routes from being discovered. In addition, adaptive route rediscovery will be applied to determine when new routes should be discovered. It can handle various attacks and introduces little overhead into the existing protocols. Both analysis and simulation studies have confirmed the effectiveness of the defense mechanisms.

Introduction and background

A mobile ad hoc network is a group of mobile nodes not requiring centralized administration or a fixed network infrastructure, in which nodes can communicate with other nodes beyond their direct transmission ranges through cooperatively forwarding packets for each other. One underlying assumption is that they communicate through wireless connections.

Spectrum auction is one important approach for dynamic spectrum allocation, in which secondary users lease some unused bands from primary users. However, spectrum auctions are different from existing auctions studied by economists, because spectrum resources are interference-limited rather than quantity-limited, and it is possible to award one band to multiple secondary users with negligible mutual interference. To accommodate this special feature in wireless communications, in this chapter, we present a novel multi-winner spectrum auction game that does not not exist in the auction literature. Since secondary users may be selfish in nature and tend to be dishonest in pursuit of higher profits, we develop effective mechanisms to suppress their dishonest/collusive behaviors when secondary users distort their valuations about spectrum resources and interference relationships. Moreover, in order to make the game scalable when the size of the problem grows, the semi-definite programming (SDP) relaxation is applied to reduce the complexity significantly. Finally, simulation results are presented in order to evaluate the auction mechanisms, and to demonstrate the reduction in complexity.

Introduction

With the development of cognitive radio technologies, dynamic spectrum access has become a promising approach. This allows unlicensed users (secondary users) dynamic and opportunistic access to the licensed bands owned by legacy spectrum holders (primary users) in either a non-cooperative fashion or a cooperative fashion.

In non-cooperative dynamic spectrum sharing, secondary users' existence is transparent to primary users, and secondary users frequently have to sense the radio environment to detect the presence of primary users.

In this chapter we address cooperation stimulation in realistic yet challenging contexts where the environment is noisy and the underlying monitoring is imperfect. We have first explored the underlying reasons why stimulating cooperation under such scenarios is difficult. Instead of trying to force all nodes to act fully cooperatively, our goal is to stimulate cooperation in a hostile environment as much as possible through playing on conditional altruism. To formally address the problem, we have modeled the interactions among nodes as secure-routing and packet-forwarding games under noise and imperfect observation, and devised a set of reputation-based attack-resistant cooperation strategies without requiring any tamper-proof hardware or a central banking service. The performance of the devised strategies has also been evaluated analytically. The limitations of the game-theoretic approaches and the practicability of the devised strategies have also been investigated through both theoretical analysis and extensive simulation studies. The results have demonstrated that, although sometimes there may exist a gap between the ideal game model and the reality, game-theoretic analysis can still provide thought-provoking insights and useful guidelines when designing cooperation strategies.

Introduction

In this chapter, instead of trying to force all nodes to act fully cooperatively, our goal is to stimulate cooperation among selfish nodes as much as possible without relying on any tamper-proof hardware or a central banking service.

In wireless ad hoc networks, autonomous nodes are reluctant to forward others' packets because of the nodes' limited energy. However, such selfishness and non-cooperation causes deterioration both of the system's efficiency and of nodes' performance. Moreover, distributed nodes with only local information might not know the cooperation point, even if they are willing to cooperate. Hence, it is crucial to design a distributed mechanism for enforcing and learning cooperation among the greedy nodes in packet forwarding. In this chapter, we consider a self-learning repeated-game framework to overcome the problem and achieve the design goal. We employ the self-transmission efficiency as the utility function of an individual autonomous node. The self-transmission efficiency is defined as the ratio of the power for self packet transmission over the total power for self packet transmission and packet forwarding. Then, we present a framework to search for good cooperation points and maintain cooperation among selfish nodes. The framework has two steps. First, an adaptive repeated-game scheme is designed to ensure cooperation among nodes for the current cooperative packet-forwarding probabilities. Second, self-learning algorithms are employed to find the better cooperation probabilities that are feasible and benefit all nodes. We then discuss three learning schemes for different information structures, namely learning with perfect observability, learning through flooding, and learning through utility prediction. Starting from non-cooperation, the above two steps are employed iteratively, so that better cooperating points can be achieved and maintained in each iteration.

The topic of frequency responses of the transmitting and receiving filters has not come into our earlier discussions of transceiver design. The frequency characteristics of the filters are also an important aspect of transceiver designs. The stopband attenuation of the transmitting (receiving) filters determines how well separated the subchannels are in the frequency domain at the transmitter (receiver). Frequency separation at the transmitter side is important for the control of spectral leakage, i.e. undesired out-of-band spectral components. Poor separation will lead to significant spectral leakage. This could pose a problem in applications where the power spectrum of the transmitted signal is required to have a large rolloff in certain frequency bands. Wired applications with frequency division multiplexing, e.g. ADSL and VDSL, are such examples [7, 8]. The power spectrum of the transmitted signal should be properly attenuated in the transmission bands of the opposite direction to avoid interference. The power spectrum should also be attenuated in amateur radio bands to reduce interference to radio transmission, called egress emission [8]. On the other hand, poor frequency separation at the receiver side results in poor out-of-band rejection. In ADSL and VDSL applications. some of the frequency bands are also used by radio transmission systems such as amplitude-modulation stations and amateur radio. The radio frequency signals can be coupled into the wires and this introduces radio frequency interference (RFI) or ingress [29]. Poor frequency selectivity of the receiving filters means many neighboring tones can be affected. The signal to interference noise ratios of these tones are reduced and the total transmission rate decreased.

We found in Chapter 6 that in the DMT transceiver the transmitting and receiving filters come from rectangular windows. The spectral sidelobes of these filters are often inadequate to provide sufficient subchannel separation. In this chapter, we will use a filter bank approach to improving frequency separation among subchannels. Based on the filter bank representation of the DMT transceiver, we will introduce what we call subfilters in the subchannels.

In a dynamically changing spectrum environment, it is very important to consider the statistics of different users' spectrum access so as to achieve more efficient spectrum allocation. In this chapter, we study a primary-prioritized Markov approach for dynamic spectrum access through modeling the interactions between the primary and the secondary users as continuous-time Markov chains (CTMCs). Using the CTMC models, to compensate for the throughput degradation due to the interference among secondary users, we derive the optimal access probabilities for the secondary users, by which means the spectrum access of the secondary users is optimally coordinated and the spectrum dynamics clearly captured. Therefore, a good tradeoff between the spectrum efficiency and fairness can be achieved. The simulation results show that the primary-prioritized dynamic spectrum access approach under the criterion of proportional fairness achieves much higher throughput than do the CSMA-based random access approaches and the approach achieving max–min fairness. Moreover, it provides fair spectrum sharing among secondary users with only small performance degradation compared to the approach maximizing the overall average throughput.

Introduction

Efficiently and fairly sharing the spectrum among secondary users in order to fully utilize the limited spectrum resources is an important issue, especially when multiple dissimilar secondary users coexist in the same portion of the spectrum band. Although existing dynamic spectrum access schemes have successfully enhanced spectrum efficiency, most of them focus on spectrum allocation among secondary users in a static spectrum environment.

In autonomous mobile ad hoc networks (MANETs) where each user is its own authority, the issue of cooperation enforcement must be solved first in order to enable networking functionalities such as packet forwarding, which becomes very difficult under noise and imperfect monitoring. In this chapter, we consider cooperation enforcement in autonomous MANETs under noise and imperfect observation and study basic packet forwarding among users using repeated-game models with imperfect information. A belief-evaluation framework is presented to obtain cooperation-enforcement packet-forwarding strategies that are based solely on each node's private information, including its own past actions and imperfect observation of other nodes' information. More importantly, we not only show that the strategy with a belief system can maintain the cooperation paradigm but also establish its performance bounds. The simulation results illustrate that the belief-evaluation framework can enforce cooperation with only a small performance degradation compared with the unconditionally cooperative outcomes when noise and imperfect observation exist.

Introduction

One major drawback of the existing game-theoretic analyses on cooperation in autonomous ad hoc networks is that all of them have assumed perfect observation, and most of them have not considered the effect of noise on the strategy design. However, in autonomous ad hoc networks, even when a node has decided to forward a packet for another node, this packet may still be dropped due to link breakage or transmission errors.

Introduction

In this chapter we present the applicability of probability theory and random variables to the formulation of information theory pioneered by Claude Shannon in the late 1940s. Information theory introduces the general idea of source coding and channel coding. The purpose of source coding is to minimize the bit rate required to represent the source (represented mathematically by a discrete random variable) with a specified efficiency at the output of the source coder. On the other hand, the goal of channel coding is to maximize the bit rate at the input of the channel encoder so that code words can be transmitted through the channel with a specified reliability. Both source coding and channel coding can be achieved with the knowledge of the statistics of both the source and channel.

Entropy of a discrete source

Information comes from observing the outcome of an event. Common events occur frequently (high probability) and therefore carry little information. On the other hand, rare events occur infrequently (low probability) and hence carry much more information. In 1928 R. V. L. Hartley proposed a logarithmic measure of information that illustrates this observation.