Competitive Spectrum Pricing under Centralized Dynamic Spectrum Allocation

Dynamic Spectrum Allocation (DSA) has been viewed as a promising approach to improving spectrum efficiency. With DSA, Wireless Service Providers (WSPs) that operate in fixed spectrum bands allocated through static allocation can solve their short-term spectrum shortage problems resulting from the bursty nature of wireless traffic. Such DSA mechanisms should be coupled with dynamic pricing schemes to achieve the most efficient allocation. This chapter models the DSA problem where a centralized spectrum broker manages “white space” in the spectrum of TV broadcasters and sells the vacant spectrum bands to multiple WSPs, as a multi-stage non-cooperative dynamic game. Furthermore, an economic framework for DSA is presented and a centralized spectrum allocation mechanism is proposed. The simulation results show that the centralized spectrum allocation mechanism with dynamic pricing achieves a DSA implementation that is responsive to market conditions as well as enabling efficient utilization of the available spectrum.

Risk Engine Design as a Key Security Enhancement to the Standard Architecture for Cognitive Radio

Even though security research in cognitive radio offers specific countermeasures to address known threats, there are a number of unknown conditions or influences that will shape its eventual realization once it reaches capability maturity. To attempt to secure against such unknowns, this chapter describes a risk engine that can incorporate a risk assessment cognition cycle. In various business sectors, risk management is the preferred mechanism to address unknown conditions and therefore offers promise in this context. The chapter describes how the risk engine can potentially address the vulnerabilities inherent to radio operation: in the sensing/perception of spectrum, in the cognition cycle, or in the device infrastructure. It highlights some well-defined threats, their associated countermeasures, and suggests conceptual approaches for a risk engine to intervene in those scenarios. Finally, a case study is introduced to demonstrate an example risk engine's ability to accurately assess particular risks in a given operational environment as well as potentially detect adversarial actions.

Nonparametric Bayesian Prediction of Primary Users' Air Traffics in Cognitive Radio Networks

In cognitive radio networks a secondary user needs to estimate the primary users' air traffic patterns so as to optimize its transmission strategy. In this chapter, the authors describe a nonparametric Bayesian method for identifying traffic applications, since the traffic applications have their own distinctive air traffic patterns. In the proposed algorithm, the collapsed Gibbs sampler is applied to cluster the air traffic applications using the infinite Gaussian mixture model over the feature space of the packet length, the packet inter-arrival time, and the variance of packet lengths. The authors analyze the effectiveness of their proposed technique by extensive simulation using the measured data obtained from the WiMax networks.

Cross-Layer Optimization and Link Adaptation in Cognitive Radios

Cognitive Radio (CR) systems are smart systems capable of sensing the surrounding radio environment and adapting their operating parameters in order to efficiently utilize the available radio spectrum. To reach this goal, different transmission parameters across the Open Systems Interconnection (OSI) layers, such as transmit power, modulation scheme, and packet length, should be optimized. This chapter discusses the Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm as an efficient algorithm for optimizing and adapting CR operating parameters from physical, MAC, and network layers. In addition, the authors present two extensions for the proposed algorithm. The first one is Automatic Repeat reQuest-ADPSO (ARQ-ADPSO) for efficient spectrum utilization. The second one is merging ARQ-ADPSO and Case-Based Reasoning (CBR) algorithms for autonomous link adaptation under dynamic radio environment. The simulation results show improvements in the convergence time, signaling overhead, and spectrum utilization compared to the well-known optimization algorithms such as the Genetic Algorithm (GA).

Complexity Issues within Eigenvalue-Based Multi-Antenna Spectrum Sensing

This chapter provides a deep insight into multiple antenna eigenvalue-based spectrum sensing algorithms from a complexity perspective. A review of eigenvalue-based spectrum-sensing algorithms is provided. The chapter presents a finite computational complexity analysis in terms of Floating Point Operations (flop) and a comparison of the Maximum-to-Minimum Eigenvalue (MME) detector and a simplified variant of the Multiple Beam forming detector as well as the Approximated MME method. Constant False Alarm Performances (CFAR) are presented to emphasize the complexity-reliability tradeoff within the spectrum-sensing problem, given the strong requirements on the sensing duration and the detection performance.

Asynchronous Channel Allocation in Opportunistic Cognitive Radio Networks

In the case of Opportunistic Spectrum Access (OSA), unlicensed secondary users have only limited knowledge of channel parameters or other users' information. Spectral opportunities are asymmetric due to time and space varying channels. Owing to this inherent asymmetry and uncertainty of traffic patterns, secondary users can have trouble detecting properly the real usability of unoccupied channels and as a consequence visiting channels in such a way that they can communicate with each other in a bounded period of time. Therefore, the channel service quality, and the neighborhood discovery (NB) phase are fundamental and challenging due to the dynamics of cognitive radio networks. The authors provide an analysis of these challenges, controversies, and problems, and review the state-of-the-art literature. They show that, although recently there has been a proliferation of NB protocols, there is no optimal solution meeting all required expectations of CR users. In this chapter, the reader also finds possible solutions focusing on an asynchronous channel allocation covering a channel ranking.

Distributed Mechanisms for Multiple Channel Acquisition in a System of Uncoordinated Cognitive Radio Networks

Due to the constraint imposed by the Dynamic Spectrum Access paradigm, Cognitive Radio (CR) networks are entangled in persistent competition for opportunistic access to underutilized spectrum resources. In order to maintain quality of service, each network faces the challenge of acquiring dynamic enough channels to meet channel size requirement. The main goal of every CR network is to minimize the amount of contention experienced during channel acquisition and to maximize the utility derived from acquired channels. This is a major challenge, especially without a global communication protocol that can facilitate communication between the networks. This chapter discusses self-coexistence of CR networks in a decentralized system with no support for coordinated radio transmission activities. Channel acquisition mechanisms that can help networks minimize contention and maximize utility are also discussed. The mechanisms guarantee fast convergence of the system leading to an equilibrium state whereby networks are able to operate on acquired channel with minimal or zero contention.

On Fuzzy Logic-Based Channel Selection in Cognitive Radio Networks

Cognitive radio networks are a new technology based on which unlicensed users are allowed access to licensed spectrum under the condition that the interference perceived by licensed users is minimal. That means unlicensed users need to learn from environmental changes and to make appropriate decisions regarding the access to the radio channel. This is a process that can be done by unlicensed users in a cooperative or non-cooperative way. Whereas the non-cooperative algorithms are risky with regard to performance, the cooperative algorithms have the capability to provide better performance. This chapter shows a new fuzzy logic-based decision-making algorithm for channel selection. The underlying decision criterion considers statistics of licensed user channel occupancy as well as information about the competition level of unlicensed users. The theoretical studies indicate that the unlicensed users can obtain an efficient sharing of the available channels. Simulation results are reported to demonstrate the performance and effectiveness of the suggested algorithm.

Energy-Efficient Cooperative Spectrum Sensing for Cognitive Radio Networks

One of the main problems of Cooperative Spectrum Sensing (CSS) in cognitive radio networks is the high energy consumption. Energy is consumed while sensing the spectrum and reporting the results to the fusion centre. In this chapter, a novel partial CSS is proposed. The main concern is to reduce the energy consumption by limiting the number of participating users in CSS. Particularly, each user individually makes the participation decision. The energy consumption in a CSS round is expected by the user itself and compared to a predefined threshold. The corresponding user will participate only if the expected amount of energy consumed is less than the participation threshold. The chapter includes optimizing the participation threshold for energy efficiency maximization. The simulation results show a significant reduction in the energy consumed compared to the conventional CSS approach.

Heterogeneous Service-Oriented Spectrum Trading

Multiple characters of spectrum resource bring many challenges to spectrum trading. The demanders may not find the full-matching spectrum resource. Meanwhile, the optimal matching strategy cannot be determined if the demanders have different matching ratios. This chapter proposes an algorithm called HSO-ST (Heterogeneous Service-Oriented Spectrum Trading) with the target of maximum matching number under the priority restriction. This algorithm can satisfy as many secondary users as possible. Compared with other spectrum trading strategies, HSO-ST can greatly improve the spectrum demand-matching ratio.