Secondary Use of Radio Spectrum by High Altitude Platform

Traditional spectrum licensing enables guaranteed quality of service but could lead to inefficient use of the spectrum. The quest to achieve higher usage efficiency for the spectrum has been the hottest research topic worldwide recently. More efficient transmission technologies are being developed, but they alone cannot solve problems of spatially and temporally underused spectrum and radio resources. In this chapter, the authors review major challenges in traditional spectrum sharing and mechanisms to optimize the efficiency of spectrum usage. They investigate and assess incentives of a primary terrestrial system and secondary system based on a High-Altitude Platform (HAP) to share spectrum towards common benefits. The primary terrestrial system is defined to have exclusive rights to access the spectrum, which is shared by the secondary HAP system upon request. The Markov chain is presented to model two spectrum-sharing scenarios and evaluate the performance of spectrum sharing between primary terrestrial and secondary HAP systems. Simulation results show that to reserve an amount of spectrum from a primary system could encourage spectrum sharing with a secondary system, which has a frequent demand on requesting spectrum resources.

Genetic Algorithms for Decision-Making in Cognitive Radio Networks

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users dynamically allocate the empty frequencies within the licensed frequency band according to their requested QoS (Quality of Service) specifications. This dynamic decision-making is a multi-criteria optimization problem, which the authors propose to solve using a genetic algorithm. Genetic algorithms traverse the optimization search space using a multitude of parallel solutions and choosing the solution that has the best overall fit to the criteria. Due to this parallelism, the genetic algorithm is less likely than traditional algorithms to get caught at a local optimal point.

Technical Challenges in 4G Cognitive Femtocell Systems

This chapter studies the application of femtocells as part of the future cognitive 4G networks. It starts with a demonstration for the evolution of cellular and wireless networks. The developing technology that leads towards a converged LTE-Femtocell wireless environment is described in detail. The chapter presents the key challenges of deploying cognitive femtocell in the macrocell networks. As spectrum utilisation management is the main concern in the future network, the main models for spectrum allocation used to provide enough bandwidth to the femtocell in coexistence with the LTE systems are incorporated for further investigation. In addition, the Quality of Service (QoS) provisioning and the main approaches for measuring end user performance are given as function small range transmission domains. The requirement of an effective mobility management solution in such systems is analysed for future development. The chapter is concluded with a summary.

MAC Layer Protocols for Cognitive Radio Networks

In the last few decades, the Cognitive Radio (CR) paradigm has received huge interest from industry and academia. CR is a promising approach to solve the spectrum scarcity problem. Moreover, various technical issues still need to be addressed for successful deployment of CRNs, especially in the MAC layer. In this chapter, a comprehensive survey of the Medium Access Control (MAC) approaches for CRN is presented. These MAC technologies under analysis include spectrum sharing, multiple antenna techniques, cooperation, relays, distributed systems, network convergence, mobility, and network self-optimization. Moreover, various classifications of MAC protocols are explained in this chapter on the basis of some parameters, like signaling technique, type of architecture, sharing mode, access mode, and common control channel. Additionally, some case studies of 802.11, 802.22, and Mobile Virtual Node Operator (MVNO) are also considered for the case study. The main objective of this chapter is to assist CR designers and the CR application engineers to consider the MAC layer issues and factors in the early development stage of CRNs.

A Survey of High Performance Cryptography Algorithms for WiMAX Applications Using SDR

As wireless broadband technology has become very popular, the introduction of Worldwide Interoperability for Microwave Access (WiMAX) based on IEEE 802.16 standard has increased the demand for wireless broadband access in the fixed and the mobile devices. This development makes wireless security a very serious concern. Even though the Advanced Encryption Standard (AES) has been popularly used for protection in WiMAX applications, still WiMAX is exposed to various classes of wireless attack, such as interception, fabrication, modification, and reply attacks. The complexity of AES also produces high power consumption, long processing time, and large memory. Hence, an alternative cryptography algorithm that has a lower power consumption, faster and smaller memory, is studied to replace the existing AES. A Software Defined Radio (SDR) is proposed as a different way of proving the performance of the cryptography algorithm in real environments because it can be reprogrammed, which leads to design cost and time reductions.

MAC Layer Spectrum Sensing in Cognitive Radio Networks

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users can dynamically allocate the empty frequencies within the licensed frequency band, according to their requested quality of service specifications. In this chapter, the authors investigate and assess the performance of MAC layer sensing schemes in cognitive radio networks. Two performance metrics are used to assess the performance of the sensing schemes: the available spectrum utilization and the idle channel search delay for reactive and proactive sensing schemes. In proactive sensing, the adapted and non-adapted sensing period schemes are also assessed. Simulation results show that proactive sensing with adapted periods provides superior performance at the expense of higher computational cost performed by network nodes.

MAC Protocols for Cognitive Radio Ad Hoc Networks

In contrast to infrastructure-based networks, in wireless ad hoc networks nodes can discover and communicate with each other directly without involving central access points. In this mode of multi-hop networks, all nodes have equal right to access the medium. Hence, the performance of wireless ad hoc networks is mostly limited by traffic congestion. To alleviate such a problem, Cognitive Radio (CR) technology can be used. In this chapter, a CR-based Medium Access Control (MAC) layer for wireless ad hoc networks is investigated. The authors focus on Cognitive MAC protocols for an unlicensed user, which can be enabled to access the large amount of unused spectrum allocated for a licensed user in an intelligent way without causing any harmful interference. They propose a cognitive MAC protocol based on the theory of the Partially Observed Markov Decision Process (POMDP), which sense the radio spectrum, detect the occupancy state of different primary channels, and then opportunistically communicate over unused channels. The objective is to benefit as much as possible from the available spectrum opportunities by making efficient decisions on which channels to access, which ensures maximization of the throughput of the secondary user.

Self-Organization in IEEE Standard 1900.4-Based Cognitive Radio Networks

Distributing Radio Resource Management (RRM) in heterogeneous wireless networks is an important research and development axis that aims at reducing network complexity, signaling, and processing load in heterogeneous environments. Performing decision-making involves incorporating cognitive capabilities into the mobiles such as sensing the environment and learning capabilities. This falls within the larger framework of cognitive radio (Mitola, 2000) and self-organizing networks (3GPP, 2008). In this context, RRM decision making can be delegated to mobiles by incorporating cognitive capabilities into mobile handsets, resulting in the reduction of signaling and processing burden. This may however result in inefficiencies such as those known as the “Tragedy of commons” (Hardin, 1968) that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized network architectures and protocols keep being considered and being compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal-centric RRM schemes. Instead of taking part within the debate among the supporters of each solution, the authors propose a hybrid scheme where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information (Elayoubi, 2010). At some system’s states, the network manager may impose his decisions on the network users. In other states, the mobiles may take autonomous actions in reaction to information sent by the network. Specifically, the authors derive analytically the utilities related to the Quality of Service (QoS) perceived by mobile users and develop a Bayesian framework to obtain the equilibria. They then analyze the performance of the proposed scheme in terms of achievable throughput (for both mobile terminals and the network) and evaluate the price of anarchy which measures how good the system performance is when users play selfishly instead of playing to achieve the social optimum (Johari, 2004). Numerical results illustrate the advantages of using the hybrid game framework in a network composed of HSDPA and 3G LTE system that serve streaming and elastic flows. Finally, this chapter addresses current questions regarding the integration of the proposed hybrid Stackelberg scheme in practical wireless systems, leading to a better understanding of actual cognitive radio gains.

Iterative Optimization of Energy Detector Sensing Time and Periodic Sensing Interval in Cognitive Radio Networks

In this chapter the authors propose a new approach for optimizing the sensing time and periodic sensing interval for energy detectors in cognitive radio networks. The optimization of the sensing time depends on maximizing the summation of the probability of right detection and transmission efficiency, while the optimization of periodic sensing interval is subject to maximizing the summation of transmission efficiency and captured opportunities. Since the optimum sensing time and periodic sensing interval are dependent on each other, an iterative approach to optimize them simultaneously is proposed and a convergence criterion is devised. In addition, the probability of detection, probability of false alarm, probability of right detection, transmission efficiency, and captured opportunities are taken as performance metrics for the detector and evaluated for various values of channel utilization factors and signal-to-noise ratios.

Interference Suppression Capabilities of Smart Cognitive-Femto Networks (SCFN)

Cognitive Radios are considered a standard part of future heterogeneous mobile network architectures. In this chapter, a two tier heterogeneous network with multiple Radio Access Technologies (RATs) is considered, namely (1) the secondary network, which comprises of Cognitive-Femto BS (CFBS), and (2) the macrocell network, which is considered a primary network. By exploiting the cooperation among the CFBS, the multiple CFBS can be considered a single base station with multiple geographically dispersed antennas, which can reduce the interference levels by directing the main beam toward the desired femtocell mobile user. The resultant network is referred to as Smart Cognitive-Femto Network (SCFN). In order to determine the effectiveness of the proposed smart network, the interference rejection capabilities of the SCFN is studied. It has been shown that the smart network offers significant performance improvements in interference suppression and Signal to Interference Ratio (SIR) and may be considered a promising solution to the interference management problems in future heterogeneous networks.