Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks

Abstract

Recent technological advances have resulted in the development of wireless ad hoc networks which are envisioned to provide rapid on-demand network deployment due to their self-configurability and lack of pre-deploy infrastructure requirements. These devices generally have small form factors, and have embedded storage, processing and communication ability I. F. Akyildiz (2009). With the growing proliferation of such wireless devices, the spectrum is increasingly getting congested. However, it has also been pointed out in several recent measurement reports that the spectrum are highly under-utilized FCC (2002). In order to achieve much better spectrum utilization and viable frequency planning, Cognitive Radios (CRs) are under development to dynamically capture the unoccupied spectrum J. Mitola (1999). Many challenges arise with such dynamic and hierarchical means of accessing the spectrum, especially for the dynamic resource allocation of CR users by adapting their transmission and reception parameters to the varying spectrum condition while adhering to power constraints and diverse quality of service (QoS) requirements (see, for example, S. Tao (2006); Q. Zhao (2007)). In this chapter, an energy constrained wireless CR ad hoc network is considered, where each node is equipped with CR and has limited battery energy. One of the critical performance measures of such networks is the network lifetime. Additionally, due to the infrastructureless nature of ad hoc networks, distributed resource management scheme is desired to coordinate and maintain communications between each transmitting receiving pair. In this context, the present chapter provides a framework of distributed energy efficient spectrum access and resource allocation in wireless CR ad hoc networks that employ orthogonal frequency division multiple access (OFDMA) K. Fazel (2003); A. Pandharipande (2002) at the physical layer. OFDMA is well suited for CR because it is agile in selecting and allocating subcarriers dynamically and it facilitates decoding at the receiving end of each subcarrier J. Bazerque (2007). In addition, multi-carrier sensing can be exploited to reduce sensing time I. F. Akyildiz (2006). Each emerging CR user will select its subcarriers and determine its transmission parameters individually by solving an optimization problem. The optimization objective is to minimize its energy consumption per bit1 while satisfying its QoS requirements and power limits.