Energy-Efficient Cooperative Spectrum Sensing for Cognitive Radio Networks

Energy-Efficient Cooperative Spectrum Sensing for Cognitive Radio Networks

Saud Althunibat, Sandeep Narayanan, Marco Di Renzo, Fabrizio Granelli
DOI: 10.4018/978-1-4666-6571-2.ch004
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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.
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1. Introduction

Recently, energy efficiency in wireless networks has received a significant amount of research. This is because mobile users are usually battery-powered. The limited energy resources represent a challenge hindering wide implementation of some recent technologies (Fettweis & Zimmermann, 2008). Some wireless systems, such as cognitive radio (CR), implies more energy consumption than other systems. Cognitive radios in general require more energy to operate, as compared to the conventional transceivers due to the additional tasks required to perform cognitive transmission. In CR, a licensed spectrum can be exploited by unlicensed users when it is (temporarily and spatially) unused by licensed users. This requires awareness of spectrum status, which is performed by a process termed as spectrum sensing (Mitola & Maguire, 1999), (Haykin, 2005).

In order to identify the unused spectrum portions, the unlicensed users, also called cognitive users (CUs), are enforced to sense it for specific period, inducing energy consumption which does not exist in the typical wireless systems. Moreover, aiming at improving the reliability of spectrum sensing, cooperative spectrum sensing (CSS) is proposed (Mishra, Sahai & Brodersen, 2006), (Di Renzo, Imbriglio, Graziosi & Santucci, 2009), (Ghasemi & Sousa, 2007). In CSS, the local sensing results are reported to a central entity, called fusion centre (FC). The FC is in charge of making a global decision regarding the spectrum occupancy by applying a specific fusion rule (FR). Although CSS decreases the probability of erroneous decision considerably by mitigating the effects of multipath fading and shadowing, it causes extra delay, security risks (I.F. Akyildiz, Lo & Balakrishnan, 2011) (Di Renzo Graziosi & Santucci, April 2009) and more energy consumption.

The high energy expenditure in CSS is caused by the individual sensing and reporting the sensing results to the FC. In case of large number of CUs and/or large number of sensed channels, energy-efficient CSS becomes a pressing need for CR systems. Aiming at reducing energy consumption, limiting the amount of reported results has been widely investigated. In general, two well-known schemes for results’ reporting (S. Chaudhari, Lunden, Koivunen & Poor, 2012) (Viswanathan & Varshney, 1997), soft scheme (SS) and hard scheme(HS). In SS, the sensing result of each CU is quantized locally by a multiple number of bits and sent to the FC. On the other hand, the result is quantized by only one bit in HS. As a CU employing HS reports only one bit, it is clear that the energy consumption is lower than if SS is employed (Maleki, Chepuri, & Leus, 2011). Thus, in this work, we consider only HS.

Many works have investigated the reduction of energy consumption in CSS. These works can be classified into four different approaches: (i) Reducing the number of sensing users, (ii) Reducing the sensing time, (iii) Reducing the reported sensing data, and (ii) Optimizing the decision-making rule.

In the first approach, (Maleki et al., 2011), and (Pham, Zhang, Engelstad, Skeie & Eliassen, 2010) have proposed algorithms that use the minimum number of sensing users based on different setups while satisfying predefined thresholds on the detection accuracy. In (Cheng et al.,2012), the CUs are divided into non-disjoint subsets such that only one subset senses the spectrum while the other subsets enter a low power mode. The energy minimization problem is formulated as a network lifetime maximization problem with constraints the detection accuracy. An algorithm for user selection is proposed in (Najimi et. al., 2013), where the user subset that has the lowest cost function and guarantees the desired detection accuracy is selected. The cost function is related to the system energy consumption.

Key Terms in this Chapter

Wireless Networks: A type of communication networks that uses wireless data transfer techniques.

Wireless Communications: The transfer of data between two or more devices that are not electrically connected to each other.

Cognitive Radio: A wireless technology proposed to improve spectrum efficiency. It is based on exploiting the temporarily unused portions of the spectrum by unlicensed users.

Cognitive Radio Networks: A type of wireless networks that consists a set of users cooperating to use a specific spectrum based on cognitive radio technology.

Cooperative Spectrum Sensing: An approach proposed to enhance the reliability of the spectrum sensing process. It implies sharing the local sensing results of several users at a central entity, aiming at improving the reliability of the process decision.

Energy Efficiency: The ratio of the average successfully transmitted data in bits to the average consumed energy in Joule.

Spectrum Sensing: A method used to identify the spectrum status either used or unused.

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