Non-Gradient Based PDF Approximation for Sensor Selection in Cognitive Sensor Networks

Non-Gradient Based PDF Approximation for Sensor Selection in Cognitive Sensor Networks

Mohammad Reza Ghavidel Aghdam, Reza Abdolee, S. K. Seyyedi Sahbari, Behzad Mozaffari Tazehkand
DOI: 10.4018/IJITN.2019010101
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Energy consumption in detection is a key objective for cognitive sensor network. Therefore, measuring the energy consumption is an important issue for efficient spectrum sensing. In order to compute the consumed energy at sensor nodes, their energy probability density function (PDF) is often required. In this article, the authors study the problem of spectrum sensing in cognitive networks and focus on strategies that can substantially affect the energy efficiency and complexity of such algorithms. In particular, they consider an energy detection mechanism in cooperative spectrum sensing where the knowledge of the energy PDF is the key. Since in practice the true value of such a PDF is unavailable, the authors propose to use non-gradient based optimization algorithms to find the parameters of approximated PDF function. In the proposed method, the corresponding PDF parameters are computed iteratively using Genetic and PSO algorithms. The numerical results show that the proposed technique outperforms prior methods.
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The demand for the radio spectrum has significantly increased in recent years. The Federal Communications Commission (FCC) (Kolodzy, 2002) states that the fixed spectrum allocation can be inefficient, and the licensed spectrum may remain unoccupied for a long time. In its recent Report and Order (Federal Communications Commission, 2008), the FCC permitted the operation of networks consisting of low-power portable devices and sensors in the VHF-UHF band. The FCC is also in the process of seeking comments on the Secondary Users allocation of the 2.36–2.4 GHz band for body sensor network operation to offer wireless healthcare services (Federal Communications Commission, 2009).

Cognitive radios achieve secondary spectrum access while limiting harmful interference to licensed primary users. To achieve this, spectrum sensing of radio channels is employed to identify channels that may be vacant. Transmission is then limited on channels determined to be idle in order to avoid interference with primary users. Reliable determination of idle channels is thus a critical problem. The hidden terminal problem as well as fading effects can adversely affect the performance reliability of a cognitive radio.

One of the main objectives of cognitive radio is to make use of the spectrum holes opportunistically. The SUs should sense the arrival of the PUs and move to another unused spectrum holes without causing interference to the PUs. There are two types of sensing technique a) Signal processing technique b) Cooperative sensing technique. The signal processing technique is further divided into Energy Detection(ED), Matched Filter Detection (MFD), cyclostationary based detection technique and other techniques. On the other hand, cooperative sensing technique can be further divided into centralized spectrum sensing, decentralized spectrum sensing and hybrid spectrum sensing techniques (Joshi, 2013). It has been shown that in a network of cognitive radios, decentralized spectrum sensing improves the detection performance (Mishra, 2006).

The energy detection technique is simple strategy to obtain information about the PU signal for channel sensing. Using this technique, the received energy on a licensed band is measured. If the measured energy exceeds a predefined threshold level, primary user is present; otherwise a spectrum hole is confirmed. Through spectrum sensing, the SUs can detect the unused spectrum of primary users (white space spectrum), and utilize those licensed channels. Cooperative spectrum sensing is an effective method to improve the spectrum sensing performance. Ghasemi (2008) is shown that the required detector sensitivity and sensing time is reduced by increasing the number of cooperative nodes whereas the communication overhead over the network increases.

Censoring has been considered in the context of wireless sensor networks and cognitive radios (Sun, 2007), (Appadwedula, 2008; Appadwedula, 2005; Appadwedula, 2002) and shown to be effective in saving energy. The design of censoring regions under different optimization settings related to the detection performance has been considered in (Rago, 1996) for minimization of the miss detection probability with constraints on the false alarm rate and the network energy consumption. Further, (Appadwedula, 2008), (Appadwedula, 2008), and (Rago, 1996) consider minimization of the detection error probability subject to the network energy consumption.

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