Journal of Computer Networks and Communications

Volume 2016, Article ID 9706781, 9 pages

http://dx.doi.org/10.1155/2016/9706781

## Performance Analysis of Cooperative Spectrum Sensing under Guaranteed Throughput Constraints for Cognitive Radio Networks

Department of Electrical and Electronics Engineering, University of Bahrain, P.O. Box 32038, Isa Town, Bahrain

Received 7 November 2015; Revised 23 March 2016; Accepted 10 April 2016

Academic Editor: Rui Zhang

Copyright © 2016 H. F. Al-Doseri and M. A. Mangoud. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

One of the main challenges in cognitive radio networks is the ability of secondary users to detect the primary user presence with high probability of detection. In previous research, optimizing cooperative sensing in cognitive radio networks is performed for either a targeted probability of detection or a false alarm. After setting one of the probabilities as an optimization constraint, the other is optimized. In this paper, a guaranteed constant throughput at the secondary users is introduced as a target while optimizing probability of detection for cooperative sensing. Both sensing time values and number of cooperated cognitive radio secondary users are investigated to maximize the probability of detection of primary user. AND and OR hard decision schemes are considered and compared with soft decision scheme which is weighted modified deflection coefficient scheme (W-MDC). It is illustrated that cooperation of all users and utilizing full frames for sensing time will not provide maximum probability of detection. A tradeoff between performances of cognitive radio networks with and without optimization is presented. The effects of varying network sizes, normalized target throughput, maximum frame duration times, and received signal-to-noise ratio at the fusion center are investigated for different fusion rules.

#### 1. Introduction

Cognitive radios (CRs) have been widely considered as a promising solution to efficiently utilize the radio spectrum by allowing secondary users (SUs) to access the spectrum of licensed primary users (PUs) [1, 2]. Recently, the Federal Communications Commission (FCC) has opened the TV white space (TVWS), which is the unused TV band in time and space for cognitive radios [3]. Consequently IEEE has formed various working task groups (TGs) such as IEEE 802.11af, 802.15.4m, 802.19.1, and 802.22b to regulate the unlicensed applications of underutilized TVWS. One of the main challenges of these CRs is that the operation of SUs needs to be maintained at maximum possible throughput without causing disruptive interference to the PUs; this is known as sensing-throughput tradeoff [4]. Cooperative spectrum sensing with different fusion schemes is used to overcome many problems facing the individual sensing to achieve the optimum probability of detection () and probability of false alarm (). Previously, it is demonstrated that there is an optimal number of cooperated users less than the total network size that gives the best performance of a secondary users network using AND and OR hard decision fusion rules [5]. Further CR network performance enhancement was achieved by applying linear cooperation of local test statistics [6], where modified deflection coefficients (MDC) were optimized to find the weight vector that combats the distractive channel effects. Apparently, soft decision is superior to hard decision since it imposes more sensing information between the CR users and the fusion center (FC). Cooperative spectrum sensing literature includes many research contributions to maximize the channel efficiency or the normalized SUs throughput by optimizing thresholds values, number for SUs, and detection/sensing time [7–11]. Earlier optimization studies of optimizing cooperative users’ numbers focus on the primary users’ perspective and minimize under the constraint of fixed as constant detection rate (CDR) case in [4]. Other studies lay emphasis on the problem of designing the sensing slot duration and maximize the achievable throughput for the secondary users under the constraint that the primary users are sufficiently protected [7]. However, when targeting fixed probability of detection, this does not guarantee a constant throughput for secondary users. In this paper, we look at the optimization problem from SUs point of view while satisfying the PU requirement. The main contributions of this paper are as follows. A simultaneous optimization of both number of users and sensing time under novel constraint will be presented. The target will be achieving high protection of primary user () under a guaranteed normalized throughput for secondary users. Furthermore, performances of different CR network scenarios are compared and sensing-throughput tradeoff is discussed for different fusion schemes. Hard decision fusion rules AND and OR are compared with weighted modified deflection coefficient scheme (W-MDC) soft decision rule. Investigations of effect of network sizes, targeted throughput values, total sensing/detection time frames, and received SNR levels are to be presented. The rest of the paper is organized as follows. Section 2 presents network model description and normalized SUs throughput definition. In Section 3, expressions for are derived under constant normalized throughput constraint for different fusion schemes to formulate the optimization problem. Optimizing numbers of cooperative users and sensing time for CRs will be discussed in Section 4 along with investigations of the effect of key parameters such as network size, targeted efficiency rate, total sensing time, and received SNR levels. Finally, the paper will be concluded in Section 5.

#### 2. Network Model and Throughput Tradeoff

##### 2.1. Single User Energy Detection

Suppose a cognitive radio network with cooperative secondary users and one fusion center that make a decision on the primary user activity based on the received decisions from the different secondary users. Assume that energy detection is utilized at each single secondary user (SU). Let and be the hypotheses of the primary user being inactive or active, respectively. is the received signal by the th SU at sample . is the PU signal and it is assumed to be independent and identically distributed (i.i.d.) random process with zero mean and variance of . is assumed to be a complex PSK modulated signal.

is the noise signal that is considered as i.i.d. Gaussian distribution with zero mean and variance of .

For a single CR spectrum sensing scheme [5–8], the local decision rule is modelled aswhere is the test statistic of the th secondary user using energy detection over a detection interval of samples which is calculated as and is the corresponding decision threshold, since is the sum of squares of Gaussian random variables. It is shown previously in [6] that the standard Gaussian variable follows a central chi square distribution with degrees of freedom if is true. In case is true the standard Gaussian variable would follow a noncentral chi square distribution with degrees of freedom. According to the central limit theorem, if the number of samples is large enough, test statistics are asymptotically Gaussian distributed. Define the SNR at the th user which is the primary user signal power to noise ratio measured at the SU receiver of interest as . The probabilities of detection and false alarm ( and ) at the th secondary user in terms of targeted , and and sampling size , as introduced in [3], are given bywhere denotes the complementary cumulative distribution function of a zero mean, unit variance Gaussian distribution.

##### 2.2. Throughput Calculations

Cognitive user is considered to operate in a frame basis transmission. As shown in Figure 1, the cognitive user performs samples in a periodic sensing and transmission frame duration every ; if is the sampling time, then . The frame comprises sensing interval time that has detection samples used for sensing PU. The second part of the frame is the data transmission interval (active or idle) with duration of that has - samples, where .