I. INTRODUCTION
A survey carried out by the Federal Communications Commission (FCC) on spectrum utilization has indicated that the actual licensed spectrum whose sole owner is primary user (PU) is largely under-utilized in vast geographical dimensions [Reference Force1]. Cognitive radio (CR) provides opportunistic access to unused bands to the secondary users (SUs) [Reference Mitola and Maguire2,Reference Haykin3]. Sensing accuracy is important for avoiding interference to the PU in CR technology. Reliable spectrum sensing is not always guaranteed due to multipath fading, shadowing, and hidden terminal problem. In the recent years, cooperative spectrum sensing scheme (CSS) has thus become a popular technique to solve the efficiency of spectrum usage and provide a high level of protection to the PU from SUs. CSS has been widely studied in [Reference Ganesan and Ye4–Reference Lu and Wei8]. The CSS has two successive stages; sensing and reporting. In sensing stage, spectrum sensing is done by several local SUs. Then in next stage, sensing decisions of the SUs is sent to the fusion center (FC) where it is combined by using some fusion rule to make an overall decision regarding the presence or absence of PU. Among several spectrum sensing techniques, energy detector (ED) is one of the most popular detectors employed for spectrum sensing. Measuring only the received signal power and comparing it with a pre-defined threshold, the ED is a non-coherent detection device with low-implementation complexity and is more energy efficient. It is to be noted that ED performance is highly degraded under low signal to noise ratio (SNR) and noise uncertainty condition [Reference Park9]. Also, ED cannot well differentiate between the signal and noise, if the detected observational values lie in the confused region i.e., between signal and noise. Another detector, preamble detector (PD) [Reference Shin, Ramachandran and Roy10] is quite reliable as it takes advantage of the processing gain inherent in the preamble. Its power consumption, however, may be usurious.
A censoring scheme using double threshold based on ED was proposed in [Reference Sun, Zhang and Letaief11]. If the detected observational energy values (O i ) by the SUs lie in the confused region, they will not report their decision to the FC. The authors showed that their proposed method can reduce the sensing time. However, at the same time, it also created sensing failure problem. An adaptive double threshold energy detection based on Markov model (ADEMM) for CR was proposed in [Reference Liu12]. Time-varying characteristic of the channel was taken into consideration to overcome the issue of confused region of the occupied channel. A hybrid double-threshold-based CSS over fading channels using ED was proposed in [Reference Vien, Nguyen, Trestian, Shah and Gemikonakli13]. The authors also proposed an optimal SU selection algorithm for forwarding local decisions to the FC. Another method using double threshold based on ED to increase the detection performance as compared with the conventional ED, proposed in [Reference Zhu, Xu, Wang, Huang and Zhang14]. In this method, SU will make the local decision by comparing their O i of the clear region with the two pre-fixed thresholds of ED. If the O i lies in the confused region i.e. between these two pre-fixed thresholds then the SU will forward it to the FC. The FC will make an overall decision by considering the local decision of SU of clear region and comparing the O i of confused region with another final threshold value of ED. We have considered ref. [Reference Zhu, Xu, Wang, Huang and Zhang14] as a baseline approach for our proposed method and have compared it in Section IV. A two-step fast-and-fine sensing has been proposed in [Reference Choi, Xin and Rangarajan15] where the second step of fine sensing is triggered when it is difficult to decide on the presence of PUs in the first step of energy detection. Also, a two step selectively triggered CSS in CRNs is proposed in [Reference Paul, Pak and Choi16] to alleviate the problems of ED. Here, as a first step, non-cooperative ED is used and as a second step, again ED is used for CSS to arrive at a final decision. There have also been similar approaches such as two-step hard and soft combination techniques, proposed in [Reference Alemseged, Sun, Tran and Harada17], where the second step of the soft combination is initiated when it is difficult to arrive at a decision in the first step of the hard combination. However, it uses only one detector i.e. ED for decision-making process which is completely different from our proposed approach. Further, a novel cascaded-clear channel assessment (CCA) method: cascaded ED matched filter (CEM) for wireless network was proposed in [Reference Triwicaksono, Rauniyar and Shin18]. CEM cartel the advantages of two CCA mechanisms, energy efficient ED, and reliable matched filter (MF) to devise a new approach of CCA in wireless network. However, CEM scheme considers multi-stage detection through ED and MF detector. In CEM scheme, the second step detector MF is only triggered when the probability of false alarm of ED is set as 10, 20, 30, and 40%.
To overcome the noise uncertainty problem of ED and to increase the detection performance, we propose a CSS technique using adaptive activation of energy efficient ED and reliable PD. As the first step of our proposed method, we have used ED to take decision in the clear region where the detector can easily differentiate between signal and noise and makes its own local decision. There are two thresholds for the measured energy in the first step and ED is used in the first step. If the sensed energy in the first step is between these two thresholds, the second step which involves the activation of PD is activated in a cooperative way. Otherwise, the second step detector PD is not activated at all. In this way, we can enhance the detection performance and energy efficiency at the same time by using the collaborative advantages of ED and PD.
The rest of the paper is organized as follows: Section II presents system brief working and explanation of ED and PD. Section III describes our proposed model. Numerical results are shown in Section IV. Finally, conclusion is drawn in Section V.
II. STRUCTURES OF ENERGY DETECTOR AND PREAMBLE DETECTOR
The main goal of CR is to correctly identify the presence and absence of PU and allows the SUs to utilize the unused spectrum if it is not used by PUs. Under binary hypothesis testing, we consider the occurrence of two input events in observing signal x i in some observation interval denoted by [Reference Lehtomäki, Vartiainen, Vuohtoniemi and Saarnisaari19]
$$\eqalign{H_0 &: x_i = n_i \cr H_1 &: x_i = s_i + n_i,}$$
where i = 1, 2, 3, … N is number of samples. H
0 represents the hypothesis that the observation vector consists of noise. H
1 represents the hypothesis that the observation vector consists of noise and signal. The noise component n
i
is assumed to be Additive White Gaussian random variable which is independent and identically-distributed (i.i.d) with zero mean normal distribution with variance n
i
~
${\cal N}(0,\,\sigma^{2})$
, and s
i
is the signal.
A) Energy detector
ED is non-coherent detector which detects the presence of signals by simply squaring its energy and comparing that energy around the carrier frequency with a certain threshold. The ED consists of a quadrature receiver with y I and y Q representing samples from In-phase and Quadrature branch, respectively. The samples after passing the squaring device, output of the integrator is denoted by
$$y_I = y_Q = \left({1 \over N_0}\right)\int^T_0 r^2(t)\,dt,$$
where r(t) is input signal, N 0 is noise spectral density.
Within observed sensing period, test statistic ED can be approximated as Y ED = y I + y Q . At the observation time t, decision variable Y ED will be compared with a detection threshold of ED denoted by λ ED . Threshold value is set to meet the target probability of false alarm p fa according to the noise power. In the case of p fa , the detector classifies the channel as busy when the actual channel is free. Probability of detection (p d ) is the probability of correctly detecting the presence of PU when the actual channel is in busy state. The expression for p fa and p d can be given as [Reference Park9]
$$p_{fa}^{ED}= 1 - F_{\chi}\left({\lambda^{ED} \over \sigma^2},2n_T\right),$$
where F χ is cumulative distribution function (CDF) of standard chi-square random variable with k degree of freedom, n T is the number of bits during the observation interval T, and has a variance σ = 1,
$$p_{d}^{ED}= {\cal Q}\left(\sqrt{\psi_{ED}},\sqrt{{\lambda^{ED} \over \sigma^2}}\right),$$
where
${\cal Q}$
is generalized Marcum-Q function with non-central chi-square distribution of non-centrality parameter
$s^{2}= \psi_{ED} = n_{T}(SNR)$
.
ED is a robust, universal detector which can be deployed in all systems without requiring any prior knowledge of the type of underlying modulation scheme employed. Also, ED is more energy efficient in terms of energy consumption and ED's computational complexity is lower than the most of the detectors. However, ED is inherently less reliable at low SNR and noise uncertainty condition.
B) Preamble detector
For coherent detection of signals, the sensing node has to attain time synchronism with the ongoing transmission. In packet-based systems, the process of acquiring time synchronism is facilitated by the transmission of a preamble in front of every packet, typically consisting of repetitions of a sequence of known symbols. The receiver performs a correlation of the known sequence with the received signal with varying time offsets. At the offset corresponding to time synchronism, the correlation is high due to the processing gain resulting from the repetition of the known symbols. This high correlation is both indicative of the signal presence and provides an estimate of the time offset. This carrier-sense based CCA using the correlation of the known preamble with the received signal is called PD [Reference Shin, Ramachandran and Roy10].
In simple terms, PD [Reference Shin, Ramachandran and Roy10,Reference Nagaraj, Khan, Schlegel and Burnashev20] performs correlation of the known signal, thus the high processing gain due to correct offset gives higher reliability than ED. However, for the successful operation of PD, it consumes a high amount of power. Hence, PD is also considered as power hungry detector. Also, the computational complexity of PD is much higher than ED.
The expression for p fa and p d of a PD can be given as [Reference Shin, Ramachandran and Roy10]
$$p_{fa}^{PD}= 1 - F_{\chi}\left({\lambda^{PD} \over \sigma},2\right),$$
$$p_d^{PD} = {\cal Q}\left(\sqrt{\psi_{PD}},\sqrt{{\lambda^{PD} \over \sigma^2}}\right),$$
where λ
PD
is the threshold setting for PD, the non-centrality parameter
$s^{2}= \psi_{PD}=2n_{T}(SNR)$
is the output of the filters in in-phase, and quadrature branches at the correct offset. The correlation process of PD has a central chi-square distribution with 2 degree of freedom with a variance
$(\sigma= \sqrt{n_{T}})$
.
III. PROPOSED SCHEME
Our proposed scheme is based on adaptive activation of energy efficient ED and reliable PD. As the first step of our proposed method, ED is used where it can easily make its own local decision. There are two thresholds λ1 ED and λ2 ED for the measured energy in the first step. If the sensed energy in the first step is between these two thresholds, the second step which involves the activation of PD is triggered to make an appropriate decision on the presence or absence of PU signal. Otherwise, the second step detector PD is not activated. In this way, we can enhance the detection performance and energy efficiency at the same time.
In our proposed scheme, when an SU confirms the presence or absence of the signal of PU, the second step PD detector is not activated at all; only the first step ED detector is sufficient to take the decision and increase the energy efficiency of our proposed scheme. The determination of double threshold values for ED is discussed in paper [Reference Zhu, Xu, Wang, Huang and Zhang14,Reference Peh, Liang, Guan and Zeng21]. For threshold setting, first, the boundary values
$\bigtriangleup_{0}$
and
$\bigtriangleup_{1}$
is defined according to the chosen false alarm probability of ED to determine the region of uncertainty. Based on
$\bigtriangleup_{0}$
and
$\bigtriangleup_{1}$
, the two threshold value λ1
ED
and λ2
ED
of ED is set accordingly. It should be noted that the region of uncertainty depends on the value of
$\bigtriangleup_{0}$
and
$\bigtriangleup_{1}$
chosen. If we choose the higher values for
$\bigtriangleup_{0}$
and
$\bigtriangleup_{1}$
, there is a greater region of uncertainty i.e. sensed energy is in between two thresholds values λ1
ED
and λ2
ED
. Hence, there is a higher chance of activation of second step detector PD and vice versa. The threshold setting λ3
PD
for PD is set according to the chosen false alarm probability which is the same false alarm probability of first step detector ED.
In step 1, if the sensed energy level by the SU is above threshold λ2 ED , then decision D i ‘11’ will be taken and it indicates the presence of a PU's signal i.e. condition H 11 and if it is below threshold λ1 ED , then decision D i ‘00’ will be taken and it indicates the absence of a PU's signal i.e. condition H 00. When the received energy of the PU's signal at the SU is between these two thresholds i.e. in the confused region then ED cannot take any decision D i and further reports either ‘01’ or ‘10’ to FC to report uncertainty of the decision. For simplicity, it should be noted that ‘01’ or ‘10’ represents the same uncertainty of decision in our scheme. Now, the second step PD detector is activated. In the second step, the FC orders all the SUs to sense. The SUs sensing in the second step only need to report a one-bit decision; that is, ‘1’ means the presence of PU and ‘0’ means the absence of PU. Also, it should be noted that, in our proposed scheme, if SU is uncertain about the decision, it triggers the activation of second step PD to arrive at a concrete decision; the sensing result from the first step ED is no more considered and FC instructs all the SUs for sensing in the second step using reliable PD where the threshold is pre-defined for PD. Finally, the FC makes a decision by using a given hard decision fusion rule such as Majority, AND, or OR rule.
The decision D i in the first step when ED is active is given as:
$$D_i = \hbox{if} \left\{\matrix{O_i \lt \lambda_1^{ED},\hfill & H_{00}\hfill\cr \lambda_1^{ED} \leq O_i \leq \lambda_2^{ED},\hfill & H_{10}\,\hbox{or}\,H_{01}\hfill \cr O_i \geq \lambda_2^{ED},\hfill & H_{11}\hfill} \right.$$
In the second step, when the PD is activated, we find the decision variable V from Y PD over M offset bits. Y PD is the output from the PD receiver. Variable V is compared with PD threshold λ3 PD to decide the presence or absence of PU signal.
The decision D i in the second step when PD is active is given as:
$$D_i=V=Y_{PD}^m \left\{\matrix{0 \hfill & 0 \leq \sum\limits_{i=1}^{N-K} O_i \leq \lambda_3^{PD} \hfill \cr 1 \hfill &\sum\limits_{i=1}^{N-K} O_i \gt \lambda_3^{PD} \hfill}, \quad m = 1, \ldots M,\right.$$
where m = 1, … M is the varying time offset bits where the PD receiver performs a correlation of the known sequence with the received signal.
The FC makes a final decision based on a voting rule. In this paper, we have considered widely used hard decision voting rule, ‘K out of N rule’. In this rule, the presence of PU is declared, if K SUs out of total N SUs declare the presence of PU. Similarly, the absence of PU is declared if K SUs out of total N SUs declare the absence of PU. The procedural flow of our proposed scheme block diagram is shown in Fig. 1.
Procedural flow of proposed scheme.
Based on above discussion, we can say that our proposed scheme is energy efficient than PD as in our scheme the second step PD is only triggered when the O i of first step ED lies in the confused region. The detection performance of our proposed scheme is higher as compared with ED due to the involvement of reliable PD. Since the involvement of cooperative PD is only triggered if the O i values lies in confused region, we can say that the computational complexity of our proposed scheme is higher than ED and lower than PD i.e. in between ED and PD. Thus, making our proposed scheme as both energy efficient than PD and reliable than ED.
First Step Algorithm-Activation of Non-cooperative ED
Second Step Algorithm-Activation of Cooperative PD
A) False alarm and detection probabilities of proposed adaptive method
The use of double thresholds for ED allow the probabilities of false alarm and miss detection to be set arbitrarily low at the expense of an increased uncertainty region. This means that when the region of uncertainty increases, there is a involvement of reliable PD which keeps the low probabilities of false alarm and miss detection in the system. When the ED detector is active in first step, the false alarm probability
$p_{fa,Step1}^{ED}$
, probability of no false alarm
$p_{No \hbox{-}fa,Step1}^{ED}$
, probability of detection
$p_{d,Step1}^{ED}$
, and miss detection probability
$p_{m,Step1}^{ED}$
concerned with double thresholds of ED are given as follows:
$$\eqalign{p_{fa,Step1}^{ED}&=\,P\{O_i \gt \lambda_2^{ED}\vert H_{00}\}\cr &=\,1 - F_{\chi}\left({\lambda_2^{ED} \over \sigma^2},2n_T\right),}$$
$$\eqalign{p_{No-fa,Step1}^{ED}&=\,P\{O_i \leq \lambda_1^{ED}\vert H_{00}\} \cr &=\,1-\left(1 - F_{\chi}\left({\lambda_1^{ED} \over \sigma^2},2n_T\right)\right),}$$
$$\eqalign{p_{d,Step1}^{ED}&=\,P\{O_i \gt \lambda_2^{ED} \vert H_{11}\} \cr &=\,{\cal Q}\left(\sqrt{\psi_{ED}},\sqrt{{\lambda_2^{ED} \over \sigma^2}}\right),}$$
$$\eqalign{p_{m,Step1}^{ED}&=\,P\{O_i \leq \lambda_1^{ED}\vert H_{11}\} \cr &=1-\left({\cal Q}\left(\sqrt{\psi_{ED}},\sqrt{{\lambda_2^{ED} \over \sigma^2}}\right)\right).}$$
The probabilities of uncertainty under H
0 and H
1 are denoted by
$\bigtriangleup_{0}$
and
$\bigtriangleup_{1}$
, respectively, and are given by
$$\eqalign{\bigtriangleup_{0}&=\,P\{\lambda_1^{ED} \lt O_i \leq \lambda_2^{ED}\vert H_{00}\} \cr &=\,1-p_{fa,Step1}^{ED} - p_{No-fa,Step1}^{ED},}$$
$$\eqalign{\bigtriangleup_{1}&=\,P\{\lambda_1^{ED} \lt O_i \leq \lambda_2^{ED}\vert H_{11}\} \cr &=\,1-p_{d,Step1}^{ED} - p_{m,Step1}^{ED}.}$$
As we know in our proposed scheme, when the ED in first step is uncertain about the decision, it will trigger the activation of cooperative PD in second step. When the PD detector is active in second step, the false alarm probability
$p_{fa,Step2}^{PD}$
, probability of no false alarm
$p_{No-fa,Step2}^{PD}$
, probability of detection
$p_{d,Step2}^{PD}$
, and miss detection probability
$p_{m,Step2}^{PD}$
concerned with PD are given as follows:
$$\eqalign{p_{fa,Step2}^{ED}&=\,P\{V \gt \lambda_3^{PD}\vert H_{0}\}\cr &=\,1 - F_{\chi}\left({\lambda_3^{PD} \over \sigma},2\right),}$$
$$\eqalign{p_{No \hbox{-}fa,Step2}^{ED}&=\,P\{V \leq \lambda_3^{PD}\vert H_{0}\} \cr &=\,1-\left(1 - F_{\chi}\left({\lambda_3^{PD} \over \sigma},2\right)\right),}$$
$$\eqalign{p_{d,Step2}^{PD}&=\,P\{V \gt \lambda_3^{PD}\vert H_{1}\} \cr &=\,{\cal Q}\left(\sqrt{\psi_{PD}},\sqrt{{\lambda_3^{PD} \over \sigma^2}}\right),}$$
$$\eqalign{p_{m,Step2}^{PD}&=\,P\{V \leq \lambda_3^{PD}\vert H_{1}\} \cr &=\,1-\left({\cal Q}\left(\sqrt{\psi_{PD}},\sqrt{{\lambda_3^{PD} \over \sigma^2}}\right)\right),}$$
since we have used K out of N rule for the second step i.e. when the PD is active, the cooperative probabilities of false alarm
$p_{Coop \hbox{-}fa,Step2}^{PD}$
and cooperative detection probabilities
$p_{Coop \hbox{-}d,Step2}^{PD}$
can be respectively obtained as follows:
$$p_{Coop \hbox{-}fa,Step2}^{PD} = \sum\limits_{K}^{N} \left(\matrix{N \cr K} \right) (1-p_{fa,Step2}^{PD})^{N-K} (p_{fa,Step2}^{PD})^K,$$
$$p_{Coop \hbox{-}d,Step2}^{PD} = \sum\limits_{K}^{N} \left(\matrix{N \cr K} \right) (1-p_{d,Step2}^{PD})^{N-K} (p_{d,Step2}^{PD})^K.$$
The overall probability of detection for our adaptive activation of ED and PD scheme can be given as
$$p_{d}^{ov} = p_{d,Step1}^{ED} + \bigtriangleup_{1} p_{coop-fa,Step2}^{PD}.$$
Similarly, the overall probability of no false alarm and false alarm for our scheme can be respectively given as
$$p_{No \hbox{-}fa}^{ov} = p_{No-fa,Step1}^{ED} + \bigtriangleup_{0} p_{No-fa,Step2}^{PD}$$
$$\eqalign{p_{fa}^{ov} &=\, 1-p_{No-fa}^{ov}\cr &=\,p_{fa,step1}^{ED} - \bigtriangleup_{0} (1-p_{fa,Step2}^{PD})}$$
IV. NUMERICAL RESULTS
Our simulation was conducted in MATLAB to investigate the performance of our proposed scheme. Additive White Gaussian Noise is imposed on the original signal x i either for H 0 or H 1 condition. We assumed that there exists an error-free control channel available between the SUs and the FC for sending local decisions and observational values O i of the confused region. For a fair comparison of our proposed scheme, we compared our scheme with conventional ED where only a single threshold based on the probability of false alarm of ED is defined for decision making process and double threshold using ED method of [Reference Zhu, Xu, Wang, Huang and Zhang14].
The receiver operating characteristics (ROC) curves of our proposed scheme as compared with other schemes is shown in Fig. 2. The ROC curve is obtained with SNR = 5 dB, Number of cooperative SUs = 6,
$\bigtriangleup_{0} = \bigtriangleup_{1} = 0.1$
, time bandwidth product u = 5. As we can clearly see from Fig. 2, our proposed scheme has the higher detection performance compared with double threshold method using ED and conventional ED methods. Our scheme takes the advantage of reliable activation of PD at the second step if the decision from first step using ED is uncertain.
ROC of proposed adaptive activation of ED and PD detector scheme.
Figure 3 shows the cooperative probability of detection curves against different SNR values when
$\bigtriangleup_{0} = \bigtriangleup_{1} = 0.01$
. Figure 3 is plotted using
$\bigtriangleup_{0} = \bigtriangleup_{1} = 0.01$
, SNR values ranges from −10 dB to 10 dB, number of cooperative SUs = 6, p
fa
is set at 0.1, time bandwidth product u = 5. It is clear from Fig. 3 that our proposed adaptive activation of ED and PD scheme is better in detecting PU signal than the other schemes at different SNR ranges. Even at −10 dB SNR value, our scheme is clearly able to detect the PU signal. The scheme using double threshold ED and conventional ED suffer greatly at low SNR region due to the fact that the energy detector is highly susceptible to the noise uncertainty at the low SNR. The uncertainty of the decision in the confused region by first step ED detector activates the second step which is back-off by reliable PD detector in our scheme. Hence, our proposed scheme performance is superior to all other schemes. It should be noted that detection performance of PD is obviously higher and is almost equal to “1” as shown by the reference [Reference Shin, Ramachandran and Roy10], hence it is not shown in our simulation results.
Comparison of probability of detection of our proposed scheme with other methods at different SNR when
$\bigtriangleup_0 = \bigtriangleup_1 = 0.01$
.
Figure 4 shows the cooperative probability of detection curves against different SNR values when
$\bigtriangleup_{0} = \bigtriangleup_{1} = 0.1$
. Figure 4 is plotted using
$\bigtriangleup_{0} = \bigtriangleup_{1} = 0.1$
, SNR values ranges from −10 dB to 10 dB, number of cooperative SUs = 6, p
fa
is set at 0.1, time bandwidth product u = 5. As we increase the uncertainty region, the second step detector PD is triggered to make the reliable detection. Hence it is clear from Fig. 4, that in our proposed adaptive activation of ED and PD scheme, PD is activated most of the time. so, the detection probability is higher for our scheme over all ranges of SNR as compared with other schemes.
Comparison of probability of detection of our proposed scheme with other methods at different SNR when
$\bigtriangleup_0= \bigtriangleup_1 = 0.1$
.
V. CONCLUSION
In this paper, we have proposed a cooperative spectrum sensing based on adaptive activation of ED and PD for CR networks. The ED performance is highly degraded under low SNR and noise uncertainty condition. To alleviate the problem of ED, as the first step in our proposed method, ED was used to take a decision in clear region where it can easily make its own local decision. Two thresholds were pre-defined for the measured energy in the first step for ED. If the sensed energy in the first step is between these two thresholds, the second step which involves the activation of cooperative PD is triggered to make an appropriate decision on the presence or absence of the PU's signal. Otherwise, the second step cooperative PD detector is not activated. In this way, we enhanced the detection performance and energy efficiency at the same time by taking the collaborative advantage of energy efficient ED and reliable PD detector. The effectiveness of our proposed method as compared with conventional schemes was validated through MATLAB simulations. Our proposed method gives significantly better detection performance compared with other conventional methods.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
ACKNOWLEDGEMENTS
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2014-0-00639) supervised by the IITP(Institute for Information & Communications Technology Promotion).
Ashish Rauniyar was born in 1988. He received his undergraduate degree in Computer Science and Technology from the University of Mysore, India in 2011. After his undergraduate degree, he worked as a software developer for WIPRO Technologies, Bangalore, India from 2011 to 2012. He was a graduate research assistant at Wireless Emerging Networking System Lab (WENS), while completing his Master degree in IT Convergence Engineering at Kumoh National Institute of Technology, South of Korea. Currently, he is working as a Ph.D. Research Fellow at OsloMet-Oslo Metropolitan University and University of Oslo (UiO), Oslo, Norway. His main research area includes Internet of Things, Cognitive Radio Network, Machine Learning, Embedded Systems and Networks, Software Engineering.
Soo Young Shin was born in 1975. He received his B.S., M.S., and Ph. D degrees in Electrical Engineering and Computer Science from Seoul National University, South-Korea in 1999, 2001, and 2006, respectively. His research interests include wireless LAN, WPAN, WBAN, wireless mesh network, sensor networks, coexistence among wireless networks, industrial and military network, cognitive radio networks, MIMO, OFDM, NOMA, and next-generation mobile wireless broadband networks (4G/5G). He was a visiting scholar in FUNLab at University of Washington, US, from July 2006 to June 2007. After 3 years working in WiMAX design laboratory of Samsung Electronics, he is now an assistant professor in School of Electronics in Kumoh National Institute of Technology since September 2010.
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