Skip to content

Advertisement

Open Access

An enhanced sum rate in the cluster based cognitive radio relay network using the sequential approach for the future Internet of Things

Contributed equally
Human-centric Computing and Information Sciences20188:16

https://doi.org/10.1186/s13673-018-0139-4

Received: 18 January 2018

Accepted: 22 May 2018

Published: 7 June 2018

Abstract

The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with a conventional non-sequential approach, therefore making it applicable for the future Internet of Things. In addition, the sum rate of the primary network and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the n-out-of-k rule. By simulation, we show that the proposed sequential approach with a relay (Lemma 2) provides a significant sum rate gain compared to the conventional non-sequential approach with no relay (Lemma 1) under any condition.

Keywords

Cognitive radioChannel sensingAmplify and forward relayFusion centerNon-sequential approachSequential approachInternet of Things

Introduction

Motivation

Cognitive radio (CR) is a promising wireless communication technology that improves spectrum band utilization by using it more flexibly, intelligently and efficiently [1]. When a secondary user (SU) supports the spectrum sensing capability in a cognitive radio network (CRN), they can discover and select frequency bands that are most suitable for use. A precondition of SU access is that SUs opportunistically use the spectrum allocated to the primary network (PN) without causing harmful interference to the primary user (PU) and instantly vacate the allocated spectrum when the PU appears. For this reason, devising optimal spectrum sensing strategies is one of the most important research topics in a CRN [2]. Thus the performance of a CRN significantly depends on the capability of the SU transmitters to sense the primary channel.

Internet of Things (IoT) is a promising technology that allows communications among sensor nodes, a continuous exchange of context between sender and receiver, and the ability to join and leave the network spontaneously [36]. IoT has two essential properties: self-adaptation and self-organization. However, the great challenges for future IoT based multimedia applications are the spectrum scarcity problem, high implementation cost, high energy consumption, and low sum rate as compared with more general radio platforms due to the rapid increase in the number of wireless devices present in future IoT [6] systems. In order to support the applicability of CR for future IoT, the cluster based cognitive radio relay network (CCRRN) which utilises reporting frameworks is a promising approach. We propose a sequential approach with a relay which can be enhanced to more optimally consume the available spectrum and improve the sum rate in transmission.

Spectrum sensing plays a vital role in CRNs and can be broadly classified into two main categories: (1) non-cooperative spectrum sensing (NCSS) [7, 8] and (2) cooperative spectrum sensing (CSS) [9, 10]. In (1), some of the most important techniques in the literature are, matched filter detection [11], cyclostationary based detection [12] and energy detection (ED) [13, 14]. The matched filter is a faster and more optimal sensing technique which depends on the prior knowledge of the PU transmission including modulation type, bandwidth, carrier frequency, pulse shaping and frame format, etc. In cyclostationary based detection, overall performance is good, but it still requires partial knowledge of the PU’s signal such as the properties of cyclic frequencies. On the other hand, the ED is considered as an attractive method due to the fact that it does not require any prior knowledge of the incumbent signal, its easy implementation and low complexity. However, the spectrum sensing accuracy is compromised because of fading, shadowing, uncertainty and the hidden terminal problem [1517]. In (2), it reduces sensing delay and improves sensing accuracy as it mitigates against the effects of fading, shadowing, uncertainty and the hidden terminal problem. It can be broadly classified into three main categories: (a) centralized, which declares one SU as a central node to make a final decision, (b) distributed, which makes a final decision through the linear combination of independent decisions and (c) relay-based spectrum sensing, which obtains higher PU detection probability due to the presence of a relay to assist the SU with lower PU detection probability [18]. In the centralized case, each SU performs local sensing independently and then forwards the sensing results to the fusion center (FC) through the noise free reporting channel between the SUs and the FC. The FC makes a final decision according to some fusion rules. These fusion rules can be classified as soft decision fusion (SDF) or hard decision fusion (HDF).

When we consider noisy reporting channels, the advantages of cooperative sensing can be limited [1922]. To mitigate against this problem [2325], a cluster based cooperative sensing scheme divides all the SUs into a number of clusters and selects the most favorable SU in each cluster as a cluster head (CH) to report sensing results. This can dramatically lessen the performance deterioration caused by the fading of the wireless channels. In these schemes, the SU selected as the CH has to fuse sensing data from all the cluster members (the SUs in this cluster). However, the existing spectrum sensing strategies have been evaluated by using rigid sensing time slots in a CRN. This means that each SU’s reporting time slot and the CH reporting time slot offer no contribution to spectrum sensing, while SU sensing and reporting times and CH reporting times are in different time slots.

Contributions

In this paper, we have achieved the following major contributions:
  • We propose an enhanced sum rate in the CCRRN using the sequential approach with a relay for future IoT systems, and formulate the sensing performance and the sum rate maximization problem in the conventional non-sequential approach with no relay [38]. The proposed sequential approach with and without a relay, requires us to use a utilization reporting framework to solve the maximization problem for the conventional non-sequential approach.

  • The proposed sequential approach in the CCRRN with and without a relay, is considered a noisy reporting channel between the SU and the CH due to SU members in the cluster often being at a distance from each other.

  • We propose an efficient reporting mechanism in which each SU achieves a longer/flexible sensing time slot to sense the PU signal due to a utilization reporting framework that employs the sequential approach in the CCRRN with a relay.

  • We empirically examine the sensing performances at SUs, CHs and the FC by extending the sensing time slots with and without a relay using the SDF scheme i.e., \(\text{`}n-out-of-k-rule {\text{'}}\).

  • Based on the false alarm and detection probabilities, the sum rate of the PN and CCRRN are analyzed using the conventional non-sequential approach in the CCRRN with no relay and the proposed sequential approach in the CCRRN with and without a relay.

  • We calculate the optimal false alarm probability (Lemma 3) which enhances the sum rate in the proposed sequential approach in the CCRRN with and without a relay; compared with the conventional non-sequential approach in the CCRRN with no relay.

Organization

The remainder of the paper is organized as follows. The general motivation and the background of this paper is summarized in the “Related works” section. “System model” section describes the system model which consists of a PN and a CCRRN. “Energy detection technique” section describes how each SU estimates its own measured energy from the PU signal. “The conventional non-sequential approach in the CCRRN” section describes the conventional non-sequential approach in the CCRRN where each SU underutilizes the reporting framework. “The proposed sequential approach in the CCRRN utilizing the reporting framework” section describes the sensing performance and the sum rate analysis where each SU utilizes the reporting framework. The simulation parameters of the proposed approach and comparisons are given in the “Simulation results and discussion” section. We compare our proposed approach with other existing approaches which demonstrates better gain in terms of detection performance and sum rate maximization with low complexity. Finally, our conclusions and future work are addressed in the “Conclusions and future works” section. In order to make the paper more readable, the main parameters are listed in Table 1.
Table 1

Main parameters

Parameters

Meaning

\(H(H_0/H_1)\)

Hypotheses (absent/present)

\(\tau _s(\tau _s^{con}/\tau _s^{prop})\)

Sensing time slot (in the conventional non-sequential approach/ in the proposed sequential approach)

\(\tau _r(\tau _{r,SU}/\tau _{r,CH})\)

Reporting time slot (reporting time slot at the SU/CH)

\(P_{f,k}^{con(0)}\)

Probability of false alarm in the conventional non-sequential approach with no relay at the kth cluster

\(P_{f,k}^{prop(1)}\)

Probability of false alarm in the proposed sequential approach with a relay at the kth cluster

\(P_{f,k}^{prop(2)}\)

Probability of false alarm in the proposed sequential approach with no relay at the kth cluster

\(P_{d,k}^{con(0)}\)

Probability of detection in the conventional non-sequential approach with no relay at the kth cluster

\(P_{d,k}^{prop(1)}\)

Probability of detection in the proposed sequential approach with a relay at the kth cluster

\(P_{d,k}^{prop(2)}\)

Probability of detection in the proposed sequential approach with no relay at the kth cluster

\(P_{f, k}^{*}\)

The optimal probability of false alarm at the kth cluster

\(\lambda _k^{con}\)

Decision threshold in the conventional non-sequential approach with no relay at the kth cluster

\(\lambda _k^{prop}\)

Decision threshold in the proposed sequential approach with a relay at the kth cluster

\(P_{f,FC}^{m}/P_{d,FC}^{m}\)

Global decision under binary hypotheses at the FC, here m stands for (0, 1, 2)

\(P_t/\gamma _j/\gamma _k\)

Amplify power at each SU/ the signal to noise ratio at the jth SU/ the signal to noise ratio at the kth cluster

\(Q(\cdot)/Q^{-1}(\cdot)\)

Gaussian/ inverse Gaussian tail function

\(\delta _d\)

The network end-to-end delay in the conventional non-sequential approach with no relay/the proposed sequential approach with and without a relay

Related works

CCRRNs with a relay scheme play an important role in utilizing the reporting framework. Thus, several publications are listed below that provide the background and recent research contributions of a sequential approach with and without a relay, and non-sequential approach with no relay schemes. The analysis of NCSS in a CRN is presented in [7, 8]. An analysis of CSS in a CRN is presented in [9, 10]. An efficient transmission mode selection based on reinforcement learning for cooperative CRNs is studied in [10]. It is shown that the spectrum sensing accuracy is compromised because of fading, shadowing, uncertainty and the hidden terminal problem [1517]. In order to mitigate the fading problem, the relay based spectrum sensing in the CRN is studied in [18]. The advantages of CSS can be limited due to noisy reporting channels, which are studied in [1922]. To mitigate against this problem, cluster based CSS schemes are studied in [2325]. Effective implementation of security based algorithmic approaches in mobile ad-hoc network and stochastic approaches for dynamic power management in wireless sensor network (WSN) are studied in [26, 27]. In a CRN with a large number of SUs, CSS requires many reports from SUs to the FC through the control channel, which can result in overhead traffic of the control channel. Some methods have been proposed to solve these problems with CSS, such as cluster-based CSS [28] and sequential CSS [2931]. Vu-Van et al. [32] proposed a cluster based sequential CSS scheme for CRNs in order to significantly reduce the number of direct reports from SUs to the FC while keeping the similar sensing performance to that of the conventional CSS scheme. In this scheme, each SU performs local sensing independently and then forwards the sensing of hard decisions to the corresponding CH through the noise free reporting channel. All CHs forward their hard decisions to the corresponding FC through the noise free reporting channel. However, the sensing performance of this scheme may be decreased due to fading effects of reporting channels in real environments. Therefore, an efficient reporting mechanism is needed in the cluster based CSS scheme for enhanced spectrum sensing and sum rate maximization. Moreover, the sensing performance is also decreasing when we are using the HDF scheme, i.e., \(\text{`}OR-rule{\text{'}}\) and \(\text{`}AND-rule{\text{'}}\). Therefore, the SDF scheme is needed in the cluster based CSS for further enhanced spectrum sensing and sum rate maximization. In [1922, 33], with the objective of maximizing the sum rate of the CRN, a cluster-based CSS is proposed to obtain a proper assignment policy. However, the reporting channels are considered as error-free. Moreover, the sum rate was not analyzed as a utilization reporting framework. In [2325, 34], the authors propose a cluster-based CSS strategy to maximize an achievable sum rate scheme in the non-error-free environment. However, the sum rate was not analyzed as a utilization reporting framework. Hung et al. [35] proposed cognitive cooperative networks with a cluster based relaying scheme in which the secondary base station (SBS) transmits signals to multiple secondary receivers (SU-Rx) through the help of multiple secondary relays. However, the reporting channel between the SBS and SU-Rx is noise free. Yu H [36, 37] proposed the optimal channel sensing maximizing sum rate in CRNs with multiple SUs with the capacity of the CRN being analyzed. Moreover, some interesting characteristics including asymptotic results were observed. However, the sum rate was not analyzed as a utilization reporting framework approach in the CCRRN. Miah et al. [38] proposed maximization of the sum rate in amplify and forward (AF)-cognitive radio networks using the superposition approach with the sum rate of CRNs being analyzed. However, the sum rate was not analyzed based on the optimal probability of false alarm. Moreover, the sequential approach was not analyzed which would be a more favorable approach due to the limited reporting control channel.

System model

The proposed system model consists of a PN and a CCRRN as shown in Fig. 1 in which the SU is denoted as an unlicensed user that opportunistically accesses the spectrum of the PU without causing interference, whilst the PU remains the licensed user of the spectrum. The PN consists of PUs, i.e., primary transmitters and receivers. The operation of the PU is considered to be time division multiplexing access (TDMA). We assume that for each slot the PU transmitter sends data to its receiver independently with a probability \(\rho \in [0, 1],\) which is defined as the primary activity factor. On the other hand, it is assumed that the SU network is also a time slot-based network. The CCRRN consists of N SUs, i.e., secondary user transmitters and receivers that will simply act in an AF relay manner, K CHs and a FC where the N SUs are grouped into a cluster controlled by a CH based on a low energy adaptive clustering hierarchy-centralized (LEACH-C) protocol [3942] as shown in Fig. 1a. The process of the LEACH-C protocol is the build up of rounds; each round consists of two phases as follows: (1) the setup phase and (2) the steady state phase.

In (1), each SU sends a \(\text{`}request-message {\text{'}}\) \(=(SU_{ID}, LOC(C), SNR)\) to the FC. Based on this \(\text{`}request-message {\text{'}}\), the FC determines CHs among all SUs, while the remaining SUs will act as cluster members. After the CHs are determined, the FC broadcasts a \(\text{`}reply-message {\text{'}}\) \(=(CH_{ID}, SYN(T))\). If the SUs CH ID matches its own ID, then the SU is a CH; otherwise, the SU is a cluster member and goes to sleep. Here \(SU_{ID}\), \(CH_{ID}\), LOC(C), SNR and SYN(T) stands for the identification number at the SU, the identification number at the CH, the current SU location, the signal to noise ratio and the time synchronization, respectively. Moreover, the primary and secondary users are synchronized.

In (2), the SUs start to share their measurement of the received PU signal to the corresponding CH, and then the CH collects their measurements and makes the cluster decision and relays it to the FC. Afterwards, the FC combines the received cluster decisions to make the global decision and then broadcasts it back to all CHs and the CHs send it to their cluster members.

In the proposed CCRRN with no relay scheme, the source SU sends the data to the destination SU during the time slot using a direct link. For the proposed CCRRN with a relay scheme, the source SU sends the data to the destination SU during the time slot and the relay SU receives the data on the same time slot due to the broadcast nature of communication, and then finally forwards the data to the destination SU in the manner of the AF protocol as shown in Fig. 1b.
Figure 1
Fig. 1

The proposed system model: a all SUs build up a cluster based on the LEACH-C protocol; b SU interaction with and without a relay

Let \(H_1\) and \(H_0\) be the hypotheses representing the presence and absence of primary signals, respectively. Under this binary hypothesis testing problem, the spectrum sensing can be formulated as follows
$$\begin{aligned} {\left\{ \begin{array}{ll} H_1{:} &{} \text{ if } \text{ PU } \text{ is } \text{ present } \\ H_0{:} &{} \text{ if } \text{ PU } \text{ is } \text{ absent } \end{array}\right. } \end{aligned}$$
(1)
Depending on the packet transmission of the PU, the received signals of the jth SU can be formulated as follows
$$\begin{aligned} y_j(l)|_{l=1}^{L}=\theta h_j(l)x(l)+w_j(l) \end{aligned}$$
(2)
where \(\theta =1\) denotes the presence of the PU while \(\theta =0\) denotes the absence of the PU, \(y_j(l)\) denotes the sensing signal received by the jth SU, \(h_j(l)\) is the channel gain between the jth SU and the PU with \(h=\sum _{j=1}^Nh_j\). Moreover, x(l) is the PU transmit signal which is modulated on a binary phase shift keying (BPSK) signal with power \(p_x^2\), \(w_j(l)\) is a circularly symmetric complex Gaussian (CSCG) noise with the variance of \(\sigma _{w,j}^2\) by the jth SU, and L is the number of samples.
Each SU will simply act in an AF manner, the N SUs relay their individual measurements of the PUs signal, \(y_j(l)\) to the kth corresponding CH through a noisy control channel in the sequential manner. At the CH, the relay signal received by the corresponding CH from the jth SU will be given as follows
$$\begin{aligned} r_j(l)={\left\{ \begin{array}{ll} \sqrt{P}_t g_j(l)y_j(l)+z_j(l); &{} \text{ with } \text{ AF } \text{ manner } \\ g_j(l)y_j(l)+z_j(l); &{} \text{ without } \text{ AF } \text{ manner } \end{array}\right. } \end{aligned}$$
(3)
where \(\sqrt{P}_t\) denotes the transmit power of each SU relay, \(g_j\) denotes the amplitude of the channel gain of the jth SU and kth CH link, i.e., \(g=\sum _{j=1}^{N}g_j(l)\), and \(z_j(l)\) is the noisy reporting channel of the jth SU and kth CH link which has zero mean and an additive white Gaussian noise with variance, i.e., \(\sigma _{z,j}^2\). Then the signal to noise ratio (SNR) at the kth CH is given as follows
$$\begin{aligned} \gamma _k=\sum _{j=1}^{N}\gamma _j=\frac{p_x^2}{\sigma _{w+r}^2=\sum _{j=1}^N(\sigma _{w,j}^2+\sigma _{z,j}^2)} \end{aligned}$$
(4)
where \(\gamma _k\) is the SNR at the kth CH, which is defined as the ratio of the signal power to noise power.

Energy detection technique

The ED technique [4345] is widely used in the spectrum sensing of the SU, because it can be implemented easily without acquiring any prior information of the PU signal. We assume that each SU transmitter senses the PU signal using the ED technique. The structure of the channel sensing process at the SU transmitter employing the ED is shown in Fig. 2.
Figure 2
Fig. 2

A block diagram of the energy detection technique [4345]

At the particular cluster with N SUs, the sensing result, \(r_j(l)\) received by the jth SU transmitter is the signal power in a particular frequency in the time domain, a band-pass filter is applied to the received signal, then the output of this filter is transformed by an analog-to-digital converter (ADC), which will be individually averaged and squared using the ED to estimate its own measured energy as given by
$$\begin{aligned} E_j=\frac{1}{L}\sum _{l=1}^L|r_j(\frac{l}{F_s})|^2 \end{aligned}$$
(5)
where \(r_j(\frac{l}{F_s})\) is the lth sample of a received signal by the jth SU at the CH which is defined as \(L=\tau _sF_s\), here \(\tau _s\) is the sensing duration which is commonly used by all SUs to sense the PU signal and \(F_s\) is the sampling frequency of the PU signal.
The probability distribution function (PDF) can be approximated as a Gaussian random variable using the central limit theorem (CLT) as follows
$$\begin{aligned} {\left\{ \begin{array}{ll} \aleph (\mu _0=\sum\limits _{j=1}^N\mu _{0,j}, \quad&{} \sigma _0^2=\sum\limits_{j=1}^N\sigma _{0,j}^2) \\ \aleph (\mu _1=\sum\limits_{j=1}^N\mu _{1,j}, \quad&{}\sigma _1^2=\sum\limits_{j=1}^N\sigma _{1,j}^2) \end{array}\right. } \end{aligned}$$
(6)

Conventional non-sequential approach in the CCRRN

In the conventional non-sequential approach with no relay, all SUs sense the PU signal at a time during the rigid sensing time slot and forward their sensing results to the CH during the reporting time slot. In this conventional approach, we do not utilize the reporting framework as all SUs forward their sensing results to the CHs in a non-sequential manner. As an example, the 2nd SU in the 1st cluster cannot utilize the rigid reporting time slot of the 1st SU for sensing the PU signal, and the 3rd SU in the 1st cluster cannot utilize the rigid reporting time slots of the previous 1st SU and 2nd SU for sensing the PU signal, and so on; similarly, the 1st SU in the 2nd cluster cannot utilize the rigid reporting time slot of the 1st cluster head for sensing the PU signal, and the 1st SU in the 3rd cluster cannot utilize the rigid reporting time slots of the previous 1st cluster head and 2nd cluster head for sensing the PU signal, and so on [13]. Figure 3 shows the frame structure for the conventional non-sequential approach in the CCRRN with no relay and under utilizing the reporting framework. Under the given frame structure, all SUs sense the PU channel with a rigid sensing time slot \(\tau _s\), as shown in Algorithm 1. During a rigid sensing time slot \(\tau _s\), the conventional non-sequential approach in the CCRRN with no relay will not be applicable for the future IoT as the detection gain is not sufficient.
Figure 3
Fig. 3

Frame structure of a time slot for sensing, reporting and packet transmission in the conventional non-sequential approach in the CCRRN with no relay by under utilizing reporting framework [13, 14]

Lemma 1

When the PU signal, x(l) is a BPSK-modulated signal, the channel noise between the PU and the jth SU, \(w_j(l),\) is a CSCG, and the reporting channel between the jth SU and the kth CH is noise-free. An estimation of the received signal power is given by all SUs in the conventional non-sequential approach in the CCRRN with no relay as follows [38]
$$\begin{aligned} E={\left\{ \begin{array}{ll} \mu _0=L\sigma _w^2, \quad&{} \sigma _0^2=L\sigma _w^4 \\ \mu _1=L(1+|h|^2\gamma )\sigma _w^2, \quad&{} \sigma _1^2=L(1+2|h|^2\gamma )\sigma _w^4 \end{array}\right. } \end{aligned}$$
(7)

Proof

Please see Appendix.

Now, the kth CH calculates a cluster decision test statistic from all the individual test statistics \([E_1, E_2,\ldots, E_N]\) of an individual SU and multiplies the weight-coefficient of the SU by a linear statistic combination (LSC) manner as follows
$$\begin{aligned} C_k|_{k=1}^K=\sum _{j=1}^N\omega _jE_j \end{aligned}$$
(8)
where \(\omega _j\) is the weight-coefficient assigned on the jth SU at the kth cluster.
Based on the Eq. (8), we can calculate the kth cluster probability of false alarm \(P_{f,k}^{con}=Pr[H_1|H_0]\) and the probability of detection \(P_{d,k}^{con}=Pr[H_1|H_1]\) as given for a pre-selected threshold of \(\lambda _k^{con}\)
$$\begin{aligned} \begin{array}{lll} P_{f,k}^{con} &{}=&{}Pr[C_k\ge \lambda _k^{con}|H_0] \\ &{}=&{}Q\left( \left( \frac{\lambda _k^{con}}{\sigma _w^2}-1\right) \sqrt{\tau _sF_s}\right) \end{array} \end{aligned}$$
(9)
$$\begin{aligned} \begin{array}{lll} P_{d,k}^{con} &{}=&{}Pr[C_k\ge \lambda _k^{con}|H_1] \\ &{}=&{}Q\left( \left( \frac{\lambda _k^{con}}{\sigma _w^2}-|h|^2\gamma -1\right) \sqrt{\frac{\tau _sF_s}{(1+2|h|^2\gamma )}}\right) \end{array} \end{aligned}$$
(10)
where Q(t) denotes a Gaussian tail function which is defined as \(Q(t)=\frac{1}{\sqrt{2\pi }}\int _t^\infty e^{-\frac{x^2}{2}}dx\).
From Eqs. (9) and (10), we can calculate the decision threshold \(\lambda _k^{con}\) at the kth CH as follows
$$\begin{aligned} \lambda _k^{con}={\left\{ \begin{array}{ll} \left[ \frac{Q^{-1}(P_{f,k}^{con})}{\sqrt{\tau _sF_s}}+1\right] \sigma _w^2; \quad&{} H_0\\ \left[ \frac{Q^{-1}(P_{d,k}^{con})}{\sqrt{\frac{\tau _sF_s}{(1+2|h|^2\gamma )}}}+1+|h|^2\gamma \right] \sigma _w^2; \quad&{} H_1 \end{array}\right. } \end{aligned}$$
(11)
where \(Q^{-1}(t)\) denotes an inverse-Gaussian tail function.
We can calculate the sensing time in the conventional non-sequential approach in the CCRRN with no relay and guarantee the sensing performance given by the probability of false alarm and the probability of detection using Eqs. (9) and (10) as follows [38]
$$\begin{aligned} \tau _s^{con}=\frac{1}{F_s|h|^4\gamma ^2}\left[ Q^{-1}(P_{f,k}^{con})-Q^{-1}(P_{d,k}^{con})\sqrt{(1+2|h|^2\gamma )}\right] \end{aligned}$$
(12)

Proposition 1

In the conventional non-sequential approach in the CCRRN with no relay, all the SUs at the kth cluster have obtained the same fixed/rigid sensing time slot which is denoted as
$$\begin{aligned} \tau _s^{kj}=\tau _s^{con} \end{aligned}$$
(13)
where \(\tau _s^{kj}\) denotes the flexible sensing time slot for the jth SU at the kth cluster that is equal to \(\tau _s^{con}\) which denotes the fixed/rigid sensing time slot for all SUs.

Proof

Please see Appendix.

In Algorithm 1, all SUs are under utilizing the reporting framework as obtained fixed/rigid sensing time slot (see line 5). Then, it computes the probability of false alarm (see line 7) and the probability of detection (see line 8) at the kth cluster in the conventional non-sequential approach with no relay (\(P_t = 1\)).

Proposed sequential approach in the CCRRN utilizing the reporting framework

In the proposed sequential approach in the CCRRN, the sensing performance is discussed in the “Spectrum sensing analysis” section and the system sum rate is discussed in the “Sum rate analysis” section.

Spectrum sensing analysis

The proposed scheme utilizing the reporting framework, namely the proposed sequential approach in the CCRRN, is a promising solution for the spectrum scarcity problem [4648] of the future IoT multimedia applications. In this approach, each SU can obtain a longer/flexible sensing time slot due to the rest of the SUs reporting time slots and the CH reporting time slots being combined to the longer/flexible sensing time slot for that as shown in Fig. 4 and Algorithm 2. A major challenge in the proposed sequential approach is utilizing the spectrum more efficiently which will be more applicable for the future IoT systems.
Figure 4
Fig. 4

Frame structure of a time slot for sensing, reporting and packet transmission in the proposed sequential approach in the CCRRN utilizing the reporting framework

In Fig. 4, the 2nd SU in the 1st cluster can utilize the rigid reporting time slot of the 1st SU for sensing the PU signal, and the 3rd SU in the 1st cluster can utilize the fixed/rigid reporting time slots of the previous 1st SU and 2nd SU for sensing the PU signal, and so on. Moreover, the 1st SU in the 2nd cluster can utilize the rigid reporting time slot of the 1st cluster head \((CH_1)\) for sensing the PU signal, and the 1st SU in the 3rd cluster can utilize the rigid reporting time slots of the previous 1st cluster head \((CH_1)\) and 2nd cluster head \((CH_2)\) for sensing the PU signal, and so on.

Lemma 2

When the PU signal x (l) is a BPSK-modulated signal, \(w_j(l)\) is a CSCG channel noise between the PU and the jth SU and \(z_j(l)\) is the reporting channel noise between the jth SU and the kth CH which is defined as CSCG. An estimation of the received signal power is given by all SUs in the proposed sequential approach with a relay as given by
$$\begin{aligned} {\left\{ \begin{array}{ll} \aleph (L\sigma _{w+z}^2, \quad&{} L\sigma _{w+z}^4) \\ \aleph (L(1+P_t|h|^2|g|^2\gamma )\sigma _{w+z}^2, \quad&{} L(1+2P_t|h|^2|g|^2\gamma )\sigma _{w+z}^4) \end{array}\right. } \end{aligned}$$
(14)
where \(\sigma _{w+z}^2=\sum _{j=1}^N(\sigma _{w,j}^2+\sigma _{z,j}^2),\) \(\sigma _{w+z}^4=\sum _{j=1}^N(\sigma _{w,j}^4+\sigma _{z,j}^4),\) and \(\gamma =\sum _{j=1}^N\gamma _j\)

Proof

Please see Appendix.

Proposition 2

In the proposed sequential approach in the CCRRN with and without a relay, all the SUs at the kth clusters have obtained the longer/flexible sensing time slot which is denoted as follows
$$\begin{aligned} \tau _s^{kj}=\tau _s+(j-1)\tau _{r,SU}+(k-1)\tau _{r,CH} \end{aligned}$$
(15)
where \(\tau _s^{kj}\) denotes the longer/flexible sensing time slot for the jth SU at the kth cluster, \(\tau _{r,SU}\) denotes the reporting time slot for each SU and \(\tau _{r,CH}\) denotes the reporting time slot for each CH.

Proof

Please see Appendix.

Based on the Eq. (15), we can calculate the probability of false alarm and the probability of detection in the proposed sequential approach with a relay at the kth cluster as follows
$$\begin{aligned} \begin{array}{lll} P_{f,k}^{prop} &{}=&{}Pr[C_k\ge \lambda _k^{prop}|H_0] \\ &{}=&{}Q\left( \left( \frac{\lambda _k^{prop}}{\sigma _{w+z}^2}-1\right) \sqrt{\tau _s^{kj}F_s}\right) \end{array} \end{aligned}$$
(16)
$$\begin{aligned} \begin{array}{lll} P_{d,k}^{prop} &{}=&{}Pr[C_k\ge \lambda _k^{prop}|H_1] \\ &{}=&{}Q\left( \left( \frac{\lambda _k^{prop}}{\sigma _{w+z}^2}-P_t|h|^2|g|^2\gamma -1\right) \sqrt{\frac{\tau _s^{kj}F_s}{(1+2P_t|h|^2|g|^2\gamma )}}\right) \end{array} \end{aligned}$$
(17)
From Eqs. (16) and (17), we can calculate the decision threshold, \(\lambda _k^{prop}\) at the kth CH as follows
$$\begin{aligned} \lambda _k^{prop}={\left\{ \begin{array}{ll} \left[ \frac{Q^{-1}(P_{f,k}^{prop})}{\sqrt{\tau _s^{kj}F_s}}+1\right] \sigma _{w+z}^2; \quad&{} H_0 \\ \left[ \frac{Q^{-1}(P_{d,k}^{prop})}{\sqrt{\frac{\tau _s^{kj}F_s}{(1+2P_t|h|^2|g|^2\gamma )}}}+1+P_t|h|^2|g|^2\gamma \right] \sigma _{w+z}^2; \quad&{} H_1 \end{array}\right. } \end{aligned}$$
(18)
At the FC, all cluster decisions received will be combined to make a global decision \((P_{f,FC}^{m}/P_{d,FC}^{m})\) about the presence or absence of the PU signal by using the \(n-out-of-k\) rule test as follows
$$\begin{aligned} P_{f,FC}^{m}= \sum _{j=\beta }^K\sum _{A(j)\subset (1,\ldots ,K)}\left[ \prod _{k\subset A(j)}P_{f,k}^{m}\prod _{k\not \subset A(j)}(1-P_{f,k}^{m}) \right] \end{aligned}$$
(19)
$$\begin{aligned} P_{d,FC}^{m}= \sum _{j=\beta }^K\sum _{A(j)\subset (1,\ldots ,K)}\left[ \prod _{k\subset A(j)}P_{d,k}^{m}\prod _{k\not \subset A(j)}(1-P_{d,k}^{m}) \right] \end{aligned}$$
(20)
where the second summation with a subscript of \(A(j)\subset {1,2,...,K}\) denotes the sum of all possible subsets with the jth SUs in the kth cluster and \(\beta\) denotes the global threshold at the FC. Moreover, m stands for index (0, 1, 2) here, \(m=0\) indicates the conventional non-sequential approach with no relay, \(m=1\) indicates the proposed sequential approach with a relay and \(m=2\) indicates the proposed sequential approach with no relay.

The proposed Algorithm 2 exhibits the whole idea. In Algorithm 2, it checks \(N=1, K=1\) (see line 4) as the 1st SU of the 1st cluster in this case the reporting framework is not being utilized as it is obtaining a fixed/rigid sensing time slot (see line 5); otherwise, all SUs are utilizing the reporting framework and are obtaining a longer/flexible sensing time slot (see line 7). Then, it computes the probability of false alarm (see line 11) and the probability of detection (see line 12) at the kth cluster in the proposed sequential approach with a relay (\(P_t \ne 1\)) (see line 10), otherwise, it computes the probability of false alarm (see line 14) and the probability of detection (see line 15) at the kth cluster in the proposed sequential approach with no relay (\(P_t=1\)). After then, it computes a global decision at the FC which is defined as \((P_{f,FC}^{m}/P_{d,FC}^{m})\) (see line 18/line 19).

The network end-to-end delay is another important factor in the CRN [30]. Based on Figs. 3 and 4, the network end-to-end delay of the conventional CCRRN with no relay and the proposed CCRRN with and without a relay are the same, denoted as \(\delta _d\) which is defined as follows
$$\begin{aligned} \begin{array}{lll} \delta _d &{}=&{}N\tau _{r,SU}+K\tau _{r,CH}\\ &{}=&{}\left( N+K\right) \tau _r \end{array} \end{aligned}$$
(21)
Here, if \(\tau _{r,SU}=\tau _{r,CH}\), then the reporting time slot is denoted as \(\tau _r\).

Sum rate analysis

With the frame structure and sensing performance in the above section, we can analyze the system sum rate with several assumptions. In the transmission slot, if the SU transmitter does not detect the PU signal, it determines that the channel is available and transmits data to its own receiver; otherwise, it waits until the channel becomes available for its transmission that are scheduled in a round-robin manner.

When sensing accuracy perfect, e.g. when the PU is absent and the absence of the PU is accurately detected by the SU, the SU can access the primary spectrum with the probability \((1-P_{f,FC}^{m})\); otherwise, when the PU is present and the presence of the PU is accurately detected by the SU, the SU can not access the primary spectrum with the probability \(P_{d,FC}\). In this case, the sum rate of all users including both PU and SUs in a round-robin manner is calculated as follows [36, 37]
$$\begin{aligned} R_{sum}=\rho P_{d,FC}^{m}C_{PU}+(1-\rho )(1-P_{f,FC}^{m})C_{j,SU} \end{aligned}$$
(22)
where \(C_{PU}\) denotes the channel capacity of the PU link, \(C_{j,SU}\) denotes the channel capacity of the jth SU link, and \(\rho \in [0,1]\) denotes the primary activity factor which means the probability of the PUs transmitting in a given frame.
The \(C_p\) and \(C_{j,SU}\) are given as follows
$$\begin{aligned} C_{PU}=log_2(1+SNR_{PU}) \end{aligned}$$
(23)
$$\begin{aligned} C_{j,SU}=\frac{T-\tau _s^{prop}}{T}log_2(1+SNR_{j,SU}) \end{aligned}$$
(24)
where \(SNR_{PU}\) denotes the SNR of the PUs link, \(SNR_{j,SU}\) denotes the SNR of the jth SU link in the CCRRN and T denotes the total frame length.

Lemma 3

If the optimal probability of false alarm \(P^*_{f,k}\) is a non-decreasing function of \(\rho,\) then the \(P^*_{f,k}\) is maximizing the sum rate as follows
$$\begin{aligned} \max _{P_{f,k}=P_{f,k}^*} R_{sum} \end{aligned}$$
(25)

Proof

Please see Appendix.

Simulation results and discussion

In this section, we verify the theoretical results and evaluate the performance of the proposed sequential approach with and without a relay. This is done through numerical simulations via Mathlab.

Monte-Carlo simulations were carried out using the simulation parameters listed in Table 2 below.
Table 2

Simulation parameters

Parameters

Value

The total number of SUs, N

12

The total number of CHs, K

3

The sampling frequency, \(F_s\)

300 kHz

The sensing time, \(\tau _s\)

300 ms

The reporting time at the SU, \(\tau _{r,SU}\)

30 ms

The reporting time at the CH, \(\tau _{r,CH}\)

30 ms

The PUs signal, x(l)

BPSK

The channel noise, w(l) and the reporting channel noise, z(l)

CSCG

The SNR of PU, \(SNR_{PU}\)

10 dB

Figures 5, 6 and 7 respectively show cluster and global sensing performance, receiver operating characteristic (ROC) curves for the conventional non-sequential approach with no relay, the proposed sequential approach with no relay and the proposed sequential approach with a relay. In the conventional non-sequential approach with no relay, 3 CHs have the same performance, then just one curve is shown in Fig. 5. The reason is that the rigid sensing time \(\tau _s^{kj}\) in the Eq. (13) for the case of the conventional non-sequential approach has no contribution on sensing gain, as mentioned above. As shown in Fig. 6, the third cluster has a better sensing performance than the others because the SUs in this cluster have a longer sensing duration than those in the other first and second clusters due to the proposed sequential approach in Eq. (15) for the case that the reporting time has contributed to sensing gain, as mentioned above. As shown in Fig. 7, the third cluster has a much better sensing performance than the others because the SUs in this cluster have a longer sensing duration due to the sequential approach than those in the other first and second clusters. The reasons are firstly, the flexible sensing time \(\tau _s^{kj}\) in Eq. (15) has contributed to the sensing gain; and secondly, each SU acts in an AF manner in the proposed sequential approach with a relay based on Eqs. (16) and (17), as mentioned above.
Figure 5
Fig. 5

ROC curves at CH and FC for the conventional non-sequential approach with no relay

Figure 6
Fig. 6

ROC curves at CH and FC for the proposed sequential approach with no relay

Figure 7
Fig. 7

ROC curves at CH and FC for the proposed sequential approach with a relay

As shown in Fig. 8, by comparing these ROCs at the FC based on Eqs. (19) and (20) for the conventional non-sequential approach with no relay, the proposed sequential approach with no relay and proposed sequential approach with a relay, it can be seen that the proposed sequential approach with and without a relay shows better sensing capabilities than the conventional non-sequential approach with no relay which can be applicable for the future IoT systems. In addition, the proposed sequential approach with a relay shows much better sensing performance than the proposed sequential approach with no relay because each SU simply acts as a relay in the proposed sequential approach with a relay. It is found that the proposed sequential approach with a relay outperforms the other approaches for all cases which makes it more applicable for the future IoT systems.
Figure 8
Fig. 8

ROC curves at FC for the conventional non-sequential approach with no relay, the proposed sequential approach with no relay and the proposed sequential approach with a relay

Figure 9 shows the sum rates for the conventional non-sequential approach with no relay, the proposed sequential approach with no relay and the proposed sequential approach with a relay depending on the false alarm probability of a SU, i.e., the sum rate is a function of \(P_f\). The sum rate of the proposed sequential approach with a relay is higher than when compared with both the conventional non-sequential approaches and the sequential approach with no relay for the entire range of \(P_f\), which can be more applicable for the future IoT.
Figure 9
Fig. 9

Sum rate curves vs. probability of false alarm \(P_f\) for the SU in the conventional non-sequential approach with no relay, the sequential approach with no relay and the sequential approach with a relay when the primary activity factor is \(\rho\) = 0.7

Moreover, the sum rate curve is a function of \(\rho\) for a given probability of false alarm (\(P_f=0, 0.1, 0.2, 0.3, 0.5, 1\)). Therefore, we show the sum rate of the conventional non-sequential approach with no relay, the proposed sequential approach with no relay and the proposed sequential approach with a relay, respectively as shown in Fig. 10.
Figure 10
Fig. 10

Sum rate curves vs. the primary activity factor \(\rho\) for the PU in the conventional non-sequential approach with no relay, the sequential approach with no relay and the sequential approach with a relay when the entire probability of false alarm \(P_f\)

Additionally, the sum rate curve is a quasi-concave function of \(P_f\) for a given primary activity factor \(\rho\). Therefore, there exists the optimal value of \(P_f\) which enhances the sum rate for a given \(\rho\). For the sum rate of the proposed sequential approach with a relay when \(\rho =0.8\), the optimal \(P_f\) is given by 0.38. In the case of the proposed sequential approach with no relay and the conventional non-sequential approach with no relay respectively, the optimal probability of false alarm \(P_f^*\) is 0.41 and 0.47 as shown in Fig. 11.
Figure 11
Fig. 11

Optimal probability of false alarm \(P_f^*\) vs the primary activity factor \(\rho\) in the conventional non-sequential approach with no relay, the sequential approach with no relay and the sequential approach with a relay

In order to compare the sensing gain at \(CH_3\), the proposed sequential approach with a relay and with no relay, can detect the spectrum with \(72\%\) and \(68\%\) detection accuracy, respectively whereas the conventional non-sequential approach with no relay detects the PU’s signal with \(54\%\) as shown in Table 3.
Table 3

Different approaches vs. the profferent approaches vs. the probability ofbability of detection and probability of false alarm

Approaches

Probability of detection\(P_d\)

Probability of false alarm \(P_f\)

Figures

Conventional approach

\(CH_1\)

\(CH_2\)

\(CH_3\)

FC

\(CH_1\)

\(CH_2\)

\(CH_3\)

FC

Fig. 5

0.54

0.54

0.54

0.70

0.20

0.20

0.20

0.20

 

Proposed approach with no relay

0.58

0.62

0.68

0.80

0.20

0.20

0.20

0.20

Fig. 6

Proposed approach with a relay

0.59

0.68

0.72

0.84

0.20

0.20

0.20

0.20

Fig. 7

In order to compare the sensing gain at a FC, the proposed sequential approach with a relay and with no relay can detect the spectrum with \(84\%\) and \(80\%\) detection accuracy, respectively whereas the conventional non-sequential approach with no relay detects the PU’s signal with \(70\%\) as shown in Table 4.
Table 4

Sensing gain at the FC vs. different approaches

Items

Conventional approach

Prop. approach with no relay

Prop. approach with a relay

Figure

\(P_d\)

0.70

0.80

0.84

Fig. 8

\(P_f\)

0.20

0.20

0.20

 
In order to compare the sum rate, the proposed sequential approach with no relay and with a relay can be obtained as an enhanced sum rate with 2.62Hz and 2.75Hz, respectively compared to the conventional non-sequential approach with no relay is 2.50Hz as shown in Table 5.
Table 5

The sum rate vs. the probability of false alarm

Items

Conventional approach

Prop. approach with no relay

Prop. approach with a relay

Figure

Sum rate

2.50

2.62

2.75

Fig. 9

\(P_f\)

0.31

0.31

0.31

 
In order to compare the optimal probability of false alarm \((P_f^*)\), the proposed sequential approach with no relay and with a relay achieves an accuracy with respect to false alarm of \(41\%\) and \(39\%\), respectively, compared to the conventional non-sequential approach with no relay which achieves an accuracy of \(49\%\) as shown in Table 6.
Table 6

The optimal probability of false alarm vs. primary activity factor

Items

Conventional approach

Prop. approach with no relay

Prop. approach with a relay

Figure

\(P_f^*\)

0.49

0.41

0.39

Fig. 11

\(\rho\)

0.80

0.80

0.80

 

The results listed in Tables 3, 4, 5 and 6 show that the proposed sequential approach with a relay achieves better sensing gain, enhanced sum rates and lower optimal probability of false alarm \(P_f^*\) compared to the conventional non-sequential approach with no relay which is more applicable for the future IoT.

Conclusion and future works

The main purpose of the proposed sequential approach in the CCRRN with and without a relay is to achieve not only better sensing gain of SUs, but also to maximize the sum rate of the SU’s transmitter and receiver. In this paper, an efficient reporting mechanism scheme based on the sequential approach in the CCRRN has been proposed to gain a better detection and enhanced sum rate by utilizing the reporting frameworks of SUs and CHs. In detection gain, the probability of detection in the proposed sequential approach in the CCRRN with a relay are 4% and 14% over the proposed sequential approach in the CCRRN with no relay and the conventional non-sequential approach in the CCRRN with no relay, respectively. In addition, the proposed sequential approach in the CCRRN with no relay and with a relay achieved an optimal probability of false alarm \(P_f^*\) which maximizes the sum rate for a given primary activity factor \(\rho\) over the conventional non-sequential approach in the CCRRN with no relay. In sum rate maximization, the sum rate of the proposed sequential approach in the CCRRN with a relay are \(13\%\) and \(25\%\) over the proposed sequential approach in the CCRRN with no relay and the conventional non-sequential approach in the CCRRN with no relay, respectively. Therefore, we conclude that our proposed sequential approach in the CCRRN with a relay will be more applicable for the future IoT due to the fact that it mitigates the spectrum scarcity problem.

For future work, we will analyze the sum rate based on an efficient reporting mechanism whilst considering the interference to the PUs. Moreover, we will discuss the complexity of the proposed Algorithm 2 compared to the conventional Algorithm 1. Also, we will analyze detection and sum rate performance for scenarios when all SUs and CHs are moving.

Notes

Abbreviations

CR: 

cognitive radio

SU: 

secondary user

CRN: 

cognitive radio network

PN: 

primary network

PU: 

primary user

CCRRN: 

cluster based cognitive radio relay network

IoT: 

Internet of Things

NCSS: 

non-cooperative spectrum sensing

CSS: 

cooperative spectrum sensing

ED: 

energy detection

FC: 

fusion center

SDF: 

soft decision fusion

HDF: 

hard decision fusion

CH: 

cluster head

WSN: 

wireless sensor network

SBS: 

secondary base station

SU-Rx: 

secondary receivers

AF: 

amplify and forward

TDMA: 

time division multiplexing access

LEACH-C: 

low energy adaptive clustering hierarchy-centralized

BPSK: 

binary phase shift keying

CSCG: 

circularly symmetric complex Gaussian

SNR: 

signal to noise ratio

ADC: 

analog to digital converter

PDF: 

probability distribution function

CLT: 

central limit theorem

LSC: 

linear statistic combination

ROC: 

receiver operating characteristic

Declarations

Authors' contributions

MS and EB provided the guideline to focus on issues, requiring solutions, and reviewed the overall manuscript. MSM conceived the study, drafting the article, revising it critically for intellectual content of the whole manuscript. They reviewed the technical contribution of the work and approved the final. All authors read and approved the final manuscript.

Authors information

Md Sipon Miah received his BSc (Hon’s), and MSc in Information and Communication Engineering (ICE) from the Islamic University (IU), Kushtia-7003, Bangladesh, in 2006 and 2007, respectively. Since 2010, he has been with the Department of Information and Communication Engineering (ICE), in the Islamic University (IU), Kushtia-7003, Bangladesh. He is currently an Associate Professor in the same Department. Sipon is currently pursuing a Structured Ph.D. in computer science in the Department of Information Technology (IT), National University of Ireland Galway (NUIG), Galway, Ireland. In 2016 Sipon was awarded the prestigious Hardiman Scholarship. His research interests include Spectrum Sensing, Energy Harvesting, MU-MIMO based Cognitive Radio Networks and Massive MIMO based Cognitive Radio Networks.

Dr. Michael Schukat is a lecturer and researcher in the Discipline of Information Technology at the National University of Ireland Galway (NUIG), Galway, Ireland. He is principal investigator of both the OSNA (Open Sensor Network Authentication) cyber security research group (http://www.osna-solutions.com) and the Performance Engineering Laboratory @ NUI Galway. His main research interests include security / privacy problems of connected real-time /time-aware embedded systems (i.e. industrial control, IoT and cyber-physical systems) and their communication / time synchronisation protocols. He is actively involved in various security working groups on a European (e.g. COST Action Cryptacus) and International level (e.g. US-NIST CPS Public WG). Originally from Germany, Dr. Schukat studied Computer Science and Medical Informatics at the University of Hildesheim, where he graduated with a M.Sc. (Dipl. Inf.) in 1994 and a Ph.D. (Dr. rer. nat.) in 2000. Between 1994 and 2002 he worked in various industry positions where he specialised in deeply embedded real-time systems across diverse domains, such as industrial control, medical devices, automotive and network storage.

Dr. Enda Barrett is a Lecturer and researcher at the National University of Ireland Galway (NUIG), Galway, Ireland. In 2013, Enda received his Ph.D. in Computer Science from NUI Galway. His Ph.D. research investigated the application of a subset of machine learning techniques known as reinforcement learning to automate resource allocations and scale applications in infrastructure as a service cloud computing environments. Upon completion of his Ph.D., Enda joined Schneider Electric as a research engineer on a globally distributed innovation team. His main research interests include machine learning, distributed computing, cyber security and networking.

Acknowledgements

This work was supported in part by the Discipline of Information Technology (IT), National University of Ireland Galway (NUIG), Galway, Ireland and in part by the Hardiman Scholarship Postgraduate Research Foundation of National University of Ireland Galway (NUIG), Grant No. 16239003, Galway, Ireland.

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Information Technology, National University of Ireland Galway, Galway, Ireland
(2)
Department of Information and Communication Engineering, Islamic University, Kushtia, Bangladesh

References

  1. Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. IEEE Personal Commun 6(4):13–18. https://doi.org/10.1109/98.788210 View ArticleGoogle Scholar
  2. Senthilmurugan S, Venkatesh TG (2017) Optimal channel sensing strategy for cognitive radio networks with heavy-tailed idle times. IEEE Trans Cogni Commun Netw 3(1):26–36. https://doi.org/10.1109/TCCN.2017.2669985 View ArticleGoogle Scholar
  3. Afzal A, Zaidi SAR, Shakir MZ, Imran MA, Ghogho M, Vasilakos AV, McLernon DC, Qaraqe K (2015) The cognitive Internet of Things: a unified perspective. Mob Netw Appl 20(1):72–85. https://doi.org/10.1007/s11036-015-0583-6 View ArticleGoogle Scholar
  4. Li T, Yuan J, Torlak M (2018) Network throughput optimization for random access narrowband cognitive radio Internet of things (NB-CR-IoT). IEEE Internet Things J 99:1–13. https://doi.org/10.1109/JIOT.2017.2789217 Google Scholar
  5. Park JH (2017) Advances in algorithm, multimedia, and network for future IT. J Inf Proces Syst 13(2):197–203. https://doi.org/10.3745/JIPS.00.0004 Google Scholar
  6. Zhu J, Song Y, Jiang D, Song H (2017) A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of Things. IEEE Internet Things J 99:1–11. https://doi.org/10.1109/JIOT.2017.2759728 Google Scholar
  7. Yawada PS, Wei AJ (2016) Comparative study of spectrum sensing techniques base on techniques non-cooperative in cognitive radio networks. In: Proceedings of the 5th international conference on computer science and network technology (ICCSNT). IEEE, Changchun, pp 517–520Google Scholar
  8. Suseela B, Sivakumar D (2015) Non-cooperative spectrum sensing techniques in cognitive radio-a survey. In: 2015 IEEE technological innovation in ICT for agriculture and rural development (TIAR). IEEE, Chennai, pp 127–133Google Scholar
  9. Nguyen VD, Shin OS (2018) Cooperative prediction-and-sensing-based spectrum sharing in cognitive radio networks. IEEE Trans Cogni Commun Netw 4(1):108–120View ArticleGoogle Scholar
  10. Rahman MA, Lee YD, Koo I (2016) An efficient transmission mode selection based on reinforcement learning for cooperative cognitive radio networks. Hum centric Comput Inf Sci 6(2):1–14. https://doi.org/10.1186/s13673-016-0057-2 Google Scholar
  11. Lv Q, Gao F (2015) Mathched filter based spectrum sensing and power level recognition with multiple antennas. In: 2015 IEEE China summit and international conference on signal and information processing (ChinaSIP). IEEE, Chengdu, pp 305–309Google Scholar
  12. Enserink S, Cochran D (1994) A cyclostationary feature detector. In: Proceedings of the 28th asilomar conference on signals, systems and computers, vol. 2, pp 806–810Google Scholar
  13. Anaand PP, Charan C (2016) Two stage spectrum sensing for cognitive radio networks using ED and AIC under noise uncertainty. In: 2016 IEEE international conference on recent trends in information technology. IEEE, Chennai, pp 1–6Google Scholar
  14. Miah MS, Rahman MM (2014) An eigenvalue and superposition approach based cooperative spectrum sensing in cognitive radio networks. In: 2014 IEEE international conference on electrical engineering and information and communication technology (ICEEICT). IEEE, Dhaka, pp 1–7Google Scholar
  15. Mu J, Jing X, Huang H, Gao N (2018) Subspace-based method for spectrum sensing with multiple users over fading channel. IEEE Commun Lett 22(4):848–851View ArticleGoogle Scholar
  16. Chandrasekaran G, Kalyani K (2015) Performance analysis of cooperative spectrum sensing over \(k-\mu\) shadowed fading. IEEE Wirel Commun Lett 4(5):553–556View ArticleGoogle Scholar
  17. Sun M, Zhao C, Yan S, Li B (2017) A novel spectrum sensing for cognitive radio networks with noise uncertainty. IEEE Trans Veh Technol 66(5):4424–4429View ArticleGoogle Scholar
  18. Chen Q, Motani M, Wong WC (2010) Cooperative spectrum sensing strategies with multiple relays. In: 2010 IEEE international conference on communication systems (ICCS). IEEE, Singapore, pp 540–544Google Scholar
  19. Gupta M, Verma G, Dubey RK (2016) Cooperative spectrum sensing for cognitive radio based on adaptive threshold. In: Proceedings of the 2nd international conference on computational intelligence and communication technology (CICT). IEEE, Ghaziabad, pp 444–448Google Scholar
  20. Homayounzadeh A, Mahdavi M (2015) Performance analysis of cooperative cognitive radio networks with imperfect sensing. In: 2015 IEEE international conference on communications, signal processing, and their applications (ICCSPA). IEEE, Sharjah, pp 1–6Google Scholar
  21. Liao Y, Wang T, Song L, Jiao B (2014) Cooperative spectrum sensing for full-duplex cognitive radio networks. In: 2014 IEEE international communication systems (ICCS). IEEE, Macau, pp 56–60Google Scholar
  22. Aysal TC, Kandeepan S, Piesiewicz R (2009) Cooperative spectrum sensing with noisy hard decision transmissions. In: IEEE international conference on communications (ICC’09). IEEE, Dresden, pp 1–5Google Scholar
  23. Sun C, Zhang W, Letaief KB (2007) Cluster-based cooperative spectrum sensing in cognitive radio systems. In: IEEE international conference on communications (ICC’07). IEEE, Glasgow, pp 2511–2515Google Scholar
  24. Althunibat S, Denise BJ, Granelli F (2014) Secure cluster-based cooperative spectrum sensing against malicious attackers. In: Proceedings of IEEE globecom workshops. IEEE, Austin, pp 1284-1289Google Scholar
  25. Rasheed T, Rashdi A, Akhtar AN (2015) A cluster based cooperative technique for spectrum sensing using relay factor. In: Proceedings of the 12th international Bhurban conference on applied sciences and technology (IBCAST). IEEE, Islamabad, pp 588–590Google Scholar
  26. Singh R, Singh P, Duhan M (2014) An effective implementation of security based algorithmic approach in mobile adhoc networks. Hum centric Comput Inf Sci 4(1):7. https://doi.org/10.1186/s13673-014-0007-9 View ArticleGoogle Scholar
  27. Pughat A, Sharma V (2015) A review on stochastic approach for dynamic power management in wireless sensor networks. Hum centric Comput Inf Sci 5(1):4. https://doi.org/10.1186/s13673-015-0021-6 View ArticleGoogle Scholar
  28. Chen R, Park JM, Bian K (2008) Robust distributed spectrum sensing in cognitive radio networks. INFOCOM. IEEE, Phoenix, pp 1876–1884Google Scholar
  29. Zou Q, Zheng S, Sayed AH (2010) Cooperative sensing via sequential detection. IEEE Trans Signal Proces 58(12):6266–6283MathSciNetView ArticleGoogle Scholar
  30. Xia W, Wang S, Liu W, Chen W (2009) Cluster-based energy efficient cooperative spectrum sensing in cognitive radios. In: Proceedings of the 5th international conference on wireless communications, networking and mobile computing (WiCom’09). IEEE, Beijing, pp 1–4Google Scholar
  31. Lee IH, Lee H (2017) Achievable rate analysis for opportunistic non-orthogonal multiple access-based cooperative relaying systems. J Inf Proces Syst 13(3):630–642Google Scholar
  32. Vu-Van H, Koo I (2014) A cluster-based sequential cooperative spectrum sensing scheme utilizing reporting framework for cognitive radios. IEEJ Trans Electr Electron Eng 9(3):282–287View ArticleGoogle Scholar
  33. Zhang W, Yang Y, Yeo CK (2015) Cluster-based cooperative spectrum sensing assignment strategy for heterogeneous cognitive radio network. IEEE Trans Veh Technol 64(6):2637–2647View ArticleGoogle Scholar
  34. Mashreghi M, Abolhassani B (2017) A cluster-based cooperative spectrum sensing strategy to maximize achievable throughput. Wirel Personal Commun 96(3):4557–4584View ArticleGoogle Scholar
  35. Tran H, Zepernick HJ, Phan H, Quang TP (2013) Cognitive cooperative networks with cluster-based relaying under interference constraints. In: Proceedings of the 5th international conference on ubiquitous and future networks (ICUFN). IEEE, Da Nang, pp 542–546Google Scholar
  36. Yu H (2013) Optimal channel sensing maximising sum rate in cognitive radio with multiple secondary links. Trans Emerg Telecommun Technol 24(7–8):777–784. https://doi.org/10.1002/ett.2725 View ArticleGoogle Scholar
  37. Yu H (2013) Optimal channel sensing for heterogeneous cognitive networks: an analytical approach. KSII Trans Internet Inf syst 7(12):2987–3002View ArticleGoogle Scholar
  38. Miah MS, Schukat M, Barrett E (2017) Maximization of sum rate in AF-cognitive radio networks using superposition approach and n-out-of-k rule. In: Proceedings of the 28th Irish signals and systems conference (ISSC). IEEE, Killarney, pp 1–6Google Scholar
  39. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670. https://doi.org/10.1109/TWC.2002.804190 View ArticleGoogle Scholar
  40. Sinha A, Lobiyal DK (2013) Performance evaluation of data aggregation for cluster-based wireless sensor network. Hum Centric Comput Inf Sci 3(1):13. https://doi.org/10.1186/2192-1962-3-13 View ArticleGoogle Scholar
  41. Rahman MM, Miah MS, Sharmin D (2016) Cognitive improved LEACH (CogILEACH) protocol for wireless sensor network. Trans Netw Commun 4(6):1. https://doi.org/10.14738/tnc.46.2301 Google Scholar
  42. Singh B, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Hum Centric Comput Inf Sci 2(1):13. https://doi.org/10.1186/2192-1962-2-13 View ArticleGoogle Scholar
  43. Urkowitz H (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531View ArticleGoogle Scholar
  44. Kostylev VI (2000) Characteristics of energy detection of quasideterministic radio signals. Radiophys Quantum Electron 43(10):833–839. https://doi.org/10.1023/A:1010387403443 View ArticleGoogle Scholar
  45. Kostylev VI (2002) Energy detection of a signal with random amplitude. In: Proceedings of the IEEE international conference on communications (ICC’02). IEEE, New York, pp 1606–1610Google Scholar
  46. Zhu J, Song Y, Jiang D, Song H (2016) Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the Internet of Things. IEEE Access 4:4609–4617View ArticleGoogle Scholar
  47. Ambede A, Vinod AP, Shreejith S (2017) Efficient FPGA implementation of a variable digital filter based spectrum sensing scheme for cognitive IoT systems. In: Proceedings of the IEEE global Internet of things summit (GIoTS). IEEE, Geneva, pp 1–4Google Scholar
  48. Liu X, Xie JL (2017) Priority-based spectrum access in cognitive D2D networks for IoT. In: Proceedings of the IEEE international conference on communications (ICC). IEEE, Paris, pp 1–6Google Scholar
  49. Boyed S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, CambridgeView ArticleGoogle Scholar
  50. Mathis H (2001) On the kurtosis of digitally modulated signals with timing offsets. In: Proceedings of the 3rd workshop on signal processing advances in wireless communications (SPAWC’01). IEEE, Taiwan, pp 86–89Google Scholar

Copyright

© The Author(s) 2018

Advertisement