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Sensing in Cognitive Radio Networks: A Survey

AbstractWireless Sensor networks are increasing in demand day to day. Increase in demand increases the challenges in providing spectrum to the wireless spectrums and spectrum utilization. The wireless spectrum suffers underutilization of the available spectrum. To overcome this challenge an intelligent spectrum sensing and radio allocation schemes are used to perform dynamic and efficient spectrum allocation of sensor nodes in the network. Optimization is also required to improve the efficiency of the spectrum. In this paper, we discuss about the background, framework, classification and the algorithms involved in this process of spectrum sensing

 

Index Terms—  Wireless network, Cognitive Radios (CRs), Distributed, Reliability, Spectrum Sensing, Sensing Nodes.

I.     Introduction

he Wireless network spectrum are highly expensive and scare resource which is very expensive for only a few Megahertz chunks. For an example signal with the frequency range of 2.6 GHz spectrum are around 1.2 billion USD [1]. This huge price for the wireless spectrum is due to sharp increase in the demand and development of the wireless network. Another survey gives us the idea that the actual utilization of the spectrum is very less of about 5 to 10 percent of the entire reuse band [2]. Most of the time the actual spectrum is under-utilized. To overcome the under utilization of the spectrum concept of Cognitive Radio (CR) Technology immerged. With the use of the CR technology, the wireless devices can detect the unused portion of the wireless spectrum of the licensed user and at a given location and at a given time [3]. This is known has “spectrum holes”.  The network using the CR technology is known has the cognitive radio network or secondary network. Cognitive Radio system have two types of users: licensed user known has primary user and unlicensed users known as the secondary user. The main objective of the secondary user is not to interfere with primary users and not to provide Quality of service (QOS). Figure 1 show the utilization of the spectrum.

Figure 1: overview of spectrum usage.

Energy efficient spectrum sensing algorithm is explained in the papers [4,5,6,7]. Figure2 illustrate the architecture of the cognitive spectrum sensing algorithm. The cognitive spectrum performance spectrum depends onto basic metrics such as probability of spectrum and probability of false alarm.

Figure 2:Cognitive spectrum algorithm [16]

Probability of false alarm is that CR user declaration that PU is present when the spectrum is free, and the probability of the spectrum is that probability CR detects the presence of PU correctly. Maximized probability of detection results in an optimal spectrum sensing because increased probability of false alarm reduces the spectral efficiency. The performance of detection of spectrum sensing is affected by factors like multipath fading, shadowing and the receiver uncertainty [8-9]. Figure 3 gives the overview of the above-mentioned algorithms which shows that the CR1 and CR2 are present inside the transmission boundary while CR3 is outside transmission range. CR2 experience the multipath and shadow fading due to the multiple transmission PU which is due to blocking of the intermediate obstacle.

Figure 3:Receiver uncertainty and multipath/shadow fading [10]

CR3 suffers from the receiver uncertainty which will cause the PU receiver to not know the existing PU receiver. CR1 which has the strong transmission signal requests other CR users and shares the sensing results. These information’s about the shared sensing results is used to increase the overall detection performance proving that the cooperative sensing technique is a better approach for signal which suffers multipath and shadow fading and even the receiver uncertainty problem in the transmitted signal.

The information’s from the spatially located CR user can be used to make combined accurate decision. Due to multipath fading and shadowing the detection of CR becomes cumbersome because of its lower SNR, but increases in sensitivity cannot increase the detection performance when SNR of PU is below certain value. Improved detection with relaxed sensitivity does not limit cooperative gain, when cooperation reduces the sensing time, the CR users will have more time for data transmission and to improve their throughput [11]. Hence, well-designed cooperation scheme for cooperative sensing can contribute to a variety of achievable cooperative gain

Cooperative gain can be limited by cooperation overhead. The delay in sensing time is more in operations and energy towards cooperative sensing. Security vulnerabilities and performance degradation due to correlated shadowing cause cooperation overhead. Hence, it is required to effectively realize cooperation to achieve optimal cooperative gain without being compromised by the cooperation overhead. So, every Cooperative sensing scheme should address three fundamental questions of cooperation Method, Cooperative gain and Cooperation Overhead.

II.   Background

A wireless network is a network of nodes that controls the environment by enabling the interaction between the user and computer. Wireless sensor network consists of actuator and sensors which are in-between the gateway and the client. A huge amount of the sensors is deployed inside and outside the self-organized networks. These nodes are used to gather the information of the data packets and hops to transmit along the sensor nodes. During the transmission along the pathway takes a multi-hop routing through the internet and satellite. A wireless sensor consists of many autonomous computing nodes deployed over a large area. User configures and manages the wireless network, which is the public mission. The small autonomous nodes which are employed in the large area is used for the various application like forest fire, remote surveillance, collecting weather statics and many more applications. The requirement of each applications varies but most of the applications in common requires high bandwidth, network robustness, energy conservation, and continuous access to the nodes [12]. We can discuss some of the important terms used for the WSNs.

Bandwidth:

Capacity of a medium to transmit and receive data between (or among) nodes. The large bandwidth requirement in WSNs is increasing day by day as more networks are being deployed for multiple purpose. Each network utilizes its assigned spectrum band. Node density in the network is defined in such a way that the number of transmitting node is between the available bandwidth.

Latency:

It is the amount of end-to-end delay between sender and receiver which is a well-known measure in the field of digital networks. In the context of WSNs, several protocols have been developed to allow fast data transfer between the nodes, reduction in latency in these networks.

Robustness:

Robustness is the ability of a network to perform continuously its operations without degradation. The robustness itself has two aspects of reliability and resilience. IT is the ability to operate continuously without errors and the resilience aspect deals with its ability to operate under constrained environment e.g. any failures or catastrophe. WSNs are usually designed to be more resilient as compared to their wired counterparts, and other wireless networks, as they are usually deployed in harsh environments and are required to transmit their sensed data for a long period of time in any kind of climate conditions.

Energy consumption:

Energy consumption is another important aspect of the wireless network has most of the wireless mobile network are battery powered and hence saving battery life will be main goal of the wireless network. Energy constraints have been much investigated at all the layers of these networks.

Congestion:

WSNs have a specific density constraint and when the number of nodes in the medium is higher than the number of nodes in the wireless network it leads to the congestion, which introduces the problem of congestion nodes. Therefore, a new concept has recently emerged wherein the sensor nodes are proposed to have CR capabilities so that available radio spectrum can be efficiently utilized. Although developing a cognitive radio sensor network (CRSN) has a benefit of efficient spectrum utilization, there exists several challenges to be addressed before making best use of this concept. For example, energy consumption is an important consideration as the nodes cannot use their limited battery power to continuously sense the spectrum for opportunistic access. There exists a need to propose novel techniques to reduce both the computational and spectrum sensing overheads.

Cognitive sensor networks have low burst traffic, dynamic availability of multiple channels and spectrum mobility, energy limited and memory limited cognitive radio and opportunistic access in the presence of primary user activity. Due to the presence of the distinguished features in the cognitive network., resource allocation cannot be implied directly into the cognitive radio sensor networks. While designing resource allocation some of the optimization techniques should be applied for the spectrum access. Some of the optimization techniques are given as follows,

Considerations of fairness:

The resource allocation scheme for the cognitive radio sensor networks is to consider fairness among the several resource competitive senor nodes. The fairness among the priority data is highly important [14]. In some schemes where throughput maximization is required employs allocation of n number of sensor and for the rest partially no sensors are employed. Whereas in the fair resource allocation schemes, resource ensures that the nodes will get fairly allocated to the resources.

In this scheme, the resources are allocated according to the priority. Network aim for the fair dynamic spectrum sharing among the sensor nodes. The scheme gives the fair spectrum allocation among the n number of sensor nodes and consider the priority among the sensed data. This approach considers QOS and support heterogeneous traffic of smart grid system where each traffic type has an associated priority.

The disadvantage of schemes considering fairness and priority is that they will not achieve maximum performance in network. Moreover, fair resource allocation does not ensure QOS support. Fair resource allocation is necessary for Cognitive radio sensor networks where collaborative effort of sensors is needed, and individual measurement of each sensor is important. These networks also have potential applications in other priority based systems, e.g. Priority based health systems. In this type of applications, schemes with priority consideration can be used.

Energy Efficiency:

Energy-efficiency is generally required to extend the lifetime of the network. These schemes are highly desirable for Cognitive radio sensor networks, [15] since the sensor nodes in Cognitive radio sensor networks have limited power supply capability. However, these schemes are focused on energy conservation and energy minimization and cannot achieve maximum performance. Energy-efficiency is very important for energy-limited sensor nodes and is therefore desirable for all types of application of Cognitive radio sensor networks[44]. However, there are certain applications where replenishing the battery of sensor nodes may be inconvenient e.g., underground mines monitoring, forests fire detection, situation management in disaster affected areas, etc. In this type of applications, the energy-efficient schemes should be used to achieve energy efficiency and prolong the sensors as well as the network lifetime.

III. ARCHITECTURE OF COGNITIVE SENSING SPECTRUM

The cooperative sensing framework consists of the primary users, cooperating cognitive radio users or secondary users including a fusion center, all the cooperative sensing elements, the licensed radio frequency channels acting as R-Channels and S-Channels and an optimal remote database for the knowledge database [16-18]. Fig 4 illustrates the centralized cooperative sensing framework from the perspective of physical layer.

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Figure 4:Framework of the centralized Co-operative system [10]

In this framework, the cooperating CR users perform local sensing. For data transmission or spectrum sensing we can configure the RF front end. The RF frontend includes the down-conversion of RF signals and using an analog-to-digital converter (ADC) for sampling the signal at Nyquist rate. The RF frontend’s output of raw sensing data can be directly connected to the FC or be internally treated for local decision. Certain internal processing is usually required to decrease the bandwidth requirement of the channel. In order to do local decision it does two processing steps namely the calculation of test statistics, and a threshold device. A medium access control (MAC) scheme should contact the control channel for reporting the sensing results when the local decisions or the raw sensing data is ready. Higher network protocol layers use the sensing results for spectrum-aware routing selection [19] for example. An FC is a powerful CR user that is capable of acting as a regular CR with additional user selection capacity aided by the embedded knowledge base. The powerful FC acts as a base station and it stores information about the PU activities and white space information. In a distributed cooperative sensing, all CR users behave as individual FC and can perform final decision about the PU status iteratively.

IV.     Cognitive sensing classification

Cognitive sensing network can be classified into three categories: Centralized [20,21], Distributed [22], and relay-assisted [23-25] and they are discussed briefly as following

Centralized Cognitive sensing (CCS):

In centralized cooperative spectrum sensing, a CR user acts as a central identity called fusion center, that controls the three-step process of cooperative sensing. The initial step involves the Fusion center selecting a frequency band of interest for sensing and then it instructs all the cooperating CR users to perform local sensing individually. Further steps involve reporting of the collected sensing information to the Fusion center by all cooperating CR users. In the end, the Fusion center combines the received local sensing information and determines the presence of PUs, and sends the decision back to all cooperating CR users in the network. Fig 5 gives us the overview of the centralized cognitive sensing.

Figure 5:Centralized cognitive sensing spectrum [10]

Here CR0 acts as the Fusion center and all other CR users CR1-CR5 perform local sensing and share the sensed information with fusion center which is the CR0. To perform local sensing, all local CR users tune to the PU licensed channel or frequency band, known as the sensing band. Similarly, a control channel is established between every CR user and the Fusion center to transmit the sensed data. In centralized CR networks, a CR base station (BS) is naturally the FC.

Distributed Cognitive sensing (DCS):

The distributed cognitive sensing does not have the fusion center unlike centralized system. CR communicates among themselves and they share the sensed information of them and to perform a converged decision. Fig 5 gives us the overview of the distributed sensing spectrum. Form the illustration we can see that the local sensing is performed by all the CR1-CR5. They transmit the sensed information to the other CR which are in there transmitting range. These sensed information’s are combined with the own CR information and with the other users CR to perform the decision of the presence and absence of the PU by using the local criteria of the information of the system. Ever CR shares information with the user and hence distributed sensing system requires more steps to come to an end decision.

Figure 6: Distributed cognitive sensing

Relay-Assisted sensing spectrum:

The relay-assisted sensing spectrum consists of CR users with strong and weak sensing/control channels. Fig 7 shows relay-assisted. Here, CR1, CR4, CR5 observe strong PU signals thereby have a strong sensing channel while CR2 and CR3 have weak PU sensing channel. Alternatively, CR2 and CR3 have strong control channel while CR1, CR4 and CR5 have weak control channel. In this case, these CR users with strong and weak sensing and control channels can complement and cooperate each other to improve the performance of cooperative sensing., a CR user observing a weak sensing channel               and a CR user with a strong sensing channel, strong report channel and a weak report channel, for example, can complement and cooperate with each other to improve the performance of cooperative sensing. (i.e.) the control channels form CR2 and CR3 can act as relay channels for other weak control channel CR users. Hence known as relay channels. Fig 7, though it shows a centralized structure, the relay-assisted cooperative sensing can exist in distributed scheme.

 

Figure 7: Relay-assisted sensing spectrum.

V.  Cooperative Spectrum allocation scheme And Algorithms

Several cooperative spectrum sensing algorithms are discussed. In this following topic we will discuss about the fundamental models of the system using the PU and various SUs. Algorithms involves the use of centralized, distributed and relay-assisted cooperative spectrum. We can observe that the centralized system models use the central fusion center to make the end decision, comparing to the distributed system uses the fusion center. Four different algorithms are discussed.

A.      Reliability-Bases cognitive sensing algorithm:

In this paper [26], the author talks about a cooperative spectrum sensing algorithm to improve the sensing performance in the low signal to noise ratio (SNR). This algorithm, a new kind of detection probability and false alarm probability is introduced to evaluate the reliability of sensing node to select the more reliable nodes to participate in fusion according to the local historical sensing information of sensing nodes[43].

The system model describes the use of one PU and N number of SUs in a cognitive radio network. The tested system involves uses of variables H0 and H1, where H0 represents when PU is present while H1 represents when PU is absent. The spectrum detection of the ithSU can be modeled as binary hypothesis testing problem as,

where xi(t) is the signal received of ith sensing node, s(t) is the

signal transited of the PU, hi(t) is the channel gain between ith SU to the PU, ni(t) is the additive Gauss white noise of the channel.

Figure 8:Reliability-based Scheme [26]

In Fig 8, the process of reliability-based cooperative sensing is depicted. In this scheme, the detection probability and the false alarm probability for each sensing node is obtained during local sensing. The algorithm is explained as following steps,

Step 1: Calculate the detection probability and the false alarm probability of all sensing nodes in lase fusion;

Step 2: Get the local decision of all sensing nodes;

Step 3: Calculate the reliability of the all sensing nodes;

Step 4: Calculate the average reliability and choose the sensing nodes which reliability is larger than the average reliability to participate in fusion;

Step 5: Calculate the decision threshold;

Step 6: Calculate the decision statistics in the fusion center

Step 7: Makes the global decision. If the statistics is larger than the threshold, the PU is present; otherwise, the PU is absent;

Step 8: End.

If Hr(k) > Gr(k), the hypothesis H1 is true and the PU is present else if Hr(k) <= Gr(k), the hypothesis H1 is false and the PU is absent. Fig 9 gives us the Detection Probability of 4 algorithm and fig 10 gives different

Figure 9:Detection Probability of 4 algorithms

Figure 10:Detection Probability in different attenuation factor

The algorithm improves the sensing performance by alleviating the harmful effects of the accidental sensing errors, even the malicious nodes, on cooperative spectrum sensing. Simulation results showed that the algorithm proposed has 2-4 dB advantage over the other three algorithms namely weighted cooperative sensing algorithm (WCSA), detection probability based weighted (DPW) algorithm and average weighted (AW) algorithm.

B.      Hierarchical Fusion-Based CSS scheme in CRNs [27]

In this paper, the author discusses a Cluster-based Hierarchical Fusion cooperative spectrum sensing scheme for CRNs based on the energy consumption and the performance of the detection. The authors propose clustering to avoid large data processing in the Fusion Centers (FCs). Fig 11 shows cluster based CRNs using an FC and Cluster Head (CH). Several other cluster techniques have been discussed in [28-30].

The System model proposed included one PU, one FC at the center and N Cooperative SUs at a distance as shown in the fig 11. The authors use energy detection (ED) method [31] which is the differences of the received signal power in the presence and absence of a PU. If the energy detected is above threshold, then the PU exists else the PU is absent. Once each SU decides their one-bit decisions, these are sent to Fusion Center for next level of decision. The problem with this method is that the Fusion Center uses the AND/OR rules to decide if a PU is present or not.

Figure 11: cluster-based sensing scheme [33]

In the proposed cluster based model, this N number of SUs are grouped into K clusters, each cluster having a CH, and two levels of cooperation are proposed in this scheme. The first level of cooperation takes place at the CH of each cluster, while the second level of cooperation happens at a higher layer using decisions from individual CHs and the FC. In the first level data is collected by each SU at the CH of each cluster which is combined using the energy detection to make the cluster decision. In second level, the one-bit decision is combined with the majority rule at the Fusion center making the final decision. A modified Majority rule assigns weight coefficient [32] to the cluster level decisions based on the reputation degree of the cluster.

The simulation results as shown in Fig 12 prove that the cluster based hierarchical scheme that uses modified MAJORITY rule performs better than three other classical hierarchical models such as MAJORITY, AND, OR [33].

Figure 12: Performance comparison of 4 hierarchical scheme [27]

C.     Soft Decision with Noise Uncertainty Reduction [34]

In this paper, the author proposes an enhanced fusion rule for the ED based soft decision. The scheme has been proposed to enhance the spectrum sensing in CRNs with low computational complexity while in hard decision, the PU activity is based on its own energy measurements. The final decision is made by the FC where AND, OR or majority rule is applied to the collected CR decisions while in soft decision [35] PU sends energy measurement to the FC which acts as energy sensors for the FC.

One of the three energy combining schemes namely square law combining (SLC), maximum ratio combining (MRC) and square law selection (SLS) is applied on the collected data to arrive at the final decision about the current PU. This is stated as the conventional soft decision CSS. Several other soft fusion schemes are described in [36, 37].

The paper describes about the combining schemes where SLC adds up all the data from each node for a time duration of L for the threshold period.In MRC, weights, which are proportional to the SNR, are multiplied with energy measurements from CR users. In SLS, the FC selects the CR user with highest energy. The energy values obtained are compared with threshold to determine the PU status. The system model consisted of the fundamental model with one PU, one FC and several CR users.

In the proposed fusion rule, the effect of noise uncertainty was considered when arriving at a final decision about the current PU existence. The idea behind the proposed soft decision CSS is that it utilizes the information about the recent PU activities inborn in the latest collected PU energy measurements and combined by the FC at the recent quiet periods i, 1 ≤ i ≤ L − 1, to contribute the final decision about the current PU activity at the sensing period L. An activity profile of the PU, which store the past L – 1 combined energy values, is created using the collected data from L – 1 sensing periods, which is used for the pre-estimation of its current activity at the sensing period L.

Eavg(L) and ρ(L), which are the average of energy values combined using the techniques mentions above and the noise uncertainty factor respectively, is given by the

Having able to predict the current PU activity, it alleviates the effect of noise uncertainties in the PU energy measurements made by the CRs in the sensing period L by dynamically adjusting the threshold level used by the FC, which improves the accuracy of the final decision about the current PU existence made by the FC.

Figure 13: Performance comparison between proposed soft decision and conventional one  [27]

Figure 14: Complexity between proposed SLC and the soft decision and the conventional

To brief, Predicting the current PU status was investigated using energy measurements successively collected by the FC from the CRs during several sensing periods. Based on predicting the current PU status, two dynamic thresholds were toggled to increase the probability of detection and decrease the probability of false alarm. The threshold values are dynamically adjusted based on the estimated value of the noise uncertainty factor of the current sensing event. Theoretical and simulation analysis results of the proposed scheme proved the high efficiency compared to the conventional soft decision CSS, in which noise uncertainty effect is not considered. The proposed design is not exposed to the effect of SNR wall [34], and at SNR of −25 dB it succeeded to reduce the number of used CRs compared to the conventional soft decision CSS by 80%, 70%, and 70% to obtain the same target performance of Pd = 0.9 and Pfa = 0.1 fig 14 , using MRC, SLC and SLS respectively. Also, a high increase in spectrum and energy efficiency are obtained by using the proposed soft decision CSS instead of using the conventional one.

D.     A Distributed Consensus Based CSS Scheme in CRs [40]

In this paper, the author has discussed a distributed CSS for CRNs. The have presented a fully distributed and scalable scheme for spectrum sensing based on recent advances in consensus algorithms [38]. The scheme sets up a neighbourhood with users having desired channel characteristics. The consensus of the CR users to make the final decision and thereby leverage the detection results in severe wireless fading networks. The proposed algorithm can work with two underlying network models, fixed bidirectional graphs and random graphs. This bio inspired algorithm provides insights on the future design of CRNs.

Being a distributed CSS, the scheme is modeled as a multiagent coordination problem and the CR users or Secondary users cooperate based on only local information exchange without a FC.  This is because a common receiver is not available in mobile and ad-hoc CRNs. The fundamental requirement is the secondary users do local sensing after which they exchange sensed information among other CR users and converge on a decision unanimously about the presence of PU based on the received data and local sensing.

The proposed consensus scheme has two stages like any other CSS. The first stage is the local sensing by the CR users while the second stage is the exchange of collected information among the network of CR users and then calculate to make a local decision. The spectrum sensing model used in the paper is ED because of its simplicity in implementation. So, each secondary user i detects the energy and get the measurement Ywhich is the result of the first[41] stage, the local sensing information.

The second stage requires the secondary users to establish communication links with the neighbours. So, a network is formed between them based on the standard graph model. The network model is illustrated as an undirected graph G(N, E) where N is the set of nodes (Secondary users) and E is the set of edges and ϵ N×N. Existence of an edge (i,j) denotes there exists a direct link between the nodes i and j. The neighbour of i, which is j, is denoted by a set N= {j|(j,i) ϵ E}  N. Each node is assigned a consensus variable xithat stores the energy estimate. By reaching consensus it is meant that, the individual states xi converge to a common value x* given by xi(k)  x* as  ∞, for each ϵ N, where k is the discrete time and xi(k) is updated based on previous states of node i and its neighbours

As stated earlier, the proposed scheme can work with fixed as well as random network topologies. Fixed topologies have duplex wireless links with the desired neighbours and the link remain stable until the consensus is reached while random network topologies have fixed topologies but experience random link failures due to fading of wireless signals. The network topologies undergo random changes and the primary user may arbitrarily enter and leave the network, a protocol is essential to quickly decide when the harmony is practically reached. If the secondary users cannot efficiently form a decision in limited steps, the energy measurements obtained at the initial stage may become outdated. To address this finite time-detection issue, in implementations, a certain toleration threshold may be used by the users. A secondary user may stop the iteration if it finds that the difference between the states of the simulation, it can be determined that the threshold may be chosen to be around a fraction of 1 dB or close to 1 dB

In this paper the authors simulate under three test conditions. The first condition involves all the nodes having the same average SNR, in the second condition, they use varying SNR between 5 and 9 dB for each user and finally the third condition uses average SNR varying from 5 and 15 dB [45]. The results of all these simulations shows that the missing probability (Pm) and the false alarm probability (Pf) perform better than those of the existing methods and the results highlights the threshold robustness of the proposed consensus algorithm. when the average SNR increases, Pm drops for a given threshold while Pf remains the same

Figure 15:Pm vs Pf [40]

One major limitation of the scheme is that the step size is dependent on the degree of a node in the network. This means the scheme requires to have a prior knowledge of the upper bound of the max degree of the network. However, an alternative approach, the Metropolis weights approach [39] can be used to solve the problem as this approach does not need to have a prior knowledge about the degree of the network.

VI.     Conclusion

In This paper we have discussed that the cooperative sensing technique is used to improve the spectral efficiency of the signal. Many cognitive spectrum performances are discussed. Background of the cognitive network is discussed in brief. Architecture and framework are widely explained in this paper. Various cooperative sensing algorithms are discussed namely fusion-based, hierarchical based, distributed and soft decision making. These algorithms are compared with the conventional and proposed model. Future works involves the study of model which affects the cognitive sensing networks.

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