Cognitive Radio Network

Abstract — In this paper, we propose a model of mobile cognitive radio network after fully considering the analysis method of network connectivity based on random way-point (RWP) mobility scheme. The closed-form solution of connectivity probability has been derived to evaluate the network performance in this model. And it shows that the network connectivity is related to the size of mobile cognitive radio network, the number of secondary users, the transmission range of secondary users and primary users, the interference range of secondary users and primary users, the number of primary users and the activity factor of primary users. Simulation results are good agreement with numerical results, which verifies our theoretical analysis is correct and reasonable.

As smartphone and other wireless equipment have become such integral part of our daily lives, we demand to be connected to anyone, anytime, and anywhere. This poses an exciting challenge to the wireless communication research community to explore new ways to exploit the limited radio spectrum band. The fixed spectrum assignment strategy employed by existing wireless networks is extremely inefficient. A field test taken in New York City reported that the maximum total spectrum occupancy is only 13.1% in the 30-MHz to 3-GHz band [1]. Similar studies showed that only 22% of assigned spectrum is utilized in urban areas and less than 3% in rural areas [2]. The Federal Communications Commission estimates that the utilization rate for existing spectrum is between 15% to 85% with high variance in time and location [3]. Cognitive radio network (CRN) shows great promise to exploit these deficiencies by applying dynamic spectrum assignment.

In order to test the functionality of the cognitive radio network under realistic environments, several test beds are currently being implemented. In Chen, Guo, and Qiu (2011), architecture for cognitive radio network test beds with functional architecture for cognitive radio network nodes is proposed. In Qiu et al. (2010), a real-time cognitive radio test bed with considerations on the design architec- ture, hardware platform, and key algorithms is built. Cognitive radio network is taken into considera- tion for smart grid applications as well. With consideration in ECMA-392 international standard, in Franklin et al. (2010), an ultra-high frequency band cognitive radio test bed is built based on ECMA- 392 international standard which defines the MAC and physical (PHY) operation for television wide space (TVWS) communication between portable devices. The test bed is verified to be operated in line with the FCC rules. In the near future, advanced test beds with complete functionalities can be used to further develop and test cognitive radio networks under realistic environment.

For reducing the effect of Inter-Symbol Interference (ISI) in frequency domain OFDM system, the last Ng samples of the time domain OFDM symbol is copy to the beginning of the symbol to form a guard time or cyclic prefix. Therefore, the OFDM block has a period of Ts = (Ns + Ng)/Fs where Fs is the sampling frequency. At the receiver, the inverse block is applied. After time synchronization (frame detection, start of symbol timing, and SFO estimation and compensation) and frequency synchronization (CFO estimation and correction), the cyclic prefix is removed. Then, the received OFDM symbol are transformed into the frequency domain through an Ns point DFT. The channel is then estimated and the received data is equalized. The complex data output is then mapped to bits again through the De- mapper. De-interleaving, decoding, and randomization is applied later to the received block to recover the original source bits. From the network point of view, we consider a cognitive radio network of K SUs and one PU. The PU occupies a spectrum of a certain bandwidth for its transmission, while the same sensed spectrum is shared by the SUs. In fact, the spectrum is totally utilized by one SU (the master node or the fusion node) to send different data to the other K -1 SUs (the slave nodes).

The Cognitive Radio is an intelligent radio that can be programmed and configured dynamically. The CR is a transceiver which is designed to utilize the best wireless channel in its vicinity. Such a radio automatically detects applicable channels in wireless spectrum, then consequently changes its transmission or reception parameters to allow more concurrent wireless communication in a given spectrum band at one region. The Cognitive Radio network as shown in Fig.1. Cognitive Radio (CR) technology has gained attention due to the FCC mandate that does allows unlicensed radios to operate in the unused portions of the UHF band. Such channels, however, need to be relinquished when primary (or incumbent) users begin using them. Solutions that can opportunistically use such channels can help alleviate the congestion in the ISM bands. Advances inhardware technologies have made it possible to simultaneously use the capacity of a large number of channels that may share or may not be contiguous by the use of non-contiguous OFDMA.

The redundancy nature of nodes is exploited in OR to transmit packets to nodes that are available for routing, which gains benefit from the broadcast characteristics of wireless transmission. Following the network conditions, the link can change dynamically making it appropriate for cognitive radio network which has rapid variation of spectrum availability. In OR, several nodes are potentially chosen as next hop node for forwarding unlike conventional routing where single specific node is preselected as a forwarder for a packet. Thus multiple potential paths may be used by the source to deliver the packets to the sink, where the reliable link is chosen using the metric Expected Transmission Count (ETC). ETX is the average number of transmissions necessary to send a packet reliably across a route or a link counting retransmissions also. The ETC of a single path is given by the addition of ETX of every link in the route. ETC is computed as inverse quantized value of Received Signal Strength Indicator.

Communication is a transfer of information from one point to another. Today’s communication is very advance; we use many new technologies like Cognitive radio network is latest one. The term Cognitive Radio was first officially presented by Mitola and Maguire in 1999 [1].Cognitive radio network is a network in which an un-licensed user can use an empty channel in a spectrum band of licensed user. Cognitive Radio Networks (CRNs) is an intelligent network that adapt to changes in their network to make a better use of the spectrum. CRNs solve the spectrum shortage problem by allowing unlicensed users to use spectrum band of licensed user without interference. Generally licensed users are known as primary users and un-licensed users are secondary users.When information is send through a licensed spectrum band is a primary user, only some channel of band is used, others are empty. These empty channels are used by un-licensed user called secondary user. Secondary users always watch the activities of primary user, and detect the empty channel and occupy the channel without disturbing the primary user. When the primary users are active, the secondary user should either avoid using the channel. An Empty channel also known as spectrum holes.

Spectrum sensing plays a very provocative role in cognitive radio network. In order to utilize spectrum more efficiently and to exploit the primary user, spectrum sensing is accomplished. We proposed a new hybrid algorithm for detection of primary user in cognitive radio network. The theoretical analysis and simulation is also presented in this paper. This research work includes an analogy with Energy Based Detection and Cyclostationary Feature Detection. Our proposed algorithm is a flexible algorithm, the Cyclostationary feature algorithm act as feature extractor when primary user is present and function as detector when primary user is absent. The results show that it is optimum spectrum sensing algorithm under different SNR values. It has removed the shortcomings faced by both sensing algorithms i.e. Energy Based Detection and Cyclostationary Feature Detection.

Automatic gain control (AGC): The AGC maintains the gain or output power level of an amplifier. The conitive radio network can be used efficiently in network centric, distributed, adhoc, mesh architecture, and serve the needs of both licenced and unlicenced applications. The basic components of cognitive radio network are mobile station, base station and backbone networks depending on this there are three kinds of network architecture in cognitive radio i.e, Infrastructure, ad hoc and mesh architecture.

This paper deals with spectrum detection and spectrum handoff mechanism in cognitive radio network. Spec- trum analyzer is used to emulate cognitive radio to do spectrum sensing. The whole procedure of spectrum sensing including sensing setup, instrument control, sensing capability, sensing scenario and sensing result is presented in detail. The main advantage of equipment-based spectrum sensing is to perform quick and semi- continuous measurements. The time needed for each measurement is around 80 - 110 ms. Spectrums of CDMA signal, GSM signal and DTV signal are shown in this paper. Based on this information, the information about the spectrum is embedded in packets and broadcasted to every user. Moreover these spectrum measurements

The demand for wireless radio spectrum is increasing rapidly. As the number of users and data rates is increasing day by day it is very difficult to accommodate them within the limited radio frequency spectrum. Federal communication commission (FCC) allocates spectrum to licensed users. Most of the spectrum is not efficiently used by them. Licensed users are called primary users and the unlicensed users are called secondary users. Cognitive radio technology is the intelligent network which makes use of unused spectrum of primary users. Spectrum scarcity problem is solved by cognitive radio network by allowing the unlicensed or secondary users to make use of primary user’s unused spectrum without causing interference. The essential security mechanism is necessary for the successful implementation of cognitive radio network and the realization of benefits. The taxonomy of security threat is presented and the active threat related to cognitive radio is shown. Different types of active attacks related to spectrum sensing manipulation like primary emulation attacks, spectrum sensing data falsification are discussed. Network layer attacks such as the sinkhole attack, hello flood attack, and transport layer attack like lion attacks, sybil attacks are discussed here. 2. OVERVIEW OF COGNITIVE RADIO NETWORK

Spectrum is a very relevant resource in wireless communication systems and it has been a predominant research topic from the recent several decades. The improvement of the cognitive radio network requires the involvement and interaction of many recent techniques, including distributed spectrum sensing, interference management, cognitive radio reconfiguration management, and cooperative communications. Furthermore, in order to fully realize the CR system in wireless communications for efficient utilization of scarce RF spectrum, the method used in recognizing the interference and/or spectrum sensing should be steady and prompt so that the primary user will not get affected from the CR system to utilize their licensed spectrum. Infuture, the analysis of energy detection based channel detection method and a threshold determination method which is functional and able to detect PUs even in low SNR may be carried out.

In this paper the analytical and simulation results of probability of detection and false alarm of a co-operative cognitive radio network are compared under both awgn and Rayleigh fading environment. After getting the confidence level of above 95% from the simulation, a neural network (NN) is trained with simulation data where the analytical result is given as the target of the NN. Finally the results are verified with the profile of MSE (mean square er- ror) of three data set (train, validation and test), regression on data set, con- fusion matrices and error histogram. Here we use Backpropagation algorithm and Hopfield model, all the results yield error of less than 4.5%. The concept of paper is applicable at fusion center (FC) to make proper judgment of presence of primary user (PU).

changes with types of traffic and network. The QoS is a function of multiple contradictory parameters and those are not certain and exact. Application of fuzzy logic gives more effective solution when the available information is vague and imprecise. This paper proposes to use rule based fuzzy logic for assessing the influence of three critical parameters on QoS in a cognitive radio network. At the onset, fuzzy logic found applications in control system 8,9

Energy harvesting network (EHN) is a trending topic among the recent researches. This substantial attention is due to the limitations, operational cost and risks of the conventional power suppliers, such as fossil fuel and batteries. Moreover, EHN are expected to enhance energy efficiency by harvesting energy of RF and renewable sources. In contemporary research works, EHN is applied to CR technology. This energy harvesting cognitive radio network (EH- CRN) is expected to utilize both energy and electromagnetic spectrum efficiently. However, EH-CRN is facing enormous challenges related to technical design. Some of these challenges are reviewed in recent surveys. However, other challenges such as optimizing the network throughput and EH-CRN implementation models were not the focus of these researches. Therefore, the aim of this survey is to review EH-CRN research works by focusing the survey perspective on maximizing the network throughput and the implementation models.

In this paper, a new architecture of cognitive radio network (CRN) is presented for future dynamic spectrum sharing in time-variant flat fading (TVFF) channels. We consider a practical scenario where secondary users (SUs) are able to access the idle spectrum by secondary user routers (SU_Rs). Managed by a fusion center (FC), SU_Rs can work together to capture the idle spectrum, and then assign to the SUs. Besides, it is imperative to guarantee the wireless communication quality between primary base station (P_BS) and SU_Rs. Therefore, a new cooperative spectrum sensing (CSS) algorithm is suggested to recursively estimate the channel state information (CSI) while capturing the idle licensed band. The united mathematics model relies on a dynamic state-space model (DSM) and a Bernoulli filters (BF) algorithm. TVFF channels are modeled as finite-state Markov channel (FSMC). In order to reduce complexity of CSS, the particles are manipulated and reconstructed. Experimental simulations demonstrate that, by exploiting dynamic CSI, sensing performance of the new CSS algorithm will surpass the traditional schemes and this new architecture can be used in a realistic spectrum sharing system.

A cognitive radio is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones. This intelligently avoids interference amongst users and provides the ability to use up all available bandwidth on the RF spectrum. There are many ways in which the communication can be demonstrated. This paper examines how we have implemented a cognitive radio network testbed using software defined radios (SDRs) for multimedia communications, where components that have been traditionally implemented in hardware are instead implemented by means of software on a personal computer or embedded system to communicate and transfer a file between each other. We attempt to demonstrate potential use of SDRs for future multimedia applications.

ABSTRACT: The wireless communications are generating the spectrum shortage problems with the Reference to the latest developments. The more challenges in the use of licensed wireless networks or unlicensed wireless networks with opportunistic use of the spectrum with limited rules or without limited rules. The different frequency bands are used by different wireless networks. So it’s very important to use attenuation bands when there is no activity occurs on them. A new technology which leads like Cognitive radio is to solve these problems through dynamically utilization of rules and spectrum. Several spectrum sharing schemes have been proposed in cognitive radio. The major and challenging issues are security in cognitive radio network. The attackers in cognitive radio technology as compared to the wireless networks in a general form chances are prearranged. Mobile station equipment may switch to any available frequency band in the cognitive radio, and make list of free channel and take handoff decision. So whenever handoff is made there will be a chance that malicious attacker may hack ongoing traffic. He may even break off established traffic by imitating any kind of active or passive attack like spoofing denial of service, interception etc. This paper discovers the key challenges to give security in cognitive radio networks. And discusses the current security carriage of rising IEEE 802.22 cognitive radio typical and recognizes security threats and vulnerabilities along with the countermeasures and solutions.

CR network is having a dynamic characteristic due to which it is difficult to detect the selfish attack in the wireless network. The COOPON technique is used for detection of selfish attack. Here it concentrates on narrow minded assaults of SUs towards different direct access in subjective radio specially appointed systems. It accepts that a single secondary user obliges different channels. Each secondary user will frequently telecast the current station distribution data to the majority of its neighboring secondary users. It includes the quantity of stations in use and the quantity of accessible stations [1] [14]. Among auxiliary ad hoc system CCC (common control channel) has been utilized to show and watch data. Type 3 selfish attack can be found out using the COOPON tech- nique. The secondary users will use the information distributed through CCC to access channel for transmission. In this technique, it considers one node as target node and the other as neighboring node, and it will check the selfish attack of the target node.

Over the last decade, the development of wireless communication technology has shown exponential increase and wireless services have evolved from traditional voice service to a wide range of multimedia services. The bandwidth requirement varies from application to application. Hence, providing QoS to these applications according to their requirements of bandwidth is a critical and challenging task. QoS is the performance level of a service offered by the network to the user in order to achieve deterministic behavior by proper utilization of the network resources. The meaning of QoS changes as per the requirements of the application field. It is the process by which the performance and reliability of a network is controlled. It is a complex process and the modeling of QoS is a tough and difficult task. ISO 9000 defined QoS as “the degree to which a set of inherent characteristics fulfils requirements”. ITU-T 16-17 defined QoS as “the collective effect of