In wireless communication systems it is important to identify the suitable frequency spectrum bands for future functionalities. The radio spectrum is allocated to various functions, services and applications but most part of the spectrum is not utilized efficiently. To solve this underutilization of frequency spectrum problem Cognitive Radio (CR) concept has been put forwarded. This survey paper brings the importance of cognitive radio in the dynamic spectrum access and the various CR attacks. The main objective of Cognitive Radio is to utilize the limited and under-utilized frequency spectrum effectively without disturbing the primary users. As the effect of it, Cognitive radio has to interact with the environment in which it is operating and to find the unused band of spectrum to transmit accordingly and subsequently adapts to the environment in which it is operating. This paper categories the various Cognitive RadioNetwork attacks and various issues related to it.
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 radionetwork. At the onset, fuzzy logic found applications in control system 8,9
The RadioNetwork Design Problem (RND) originated in the context of wireless communication technologies. An efficient design of a radio transmitting network is a rele- vant issue due to the continuous increase in the user population of the radio-communications-associated ser- vices which demand more efficient coverage in wide geographic areas. The RND problem is an optimization problem belonging to NP-Hard class: there are a great number of possible solutions, prohibiting the determina- tion of the optimal one through their sequential evalua- tion. That is why several optimization algorithms are normally used instead.
In mobility management, detection of spectrum hole plays very important role for doing spectrum handoff mechanism. Operation of the cognitive radio depends on the spectrum sensing which is the most important task in case of cognitive radionetwork. Detection of spectrum holes (underu- tilized sub-bands of the radio spectrum) estimates average power, throughput and utilized time for each spectrum hole. But in case of reactive spectrum handoff mechanism, spectrum handoff delay becomes tedious task. So in order to make spectrum handoff mechanism efficient our scheme proposed spectrum handoff mechanism with negligible spectrum handoff delay. In this paper we had real time experimental setup with the spectrum handoff mechanism. We investigate algorithm for spectrum handoff mechanism which strongly minimizes spectrum handoff delay of cognitive radionetwork using spectrum detection method for sensing spectrum holes.
A Cognitive Radio having ability to suit multi-dimensionally intelligent wireless communication system that having an importance to fulfill the consumer needs. A Cognitive Radio is capable of: 1) sensing its environment 2) adapting its physical layer functionality. The two basic objectives of cognitive radio are: highly trusted communications whenever and wherever needed and utilization properly the radio spectrum. The cognitive radio is an approach that can be extended to cognitive networks. A cognitive radionetwork is intelligent multiuser wireless communication systems that believe the radio-scene, except the variations in the environment, provides communication between users by cooperation, and controls. The communication through proper allocation of resources. The cognitive network encompasses a cognitive process that can perceive current network conditions, and then plan, decide, and act on those conditions. Cognitive networks require a software adaptable network to implement the actual network functionality and allow the cognitive process to adapt the network. The most general theory in telecommunications is information theory which can be classified into syntactic, semantic, and pragmatic levels. Syntactic represents the lowest level which includes the study of relations of signs to other signs. Semantics is the study of the relations of signs to what they represent. This level thus considers the meaning of the signs. Pragmatics represents the highest level which includes the study of the interpretation of signs to their users. This level considers the value and utility of the signs.
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 radionetwork 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.
is no reason to limit the number of base stations. Transmit power control at the base stations results in a moderate reduc- tion of the exposure to electromagnetic fields. However, mo- bile stations will benefit from transmit power control in com- bination with small cell sizes due to their intelligent power control algorithms. Radionetwork planning that keeps the request of low transmit powers at the mobile station in mind is an effective means to achieve a significant reduction of the human exposure to electromagnetic fields.
Communication is a transfer of information from one point to another. Today’s communication is very advance; we use many new technologies like Cognitive radionetwork is latest one. The term Cognitive Radio was first officially presented by Mitola and Maguire in 1999 .Cognitive radionetwork 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.
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 radionetwork (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.
ABSTRACT: Cognitive Radios emerged as a means of improving the efficiency of current spectrum allocation policy by utilizing the available unused spectrum. There are many challenges in the systematization of Cognitive RadioNetwork, and the most obvious is to fulfil the awareness requirement. The nodes must be able to share the spectral information which requires inter node communication, hence that shared channel is needed that can be operated under various mode activities from the primary users. MAC layer (IEEE 802.22) protocol is suitable for channel sharing and proved that the resulting controlled channel sharing performs better than the traditional sensing based MAC algorithms, the proposed algorithm for solving this problem has provably tight bounds in terms of the max-min throughput. In order to make less number of reconfigurations in the network, localized version share algorithm is preferred in CR network. Thus helps to combine the centralized algorithm and its localized version and hence the complete protocol is able to achieve high fairness and high network throughput with few channel reconfigurations.
Motivated by successful mobile broadband subscription uptake in some countries, the cellular communication industry around the world is currently aggressively ex- panding third-generation (3G) systems using wideband code division multiple access (WCDMA). Given the stiff competition in the telecommunication industry, operators seek fast go-to-market solutions for any new types of service to be introduced. Furthermore, operators need to make informed decisions on network expansion to satisfy performance requirements efficiently. This drives the need for efficient radionetwork planning and rapid esti- mation of deployment cost. A typical WCDMA radionetwork planning process involves three stages: initial dimensioning, detailed planning, and network optimiza- tion . Initial dimensioning is a rough radionetwork planning (RNP) process for estimating the number of networking equipment needed to support the required quality of service (QoS) for a given targeted number of subscribers while fulfilling operators’ constraints.
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 radionetwork. 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.
Students will plan a simple Private Mobile RadioNetwork using Radio Mobile application. The network is assumed to be located in the north of Portugal, along the Douro River, providing communications in the 170 MHz band with 20 W transmitters. Initially, only three sites constitute the fixed network, but it is recognized that these sites result into insufficient coverage for mobiles. Students should then find suitable locations for two additional sites, to obtain adequate coverage of the area.
In this paper the analytical and simulation results of probability of detection and false alarm of a co-operative cognitive radionetwork 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).
In order to test the functionality of the cognitive radionetwork under realistic environments, several test beds are currently being implemented. In Chen, Guo, and Qiu (2011), architecture for cognitive radionetwork test beds with functional architecture for cognitive radionetwork 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 radionetwork 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.
Abstract—This is the second paper in a series of using cognitive radionetwork as wireless sensor network. The motivation of the paper is to push the convergence of radar and communication systems into a unified cognitive network. This paper studies this vision from a secure point of view. We propose two methods for robust spectrum sensing in the same framework of cognitive radionetwork. The first method is based on robust principal component analysis (PCA), to separate spectrum sensing results into the low rank signal matrix and the sparse attack matrix. Using sparse attack cancellation in least squares, the second method iteratively estimates the relative transmitted power of primary user under the threats of attackers. Then the relative transmitted power of primary user can be calculated from the recovered signal matrix. Both two methods can detect the sparse compromised cognitive radio nodes and effectively obtain the relative transmitted power.
ABSTRACT: In recent years, cognitive radio acts as a better technology for future generation networks because of its efficient capability intelligent system which can be used to sense, learn and optimized. Cognitive radio grants unlicensed users to adopt to the licensed frequency bands by way of dynamic spectrum access in order to reduce spectrum deficiency. This craves intelligent spectrum sensing techniques like co-operative sensing which generates use of information from number of users. The main defiance in any cognitive radio system is to improvise secondary user’s throughput while limiting objection foisted on licensed users. In wireless communication system, CR is the most challenging and promising concept for an efficient usage of the radio spectrum.Spectrum sensing is one of the most critical aspects of the cognitive radio as it offers the awareness of the spectrum holes in the networking environment.Deciding the optimal sensing, transmission timing strategies and accurate sensing techniques are of tremendous emphasized in a cognitive radionetwork. Spectrum sensing is significant enough to make real time decisions about which it bands to sense, when and for how long to hold the spectrum using the sensed spectrum information. In this paper, the cognitive radio concepts and the review of different spectrum sensing techniques are discussed in a broader sense.
For this reason we have often been sought out by operators, vendors and independent service providers wanting to get experience with our tools and benefit from our global knowledge. Consequently we have decided to offer a structured training program in LTE radionetwork planning and optimization utilizing the Atoll™ planning tool and the Symena Automatic Cell Planning and optimization tool, Capesso™.
In Kuala Lumpur, the number of mobile subscribers increase drastically caused by the increasing number of population. This condition results traffic congestion since the existing network cannot provide enough capacity for users to make a call especially during peak time (working hours). Thus, the need for more installed capacity is rising. To provide more capacity for certain area, the possible solutions are installing more Transceiver (TRX) on the existing BTS or implementing additional radionetwork or BTS.
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 radionetwork 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).