Discuss about the Full-Duplex Communication In Cognitive Radio Network.
The cognitive radio or CR is a kind of modern day wireless communications where transceiver intelligently detects different communication channels that are to be used. It has been optimizing the using of various kinds of available radio-frequency spectrums. These have been minimizing the interference to multiple kinds of current users.
The CR in its primary form is a hybrid technology that has included SDR or “Software Defined Radio”. This applies to spread various spectrum communications. Different possible functions of CR involved a transceiver’s ability to find out the geographic location, recognize and authorize the users, encrypt and then decrypt the signals (Li et al. 2016). Further, it has included a sense of neighboring wireless devices in action. This has also added the adjusting of modulation characteristics and output powers.
Moreover, there have been two primary kinds of cognitive radio. They are complete cognitive radio and present day spectrum sensing cognitive radios. For instance, the full cognitive radio considers every parameter that any wireless network or nodes are aware of. Again, on the other hand, spectrum sensing cognitive radio is been effectively utilized in detecting various channels within the radio frequency spectrum. The CR has been supporting the dynamic spectrum access. This policy has been addressing issues with spectrum scarcity. This is encountered in various countries. In this way, CR is broadly regarded as one of the outstanding and promising innovations as far as future wireless communication is considered. In order to make wireless networks and radios cognitive in real sense is not an easy job. However, this by no means has needed collaborative effort from different research communities. This has included network engineering, signal processing, software-hardware joint design, game theory, radio-frequency design and reconfigurable.
The cognitive radio is a popular idea and at many situations they can create network of radios. This is done through connecting various cognitive radio nodes. Thus various elements of those performances are developed considerably. At many cases any single cognitive radio communicates with various non-cognitive radio stations. For example Li et al. (2015) shows the instance of femtocell that needs cognitive functionality for setting itself up. It is needed to communicate with non-cognitive cell-phones. However, at many instances, cognitive radios are able to perform a network and then act as the complete CR network. At this scenario, there are various benefits to improve performances of the compete network beyond the individual elements.
The CR wireless network is the critical candidate sectors where the techniques are used for various opportunistic spectrum accesses. The research in these sectors has been in its nascent stage. However, it has been developing very fast. Moreover, Youssef et al. (2014) has classified the quick emerging application sector of CR wireless sensor networks. These have been highlighting the primary research that is undertaken and indicating open issues. Besides, Ng, Lo and Schober (2016) has demonstrated various benefits of cognitive radio wireless sensor networks, the distinction between different ad hoc cognitive radio networks. This has also included CR radio wireless sensor networks, application areas of cognitive radio wireless sensors along with research and challenges trend. All of these have been under cognitive radio wireless sensor networks. Furthermore, different sensing schemes that are suitable for these CR networks are been analyzed in the article of Hawa et al. (2017) with the emphasis over spectrum and cooperation access methods assuring the availability of needed QoS.
Using cognitive radio network delivers various benefits as compared to the cognitive radio operating entirely in an autonomous manner. The first benefit as highlighted by Elkashlan et al. (2015) is improved spectrum sensing. Through using cognitive radio network, it has been possible to achieve essential benefits according to spectrum sensing. Next, there is improved coverage. Through setting various CR network, it is possible to relay information from a node to the other. Thus the power levels are decreased, and the overall performance is sustained.
The “dumb” or traditional radios are also been designed. This is done with an assumption that has been operating under the spectrum band. This has been totally free of various kinds of interfaces. Thus there is no reason to endow the radios that have possessed the capability to modify the parameters dynamically, spectrum bands and channels against interferences. These radios, as mentioned by Zou et al. (2015) have been not surprisingly dedicated or licensed spectrum to operate.
For example, Tsiropoulos et al. (2016) highlighted that the instance of xMax cognitive radios. They are engineered from the base to function the challenging situations. This, unlike the conventional counterparts, can see the environment in considerable details for determining spectrum that is not used. It is helpful to tune the frequency to transmit and receive signals quickly. It helps in quick tuning that frequency to transfer and receive messages. Moreover, they have the capabilities to find swiftly spectrums of interfaces that are recognized over the rates used. In the case of xMax, the samples detect and determine whether the interfaces have reached various unacceptable levels.
In the following diagram, the operations of xMax cognitive radios are illustrated in comparison to traditional ones. This has further shown the careen captures of readings of spectrum analyzers that is taken from xMaz network tower within Ft Lauderdale. Here the frequencies are calculated that are within the unlicensed band. Since the spectrum is unlicensed or free of charge to use, it is utilized by many. The xMax cognitive network, in short, has been fetching various opportunities where the radios have been barriers to interference. To decrease the unnecessary and trashing channel switching because of temporary and very short-lived interference phenomenon or different degraded network situations, the handovers and actual channels are created through trending multiple samples and calculations as per Liu et al. (2014). Here, the system has been transferring from the present circuit as high levels of interferences exceed the built-in inference mitigation abilities.
The cognitive radios have been helping radio devices for using spectrums or radio frequencies entirely in sophisticated and new ways. The cognitive radios have been able to control, sensor and detect conditions to operate the environment and then dynamically reconfigure the individual characteristics for matching those conditions. Through using complicated measures, xMax has included cognitive radios to identify the potential impairments for communications such as path loss, interference, multipath fading and shadowing according to Lu et al. (2014). Moreover, they can adjust the transmitting parameters like frequency, power output and modulations to assure the optimised experiences regarding communications like users.
The capability of xMax cognitive radios has been making various real-time autonomous decisions and changes the frequencies. This is denoted as the DSA or dynamic spectrum access. This has been helpful to permit them to intelligently share the spectrum and retrieve more and more bandwidth. This has been developing the entire spectrum efficiency. This has been helpful to achieve the opportunistic usage of shared frequencies such as unlicensed spectrum (Ahmed et al. 2016). Here, the xMax cognitive radio technology has been developed in a frequency agnostic. Its cognitive identifies and utilization spectrum sensing techniques are used in powering at any frequency band. This has been helpful because wireless regulatory bodies and FCCs have been present across the worlds that are within the process to open up new spectrums. This has been reclassifying current spectrums to be present for various opportunistic uses through cognitive radios (Bi An, K., P Ark, and G Ao 2014). This helps new market entrants, uses, enterprises, public safeties and current wireless operators for offering new services, extra bandwidth and high ability instead of any need for the entities to purchase the expensive and scare wireless kinds of spectrums.
Maximum of the researches done under the field of cognitive radio has been restricted to Dynamic Spectrum Access under radio devices. Saleem and Rehmani (2014) has shown that xG technology has been expanding cognitive technique application beyond the DSA at every radio utilised through xMax system. This has been leveraging cognitive technology in various aspects of the operations of communications and around the complete xMax wireless networks.
Here, one of the breakthroughs as shown by Saleem and Rehmani (2014), is that xG has made xMax solutions beyond the competitive CRs apart from patent and sophisticated pending to interference mitigations. Here, the interference mitigations tools have been allowing xMax cognitive radios to rise the dwelling time over any channel. However, the presence of interference has been causing conventional radios to fail. It has raised the entire spectrum bandwidth available for using xMax systems as compared to various other radio systems. It has included the developments of the reliability of xMax network in harsh RF conditions (Jing et al. 2015). The xMax cognitive radios has been including MIMO antennas and developed signal processing algorithms for withstanding the higher levels of jamming, noise, common interference than the traditional radios and compete the various solutions of cognitive radios.
At these cognitive radio networks, different secondary users are also been permitted to utilize various vacant spectrum. This must be done opportunistically. This is done without interfering with the transmission. Amjad et al. (2017) showed that one of the primary challenges for those users has been to know the time to leave and occupy that channels or spectrums for the transmission of main users. For handling those issues, different users should be able to predict the availability of the channels of fundamental users. This indicates whether the status of PU channels has been busy or idle. It is helpful to leave or occupy the channels for PU transmission. The problem of spectrum occupancy has been investigated broadly by Guimarães et al. (2014). For instance, the concept of predictive spectrum access was initially introduced where the authors used HMM or Hidden Markov Model. This is to resolve the prediction problems of various kinds of spectrum occupancy. After that, HMM-based model or spectrum prediction gained massive attention in their study. Since, the HMM-based approaches have needed previous knowledge of PU’s traffic pattern. This has also included various machine learning approaches like neural networks, SVR or Support Vector regression and Bayesian inference. It is adopted by Zheng et al. (2014) for predicting PU channels availability. Besides, the techniques of predictions have been considered to time-invariant for PU models. As in real-world CR systems, the patterns of PU traffics have been exhibiting the time-variant traffic machine learning algorithms.
The BOL or “Bayesian online learning algorithm”, on the other hand, has possessed the ability to track the time-invariant and time-variant dual-states behaviors of switching time series. As motivated by those facts the nature of PUs channels state availability is modeled as the switching time series of dual-status. Here, new spectrum occupancy is proposed or channels state prediction strategy has been using BOL in performing PU channels availability predictive in CR networks. This motivated by the fact the kind of PU channel state availability is models as dual-status switching series of time. Here, Yu et al. (2015) has proposed a latest spectrum occupancy prediction tool using BOL in performing PU channels availability predictions in CR networks
This experimental outcome has shown the efficiency of BOL algorithms to predict and change various points of time series generated to capture the PU channel availabilities. For example, Naeem et al. (2014) has also studied the problem spectrum prediction of occupancies for one use series of first user channel state availabilities by a Bayesian model of online learning. This has modelled the detecting of series of main user channels state availabilities as the time series changes in time between PU occupied, and PU idles between two states. This is helpful to analyse the performance of the algorithm though simulated PU detection series. These are helpful to verify the efficiency of BOL model to predict PU channel state availability.
The drawbacks has included sensing of data that becomes outdated due to fast because of problems like receiver uncertainties and channels failures as per future decision making considered. This also needs previous data of main users in operations needed to match filtering features. This has needed prior information of main users in operations needed to match filtering features. This is considered to be complex to get them particularly under tactical scenarios. The interference of channel is highly since the nature of wireless medium particularly for information that is created from resources having larges sensitivity (Cheng, Zhang and Zhang 2015). Further, there has been spectrum mobility under time domain. The CR networks has been adapting to various kinds of wireless spectrum. These are done as per different available bands. Since, the available channels has been changing in due time. This QoS has been also challenging
Next, there have been problems arising from the standard control channels (SCC). This has been facilitating various spectrums of sharing functionalities. As these channels are vacated as the primary users opts for channels the deployments of fixed CC has been feasible. Moreover, there has been range of radio that has been dynamic (Mohjazi et al. 2015).
The above technologies discussed have been effective, quicker and more effective way to transfer information to and from specific wireless, mobile and fixed communication devices. They are aware of their operating environments. Further, they are able to automatically adjust it to maintain intended communications. The technologies has been possessing trained operator within radio making. This leads to consistent adjustments for utmost performance. Thus the future generation c0mmunicarion technologies are expected to be much more intelligent in nature. Moreover, it must deliver the platform for operators to exploit their network resources effectively. For example, smart cities can be developed on the basis of cognitive radio that is intended for spatial sensing and spectrum sensing.
Various researchers have been presently found to be engaged in developing technologies regarding communications. This has also included different protocols needed for different types of CR networks. In this way it can be ensured that the spectrum-aware contact is required, for future research along the lines that are been discussed in the survey.
The problems are organized in the following discussions.
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Spectrum sensing |
This is seen as the key to enable the cognitive radio for not interfacing with the primary users through reliability in detecting main user signals. |
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Advance Spectrum Management |
The cognitive radios have been possessing high potential in improving the spectrum through helping users to access those spectrums dynamically. This is done instead of disturbing the licensed primary broadcasts. Here, the primary challenge to operate those radios as networks has been to implement effective medium access mechanism control. This has been efficiently and adaptively allocating powers of transmission and spectrums. This is done among cognitive radios as the surrounding environment. |
|
Unlicensed spectrum usages |
This the discrepancy taking place between FCC allocations and real usages indicating the new approach in which the spectrum licensing is required. |
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Spectrum sharing strategies |
This is an allocation of the prodigious quantity of spectrum used for shared and unlicensed services. |
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Issues with hidden nodes and sharing |
The cognitive radio-sensitivity has been outperforming receivers of primary users through large margin preventing what is a hidden node issue. |
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Security and trusted access |
Here, with the rise in focus over the previous years over system survivability and securities, it is vital to note that those distributed intelligent systems like cognitive radios have been offering benefits in various events of attacks. The military applications and intelligence have needed different application-specific safe, wireless networks. |
|
Cross-layer design |
Flexibilities of cognitive radios have possessed essential implications to design cross-layer algorithms adapting to changes in radio interferences physical link qualities, radio node densities, network topologies or traffic demands. This is expected to nee advanced control and management system supporting the cross-layer information. |
|
Hardware and software architecture |
Here, the potential for cognitive radio has been the active method and extension of software-defined radios. This has been transmitting and receiving data over different wireless communication devices. |
The above study has provided an easy and effective outline of cognitive radio and present challenges in researchers. This is helpful for the researchers across the world to accept the idea of CR quickly and then work on it. Notably, various developments and researchers are presented here within cognitive radio networks. However, the numerous unanswered questions lying here are as following.
The recommendation systems help in developing efficiency of spectrum access to cognitive radio networks. Here the possibility of various activities can be adjustable or fixed. The various recommendations are demonstrated below.
Conclusion drawn from the above analysis:
In the above report the processes to analyze and modeling of interactions of cognitive radios is discussed. This helps in improving designing of cognitive radio. This also includes distributed radio resource managements algorithms. This comprises of specific interests towards characterizing those algorithms and stability properties. Further, this is followed through assimilating conventional engineering and techniques of nonlinear programming analysis. This permits rapid characterization of cognitive radio algorithm properties. The study helps in understanding that through opportunistically that has been exploiting various current wireless spectrums. Further, the CR networks are also been developed to resolve problems that are found to be originated from various types of limited available range. This also includes the inefficiency in using scales. Besides, CR networks are also been equipped with different intrinsic abilities of cognitive radios. These have been providing ultimate communications of spectrum-aware paradigm within wireless communications. In the above discussion, the inherent properties and challenges of present researchers of spectrum management under CR networks are presented. Particularly novel spectrum management functionalities are investigated in the above study. This is helpful to understand the spectrum mobility, sharing, decision and sensing.
References:
Ahmed, Z.I.A.M., Bilal, D.K.H. and Alhassan, D.M., 2016. Cognitive Radio Network Review. International Journal of Engineering”, Applied and Management Sciences Paradigms, 13(1), pp.320-327.
Amjad, M., Akhtar, F., Rehmani, M.H., Reisslein, M. and Umer, T., 2017. Full-duplex communication in cognitive radio networks: A survey. IEEE Communications Surveys & Tutorials.
Bi An, K., P Ark, J.M. and G Ao, B., 2014. Cognitive Radio Networks. Springer International Publishing.
BLOG, x., xG, C., Brands, V., (IMT), I., Overview, x., Line, x., System, x., Point, x., Modem, x., Modem, x., Hotspot, x., Point, x., (MCC), x., Tools, N., Solutions, x., Management, E., Military, D., Infrastructure, C., Architecture, N., Deployment, F., Software, A., Training, S., Federal, x., Networks, C., Mitigation, I., Technologies, V., Events, N., Coverage, M., Releases, P., Events, x., Blog, x., Reports, W., Sheets, P., Directors, B., Team, M., Information, I., Content, S., Reports, F., Governance, C., Technology, C., xG, P., xG, P. and Partners, T. (2018). Cognitive Radio Networks – xG Technology. [online] xG Technology. Available at: https://www.xgtechnology.com/innovations/cognitive-radio-networks/ [Accessed 10 Jun. 2018].
Cheng, W., Zhang, X. and Zhang, H., 2015. Full-duplex spectrum-sensing and MAC-protocol for multichannel nontime-slotted cognitive radio networks. IEEE Journal on Selected Areas in Communications, 33(5), pp.820-831.
Elkashlan, M., Wang, L., Duong, T.Q., Karagiannidis, G.K. and Nallanathan, A., 2015. On the security of cognitive radio networks. IEEE Transactions on Vehicular Technology, 64(8), pp.3790-3795.
Guimarães, F.R.V., da Costa, D.B., Tsiftsis, T.A., Cavalcante, C.C. and Karagiannidis, G.K., 2014. Multiuser and multirelay cognitive radio networks under spectrum-sharing constraints. IEEE Transactions on Vehicular Technology, 63(1), pp.433-439.
Hawa, M., AlAmmouri, A., Alhiary, A. and Alhamad, N., 2017. Distributed opportunistic spectrum sharing in cognitive radio networks. international journal of communication systems, 30(7).
Jing, T., Zhu, S., Li, H., Xing, X., Cheng, X., Huo, Y., Bie, R. and Znati, T., 2015. Cooperative relay selection in cognitive radio networks. IEEE Transactions on Vehicular Technology, 64(5), pp.1872-1881.
Li, F., Shi, P., Wu, L., Basin, M.V. and Lim, C.C., 2015. Quantized control design for cognitive radio networks modeled as nonlinear semi-Markovian jump systems. IEEE Transactions on Industrial Electronics, 62(4), pp.2330-2340.
Li, Y., Zhou, L., Zhu, H. and Sun, L., 2016. Privacy-preserving location proof for securing large-scale database-driven cognitive radio networks. IEEE Internet of Things Journal, 3(4), pp.563-571.
Liu, Y., Ding, Z., Elkashlan, M. and Yuan, J., 2016. Nonorthogonal multiple access in large-scale underlay cognitive radio networks. IEEE Transactions on Vehicular Technology, 65(12), pp.10152-10157.
Lu, X., Wang, P., Niyato, D. and Hossain, E., 2014. Dynamic spectrum access in cognitive radio networks with RF energy harvesting. IEEE Wireless Communications, 21(3), pp.102-110.
Mohjazi, L., Dianati, M., Karagiannidis, G.K., Muhaidat, S. and Al-Qutayri, M., 2015. RF-powered cognitive radio networks: Technical challenges and limitations. IEEE communications Magazine, 53(4), pp.94-100.
Naeem, M., Anpalagan, A., Jaseemuddin, M. and Lee, D.C., 2014. Resource allocation techniques in cooperative cognitive radio networks. IEEE communications surveys & tutorials, 16(2), pp.729-744.
Ng, D.W.K., Lo, E.S. and Schober, R., 2016. Multiobjective resource allocation for secure communication in cognitive radio networks with wireless information and power transfer. IEEE transactions on vehicular technology, 65(5), pp.3166-3184.
Reyes, H., Subramaniam, S., Kaabouch, N. and Hu, W.C., 2016. A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks. Computers & Electrical Engineering, 52, pp.319-327.
Saleem, Y. and Rehmani, M.H., 2014. Primary radio user activity models for cognitive radio networks: A survey. Journal of Network and Computer Applications, 43, pp.1-16.
Tsiropoulos, G.I., Dobre, O.A., Ahmed, M.H. and Baddour, K.E., 2016. Radio resource allocation techniques for efficient spectrum access in cognitive radio networks. IEEE Communications Surveys & Tutorials, 18(1), pp.824-847.
Youssef, M., Ibrahim, M., Latif, M.A., Chen, L. and Vasilakos, A.V., 2014. Routing Metrics of Cognitive Radio Networks: A Survey. IEEE Communications Surveys and Tutorials, 16(1), pp.92-109.
Yu, R., Zhang, Y., Liu, Y., Gjessing, S. and Guizani, M., 2015. Securing cognitive radio networks against primary user emulation attacks. IEEE Network, 29(4), pp.68-74.
Zheng, G., Ho, Z.K.M., Jorswieck, E.A. and Ottersten, B.E., 2014. Information and Energy Cooperation in Cognitive Radio Networks. IEEE Trans. Signal Processing, 62(9), pp.2290-2303.
Zou, Y., Zhu, J., Yang, L., Liang, Y.C. and Yao, Y.D., 2015. Securing physical-layer communications for cognitive radio networks. IEEE Communications Magazine, 53(9), pp.48-54.
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