As an MSc research student, you are expected to understand how other areas of computer science relate to your chosen specialism. You must have a grasp of significant current open problems in other specialisms, as well as an understanding of how techniques and methods from other research areas can be applied within your specialism.
In this assignment you must provide an overview of two MSc Research specialism lectures (AI / Networking / Cyber Security / Software Engineering / Data Science) which are NOT the specialism you discussed in Assignment 1.
For each of these two areas, you must discuss the research question presented within the relevant specialist lecture for this specialism, propose some research approaches to investigate this question and identify further work you might undertake which builds on this. You must also explore your personal strengths in this area.
SPECIALISM RESEARCH REPORT
Spectrum sensing can be determined as an important aspect in the management of cognitive radio network-management technology. This is an important challenging factor in the management of cognitive radio networks. The particular area of this research study is the determination of usage of energy-efficient technology of spectrum sensing in networks of cognitive radio. Spectrum sensing technology helps radio networks gather information about different environments and spectrum availability for smooth networking. The mainly used technology in spectrum sensing is detection energy and matching of filter detection to reduce any false alarm issue. Metrics that help in measuring the trustworthiness in sensing are the detection probability (Pd), and false alarms probability (Pfa) are essential. Effective spectrum sensing helps to protect the primary users from any interference and provide various opportunities for secondary users (SUs). Details of open research questions, existing work, and approach of research along with personal investment will be discussed here.
- What is the novel, intelligent and energy-efficient spectrum sensing mechanism for the improvement of efficiency of cognitive radio networks?
Spectrum sensing indicates a systematic process of monitoring some specific frequency band for detection of primary as well as secondary users. This research question is scientifically interesting to answer the details of spectrum sensing technology application in the real world to improve radio networks. According to Arjoune and Kaabouch (2019), the cognitive radio technique has enough potential to provide details about any shortage of the respective radio networks spectrum after enabling techniques of accessing dynamic spectrum. This research question creates interest as it can answer the novel and intelligent spectrum sensing technology application for measuring the efficiency of radio networks. Spectrum sensing is compelling enough for secondary users in detention of respective spectrum holes during sensing. Spectrum sensing is required as it can effectively prevent any harmful interference and improve the utilisation strategy for primary and secondary users.
This question has enough capability to answer the possible real-world advantages and limitations of the implementation of the technique of spectrum sensing in radio networks. Based on the provided case study, the Markov two-state chain model system indicates the occupancy model of spectrum for the arrival of traffic for primary users (Syed and Safdar, 2015).
Based on this two-stage model of Markov, the first state represents the ON state, whereas the second state represents the OFF state. This research question will be beneficial to determine the effectiveness of this model and answer the importance of implementing this model concept in spectrum sensing for radio network improvement. The reason for using databases for understanding the history of spectrum sensing for radio networks will be achievable from this research question.
Improper implementation of energy-efficient spectrum sensing strategy creates barriers for improvement of cognitive radio networking improvement.
Cognitive radio networking is termed as a genuine solution against the issue of scarcity of spectrum in modern wireless technology. According to Gupta and Kumar (2019), cooperation among different cognitive radio networks plays a critical role after providing comparatively low power radios for achieving high-quality networks. Spectrum mobility on cognitive networking is an important feature that supports effective data transmission for secondary users. On the contrary, Xiong et al. (2018) have explained that spectrum sensing strategy in the networking of cognitive radio for secondary users includes the spectrum’s usage monitoring and tracking the underutilised portion of the respective spectrum. Therefore, it can be determined that cooperation is essential in cognitive radio networking for tracking the usage and underutilised proportion of spectrum to eliminate misuse of the spectrum. Spectrum sensing has a higher potential in cognitive radio networking improvement as it provides opportunities for future cognitive radio networking improvement (Xionget al. 2018). Limited availability of effective spectrum sensing techniques and inefficiency in their use can reduce the effectiveness of wireless technology.
Different advantages and limitations are associated with spectrum seasoning for cognitive radio. Advantages include its simplicity in use, optimal detection facility, and effective solution for shadowing and robust structure. Spectrum sensing is effective for filter detection and energy detection that causes comparatively low computational and implementation costs for cognitive radio networking improvement (Dibal et al. 2018). Sensing is essential in cognitive radio networking improvement as it helps in providing next-generation wireless technology. On the other hand, Olawole et al. (2019) have explained that non-cooperative sensing of the spectrum indicates the coexistence of secondary users and primary users on a channel that is another advantage. Therefore, these advantages of spectrum sensing support effective cognitive radio networking. Further, the availability of energy efficiency features in spectrum sensing has added advantage in managing this technology for cognitive radio.
Based on this literature review, it is identified that all of this literature is lacking enough to answer the in-depth analysis of sensing of spectrum use for cognitive radio. In-depth analysis of respective challenges during primary user signals for detection of the effective strength of signals is important in spectrum sensing (MacDonald et al. 2017). This analysis is not properly done in all of this provided literature. Therefore, one open problem is the lack of details about the advantages of energy-efficient spectrum sensing technology for improving cognitive radio. Another open problem is that respective challenges related to spectrum sensing are not well mentioned here.
Based on the outcome after analysis of the provided research paper, it is observed that the approach used here is history assisted database analyst technology using the conventional technique of spectrum sensing and detection of energy method. Fast spectrum sensing with accuracy is a crucial challenge for the implementation of spectrum sensing in cognitive radio networking (Ning et al. 2020). Analysis of databases by applying advanced technology for the identification of energy efficiency of spectrum sensing in cognitive radio networking is adequate. The energy detector method is mainly used for its simplicity and low computation effect when analysing existing databases. Conventional spectrum sensing includes different stages for analysis of the effectiveness of spectrum sensing in the networking of cognitive radio. Modulation classification can be another vital technique for spectrum sensing that can neutralise the negativity of conventional spectrum sensing (Bahloul et al. 2017). This research is effective enough to answer the research question based on a historical analysis of databases for understanding the effectiveness of spectrum seasoning in improving networks of cognitive radio.
The advantages of using this database for analysis of the effectiveness of spectrum sensing have provided a wide range of data for gathering effective results. Analysis of present historical dataset analysis provides possible ranges of scan threshold that is 1 to 5 that helps identify occupied levels of a band (Syed and Safdar, 2015). Advantages of storing, digesting, and intelligently processing are the other advantages that this individual approach has provided in this provided research study. Continuous update of dataset during an analysis of database has added advantage by improving quality of data. Effective implementation of technology for analysis of dataset has added simplicity and easy implementation of concepts in this provided research approach. Availability of data from Pd and Pfa has added extra advantage in the analysis of this dataset. However, disadvantage indicates non-availability of a primary dataset that sometimes has created an issue in maintaining authenticity in the outcome. Database analysis technique has not provided a proper outcome regarding the probability of energy consumption in spectrum sensing, another disadvantage.
Along with database analysis, the collection of primary data on the importance of sensing of spectrum for improving cognitive radio networking will be beneficial to get accurate data. Primary data and secondary historical datasets can provide a vast range of in-depth datasets that can provide effective results in the research. Maintaining authenticity in the research result is essential for which primary data collection will be beneficial.
Interest in the analysis of this respective research question indicates its potential to answer the effective strategy of spectrum sensing to improve the networking of cognitive radio. Previous cooperative spectrum sensing technology has used omnidirectional antennas to track users’ geographic location (Na et al. 2017). However, this system is not so prominent to provide an energy-efficient strategy of spectrum sensing. Therefore, this research question will help discuss historic data to guide improvement strategies for cognitive radio networking techniques. Further, analysis of challenges regarding implementation of spectrum sensing strategy can be achieved from this research question.
Personal strength along with knowledge will effectively fit for carrying out this research due to adequate knowledge on cognitive radio networking and spectrum sensing. This study has provided enough historical aspects regarding spectrum sensing and cognitive radio. Application of those concepts will fit for researching future based on this topic.
Arjoune, Y. and Kaabouch, N., 2019. A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions. Sensors, 19(1), p.126. https://www.mdpi.com/1424-8220/19/1/126/pdf
Bahloul, M.R., Yusoff, M.Z., Abdel-Aty, A.H., Saad, M.N.M. and Kudsi, L., 2017.Spectrum sensing for cognitive radio systems using modulation classification: A review. Applied Mathematics Information Sciences, 11(3), pp.857-865. https://www.researchgate.net/profile/Mohammad-Bahloul/publication/316979249_Spectrum_sensing_for_cognitive_radio_systems_using_modulation_classification_A_review/links/591b60514585153b614fa4ef/Spectrum-sensing-for-cognitive-radio-systems-using-modulation-classification-A-review.pdf
Dibal, P.Y., Onwuka, E.N., Agajo, J. and Alenoghena, C.O., 2018. Application of wavelet transform in spectrum sensing for cognitive radio: A survey. Physical Communication, 28, pp.45-57. http://repository.futminna.edu.ng:8080/jspui/bitstream/123456789/10819/1/Inter%20J%2002%20Application_of_wavelets_spectrum_sensing_survey_paper.pdf
Gupta, M.S. and Kumar, K., 2019. Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues. Journal of Network and Computer Applications, 143, pp.47-76. https://www.sciencedirect.com/science/article/pii/S1084804519301936
MacDonald, S., Popescu, D.C. and Popescu, O., 2017.Analyzing the performance of spectrum sensing in cognitive radio systems with dynamic PU activity. IEEE Communications Letters, 21(9), pp.2037-2040. https://ieeexplore.ieee.org/abstract/document/7930390/
Na, W., Yoon, J., Cho, S., Griffith, D. and Golmie, N., 2017. Centralized cooperative directional spectrum sensing for cognitive radio networks. IEEE Transactions on Mobile Computing, 17(6), pp.1260-1274. https://ieeexplore.ieee.org/abstract/document/8093693/
Ning, W., Huang, X., Yang, K., Wu, F. and Leng, S., 2020. Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks. Journal of Communications and Networks, 22(1), pp.12-22. https://ieeexplore.ieee.org/iel7/5449605/9042893/09042894.pdf
Olawole, A.A., Takawira, F. and Oyerinde, O.O., 2019. Cooperative spectrum sensing in multichannel cognitive radio networks with energy harvesting. IEEE Access, 7, pp.84784-84802. https://ieeexplore.ieee.org/iel7/6287639/8600701/08744509.pdf
Syed, T.S. and Safdar, G.A., 2015. On the usage of history for energy efficient spectrum sensing. IEEE Communications Letters, 19(3), pp.407-410. https://ieeexplore.ieee.org/abstract/document/7004859/ Xiong, T., Yao, Y.D., Ren, Y. and Li, Z., 2018. Multiband spectrum sensing in cognitive radio networks with secondary user hardware limitation: Random and adaptive spectrum sensing strategies. IEEE Transactions on Wireless Communications, 17(5), pp.3018-3029. https://ieeexplore.ieee.org/abstract/document