@article{shakila_zaman_security_2021,
	title = {Security {Threats} and {Artificial} {Intelligence} {Based} {Countermeasures} for {Internet} of {Things} {Networks}: {A} {Comprehensive} {Survey}},
	volume = {9},
	doi = {10.1109/ACCESS.2021.3089681},
	abstract = {The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms– branches of Artificial Intelligence (AI)– can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.},
	journal = {IEEE Access},
	author = {Shakila Zaman and {Mohammed A. Aseeri} and {Muhammad Raisuddin Ahmed} and {Risala Tasin Khan} and {M Shamim Kaiser} and {Mufti Mahmud}},
	month = jun,
	year = {2021},
	pages = {94668 -- 94690},
}

The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms– branches of Artificial Intelligence (AI)– can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

@article{rahman_review_2021,
	title = {A {Review} of {Contact} {Tracing} {Approaches} for {Controlling} {COVID}-19 {Pandemic}},
	issn = {0975-4172},
	url = {https://computerresearch.org/index.php/computer/article/view/2014},
	doi = {10.34257/GJCSTHVOL21IS1PG31},
	language = {en-US},
	urldate = {2021-05-17},
	journal = {Global Journal of Computer Science and Technology},
	author = {Rahman, Md Tanvir and Shuva, Taslima Ferdaus and Khan, Risala Tasin and Nasir, Mostofa Kamal},
	year = {2021},
}

@article{lima_performance_2021,
	title = {Performance {Analysis} of 2x2 and 3x3 {MIMO} with {QPSK} in {Rayleigh} {Fading} {Channel}},
	volume = {9},
	issn = {23219653},
	url = {https://www.ijraset.com/fileserve.php?FID=33119},
	doi = {10.22214/ijraset.2021.33119},
	abstract = {A promising approach to improve the performance of wireless communication is multiple input multiple output (MIMO) system. MIMO can achieve high data rate without extra bandwidth. Space-Time Block Coding (STBC) is an emerging concept in MIMO which provides efficient performance in case of Bit Error Rate (BER) and diversity gain. In this paper, mathematical equations of channel capacity and Bit Error Rate (BER) for 3x3 MIMO system has been derived and simulation has been done to compare the performance of Average Channel Capacity vs. SNR and BER vs. SNR for 2X2 and 3X3 antennas. By varying the SNR value we observe the channel capacity and BER of the proposed system. In case of probability of error it is decreased when SNR value is increased but not tends to zero. But in case of channel capacity, it is increased with the increased value of SNR. By comparing these two relations we see that channel capacity is increasing without extra band-width and offers faster communication. Then a comparison among the existing MIMO system with the proposed system has been done. The result shows 3x3 MIMO system gives better performance for channel capacity compared to 2X2 MIMO.},
	language = {en},
	number = {2},
	urldate = {2021-05-17},
	journal = {International Journal for Research in Applied Science and Engineering Technology},
	author = {Lima, Jakia Sultana and {Risala T Khan} and {Fahima Tabassum}},
	month = feb,
	year = {2021},
	pages = {438--444},
}

A promising approach to improve the performance of wireless communication is multiple input multiple output (MIMO) system. MIMO can achieve high data rate without extra bandwidth. Space-Time Block Coding (STBC) is an emerging concept in MIMO which provides efficient performance in case of Bit Error Rate (BER) and diversity gain. In this paper, mathematical equations of channel capacity and Bit Error Rate (BER) for 3x3 MIMO system has been derived and simulation has been done to compare the performance of Average Channel Capacity vs. SNR and BER vs. SNR for 2X2 and 3X3 antennas. By varying the SNR value we observe the channel capacity and BER of the proposed system. In case of probability of error it is decreased when SNR value is increased but not tends to zero. But in case of channel capacity, it is increased with the increased value of SNR. By comparing these two relations we see that channel capacity is increasing without extra band-width and offers faster communication. Then a comparison among the existing MIMO system with the proposed system has been done. The result shows 3x3 MIMO system gives better performance for channel capacity compared to 2X2 MIMO.

@article{zaman_thinking_2021,
	title = {Thinking {Out} of the {Blocks}: {Holochain} for {Distributed} {Security} in {IoT} {Healthcare}},
	shorttitle = {Thinking {Out} of the {Blocks}},
	url = {http://arxiv.org/abs/2103.01322},
	abstract = {The Internet-of-Things (IoT) is an emerging and cognitive technology which connects a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. IoT is increasingly being implemented in distributed settings, making footprints in almost every sector of our life. Unfortunately, for healthcare systems, the entities connected to the IoT networks are exposed to an unprecedented level of security threats. Relying on a huge volume of sensitive and personal data, IoT healthcare systems are facing unique challenges in protecting data security and privacy. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from major setbacks of increasing storage and computation requirements with the network size. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes these challenges and is particularly suited for resource constrained IoT scenarios. The performance and thorough security analyses demonstrate that a holochain-based IoT healthcare system is significantly better compared to blockchain and other existing systems.},
	urldate = {2021-05-17},
	journal = {arXiv:2103.01322 [cs]},
	author = {Zaman, Shakila and Khandaker, Muhammad R. A. and Khan, Risala T. and Tariq, Faisal and Wong, Kai-Kit},
	month = mar,
	year = {2021},
	note = {arXiv: 2103.01322},
	keywords = {Computer Science - Cryptography and Security},
}

The Internet-of-Things (IoT) is an emerging and cognitive technology which connects a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. IoT is increasingly being implemented in distributed settings, making footprints in almost every sector of our life. Unfortunately, for healthcare systems, the entities connected to the IoT networks are exposed to an unprecedented level of security threats. Relying on a huge volume of sensitive and personal data, IoT healthcare systems are facing unique challenges in protecting data security and privacy. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from major setbacks of increasing storage and computation requirements with the network size. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes these challenges and is particularly suited for resource constrained IoT scenarios. The performance and thorough security analyses demonstrate that a holochain-based IoT healthcare system is significantly better compared to blockchain and other existing systems.

@inproceedings{md_shams_sayied_haque_voice_2020,
	address = {Institute of Information Technology, Jahangirnagar University},
	title = {Voice {Assistant} and {Touch} {Screen} {Operated} {Intelligent} {Wheelchair} for {Physically} {Challenged} {People} {\textbar} {SpringerLink}},
	url = {https://link.springer.com/chapter/10.1007/978-981-33-4673-4_32},
	urldate = {2021-05-17},
	author = {{Md. Shams Sayied Haque} and {Md. Tanvir Rahman} and {Risala T Khan} and {Mohammad Shibli Kaysar}},
	month = dec,
	year = {2020},
}

@article{samsunnahar_khandakar_recognition_2020,
	title = {Recognition of {Bangla} {Handwritten} {Number} {Using} {Combination} of {PCA} and {FIS} with the {Aid} of {DWT}},
	volume = {8},
	url = {https://www.scirp.org/journal/paperinformation.aspx?paperid=103180},
	abstract = {The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: “one and nine” and “five and six”. We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8\% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.},
	number = {9},
	urldate = {2021-05-17},
	journal = {Journal of Computer and Communications},
	author = {{Samsunnahar Khandakar} and {Md Imdadul Islam} and {Fahima Tabassum} and {Risala T Khan}},
	month = sep,
	year = {2020},
}

The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: “one and nine” and “five and six”. We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.

@inproceedings{zaman_towards_2020,
	title = {Towards {SDN} and {Blockchain} based {IoT} {Countermeasures}: {A} {Survey}},
	shorttitle = {Towards {SDN} and {Blockchain} based {IoT} {Countermeasures}},
	doi = {10.1109/STI50764.2020.9350392},
	abstract = {Security vulnerabilities have become significant concerns due to growing demand of Internet of Things (IoT) not only for home automation systems, but also for various industrial applications. Central security technologies are vulnerable to single-point failure that could reduce attack handling technology efficiency. Distributed security frameworks are used in resource-constrained heterogeneous IoT networks. Conventional security strategies are insufficient to protect the distributed and dynamic existence of IoT networks. Conversely, Software Define Network (SDN) and Blockchain are two emerging techniques expected to solve heterogeneous IoT network security. This analysis work therefore mainly seeks to examine IoT network features, security specifications, and challenges. Thereafter, well-known threats or attacks are analyzed in IoT. SDN and blockchain-based countermeasures are addressed for IoT network security with a case study. Finally, to enhance security and privacy , numerous open issues are discussed.},
	booktitle = {2020 2nd {International} {Conference} on {Sustainable} {Technologies} for {Industry} 4.0 ({STI})},
	author = {Zaman, Shakila and Kaiser, M. Shamim and Tasin Khan, Risala and Mahmud, Mufti},
	month = dec,
	year = {2020},
	keywords = {Authentication, Blockchain, Communication networks, DDoS, Distributed security, Internet of Things, Network Function Virtualization, Privacy, Safety, Security, Sensors, Software, Threats},
	pages = {1--6},
}

Security vulnerabilities have become significant concerns due to growing demand of Internet of Things (IoT) not only for home automation systems, but also for various industrial applications. Central security technologies are vulnerable to single-point failure that could reduce attack handling technology efficiency. Distributed security frameworks are used in resource-constrained heterogeneous IoT networks. Conventional security strategies are insufficient to protect the distributed and dynamic existence of IoT networks. Conversely, Software Define Network (SDN) and Blockchain are two emerging techniques expected to solve heterogeneous IoT network security. This analysis work therefore mainly seeks to examine IoT network features, security specifications, and challenges. Thereafter, well-known threats or attacks are analyzed in IoT. SDN and blockchain-based countermeasures are addressed for IoT network security with a case study. Finally, to enhance security and privacy , numerous open issues are discussed.

@article{tabassum_human_2020,
	title = {Human face recognition with combination of {DWT} and machine learning},
	issn = {1319-1578},
	url = {https://www.sciencedirect.com/science/article/pii/S1319157819309395},
	doi = {10.1016/j.jksuci.2020.02.002},
	abstract = {To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56\% for the worst case and 93.34\% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.},
	language = {en},
	urldate = {2021-05-17},
	journal = {Journal of King Saud University - Computer and Information Sciences},
	author = {Tabassum, Fahima and Imdadul Islam, Md. and Tasin Khan, Risala and Amin, M. R.},
	month = feb,
	year = {2020},
	keywords = {Accuracy of recognition, Eigen vectors, FC, ROI, Wavelet coherence},
}

To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56% for the worst case and 93.34% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.

@article{rahman_automated_2020,
	title = {An {Automated} {Contact} {Tracing} {Approach} for {Controlling} {Covid}-19 {Spread} {Based} on {Geolocation} {Data} {From} {Mobile} {Cellular} {Networks}},
	volume = {8},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2020.3040198},
	abstract = {The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this article, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.},
	journal = {IEEE Access},
	author = {Rahman, Md. Tanvir and Khan, Risala T. and Khandaker, Muhammad R. A. and Sellathurai, Mathini and Salan, Md. Sifat A.},
	year = {2020},
	note = {Conference Name: IEEE Access},
	keywords = {Bluetooth, COVID-19, Coronavirus, Covid-19, Government, Mobile handsets, Pandemics, Privacy, Viruses (medical), contact tracing, geolocation, pandemic},
	pages = {213554--213565},
}

The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being conceptually possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this article, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.

@inproceedings{li_learning_2019,
	title = {Learning the {Wireless} {V2I} {Channels} {Using} {Deep} {Neural} {Networks}},
	doi = {10.1109/VTCFall.2019.8891562},
	abstract = {For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.},
	booktitle = {2019 {IEEE} 90th {Vehicular} {Technology} {Conference} ({VTC2019}-{Fall})},
	author = {Li, Tian-Hao and Khandaker, Muhammad R. A. and Tariq, Faisal and Wong, Kai-Kit and Khan, Risala T.},
	month = sep,
	year = {2019},
	note = {ISSN: 2577-2465},
	keywords = {Biological neural networks, Channel estimation, Machine learning, OFDM, Wireless communication},
	pages = {1--5},
}

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.

@inproceedings{gao_deep_2019,
	title = {Deep {Neural} {Network} {Based} {Resource} {Allocation} for {V2X} {Communications}},
	doi = {10.1109/VTCFall.2019.8891446},
	abstract = {This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.},
	booktitle = {2019 {IEEE} 90th {Vehicular} {Technology} {Conference} ({VTC2019}-{Fall})},
	author = {Gao, Jin and Khandaker, Muhammad R. A. and Tariq, Faisal and Wong, Kai-Kit and Khan, Risala T.},
	month = sep,
	year = {2019},
	note = {ISSN: 2577-2465},
	keywords = {Copper, Interference, Machine learning, Resource management, Training, Vehicle-to-everything},
	pages = {1--5},
}

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.

@inproceedings{risala_t_khan_performance_2017,
	title = {Performance {Evaluation} of {Cognitive} {Radio} {Network} under {Matched} {Filter} {Detection}},
	url = {https://ieeexplore.ieee.org/abstract/document/8455111/},
	abstract = {The performance of cognitive radio network depends on how efficiently the Secondary Users (SUs) can sense a Primary User (PU) on a physical channel. In matched filter detection the maximum signal to noise ratio (SNR) is achieved at receiving end can be used as the detection of technique of cognitive radio network. In this paper we analyzed the impact of False Alarm (FA), length of test statistics and threshold SNR in acquiring proper detections or sensing of PU by SUs. From the profile of probability of detection against SNR taking FA as a parameter we determine the optimum condition of the network based on matched filter detection. Finally the analytical results are verified by simulation with sufficient confidence level.},
	language = {en-US},
	urldate = {2021-05-17},
	author = {{Risala T Khan} and {Shakila Zaman} and {Md Imdadul Islam} and {MR Amin}},
	month = dec,
	year = {2017},
}

The performance of cognitive radio network depends on how efficiently the Secondary Users (SUs) can sense a Primary User (PU) on a physical channel. In matched filter detection the maximum signal to noise ratio (SNR) is achieved at receiving end can be used as the detection of technique of cognitive radio network. In this paper we analyzed the impact of False Alarm (FA), length of test statistics and threshold SNR in acquiring proper detections or sensing of PU by SUs. From the profile of probability of detection against SNR taking FA as a parameter we determine the optimum condition of the network based on matched filter detection. Finally the analytical results are verified by simulation with sufficient confidence level.

@article{khan_comparison_2017,
	title = {Comparison of {Detection} of {Bayesian}, {Match} {Filter}, {Wiener} {Filter} and {Energy} {Models} of {CRN}},
	abstract = {Recent literature deals with several detection models to measure the performance of cognitive radio network (CRN). In this paper we bring fours detection techniques: Energy detection (ED), Match filter detection (MFD), Bayesian detection (BD) and Wiener filter detection (WFD) in a single graph to observe relative performance. Here we consider only probability false alarm and that of detection under both Rayleigh fading channel and additive white Gaussian noise (AWGN) environment. For probability false alarm, the match filter shows the best performance and Bayesian detection is the best for the case of probability of detection but the phenomenon does not mean that above two detections are the best. In CRN better sensitivity of detection deteriorates the false alarm and vice versa. Combing the two parameters of the paper match filter detection is the best then comes wiener filter detection. Finally, the analytical results are verified by simulation.},
	journal = {IEEE R10 Humanitarian Technology Conference},
	author = {Khan, Risala and {Shakila Zaman} and {Md Imdadul Islam} and {MR Amin}},
	month = dec,
	year = {2017},
}

Recent literature deals with several detection models to measure the performance of cognitive radio network (CRN). In this paper we bring fours detection techniques: Energy detection (ED), Match filter detection (MFD), Bayesian detection (BD) and Wiener filter detection (WFD) in a single graph to observe relative performance. Here we consider only probability false alarm and that of detection under both Rayleigh fading channel and additive white Gaussian noise (AWGN) environment. For probability false alarm, the match filter shows the best performance and Bayesian detection is the best for the case of probability of detection but the phenomenon does not mean that above two detections are the best. In CRN better sensitivity of detection deteriorates the false alarm and vice versa. Combing the two parameters of the paper match filter detection is the best then comes wiener filter detection. Finally, the analytical results are verified by simulation.

@article{zaman_comparative_2017,
	title = {Comparative {Study} of {Bayesian} and {Energy} {Detection} {Including} {MRC} {Under} {Fading} {Environment} in {Collaborative} {Cognitive} {Radio} {Network}},
	volume = {8},
	issn = {21565570, 2158107X},
	url = {http://thesai.org/Publications/ViewPaper?Volume=8&Issue=5&Code=ijacsa&SerialNo=50},
	doi = {10.14569/IJACSA.2017.080550},
	abstract = {The most important component of Cognitive Radio Network (CRN) is to sense the underutilised spectrum efficiently in fading environment for incorporating the increasing demand of wireless applications. The result of spectrum sensing can be affected by incorrect detection of the existence of Primary User (PU). In this paper, we have considered Collaborative spectrum sensing to maximise the spectrum utilisation of Cognitive Radio (CR) user. We proposed a new architecture and algorithm that shows the step by step spectrum sensing procedure using Energy detection and Bayesian detection in collaborative environment for an optimal number of users. This algorithm also includes Maximal Ratio Combining (MRC) diversity techniques in fusion centre to make a final decision under fading condition. The simulation result shows the significant optimisation of detection performance with less misdetection for large number of users. It is also observed that MRC produces better results in collaborative manner under Nakagami-m, Rayleigh and Normal fading. Finally in this paper, we have analysed the relative performance of different wireless channels for various SNR levels and from that analysis it concludes that ED technique works better in high SNR and BD technique works for low SNR.},
	language = {en},
	number = {5},
	urldate = {2021-05-17},
	journal = {International Journal of Advanced Computer Science and Applications},
	author = {Zaman, Shakila and Tasin, Risala and Imdadul, Md.},
	year = {2017},
}

The most important component of Cognitive Radio Network (CRN) is to sense the underutilised spectrum efficiently in fading environment for incorporating the increasing demand of wireless applications. The result of spectrum sensing can be affected by incorrect detection of the existence of Primary User (PU). In this paper, we have considered Collaborative spectrum sensing to maximise the spectrum utilisation of Cognitive Radio (CR) user. We proposed a new architecture and algorithm that shows the step by step spectrum sensing procedure using Energy detection and Bayesian detection in collaborative environment for an optimal number of users. This algorithm also includes Maximal Ratio Combining (MRC) diversity techniques in fusion centre to make a final decision under fading condition. The simulation result shows the significant optimisation of detection performance with less misdetection for large number of users. It is also observed that MRC produces better results in collaborative manner under Nakagami-m, Rayleigh and Normal fading. Finally in this paper, we have analysed the relative performance of different wireless channels for various SNR levels and from that analysis it concludes that ED technique works better in high SNR and BD technique works for low SNR.

@article{department_of_computer_science_and_engineering_daffodil_international_university_dhaka_1207_bangladesh_efficient_2017,
	title = {Efficient {Sensor}-{Cloud} {Communication} using {Data} {Classification} and {Compression}},
	volume = {9},
	issn = {20749007, 20749015},
	url = {http://www.mecs-press.org/ijitcs/ijitcs-v9-n6/v9n6-2.html},
	doi = {10.5815/ijitcs.2017.06.02},
	abstract = {Wireless Sensor Network, a group of specialized sensors with a communication infrastructure for monitoring and controlling conditions at diverse locations, is a recent technology which is getting popularity day by day. Besides, cloud computing is a type of high-performance computing that uses a network of remote servers which simultaneously provides the service to store, manage and process data rather than a local server or personal computer. An architecture called sensor-cloud is also providing good services by combining the capabilities from both ends. In order to provide such services, a large volume of sensor network data needs to be transported to cloud gateway with a high amount of bandwidth and time requirement. In this paper, we have proposed an efficient sensor-cloud communication approach that minimizes the enormous bandwidth and time requirement by using statistical classification based on machine learning as well as compression using deflate algorithm with a minimal loss of information. Experimental results describe the overall efficiency of the proposed method over the traditional and related research.},
	language = {en},
	number = {6},
	urldate = {2021-05-17},
	journal = {International Journal of Information Technology and Computer Science},
	author = {{Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1207, Bangladesh} and Rahman, Md. Tanvir and Salan, Md. Sifat Ar and Shuva, Taslima Ferdaus and Khan, Risala Tasin},
	month = jun,
	year = {2017},
	pages = {9--17},
}

Wireless Sensor Network, a group of specialized sensors with a communication infrastructure for monitoring and controlling conditions at diverse locations, is a recent technology which is getting popularity day by day. Besides, cloud computing is a type of high-performance computing that uses a network of remote servers which simultaneously provides the service to store, manage and process data rather than a local server or personal computer. An architecture called sensor-cloud is also providing good services by combining the capabilities from both ends. In order to provide such services, a large volume of sensor network data needs to be transported to cloud gateway with a high amount of bandwidth and time requirement. In this paper, we have proposed an efficient sensor-cloud communication approach that minimizes the enormous bandwidth and time requirement by using statistical classification based on machine learning as well as compression using deflate algorithm with a minimal loss of information. Experimental results describe the overall efficiency of the proposed method over the traditional and related research.

@article{sultana_performance_2016,
	title = {Performance {Evaluation} of {CognitiveRadio} {Network} under different pathloss models using frequency range of 1900 and 2100 {MHz}},
	volume = {7},
	issn = {22295518},
	url = {http://www.ijser.org/onlineResearchPaperViewer.aspx?Performance-Evaluation-of-CognitiveRadio-Network-under-different-pathloss-models-using-frequency-range-of-1900-and-2100-MHz.pdf},
	doi = {10.14299/ijser.2016.06.001},
	abstract = {The correct detection of the presence of licensed user (Primary user) is the most essential requirement of Cognitive Radio Network (CRN). Otherwise the PU will face jamming signal from unlicensed user (Secondary User) and therefore will not be able to transmit. At the same time if a PU is not in transmitting mode but SU senses the presence of PU in transmitting mode then the SU will stop transmission even though the frequency band is free to use. In this situation, different path loss models incorporating with different fading channels have been proposed previously to measure the performance of CRN. Fading is generally a signal loss due to sudden alteration in channel response. In this paper, the aim is to evaluate the performance under different types of fading condition (such as Rayleigh, Nakagami-m, Weibull and Normal) with the incorporation of different types of path loss models such as Lee's path loss Model, COST-231 Walfisch Ikagami Model and ECC-33/ Hata Okumura Extended Model along with MRC and Selection combining Scheme under frequency ranges of 1900 MHz and 2100 MHz as this range is well suited for 4G technology.},
	language = {en},
	number = {6},
	urldate = {2021-05-17},
	journal = {International Journal of Scientific and Engineering Research},
	author = {Sultana, Zubyda and Tasin Khan, Risala and Nahar, Lailatun},
	month = jun,
	year = {2016},
	pages = {1144--1149},
}

The correct detection of the presence of licensed user (Primary user) is the most essential requirement of Cognitive Radio Network (CRN). Otherwise the PU will face jamming signal from unlicensed user (Secondary User) and therefore will not be able to transmit. At the same time if a PU is not in transmitting mode but SU senses the presence of PU in transmitting mode then the SU will stop transmission even though the frequency band is free to use. In this situation, different path loss models incorporating with different fading channels have been proposed previously to measure the performance of CRN. Fading is generally a signal loss due to sudden alteration in channel response. In this paper, the aim is to evaluate the performance under different types of fading condition (such as Rayleigh, Nakagami-m, Weibull and Normal) with the incorporation of different types of path loss models such as Lee's path loss Model, COST-231 Walfisch Ikagami Model and ECC-33/ Hata Okumura Extended Model along with MRC and Selection combining Scheme under frequency ranges of 1900 MHz and 2100 MHz as this range is well suited for 4G technology.

@article{sultana_performance_2016,
	title = {Performance {Evaluation} of {CognitiveRadio} {Network} under different pathloss models using frequency range of 1900 and 2100 {MHz}},
	volume = {7},
	issn = {22295518},
	url = {http://www.ijser.org/onlineResearchPaperViewer.aspx?Performance-Evaluation-of-CognitiveRadio-Network-under-different-pathloss-models-using-frequency-range-of-1900-and-2100-MHz.pdf},
	doi = {10.14299/ijser.2016.06.001},
	abstract = {The correct detection of the presence of licensed user (Primary user) is the most essential requirement of Cognitive Radio Network (CRN). Otherwise the PU will face jamming signal from unlicensed user (Secondary User) and therefore will not be able to transmit. At the same time if a PU is not in transmitting mode but SU senses the presence of PU in transmitting mode then the SU will stop transmission even though the frequency band is free to use. In this situation, different path loss models incorporating with different fading channels have been proposed previously to measure the performance of CRN. Fading is generally a signal loss due to sudden alteration in channel response. In this paper, the aim is to evaluate the performance under different types of fading condition (such as Rayleigh, Nakagami-m, Weibull and Normal) with the incorporation of different types of path loss models such as Lee's path loss Model, COST-231 Walfisch Ikagami Model and ECC-33/ Hata Okumura Extended Model along with MRC and Selection combining Scheme under frequency ranges of 1900 MHz and 2100 MHz as this range is well suited for 4G technology.},
	language = {en},
	number = {6},
	urldate = {2021-05-17},
	journal = {International Journal of Scientific and Engineering Research},
	author = {Sultana, Zubyda and Tasin Khan, Risala and Nahar, Lailatun},
	month = jun,
	year = {2016},
	pages = {1144--1149},
}

The correct detection of the presence of licensed user (Primary user) is the most essential requirement of Cognitive Radio Network (CRN). Otherwise the PU will face jamming signal from unlicensed user (Secondary User) and therefore will not be able to transmit. At the same time if a PU is not in transmitting mode but SU senses the presence of PU in transmitting mode then the SU will stop transmission even though the frequency band is free to use. In this situation, different path loss models incorporating with different fading channels have been proposed previously to measure the performance of CRN. Fading is generally a signal loss due to sudden alteration in channel response. In this paper, the aim is to evaluate the performance under different types of fading condition (such as Rayleigh, Nakagami-m, Weibull and Normal) with the incorporation of different types of path loss models such as Lee's path loss Model, COST-231 Walfisch Ikagami Model and ECC-33/ Hata Okumura Extended Model along with MRC and Selection combining Scheme under frequency ranges of 1900 MHz and 2100 MHz as this range is well suited for 4G technology.

@article{nahar_medium_2016,
	title = {Medium {Access} {Probability} of {Cognitive} {Radio} {Network} {Under} {ECC}-33/{Hata}-{Okumura} {Extended} {Model} {Using} {Different} {Fading} {Channels} at {1900MHz} and {2100MHz}},
	volume = {06},
	abstract = {Cognitive radio detects the presence or absence of Primary User (PU) in its sensing region to provide the free radio spectrum to its Secondary user (SU). It is widely accepted a SU is only allowed to access a network of PU when no PU of that network is accessing the network at that moment. Sometimes SU misjudges the presence of PU inside the sensing region though the PU is in transmitting mode outside the sensing region which is termed as spatial false alarm. The incorporation of spatial false alarm makes the task more difficult. Previous literature performs this task using Lee’s path loss model .In our paper we have considered ECC-33/ Hata-Okumura Extended Model for two frequencies 1900MHz and 2100MHz as its frequency range is up to 3.5 GHz and compare the performance using different fading channels such as Rayleigh, Nakagami-m, Normal or Gaussian, Weibull, MRC Rayleigh and Selection Combining Rayleigh.},
	language = {en},
	number = {07},
	journal = {International Journal of Computational Engineering Research},
	author = {Nahar, Lailatun and Akter, Shireen and Khan, Risala Tasin},
	month = jul,
	year = {2016},
	pages = {1--6},
}

Cognitive radio detects the presence or absence of Primary User (PU) in its sensing region to provide the free radio spectrum to its Secondary user (SU). It is widely accepted a SU is only allowed to access a network of PU when no PU of that network is accessing the network at that moment. Sometimes SU misjudges the presence of PU inside the sensing region though the PU is in transmitting mode outside the sensing region which is termed as spatial false alarm. The incorporation of spatial false alarm makes the task more difficult. Previous literature performs this task using Lee’s path loss model .In our paper we have considered ECC-33/ Hata-Okumura Extended Model for two frequencies 1900MHz and 2100MHz as its frequency range is up to 3.5 GHz and compare the performance using different fading channels such as Rayleigh, Nakagami-m, Normal or Gaussian, Weibull, MRC Rayleigh and Selection Combining Rayleigh.

@inproceedings{saha_relay_2016,
	title = {Relay selection and beamwidth adaptation to overcome blockage in mm-wave wireless private area network},
	doi = {10.1109/IWCI.2016.7860377},
	abstract = {This work presents relay selection and beamwidth adaptation algorithms for millimeter-wave communication network by selecting best relay. Due to the high path loss, the mm-Wave links suffer from shadowling. Thus improving link availability is a challenge for the system designer. In this work, the link availability is further improved by optimal power allocation and beamwidth adaptation. The channel model considered the path loss and shadowing. The throughput and link availability of the proposed algorithms have been evaluated. The results have been compared with the existing state of art methods. It has been found that the proposed algorithms for the relay selection and beamwidth adaptation outperformed the existing results.},
	booktitle = {2016 {International} {Workshop} on {Computational} {Intelligence} ({IWCI})},
	author = {Saha, Anindita and Ashrafi, Farah and Islam, Sadia and Khan, Risala Tasin and Kaiser, M. S.},
	month = dec,
	year = {2016},
	keywords = {Beamwidth, Blocking, Interference, Power allocation, Relay selection, Relays, Reliability, Shadow mapping, Signal to noise ratio, Throughput, Wireless personal area networks, mm-Wave},
	pages = {259--263},
}

This work presents relay selection and beamwidth adaptation algorithms for millimeter-wave communication network by selecting best relay. Due to the high path loss, the mm-Wave links suffer from shadowling. Thus improving link availability is a challenge for the system designer. In this work, the link availability is further improved by optimal power allocation and beamwidth adaptation. The channel model considered the path loss and shadowing. The throughput and link availability of the proposed algorithms have been evaluated. The results have been compared with the existing state of art methods. It has been found that the proposed algorithms for the relay selection and beamwidth adaptation outperformed the existing results.

@article{tasin_optimum_2016,
	title = {Optimum {Access} {Analysis} of {Collaborative} {Spectrum} {Sensing} in {Cognitive} {Radio} {Network} using {MRC}},
	volume = {7},
	issn = {21565570, 2158107X},
	url = {http://thesai.org/Publications/ViewPaper?Volume=7&Issue=7&Code=ijacsa&SerialNo=52},
	doi = {10.14569/IJACSA.2016.070752},
	abstract = {The performance of cognitive radio network mainly depends on the finest sensing of the presence or absence of Primary User (PU). The throughput of a Secondary User (SU) can be reduced because of the false detection of PU which causes an SU from its transmission opportunity. The factorization of the probability of correct decision is a really hard job when the special false alarm is incorporated into it. Previous works focus on collaborative sensing on the normal environment. In this paper, we have proposed a collaborative sensing method in Cognitive radio network for optimal access of PU licensed band by SU. It is shown performance analysis of energy detection through different cognitive users and conducts a clear comparison between local and collaborative sensing.In this paper, the maximal ratio combining diversity technique with energy detection has been employed to reduce the false alarm probability in the collaborative environment. The simulation result showssignificant reduction of the probability of misdetection with increasing in the number of collaborative users.We also analyze that MRC scheme exhibits the best detection performance in collaborative environment.},
	language = {en},
	number = {7},
	urldate = {2021-05-17},
	journal = {International Journal of Advanced Computer Science and Applications},
	author = {Tasin, Risala and Zaman, Shakila and Imdadul, Md. and R., M.},
	year = {2016},
}

The performance of cognitive radio network mainly depends on the finest sensing of the presence or absence of Primary User (PU). The throughput of a Secondary User (SU) can be reduced because of the false detection of PU which causes an SU from its transmission opportunity. The factorization of the probability of correct decision is a really hard job when the special false alarm is incorporated into it. Previous works focus on collaborative sensing on the normal environment. In this paper, we have proposed a collaborative sensing method in Cognitive radio network for optimal access of PU licensed band by SU. It is shown performance analysis of energy detection through different cognitive users and conducts a clear comparison between local and collaborative sensing.In this paper, the maximal ratio combining diversity technique with energy detection has been employed to reduce the false alarm probability in the collaborative environment. The simulation result showssignificant reduction of the probability of misdetection with increasing in the number of collaborative users.We also analyze that MRC scheme exhibits the best detection performance in collaborative environment.

@inproceedings{khan_comparison_2016,
	title = {Comparison of cyclostationary and energy detection in cognitive radio network},
	booktitle = {2016 {International} {Workshop} on {Computational} {Intelligence} ({IWCI})},
	publisher = {IEEE},
	author = {Khan, Risala Tasin and Islam, Md Imdadul and Zaman, Shakila and Amin, M. R.},
	year = {2016},
	pages = {165--168},
}

@article{md_fazlay_rabbi_performance_2015,
	title = {Performance {Evaluation} of {Cognitive} {Radio} {Network} {Based} on 2-{D} {Markov} {Chain}},
	url = {http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-64181e8f-ba61-45cd-81ce-550732069b45},
	abstract = {Now-a-days optimum utilization of spectrum of a wireless network is performed based on Cognitive Radio (CR) system. In this paper performance of such network is evaluated using two dimensional Markov chain under limited user case. The closed form expression for throughput and call blocking probability is derived in terms of traffic parameters. The impact of number of channel, number of primary and secondary user on throughput and call blocking probability is analyzed.},
	urldate = {2021-05-17},
	journal = {Journal of Telecommunications and Information Technology},
	author = {{Md Fazlay Rabbi} and {Risala T Khan} and {Jesmin Akhter} and {Md Imdadul Islam}},
	year = {2015},
}

Now-a-days optimum utilization of spectrum of a wireless network is performed based on Cognitive Radio (CR) system. In this paper performance of such network is evaluated using two dimensional Markov chain under limited user case. The closed form expression for throughput and call blocking probability is derived in terms of traffic parameters. The impact of number of channel, number of primary and secondary user on throughput and call blocking probability is analyzed.

@article{khan_medium_2015,
	title = {Medium {Access} {Probability} of {Cognitive} {Radio} {Network} at 1900 {MHz} and 2100 {MHz}},
	volume = {124},
	url = {https://www.ijcaonline.org/research/volume124/number5/khan-2015-ijca-905490.pdf},
	abstract = {Conventionally fading analysis of wireless LAN or MAN means small scale fading i.e. wide fluctuation of received signal with small variation of time and distance. In analysis of fading channel we consider the received signal in power, voltage or SNR as a random variable then statistical probability density function (pdf) like Rayleigh, Rician or Nakagami-m is used to get the probability of different phenomena. Most of the pdf is governed by two parameters: mean and variance of the random variable. In recent literature the mean value is taken constant but in this paper we consider the mean value as a slowly varying random variable and depend on the parameters of large scale fading. In this paper the concept of large and small scale fading is combined, in analysis of performance of cognitive radio network in context of medium access probability specially at 1900MHz and 2100MHz.},
	number = {5},
	journal = {International Journal of Computer Applications},
	author = {Khan, Risala Tasin and Dhaka, Savar and Amin, M. R.},
	month = aug,
	year = {2015},
	pages = {42--45},
}

Conventionally fading analysis of wireless LAN or MAN means small scale fading i.e. wide fluctuation of received signal with small variation of time and distance. In analysis of fading channel we consider the received signal in power, voltage or SNR as a random variable then statistical probability density function (pdf) like Rayleigh, Rician or Nakagami-m is used to get the probability of different phenomena. Most of the pdf is governed by two parameters: mean and variance of the random variable. In recent literature the mean value is taken constant but in this paper we consider the mean value as a slowly varying random variable and depend on the parameters of large scale fading. In this paper the concept of large and small scale fading is combined, in analysis of performance of cognitive radio network in context of medium access probability specially at 1900MHz and 2100MHz.

@inproceedings{ahmed_automated_2015,
	title = {Automated {CV} processing along with psychometric analysis in job recruiting process},
	doi = {10.1109/ICEEICT.2015.7307521},
	abstract = {In this paper we have proposed automated job recruiting process along with psychometric analysis. Here the focus has been given in automating the job applying and CV processing system. A social networking website for the job seekers and employers is proposed to develop which will forward CV to the desired company or organizations automatically by matching the required criteria instead of traditional job searching and applying process. With the help of the website, the job organizations would be able to choose the efficient and right person for the right job among the applicants on the basis of Psychometric analysis and also it will increase the job satisfaction among the employees.},
	booktitle = {2015 {International} {Conference} on {Electrical} {Engineering} and {Information} {Communication} {Technology} ({ICEEICT})},
	author = {Ahmed, Firoz and Anannya, Mehrin and Rahman, Tanvir and Khan, Risala Tasin},
	year = {2015},
	keywords = {Companies, Lead, Reliability, Sociology, Statistics, Switches, formatting, insert, style, styling},
	pages = {1--5},
}

In this paper we have proposed automated job recruiting process along with psychometric analysis. Here the focus has been given in automating the job applying and CV processing system. A social networking website for the job seekers and employers is proposed to develop which will forward CV to the desired company or organizations automatically by matching the required criteria instead of traditional job searching and applying process. With the help of the website, the job organizations would be able to choose the efficient and right person for the right job among the applicants on the basis of Psychometric analysis and also it will increase the job satisfaction among the employees.

@article{kuri_cross-layer_2014,
	title = {Cross-{Layer} {Analytical} {Model} for {Cognitive} {Radio} {Network} under {Engset} and {M}/{G}/1/m {Traffic}},
	volume = {15},
	abstract = {This paper deals with the performance of secondary users (SUs) based on dynamic scheduling of users based on cross-layer analytical model. Under this scheme the number secondary users in a network are kept limited to discourage malicious users hence Engset's traffic model is more appropriate than the Erlang's model of previous literature. We also incorporate M/G/1(m) traffic model, applicable for ATM/TCP-IP traffic, in cognitive radio network under cross-layer analytical model. Finally, impact of spectrum sensing period and probability of false alarm on blocking probability of secondary users are analyzed.},
	journal = {Jahangirnagar University Journal of Electronics and Computer Science},
	author = {Kuri, Sajib Kumar and Khan, Risala and Islam, Md},
	month = jun,
	year = {2014},
	pages = {1--5},
}

This paper deals with the performance of secondary users (SUs) based on dynamic scheduling of users based on cross-layer analytical model. Under this scheme the number secondary users in a network are kept limited to discourage malicious users hence Engset's traffic model is more appropriate than the Erlang's model of previous literature. We also incorporate M/G/1(m) traffic model, applicable for ATM/TCP-IP traffic, in cognitive radio network under cross-layer analytical model. Finally, impact of spectrum sensing period and probability of false alarm on blocking probability of secondary users are analyzed.

@article{khan_traffic_2014,
	title = {Traffic {Analysis} of a {Cognitive} {Radio} {Network} {Based} on the {Concept} of {Medium} {Access} {Probability}},
	volume = {10},
	issn = {1976-913X},
	url = {http://koreascience.or.kr/article/JAKO201401657906036.page},
	doi = {10.3745/JIPS.03.0019},
	abstract = {The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.},
	language = {eng},
	number = {4},
	urldate = {2021-05-17},
	journal = {Journal of Information Processing Systems},
	author = {Khan, Risala T. and Islam, Md Imdadul and Amin, M. R.},
	year = {2014},
	note = {Publisher: Korea Information Processing Society},
	pages = {602--617},
}

The performance of a cognitive radio network (CRN) solely depends on how precisely the secondary users can sense the presence or absence of primary users. The incorporation of a spatial false alarm makes deriving the probability of a correct decision a cumbersome task. Previous literature performed this task for the case of a received signal under a Normal probability density function case. In this paper we enhance the previous work, including the impact of carrier frequency, the gain of antennas on both sides, and antenna heights so as to observe the robustness against noise and interference and to make the correct decision of detection. Three small scale fading channels: Rayleigh, Normal, and Weibull were considered to get the real scenario of a CRN in an urban area. The incorporation of a maximal-ratio combining and selection combing with a variation of the number of received antennas have also been studied in order to achieve the correct decision of spectral sensing, so as to serve the cognitive users. Finally, we applied the above concept to a traffic model of the CRN, which we based on a two-dimensional state transition chain.

@article{khan_enhancement_2012,
	title = {Enhancement of {Performance} of {Cognitive} {Radio} {Network} with {Incorporation} of {MRC} {Scheme} at {Secondary} {Receiver}},
	url = {/paper/Enhancement-of-Performance-of-Cognitive-Radio-with-Khan-Shabnam/ea18658953a89768a47fedcf19ddfb5569d532d2},
	abstract = {The faithful detection of presence of a primary user (PU) is the most essential requirement of a cognitive radio network. Otherwise the PU will experience jamming from a secondary user (SU) which will eventually lead to reduction in throughput of the PU. Similarly, the false detection of a PU will abstain a SU from its transmission opportunity hence reduce the throughput of the SU. Under this situation we propose a cognitive receiver equipped with multiple antenna and maximal ratio combining scheme (MRC) to detect the presence of a PU. The rest of the communication links like PU to PU or SU to PU uses single antenna. In this paper the concept of test statistics of fusion center from a previous literature is applied in the derivation of the probability of false alarm, probability of detection, channel capacity and symbol error rate of the network. The performance of a cognitive radio network under MRC scheme at receiving mode of SU is found better than the case of a single antenna.},
	language = {en},
	urldate = {2021-05-17},
	journal = {undefined},
	author = {Khan, R. T. and Shabnam, T. and Islam, M. I. and Amin, M.},
	year = {2012},
}

The faithful detection of presence of a primary user (PU) is the most essential requirement of a cognitive radio network. Otherwise the PU will experience jamming from a secondary user (SU) which will eventually lead to reduction in throughput of the PU. Similarly, the false detection of a PU will abstain a SU from its transmission opportunity hence reduce the throughput of the SU. Under this situation we propose a cognitive receiver equipped with multiple antenna and maximal ratio combining scheme (MRC) to detect the presence of a PU. The rest of the communication links like PU to PU or SU to PU uses single antenna. In this paper the concept of test statistics of fusion center from a previous literature is applied in the derivation of the probability of false alarm, probability of detection, channel capacity and symbol error rate of the network. The performance of a cognitive radio network under MRC scheme at receiving mode of SU is found better than the case of a single antenna.

@article{bulbul_ahmed_risala_t_khanimdadul_islam_wlan-lte_nodate,
	title = {{WLAN}-{LTE} {Integrated} {Traffic} {Model} under {Unlicensed} {Spectrum}},
	volume = {17},
	url = {https://www.academia.edu/38794998/WLAN_LTE_Integrated_Traffic_Model_under_Unlicensed_Spectrum},
	language = {en},
	number = {7},
	urldate = {2021-05-17},
	journal = {International Journal of Computer Science and Information Security},
	author = {Bulbul Ahmed, Risala T Khan,Imdadul Islam},
}