Aamir Z . Shaikh | NED University of Engineering and Technology (original) (raw)
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Papers by Aamir Z . Shaikh
We derive probability of detection Pd and false alarm Pf for spectrum sensing cognitive devices, ... more We derive probability of detection Pd and false alarm Pf for spectrum sensing cognitive devices, employing correlated multiple antenna elements using linear test statistic. Detection performance of such sensors is severely degraded due to the correlation among antennas, in addition to that fading channel conditions may further deteriorate the performance. We propose a simple hard decision fusion strategy at the secondary Base Station to improve the performance by exploiting collaborative gain. Region of Convergence (ROC) is also evaluated under OR based fusion strategy. Numerical results certify the proposed proposal. Keywords correlated multiple antenna energy detector, linear statistic,
International Journal of Computer Applications, 2012
2013 3rd IEEE International Conference on Computer, Control and Communication (IC4), 2013
ABSTRACT
Wireless Personal Communications, 2015
ABSTRACT
International Journal of Advanced Computer Science and Applications
Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under... more Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under the umbrella of 6G and 6G+ communication standard. The expected new services that will be introduced in 6G communication will require high data rates for transmission. The learning based algorithms will play a key role towards successful implementation of these novel technologies and evolving next generation wireless standards for providing ubiquitous connectivity. This paper investigates performance of two artificial neural network (ANN) based algorithms for Karachi. These include Nonlinear autoregressive exogenous Algorithm (NARX) and cascade feed forward back propagation neural network (CFFBNN) scheme. A dataset for Karachi is also developed for 1805 MHZ. The results of the two algorithms are compared that show Mean Square Error (MSE) for CFFBNN is 6.8877e-5 at epoch 16 and MSE for NARX is 3.1506e-11 at epoch 26. Hence, exploiting computational performance, NARX performs much superior than the classis CFFBNN algorithm.
International Journal of Future Generation Communication and Networking
The number of wireless devices are increasing rapidly with the advent of technological innovation... more The number of wireless devices are increasing rapidly with the advent of technological innovation. Many of these devices require large bandwidth, hence as a consequence spectrum scarcity is also increasing rapidly. Cognitive radio is a promising technology that can provide solution to spectrum scarcity by dynamic frequency allocation. So far, frequency allocation has been static. Dynamic spectrum allocation allows secondary users to use frequencies assigned to primary (licensed) users without causing interference to primary users. In this paper, we have developed a dataset utilization of different frequencies by spectrum sensing. This data is used as an input to machine learning algorithm in order to predict white spaces and transmission levels at which secondary users can transmit without creating any interference with primary users. For creation of the database we have performed spectrum sensing on different frequency bands. After analysing the data, we found out spectrum holes (white spaces, where primary user isn't transmitting) and transmission levels of primary user so that secondary user can transmit without creating interference. From this dataset, we trained our machine learning algorithm to accurately predict spectrum holes and transmission levels for secondary users. Using machine learning we were able to predict accurately within a fraction of time. Our proposed methodology increases accuracy and at the same time reduces interference, power consumption and frequency allocation time and then created dataset is import to the Artificial Neural Network (ANN) for the prediction of best available spectrum slots.
International Journal of Future Generation Communication and Networking
The emergence of Internet of Things and other applications of wireless communication has resulted... more The emergence of Internet of Things and other applications of wireless communication has resulted in increase of air interference among various wireless devices. In upcoming time we will be connecting more and more devices wirelessly. In addition to an increase in number of devices, many devices also demand higher bandwidth. We have a limited spectrum available for communication and as the demand increases it creates more and more congestion in the available spectrum. Besides this scarcity of spectrum, it has been observed that all available frequencies in this spectrum are not efficiently utilized. Some frequency bands face congestion while others are underutilized. The solution of all these issues is Cognitive Radio. The fundamental theory of cognitive radio deals with the issues mentioned above and provides efficient utilization of available spectrum. In cognitive radio when a frequency is not utilized by primary user (Licensed user), it is allocated to secondary user (Unlicensed user) who can use the frequency until there is no primary user. For searching primary and secondary user we use spectrum sensing. Depending on the type of users and the environment, this spectrum sensing can be a time consuming task which can severely impact the QoS. To deal with this critical issue we use machine learning techniques, which predict spectrum holes in an available frequency band. This in turn reduces spectrum sensing time and power consumed in sensing. Among various Machine learning techniques, Artificial Neural Networks is one of the most popular and widely used technique. Unlike other Machine learning Techniques, Neural Network doesn't require prior knowledge of the system and in most cases it doesn't require the model to be retrained an every instance. These advantages makes it one of the most popular technique for cognitive radios. So far a lot of work has been done on implementing Artificial Neural Network models for predicting the most suitable frequency for a secondary user. In this paper a comprehensive survey has been conducted on various ANN techniques, its comparison with other machine learning techniques and discussion on various learning models to increase the decision making ability of cognitive radio's cognitive engine. ANN uses supervised learning and this paper compares it with other supervised learning techniques (like SVM) and also unsupervised learning techniques and statistical models. The paper provides detailed knowledge about what factors influence the use of ANN in cognitive engines and under certain conditions which ANN technique is most suitable.
International Journal of Advanced Computer Science and Applications, 2016
Lighting up radio is a driving improvement in remote correspondence that gives created utilizatio... more Lighting up radio is a driving improvement in remote correspondence that gives created utilization of range. Range seeing is performed to make an unequivocally hot radio structure. Careful radio extra things with to use unused odd social events without causing impedance with ensured rule customers. Among the specific range seeing frameworks Energy zone and conviction based ID structures are less surprising and dull. The two structures do bar past valuation for the standard customer signal for range seeing. Range referencing can be improved by taking a bewildering choice. Explanation behind this record is to completed centrality sponsorship and eigenvalue based boggling degree finding in NI-USRP mechanical party structure and get its introduction. In this paper the contraption is executed using one squashing customer transmitter and two novel radio customers. The utilization is done using LABVIEW and clear introduction execution is surged down.
In this paper, time of arrival and angle of arrival statistics for wireless propagation environme... more In this paper, time of arrival and angle of arrival statistics for wireless propagation environments are evaluated in closed form using three dimensional ellipsoidal model. Scatterers are assumed to be uniformly distributed inside the ellipsoid whose foci collocate with the transmitter and the receiver. This model finds numerous applications in micro- and pico-cell outdoor as well as for indoor environments where scatterers significantly impact the transmitter and receiver characteristics. Joint and marginal probability density functions of time of arrival and angles (both azimuth and elevation) of arrival at the receiver side are evaluated in closed form. Numerical simulations are used to validate the analytical derivations.
Collaborative spectrum sensing for detection of white spaces helps in realizing reliable and effi... more Collaborative spectrum sensing for detection of white spaces helps in realizing reliable and efficient spectrum sensing algorithms, which results in efficient usage of primary spectrum in secondary fashion. Collaboration among cognitive radios improves probability of detecting a spectral hole as well as sensing time.
We derive probability of detection Pd and false alarm Pf for spectrum sensing cognitive devices, ... more We derive probability of detection Pd and false alarm Pf for spectrum sensing cognitive devices, employing correlated multiple antenna elements using linear test statistic. Detection performance of such sensors is severely degraded due to the correlation among antennas, in addition to that fading channel conditions may further deteriorate the performance. We propose a simple hard decision fusion strategy at the secondary Base Station to improve the performance by exploiting collaborative gain. Region of Convergence (ROC) is also evaluated under OR based fusion strategy. Numerical results certify the proposed proposal. Keywords correlated multiple antenna energy detector, linear statistic,
International Journal of Computer Applications, 2012
2013 3rd IEEE International Conference on Computer, Control and Communication (IC4), 2013
ABSTRACT
Wireless Personal Communications, 2015
ABSTRACT
International Journal of Advanced Computer Science and Applications
Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under... more Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under the umbrella of 6G and 6G+ communication standard. The expected new services that will be introduced in 6G communication will require high data rates for transmission. The learning based algorithms will play a key role towards successful implementation of these novel technologies and evolving next generation wireless standards for providing ubiquitous connectivity. This paper investigates performance of two artificial neural network (ANN) based algorithms for Karachi. These include Nonlinear autoregressive exogenous Algorithm (NARX) and cascade feed forward back propagation neural network (CFFBNN) scheme. A dataset for Karachi is also developed for 1805 MHZ. The results of the two algorithms are compared that show Mean Square Error (MSE) for CFFBNN is 6.8877e-5 at epoch 16 and MSE for NARX is 3.1506e-11 at epoch 26. Hence, exploiting computational performance, NARX performs much superior than the classis CFFBNN algorithm.
International Journal of Future Generation Communication and Networking
The number of wireless devices are increasing rapidly with the advent of technological innovation... more The number of wireless devices are increasing rapidly with the advent of technological innovation. Many of these devices require large bandwidth, hence as a consequence spectrum scarcity is also increasing rapidly. Cognitive radio is a promising technology that can provide solution to spectrum scarcity by dynamic frequency allocation. So far, frequency allocation has been static. Dynamic spectrum allocation allows secondary users to use frequencies assigned to primary (licensed) users without causing interference to primary users. In this paper, we have developed a dataset utilization of different frequencies by spectrum sensing. This data is used as an input to machine learning algorithm in order to predict white spaces and transmission levels at which secondary users can transmit without creating any interference with primary users. For creation of the database we have performed spectrum sensing on different frequency bands. After analysing the data, we found out spectrum holes (white spaces, where primary user isn't transmitting) and transmission levels of primary user so that secondary user can transmit without creating interference. From this dataset, we trained our machine learning algorithm to accurately predict spectrum holes and transmission levels for secondary users. Using machine learning we were able to predict accurately within a fraction of time. Our proposed methodology increases accuracy and at the same time reduces interference, power consumption and frequency allocation time and then created dataset is import to the Artificial Neural Network (ANN) for the prediction of best available spectrum slots.
International Journal of Future Generation Communication and Networking
The emergence of Internet of Things and other applications of wireless communication has resulted... more The emergence of Internet of Things and other applications of wireless communication has resulted in increase of air interference among various wireless devices. In upcoming time we will be connecting more and more devices wirelessly. In addition to an increase in number of devices, many devices also demand higher bandwidth. We have a limited spectrum available for communication and as the demand increases it creates more and more congestion in the available spectrum. Besides this scarcity of spectrum, it has been observed that all available frequencies in this spectrum are not efficiently utilized. Some frequency bands face congestion while others are underutilized. The solution of all these issues is Cognitive Radio. The fundamental theory of cognitive radio deals with the issues mentioned above and provides efficient utilization of available spectrum. In cognitive radio when a frequency is not utilized by primary user (Licensed user), it is allocated to secondary user (Unlicensed user) who can use the frequency until there is no primary user. For searching primary and secondary user we use spectrum sensing. Depending on the type of users and the environment, this spectrum sensing can be a time consuming task which can severely impact the QoS. To deal with this critical issue we use machine learning techniques, which predict spectrum holes in an available frequency band. This in turn reduces spectrum sensing time and power consumed in sensing. Among various Machine learning techniques, Artificial Neural Networks is one of the most popular and widely used technique. Unlike other Machine learning Techniques, Neural Network doesn't require prior knowledge of the system and in most cases it doesn't require the model to be retrained an every instance. These advantages makes it one of the most popular technique for cognitive radios. So far a lot of work has been done on implementing Artificial Neural Network models for predicting the most suitable frequency for a secondary user. In this paper a comprehensive survey has been conducted on various ANN techniques, its comparison with other machine learning techniques and discussion on various learning models to increase the decision making ability of cognitive radio's cognitive engine. ANN uses supervised learning and this paper compares it with other supervised learning techniques (like SVM) and also unsupervised learning techniques and statistical models. The paper provides detailed knowledge about what factors influence the use of ANN in cognitive engines and under certain conditions which ANN technique is most suitable.
International Journal of Advanced Computer Science and Applications, 2016
Lighting up radio is a driving improvement in remote correspondence that gives created utilizatio... more Lighting up radio is a driving improvement in remote correspondence that gives created utilization of range. Range seeing is performed to make an unequivocally hot radio structure. Careful radio extra things with to use unused odd social events without causing impedance with ensured rule customers. Among the specific range seeing frameworks Energy zone and conviction based ID structures are less surprising and dull. The two structures do bar past valuation for the standard customer signal for range seeing. Range referencing can be improved by taking a bewildering choice. Explanation behind this record is to completed centrality sponsorship and eigenvalue based boggling degree finding in NI-USRP mechanical party structure and get its introduction. In this paper the contraption is executed using one squashing customer transmitter and two novel radio customers. The utilization is done using LABVIEW and clear introduction execution is surged down.
In this paper, time of arrival and angle of arrival statistics for wireless propagation environme... more In this paper, time of arrival and angle of arrival statistics for wireless propagation environments are evaluated in closed form using three dimensional ellipsoidal model. Scatterers are assumed to be uniformly distributed inside the ellipsoid whose foci collocate with the transmitter and the receiver. This model finds numerous applications in micro- and pico-cell outdoor as well as for indoor environments where scatterers significantly impact the transmitter and receiver characteristics. Joint and marginal probability density functions of time of arrival and angles (both azimuth and elevation) of arrival at the receiver side are evaluated in closed form. Numerical simulations are used to validate the analytical derivations.
Collaborative spectrum sensing for detection of white spaces helps in realizing reliable and effi... more Collaborative spectrum sensing for detection of white spaces helps in realizing reliable and efficient spectrum sensing algorithms, which results in efficient usage of primary spectrum in secondary fashion. Collaboration among cognitive radios improves probability of detecting a spectral hole as well as sensing time.