Technological advances in communications and computation have enabled the development of low-cost... more Technological advances in communications and computation have enabled the development of low-cost, low-power, small in size, and multifunctional sensor nodes in a wireless sensor network. Since the radio transmission and reception consumes a lot of energy, one of the important issues in wireless sensor network (WSN) is the inherent limited battery power within network sensor nodes. In addition to maximizing the lifespan of sensor nodes, it is preferable to distribute the energy dissipated throughout the wireless sensor network in order to maximize overall network performance. Packet loss that occurs due to mobility of the sensor nodes is one of main challenge in Wireless Sensor Network (WSN) and it comes in parallel with energy consumption. CBRP can change TDMA scheduling adaptively according to traffic and mobility characteristics. In this protocol the cluster head receive data from not only its member during the TDMA allocated time slot but also other sensor nodes that just enter ...
2015 International Conference on Pervasive Computing (ICPC), 2015
Nowadays wireless networks are regulated by fixed spectrum access policy. But all the fixed assig... more Nowadays wireless networks are regulated by fixed spectrum access policy. But all the fixed assigned spectrums are not utilized fully at all times. At the same time frequency spectrum need has increased drastically as the spectrum is being used for different multimedia applications with voice communication. It gives rise to a need to dynamically access the available frequency spectrum. Cognitive Radio (CR) is the intelligent technology which can sense vacant frequency spectrum i.e. the spectrum not being used by licensed users at the time when CR requires it. So sensing vacant channel is very important in CR technology. Sometimes due to fading and shadowing effects single CR user can't detect the presence of licensed (Primary) user. Co-operative spectrum sensing helps in improving the sensing process and getting more reliable results. Here we are discussing three different co-operative spectrum sensing algorithms with energy detection for local spectrum sensing. We will discuss traditional co-operative spectrum sensing (TCSS), SNR weighted co-operative spectrum sensing (SWCSS) and selective SNR weighted co-operative spectrum sensing (SSWCSS). Simulation shows that SWCSS gives better results than TCSS and the numbers of co-operating secondary users (SU) are reduced and consumption of system resource is minimized. SSWCSS is similar to SWCSS but the number of co-operating secondary users (SU) can be reduced further.
International Journal of Innovative Research in Computer and Communication Engineering, 2015
Compressive Sensing acquire sparse signal significantly at very lower rate than Nyquist sampling ... more Compressive Sensing acquire sparse signal significantly at very lower rate than Nyquist sampling rate. For this, a low complexity compressed sensing operation is defined and it is the combination of sampling and compression. The signals formed from compressed sensing operation are compressible signals and a set of random linear measurements accurately reconstructs compressible signals with the use of nonlinear or convex reconstruction algorithms. Basis Pursuit algorithm is one of the convex optimization algorithms to reconstruct the sparse signal. The l1 minimization theory for linear programming problems is used to formulate the compressive sensing method. Interior point method is used to solve the basis pursuit algorithm for sparse signal reconstruction. In this paper, the methodology of reconstructing sparse signal using basis pursuit algorithm is discussed.
International Journal of Electrical and Computer Engineering (IJECE), 2019
Over the past few years, Cognitive Radio has become an important research area in the field of wi... more Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications
Technological advances in communications and computation have enabled the development of low-cost... more Technological advances in communications and computation have enabled the development of low-cost, low-power, small in size, and multifunctional sensor nodes in a wireless sensor network. Since the radio transmission and reception consumes a lot of energy, one of the important issues in wireless sensor network (WSN) is the inherent limited battery power within network sensor nodes. In addition to maximizing the lifespan of sensor nodes, it is preferable to distribute the energy dissipated throughout the wireless sensor network in order to maximize overall network performance. Packet loss that occurs due to mobility of the sensor nodes is one of main challenge in Wireless Sensor Network (WSN) and it comes in parallel with energy consumption. CBRP can change TDMA scheduling adaptively according to traffic and mobility characteristics. In this protocol the cluster head receive data from not only its member during the TDMA allocated time slot but also other sensor nodes that just enter ...
2015 International Conference on Pervasive Computing (ICPC), 2015
Nowadays wireless networks are regulated by fixed spectrum access policy. But all the fixed assig... more Nowadays wireless networks are regulated by fixed spectrum access policy. But all the fixed assigned spectrums are not utilized fully at all times. At the same time frequency spectrum need has increased drastically as the spectrum is being used for different multimedia applications with voice communication. It gives rise to a need to dynamically access the available frequency spectrum. Cognitive Radio (CR) is the intelligent technology which can sense vacant frequency spectrum i.e. the spectrum not being used by licensed users at the time when CR requires it. So sensing vacant channel is very important in CR technology. Sometimes due to fading and shadowing effects single CR user can't detect the presence of licensed (Primary) user. Co-operative spectrum sensing helps in improving the sensing process and getting more reliable results. Here we are discussing three different co-operative spectrum sensing algorithms with energy detection for local spectrum sensing. We will discuss traditional co-operative spectrum sensing (TCSS), SNR weighted co-operative spectrum sensing (SWCSS) and selective SNR weighted co-operative spectrum sensing (SSWCSS). Simulation shows that SWCSS gives better results than TCSS and the numbers of co-operating secondary users (SU) are reduced and consumption of system resource is minimized. SSWCSS is similar to SWCSS but the number of co-operating secondary users (SU) can be reduced further.
International Journal of Innovative Research in Computer and Communication Engineering, 2015
Compressive Sensing acquire sparse signal significantly at very lower rate than Nyquist sampling ... more Compressive Sensing acquire sparse signal significantly at very lower rate than Nyquist sampling rate. For this, a low complexity compressed sensing operation is defined and it is the combination of sampling and compression. The signals formed from compressed sensing operation are compressible signals and a set of random linear measurements accurately reconstructs compressible signals with the use of nonlinear or convex reconstruction algorithms. Basis Pursuit algorithm is one of the convex optimization algorithms to reconstruct the sparse signal. The l1 minimization theory for linear programming problems is used to formulate the compressive sensing method. Interior point method is used to solve the basis pursuit algorithm for sparse signal reconstruction. In this paper, the methodology of reconstructing sparse signal using basis pursuit algorithm is discussed.
International Journal of Electrical and Computer Engineering (IJECE), 2019
Over the past few years, Cognitive Radio has become an important research area in the field of wi... more Over the past few years, Cognitive Radio has become an important research area in the field of wireless communications. It can play an important role in dynamic spectrum management and interference identification. There are many spectrum sensing techniques proposed in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications
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Papers by Sheetal Borde
in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver
parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation
type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even
in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications
in literature for cognitive radio, but all those techniques detect only presence or absence of the primary user in the designated band and do not give any information about the used modulation scheme. In certain applications, in cognitive radio receiver, it is necessary to identify the modulation type of the signal so that the receiver
parameters can be adjusted accordingly. Most of the modulated signals exhibit the property of Cyclostationarity that can be used for the purpose of correct detection of primary user and the modulation
type. In this paper, we have proposed an enhanced signal detection algorithm for cognitive radio receiver which makes use of cyclostationarity property of the modulated signal to exactly detect, the modulation type of the received signal using a trained neural network. The algorithm gives better accuracy of signal detection even
in low SNR conditions. The use of a trained neural network makes it more flexible and extendible for future applications