D RAGHUNATHA RAO
@jntua, anantapuramu
ASSOCIATE PROFESSOR and ECE Department
SVR ENGINEERING COLLEGE (AUTONOMOUS)
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Computer Networks and Communications, Signal Processing, Computer Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
D. Raghunatha Rao, T. Jayachandra Prasad, and M. N. Giri Prasad
Springer Science and Business Media LLC
D Raghunatha Rao, T Jayachandra Prasad, and M N Giri Prasad
Auricle Technologies, Pvt., Ltd.
The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.
D Raghunatha Rao, T Jayachandra Prasad, and M N Giri Prasad
IEEE
Cooperative sensing is a solution to augment the performance of detection, in which Secondary Users (SU) cooperate with each other for sensing the spectrum to discover the spectrum availability. Spectrum sensing is one of the effective components of Cognitive Radio Networks (CRN). Spectrum sensing allows a CR, which contains information about its spectrum availability as well as the environment. In this paper, the spectrum availability sensing is carried out using the Deep Residual Network test statistic refining scheme (DRN test statistic refining scheme) in which the DRN contains several layers, such as convolutional layer(cony), pooling (pool), activation function, batch normalization, residual blocks and linear classifier. Here, the linear classifier provides the sensing output by considering the fused data and sensing data matrix as an input. In order to improve spectrum availability sensing, data cleansing algorithm along with data fusion have been implemented. In addition, all the sensing processes are carried out in Cooperative Spectrum sensing (CSS) with Crowd sensors. Moreover, the experimental result demonstrates that the devised DRN test statistic refining scheme attained the better detection probability of 0.9578 for the matrix size of 10 X 5 using Rician channel.
D. Raghunatharao, T. Jayachandra Prasad, and M. N. Giri Prasad
Springer International Publishing
D. Raghunatharao, T. Jayachandra Prasad, and M. N. Giri Prasad
Springer Science and Business Media LLC