Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks

This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with as few as 4 jammer hopping samples. The model is also capable of determining if the jammer is utilizing a sequence that the model is trained for in as few as 25 jammer hopping samples

Frequency hopping in wireless sensor networks

Wireless sensor networks (WSNs) are nowadays being used to collectively gather and spread information in different kinds of applications, for military, civilian, environmental as well as commercial purposes. Therefore the proper functioning of WSNs under different kinds of environmental conditions, especially hostile environments, is a must and a lot of research currently ongoing. The problems related to the initialization and deployment of WSNs under harsh and resource limited conditions are investigated in this thesis. Frequency hopping (FH) is a spread spectrum technique in which multiple channels are used, or hoped, for communications across the network. This mitigates the worst effects of interference with frequency agile communication systems rather than by brute force approaches. FH is a promising technique for achieving the coexistence of sensor networks with other currently existing wireless systems, and it is successful within the somewhat limited computational capabilities of the sensor nodes hardware radios. In this thesis, a FH scheme for WSNs is implemented for a pair of nodes on an application layer. The merits and demerits of the scheme are studied for different kinds of WSN environments. The implementation has been done using a Sensinode NanoStack, a communication stack for internet protocol (IP) based wireless sensor networks and a Sensinode Devkit, for an IPv6 over low power wireless personal area network (6LoWPAN). The measurements are taken from the developed test bed and channel simulator for different kinds of scenarios. The detailed analysis of the FH scheme is done to determine its usefulness against interference from other wireless systems, especially wireless local area networks (WLANs), and the robustness of the scheme to combat fading or frequency selective fading

Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically