Djamel Teguig

Throughput Optimization for Cooperative Spectrum Sensing in Cognitive Radio Networks

In this paper, the channel utilization (throughput vs. sensing time relationship) is analyzed for cooperative spectrum sensing under different combining rules and scenarios. The combining rules considered in this study are the OR hard combining rule, the AND hard combining rule, the Equal Gain Soft combining rule and the two-bit quantized (softened hard) combining rule. For all combining rules, the detection performance, with a Gaussian distribution assumption, is expressed in two different scenarios, CPUP (Constant Primary User Protection) and CSUSU (Constant Secondary User Spectrum Usability). A comparison, based on simulations, is conducted between these proposed schemes in both scenarios, in terms of detection performance and throughput capacity of the CR network.

Consensus algorithms for distributed spectrum sensing based on goodness of fit test in cognitive radio networks

In this paper, we study a consensus algorithm for distributed spectrum sensing (DSS) in cognitive radio networks (CRN) integrating a Goodness of Fit based spectrum sensing scheme. Existing work in this area often applies energy detector as a local spectrum sensing method for DSS, however in this case one needs to make the assumption that the noise level is the same at every node in the network, otherwise the threshold can not be set properly. In GoF based spectrum sensing, the threshold for the binary test depends only on the desired false alarm probability and not on the local noise powers. Motivated by this nice feature of GoF based spectrum sensing, we consider the goodness of fit (GoF) test statistic to be exchanged among cognitive radio (CR) users (consensus variable) instead of the energy. Moreover, a weighted consensus based DSS scheme is proposed and compared to the conventional consensus based on DSS. Simulations are conducted to show the effectiveness of the consensus algorithm based on GoF test.

Wideband spectrum sensing technique based on Goodness-of-Fit testing

Recently, Goodness-of-Fit (GoF) based spectrum sensing has been proposed as a method for blind narrowband spectrum sensing. GoF based spectrum sensing has the nice feature that, compared to Energy Detection, it needs fewer samples to achieve the same sensing performance. In this paper, we present how GoF based spectrum sensing can be integrated in a conventional wideband spectrum sensing scheme, resulting in an accurate technique with a very short sensing time, which makes this method attractive for cognitive radio applications.

GPR modelling applied to vertical and horizontal resolution of buried objects

In this paper we are interested in ground penetrating radar (GPR) modelling applied to horizontal and vertical resolution of buried objects using the finite difference in time domain (FDTD). Our objective was to assess the vertical and the horizontal resolution of three tablecloths of pipes like metallic, empty PVC and PVC filled with water in three depths as the model of “site test of laboratoire central des ponts et chaussées (LCPC renamed actually as IFSTAR) of Nantes-France”, when the soil was attenuating low dispersive. When using the effect of frequency excitation source and depth, results thus found, after having to determine all the parameters which influenced resolution, it was necessary to also see the nature of the targets, this is why, this resolution differs from one target to another. For vertical resolution, metallic targets or which presented a very high permittivity, it was necessary to have a distance from a little high separation because it could mask the targets behind and what appeared at the time of the study of this resolution and that in end the ΔV would be smaller for the case of the empty plastic. Experimental and numerical results were made and showed a great agreement by using a processing as migration what was not obligatorily the case of horizontal resolution which in is made based on a direct visibility..