[1] A new integrated approach for identifying the shallow subsurface electric properties from ground-penetrating radar (GPR) signal is proposed. It is based on an ultrawide band (UWB) stepped frequency continuous wave (SFCW) radar combined with a dielectric filled transverse electric and magnetic (TEM) horn antenna to be used off the ground in monostatic mode; that is, a single antenna is used as emitter and receiver. This radar configuration is appropriate for subsurface mapping and allows for an efficient and more realistic modeling of the radar-antenna-subsurface system. Forward modeling is based on linear system response functions and on the exact solution of the three-dimensional Maxwell equations for wave propagation in a horizontally multilayered medium representing the subsurface. Subsurface electric properties, i.e., dielectric permittivity and electric conductivity, are estimated by model inversion using the global multilevel coordinate search optimization algorithm combined sequentially with the local NelderMead simplex algorithm (GMCS-NMS). Inversion of synthetic data and analysis of the corresponding response surfaces proved the uniqueness of the inverse solution. Laboratory experiments on a tank filled with a homogeneous sand subject to different water content levels further demonstrated the stability and accuracy of the solution toward measurement and modeling errors, particularly those associated with the dielectric permittivity. Inversion for the electric conductivity led to less satisfactory results. This was mainly attributed to the characterization of the frequency response of the antenna and to the high frequency dependence of the electric conductivity.

A common way of describing antennas in the time domain is by means of their impulse response.When the time domain antenna equations are expressed in terms of the normalised impulse response (normalised IR), they become very simple to use, because all frequency dependent antenna characteristics are included in the normalised IR. This paper describes a method for measuring the normalised IR experimentally, using a vector network analyser. The normalised IRs of different air and dielectric-filled TEM horn antennas are compared and discussed. The normalised IR is found to be a powerful tool for simulating antenna behaviour directly in the time domain. Thanks to the introduction of the virtual source, (i.e. an apparent point in the antenna from which the radiated field degrades by a factor 1/r), the time domain antenna equations can also be used near the TEM horns, although still in the far field of the antenna. Some examples of time domain simulations and system modelling using the normalised IR are presented. In each example, the simulations are compared with measured data.

In cognitive radio, spectrum sensing is a challenging task. In this letter, a new spectrum sensing method is proposed based on Goodness of Fit test (GoF) of the energy of the received samples with a chi-square distribution. We derive the test statistic and evaluate the performance of the proposed method by Monte Carlo simulations. It is shown that our proposed spectrum sensing method outperforms the conventional energy detection (ED) without increasing the complexity of the sensing.

The jamming attack is one of the most severe threats in cognitive radio networks, because it can lead to network degradation and even denial of service. However, a cognitive radio can exploit its ability of dynamic spectrum access and its learning capabilities to avoid jammed channels. In this paper, we study how Q-learning can be used to learn the jammer strategy in order to pro-actively avoid jammed channels. The problem with Q-learning is that it needs a long training period to learn the behavior of the jammer. To address the above concern, we take advantage of the wideband spectrum sensing capabilities of the cognitive radio to speed up the learning process and we make advantage of the already learned information to minimize the number of collisions with the jammer during training. The effectiveness of this modified algorithm is evaluated by simulations in the presence of different jamming strategies and the simulation results are compared to the original Q-learning algorithm applied to the same scenarios.

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, AND the 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.