ii Performance Analysis of DS/CDMA Systems with Shadowing and Flat Fading

integration; Padé approximation. A new method is developed for evaluating the error probability (Pe) for Direct Sequence, Code Division Multiple Access (DS/CDMA) wireless systems that includes the effects of shadowing and fading. The method is based on saddle point integration (SPI) of the test statistic’s moment generating function (MGF) in the complex plane. The SPI method is applicable to both ideal and wireless channels. For wireless channels, a Padé approximation (PA) of the MGF, which is derived from the moments of the channel’s shadowing and fading distributions, allows efficient evaluation of the Pe. The SPI method can be used to model independent channels using separate shadowing and fading moments for each individual channel. The relative error between the probability density function (PDF) of the composite variate representing log-normal shadowing and Rayleigh fading and the PDF found from the inverse Laplace transform of the PA is negligible. Results show that log-normal shadowing increases the Pe by 100 % to 1000% compared to channels exhibiting fading only.

On the deployment of heterogeneous sensor networks for detection of mobile targets

Abstract — Detecting targets moving inside a field of interest is one of the fundamental services of Wireless Sensor Networks. The network performance with respect to target detection, is directly related to the placement of the sensors within the field of interest. In this paper, we address the problem of wireless sensor deployment, for the purpose of detecting mobile targets. We map the target detection problem to a line-set intersection problem and derive analytic expressions for the probability of detecting mobile targets. Compared to previous works, our mapping allows us to consider sensors with heterogeneous sensing capabilities, thus analyzing sensor networks that employ multiple sensing modalities. We show that the complexity of evaluating the target detection probability grows exponentially with the network size and, hence, derive appropriate lower and upper bounds. We also show that maximizing the lower bound is analogous to the problem of minimizing the average symbol error probability in 2-dimensional digital modulation schemes over Additive White Gaussian Noise, that is, in turn, addressed using the circle packing problem. Using this analogy, we derive sensor constellations from well known signal constellations with low average symbol error probability. I