An optimization in computation time for the prediction of radio coverage zones

Abstract

[1] This paper presents a method which optimizes the computation time for radio coverage prediction, whatever propagation model is used. The principle consists in reducing, in comparison with classical techniques, the number of application points of a propagation model. The proposed method is based on a multiresolution analysis of measured signals operating at 1.8 GHz and on an electromagnetic analysis of the propagation environment. The method is compared with a classical technique and is evaluated in terms of a reduction in computation time and of an increase in accuracy. Satisfactory results were obtained for microcells and small cells, and gains in computation time, with values close to 3 and 80, were achieved for scalar and vectorial models, respectively. As 90% of the estimation errors relating to the received signal level are less than 2.2 dB, a high level of accuracy was also assured.

Hybrid propagation channel modelling for city area land mobile satellite communications

“…This is justified because the value 16 corresponds to 1R0D, in other words, paths reflected only once and not diffracted. This is the predominant component in the absence of the direct path, as suggested in the studies [11,13]. As a result, all sample points with Σ CI <16 are in NLOS severe areas, while others (16 ≤ Σ CI <32) are in NLOS weak areas.…”

“…Firstly, the CI associated with less attenuation will be assigned with a greater value. The order of magnitude for CIs can be easily determined: paths with more interactions will lose more power, so 0R0D is greater than 1R0D; diffractions result in greater attenuation than reflections [13], so 1R0D is greater than 0R1D. Secondly, we have chosen a series of binary weights in order to make unique correspondence between Σ CI values and CI groups.…”

Consistency constraints and 3D building reconstruction

“…Our 3D model can be used on the one hand for lighting simulation based on the room and portal volumes [6][7][8] and on the other hand for radio-wave propagation based on wall volumes [9,10]. We aim at performing other types of simulation such as acoustics, heat transfer and so on.…”

“…In addition, the XLim/SIC laboratory also proposes a radio-wave propagation simulation system adapted to indoor scenes [9,10]. The input data structure used is only composed of wall volumes.…”

“…In addition, cell and portal structures often used in the context of large buildings [4][5][6][7][8] can be naturally produced from our structure. We have also produced files for radio-wave propagation simulations used in our laboratory [9,10] …”