LAUSR

Sector antenna azimuth plays an important role in the operation and maintenance of mobile networks. It is adjusted frequently so as to guarantee the high quality of coverage and low interference among neighboring cells. As one of the key elements of base station almanac (BSA), the azimuth of all the base stations need to be precisely managed by the operator. However, currently it is generally acquired by on-site measurement and updated manually, which is neither timely nor cost-effective. In addition, it is not open for third parties who need the information for network analysis. In view of this, by transforming the problem of azimuth estimation into one of searching for the optimal cell boundaries under the constraint of site location, this paper proposes an azimuth estimation method based on an improved multi-class soft-margin support vector machine (SVM). The explicit expression of the objective function of the problem is deduced through Lagrange duality. A circular one-versus-one strategy is utilized in dealing with the multi-class classification problem. In addition, a so-called average confusion index is designed to evaluate the degree of boundary separability of a base station so as to prejudge whether it is worthwhile to estimate its azimuths. Experiments are undertaken to validate the proposed algorithm by utilizing the spatial-temporal wireless signal dataset crowdsensed from massive user terminals in the 4G network. The experiments show that the proposed algorithm gives a significantly higher accuracy compared to that of two existing methods, namely the Gauss and radial rasterization methods. The impacts of data volume, penalty term and the boundary separability on the azimuth estimation is also discussed. The new algorithm is a promising alternative to the conventional manual solution with a much higher efficiency and lower cost, thus improving the intelligence of mobile network operation.