This paper presents a method of synthesizing covariance matrix elements of array input signal for high resolution 2-D Direction-Of-Arrival (DOA) estimation via antenna (sensor) switching. Antenna array generally has the same number of array elements and receiver modules which often leads large receiver hardware cost. Two of the authors have already studied a way of antenna switching to reduce receiver cost, but it can be applied only for periodic incident signals like sinusoid. In this paper, we propose two simple methods of DOA estimation from sparse data by synthesizing covariance matrix elements of array input signal via antenna switching, which can also be applied to DOA estimation of antiperiodic incident signals. Performance of the proposed approach is evaluated in detail through some computer simulation.

Transmit power planning in the W-CDMA downlink, especially planning of the total transmit power of base stations and their apportionment to common control channels, is discussed taking cell coverage planning into account. A transmit power planning procedure is proposed for common control channels based on both link-level and system-level simulations. An analysis on optimum power allocation for common pilot channels is also presented. Link budgets that are applicable to downlink common control channels are developed and presented.

This paper presents a simple 3-D array configuration for high-resolution 2-D Direction-Of-Arrival (DOA) estimation. Planar array structures like Uniform Rectangular Array (URA) or Uniform Circular Array (UCA) often well estimate azimuth angle but cannot well estimate elevation angle because of short antenna aperture in elevation direction. One may put more number of array elements to improve elevation angle estimation accuracy, however it will require very large hardware and software cost. This paper presents a simple 3-D array structure for high-resolution 2-D DOA estimation only by modifying the height of some array elements in a planar array. Based on the analysis of Cramer-Rao Lower Bound (CRLB) formulation and its dependency on the height of array elements, we develop a simple 3-D array structure which improves elevation angle estimation accuracy while preserving azimuth angle estimation accuracy.

In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.