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The TD-LTE wireless private network for electric power systems is an important component of smart grids, and coverage analysis and interference identification are essential in operating and optimizing of power wireless private networks. This paper presents an approach to analyzing indoor and outdoor coverage scopes of cell radio signals and recognizing locations and sources of intra-network interference in the network deployed in a dense urban environment. This approach takes scenario modeling to represent terrain and on-ground objects given by digital maps, utilizes ray tracing to track the signal propagation trajectories, and calculates strength attenuation of radio signals due to signal propagation such as direct transmission, reflection, diffraction and refraction. The uniform grid is employed as the acceleration structure to speed up tracing signal propagation paths, and drive-testing measurement data and scenario-oriented propagation model calibration are used to improve analysis accuracy. Weak coverage spots and interference defect spots are defined and used to identify interference types and sources. We applied the approach to a tentative TD-LTE power wireless private network in a southern city in China, proving that the ray-tracing-based scheme is able to make precise analysis on coverage and interference in practical networks. [ABSTRACT FROM AUTHOR]

When adopting transmit diversity in the Chinese digital television terrestrial broadcasting (DTTB) system, there exists two kinds of interference: training sequences (TSs) from different transmit antennas are overlapped; and the TSs are interfered with OFDM data due to multipath fading. In order to distinguish different TSs, this paper proposes a method to design orthogonal TS set, which also have good correlation characteristic. Subsequently, an iterative algorithm to eliminate the interference between TSs and data is proposed and theoretically analyzed. Finally, simulations are performed and the results show that the system with the proposed scheme can achieve great diversity gain under acceptable computational complexity. [ABSTRACT FROM PUBLISHER]