Leaky coaxial (LCX) cable has been employed as antennas for wireless traffic over many linear-cell scenarios such as railway station, tunnels and shopping malls. In addition, LCX can be used for user localization and wireless power transfer (WPT). Compared with the equal power allocation method, the power allocation method for LCX system using positional information (PI) can improve its capacity with the same level of computational complexity. In this paper, we will investigate the level of capacity loss on the 2.4 GHz and 5 GHz band for the conventional equal power (EP) allocation method, the water-filling (WF) based power allocation, and our proposed low-complexity power allocation method for LCX system with PI. The results show that LCX system with our proposed method using PI can reduce the capacity loss due to localization error than that of others.

Received signal strength indicator (RSSI) based indoor localization technology has its own irreplaceable advantages for many location-aware applications. It is becoming obvious that in the development of fifth-generation (5G) and future communication technology, indoor localization technology will play a key role in location-based application scenarios including smart home systems, manufacturing automation, health care, and robotics. Compared with wireless coverage using conventional monopole antenna, leaky coaxial cables (LCX) can generate a uniform and stable wireless coverage over a long-narrow linear-cell or irregular environment such as railway station and underground shopping-mall, especially for some manufacturing factories with wireless zone areas from a large number of mental machines. This paper presents a localization method using multiple leaky coaxial cables (LCX) for an indoor multipathrich environment. Different from conventional localization methods based on time of arrival (TOA) or time difference of arrival (TDOA), we consider improving the localization accuracy by machine learning RSSI from LCX. We will present a probabilistic neural network (PNN) approach by utilizing RSSI from LCX. The proposal is aimed at the two-dimensional (2-D) localization in a trajectory. In addition, we also compared the performance of the RSSI-based PNN (RSSI-PNN) method and conventional TDOA method over the same environment. The results show the RSSI-PNN method is promising and more than 90% of the localization errors in the RSSI-PNN method are within 1 m. Compared with the conventional TDOA method, the RSSI-PNN method has better localization performance especially in the middle area of the wireless coverage of LCXs in the indoor environment.

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