Abstract-Radio location by time advance for GSM systems had been published. But the resolution of time advance in GSM systems is too rough to locate the mobile position. This paper proposes a mobile location estimation based on the differences of downlink signal attenuations. This provides the possible mobile locations if the relationship between distances and signal attenuation is determined. Then, the mobile location can be estimated from those possible locations. The error of the proposed method is much smaller than the error of cell-ID method in the practical microcell system. The most advantage of this method is the non-necessity of a known and accurate path loss modeling and the reduction of shadowing effect.

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users' resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.

A signal attenuation difference of arrival (SADOA) scheme is proposed to combine with the time difference of arrival (TDOA) method for mobile location estimation. On the basis of ratio of distances between the mobile and base stations derived from differences of signal attenuations, each SADOA measurement yields a circle on which the mobile may lie. Meanwhile, each TDOA measurement defines a hyperbola on which the mobile may reside. The proposed hybrid SADOA/TDOA scheme uses Taylor-series expansion to linearise the circles and hyperbolas and iteratively computes the mobile position based on least-squares estimation. Without perfect path loss modelling and hardware modification, the proposed scheme reduces location errors compared with either technique separately. Simulations demonstrate encouraging performance with 50% improvement over the conventional TDOA method in shadowing and non-line-of-sight propagation environments.

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