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This paper intends to propose a novel handover algorithm to balance the load of the layers in a multi-reuse scenario. Each layer works independently from each other and it has its own scheduling process, coverage and mobile stations attached. The algorithm proposed balances the resources among layers by moving mobile stations from one to another layer according their QoS requirements and channel condition. Results show that it can increase the cell coverage and still keeping a satisfactory quality for the VoIP calls. Effective spectral efficiency also increased when comparing with single tri-sectorized reuse 1 scenario. The algorithm is also prepared to avoid ping-pong handovers and to work for any number of layers.

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Machine learning techniques applied to radio frequency (RF) signals are used for many applications in addition to data communication. In this paper, the authors propose a machine learning solution for classifying the number of people within an indoor ambient. The main idea is to identify a pattern of received signal characteristics according to the number of people. Experimental measurements are performed using a software-defined radio platform inside a laboratory. The data collected is post-processed by applying a feature mapping technique based on mean, standard deviation, and Shannon information entropy. This feature-space data is then used to train a supervised machine learning network for classifying scenarios with zero, one, two, and three people inside. The proposed solution presents significant accuracy in classification performance.