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We characterize third-dimension (3D) of channel measurements based on two dimension channel model with fully synchronize

This paper considers an energy-efficient transmit design in a general multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Specifically, a transmitter sends one confidential message to a legitimate receiver, which must be kept perfectly secure from several external multi-antenna eavesdroppers. Assuming imperfect channel state information (CSI) at the transmitter, our goal is to design the input transmit covariance, such that the worst-case secrecy energy efficiency (WC-SEE) is maximized, subject to the quality of service (QoS) and total transmit power constraints.

Signal processing techniques can create new applications for the data captured by existing sensor systems. Decades old sensor technology for monitoring the structural health of a building can serve a new role as a novel source of indoor localization data. Specifically, when a person's footstep-generated floor vibrations can be detected and located then it is possible to locate persons moving within a building. This emergent cyber-physical system holds the potential for an ambient localization service.

This paper proposes a novel framework for activity recognition from 3D motion capture data using topological data analysis (TDA). We extract point clouds describing the oscillatory patterns of body joints from the principal components of their time series using Taken's delay embedding. Topological persistence from TDA is exploited to extract topological invariants of the constructed point clouds. We propose a feature extraction method from persistence diagrams in order to generate robust low dimensional features used for classification of different activities.

In this paper we propose a hierarchical activity clustering methodology which incorporates the use of topological persistence analysis. Our clustering methodology captures the hierarchies present in the data and is therefore able to show the dependencies that exist between these activities. We make use of an aggregate persistence diagram to select robust graphical structures present within the dataset. These models are stable over a bound and provide accurate classification results.

A low-density spatial downlink reference signal (LDS-RS) design is proposed for frequency-division duplex (FDD) massive full-dimensional multiple-input multiple-output (FD-MIMO) systems. By exploiting the spatial correlation between the channels of different antennas, this scheme can efficiently reduce the downlink RS overhead and therefore enhances the achievable spectral efficiency significantly.

The construction of complementary sets of unimodular sequences of length N, with low correlation and complementary correlation coefficients is addressed. The design criterion is based on the minimisation of a cost function that penalizes the integrated side lobe as well as the sum of the complementary correlations of the sequences in the set. Numerical solution to the proposed cost function is obtained using conventional optimization methods.