The performance of Global Navigation Satellite System (GNSS)-based positioning techniques degrades in tunnels, urban canyons and other areas, in which GNSS coverage is poor. Recent advances indicate that radio-based positioning techniques have the potential of complementing GNSS-based positioning services in such problematic areas. In this work, we propose to combine range and angle measurements routinely exercised by fifth generation of mobile communication (5G) base stations with acceleration measurements by vehicles to generate position estimates. Specifically, we propose to utilize an extended Kalman filter to combine the range, angle and acceleration measurements. The accuracy of this positioning system is studied in millimeter-wave 5G cellular networks. Specifically, the proposed positioning system using 5G and sensor fusion is tested in a highway scenario in which 5G base stations provide cellular coverage. In this setting, we study the impact of certain parameters of the 5G network such as the available bandwidth and the propagation environment on the positioning accuracy. We find that the positioning accuracy is largely affected by the number of antennas in the base station and that the proposed scheme outperforms GNSS based schemes in the problematic areas.

Resource management schemes in multicell orthogonal frequency division multiplexing (OFDM) networks typically assume perfect channel knowledge at the transmitter or employ a statistical channel estimation error. In this paper we propose a model that explicitly captures the inherent tradeoff between the power allocated to transmitting data and pilot signals in multicell single input multiple output (SIMO) systems. We first study the asymptotic behavior of the required data power to reach a predefined signal-to-noise ratio (SNR) target as a function of the employed pilot power in a single OFDM cell, then develop a distributed multicell algorithm that strives to minimize the multi-cell sum data power with respect to a predefined signal-to-interference-and-noise-ratio (SINR) target vector. The results provide new insights in dimensioning the pilot and data transmit power levels and serve as a basis for developing distributed power control schemes in multicell systems.

Network multiple input, multiple output (MIMO) systems are built around a broadband backbone network that allows for the fast communication of channel state information (CSI) as well as user data between different base stations. Previous works have shown that multicell channel adaptive (opportunistic) power control can minimize the sum power or maximize the sum rate when the backbone is used for the exchange of CSI in network MIMO systems. In this work we investigate the gains of multicell opportunistic power control under per user fairness constraints when both CSI and user data are shared between multiple sites. We find that multicell opportunistic power control working in concert with uplink joint signal detection is an efficient means both for the capacity and the power control problems that not only minimizes sum power or maximizes overall capacity, but is also able to provide arbitrary level of fairness.