946 publications from this institution
This paper presents a complete parametrization for a three-dimensional (3-D) geometry-based stochastic radio channel model (GSCM) at 10.1 GHz based on a measurement campaign. The radio channel measurements were carried out with a vector network analyzer over a 500 MHz bandwidth in a two-story lobby environment. The measurements were conducted with 9 × 324 dual polarized virtual antenna arrays in line-of-sight (LOS) and non-LOS propagation conditions. The recorded data was post-processed using the estimation of signal parameters via rotation invariance algorithm. The statistical analysis was carried out to provide full 3-D parametrization for the GSCM. The parametrization was verified by radio channel simulations. Multiple-input multiple-output (MIMO) channel is reconstructed from the estimated propagation paths and equivalently the channel simulations are performed by quasi-deterministic radio channel generator using our measurements-based parameters. The channel capacity, Demmel condition number, and relative condition numbers are used as the verification metrics. The results show that the reconstructed MIMO channel matches well the simulated MIMO channel.
Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS's limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an l_0 minimization problem. This is a combinatorial, NP-hard problem. We approximate the l_0 minimization problem as non-combinatorial l_1-norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of the location to deploy multiple UVA-BSs simultaneously while satisfying the constraints.
