In this paper, a recently conducted measurement campaign for unmanned-aerial-vehicle (UAV) channels is introduced. The downlink signals of an in-service long-time-evolution (LTE) network which is deployed in a suburban scenario were acquired. Five horizontal and five vertical flight routes were considered. The channel impulse responses (CIRs) are extracted from the received data by exploiting the cell specific signals (CRSs). Based on the CIRs, the parameters of multipath components (MPCs) are estimated by using a high-resolution algorithm derived according to the space-alternating generalized expectation-maximization (SAGE) principle. Based on the SAGE results, channel characteristics including the path loss, shadow fading, fast fading, delay spread and Doppler frequency spread are thoroughly investigated for different heights and horizontal distances, which constitute a stochastic model.

In this contribution, an algorithm based on the space-alternating generalized expectation-maximization principle is proposed for estimating the locations of scatterers involved in the last-hops of propagation paths when a large-scale antenna array is used in a receiver for channel measurement. The underlying generic parametric model is constructed under the spherical wave-front assumption, which allows characterizing a path with a new parameter, i.e., the distance between the scatterer at the last-hop of the path and a specific receiving antenna, additional to the conventional parameters characterizing a specular path under the plane wave-front assumption. Cramér-Rao lower bounds of mean squared errors are derived for the parameter estimators in a single-path scenario, and their accuracy is evaluated through Monte Carlo simulations. The performance of the algorithm when being applied in reality is also evaluated through experiments conducted in an office with a carrier frequency of 9.5 GHz, a bandwidth of 500 MHz, and the receiver equipped with a 121-element virtual array. The proposed signal model and algorithm can be extended to the case of localizing the scatterers in the first- and last-hops of paths when large-scale antenna arrays are used in both the transmitter and the receiver.

In this paper, a measurement campaign for massive multiple-input multiple-output (MIMO) channel characterization in both line-of-sight (LoS) and non-LoS outdoor environments is introduced. The measurements are conducted at the center frequency of 15 GHz with a bandwidth of 4 GHz. A virtual 40 × 40 planar antenna array formed by stepping a vertically-polarized bi-conical omni-directional antenna (ODA) along regularly-spaced grids is used in the receiver (Rx). The transmitter is equipped with a single ODA. To investigate channel variations over the Rx array, this 1600-element Rx array is split into multiple 7 × 7 sub-arrays, and a maximum-likelihood parameter estimation algorithm implemented using the space-alternating generalized expectation-maximization principle is applied to extracting multipath components (MPCs) from sub-array outputs. The spatial variability of K-factor, composite channel spreads in delay, azimuth, and elevation of arrival are investigated. Based on the estimated MPCs' parameters, multipath clusters are identified and associated across the array to find the so-called spatial-stationary (SS) clusters. From several hundreds of SS-clusters extracted, we establish a stochastic model for their life distances in horizontal and vertical directions, two-dimensional (2-D) life region, and variations of cluster spreads. These findings are important for massive MIMO channel modeling in the cases, where 2-D largescale arrays are considered.

Graph theory channel modeling is an efficient approach to simulate multipath radio propagation including the reverberation effect of electromagnetic waves. In this contribution, without modifying the modeling framework, we proposed a semi-deterministic channel modeling approach by associating the scatterers with realistic environment objects, and by calculating the coefficients of the propagation paths on the base of a physically sound and proven diffuse scattering theory. The diffuse multipath components are then combined with the specular components (SCs) simulated by ray-tracing (RT) to obtain a complete channel representation. The proposed method is evaluated in two reference scenarios at 3.8 and 60 GHz, respectively, by comparing the simulated channel characteristics with channel measurement data. Results show that the proposed method can accurately predict the channel characteristics in both the delay domain and the angular domain. The proposed approach is appropriate to model multipath propagation in confined indoor or dense-urban environment at millimeter (mm)-wave frequencies and above, where reverberation and rough-surface scattering can be important phenomena.

Due to the decrease in cost, size and weight, Unmanned Aerial Vehicles (UAVs) are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to UAVs both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this paper, an approach to the air-to-ground channel characterization for low-height UAVs based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics like path loss, shadow fading, root mean square delay and Doppler frequency spreads and the K-factor are statistically characterized for different suburban scenarios.

In this paper, a novel fingerprint-based localization technique is proposed, which is applicable for positioning user equipments (UEs) in cellular communication networks such as the long-term-evolution (LTE) system. This technique utilizes a unique mapping between the characteristics of a radio channel formulated as a fingerprint vector and a geographical location. A feature-extraction algorithm is applied to selecting channel parameters with non-redundant information that are calculated from the LTE down-link signals. A feedforward neural network with the input of fingerprint vectors and the output of UEs’ known locations is trained and used by UEs to estimate their positions. The results of experiments conducted in an in-service LTE system demonstrate that by using only one LTE eNodeB, the proposed technique yields a median error distance of 6 and 75 meters in indoor and outdoor environments, respectively. This localization technique is applicable in the cases where the Global Navigation Satellite System (GNSS) is unavailable, e.g., in indoor environments or in dense-urban scenarios with closely spaced skyscrapers heavily blocking the line-of-sight paths between a UE and GNSS satellites.