To support the development of high-capacity airto-ground links for range extension, measurements of the lowaltitude air-to-ground channel were made at 915 MHz. Two transmit antennas were mounted on a UAV, which was flown in loops at an altitude of approximately 200 m above ground level. The received signals were recorded at each of eight antenna elements mounted on a van, at locations outside and inside the flight loop. Analysis of the measurements shows that there are regions where the spatial diversity is significant, despite the sparse multipath environment, indicating spatial decorrelation at both the ground and air terminals. The variations in spatial correlation across the receiver array indicate the presence of nonplanar wavefronts produced by the signals' interaction with objects in the array near-field, in particular, the measurement vehicle. A similar effect is probable at the UAV, and it is expected that more significant near-field effects would arise on a more conventional air platform. These support significant reductions in outage probability at both receiver locations: with appropriate signalling strategies, an airborne platform could provide a viable relay or broadcast node for high capacity communications using MIMO.

Abstract-Spectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation and network bandwidth when the bands under scanning are wide and highly contested. In this paper we propose distributed spectrum sensing with multiple sensing nodes in a UAV environment. The ground nodes in our scheme sense the spectrum in parallel using compressive sensing. Each sensor node transmits compressive measurements to a nearby UAV in the air. The UAV performs decoding on the received measurements; it decodes information with increasing resolution as it receives more measurements. Furthermore, by a property of compressive sensing decoding, frequencies of large magnitude responses are recovered first. In the proposed scheme, as soon as the UAV detects the presence of such high-power frequencies from a sensor, this information is used to aid decoding for other sensors. We argue that such collaboration enabled by UAV will greatly enhance the decoding accuracy of compressive sensing. We use packet-loss traces acquired in UAV flight experiments in the field, as well as field experiments involving software-defined radios, to validate the effectiveness of this distributed compressive sensing approach.

In this paper, we use a finite-state model to predict the performance of the Transmission Control Protocol (TCP) over a varying wireless channel between an unmanned aerial vehicle (UAV) and ground nodes. As a UAV traverses its flight path, the wireless channel may experience periods of significant packet loss, successful packet delivery, and intermittent reception. By capturing packet run-length and gap-length statistics at various locations on the flight path, this locationdependent model can predict TCP throughput in spite of dynamically changing channel characteristics. We train the model by using packet traces from flight tests in the field and validate it by comparing TCP throughput distributions for model-generated traces against those for actual traces randomly sampled from field data. Our modeling methodology is general and can be applied to any UAV flight path.

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