Modeling and performance analysis of opportunistic link selection for UAV communication

In anticipation of wide implementation of 5G technologies, the scarcity of spectrum resources for the unmanned aerial vehicles (UAVs) communication remains one of the major challenges in arranging safe drone operations. Dynamic spectrum management among multiple UAVs as a tool that is able to address this issue, requires integrated solutions with considerations of heterogeneous link types and support of the multi-UAV operations. This paper proposes a synthesized resource allocation and opportunistic link selection (RA-OLS) scheme for the air-to-ground (A2G) UAV communication with dynamic link selections. The link opportunities using link hopping sequences (LHSs) are allocated in the GCSs for alleviating the internal collisions within the UAV network, offloading the on-board computations in the spectrum processing function, and avoiding the contention in the air. In this context, exclusive technical solutions are proposed to form the prototype system. A sub-optimal allocation method based on the greedy algorithm is presented for addressing the resource allocation problem. A mathematical model of the RA-OLS throughput with above propositions is formulated for the spectrum dense and scarce environments. An interference factor is introduced to measure the protection effects on the primary users. The proposed throughput model approximates the simulated communication under requirements of small errors in the spectrum dense environment and the spectrum scarce environment, where the sensitivity analysis is implemented. The proposed RA-OLS outperforms the static communication scheme in terms of the utilization rate by over 50% in case when multiple links are available. It also enables the collaborative communication when the spectral resources are in scarcity. The impacts from diverse parameters on the RA-OLS communication performance are analyzed

Developing Reactive Distributed Aerial Robotics Platforms for Real-time Contaminant Mapping

The focus of this research is to design a sensor data aggregation system and centralized sensor-driven trajectory planning algorithm for fixed-wing aircraft to optimally assist atmospheric simulators in mapping the local environment in real-time. The proposed application of this work is to be used in the event of a hazardous contaminant leak into the atmosphere as a fleet of sensing unmanned aerial vehicles (UAVs) could provide valuable information for evacuation measures. The data aggregation system was designed using a state-of-the-art networking protocol and radio with DigiMesh and a process/data management system in the ROS2 DDS. This system was tested to consistently operate within the latencies and distances tolerated for the project while being highly extensible to sensor configurations. The problem of creating optimal trajectory planning for exploration has been modelled accurately using partially-observable Markov decision processes (POMDP). Deep Reinforcement learning (DRL) is commonly applied to approximate optimal solutions within a POMDP as it can be analytically intractable for complex state spaces. This research produces a POMDP that describes this exploration problem and applies the state-of-the-art soft actor-critic (SAC) reinforcement learning algorithm to create a policy that produces near-optimal trajectories within this new POMDP. A subset of the spatially relevant inputis used instead of complete state during training and a turn-taking sequential planner is designed for using multiple UAVs to help mitigate scalability problems that come with multi-UAV coordination. The learned policy from SAC can outperform a greedy and fixed trajectory on 1, 2, and 3 UAVs by a 30% margin on average. The turn-taking strategy provides small, but repeatable scaling benefits while the windowed input results in a 50%-60% increase in reward versus trained networks without windowed input. The proposed planning algorithm is effective in dynamic map exploration and has the potential to increase UAV effectiveness in atmospheric contaminant leak monitoring as it is expanded to be integrated on real-world UAVs

Performance Analysis of ML-based MTC Traffic Pattern Predictors

Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-of-the-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.Comment: IEEE Wireless Communications Letters Print ISSN: 2162-2337 Online ISSN: 2162-234

Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size

Taming and Leveraging Directionality and Blockage in Millimeter Wave Communications

To cope with the challenge for high-rate data transmission, Millimeter Wave(mmWave) is one potential solution. The short wavelength unlatched the era of directional mobile communication. The semi-optical communication requires revolutionary thinking. To assist the research and evaluate various algorithms, we build a motion-sensitive mmWave testbed with two degrees of freedom for environmental sensing and general wireless communication.The first part of this thesis contains two approaches to maintain the connection in mmWave mobile communication. The first one seeks to solve the beam tracking problem using motion sensor within the mobile device. A tracking algorithm is given and integrated into the tracking protocol. Detailed experiments and numerical simulations compared several compensation schemes with optical benchmark and demonstrated the efficiency of overhead reduction. The second strategy attempts to mitigate intermittent connections during roaming is multi-connectivity. Taking advantage of properties of rateless erasure code, a fountain code type multi-connectivity mechanism is proposed to increase the link reliability with simplified backhaul mechanism. The simulation demonstrates the efficiency and robustness of our system design with a multi-link channel record.The second topic in this thesis explores various techniques in blockage mitigation. A fast hear-beat like channel with heavy blockage loss is identified in the mmWave Unmanned Aerial Vehicle (UAV) communication experiment due to the propeller blockage. These blockage patterns are detected through Holm\u27s procedure as a problem of multi-time series edge detection. To reduce the blockage effect, an adaptive modulation and coding scheme is designed. The simulation results show that it could greatly improve the throughput given appropriately predicted patterns. The last but not the least, the blockage of directional communication also appears as a blessing because the geometrical information and blockage event of ancillary signal paths can be utilized to predict the blockage timing for the current transmission path. A geometrical model and prediction algorithm are derived to resolve the blockage time and initiate active handovers. An experiment provides solid proof of multi-paths properties and the numeral simulation demonstrates the efficiency of the proposed algorithm