Real-time, high-accuracy frequency-phase estimation is the critical mission of Doppler tracking, which is a primary technique for deep space spacecraft navigation and planetary radio science experiments. Usually, the analog intermediate frequency signal is digitalized and converted to baseband by signal processing hardware platforms called digital back-ends (DBEs) and parameter estimation is performed by extra high performance computers. In this paper, a novel real-time, high-accuracy parameter estimator called a hardware-based integrated parameter estimator (HIPE) is proposed and implemented inside DBEs. An adaptive frequency tracker is proposed to make the initial signal detection, frequency tracking, and data reduction. Then a parameter estimation is sequentially obtained by a modified dechirp technique and a high-resolution spectral analysis technique called spec-zooming. Further, a folding architecture is designed to save hardware resources when realizing spec-zooming in a field programmable gate array (FPGA). An example design is deployed on a DBE with Xilinx Virtex-6 FPGA and an ARM processor. The performance is verified by X-band observations of Mars Express (MEX) and New Horizons (NH). Under an integration time of 1 s, HIPE only takes 2.2 ms to process single-channel baseband data and provides frequency accuracies of 7 mHz and 30 mHz for the tested MEX and NH data. HIPE is implemented inside DBE, so the extra computer is no longer required and the pressure of data transmission or storage is greatly relieved. It could easily be extended to parallel multi-channel, real-time processing and would be a powerful method for Doppler measurement in deep space exploration missions, such as the Chinese mission to Mars to be undertaken by 2020.
To accommodate the exponentially increasing computation demands of vehicle-based applications, vehicular edge computing (VEC) system was introduced. This paper considers a three-layer VEC architecture and proposes an online offloading scheduling and resource allocation (OOSRA) algorithm to improve the system performance. Specifically, this study designs a game-theoretic online algorithm to solve the problem of computation task offloading scheduling, and employs an online bin-packing algorithm to compute the resource allocation modified from the First Fit algorithm, which can be adapted to various traffic flow and service attributes. Extensive simulations are conducted, and a numerical analysis of simulation results verifies the effectiveness of the OOSRA-VEC system. The algorithms proposed in this paper are online, adaptive, and distributed, which can provide useful references for future development in VEC system protocols. INDEX TERMS Intelligent vehicles, intelligent transportation systems, edge-computing, game theory.
In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and collect data from them via wireless information transmission (WIT). In this paper, we aim to minimize the time required by a UAV via jointly optimizing the trajectory of the UAV and the transmission scheduling for all the GTs. This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. To this end, we develop an iterative algorithm based on binary search and successive convex optimization (SCO) to solve it. The simulation shows that our proposed solution outperforms the benchmark algorithms.
Management of connected vehicles at unsignalised intersections is a large‐scale complex problem with safety constraints and time‐varying unsolved variables, which is crucial but hard to solve online. A faster coordination system, however, not only benefits from smaller time granularity to find optimum, but also has more robustness towards a scenario with fast‐moving vehicle nodes. This paper proposes a real‐time coordination scheme consisting of three stages. (a) Target velocity optimisation: collision‐free passage is formulated as a mixed integer linear programming problem, each approaching lane corresponding to an independent variable; (b) vehicle subgraph extraction: a directed graph is built and pruned based on the optimisation result, determining a subgraph wherein vehicle nodes pass without redundant time slot; (c) velocity profile synchronisation: velocity profile of the selected vehicles is planned synchronously, respecting inter‐subgraph constraints. The main contribution of this study is to propose a fast hierarchical optimization‐based coordination method, of which the complexity is invariant with the traffic density. Simulation has verified the effectiveness of the scheme from both microscopic behaviour and statistical characteristics, reducing single‐step computation time to 0.02 s, and saving average driving delay by 59.83% compared to the benchmark method.
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