With the continuous functional enhancement of network services, a service usually presents a directed acyclic graphic (DAG) structure. This paper models the DAG task scheduling problem as a multi-objective optimization problem to balance the task execution efficiency, network traffic, and system load balance in componentized task deployment. To produce an instant decision, we propose the Cloud-edge Collaborative Task Scheduling (CCTS) Algorithm based on hybrid reward architecture deep reinforcement learning (DRL). Specifically, to reduce the redundancy of the state space of the Markov decision process, we use directed graph convolution networks and graph convolution networks (GCN) to embed the directed task graph and undirected network graph, respectively. Simulation results show that the proposed method outperforms the compared convolutional neural networks and GCN-based DRL schemes in reducing the system latency, energy cost, network traffic, and load balance.

Based on outdoor microcellular measurements at 26 and 32 GHz, the path loss models are modified by a proposed dynamic rain model to see the difference of the path loss models between clear and rainy air with respect to rainfall intensities and transceiver distances. Moreover, a dynamic rain cell model for a multi‐user system is developed to investigate the total rain attenuation. The results show that the parameters for floating‐intercept (FI) model have a bigger change with different rainfall intensities than close‐in (CI) model. When considering the dynamic rain model with a different radius and moving speeds, the larger rain cell radius, the larger path loss exponents in CI and FI models, the smaller path loss intercept in FI model and system capacity will be achieved. For a fixed rain cell radius, the larger moving speed of the rain cell, the shorter effective time on the path loss and system capacity will be achieved. In addition, the rain cell has a bigger effect on the path loss exponent, intercept and system capacity at higher frequency band with respect to its size, moving speed, and rainfall intensity.

Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.

In this study, a non‐linear filtering algorithm for state estimation with symmetric alpha‐stable (SαS) noise is presented. The dynamic system model investigated here can be described by a linear state‐space equation and a non‐linear observation equation. The contribution of this study can be summarised as follows. First, particle filtering approach is employed for coarse estimation of the unknown parameters and then Kalman filter is performed to achieve better estimation. Second, SαS noise is considered as the additive disturbance in the observed signal and Gaussian approximation is used to compute the characteristics. Third, the calculation complexity is analysed according to the proposed algorithm. The proposed method is compared with the standard particle filter, extended Kalman filter and unscented Kalman filter for static parameter estimation of a periodic signal. As a practical application, the proposed method is used in high frequency source localisation based on time difference of arrival measurements.

In this paper, we investigate parameter estimation for generalized frequency division multiplexing (GFDM) signals over frequency-selective fading channels. Based on the characteristics of the second-order cyclostationary statistics of GFDM signals, we develop algorithms to blindly estimate pure block duration, overall block duration, symbol duration, and number of subcarriers. The proposed algorithms are robust to timing, phase, and carrier frequency uncertainties and do not require any a priori knowledge of the received signal. Simulation results verify the feasibility of the proposed algorithms.

In this paper, a non-stationary geometry-based scattering model for street wideband multi-input multi-output (MIMO) vehicle-to-vehicle (V2V) fading channel is proposed. It is assumed that the static scatterers on the both roadside are uniformly distributed on time-varying ellipses, and the mobile scatterers are uniformly distributed in time-varying segment of the road. To avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions, the channel is realized first, then the time-varying channel properties and parameters are investigated. In this work, we extend the proposed narrowband model to wideband and also introduce the carrrier frequency and bandwidth into the model.