Q-learning-based dynamic spectrum access in cognitive Industrial Internet of Things

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability

Green cell planning and deployment for small cell networks in smart cities

In smart cities, cellular network plays a crucial role to support wireless access for numerous devices anywhere and anytime. The future 5G network aims to build the infrastructure from mobile internet to connected world. Small Cell is one of the most promising technologies of 5G to provide more connections and high data rate. In order to make the best use of small cell technology, smart cell planning should be implemented to guarantee connectivity and performance for all end nodes. It is particularly a challenging task to deploy dense small cells in the presence of dynamic traffic demands and severe co-channel interference. In this paper, we model various traffic patterns using stochastic geometry approach and propose an energy-efficient scheme to deploy and plan small cells according to the prevailing traffic pattern. The simulation results indicate that our scheme can meet dynamic traffic demands with optimized deployment of small cells and enhance the energy efficiency of the system without compromising on quality-of-service (QoS) requirements. In addition, our scheme can achieve very close performance compared with the leading optimization solver CPLEX and find solutions in much less computational times than CPLEX

Neural Mixed Platoon Controller Design

Vehicle platooning can be formulated as an optimal control problem and many solving paradigms, such as Pontryagin's maximum principle-based and dynamical programming methods, have been recently developed. However, these methods usually rely on solving a group of necessary conditions or Hamilton-Jacobi-Bellman (HJB) partial differential equations, which is hard to calculate. Besides, due to the heterogeneous dynamics of different vehicles in a mixed and complex platoon which comprises of not only connected autonomous vehicles (CAVs), but also human-driven vehicles (HDVs), it is also challenging to coordinate the behaviors of different vehicles in an unified control framework. Here we provide a Neural Mixed Platoon Control (NMPC) framework, a novel control design for mixed vehicle platooning based on a neural ordinary differential equation (NODE). We first formulate an optimal control model that incorporates the heterogeneous dynamics of a leading CAV and several following HDVs. We use a neural network to parameterize a state-feedback controller and join the neural controller and the mixed platooning dynamics into the NODE solver to create a closed-loop and learnable controlled system. The resulting system can learn optimal control inputs driving the mixed platoon to evolve from a given beginning condition to the target state within a finite duration in an unsupervised manner. Finally, simulation results validate our suggested method's usefulness in terms of space headway and velocity tracking

Joint optimization of platoon control and resource scheduling in cooperative vehicle-infrastructure system

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle’s mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a biobjective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle’s mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using ϵ-constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications

Efficient robust control of mixed platoon for improving fuel economy and ride comfort

The emergence of connected and automated vehicle technology has improved the operational efficiency of mixed traffic systems. This paper studies a two-tier trajectory optimization problem for mixed platooning to improve fuel efficiency, ride comfort, and operational safety during vehicle operations. The proposed model follows a two-tier control logic to plan the trajectory of platooning vehicles with three objectives, including minimizing fuel consumption, maximizing ride comfort, and enhancing the anti-disturbance performance of the platoon. The first is the planning tier, which aims to design the optimal trajectory for Connected and Automated Vehicles (CAVs) based on the optimal fuel consumption and comfort and obtain the expected acceleration curve of CAV. The second is the control tier, which aims to ensure the safe operation of platooning vehicles in the presence of uncertain disturbances in real time. Specifically, we propose a robust tube MPC control method, which dynamically adjusts the CAV acceleration according to the reference trajectory obtained by the planning tier to resist the effects of uncertain disturbances, and the tracking behaviour of Human-Driven Vehicles (HDVs) is offline solved by the robust optimal velocity model. Finally, we design simulation experiments to verify the effectiveness of the proposed two-tier optimization framework. The experimental results show the effectiveness and advantages of the two-tier framework in terms of fuel economy, ride comfort, and robustness against different noise disturbances

Synthesis of the System Modeling and Signal Detecting Circuit of a Novel Vacuum Microelectronic Accelerometer

A novel high-precision vacuum microelectronic accelerometer has been successfully fabricated and tested in our laboratory. This accelerometer has unique advantages of high sensitivity, fast response, and anti-radiation stability. It is a prototype intended for navigation applications and is required to feature micro-g resolution. This paper briefly describes the structure and working principle of our vacuum microelectronic accelerometer, and the mathematical model is also established. The performances of the accelerometer system are discussed after Matlab modeling. The results show that, the dynamic response of the accelerometer system is significantly improved by choosing appropriate parameters of signal detecting circuit, and the signal detecting circuit is designed. In order to attain good linearity and performance, the closed-loop control mode is adopted. Weak current detection technology is studied, and integral T-style feedback network is used in I/V conversion, which will eliminate high-frequency noise at the front of the circuit. According to the modeling parameters, the low-pass filter is designed. This circuit is simple, reliable, and has high precision. Experiments are done and the results show that the vacuum microelectronic accelerometer exhibits good linearity over -1 g to +1 g, an output sensitivity of 543 mV/g, and a nonlinearity of 0.94 %