QoE-aware power management in vehicle-to-grid networks:a matching-theoretic approach

Frequency, time and places of charging and discharging have critical impact on the Quality of Experience (QoE) of using Electric Vehicles (EVs). EV charging and discharging scheduling schemes should consider both the QoE of using EV and the load capacity of the power grid. In this paper, we design a traveling plan-aware scheduling scheme for EV charging in driving pattern and a cooperative EV charging and discharging scheme in parking pattern to improve the QoE of using EV and enhance the reliability of the power grid. For traveling planaware scheduling, the assignment of EVs to Charging Stations (CSs) is modeled as a many-to-one matching game and the Stable Matching Algorithm (SMA) is proposed. For cooperative EV charging and discharging in parking pattern, the electricity exchange between charging EVs and discharging EVs in the same parking lot is formulated as a many-to-many matching model with ties, and we develop the Pareto Optimal Matching Algorithm (POMA). Simulation results indicates that the SMA can significantly improve the average system utility for EV charging in driving pattern, and the POMA can increase the amount of electricity offloaded from the grid which is helpful to enhance the reliability of the power grid

Power Control for Full-Duplex Relay-Enhanced Cellular Networks With QoS Guarantees

Full-duplex (FD) has emerged as a new communication paradigm with the potential advantage of enhancing the capacity of the wireless communication systems. In this paper, we consider an FD relay-enhanced cellular network, wherein the residual self-interference, the uplink-downlink interference, as well as the relay-access-link interference are the vital restrictions to network performance. To this end, we investigate power control design for the FD relay-enhanced cellular networks, so as to maximize the system spectral efficiency while fulfilling the quality of service (QoS) requirements of both the uplink and downlink user equipments (UEs). We characterize the properties of the optimal transmit power allocation, and propose a power control algorithm based on signomial programming to coordinate the transmit power of the uplink UE, base station, and relay stations to mitigate the interference. Meanwhile, we also derive the closed-form optimal transmit power allocation for the conventional half-duplex (HD) transmission mode. Moreover, we conduct extensive simulation experiments to study the network-level gain of the FD mode over the HD mode in the relay-enhanced cellular networks. Simulation results demonstrate that FD relaying outperforms HD relaying on improving the spectral and energy efficiency, as well as provisioning QoS guarantees for both the uplink and downlink users

Power Control for Full-Duplex Relay-Enhanced Cellular Networks With QoS Guarantees

Full-duplex (FD) has emerged as a new communication paradigm with the potential advantage of enhancing the capacity of the wireless communication systems. In this paper, we consider an FD relay-enhanced cellular network, wherein the residual self-interference, the uplink-downlink interference, as well as the relay-access-link interference are the vital restrictions to network performance. To this end, we investigate power control design for the FD relay-enhanced cellular networks, so as to maximize the system spectral efficiency while fulfilling the quality of service (QoS) requirements of both the uplink and downlink user equipments (UEs). We characterize the properties of the optimal transmit power allocation, and propose a power control algorithm based on signomial programming to coordinate the transmit power of the uplink UE, base station, and relay stations to mitigate the interference. Meanwhile, we also derive the closed-form optimal transmit power allocation for the conventional half-duplex (HD) transmission mode. Moreover, we conduct extensive simulation experiments to study the network-level gain of the FD mode over the HD mode in the relay-enhanced cellular networks. Simulation results demonstrate that FD relaying outperforms HD relaying on improving the spectral and energy efficiency, as well as provisioning QoS guarantees for both the uplink and downlink users

Power Minimization Resource Allocation for Underlay MISO-NOMA SWIPT Systems

The combination of cognitive radio and non-orthogonal multiple access (NOMA) has tremendous potential to achieve high spectral efficiency in the IoT era. In this paper, we focus on the energy-efficient resource allocation of a cognitive multiple-input single-output NOMA system with the aid of simultaneous wireless information and power transfer. Specifically, a non-linear energy harvesting (EH) model is adopted to characterize the non-linear energy conversion property. In order to achieve the green design goal, we aim for the minimization of the system power consumption by jointly designing the transmit beamformer and the receive power splitter subject to the information transmission and EH harvesting requirements of second users (SUs), and the maximum tolerable interference constraints at primary users. However, the formulated optimization problem is non-convex and hard to tackle. By exploiting the classic semi-definite relaxation and successive convex approximation, we propose a penalty function-based algorithm to solve the non-convex problem. The convergence of the proposed algorithm is further proved. Finally, simulation results demonstrate that the non-linear EH model is able to strongly reflect the property of practical energy harvester and the performance gain of the proposed algorithm than the baseline scheme

Gathering point-aided viral marketing in decentralized mobile social networks

Viral marketing is a technique that spreads advertisement information through social networks. Recently, viral marketing through online social networks has achieved huge commercial success. However, there are still very little research reported on viral marketing in decentralized mobile social networks (MSNs). Comparing with online viral marketing, viral marketing in decentralized MSNs faces many challenges, such as unreliable information diffusion and limited network knowledge. To address these problems, we propose the \textit{gathering point-aided mobile viral marketing (GP-MVM)} scheme, which contains two major components, i.e., \textit{seed selection} and \textit{information diffusion}. \textit{Seed selection} is responsible to select a set of seed nodes from which information diffusion begins. Based on a new metric called integrated contact strength (ICS), we propose two distributed seed selection schemes, i.e., \textit{ratio seeding} and \textit{threshold seeding}, while, for information diffusion, we propose the \textit{GP-aided diffusion} algorithm, which utilizes user GPs to promote information propagation. Continuous-time Markov chain-based analytical model shows that GP-MVM has a good scalability. Simulations indicate that GP-MVM outperforms two state-of-the-art information diffusion methods designed for MSNs, in terms of both diffusion proportion and diffusion speed

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

The frequency, time and places of charging have large impact on the Quality of Experience (QoE) of EV drivers. It is critical to design effective EV charging scheduling system to improve the QoE of EV drivers. In order to improve EV charging QoE and utilization of CSs, we develop an innovative travel plan aware charging scheduling scheme for moving EVs to be charged at Charging Stations (CS). In the design of the proposed charging scheduling scheme for moving EVs, the travel routes of EVs and the utility of CSs are taken into consideration. The assignment of EVs to CSs is modeled as a two-sided many-to-one matching game with the objective of maximizing the system utility which reflects the satisfactory degrees of EVs and the profits of CSs. A Stable Matching Algorithm (SMA) is proposed to seek stable matching between charging EVs and CSs. Furthermore, an improved Learning based On-LiNe scheduling Algorithm (LONA) is proposed to be executed by each CS in a distributed manner. The performance gain of the average system utility by the SMA is up to 38.2% comparing to the Random Charging Scheduling (RCS) algorithm, and 4.67% comparing to Only utility of Electric Vehicle Concerned (OEVC) scheme. The effectiveness of the proposed SMA and LONA is also demonstrated by simulations in terms of the satisfactory ratio of charging EVs and the the convergence speed of iteration

A Fair Resource Allocation Algorithm for Data and Energy Integrated Communication Networks

With the rapid advancement of wireless network technologies and the rapid increase in the number of mobile devices, mobile users (MUs) have an increasing high demand to access the Internet with guaranteed quality-of-service (QoS). Data and energy integrated communication networks (DEINs) are emerging as a new type of wireless networks that have the potential to simultaneously transfer wireless energy and information via the same base station (BS). This means that a physical BS is virtualized into two parts: one is transferring energy and the other is transferring information. The former is called virtual energy base station (eBS) and the latter is named as data base station (dBS). One important issue in such setting is dynamic resource allocation. Here the resource concerned includes both power and time. In this paper, we propose a fair data-and-energy resource allocation algorithm for DEINs by jointly designing the downlink energy beamforming and a power-and-time allocation scheme, with the consideration of finite capacity batteries at MUs and power sensitivity of radio frequency (RF) to direct current (DC) conversion circuits. Simulation results demonstrate that our proposed algorithm outperforms the existing algorithms in terms of fairness, beamforming design, sensitivity, and average throughput.</jats:p

Data and Energy Integrated Communication Networks for Wireless Big Data

This paper describes a new type of communication network called data and energy integrated communication networks (DEINs), which integrates the traditionally separate two processes, i.e., wireless information transfer (WIT) and wireless energy transfer (WET), fulfilling co-transmission of data and energy. In particular, the energy transmission using radio frequency is for the purpose of energy harvesting (EH) rather than information decoding. One driving force of the advent of DEINs is wireless big data, which comes from wireless sensors that produce a large amount of small piece of data. These sensors are typically powered by battery that drains sooner or later and will have to be taken out and then replaced or recharged. EH has emerged as a technology to wirelessly charge batteries in a contactless way. Recent research work has attempted to combine WET with WIT, typically under the label of simultaneous wireless information and power transfer. Such work in the literature largely focuses on the communication side of the whole wireless networks with particular emphasis on power allocation. The DEIN communication network proposed in this paper regards the convergence of WIT and WET as a full system that considers not only the physical layer but also the higher layers, such as media access control and information routing. After describing the DEIN concept and its high-level architecture/protocol stack, this paper presents two use cases focusing on the lower layer and the higher layer of a DEIN network, respectively. The lower layer use case is about a fair resource allocation algorithm, whereas the high-layer section introduces an efficient data forwarding scheme in combination with EH. The two case studies aim to give a better explanation of the DEIN concept. Some future research directions and challenges are also pointed out

The Upper Bounds of Cellular Vehicle-to-Vehicle Communication Latency for Platoon-based Autonomous Driving

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols