Dynamic Sleep Control in Green Relay-Assisted Networks for Energy Saving and QoS Improving

We study the relay station (RS) sleep control mechanism targeting on reducing energy consumption while improving users' quality of service (QoS) in green relay-assisted cellular networks, where the base station (BS) is powered by grid power and the RSs are powered by renewable energy. By adopting green RSs, the grid power consumption of the BS is greatly reduced. But due to the uncertainty and stochastic characteristics of the renewable energy, power supply for RSs is not always sufficient. Thus the harvested energy needs to be scheduled appropriately to cater to the dynamic traffic so as to minimize the energy saving in the long term. An optimization problem is formulated to find the optimal sleep ratio of RSs to match the time variation of energy harvesting and traffic arrival. To fully use the renewable energy, green-RS-first principle is adopted in the user association process. The optimal RS sleeping policy is obtained through dynamic programming (DP) approach, which divides the original optimization problem into per-stage subproblems. A reduced DP algorithm and a greedy algorithm are further proposed to greatly reduce the computation complexity. By simulations, the reduced DP algorithm outperforms the greedy algorithm in achieving satisfactory energy saving and QoS performance.Comment: 7 papers, 4 figure

UWB Signal Detection by Cyclic Features

Ultra-wideband (UWB) impulse radio (IR) systems are well known for low transmission power, low probability of detection, and overlaying with narrowband (NB) systems. These merits in fact make UWB signal detection challenging, since several high-power wireless communication systems coexist with UWB signals. In the literature, cyclic features are exploited for signal detection. However, the high computational complexity of conventional cyclic feature based detectors burdens the receivers. In this paper, we propose computationally efficient detectors using the specific cyclic features of UWB signals. The closed-form relationships between the cyclic features and the system parameters are revealed. Then, some constant false alarm rate detectors are proposed based on the estimated cyclic autocorrelation functions (CAFs). The proposed detectors have low complexities compared to the existing ones. Extensive simulation results indicate that the proposed detectors achieve a good balance between the detection performance and the computational complexity in various scenarios, such as multipath environments, colored noise, and NB interferences

Optimal Power Management for Failure Mode of MVDC Microgrids in All-Electric Ships

Optimal power management of shipboard power system for failure mode (OPMSF) is a significant and challenging problem considering the safety of system and person. Many existing works focused on the transient-time recovery without consideration of the operating cost and the voyage plan. In this paper, the OPMSF problem is formulated considering the mid-time scheduling and the faults at bus and generator. Two- side adjustment methods including the load shedding and the reconfiguration are coordinated for reducing the fault effects. To address the formulated non-convex problem, the travel equality constraint and fractional energy efficiency operation indicator (EEOI) limitation are transformed into the convex forms. Then, considering the infeasibility scenario affected by faults, a further relaxation is adopted to formulate a new problem with feasibility guaranteed. Furthermore, a sufficient condition is derived to ensure that the new problem has the same optimal solution as the original one. Because of the mixed-integer nonlinear feature, an optimal algorithm based on Benders decomposition (BD) is developed to solve the new one. Due to the slow convergence caused by the time-coupled constraints, a low-complexity near-optimal algorithm based on BD (LNBD) is proposed. The results verify the effectivity of the proposed methods and algorithms.Comment: 14 pages, 9 figures, accepted for publication in IEEE Transactions on Power System

Connected Vehicular Transportation: Data Analytics and Traffic-dependent Networking

With onboard operating systems becoming increasingly common in vehicles, the real-time broadband infotainment and Intelligent Transportation System (ITS) service applications in fast-motion vehicles become ever demanding, which are highly expected to significantly improve the efficiency and safety of our daily on-road lives. The emerging ITS and vehicular applications, e.g., trip planning, however, require substantial efforts on the real-time pervasive information collection and big data processing so as to provide quick decision making and feedbacks to the fast moving vehicles, which thus impose the significant challenges on the development of an efficient vehicular communication platform. In this article, we present TrasoNET, an integrated network framework to provide realtime intelligent transportation services to connected vehicles by exploring the data analytics and networking techniques. TrasoNET is built upon two key components. The first one guides vehicles to the appropriate access networks by exploring the information of realtime traffic status, specific user preferences, service applications and network conditions. The second component mainly involves a distributed automatic access engine, which enables individual vehicles to make distributed access decisions based on access recommender, local observation and historic information. We showcase the application of TrasoNET in a case study on real-time traffic sensing based on real traces of taxis

Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.Comment: 30 pages, 13 figure

Cross-Layer Scheduling for OFDMA-based Cognitive Radio Systems with Delay and Security Constraints

This paper considers the resource allocation problem in an Orthogonal Frequency Division Multiple Access (OFDMA) based cognitive radio (CR) network, where the CR base station adopts full overlay scheme to transmit both private and open information to multiple users with average delay and power constraints. A stochastic optimization problem is formulated to develop flow control and radio resource allocation in order to maximize the long-term system throughput of open and private information in CR system and ensure the stability of primary system. The corresponding optimal condition for employing full overlay is derived in the context of concurrent transmission of open and private information. An online resource allocation scheme is designed to adapt the transmission of open and private information based on monitoring the status of primary system as well as the channel and queue states in the CR network. The scheme is proven to be asymptotically optimal in solving the stochastic optimization problem without knowing any statistical information. Simulations are provided to verify the analytical results and efficiency of the scheme

Energy Efficient Resource Allocation for Time-Varying OFDMA Relay Systems with Hybrid Energy Supplies

This paper investigates the energy efficient resource allocation for orthogonal frequency division multiple access (OFDMA) relay systems, where the system is supplied by the conventional utility grid and a renewable energy generator equipped with a storage device. The optimal usage of radio resource depends on the characteristics of the renewable energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. Lyapunov optimization method is used to decompose the problem into the joint flow control, radio resource allocation and energy management without knowing a priori knowledge of system statistics. It is proven that the proposed algorithm can result in close-to-optimal performance with capacity limited data buffer and storage device. Simulation results show that the flexible tradeoff between the system utility and the conventional energy consumption can be achieved. Compared with other schemes, the proposed algorithm demonstrates better performance.Comment: 12 pages, 9 figures, IEEE System Journa

A Pre-Allocation Design for Cost Minimization and Delay Constraint in Vehicular Offloading System

To accommodate exponentially increasing traffic demands of vehicle-based applications, operators are utilizing offloading as a promising technique to improve quality of service (QoS), which gives rise to the application of Mobile Edge Computing (MEC). While the conventional offloading paradigms focus on delay and energy tradeoff, they either fail to find efficient models to represent delay, especially the queueing delay, or underestimate the role of MEC Server. In this paper, we propose a novel \textbf{P}re-\textbf{A}llocation \textbf{D}esign for vehicular \textbf{O}ffloading (\textbf{PADO}). A task delay queue is constructed based on an allocate-execute separate (AES) mechanism. Due to the dynamics of vehicular network, we are inspired to utilize Lyapunov optimization to minimize the execution cost of each vehicle and guarantee task delay. The MEC Server with energy harvesting devices is also taken into consideration of the system. The transaction between vehicles and server is decided by a Stackelberg Game framework. We conduct extensive experiments to show the property and superiority of our proposed framework

Hybrid Optimization Method for Reconfiguration of AC/DC Microgrids in All-Electric Ships

Since the limited power capacity, finite inertia, and dynamic loads make the shipboard power system (SPS) vulnerable, the automatic reconfiguration for failure recovery in SPS is an extremely significant but still challenging problem. It is not only required to operate accurately and optimally, but also to satisfy operating constraints. In this paper, we consider the reconfiguration optimization for hybrid AC/DC microgrids in all-electric ships. Firstly, the multi-zone medium voltage DC (MVDC) SPS model is presented. In this model, the DC power flow for reconfiguration and a generalized AC/DC converter are modeled for accurate reconfiguration. Secondly, since this problem is mixed integer nonlinear programming (MINLP), a hybrid method based on Newton Raphson and Biogeography based Optimization (NRBBO) is designed according to the characteristics of system, loads, and faults. This method facilitates to maximize the weighted load restoration while satisfying operating constraints. Finally, the simulation results demonstrate this method has advantages in terms of power restoration and convergence speed.Comment: 9 pages, 14 figure

Wireless Charging Lane Deployment in Urban Areas Considering Traffic Light and Regional Energy Supply-Demand Balance

In this paper, to optimize the Wireless Charging Lane (WCL) deployment in urban areas, we focus on installation cost reduction while achieving regional balance of energy supply and demand, as well as vehicle continuous operability issues. In order to explore the characteristics of energy demand in various regions of the city, we first analyze the daily driving trajectory of taxis in different regions and find that the daily energy demand fluctuates to different degrees in different regions. Then, we establish the WCL power supply model to obtain the wireless charging supply situation in line with the real urban traffic condition, which is the first work considering the influence of traffic lights on charging situation. To ensure minimum deployment cost and to coordinate the contradiction between regional energy supply-demand balance and overall supply-demand matching, we formulate optimization problems ensuring the charge-energy consumption ratio of vehicles. In addition, we rank the priority of WCL efficiency to reduce the complexity of solution and solve the Mixed Integer NonLinear Programming (MINLP) problem to determine deployment plan. Compared with the baseline, the proposed method in this paper has significantly improved the effect.Comment: 8 pages, 13 figures, conferenc