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考虑通信服务质量的5G基站与工业园区新型电力系统协同运行
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In recent years, hydrogen fueling electric vehicles
(H2EVs) have developed rapidly and drawn much attention.
Indeed, the prevalence of H2EVs may introduce a notable
interaction between the power network (PN) and transportation
network (TN) via the hydrogen fueling station (HFS). Therefore,
it is worth investigating the efficient interaction of PN and
HFSs under the constraints of coupled power flow in PN and
traffic flow in TN. To this end, this article establishes the
operation model of HFSs coupled power-traffic system, in order
to investigate their impact relationship. Then, a collaborative
operation framework with Peer-to-Peer (P2P) energy trading is
proposed to promote the interactive efficiency between PN and
HFSs, considering both energy and reserve. On this basis, the
P2P energy trading strategy with dynamic pricing and arbitrage
prevention mechanism is developed to further reduce the total
operation cost and ensure the fairness of transaction. Case studies
on the HFSs coupled power-traffic system verify the effectiveness
of our proposed operation model and collaborative framework
considering P2P energy trading, which result in a lower operation
cost, compared with the independent operation and collaborative
operation without P2P energy trading, respectively.
Cellular base stations (BSs) are equipped with backup batteries to obtain the uninterruptible power supply (UPS) and maintain the power supply reliability. While maintaining the reliability, the backup batteries of 5G BSs have some spare capacity over time due to the traffic-sensitive characteristic of 5G BS electricity load. Therefore, the spare capacity is dispatchable and can be used as flexibility resources for power systems. This paper evaluates the dispatchable capacity of the BS backup batteries in distribution networks and illustrates how it can be utilized in power systems. The BS reliability model is first established considering potential distribution network interruptions and the effects of backup batteries. Then, the analytical formula of the BS availability index is derived with respect to batteries’ backup duration. The dispatchable capacity of BS backup batteries is evaluated in different distribution networks and with differing communication load levels. Furthermore, a potential application, daily operation optimization, is illustrated. Case studies show that the proposed methodology can effectively evaluate the dispatchable capacity and that dispatching the backup batteries can reduce 5G BS electricity bills while satisfying the reliability requirement.
The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks. The proposed framework includes three layers: a perception and execution layer, a logistics layer, and a configuration layer. In addition to the collaboration among these three layers, this study addresses the collaboration among devices, edge servers, and cloud services. For effective logistics in industrial parks, task collaboration is achieved through four functions: environmental perception and map construction, task allocation, path planning, and vehicle movement. To dynamically coordinate these functions, device-edge-cloud collaboration, which is supported by 5G slices and V2X communication technology, is applied. Then, the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks. Finally, a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.
Nowadays most of the communication technologies used in the manufacturing industry are still wired, including various fieldbuses and dedicated industrial Ethernet technologies, though wireless communication technologies including WiFi and ZigBee are recently being adopted. This study is to investigate the integration of 5G wireless communication technologies with collaborative intelligent manufacturing (CIM) processes and systems. 5G technologies and typical scenarios including enhanced mobile broadband, massive machine type communications, and ultra-reliable low latency communication are introduced. Various possible applications or business slices of 5G in CIM are analysed, including human machine interfaces and production IT, process automation, factory automation, logistics and warehousing, monitoring, and maintenance. These applications are analysed by the functions as modules with the extended 5C architecture. A modular collaborative production approach is suggested to combine modules to achieve on-demand resource allocation and complete flexible tasks in CIM systems. Related research challenges and opportunities are also discussed.
Ultra-dense networking (UDN) is considered as a promising technology for 5G. In this article, we define user-centric UDN (UUDN) by introducing the philosophy of the network serving user and the "de-cellular" method. Based on the analysis of challenges and requirements of UUDN, a new architecture is presented that breaks through the traditional cellular architecture of the network controlling user. Dynamic AP grouping is proposed as the core function of UUDN, through which a user could enjoy satisfactory and secure service following her movement. Furthermore, we provide methods for mobility management, resource management, interference management, and security issues. We point out that these functions should be co-designed and jointly optimized in order to improve the system throughput with higher resource utilization, better user experience, and increased energy efficiency. Finally, future works in UUDN are discussed.
In this paper, we consider multicell processing on the downlink of a cellular network to accomplish "macrodiversity" transmit beamforming. The particular downlink beamformer structure we consider allows a recasting of the downlink beam- forming problem as a virtual linear mean square error (LMMSE) estimation problem. We exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighboring base stations. For 1-D networks, we use the Kalman smoothing framework to obtain a forward-back- ward beamforming algorithm. We also propose a limited extent version of this algorithm that shows that the delay need not grow with the size of the network in practice. For 2-D cellular networks, we remodel the network as a factor graph and present a distributed beamforming algorithm based on the sum-product algorithm. Despite the presence of loops in the factor graph, the algorithm produces optimal results if convergence occurs. Index Terms—Cooperative base stations, distributed algo- rithm, downlink beamforming, Kalman smoothing, linear mean square error (LMMSE), localized interference, message passing, multicell processing, multiple-input-multiple-output (MIMO), sum-product algorithm.
The mobile base stations (MBS) are fundamental communication devices that ensure the constant stream of interconnectivity. However, they are mostly installed in off-grid regions. This study investigates the economic-environmental energy supply of a MBS in an isolated nanogrid (ING) that also includes a hydrogen energy storage system (HES), photovoltaic (PV) system, controllable plug-in electric vehicles (PEV) and a diesel generator (DG). A novel mixed-integer second-order cone programming (MISOCP) formulation is proposed to capture the nonlinearities of the various components through a convex optimization model. The study included different uncertainties including the traffic rate of the MBS, driving schedule of the PEVs, and PV generation via a hybrid stochastic programming (SP) and robust optimization (RO) methods. The influence of the coordinated PEV charging strategy, risk-averse RO and multi-objective optimization was studied through various case studies. The outcomes show that coordinated PEV-charging can have a significant contribution in reducing the risks and curtailing both cost and emission objective functions, while using economic-environmental operation model can cut the emissions by 17.70%.
Cellular Base Stations (BSs) are equipped with backup batteries. These batteries have some spare capacity over time while maintaining the power supply reliability, so they are potential flexible resources for power systems. This letter exhibits the insight to explore the BS dispatch potential towards power system frequency regulation. For each BS, the feasible dispatch boundaries of participating in frequency regulation are estimated. Then a framework is proposed to coordinate BSs to provide frequency support. By incorporating massive distributed BSs, the power system frequency performances can be improved.
Renewable energy sources are beneficial for both distribution network and mobile network in the context of Smart Grid, but brings greater challenges with high proportions of renewable energy penetration. In this regard, a Stackelberg game-based collaborative optimization approach is proposed for distribution network and 5G mobile network based on demand response, where the distribution network operator (DNO) works as a leader who chooses proper interactive price to reduce the peak-valley difference of net load while mobile network operator (MNO) serves as a follower who minimizes its total energy cost in response to the interactive price set by DNO. Besides, an exact convex relaxation method is proposed to omit the complementarity constraint in the optimal model of MNO, which makes the model strictly convex. Then, the existence and uniqueness of the Stackelberg equilibrium (SE) are analyzed and a distributed solution algorithm is suggested to reach the SE. The simulation results demonstrate that the proposed collaborative optimization approach can not only reduce the cost of DNO and MNO, but helpful to enhance renewable energy utilization, which realizes a win–win result.
In manufacturing industry, the movement of manufacturing resources in production logistics often affects the overall efficiency. This research is motivated by a world-leading air-conditioner manufacturer. In order to provide the right manufacturing resources for subsequent production steps, excessive time and human effort has been consumed in locating the manufacturing resources in a huge industrial park. The development of Internet of Things (IoT) has made a profound impact on establish smart manufacturing workshop and tracking applications, however a growing trend of data quantity that generated from massive, heterogeneous and bottomed manufacturing resources objects pose challenge to centralized decision. In this study, the concept of edge-computing deeply integrated in collaborative tracking purpose in virtue of IoT technology. An IoT edge computing enabled collaborative tracking architecture is developed to offload the computation pressure and realize distributed decision making. A supervised learning of genetic tracking method is innovatively presented to ensure tracking accuracy and effectiveness. Finally, the research output is developed and implemented in a real-life industrial park for verification. The results show that the proposed tracking method not only performs constant improving accuracy up to 96.14% after learning compared to other tracking method, but also ensure quick responsiveness and scalability.
We study a cellular system scenario where multiple data streams originating from a base station (BS) targeted to multiple cell-edge mobile stations (MSs) are transmitted via pre-installed cooperative relay stations (RSs) with multiple antennas. Our objective is to guarantee quality-of-service (QoS) in terms of predefined signal-to-noise ratio at such users within the transmit power budgets at BS and RSs while minimizing total transmit power. We propose a novel precoder design method for power allocation between multiple data streams at BS and RS by using joint zero-forcing strategy in order to avoid multiuser interference (MUI) in the signal received by MSs via both the direct and relay links. We also propose low-complexity suboptimal power allocation algorithm. We focus on analyzing the significance of direct link transmission in providing QoS to cell-edge MSs specially when RS is not situated directly between BS and MSs. Simulation results show that considering direct link and using the proposed scheme in such case significantly improves system outage performance compared to existing schemes in the literature which do not consider direct link.
Recently, there is great interest in considering the energy efficiency aspect of cellular networks. On the other hand, the power grid infrastructure, which provides electricity to cellular networks, is experiencing a significant shift from the traditional electricity grid to the smart grid. When a cellular network is powered by the smart grid, only considering energy efficiency in the cellular network may not be enough. In this paper, we consider not only energy-efficient communications but also the dynamics of the smart grid in designing green wireless cellular networks. Specifically, the dynamic operation of cellular base stations depends on the traffic, real-time electricity price, and the pollutant level associated with electricity generation. Coordinated multipoint (CoMP) is used to ensure acceptable service quality in the cells whose base stations have been shut down. The active base stations decide on which retailers to procure electricity from and how much electricity to procure. We formulate the system as a Stackelberg game, which has two levels: a cellular network level and a smart grid level. Simulation results show that the smart grid has significant impacts on green wireless cellular networks, and our proposed scheme can significantly reduce operational expenditure and CO_2 emissions in green wireless cellular networks.
In wireless mesh networks (WMNs), the achievable throughput of a mesh client to a gateway (GW) is limited by the minimum link capacity of the intermediate nodes. In instances when the intermediate nodes experience congestion due to large relay traffic, link failure etc., this throughput may drop below an acceptable level. Since the mesh topology implies multi-path routing capability, it is possible to satisfy the client service requirements by facilitating data traversal over multiple gateways (forming a coalition). Therefore, in this paper a game theoretic coalition formation algorithm is proposed that guarantees the minimum service requirements of clients. The algorithm is modeled as a cooperative game theory and incorporates Bilateral Shapley Value (BSV) to find the best coalition, whereupon a bargaining game theory is utilized to evaluate the throughput contribution of the member gateways. Such a game theoretic approach enables the proposed algorithm to offer a Pareto efficient solution that satisfies the minimum service requirements of the clients over dynamic network conditions. Performance at the proposed algorithm is evaluated using OMNET discrete event simulator.
We propose a new graphical model approach to cooperative multiple-input multiple-output (MIMO) cellular networks. The objective is to optimize downlink transmit beamforming at each BS in order to maximize the sum throughput over the entire network. While ideal centralized beamforming requires full channel state information (CSI) sharing among all BSs in the network and huge computational complexity for combinatorial optimization, the proposed graphical model enables distributed beamforming which requires only local CSI sharing between neighboring BSs and efficiently solves the optimization problem in a distributed manner. As distributed solvers for this problem, we derive message-passing algorithms which can be implemented with polynomial-time computational complexity. Furthermore, we make a slight approximation on the objective function to derive a simpler graphical model, providing further complexity saving. Simulation results indicate that the proposed distributed downlink beamforming achieves average cell throughput typically within just 2% of ideal centralized beamforming.
We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.
Due to the characteristics of wireless channels, utility-based resource management in wireless networks requires a set of mechanisms that are different from those for wireline networks. This paper explores in detail why and how the requirements are different. In particular, we analyze the wireless network performance to find out the scheduling algorithm that maximizes total utility of the system. Unlike previous studies, this paper focuses on scenarios in which wireless networks are not fully loaded and all of the users are best-effort data users, i.e., there is no streaming user. Our first key conclusion is that Kleinrock's Conservation Law provides a valuable means to accurately capture the perceived rates of best-effort users in such systems. The queueing analysis further indicates that, within periods during which channel conditions are stable for each user, albeit differ from user to user, the max-utility scheduling algorithm can be derived using queueing theorem and can be readily implemented in actual systems for utility functions that are of exponential or log format. When further taking into account the time-variant nature of wireless channel conditions, our simulation results demonstrate that dynamic weighted fair queueing, with weights adjusted according to the channel conditions, can achieve highly desirable performance with great flexibility.
Multi-objective evolutionary algorithms (MOEAs) that use
non-dominated sorting and sharing have been criticized mainly for: (1)
their O(MN3) computational complexity (where M is the number
of objectives and N is the population size); (2) their non-elitism
approach; and (3) the need to specify a sharing parameter. In this
paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II
(Non-dominated Sorting Genetic Algorithm II), which alleviates all of
the above three difficulties. Specifically, a fast non-dominated sorting
approach with O(MN2) computational complexity is presented.
Also, a selection operator is presented that creates a mating pool by
combining the parent and offspring populations and selecting the best N
solutions (with respect to fitness and spread). Simulation results on
difficult test problems show that NSGA-II is able, for most problems, to
find a much better spread of solutions and better convergence near the
true Pareto-optimal front compared to the Pareto-archived evolution
strategy and the strength-Pareto evolutionary algorithm - two other
elitist MOEAs that pay special attention to creating a diverse
Pareto-optimal front. Moreover, we modify the definition of dominance in
order to solve constrained multi-objective problems efficiently.
Simulation results of the constrained NSGA-II on a number of test
problems, including a five-objective, seven-constraint nonlinear
problem, are compared with another constrained multi-objective
optimizer, and the much better performance of NSGA-II is observed
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception.< >
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