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Emerging power services with massive connections and real-time control have different requirements on delay, rate, reliability, etc. In order to meet the diversified requirements of power services, network slicing can provide guarantee for the wide application of 5G in power. The current research on network slicing adapting power services is still in its infancy. This paper proposes a trade-off analysis method for radio access network (RAN) slice delay and throughput based on dynamic intelligent resource allocation to meet the different QoS requirements of different services in smart grid scenarios. Firstly, clustering analysis is used to classify the communication requirements of smart grid services. A throughput demand model for enhanced mobile broadband (eMBB) slice users based on importance and a delay constraint model for ultra-reliable and low latency communication (uRLLC) slice users are constructed, and a throughput model for slice users based on cell capacity limitation is constructed. Then, the problem model of maximizing the throughput of the eMBB slice by reasonably allocating resource blocks and power under the premise of ensuring the delay of the uRLLC slice is established. The Lyapunov drift plus penalty function method is used to convert the above problem into a tractable form, and then the problem is solved by the active set method. Simulation results show that the different QoS requirements of smart grid services will significantly affect the actual queue length of the uRLLC slice as well as the throughput of the eMBB slice.

Electric Internet of things (EIoT) that integrates 5G and edge computing can provide data transmission and processing guarantee for smart grid. However, computation offloading optimization including joint optimization of server selection and computation resource allocation still faces several challenges such as difficulty in tradeoff balance among various quality of service (QoS) parameters, coupling between server selection and computation resource allocation, and multi-device competition. To address these challenges, we propose an empirical matching-based computation offloading optimization algorithm for 5G and edge computing-integrated EIoT. The optimization objective is to minimize the computation offloading delay by jointly optimizing large timescale server selection and small timescale computation resource allocation. We first model the large timescale server selection problem as a many-to-one matching problem, which can be decoupled from small timescale computation resource allocation by establishing a matching preference list based on empirical performance. Then, the large timescale server selection problem is solved by pricing-based matching with a quota algorithm. Furthermore, based on the obtained suboptimal result of large timescale server selection, the small timescale computation resource allocation problem is subsequently solved by Lagrange dual decomposition, the result of which is used to update large timescale empirical performance. Finally, extensive simulations are carried out to demonstrate the superior performance of the proposed algorithm by comparing it with existing algorithms.

5G can provide customized services for differentiated services of Energy Internet through network slicing technology. This paper carries out research on 5G network architecture and application requirements by studying typical service scenarios of electric power based on 5G network slicing. To ensure 5G network slicing end-to-end service quality of service (QoS) and to meet the QoS requirements of different service scenarios of Energy Internet, a network slicing resource allocation method of delay against throughput at the side of the Energy Internet radio access network is proposed.This paper provides a theoretical reference for the application of 5G technology to guarantee QoS in the electric power industry.

To bridge the gap between the resource-constrained mobile devices and the resource-demanding applications, mobile cloud computing (MCC) emerges for offloading complex tasks to a cloud server. Based on this concept, cloudlets, which move available resource to the vicinity of the mobile network, enhance further the system accessibility and performance. Moreover, to strengthen the network capacity in traffic intensive area, dense small cell network (DSCN) is proposed as one of the promising solutions. In this study, the operation of cloudlets and DSCN is collaboratively studied in order to further improve the system performance. On the one hand, users can select a cloudlet and dynamically adapt the connection according to the performance and the cost, which is referred to as a user-essential dynamic cloudlet selection problem. On the other hand, a cloudlet needs to set the optimal selling price and the size of resource for the users, which is considered as a cloudlet resource allocation problem. To jointly address the problems of dynamic cloudlet selection and resource allocation, the authors propose a hierarchical evolutionary game to maximise the utilities. Simulation studies are carried out to demonstrate the effectiveness of the proposed algorithms, which, indeed, improve the entire system performance significantly.

In this paper, a downlink device-to-device (D2D)-assisted cellular networks with mobile edge caching, where most popular video files are independently cached in D2D users and cellular base station (BS), are studied. In the considered system model, each user may obtain the requested video from the cache of BS or/and D2D users surrounding them. According to the different collaborative schemes of BS caching and D2D caching, it can be divided into two different resource allocation schemes. In the hybrid caching transmission scheme, users could adopt the BS caching mode or alternatively the D2D caching mode. In the joint caching transmission scheme, each user may obtain the requested files from the BS server and the adjacent D2D users, simultaneously. By taking the required data rate and the interference constraint into account, we formulate two joint resource allocation problems integrating link selection, channel allocation, and power control to maximize the system energy efficiency (EE). Leveraging on the Dinkelbach method, the EE optimization problems are transformed into mixed-integer nonlinear programming problems and can be decomposed into three subproblems: link selection, channel allocation, and power control. To solve these complicated problems, we propose two optimization algorithms that consist of a modified branch and bound method as well as Lagrange dual decomposition approach. The simulation results demonstrate the superiority of these two proposed algorithms in improving system throughput and EE compared with other algorithms.

With the combination of 5th generation mobile networks and smart grid, businesses in smart grid are becoming more diversified. Considering the different communication quality requirements of different businesses, this paper studies the problem of maximizing the system benefit function in the case of meeting the fairness and priority of multiple businesses in smart grid. This problem is studied in heterogeneous networks to improve the transmission reliability and overall efficiency of the system. The formulated problem is a mixed integer nonlinear programming (MINLP), which is difficult to solve. To deal with this problem, the target problem is divided into two sub-problems. First, we formulate the channel optimization problem, introduce the concept of exchange operation based on increased benefits, and propose a channel matching algorithm. Next, based on the channel allocation results, we formulate the power optimization problem and propose an iterative algorithm based on Lagrange dual decomposition. Simulation results show that our proposed algorithm has advantages in improving network throughput under the premise of satisfying the fairness and priority of multiple businesses.

In recent years, with the power network continuously developed and constructed, various new types of services are constantly emerging. Diversified business needs and the surge data of users and terminal devices pose new challenges to the current architecture of power communication network. Thus, the network need to be upgraded by using related technologies to meet the requirements of the so-called Energy Internet's development. Network slicing technology can build multiple logical networks with differentiated performance on the same physical infrastructure according to different service requirements. The personalized customization and security isolation brought by network slicing can better meet the needs of service requirements while saving network construction costs. The Integrated Space-Air-Ground (SAG) Communication Network can achieve a ubiquitous network coverage and be applied in emergency communication scenario. Therefore, network slicing technology can be introduced to the wireless communication part of the Integrated SAG network to solve the problems faced by current power communication network. This paper first introduces the concept and advantages of network slicing technology and analyzes current state of the power communication network, then proposes an Integrated SAG Network architecture based on network slicing and finally discusses its key technologies.

Driven by the construction of the Ubiquitous Electricity Internet of things, various services have increasingly higher requirements for wireless communication indicators. The 5G network with low latency, large connection and large bandwidth is urgently needed to fundamentally meet various business requirements and network security requirements of the smart grid. Because of the type of service arrival of smart grid is unknown and lacks prior knowledge, reinforcement learning (RL) is used to conduct this research. Considering the differentiated service characteristics of the smart grid and the challenges of flexibility and adaptability of the communication platform, this paper aims to solve the resource allocation problem of the radio access network (RAN) slice of smart grid. In this paper, we firstly introduce three typical power services and propose a service priority concept. After reviewing the fundamental concepts and proving the convergence of RL algorithm, we propose a dynamic resource allocation strategy of the RAN slice for smart grid based on RL. Finally, simulation results prove that the proposed RL algorithm can achieve resource utilization and quality of experience (QoE) improvement against the fair allocation scheme.

Massive multiple-input multiple-output (MIMO) has been regarded as one of the key technologies of fifth-generation cellular systems due to its excellent performance in spectral and energy efficiency, whose performance has also been widely studied. However, most related works focus only on the impact of the wireless channels. In fact, its performance is affected not only by the wireless channel but also by the transceiver radio frequency (RF) circuits. Random variation of RF gain would lead to a mismatch channel, where the downlink is not the transpose of the uplink in time-division duplex (TDD) mode. Therefore, the impact of the transceiver RF circuits should be considered when we evaluate the performance of the massive MIMO systems. In this paper, we develop a detailed analysis on the downlink sum rate of the massive MIMO systems and derive its closed-form expressions using maximum ratio transmission and zero-forcing (ZF) precoding. The derived results provide some good insight into how the system performance is affected by the RF mismatch parameters. Based on the analytical results, we further investigate the optimal power allocation scheme to maximize the sum rate subject to the total power constraint and lowest rate requirement. For the simplest case of user equipment side mismatch with the ZF precoding, we apply the water-filling solution, while for the other mismatch cases, we conduct a convex relaxation on the non-convex problem through lower bound inequality, variable substitution, and Taylor expansion techniques, before applying some convex optimization solving tools. In the end, we propose an iterative algorithm to successively improve the iterative results for approaching the optimal solution. Simulations demonstrate that, for the massive MIMO systems with RF mismatch, our power allocation schemes achieve significant capacity improvement relative to an equal power scheme, and it performs well for the ZF precoding in the case of the RF mismatch only at the base station.

Mobile cloud computing (MCC) is an emerging technology to relieve the tension between compute-intensive mobile applications and resource-constrained mobile terminals by offloading computing tasks to remote cloud servers. In this paper, we consider a novel MCC architecture consisting of remote cloud server, cloudlet and mobile terminal to guarantee low latency and low energy mobile consumption. To overcome the main bottlenecks of wireless bandwidth between mobile terminal and cloudlet, and the computation capability of cloudlet, the joint optimization strategy is proposed to enhance the quality of mobile cloud service. We formulate the wireless bandwidth and computing resource allocation model as a triple-stage Stackelberg game, and solve it by using backward method. In addition, the interplays of triple-stage game are discussed and the subgame optimal equilibrium for each stage is analyzed. An iterative algorithm is proposed to obtain Stackelberg equilibrium. Numerical results demonstrate the effectiveness of the proposed algorithm.

With the development of smart grids, existing power private networks fell short of the demanding requirements of industrial applications, especially with respect to bandwidth, end to end latency, and reliability. Combining with vertical industry is a major development direction of 5G mobile communication technology, while its communication capacity of large bandwidth, low latency, high reliability as well as massive connection is matched with the service requirements of smart grid service. How to apply 5G to smart grid is widely concerned. This article is a survey of the related technologies about convergence application of 5G and smart grid. It first reviews the update of the 5G physical layer technologies, which support for realizing the communication requirements of the 5G application scenarios. Then it introduces the key technologies such as 5G mobile edge computing, computational caching and network slicing, which is the basis for 5G to support vertical multi-service networks and meet different service requirements. Finally, it presents several network slicing logic architectures of the access network, transmission network and core network which are matched with power service.

As one of the important application scenarios of the Green Internet of Things (IoT), the development of smart grid is an important means to promote the energy system revolution. Future power grid will rely heavily on smart devices based on the IoT concept. As the 5G standard matures and is put into commercial use, the green interconnected 5G mobile network has shown great potential. With 5G network slicing technology, a wireless private network can be virtualized for each industry to better meet the requirements of smart grid security, reliability and flexibility. Considering the differentiated service characteristics of the smart grid and the challenges of flexibility and adaptability of the communication platform, this paper aims to solve the resource allocation problem of the radio access network (RAN) slice of smart grid. Since the type of service arrival of smart grid is unknown and lacks prior knowledge, deep reinforcement learning (DRL) is used to conduct this research. The mapping from the RAN slice resource management to DRL of the smart grid is analyzed at first, and then discussion from the elastic application and the real-time application is presented. Furthermore, a smart grid RAN slice allocation strategy based on DRL is proposed. Finally, simulation results prove that the proposed algorithm can satisfy the maximization of the resource allocation demand of the smart grid on the RAN side while reducing the cost.

Based on software defined networking (SDN) and network function virtualization (NFV) technologies, network slicing can be customized to meet the end-to-end (E2E) quality of service (QoS) requirements of various power services in smart grids. E2E mission-critical services in smart grids should satisfy high reliability and low latency requirements, such as distribution automation, etc. In this paper, we slice the resource in core networks and flexibly coordinate different service function chains (SFCs) on the substrate physical network to enable different E2E mission-critical service delivery. Since the E2E average transmission delay is related to the congestion of each link in core networks, E2E transmission delay optimization problem is equivalent to minimize the maximum link congestion factor problem, which is Mixed Integer Linear Programming (MILP). Considering that the constraint of this problem is large and there are integer variables, the Generalized Benders Decomposition is adopted. The numerical results demonstrate the effectiveness of our proposed algorithm.

With the rapid development of the power grid, the variety of power services has become more and more diverse, resulting in different service needs. As one of the important technologies of 5G, network slicing aims to accommodate different services over the same physical network. In this paper, after briefly analyzing the background of smart grid, we investigate the related research of network slicing. Each kind of services in smart grid has its own bandwidth, reliability and delay tolerance requirements. Therefore, for the purpose of service guarantees in smart grid, we propose a slice management mechanism for resource allocation based on 5G network slicing technology. Due to the good self-learning and adaptive capabilities, we use BP(back-propagation) neural network to train the appropriate weights. Correspondingly, a heuristic algorithm for dynamic weights based on BP neural network to solve the optimization problem is proposed. Finally, simulation results demonstrate the effectiveness of our algorithm within the mechanism.

To meet the diversity of smart grid services, network slicing can be considered as a cost-effective way to provide customized services for different QoS (Quality of Service) requirements of each service. Considering the downlink transmission between gNB (gNodeB) and UEs (User Equipments), this paper focuses on logically divided eMBB (Enhance Mobile Broadband) slices and uRLLC (Ultra Reliable & Low Latency Communication) slices, which formulates a multi-objective optimization problem for different QoS requirements of various services in smart grid. The throughput of the eMBB slicing service is maximized while ensuring the delay of the uRLLC slicing service. Based on this, the Lyapunov optimization theorem is used to convert it into an equivalent drift-plus-penalty minimization problem, which could be solved by Lagrange dual decomposition method. Furthermore, simulation results demonstrate the effectiveness of our proposed RA-DT algorithm.

With the rapid development of the new generation of smart grid, the variety of smart grid services are becoming more and more diversified, which lead to various service demands. According to different service requirements, it is necessary to allocate network resources flexibly and dynamically. In order to solve the problem of resource allocation among network slices in smart grid, we propose a two-layer dynamic resource scheduling strategy for network slicing. It can be divided into inter slice resource allocation and intra slice resource allocation. The former refers to the allocation of network resources to network slices. The latter refers to that the network slice allocates the allocated resources to the users in the slice. The simulation results show that the proposed two-layer network slicing dynamic resource scheduling strategy is superior to the current resource scheduling strategy in terms of quality of experience (QoE) of users and system throughput, which can better meet the needs of users.

Emerging mobile cloud computing (MCC) technology offers great potential for the mobile terminals to support running highly sophisticated applications. However, supporting such applications needs efficient resource management from cloud servers and mobile terminals. Moreover, the resource management technologies based on quality of experiences (QoE) can meet the demand of end users in a better way than those based on quality of services. In this paper, we address the resource management problem of MCC network with an acceptable QoE. Based on multi-leader multi-follower two-stage Stackelberg game model, the proposed scheme maximizes the utility function of MCC networks. Considering the scenario in which the cloud servers and mobile terminals are selfish to maximize their own interests, the network performance is greatly affected by their greediness. To achieve better network performance, control decisions are coupled with one another. Utility function considers not only the spectral efficiency and user satisfaction in the mobile terminal but also the pricing information in the cloud. Our proposed scheme can obtain a well-balanced performance between mobile terminals and cloud servers. In addition, the existence of Nash equilibrium in the proposed scheme is investigated. Theoretically, the maximum and minimum selling prices of bandwidth are deduced. Simulation results show that the effectiveness of the proposed algorithm and our proposed scheme outperforms equal allocation scheme in terms of the user satisfaction and network revenue.

In smart cites, more and more smart mobile devices (SMDs) have many computation-intensive applications to be processed. Mobile cloud computing (MCC) as an effective technology can help SMDs reduce their energy consumption and processing delay by offloading the tasks on the distributed cloudlet. However, due to long transmission delay resulting from the unstable wireless environment, the SMD may be out of the serving area before the cloudlet transmits responses to the user. Thus, delay is a crucial problem for the MCC offloading. In this paper, we consider a multi-SMDs MCC system, where each SMD having an application to be offloaded asks for computation offloading to a cloudlet. In order to minimize the total delay of the SMDs in the system, we jointly take the offloading cloudlet selection, wireless access selection, and computation resource allocation into consideration. We formulate the total delay minimization problem as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. We propose an improved genetic algorithm to obtain a local optimal result. Simulation results demonstrated that our proposal could effectively reduce the system delay.

In recent years, information overload has led to the development of a wide variety of different types of recommender systems (RSs), which aim at harnessing the problem in different domains. Most RSs spare no effort to improve the accuracy to help users find items matching their latest preferences. However, RSs with high accuracy not always capture user satisfaction due to that users might get bored with the items which are similar to what they liked in the past. Furthermore, they narrow the horizon of users and limiting the dynamic nature of user interests. Given this, serendipity draws more attention when evaluating RSs. In this paper, we review the studies on serendipity. Working toward this direction, we propose a new metric aiming at evaluating serendipity of RSs and a new strategy to balance the tension between accuracy and serendipity in movie recommender domain.

Cloud radio access network (C-RAN) architecture has been a key technology and trend of the evolving fifth generation (5G) cellular networks, because it can achieve efficient cooperative communications and reduce deployment and operational costs. In C-RAN systems, although the fiber fronthaul has been a great candidate, we should continue to research the wireless fronthaul for the cost saving and flexible network deployment. With the data volume growth exponentially, however, there are lots of baseband samples need to be transmitted through fronthaul between remote radio heads (RRHs) and the Base Band Unit (BBU) pool. It has been a key obstacle in the adoption of the C-RAN architecture. There is another problem in CRAN with wireless fronthaul, imperfect channel state information (iCSI) result in the gap of RRH and BBU pool. In this paper, we propose game based C-RAN baseband functions splitting and placement to address the problems. In order to increase applicability and commodity of the models furthest, we focus on two typical C-RAN structures, P2P model and RRH cluster model. Based on the model of P2P structure, we propose Courtnot douploy model with incomplete information to split the baseband functions between RRH/RRH cluster and BBU pool. In the RRH cluster structure model, we propose the Nash bargaining solution to split the baseband functions inside RRH cluster. Through the calculated equilibriums using the proposed game models, we can get the perfect tradeoff between fronthaul cost and CRAN cooperative performance in different deployment scenarios. Simulation results show that effective and efficient splitting and placement schemes can be achieved by using the proposed the Courtnot douploy model and the Nash bargaining solution.

Inter-cell interference (ICI) is always a big issue in orthogonal frequency division multiple access (OFDMA) based communication networks. In this paper, we apply power control to an Improved Fractional Frequency Reuse (IFFR) scheme and investigate the system performance. IFFR scheme divides the whole available resources into two segments: the first segment and the second segment. The first segment will be used by cell center users (CCUs) and all those users can use whole bandwidth. In the second segment, resources are divided into three main parts and are allocated to the edge region. And we use different kinds of transmission power levels in both segments. In addition, we compare the proposed scheme with the Fractional Frequency Reuse (FFR) scheme through simulation. Simulation results show that the proposed scheme with power control has noticeable improvement in terms of average cell capacity as well as cell edge users (CEUs) performance.

In the downlink wireless communication systems, opportunistic beamforming is a practical and widely used technique for user-scheduling. The critical element of the opportunistic beamforming is the beamforming. There are many studies focus on the channel fluctuations but put little eyes on the beam's angle. In this paper, we investigate system performance if the beam angle changes in a sweeping way. We consider the scheme under cellular network systems and put more eyes on the slow fading channel states. The base stations equipped with smart antennas which generate beams according to the defined algorithm. An angle sweeping scheme is proposed to change the beam angle from 0 to 2π and bring fluctuations to the channel which make the scheduling better. By combining memory function, the proposed scheme is more practical and suitable for the few user circumstance. We also demonstrate the performance of the proposed algorithm through MATLAB simulation.

The scheduling problem in downlink of the cooperative multi-cell system with random packet arrivals is investigated in this paper. On the one hand, multi-cell cooperative processing (MCP) has the potential to boost spectral efficiency and improve fairness. However, the traditional centralized multi-cell cooperative scheme, in which a central processing unit is assumed with the global knowledge of all the required downlink channels and user messages to jointly design the transmitted signals for all BSs, incurs significant overhead and complexity, and will hinder the practical implementation of MCP. On the other hand, in case of the random packets arrivals, the relevant fairness criterion will be the stabilization of all buffer queues which guarantees a bounded average delay for all users. In this paper, we adopt the decentralized cooperative scheme with adaptive linear preprocessing transmission. A queue-aware scheduling scheme is considered which maximizing the queue-length-weighted sum of the users' rates. The performance of proposed scheme is evaluated through the computer simulation. Simulation results show that the proposed scheme can achieve better tradeoff between system throughput and fairness among users.

A novel localization scheme called laser beam scan localization by combining grid and light (laser) with mobile localization policy for wireless sensor networks is designed. The scheme utilizes a moving location assistant (LA) with a laser beam, through which the deployed area is scanned. The LA sends identities to unknown nodes to obtain the locations of sensor nodes. High localization accuracy can be achieved without the aid of expensive hardware on the sensor nodes, as required by other localization systems. The scheme yields significant benefits compared with other localization methods.