多主多从Stackelberg博弈下的边缘缓存资源分配算法

为鼓励视频服务提供商参与到缓存过程中,本文提出一种基于Stackelberg博弈的激励缓存资源分配算法。与传统激励缓存资源分配方案不同,本文考虑同时存在多个网络运营商和多个视频服务提供商,视频服务提供商从网络运营商处购买存储空间以缓存热门视频。针对该场景,本文将该激励缓存模型建模为多主多从Stackelberg博弈问题,分别构建主方和从方的效用函数,证明了在网络运营商价格确定的情况下,视频服务提供商之间的非合作博弈存在纳什均衡。文章利用分布式迭代算法对该博弈模型进行求解,获得了视频服务提供商的最优缓存策略和网络运营商的最优价格策略。仿真结果表明,本文提出的激励缓存机制可使视频服务提供商获得比其他缓存分配算法更高的单位成本收益。      

Proactive caching with content‐based pricing for cooperative femtocells in two‐tier heterogeneous networks

With increase in the number of smart wireless devices, the demand for higher data rates also grows which puts immense pressure to the network. A vast majority of this demand comes from video files, and it is observed that only a few popular video files are requested more frequently during any specified time interval. Recent studies have shown that caching provides a better performance as it minimizes the network load by avoiding the fetching of same files multiple times from the server. In this paper, we propose to combine two ideas; proactive caching of files and content‐based pricing in macro‐femto heterogeneous networks. The femtocell access point (FAP) is allowed to manipulate its users' demand through content‐based pricing and serve the users' requests by proactively downloading suitable content into its cache memory which reduces the load of the femtocell. In addition, an incentive mechanism is also proposed which encourages the FAP to help macrocell users under its coverage zone by allowing access to its cached content and thereby reduces the macrocell load. The proposed content‐based pricing and proactive caching scheme for femtocells is modeled as a Stackelberg game among the macrocell base station and the FAP to jointly maximize both of their utilities. Performance analysis of the scheme is presented for a single femtocell scenario and compared with the conventional flat pricing‐based scheme via numerical examples. The results demonstrate significant reduction in network load using our proposed scheme.     

Pricing framework for almost blank subframe scheme in two‐tier heterogeneous networks

As a part of enhanced inter‐cell interference coordination (eICIC), almost blank subframe (ABS) is an efficient technique to mitigate the cross‐tier interference of two‐tier heterogeneous networks (HetNets) and enhance overall network performance. However, in small cells with closed subscriber group (CSG) mode, how to motivate small cell base stations (SBSs) to adopt ABS schemes is still one of technical challenges due to the selfish nature of SBSs. In this paper, we propose a pricing framework with ABS scheme that benefit both the macrocell and small cell tier. Within the proposed framework, each SBS with closed access policy performs ABS scheme by muting a portion of the whole frame in time domain, and then dedicating this interference‐free subframe to macrocell users (MUs) for exclusive use. In return, the macrocell base station (MBS) announces the price for these ABSs and offers a certain amount of revenue to each SBS according to the length of its ABS. Furthermore, we extend the small cell performance analysis to the scenario of hybrid access policy, which allow neighboring MUs to access small cells. To investigate the interaction between two tiers, we formulate the proposed framework as a one‐leader multiple‐follower Stackelberg game, which regards the MBS and SBSs as leader and followers, respectively. On the basis of the theoretical analysis, we prove that a unique Stackelberg equilibrium (SE) exists and obtain the optimal strategies for both tiers. Numerical results evaluate the utility performance of both tiers when SE is achieved and verify the validity of the proposed framework.     

面向软件定义无线接入网的高效协作内容缓存算法

为了缓解海量的移动业务数据与容量受限的无线接入网回传链路之间的矛盾,本文提出一种面向软件定义无线接入网（SD-RAN）的协作内容缓存网络架构.在宏蜂窝基站（MBS）的控制管理下,小蜂窝基站（SBS）可以在存储单元有序存储一些高流行度的内容.针对SBS存储单元空间受限问题,进一步提出SD-RAN网络架构下的协作内容缓存算法.该算法中,每个SBS缓存空间被分割成两部分：（1）用于存储全网流行度最高的公共内容以保证各小蜂窝小区本地命中率.（2）用于存储流行度较高的差异化的内容以促进MBS内SBS之间的协作.在此基础上,解析推导具有最优平均内容获取开销的分割参数闭合表达式.仿真结果表明该算法在不同系统参数条件下能显著降低SD-RAN的平均内容获取开销.     

When to offload in two-tier smallcell networks: a Stackelberg game approach with pre-offloading-decision

In this paper, we consider the problem of traffic offloading in a two-tier downlink smallcell network with hybrid access. We formulate a Stackelberg game with pre-offloading decision, which includes two steps, to study the problem. The utility of the MBS is designed as a piecewise function, corresponding to the two possible decisions of the MBS. We design a bonus-based incentive mechanism to motivate smallcell base stations (SBSs) serve the MUEs. We prove the existence and uniqueness of the Stackelberg equilibrium. We also consider the incomplete information game, where the SBSs have private information. A Bayesian Stackelberg game with pre-offloading decision is formulated and the existence of Bayesian Stackelberg equilibrium is proved. Our proposed approach achieves much higher utilities than the existing approach does under some circumstances in both cases, which proves the necessity and superiority of our proposed game structure.     

Dynamic theoretic proactive caching placement game for energy saving in H‐CRAN

Edge caching is an effective feature of the next 5G network to guarantee the availability of the service content and a reduced time response for the user. However, the placement of the cache content remains an issue to fully take advantage of edge caching. In this paper, we address the proactive caching problem in Heterogeneous Cloud Radio Access Network (H‐CRAN) from a game theoretic point of view. The problem is formulated as a bargaining game where the remote radio heads (RRHs) dynamically negotiate and decide which content to cache in which RRH under energy saving and cache capacity constraints. The Pareto optimal equilibrium is proved for the cooperative game by the iterative Nash bargaining algorithm. We compare between cooperative and noncooperative proactive caching games and demonstrate how the selfishness of different players can affect the overall system performance. We also showed that our cooperative proactive caching game improves the energy consumption of 40% as compared with noncooperative game and of 68% to no‐game strategy. Moreover, the number of satisfied requests at the RRHs with the proposed cooperative proactive caching scheme is significantly increased.     

基于能量效率的双层非正交多址系统资源优化算法

该文针对双层非正交多址系统(NOMA)中基于能量效率的资源优化问题,该文提出基于双边匹配的子信道匹配方法和基于斯坦科尔伯格(Stackelberg)博弈的功率分配算法。首先将资源优化问题分解成子信道匹配与功率分配两个子问题,在功率分配问题中,将宏基站与小型基站层视作斯坦科尔伯格博弈中的领导者与追随者。然后将非凸优化问题转换成易于求解的方式,分别得到宏基站和小型基站层的功率分配。最后通过斯坦科尔伯格博弈,得到系统的全局功率分配方案。仿真结果表明,该资源优化算法能有效地提升双层NOMA系统的能量效率。     

Cooperative caching game based on social trust for D2D communication networks

Content sharing via device‐to‐device (D2D) communications has become a promising method to increase system throughput and reduce traffic load. Due to the characteristic of spectrum sharing in D2D network, confidentiality is becoming a key issue in content transmission. Secure communication in D2D networks is generally guaranteed by a physical‐layer security mechanism. However, this method sacrifices the system transmission rate while ensuring security. Since mobile devices are carried by humans, we can leverage their trust relations to enhance the security of communications. As much, considering the psychology structure and social attributes of mobile users, we build a multidimensional trust evaluation mechanism to evaluate the trust relationship between users, and we pick out the trusted users based on the decision‐theoretic rough sets. By sharing content only between trust users, we can enhance the security of content transmissions without relying on physical‐layer security measures. Meanwhile, content caching is now widely used to improve accessing efficiency and reduce traffic load on cellular networks. However, caching content for other users incurs additional cost, which results in selfish and noncooperative behavior in users. Considering such selfishness, we introduce a cooperative caching game based on multidimensional trust relations to motivate users to cache contents for other devices. In this game, the trust relations and physical distance between two users are considered to formulate the cost function. Furthermore, we introduce an incentive caching algorithm based on social trust to minimize the total cost in the D2D network.     

Community oriented in‐network caching and edge caching for over‐the‐top services in adaptive network conditions to improve performance

Over‐the‐top (OTT) services such as Netflix, Amazon Prime, and YouTube generate the most dominant form of traffic on the Internet today. There is increasingly high demand for resource intensive 3D contents, interactive media, 360 media, and user‐generated contents. As the amount of contents keep increasing in multiple folds, it is important to cache contents intelligently. Caching algorithm needs to exploit in‐network caching, community‐based pre‐caching, and a combined approach. Hence, we survey CDN‐based edge caching infrastructures including OpenConnect (Netflix) and Google Edge, followed by CCN based in‐network caching. We implement and compare four different approaches for caching contents including (1) in‐network caching, (2) edge caching, (3) community‐based in‐network caching, and (4) community‐based edge caching. We run our algorithms on adaptive network conditions with different topologies, cache size, content popularity, and request arrivals in and compared the delay for all these four approaches. We verify our model by calculating important performance parameters including hop count, redundancy, and hop count variances. Hopcount is an important performance parameter as it influences the processing, queuing, and transmission delays. We focus on determining if an in‐network caching approach is any better than edge caching. We reach several conclusions. First, in most of the scenarios, community‐based in‐network caching performs the best. Second, if the cache size is lesser than 30% of the total content size then community‐based edge caching is better for less popular contents. Finally, our statistical analysis also reveals that a community‐based edge caching mechanism is least affected by varying cache sizes and dynamic user behavior, which makes it a better choice for providing Service Level Agreement.     

基于主从博弈的多方人机交互对话心理模型

针对现有的多方人机交互存在分寸感把握较差、机器人回复自主性不强的问题,该文依据心理语言学关于对话心理理论,提出一种基于主从博弈的多方人机交互对话心理模型。该模型模拟了人与人交流时交互多方的心理过程,考虑到多方交互中主导者与从属者的交流特征,采取单主多从的博弈模型加以形式化表示,并使机器人扮演从属者角色,对多方主从博弈过程中从属关系带来的收益进行考量,将这一考量结果作为机器人回复的重要决策依据。实验结果表明,扮演从属者角色的机器人在与多方进行交互时,能准确把握对话分寸,在合适的时机下进行回复,进一步提升机器人回复的合理性与自主性。     

Satisfactory content delivery scheme for QoS provisioning in delay tolerant networks

We deal in this article with the content forwarding problem in delay tolerant networks (DTNs). We first formulate the content delivery interaction as a noncooperative satisfaction game. On one hand, the source node seeks to ensure a delivery probability above some given threshold. On the other hand, the relay nodes seek to maximize their own payoffs. The source node offers a reward (virtual coins) to the relay, which caches and forwards the file to the final destination. Each relay has to solve the dilemma of accepting/rejecting to cache the source's file. Cooperation incurs energy cost due to caching, carrying, and forwarding the source's file. Yet when a relay accepts to cooperate, it may receive some reward if it succeeds to be the first relay to forward the content to the destination. Otherwise, the relay may receive some penalty in the form of a constant regret; the latter parameter is introduced to make incentive for cooperation. Next, we introduce the concept of satisfaction equilibrium (SE) as a solution concept to the induced game. Now, the source node is solely interested in reaching a file delivery probability greater than some given threshold, while the relays behave rationally to maximize their respective payoffs. Full characterizations of the SEs for both pure and mixed strategies are derived. Furthermore, we propose two learning algorithms allowing the players (source/relays) to reach the SE strategies. Finally, extensive numerical investigations and some learning simulations are carried out to illustrate the behavior of the interacting nodes and to give some insightful thoughts on how to fine‐tune the network setting.     

命名数据网络中基于内容请求相关性的协作缓存算法

针对命名数据网络(Named Data Networking, NDN)存储空间的有效利用和应答内容的高效缓存问题,该文采用差异化缓存的方式,提出一种依据内容请求序列相关性的协作缓存算法。在内容请求中,预先发送对于后续相关数据单元的并行预测请求,增大内容请求的就近响应概率;缓存决策时,提出联合空间存储位置与缓存驻留时间的2维差异化缓存策略。根据内容活跃度的变化趋势,空间维度上逐跳推进内容存储位置,时间维度上动态调整内容缓存时间,以渐进式的方式将真正流行的请求内容推送至网络边缘存储。该算法减小了内容请求时延和缓存冗余,提高了缓存命中率,仿真结果验证了其有效性。     

Coordinated caching scheme based on popular content awareness and tracking

To overcome the problems of the on-path caching schemes in the content centric networking,a coordinated caching scheme based on the node with the max betweenness value and edge node was designed.According to the topol-ogy characteristics,the popular content was identified at the node with the max betweenness value and tracked at the edge node.The on-path caching location was given by the popularity and the cache size.Simulation results show that,com-pared with the classical schemes,this scheme promotes the cache hit ratio and decreases the average hop ratio,thus en-hancing the efficiency of the cache system.     

Game-theoretical analysis of mobile contributors in mobile crowd sourcing network with word of mouth mode

The crowdsourcer who calls for sensing service can recruit enough mobile contributors quickly with the word of mouth mode,improving the quality of sensing tasks.The behavior of mobile contributors in mobile crowdsourcing with the WoM was investigated.It was supposed that each mobile contributor was rational,seeking for the highest utility.The behavior of mobile contributors with a two-level Stackelberg game was formulated.In the first-level game,a mobile contributor who directly worked for the crowdsourcer acted as the leader,while contributors invited by first-level contributors were followers called the second-level contributors.In the second-level game,the second-level contributors were the leaders and contributors invited by them were followers.The Nash equilibrium for each Stackelberg game was proved was existed and unique,and designed an algorithm to reach the equilibrium.Backward induction approach to compute the best response of each game was adopted,and the simulation results show the correctness of theoretical analysis for the interaction among contributors in crowdsoucing with WoM.     

Distributed optimization of enhanced intercell interference coordination and resource allocation in heterogeneous networks

In two‐tier heterogeneous networks (HetNets), the cross‐tier interference caused by spectrum sharing between macrocell and small cells poses obstacle to ideal overall network performance. Both enhanced intercell interference coordination (eICIC) at the macrocell base station (MBS) and resource allocation (RA) at small cell base station (SBS) have been considered as efficient approaches to mitigate the cross‐tier interference. In this paper, we propose a distributed optimization framework to jointly optimize the eICIC and RA schemes at different tiers. In specific, an enhanced almost blank subframe (ABS) scheme is performed by the MBS to encourage SBSs to adopt hybrid access policy and admit the offloaded macrocell users (MUs) so as to guarantee their QoS requirements. To model the intricate interaction between two tiers, we formulate the joint optimization problem as a one‐leader multiple‐follower Stackelberg game. We prove the existence of Stackelberg equilibrium (SE) and obtain the optimal strategies for both tiers. Numerical results are presented to show that the proposed framework with hybrid access policy converges to a unique SE and utilities for both tiers is maximized, which creates a win‐win situation.     

Dynamic Probabilistic Caching Algorithm with Content Priorities for Content‐Centric Networks

This paper presents a caching algorithm that offers better reconstructed data quality to the requesters than a probabilistic caching scheme while maintaining comparable network performance. It decides whether an incoming data packet must be cached based on the dynamic caching probability, which is adjusted according to the priorities of content carried by the data packet, the uncertainty of content popularities, and the records of cache events in the router. The adaptation of caching probability depends on the priorities of content, the multiplication factor adaptation, and the addition factor adaptation. The multiplication factor adaptation is computed from an instantaneous cache‐hit ratio, whereas the addition factor adaptation relies on a multiplication factor, popularities of requested contents, a cache‐hit ratio, and a cache‐miss ratio. We evaluate the performance of the caching algorithm by comparing it with previous caching schemes in network simulation. The simulation results indicate that our proposed caching algorithm surpasses previous schemes in terms of data quality and is comparable in terms of network performance.     

Proactive content caching by exploiting transfer learning for mobile edge computing

To address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.     

Responsive pricing modeled with Stackelberg game for next-generation networks

In this paper, we study the problem of resource allocation between users in next-generation networks. The starting assumption is that the service provider tries to come up with convincing offers of service level to subscribers, based on demand responsive pricing scheme, while trying to maximize its profit. We developed two algorithms with different usage-based pricing strategies. Both algorithms solve pricing optimization problem using Stackelberg game with service provider acting as a leader and users behaving as followers. We developed the bandwidth management server to perform automatic optimal bandwidth allocation to each user's session and maximize its expected utility while maximizing the overall service provider's revenue. For both algorithms, we also performed the procedure for optimization of the capacity offered to users.     

带有特征感知的D2D内容缓存策略

设备到设备通信(D2D)可以有效地卸载基站流量,在D2D网络中不仅需要共享大众化内容还需要个性化内容缓存。该文对缓存内容选择问题进行了深入研究,提出一种结合特征感知的内容社交价值预测(CSVP)方法。价值预测不仅可以降低时延也可以减少缓存替换次数降低缓存成本。首先结合用户特征和内容特征计算内容当前价值,然后通过用户社交关系计算未来价值。微基站根据内容的价值为用户提供个性化内容缓存服务,宏基站则在每个微基站的缓存内容中选择价值较大部分的内容。仿真结果表明,该文提出的缓存策略可以有效缓解基站流量,与其他方法相比降低时延约20%～40%。     

Hierarchical game based spectrum access optimization for anti-jamming in UAV network

For the anti-jamming spectrum access optimization problem in unmanned aerial vehicle (UAV) communication networks,considering the complex and diverse malicious jamming from jammers,a Bayesian Stackelberg game was proposed to formulate the competitive relations between UAV users and jammers.Specifically,jammers acted as the leader,whereas users acted as followers of the proposed game.Based on their different utility functions,the jammer and users independently and selfishly selected their optimal strategies and obtained the optimal channels selection.Due to the NP-hard nature,it was challenging to obtain the Stackelberg Equilibrium of the proposed game.To this end,a hierarchical learning framework was formulated,and a hierarchical channel selection-learning algorithm was proposed.Simulations demonstrate that with the proposed hierarchical learning algorithm,UAV nodes can adjust their channel selection and obtain superior performance.