Yunan Gu

Context-aware data caching for 5G heterogeneous small cells networks

In this work, we investigate the problem of context-aware data caching in the heterogeneous small cell networks (HSCNs) to provide satisfactory to the end-users in reducing the service latency. In particular, we explore the storage capability of base stations (BSs) in HSCNs and propose a data caching model consists of edge caching elements (CAEs), small cell base stations (SBSs), and macro cell BS (MBS). Then, we concentrate on how to efficiently match the data contents to the different cache entities in order to minimize the overall system service latency. We model it as a distributed college admission (CA) stable matching problem and tackle this issue by utilizing contextual information to generate the preference lists of cache entities and contents, respectively. In the CA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to find a stable matching between the contents and the cache entities. Through numerical results, we illustrate the advantages of of our proposed methods.

Prolonging the Lifetime of Large Scale Wireless Sensor Networks via Base Station Placement

Network lifetime is a critical design goal of any battery operated large scale wireless sensor networks (WSNs). In this paper, we investigate how to prolong the lifetime of large scale WSNs via optimal placement of base stations (BSs). With multiple BSs placed, the sensor nodes can send their data to nearby sinks and may reduce energy consumption of relaying packets for other sensor nodes. Due to the high cost of BSs and geographical constraints in WSNs, we can only place a limited number of BSs in a few candidate locations. Considering wireless transmission features and flow routing, we formulate this base station placement (BSP) problem in WSNs into a mixed integer nonlinear programming (MINLP) problem. In view of the NP-hardness of the formulated problem, we develop the heuristic algorithm to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithm are close to the optimal one and the proposed scheme is effective in prolonging the lifetime of large scale WSNs.

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs’ idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs’ participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.

LTE-Unlicensed Coexistence Mechanism: A Matching Game Framework

Integrating LTE-A into unlicensed spectrum using carrier aggregation, LTE-Unlicensed is considered as a promising solution with the benefits of enhanced transmission and seamless user experience. However, it is still facing some major design challenges, especially the coexistence of multiple systems (LTE/Wi-Fi) within the shared unlicensed spectrum. In this work, a matching game framework is presented to tackle the coexistence issues. Specifically, wireless users (i.e., cellular users and Wi-Fi users), which interfere with each other when they operate on the same unlicensed band, are modeled as matching players. The coexistence issues between the LTE/Wi-Fi systems can thus be modeled as the interactions between LTE/Wi-Fi users using suitable matching games. In addition, the matching framework allows definition of the preference lists and matching optimality as ways to interpret various system requirements and objectives. Together with the low-complexity and semi-distributive implemented algorithms, the matching framework can accommodate many application-oriented coexistence issues in LTE-Unlicensed. Specific implementation scenarios, as well as experimental results, are analyzed to demonstrate the potential of the matching-based approaches in the LTE-Unlicensed design.

Incentive mechanism in crowdsourcing with moral hazard

With the widely adoption of smart mobile devices, there is a rapidly development of location based services. One key feature in providing the service is the crowdsourcing in which the principal obtains essential data from a large group of users, and inversely sharing the data based service with everyone for free. In this paper, we investigate the problem of how to provide continuous incentives for users to participate in the crowdsourcing activity, which can be referred to the moral hazard problem in the contract theory. First, a performance related incentive mechanism is proposed. Then, the utility maximization problem of the principal is formulated, under the constraint that each user maximizes its own utility by choosing the optimal effort in the crowdsourcing activity. Finally, the numerical results show that by using the proposed incentive mechanism, the users obtains the continuous incentives to participate in the crowdsourcing activity, and the principal successfully maximize the utilities.

Student admission matching based content-cache allocation

As a support to the backend storage, the content caching technique is of great importance to online social networks (e.g., Facebook), in reducing the request service latency and improving user satisfaction. However, the limited caching capacity and booming user data pose great challenges for the content-cache allocation. In this paper, we propose a three-layer content caching model, and focus on how to efficiently allocation contents to caches in order to minimize the overall service latency. We try to tackle this issue by utilizing both centralized Mix Integer Linear Programming (MILP) optimization and by modeling it as a distributed student admission (SA) stable matching problem. In the SA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to yield a stable matching between contents and cache centers. We compare the performance between the centralized and distributed algorithms in terms of system welfare and computation analysis. Through numerical results, we prove the effectiveness of our proposed methods.

Matching Theory for Wireless Networks

This book provides the fundamental knowledge of the classical matching theory problems. It builds up the bridge between the matching theory and the 5G wireless communication resource allocation problems. The potentials and challenges of implementing the semi-distributive matching theory framework into the wireless resource allocations are analyzed both theoretically and through implementation examples. Academics, researchers, engineers, and so on, who are interested in efficient distributive wireless resource allocation solutions, will find this book to be an exceptional resource.

Tournament Based Incentive Mechanism Designs for Mobile Crowdsourcing

With the wide adoption of smart mobile devices, there is rapid development of location based services. One key feature of supporting a pleasant/excellent service is the access to adequate and comprehensive data, which can be obtained by mobile crowdsourcing. The main challenge in crowdsourcing is how the service provider (principal) incentivize a large group of mobile users to participate. In this paper, we investigate the problem of designing a tournament to provide continuous incentives for users by rewarding them based on the rank achieved in crowdsourcing. First, we model the users utility of reward from achieving one of the winning ranks in the tournament. Then, the utility maximization problem of the principal is formulated, under the constraint that the user maximizes its own utility by choosing the optimal effort in the crowdsourcing tournament. Furthermore, we show that, the tournament can approximate the optimal contract under full information by step function. Finally, we present numerical results to compare the system performance under the different proposed incentive mechanisms; we show that by using the tournament, the users obtain the continuous incentives to participate in the crowdsourcing activity.

A Hierarchical Game Framework for Resource Management in Fog Computing

Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.

Dynamic Path To Stability in LTE-Unlicensed With User Mobility: A Matching Framework

LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users’ performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale–Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.