Wireless network virtualization is basically the abstraction, isolation, and sharing of wireless resources among different entities. Consequently, virtualization provides great flexibility and higher network efficiency, and enables easier migration to new technologies in wireless networks. Traditionally, a wireless network virtualization controller manages the virtual resources (including radio resources and infrastructure resources) known as slices which are available to the Service Providers (SPs). The SPs then allocate their purchased resources to serve their subscribed mobile users. Such a centralized allocation decouples the Quality-of-Service (QoS) management by the SPs from the virtual resource management by the controller. In this paper, we propose a matching based wireless network virtualization resource allocation mechanism: a distributed three-sided (3D) matching between radio resources, physical infrastructure and mobile users. The Restricted Three-sided Matching with Size and Cyclic preference model (R-TMSC) is implemented to obtain a stable solution. Simulation results show that our proposed spectrum-oriented and user-oriented algorithms outperform the traditional resource allocation schemes. The spectrum-oriented algorithm enhances the user throughput and the system performance, within a lesser run time. Furthermore, for an increasing number of users, the proposed algorithms serve more users than traditional methods.

The emerging mobile crowd sensing has become a new paradigm where a crowd of mobile users utilize their smart devices to conduct complex computation and sensing tasks in mobile social networks, in which the content sharing plays a significant role. In this work, we propose a novel many-to-many content sharing framework by enabling users to exchange information through separate connections with different partners of multiple relationships simultaneously. Under the statistical channel statement information condition, the many-to-many pairing problem is formulated as a Stable Multiple Activity (SMA) matching game and solved by a distributive stable b-matching algorithm in multigraphs. Based on the matching model, we further perform the power allocation scheme to improve system performance. Simulation results demonstrate the superiority of our proposed SMA method with lower complexity.

In current wireless communication systems, there is a rapid development of location based services, which will play an essential role in the future 5G networks. One key feature in providing the service is the mobile crowdsourcing in which a central cloud node denoted as the principal collects location based data from a large group of users. In this paper, we investigate the problem of how to provide continuous incentives based on user's performances to encourage users' participation in the crowdsourcing, which can be referred to the moral hazard problem in the contract theory. We not only propose the one-dimensional performance-reward related contract, but also extend this basic model into the multi-dimensional contract. First, an incentive contract which rewards users by evaluating their performances from multiple dimensions is proposed. Then, the utility maximization problem of the principal in both one-dimension and multi-dimension are formulated. Furthermore, we detailed the analysis of the multi-dimensional contract to allocate incentives. Finally, we use the numerical results to analyze the optimal reward package, and compare the principal's utility under the different incentive mechanisms. Results demonstrate that by using the proposed incentive mechanism, the principal successfully maximizes the utilities, and the users obtain continuous incentives to participate in the crowdsourcing activity.

The surging of smartphone sensing, wireless networking and social media have enabled a great variety of applications, such as environment surveillance, marketing, health monitoring and so on. This new paradigm is typically referred to as mobile crowd sensing (MCS). Existing solutions on MCS are majorly based on text, image and video analysis using the distributively sensed data. One shortcoming of such approaches is the data processing delay. Considered as an enhanced function to the temporary social media services, for example Twitter, we propose a real-time event sharing framework that provides mobile users with the freedom of expressing their various interests regarding the event data. To achieve such goals, we introduce a novel Hashtag design, which is a combination of five types of information: time, location, keywords, data type and data size. Users, by comparing their own interests with the uploaded Hashtags in the Twitter server, can search for suitable partner users to share information with in real time. The formulated user sharing/pairing problem is modeled as the Stable Fixture (SF) matching game, and can be solved by the Irving's SF (ISF) algorithm in a distributive manner. The simulations have demonstrated the superiority of our proposed ISF method by comparing with both centralized method and heuristic algorithms.

This paper analyzes the resource allocation problem in the LTE-Unlicensed spectrum to increase performance of the unlicensed systems. The problem is modeled with a matching game approach and the proposed resource allocation algorithm consists of a heuristic based on the usage of preference lists, one for each user equipment (UE) and on unlicensed bands, to determine a many-to-many matching between UEs and component carriers (CCs), aiming at maximizing the unlicensed overall network sum rate. Hence the proposed algorithm realizes a mapping between user equipments and unlicensed channels, also considering the carrier aggregation (CA) mechanism, which is typical of LTE-U. In the simulation evaluation, we compare the proposed algorithm with the Hungarian algorithm w.r.t. the system throughput and the robustness to perturbed input data. Both algorithms have almost equivalent performances, but the Hungarian algorithm presents a higher computational complexity than the proposed one.

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.

The study item: "Feasibility Study on LTE-based V2X Services", approved at 3GPP TSG RAN #68, has aroused the interest in the study of LTE assisted vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communications in the networks of connected vehicles. By deploying the direct device-to-device (D2D) technology of traditional cellular networks into the V2X (including both V2V and V2I) communications, performance improvements can be expected, such as better reliability, lower latency, and more efficient content sharing. This paper investigates the content sharing problem in the D2D based V2X communication networks. With both vehicles and eNBs carrying multiple different classes of data, this work studies how to optimize the information exchanged within the network, and in the mean time to guarantee the system quality of service (QoS) requirements. By jointly considering the data diversity and link quality, the interactions between vehicles/eNBs, or in other words, the V2V and V2I link scheduling, is modeled as the stable fixture (SF) matching game. Different from traditional D2D communications, where each node is limited to one link, we allow multiple V2X connections for each vehicle to further optimize the content sharing. More specifically, the formations of such V2X links are independent from each other, thus more flexible than the conventional clustering formation in the Vehicular ad hoc networks (VANETs). The SF game is solved by the proposed Irving's stable fixture (ISF) algorithm. Its advantages over some heuristics are demonstrated through simulation experiments.

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.

LTE, as the advanced mobile telecommunication technology, is serving heavy mobile broadband traffic nowadays. Motivated by the potential boost in performance of LTE utilizing the unlicensed spectrum, significant efforts have been devoted into the commonly referred LTE-Unlicensed technique. In this work, we investigate the carrier aggregation of licensed and unlicensed spectrum by deploying micro-cell base stations, which have access to the unlicensed spectrum, to provide cellular users a more reliable and efficient transmission. We tackle the unlicensed resource allocation problem by modeling it as a student-project allocation matching game. In addition, a postmatching procedure of resource re- allocation is introduced to guarantee unlicensed users' quality of service (QoS), as well as the system-wide stability. The simulation evaluation shows the effectiveness and efficiency of our proposed matching-based approach.

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 user's 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.

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.

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.

In device-to-device (D2D) communication, mobile users communicate directly without going through the base station. D2D commutation has the advantage of improving spectrum efficiency. But the interference introduced by resource sharing of D2D has become a significant challenge. In this paper, we try to optimize the system throughput while simultaneously meeting the quality of service (QoS) requirements for both D2D users and cellular users (CUs). We implement matching theory to solve the resource allocation problem. We utilize two efficient stable matching algorithms to optimize the social welfare while ensuring the network stability. More importantly, we introduce the idea of cheating in matching to further improve D2D users' throughput. It is proven that the cheating mechanism benefits a subset of D2D users without hurting the performance of the rest. Through the simulation results, we demonstrate the effectiveness of both the stable matching and cheating algorithms in terms of improving both D2D users and the overall throughput in D2D communications.

In device-to-device (D2D) communication, mobile users communicate directly without going through the base station. D2D commutation has the advantage of improving spectrum efficiency. But the interference introduced by resource sharing of D2D has become a significant challenge. In this paper, we try to maximize the system throughput and simultaneously meet the Quality of Service (QoS) requirement for both D2D users and cellular users (CUs). We solve the optimization problem by solving the bipartite two-sided matching problem under preferences between the admitted sets of D2D users and CUs. Second and more importantly, we introduce the idea of cheating in matching to further improve some D2D users' system throughputs. We construct a coalition strategy to implement the cheating idea. Finally, from the simulation results, we demonstrate the effectiveness of cheating in matching for D2D pairs.

Numerous applications of wireless sensor networks (WSNs) in harsh terrains are constrained by the sensors' battery-power and face the difficulties of data collection. In this paper, we propose to exploit wireless power transfer technology to replenish the energy of sensor clusters and develop an efficient data collection scheme for those wireless rechargeable senor clusters deployed in harsh terrains. In view of the harsh terrains, we employ unmanned aerial vehicles (UAVs) to travel to the sites of sensor clusters, collect data, and recharge the sensors in corresponding clusters. With joint consideration of data collection characteristics, wireless power transfer features and travel time, we mathematically formulate the data collection in rechargeable WSNs into an optimization problem with the objective of maximizing data collection utility. Based on the matching theory, we also develop a one side matching algorithm and a greedy algorithm to solve the problem in distributed manner. Through simulations, we show that UAVs are not always matched with nearest sensor clusters, the solution of the proposed greedy algorithm is optimal, and the sensed data can be efficiently collected.

In cognitive radio (CR) networks, due to the uncertainty of primary users' (PUs) traffic, there are vacant bandwidths that can be accessed by secondary users (SUs). Thus, this provides mutual benefit opportunities for both PUs and SUs. In this paper, the objective is to select the optimal channel utilization for maximizing the system utility. To solve the problem, we employ the matching theory as the mathematical framework to form mutually beneficial relationships between PUs and SUs. In particular, we propose a distributed matching algorithm for many-to-one matching considering both SUs' and PUs' characteristics. Simulation results show that the proposed distributed algorithm achieves a performance close to the centralized method, and outperforms the greedy allocation in different scenarios.

Network lifetime is critical to any battery operated wireless sensor networks (WSNs). Especially for delay-sensitive WSNs, it's challenging to prolong the network lifetime while meeting the delay requirements of different applications. We investigate how to optimize the network lifetime of delay-sensitive WSNs with respect to energy efficient routing and sleep scheduling. Instead of treating routing and sleep scheduling as two separate approaches, we have a joint consideration of them, and mathematically formulate the lifetime maximization problem. Since the formulated problem is a mixed integer nonlinear programming (MINLP) problem and NP-hard to solve, we relax it into a linear programming (LP) problem and solve the relaxed problem for the upper bound. We also develop a heuristic algorithm-RSF for feasible solution, which yields a lower bound of WSNs' lifetime. Through extensive simulations, we show that the solution found by the proposed algorithm is close to the optimal one and the proposed scheme is effective in prolonging the lifetime of delay-sensitive WSNs.

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.