Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks

In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a novel approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing work which assumes that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as a coalition formation game with overlapping coalitions. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed overlapping coalition formation game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions

An MBS-Assisted Femtocell Transmit Power Control Scheme with Mobile User QoS Guarantee in 2-Tier Heterogeneous Femtocell Networks

This study investigates how to adjust the transmit power of femto base station (FBS) to mitigate interference problems between the FBSs and mobile users (MUs) in the 2-tier heterogeneous femtocell networks. A common baseline of deploying the FBS to increase the indoor access bandwidth requires that the FBS operation will not affect outdoor MUs operation with their quality-of-service (QoS) requirements. To tackle this technical problem, an FBS transmit power adjustment (FTPA) algorithm is proposed to adjust the FBS transmit power (FTP) to avoid unwanted cochannel interference (CCI) with the neighboring MUs in downlink transmission. FTPA reduces the FTP to serve its femto users (FUs) according to the QoS requirements of the nearest neighboring MUs to the FBS so that the MU QoS requirement is guaranteed. Simulation results demonstrate that FTPA can achieve a low MU outage probability as well as serve FUs without violating the MU QoS requirements. Simulation results also reveal that FTPA has better performance on voice and video services which are the major trend of future multimedia communication in the NGN

Multi-attribute demand characterization and layered service pricing

As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare