In this paper, a novel context-aware approach for resource allocation in two-tier wireless small cell networks (SCNs) is proposed. In particular, the SCN's users are divided into two types: frequent users, who are regular users of certain small cells, and occasional users, who are one-time or infrequent users of a particular small cell. Given such context information, each small cell base station (SCBS) aims to maximize the overall performance provided to its frequent users, while ensuring that occasional users are also well serviced. We formulate the problem as a noncooperative game in which the SCBSs are the players. The strategy of each SCBS is to choose a proper power allocation so as to optimize a utility function that captures the tradeoff between the users' quality-of-service gains and the costs in terms of resource expenditures. We provide a sufficient condition for the existence and uniqueness of a pure strategy Nash equilibrium for the game, and we show that this condition is independent of the number of users in the network. Simulation results show that the proposed context-aware resource allocation game yields significant performance gains, in terms of the average utility per SCBS, compared to conventional techniques such as proportional fair allocation and sum-rate maximization.

Cloud service providers enable tenants to elastically scale resources to meet their demands. While running cloud applications, a tenant aiming to minimize cost is often challenged with crucial tradeoffs. For instance, upon each arrival of a query, a Web application can either choose to pay for CPU to compute the response fresh, or pay for cache storage to store the response to reduce future compute costs. The Ski-Rental problem abstracts such scenarios where a tenant is faced with a to-rent-or-to-buy tradeoff; in its basic form, a skier should choose between renting or buying a set of skis without knowing the number of days she will be skiing. In the multislope version of the Ski-Rental problem, the skier can choose among multiple services that differ in their buying and renting prices. In this paper, we introduce a variant of the classical Ski-Rental problem in which we assume that the skier knows the first (or second) moment of the distribution of the number of ski days in a season. We also extend the classical multislope Ski-Rental problem, where the skier can choose among multiple services, to this setting. We demonstrate that utilizing this information leads to achieving the best worst-case expected competitive ratio performance. Our method yields a new class of randomized algorithms that provide arrivals-distribution-free performance guarantees. Simulations illustrate that our scheme exhibits robust average-cost performance that combines the best of the well-known deterministic and randomized schemes previously proposed to tackle the Ski-Rental problem.

Cloud service providers (CSPs) enable tenants to elastically scale their resources to meet their demands. In fact, there are various types of resources offered at various price points. While running applications on the cloud, a tenant aiming to minimize cost is often faced with crucial trade-off considerations. For instance, upon each arrival of a query, a web application can either choose to pay for CPU to compute the response fresh, or pay for cache storage to store the response so as to reduce the compute costs of future requests. The SkiRental problem abstracts such scenarios where a tenant is faced with a to-rent-or-to-buy trade-off; in its basic form, a skier should choose between renting or buying a set of skis without knowing the number of days she will be skiing. In this paper, we introduce a variant of the classical SkiRental problem in which we assume that the skier knows the first (or second) moment of the distribution of the number of ski days in a season. We demonstrate that utilizing this information leads to achieving the best worst-case expected competitive ratio (CR) performance. Our method yields a new class of randomized algorithms that provide arrivals-distribution-free performance guarantees. Further, we apply our solution to a cloud file system and demonstrate the cost savings obtained in comparison to other competing schemes. Simulations illustrate that our scheme exhibits robust average-cost performance that combines the best of the well-known deterministic and randomized schemes previously proposed to tackle the Ski-Rental problem.

This paper tackles the downlink user scheduling and transmit beamforming problems in MIMO-OFDMA by extending a recent algorithm that maximizes the weighted sum rate (WSR) to all users in MIMO flat fading channels. The proposed method has a complexity that is proportional to the number of OFDMA subcarriers, which makes it practically attractive. Having assigned users to each subcarrier and designed beamformers for each user in each subcarrier, it remains to find the best, in terms of rate maximization, adaptive modulation and coding (AMC) mode to use for each data stream. The latter problem is solved in the second half of the paper through viewing the channel from the base station to the k-th receiver as a multiple access channel (MAC) with Nk“users”, where Nkis the number of antennas at receiver k. The proposed method maps the available AMC modes to the space of allowed theoretical rates, using the signal-to-noise ratio (SNR) gap to capacity concept, and selects the operating point with the largest sum-rate.

In emerging small cell wireless, each femtocell access point (FAP) can either service its home subscribers exclusively (i.e., closed access) or open its access to accommodate a number of macrocell users so as to reduce cross-tier interference. In this paper, we propose a game-theoretic framework that enables the FAPs to strategically decide on their uplink access policy. We formulate a noncooperative game in which the FAPs are the players that want to strategically decide on whether to use a closed or an open access policy in order to maximize the performance of their registered users. Each FAP aims at optimizing the tradeoff between reducing cross-tier interference, by admitting macrocell users, and the associated cost in terms of allocated resources. Using novel analytical techniques, we show that the game always admits a pure strategy Nash equilibrium, despite the discontinuities in the utility functions. Further, we propose a distributed algorithm that can be adopted by the FAPs to reach their equilibrium access policies. Simulation results show that the proposed algorithm provides an improvement of 85.4% relative to an optimized open access scheme in the average worst-case FAP utility.

In this work, we study the problem of power allocation and adaptive modulation in teams of decision makers. We consider the special case of two teams with each team consisting of two mobile agents. Agents belonging to the same team communicate over wireless ad hoc networks, and they try to split their available power between the tasks of communication and jamming the nodes of the other team. The agents have constraints on their total energy and instantaneous power usage. The cost function adopted is the difference between the rates of erroneously transmitted bits of each team. We model the adaptive modulation problem as a zero-sum matrix game which in turn gives rise to a a continuous kernel game to handle power control. Based on the communications model, we present sufficient conditions on the physical parameters of the agents for the existence of a pure strategy saddle-point equilibrium (PSSPE).