Beam-searching and Transmission Scheduling in Millimeter Wave Communications

Millimeter wave (mmW) wireless networks are capable to support multi-gigabit data rates, by using directional communications with narrow beams. However, existing mmW communications standards are hindered by two problems: deafness and single link scheduling. The deafness problem, that is, a misalignment between transmitter and receiver beams, demands a time consuming beam-searching operation, which leads to an alignment-throughput tradeoff. Moreover, the existing mmW standards schedule a single link in each time slot and hence do not fully exploit the potential of mmW communications, where directional communications allow multiple concurrent transmissions. These two problems are addressed in this paper, where a joint beamwidth selection and power allocation problem is formulated by an optimization problem for short range mmW networks with the objective of maximizing effective network throughput. This optimization problem allows establishing the fundamental alignment-throughput tradeoff, however it is computationally complex and requires exact knowledge of network topology, which may not be available in practice. Therefore, two standard-compliant approximation solution algorithms are developed, which rely on underestimation and overestimation of interference. The first one exploits directionality to maximize the reuse of available spectrum and thereby increases the network throughput, while imposing almost no computational complexity. The second one is a more conservative approach that protects all active links from harmful interference, yet enhances the network throughput by 100% compared to the existing standards. Extensive performance analysis provides useful insights on the directionality level and the number of concurrent transmissions that should be pursued. Interestingly, extremely narrow beams are in general not optimal.Comment: 5 figures, 7 pages, accepted in ICC 201

Low complexity content replication through clustering in Content-Delivery Networks

Contemporary Content Delivery Networks (CDN) handle a vast number of content items. At such a scale, the replication schemes require a significant amount of time to calculate and realize cache updates, and hence they are impractical in highly-dynamic environments. This paper introduces cluster-based replication, whereby content items are organized in clusters according to a set of features, given by the cache/network management entity. Each cluster is treated as a single item with certain attributes, e.g., size, popularity, etc. and it is then altogether replicated in network caches so as to minimize overall network traffic. Clustering items reduces replication complexity; hence it enables faster and more frequent caches updates, and it facilitates more accurate tracking of content popularity. However, clustering introduces some performance loss because replication of clusters is more coarse-grained compared to replication of individual items. This tradeoff can be addressed through proper selection of the number and composition of clusters. Due to the fact that the exact optimal number of clusters cannot be derived analytically, an efficient approximation method is proposed. Extensive numerical evaluations of time-varying content popularity scenarios allow to argue that the proposed approach reduces core network traffic, while being robust to errors in popularity estimation

Τεχνικές βελτιστοποίησης και θεωρίας παιγνίων για δικτυακά συστήματα περιορισμένης ενέργειας και ευφυή δίκτυα ηλεκτρισμού

The energy needs of all sectors of our modern societies are constantly increasing. Indicatively,annual worldwide demand for electricity has increased ten-fold within the last 50 years. Thus, energyefficiency has become a major target of the research community. The ongoing research efforts are focusedon two main threads, i) optimizing efficiency and reliability of the power grid and ii) improvingenergy efficiency of individual devices / systems. In this thesis we explore the use of optimizationand game theory techniques towards both goals.Stable and economic operation of the power grid calls for electricity demand to be uniformly distributedacross a day. Currently, the price of electricity is fixed throughout a day for most users. Givenalso the highly correlated daily schedules of users, this leads to unbalanced distribution of demand.However, the recent development of low-cost smart meters enables bidirectional communication betweenthe electricity operator and each user, and hence introduces the option of dynamic pricing anddemand adaptation (a.k.a. Demand Response - DR). Dynamic pricing motivates home users to modifytheir electricity consumption profile so as to reduce their electricity bill. Eventually, users by movingdemand out of peak consumption periods lead to a more balanced total demand pattern and a morestable grid.A DR scheme has to balance the contradictory interests of the utility operator and the users.On the one hand, the operator wants to minimize electricity generation cost. On the other hand,each user aims to maximize a utility function that captures the trade-off between timely executionof demands and financial savings. In this thesis we focus on designing efficient DR schemes for theresidential sector. Initially, we introduce a realistic model of user’s response to time-varying pricesand identify the operating constraints of home appliances that make optimal demand scheduling NPHard.Thus, we devise an optimization-based dynamic pricing mechanism and demonstrate how itcan be implemented as a day-ahead DR market. Our numerical results underline the potential ofresidential DR and verify that our scheme exploits DR benefits more efficiently compared to existingones.The large number of home users though and the fact that the utility operator generally lacks the know-how of designing and applying dynamic pricing at such a large scale introduce the need fora new market entity. Aggregators act as intermediaries that coordinate home users to shift or evencurtail their demands and then resell this service to the utility operator. In this direction, we introducea three-level hierarchical model for the smart grid market and we devise the corresponding pricingmechanism for each level. The operator seeks to minimize the smart grid operational cost and offersrewards to aggregators toward this goal. Aggregators are profit-maximizing entities that competeto sell DR services to the operator. Finally, end-users are also self-interested and seek to optimizethe tradeoff between earnings and discomfort. Based on realistic demand traces we demonstrate thedominant role of the utility operator and how its strategy affects the actual DR benefits. Although theproposed scheme guarantees significant financial benefits for each market entity, interestingly usersthat are extremely willing to modify their consumption pattern do not derive the maximum financialbenefit.In parallel to optimizing the power grid itself, per device energy economy has become a goal ofutmost performance. Contemporary mobile devices are battery powered and hence characterized bylimited processing and energy resources. In addition, the latest mobile applications are particularlydemanding and hence cannot be executed locally. Instead, a mobile device can outsource its computationallyintensive tasks to the cloud over its wireless access interface, so as to maximize bothits lifetime and performance. In this thesis, we explore task offloading and Virtual Machine (VM)migration mechanisms for the mobile cloud computing paradigm that minimize energy consumptionand execution time. We identify that in order to decide whether offloading is beneficial, a mobilehas also to consider the delay and energy cost of data transfer from/to the cloud. On the other hand,the challenge for the cloud is to optimally allocate the arising VMs to its servers so as to minimizeits operating cost without sacrificing performance though. Providing quality of service guarantees isparticularly challenging in the dynamic cloud environment, due to the time-varying bandwidth of theaccess links, the ever changing available processing capacity at each server and the time-varying datavolume of each VM. Thus, we propose a mobile cloud architecture that brings the cloud closer tothe user and online VM migration policies spanning fully uncoordinated ones, in which each user orserver autonomously makes its migration decisions, up to cloud-wide ones.Nevertheless, the transceiver is one of the most power consuming components of a mobile wirelessdevice. Since the medium access layer controls when a transmission takes place, it has significantimpact on overall energy consumption and consequently on the lifetime of a device. In this direction,we investigate the potential of sleep modes when several wireless devices compete for medium access.In order to characterize the resulting energy-throughput tradeoff, we calculate the optimal throughputunder energy constraints and we model contention for wireless medium as a non-cooperative game.The strategy of each user consists of its access probability and its sleep mode schedule. We showthat the resulting game has a unique Nash Equilibrium Point and that energy constraints reduce thenegative impact of selfish behaviour, leading to bounded price of anarchy. We devise also a modifiedmedium access scheme, where the state of the medium can be sampled in the beginning of each frameand show that it leads to improved exploitation of the medium without any explicit cooperation. Finally, we move to a scenario where concurrent transmissions over the same channel are not destructivebut lead to reduced performance due to interference. In this context, we consider the problemof joint relay assignment and power control. We develop interference-aware sum-rate maximizationalgorithms that make use of a bipartite maximum weight matching formulation of the problem andgeometric programming and are amenable to distributed implementation. We also identify the importanceof interference for cell-edge users in cellular networks and demonstrate that our schemes bringtogether two main features of 4G systems, namely interference management and relaying

Low complexity algorithms for relay selection and power control in interference-limited environments

We consider an interference-limited wireless network, where multiple source-destination pairs compete for the same pool of relay nodes. In an attempt to maximize the sum rate of the system, we address the joint problem of relay assignment and power control. Initially, we study the autonomous scenario, where each source greedily selects the strategy (transmission power and relay) that maximizes its individual rate, leading to a simple one-shot algorithm of linear complexity. Then, we propose a more sophisticated algorithm of polynomial complexity that is amenable to distributed implementation through appropriate message passing. We evaluate the sum rate performance of the proposed algorithms and derive conditions for optimality. Our schemes incorporate two of the basic features of the LTE-Advanced broadband cellular system, namely interference management and relaying. We also provide guidelines on how our algorithms can be incorporated in such multichannel systems

Client and server games in peer-to-peer networks

We consider a content sharing network of noncooperative peers. The strategy set of each peer comprises, (i) client strategies, namely feasible request load splits to servers, and (ii) server strategies, namely scheduling disciplines on requests. First, we consider the request load splitting game for given server strategies such as First-In-First-Out or given absolute priority policies. A peer splits its request load to servers to optimize its performance objective. We consider the class of best response load splitting policies residing between the following extremes: a truly selfish, or egotistic one, where a peer optimizes its own delay, and a pseudo-selfish or altruistic one, where a peer also considers incurred delays to others. We derive conditions for Nash equilibrium points (NEPs) and discuss convergence to NEP and properties of the NEP. For both the egotistic cases, the NEP is unique. For the altruistic case, each of the multiple NEPs is an optimum, a global one for the FIFO case and a local one otherwise. Next, we include scheduling in peer strategies. With its scheduling discipline, a peer cannot directly affect its delay, but it can affect the NEP after peers play the load splitting game. The idea is that peer i should offer high priority to (and thus attract traffic from) higher-priority peers that cause large delay to i at other servers. We devise two-stage game models, where, at a first stage, a peer selects a scheduling rule in terms of a convex combination of absolute priorities, and subsequently peers play the load splitting game. In the most sophisticated rule, a peer selects a scheduling discipline that minimizes its delay at equilibrium, after peers play the load splitting game. We also suggest various heuristics for picking the scheduling discipline. Our models and results capture the dual client-server peer role and aim at quantifying the impact of selfish peer interaction on equilibria. ©2009 IEEE