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

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

Energy efficient monitoring of water distribution networks via compressive sensing

The recent development of low cost wireless sensors enables water monitoring through dense wireless sensor networks (WSN). Sensor nodes are battery powered devices, and hence their limited energy resources have to be optimally managed. The latest advancements in compressive sensing (CS) provide ample promise to increase WSNs lifetime by limiting the amount of measurements that have to be collected. Additional energy savings can be achieved through CS-based scheduling schemes that activate only a limited number of sensors to sense and transmit their measurements, whereas the rest are turned off. The ultimate objective is to maximize network lifetime without sacrificing network connectivity and monitoring performance. This problem can be approximated by an energy balancing approach that consists of multiple simpler subproblems, each of which corresponds to a specific time period. Then, the sensors that should be activated within a given period can be optimally derived through dynamic programming. The complexity of the proposed CS-based scheduling scheme is characterized and numerical evaluation reveals that it achieves comparable monitoring performance by activating only a fraction of the sensors.QC 20151104</p

On Maximizing Sensor Network Lifetime by Energy Balancing

Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.QC 20160420</p

Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing

The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.QC 20151215</p