• Energy Research

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic drone small cells. A 3-tier HetNet is considered where macrocell, on#x002F;off switching micro cells, and solar-powered drone small cells are deployed to serve the networks' subscribers. In addition to energy harvesting, the drones can power their batteries via a charging station located at the macrocell site. Pre-planned locations are identified by the mobile operator for possible drones' placement. The objective of the framework is to optimally determine the positioning of the drones in addition to the micro cells status that can be turned off in order to minimize the daily energy consumption of the network. The framework takes also into account the cells' capacity and quality of service (QoS) metric defined by the minimum received power. An integer linear programming problem is formulated to optimally determine the network status during a time blocked period. The performance of this online scheme shows important advantages in terms of energy efficiency and connectivity compared to the traditional case without drones specially when the network is congested.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic drone small cells. A 3-tier HetNet is considered where macrocell, on#x002F;off switching micro cells, and solar-powered drone small cells are deployed to serve the networks' subscribers. In addition to energy harvesting, the drones can power their batteries via a charging station located at the macrocell site. Pre-planned locations are identified by the mobile operator for possible drones' placement. The objective of the framework is to optimally determine the positioning of the drones in addition to the micro cells status that can be turned off in order to minimize the daily energy consumption of the network. The framework takes also into account the cells' capacity and quality of service (QoS) metric defined by the minimum received power. An integer linear programming problem is formulated to optimally determine the network status during a time blocked period. The performance of this online scheme shows important advantages in terms of energy efficiency and connectivity compared to the traditional case without drones specially when the network is congested.

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

This paper proposes and investigates a green virtualization framework for infrastructure sharing among multiple cellular operators whose networks are powered by a combination of conventional and renewable sources of energy. Under the proposed framework, the virtual network formed by unifying radio access infrastructures of all operators is optimized for minimum energy consumption by deactivating base stations (BSs) with low traffic loads. The users initially associated to those BSs are off-loaded to neighboring active ones. A fairness criterion for collaboration based on roaming prices is introduced to cover the additional energy costs incurred by host operators. The framework also ensures that any collaborating operator is not negatively affected by its participation in the proposed virtualization. A multi-objective linear programming problem is formulated to achieve energy and cost efficiency of the networks' operation by identifying the set of inter-operator roaming prices. For the case when collaboration among all operators is infeasible due to profitability, capacity, or power constraints, an iterative algorithm is proposed to determine the groups of operators that can viably collaborate. Results show significant energy savings using the proposed virtualization as compared to the standalone case. Moreover, collaborative operators exploiting locally generated renewable energy are rewarded more than traditional ones. Accepted for publication in IEEE Transactions on Cognitive Communications and Networking

This paper proposes and investigates a green virtualization framework for infrastructure sharing among multiple cellular operators whose networks are powered by a combination of conventional and renewable sources of energy. Under the proposed framework, the virtual network formed by unifying radio access infrastructures of all operators is optimized for minimum energy consumption by deactivating base stations (BSs) with low traffic loads. The users initially associated to those BSs are off-loaded to neighboring active ones. A fairness criterion for collaboration based on roaming prices is introduced to cover the additional energy costs incurred by host operators. The framework also ensures that any collaborating operator is not negatively affected by its participation in the proposed virtualization. A multi-objective linear programming problem is formulated to achieve energy and cost efficiency of the networks' operation by identifying the set of inter-operator roaming prices. For the case when collaboration among all operators is infeasible due to profitability, capacity, or power constraints, an iterative algorithm is proposed to determine the groups of operators that can viably collaborate. Results show significant energy savings using the proposed virtualization as compared to the standalone case. Moreover, collaborative operators exploiting locally generated renewable energy are rewarded more than traditional ones. Accepted for publication in IEEE Transactions on Cognitive Communications and Networking

This paper proposes an optimized transmission scheme for Energy Harvesting (EH)-based two-way multiple- relay systems. All relays are considered as EH nodes that harvest energy from renewable and radio frequency (RF) sources, then use it to forward the information to the destinations. The power-splitting (PS) protocol, by which the EH node splits the input RF signal into two components for information transmission and energy harvesting, is adopted in the relay side. The objective is to optimize the PS ratios and the relays' transmit power levels in order to maximize the total sum-rate utility over multiple coherent time slots. An optimization approach based on geometric programming is proposed to solve the problem. Numerical results illustrate the behavior of the EH-based two-way multiple-relay system with respect to various parameters and compare the performance of the proposed approach with that of the dual problem-based approach.