Hakim Ghazzai

A Hybrid Energy Sharing Framework for Green Cellular Networks

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterward, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated, where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

Transmit Power Minimization and Base Station Planning for High-Speed Trains With Multiple Moving Relays in OFDMA Systems

A high-speed railway system equipped with moving relay stations placed on the middle of the ceiling of each train wagon is investigated. The users inside the train are served in two hops via orthogonal frequency-division multiple-access (OFDMA) technology. In this paper, we first focus on minimizing the total downlink power consumption of the base station (BS) and the moving relays while respecting specific quality-of-service (QoS) constraints. We first derive the optimal resource-allocation solution, in terms of OFDMA subcarriers and power allocation, using the dual decomposition method. Then, we propose an efficient algorithm based on the Hungarian method to find a suboptimal but low-complexity solution. Moreover, we propose an OFDMA planning solution for high-speed trains by finding the maximal inter-BS distance, given the required user data rates to perform seamless handover. Our simulation results illustrate the performance of the proposed resource-allocation schemes in the case of Third-Generation Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) and compare them with previously developed algorithms, as well as with the direct transmission scenario. Our results also highlight the significant planning gain obtained, owing to the use of multiple relays instead of the conventional single-relay scenario.

Front-End Intelligence for Large-Scale Application-Oriented Internet-of-Things

The Internet-of-things (IoT) refer to the massive integration of electronic devices, vehicles, buildings, and other objects to collect and exchange data. It is the enabling technology for a plethora of applications touching various aspects of our lives, such as healthcare, wearables, surveillance, home automation, smart manufacturing, and intelligent automotive systems. Existing IoT architectures are highly centralized and heavily rely on a back-end core network for all decision-making processes. This may lead to inefficiencies in terms of latency, network traffic management, computational processing, and power consumption. In this paper, we advocate the empowerment of front-end IoT devices to support the back-end network in fulfilling end-user applications requirements mainly by means of improved connectivity and efficient network management. A novel conceptual framework is presented for a new generation of IoT devices that will enable multiple new features for both the IoT administrators as well as end users. Exploiting the recent emergence of software-defined architecture, these smart IoT devices will allow fast, reliable, and intelligent management of diverse IoT-based applications. After highlighting relevant shortcomings of the existing IoT architectures, we outline some key design perspectives to enable front-end intelligence while shedding light on promising future research directions.

Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the users quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between competitive mobile operators. The presented simulation results measure the gain that could be obtained using these techniques compared with that of traditional scenarios. Finally, this paper discusses the issues and challenges related to the implementations of these techniques in real environments.

Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework With Base Station ON/OFF Switching

In this paper, the problem of energy efficiency in cellular heterogeneous networks (HetNets) is investigated using radio resource and power management combined with the base station (BS) ON/OFF switching. The objective is to minimize the total power consumption of the network while satisfying the quality of service requirements of each connected user. We consider the case of coexisting macrocell BS, small cell BSs, and private femtocell access points (FAPs). Three different network scenarios are investigated, depending on the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs, and HetNets with semiclosed FAPs. A unified framework is proposed to simultaneously allocate spectrum resources to users in an energy efficient manner and switch OFF redundant small cell BSs. The high complexity dual decomposition technique is employed to achieve optimal solutions for the problem. A low complexity iterative algorithm is also proposed and its performances are compared to those of the optimal technique. The particularly interesting case of semiclosed FAPs, in which the FAPs accept to serve external users, achieves the highest energy efficiency due to increased degrees of freedom. In this paper, a cooperation scheme between FAPs and mobile operator is also investigated. The incentives for FAPs, e.g., renewable energy sharing and roaming prices, enabling cooperation are discussed to be considered as a useful guideline for interoperator agreements.

Wireless RF-based energy harvesting for two-way relaying systems

In this paper, we investigate the Energy Harvesting (EH)-based two-way relaying system using Amplify-and-Forward (AF) and Decode-and-Forward (DF) strategies. The relay is considered as an EH node that harvests the received Radio Frequency (RF) signal and uses this harvested energy to forward the information. Two relaying protocols based on Time Switching (TS) and Power Splitting (PS) receiver architectures are proposed to enable EH and information processing at the relay. Analytical throughput expressions are derived and optimized for both protocols. The goal is to find the optimal TS and PS ratios that maximize the total throughput Numerical results illustrate the performance of TS and PS protocols for different strategies, and show that at high signal-to-noise ratio, PS is superior to TS, and AF is superior to DF in terms of achievable sum-rate.

Joint Demand-Side Management in Smart Grid for Green Collaborative Mobile Operators Under Dynamic Pricing and Fairness Setup

In this paper, the interactions between multiple mobile operators, owning heterogeneous cellular networks, and energy retailers existing in the smart grid are investigated. Energy procurement decisions of the cellular networks sharing common energy sources are jointly optimized while considering both dynamic energy pricing and varying pollution levels of energy sources. The objective is to enable collaboration among mobile operators in their demand-side management (DSM) to achieve economical and environmental goals. This is performed by solving a unified optimization problem aiming at maximizing a metric based on mobile operators’ profits while limiting the amount of carbon dioxide (CO2) emitted by all networks. In this paper, three utility metrics, namely, Sum, Max-min, and Proportional Fair utilities, are considered to reflect the degree of fairness among mobile operators in the optimized DSM. Closed-form expressions of the optimal procured energy from each retailer used to power the marcocell and active small cell base stations (BSs) are derived for pricing profiles and utility metrics. Furthermore, the BS sleeping strategy is applied, in a centralized or a decentralized manner, in order to reduce the network energy consumption during low traffic periods. Finally, the impact of renewable energy generation uncertainty on the energy procurement decision is examined. The behavior of the different actors in the energy procurement decision is investigated through simulations. Results show that significant CO2 emissions reduction can be achieved thanks to the optimized DSM and the BS sleeping strategy.

Optimized Energy Procurement for Cellular Networks Powered by Smart Grid Based on Stochastic Geometry

In this paper, we investigate the energy procurement decision of cellular networks powered by smart grid. Multiple retailers producing energy from different sources characterized by their prices and pollutant emission levels are available to power the network. The green cellular operator, constrained by a maximum tolerated level of CO2 emissions and quality of service (QoS) requirements, has to decide the optimal quantity of energy to procure from each retailer in order to minimize the energy cost and hence maximize its profit. Operators also provide multiple services to their subscribers characterized by the quality of the received signal and the probability of coverage. In this study, we employ stochastic geometry to determine the average power required per user to achieve the target probability of coverage which is then used to find the total base station power consumption. The resulting power requirements of the network are used in the optimization problem to find the optimal amount of energy to procure from each retailer. Our results illustrate the procurement behavior of cellular networks versus the CO2 emission threshold and the QoS requirements of the subscribers.

Achievable Rates of UAV-Relayed Cooperative Cognitive Radio MIMO Systems

We study the achievable rate of an uplink MIMO cognitive radio system where the primary user (PU) and the secondary user (SU) aim to communicate to the closest primary base station (BS) via a multi-access channel through the same unmanned aerial vehicle (UAV) relay. The SU message is then forwarded from the primary BS to the secondary network with a certain incentive reward as a part of the cooperation protocol between both the networks. A special linear precoding scheme is proposed to enable the SU to exploit the PU free eigenmodes. We analyze two scenarios in which the UAV relay gain matrix is either fixed or optimized. We derive the optimal power allocation that maximizes the achievable rate of the SU respecting power budget, interference, and relay power constraints. Numerical results highlight the cognitive rate gain of our proposed scheme with respect to various problem parameters. We also highlight the effect of UAV altitude on the SU and PU rates. Finally, when the relay matrix is optimized, we show that the PU rate is remarkably enhanced and that the SU rate is only improved at high-power regime.

A multi-relay selection scheme for time switching energy harvesting two-way relaying systems

In this paper, a multiple relay selection scheme for Energy Harvesting (EH)-based two-way relaying is investigated. All the relays are considered as EH nodes that harvest energy from renewable and radio frequency sources, then use it to forward the information to the sources. The time-switching protocol (TS), in which the receiver switches between transmitted information and harvested energy, is adopted in the relay side. The goal is to find the optimal TS ratios associated with the selected relays that maximize a rate-based utility function over multiple coherent time slots. Two metrics reflecting the degrees of fairness in the optimization are investigated. A joint-optimization solution based on binary particle swarm optimization is proposed to solve the problem. Numerical results illustrate the behavior of the TWR network according to the considered utility functions, the generated amount renewable energy, in addition to other system parameters.