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Green Cellular Networks: A Survey, Some Research Issues and Challenges

Ziaul Hasan, Hamidreza Boostanimehr, Vijay K. Bhargava

arXiv:1108.5493v3cs.NI

TL;DR

Growing cellular traffic and base-station deployment make energy efficiency important for operators’ costs and environmental effects. The paper surveys metrics, base-station savings, smaller-cell planning, and cognitive or cooperative techniques, and identifies research challenges for green cellular networks. It reports potential savings from switch-mode power amplifiers, dynamic base-station management, and heterogeneous deployment.

  • Problem

    Rising cellular energy costs and environmental effects create a need to improve energy efficiency across cellular-network infrastructure.

  • Method

    The paper surveys energy-efficiency metrics, base-station techniques, heterogeneous smaller-cell deployment, and cognitive-radio and cooperative-relaying approaches.

  • Results

    The survey identifies switch-mode power amplifiers, power-saving protocols, dynamic cell management, and smaller-cell deployment as energy-saving approaches, with reported savings of around 70%, up to 40%, and up to 60% in cited cases.

  • Takeaways & Limitations

    Green cellular networking requires coordinated work on metrics, base-station design, network planning, and energy efficiency in cognitive and cooperative systems.

Abstract

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Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technology

I. INTRODUCTION

Rapid growth in cellular traffic and infrastructure is increasing operators’ energy, cost, and environmental pressures. The paper surveys green-network approaches spanning metrics, base-station architecture, network planning, and cognitive or cooperative techniques.

  • Motivation: More than 4 million base stations each consume an average of 25MWh annually, while ICT contributes around 2% of total carbon emissions.Off-grid base stations may cost ten times more to operate than grid-connected sites.
  • Motivation: Rising energy costs and cellular-network carbon emissions have prompted network operators and bodies such as 3GPP and ITU to address energy efficiency.The paper situates green cellular networking within this emerging research and standardization activity.
  • Research directions: Energy-efficient cellular design requires technologies including efficient wireless architectures and protocols, redesigned base stations, smart grids, cognitive radio, cooperative relaying, and smaller-cell deployment.These approaches are presented as ways to reduce energy consumption while maintaining profitability.
  • Research directions: Cognitive radio and cooperative relaying remain immature for cellular deployment, and their unresolved energy concerns motivate further research.The paper highlights deployment issues and unanswered energy challenges in these technologies.
  • Scope and organization: The survey organizes green networking around green metrics, base-station architectural changes, network planning, and efficient system design.It also considers broader perspectives and gives special emphasis to cognitive and cooperative techniques.

II. MEASURING GREENNESS: THE METRICS

The paper surveys facility-, equipment-, and network-level energy-efficiency metrics while emphasizing that no single metric captures every relevant system property. Future metrics should incorporate deployment costs, quality of service, and spectral efficiency.

  • Metric taxonomy: Energy-efficiency metrics are classified into facility-level, equipment-level, and network-level categories according to the system scale they evaluate.Facility metrics assess deployment environments, equipment metrics assess individual equipment, and network metrics include broader network features.
  • Facility and equipment metrics: PUE and its reciprocal DCE assess facility-level efficiency, but PUE does not quantify the efficiency of individual equipment.PUE is the ratio of total facility power consumption to total equipment power consumption.
  • Equipment metrics: Power per user supports provider-side economic and planning tradeoffs, whereas ECR offers manufacturers insight into hardware performance.Power per user is measured in Watt/user, while ECR is measured in Watt/Gbps.
  • Metric limitations: ECR, TEER, and TEEER do not capture all system properties, and dynamic conditions such as full-load, half-load, and idle operation should complement existing metrics.The paper describes metric development as an active research area.
  • Network metrics: Network-level metrics become more difficult to define because coverage, capacity, traffic demand, and propagation effects interact with energy consumption.Rural indicators use coverage area, while urban indicators use capacity or busy-hour users.
  • Future requirements: A non-exhaustive metric set should eventually include deployment costs, backhaul, transmission delay, quality-of-service requirements, and spectral efficiency.The paper links consensus on standard metrics with progress toward research coordination and standardization.

III. ARCHITECTURE: ENERGY SAVINGS IN BASE STATIONS

The paper identifies rapidly increasing base-station deployment and data-intensive standards as important drivers of cellular-network energy consumption. This motivates energy conservation in both hardware circuitry and protocols.

  • Base-station energy pressure: Worldwide cellular base stations have grown from a few hundred thousand to many millions, increasing greenhouse gases, pollution, and operating energy costs.The passage connects this expansion with the rapid growth of mobile communication technology.

A. Minimizing BS energy consumption

Base-station energy consumption can be reduced through hardware redesign and software or system features that balance energy use against performance.

  • BS energy consumption can be reduced through improved hardware design and additional software or system features.
  • Power-amplifier efficiency is a central target because the PA dominates BS energy consumption.
  • Table I presents energy-efficiency metrics relevant to evaluating BS energy consumption.

1) Improvements in Power Amplifier:

The paper surveys power-amplifier improvements and network-level mechanisms that adapt cellular capacity to traffic, including sleep modes, cooperative cell management, and cell zooming.

  • Improvements in Power Amplifier: Radio components consume more than 80% of BS energy, with the PA accounting for almost 50% and achieving only 5%–20% total efficiency.The PA also wastes 80%–90% of its energy as heat, increasing cooling requirements.
  • Improvements in Power Amplifier: Switch-mode PAs reduce heat generation and current draw, with expected overall component efficiency of around 70%.
  • Improvements in Power Amplifier: Flexible PA architectures should adapt amplifier output to traffic demand because maximum-output operation wastes energy during low-load periods.
  • Improvements in Power Amplifier: Switching off transceivers when transmission or reception is unnecessary offers a direct power-saving mechanism for future high-speed networks.LTE incorporates this concept through power-saving protocols, while BS sleep protocols remain insufficiently addressed in current standards.
  • Energy-Aware Cooperative BS Power Management: Traffic fluctuations make static cell deployment inefficient, motivating coordinated BS sleep decisions that preserve coverage through remaining active cells.
  • Energy-Aware Cooperative BS Power Management: Selective reduction of active low-load cells can yield energy savings of the order of 20% and above.Cell zooming further adjusts cell size to balance traffic and reduce energy consumption, allowing neighboring cells to cover users when a cell sleeps.

1) Implementation:

Implementation combines network-state-aware cell reconfiguration with self-organizing techniques, while renewable power addresses costly and carbon-intensive off-grid BS operation.

  • Implementation: A cell-zooming server can sense traffic and channel quality, then coordinate neighboring cells when cells zoom in or out.
  • Implementation: Neighboring cells can reconfigure to guarantee coverage after cell-zooming adjustments.
  • Implementation: Self-organizing and cell-zooming networks can improve energy conservation and user experience, but practical deployment faces planning, threshold, coverage, and traffic-tracking challenges.A two-layer architecture is reported to achieve power savings of up to 40% over the entire day.
  • Implementation: Off-grid BSs may rely on diesel generators that are expensive and produce CO2 emissions.One generator is reported to consume an average of 1500 litres of diesel per month and cost approximately $30,000 per year.
  • Implementation: The Green Power for Mobile program targets renewable power for 118,000 off-grid BSs and could save up to 2.5 billion litres of diesel annually.The program could also cut annual carbon emissions by up to 6.8 million tonnes, with reported payback periods of less than three years at many sites.

D. Other ways to reduce BS power consumption

The paper surveys network-planning and base-station techniques for reducing cellular power consumption, emphasizing smaller-cell deployments and transmission-management strategies. These approaches require balancing energy savings against coverage, capacity, loading, and mobility effects.

  • Base-station planning: Reducing the number of base stations can directly lower network energy consumption, but balancing cell size and base-station capacity is challenging.Proposed design features include diversity, feederless sites, extended cells, low-frequency bands, six-sector sites, and smart antennas.
  • Heterogeneous deployment: Shorter propagation distances in micro-, pico-, and femtocells can reduce transmission power while supporting dense, high-data-rate traffic.Micro- and picocells serve small, dense-traffic areas, while femtocells cover still smaller areas.
  • Heterogeneous deployment: A picocell deployment can reduce network energy consumption by up to 60% compared with macrocells alone.Excessive small-cell deployment may leave the macrocell base station operating under low load, so deployment strategies require careful investigation.
  • Small-cell operation: Turning off femtocell transmissions and processing when no active call is present can save power, while self-organizing small-cell networks introduce coverage, interference, mobility, and security challenges.For a voice-traffic model, the cited mechanism provides average power savings of 37.5% and, under high traffic, five times fewer mobility events than fixed pilot transmission.

V. ENABLING TECHNOLOGIES: COGNITIVE RADIO AND COOPERATIVE RELAYING

The paper examines cognitive radio and cooperative relaying as enabling technologies for greener cellular networks. Cognitive radio targets spectrum underutilization and power saving, while cooperative relaying addresses coverage and transmission-power demands through multihop communication.

  • Cognitive radio: Cognitive radio intelligently senses spectrum use and accesses unused frequency bands to address spectrum underutilization.Its broader definition also includes adapting measurable network parameters to meet objectives such as power saving.
  • Cognitive radio: Cognitive-radio techniques can reduce energy consumption while maintaining required QoS under varied channel conditions.The paper identifies algorithmic complexity as a barrier to vendor implementation and calls for feasible, less complex, less expensive schemes.
  • Cooperative relaying: Direct transmission to distant users is power-intensive because path loss, shadowing, and fading make reliable long-range coverage costly.Cooperative communication can create virtual MIMO systems and improve coverage and capacity where large antennas cannot be installed on mobile devices.
  • Cooperative relaying: Multihop communication divides a direct path into shorter links, reducing channel impairment effects and enabling lower transmission power at base stations and relays.Prior work reports that two-hop communication consumes less energy than direct communication and that multihopping can reduce average energy per call in CDMA networks.

1) Enabling Green Communication via Fixed Relays:

The paper presents fixed relays as a way to increase spatial reuse and reduce transmission power without the cost and complexity of deploying additional base stations. It also notes that user cooperation must address the relay user's energy burden.

  • Fixed-relay motivation: With path-loss exponent 4, increasing base-station density by 1.5 times can reduce transmitting power by a factor of 5 while preserving SNR.The example motivates higher-density deployments as a route to lower energy consumption and greater spatial reuse.
  • Fixed-relay deployment: Fixed relays can substitute for new base stations because they cover smaller areas at lower power and are less costly and complex to deploy.The paper describes relays as a flexible means to improve spatial reuse and reduce system power relative to direct-transmission systems.
  • User cooperation: User cooperation can improve data rate and robustness, but the relaying user's energy consumption can make the paradigm unattractive.Self-interested user cooperation is presented as a promising future direction for improving energy efficiency in mobile networks.
  • Future green communication: A 50% initial power saving followed by a further 50% energy-efficiency improvement yields an additional 25% net saving.The calculation is presented in the context of cognitive and cooperative technologies for future mobile communication systems.

A. Low-Energy Spectrum Sensing

The paper identifies energy-aware spectrum sensing, medium access, routing, and retransmission as open design areas for cognitive and cooperative networks. The central challenge is improving energy efficiency without sacrificing QoS, reliability, or manageable complexity.

  • Low-energy spectrum sensing: Cognitive-radio sensing can consume substantial additional power because spectrum must be sensed frequently and sensor data processed.Energy detection may require long sensing times for accurate primary-signal detection, motivating cluster-based, sequential, and compressive sensing schemes.
  • Energy-aware MAC: Cooperative and cognitive MAC design must coordinate relays and account for sensing accuracy, sensing duration, and time-varying primary-user channel availability.Energy optimization can conflict with system performance, user satisfaction, and QoS.
  • Green routing: Existing joint routing and spectrum-allocation research often prioritizes throughput rather than directly incorporating power-efficiency constraints.The paper calls for cellular-network schemes that optimize energy consumption while delivering desired system performance and user satisfaction.
  • Energy-aware retransmission: HARQ can potentially reduce decoding energy and total transmission-plus-circuit energy across sources, destinations, and relays in delay-insensitive systems.The paper identifies HARQ-based MAC protocols as a potential way to reduce energy costs in cognitive and cooperative systems.
  • Energy-aware MAC: Relayed transmissions add network power consumption, so future MAC protocols should quantify QoS gains against their additional energy cost and coordinate direct and relayed access.The paper also emphasizes low-complexity designs.
  • Cognitive MAC: Cognitive MAC can improve energy efficiency by avoiding collisions between primary and secondary users while keeping distributed access complexity low.Channel-availability statistics may support QoS provisioning, but energy efficiency must remain an explicit trade-off.

C. Energy-Efficient Resource Management with Applications in Heterogeneous Networks

Energy-efficient resource management remains underdeveloped for cooperative and cognitive wireless systems, especially across varied objectives and constraints. The section identifies relay placement, relay selection, scheduling, heterogeneous deployment, and interference management as key research directions.

  • Energy-efficient resource management for cooperative and cognitive systems is not yet fully developed under varied network objectives and constraints.
  • Cooperative systems: Cooperative systems should minimize relay energy consumption while satisfying QoS requirements and address where, whom, and when to relay.
  • Cognitive radio systems: Energy per bit and low-power scheduling are proposed for cognitive radio systems, with round-robin scheduling reported as more energy efficient than opportunistic scheduling at equal performance.
  • Heterogeneous networks: Energy-aware heterogeneous networks should coordinate high-power macrocells and low-power femtocells while preventing cross-tier interference.

D. Cross-Layer Design and Optimization

The paper presents cross-layer optimization as a way to address energy scarcity across protocol layers, while also considering smart-grid coordination and embodied energy in network modeling. It highlights uncertainty in channel information and sensing as a practical boundary for cooperative and cognitive designs.

  • Independent protocol-layer optimization can be suboptimal when energy is scarce, motivating cross-layer schemes that jointly consider multiple network parameters.
  • Cross-layer optimization may jointly assign subcarriers, rates, power, channel access, and routing while accounting for sensing and other system errors.
  • Cooperative relaying among cognitive users can improve spectrum efficiency and fairness by allowing low-traffic users to relay for users with high traffic demand and limited bandwidth.
  • Uncertainty issues: Cooperative and cognitive methods commonly assume perfect CSI, while channel, sensing, interference, feedback, and hardware uncertainties remain insufficiently addressed.
  • Smart-grid coordination: Smart-grid coordination can use measurements from base stations to manage energy consumption cooperatively without adversely affecting QoS and capacity.
  • Embodied energy: Including embodied energy alongside operating energy can produce solutions that oppose simply increasing the number of lower-power base stations.

VIII. CONCLUSION

The conclusion synthesizes energy-efficiency metrics, base-station savings, smaller-cell deployment, cognitive and cooperative techniques, and broader network perspectives. It emphasizes that green cellular networking remains broad, with substantial research issues and challenges ahead.

  • The survey covers metrics, base-station energy savings, smaller-cell architectures, cognitive radio, cooperative relaying, smart grids, and embodied energy.
  • Base-station efficiency: Power-amplifier improvements can reduce base-station hardware consumption and dependence on air-conditioning, while sleep-mode protocols remain a future research area.
  • Heterogeneous deployment: Microcell, picocell, and femtocell deployment can reduce network power consumption, but excessive small-cell deployment may reduce central-base-station efficiency and increase embodied energy.
  • Emerging technologies: Cognitive and cooperative networks present research challenges involving low-energy sensing, energy-aware MAC and routing, resource management, cross-layer optimization, and uncertainty.
  • Green cellular networking has broad unresolved issues, and addressing them is relevant to minimizing the environmental and financial impacts of cellular technology.
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