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Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

Shunqing Zhang, Qingqing Wu, Shugong Xu, Geoffrey Ye Li

arXiv:1604.07918v1cs.IT

TL;DR

Green radio is valued for reducing operational expenditure. This paper surveys fundamental green tradeoffs across typical 4G and 5G technologies, summarizes theoretical achievements and practical energy-efficient schemes, and analyzes open technical issues.

  • Problem

    Green radio research matters not only theoretically but also for operational expenditure reduction in wireless communications.

  • Method

    The paper comprehensively surveys fundamental green tradeoffs in typical 4G and 5G technologies and organizes research according to this tradeoff framework.

  • Results

    The survey summarizes theoretical achievements, practical energy-efficient schemes, and open technical issues across OFDM, NOA, MIMO, HetNets, and UDNs.

  • Takeaways & Limitations

    The paper supports further research toward a fundamental breakthrough in the related aspects of green tradeoffs.

Abstract

from arXiv · show

With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: spectrum efficiency (SE) versus energy efficiency (EE), deployment efficiency (DE) versus energy efficiency (EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing (OFDM) and non-orthogonal aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous networks (HetNets). We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.

I. INTRODUCTION

Green radio addresses unsustainable growth in wireless traffic and energy consumption through a framework of four fundamental tradeoffs. This paper surveys those tradeoffs, reviews their application to major 4G and 5G technologies, and identifies related design impacts and challenges.

  • Green radio is valued for reducing operational expenditure and supporting sustainable wireless communications amid rising traffic and energy consumption.
  • Earlier research examined energy, bandwidth, deployment, and delay tradeoffs independently, motivating a more holistic framework.
  • The framework organizes four relationships: SE-EE, DE-EE, DL-PW, and BW-PW.
  • The paper reviews extensive research by categorizing efforts according to fundamental green tradeoffs and examines theoretical results, energy-efficient solutions, and hardware characteristics.
  • Coverage spans OFDM, MIMO, and HetNets for 4G, then reevaluates NOA, M-MIMO, and UDN under the same framework for 5G.
  • The paper reports that tradeoff curves have been characterized, some earlier open issues resolved, and physical limitations identified.

III. OFDM AND NOA

OFDM and its non-orthogonal extension NOA apply fundamental green tradeoffs to energy-efficient 4G and 5G resource use. OFDM analyses relate bandwidth, spectral efficiency, energy efficiency, delay, and power, while OFDMA extends these ideas through user and sub-channel adaptation.

  • OFDM and NOA represent orthogonal 4G and non-orthogonal 5G approaches to time-frequency resource utilization.
  • OFDM fundamental tradeoffs connect achievable system performance with consumed power resources.
  • OFDM energy efficiency and deployment efficiency increase with bandwidth while spectral efficiency decreases, favoring wide-band operation.
  • Because ergodic capacity increases with bandwidth, OFDM provides better bandwidth-power and delay-power tradeoffs, especially at low spectral efficiency.
  • Practical OFDM accounts for circuit power and PAPR, yet achieves better energy-efficiency and power performance than baselines.
  • OFDMA extends the framework through opportunistic sub-channel user selection and adaptive coding and modulation.
  • Downlink maximum energy efficiency is strictly quasi-concave in spectral efficiency, while minimum individual energy-efficiency maximization protects the worst user.

2) Energy Efficient Solutions:

Energy-efficient OFDM designs span downlink, uplink, and delay-sensitive settings, using power allocation, link adaptation, scheduling, and distributed optimization. The literature balances efficiency against delay, signaling, complexity, and multi-cell coordination constraints.

  • Proportional-rate energy-efficiency formulations are strictly quasi-concave in the proportional-rate parameter λ, enabling energy-efficiency-optimal algorithms.
  • Downlink energy-efficient transmission can significantly improve energy efficiency with relatively small spectral-efficiency loss.
  • Delay-sensitive OFDM optimization uses effective capacity and transforms delay guarantees into transmission-rate requirements when perfect CSI is available.
  • Lyapunov stochastic stability supports dynamic back-pressure power control for optimizing delay-power relations.
  • Distributed uplink power control can use localized channel states and transmission history, while water-filling remains applicable for fixed transmit power and selected subcarriers.
  • Optimal uplink strategies may require complicated iterative search, global information updates, and signaling overhead, limiting applicability in multi-cell environments.
  • Multi-cell noncooperative games jointly determine subcarrier allocation and distributed power control, with convergence and order-wise energy-efficiency improvement over benchmarks.
  • Large-scale methods prioritize energy and spectral efficiency, whereas delay-sensitive and power-limited applications favor approaches incorporating delay or power constraints.

3) Future Research Issues:

Future research must address realistic circuit-power variation, dynamic inter-cell interference, and the implementation costs of energy-efficient OFDM and NOA systems. The paper highlights coordination, hardware, waveform, and resource-allocation directions across 5G technologies.

  • Distributed scheduling makes sub-channel power and modulation choices unpredictable, leaving inter-cell interference difficult to control at the user side.
  • Heterogeneous circuit power consumption affects energy-efficiency evaluation and fairness, motivating formulations that include fairness, near-far effects, and circuit-power differences.
  • Potential remedies for dynamic interference include low-PAPR signals, low-noise spectrum filtering, interference-information exchange, multi-cell cooperation, and coordinated cancellation.
  • NOA: NOA aggregates time-frequency resources non-orthogonally and is proposed as a key enabling technology for 5G.
  • NOA: With proper power arrangement and interference cancellation, NOA improves sum spectral efficiency and yields a better spectral-efficiency–energy-efficiency tradeoff.
  • NOA: NOA generally requires no additional site renting or infrastructure upgrading, supporting a better deployment-efficiency–energy-efficiency tradeoff.
  • NOA: NOA can reduce queueing delay through additional virtual transmission pairs, improving power-related tradeoffs relative to orthogonal solutions.
  • NOA: In practical NOA, processing chains and static circuit power can grow with active transmission pairs, weakening ideal sum-energy-efficiency and bandwidth-power or delay-power gains.

3) Future Research Issues:

The paper identifies deployment and scheduling challenges for NOA and broader green-network design. User-specific scheme selection, delay guarantees, hardware reconfiguration, signaling, and MIMO’s energy costs remain central open issues.

  • NOA roadmap: The research roadmap organizes energy-efficient NOA questions around fundamental green tradeoffs.
  • NOA deployment: Energy-efficient NOA deployment must account for user characteristics: NOMA favors significant pathloss differences, whereas full-duplex favors closer links and lower transmit power.
  • NOA scheduling: Scheduling users with the same NOA scheme can reduce hardware cost, but guaranteeing delay requirements and avoiding frequent reconfiguration remain difficult.
  • NOA scheduling: Multiple NOA schemes require resource allocation across users, while supporting them through multiple hardware links is power- and hardware-costly.
  • MIMO: MIMO improves spectral efficiency through multiple antennas but incurs higher hardware cost or operating power consumption.
  • MIMO: The paper uses fundamental green-tradeoff analysis to examine regular and massive MIMO systems and identify energy-efficiency open issues.

1) Theoretical Achievements:

MIMO green-tradeoff analysis derives SE–EE relations for centralized and distributed systems and extends the framework to deployment, bandwidth, and delay tradeoffs. Practical circuit power changes the ideal behavior, while precoding, scheduling, antenna selection, and hardware adaptation provide energy-efficiency strategies.

  • Theoretical Achievements: The same analytical framework derives DE–EE, BW–PW, and DL–PW tradeoffs.These relations are plotted together as the fundamental green tradeoffs.
  • Theoretical Achievements: Centralized and distributed MIMO analyses derive SE–EE relations using capacity-power mappings and ordered channel-eigenvalue statistics.The distributed case has a more complicated mapping but admits a uniform SE–EE expression.
  • Theoretical Achievements: Theoretical SE–EE relations are monotonically decreasing, so MIMO EE improvement requires sacrificing SE.This follows because the inverse power-capacity mapping is monotonically increasing.
  • Theoretical Achievements: Including circuit power yields finite EE as SE approaches zero, lower low-SE EE than the ideal case, and a quasi-concave bell-shaped SE–EE tradeoff.The quasi-concave form permits an optimal EE value to be found with efficient numerical search.
  • Theoretical Achievements: Energy-efficient downlink MIMO uses precoding and user scheduling, with channel-diagonalizing precoding maximizing capacity for a given transmit power.Using all available transmit power is not always optimal for EE maximization.
  • Theoretical Achievements: Imperfect-CSI precoding still diagonalizes the estimated channel, but its transmit-power budget depends on channel-estimation errors.The approach extends to virtual MIMO and joint power-balancing algorithms.
  • Theoretical Achievements: Multiuser MIMO EE also depends on antenna-number selection, power distribution, user scheduling, RF-chain sleeping, and hardware switching.System-level validation reports that multiuser diversity and hardware switching can significantly improve EE.

capacity formula and derive the optimal EE precoding schemes for coordinated multi-point

The paper surveys MIMO energy-efficiency schemes and develops coordinated multi-point and practical power-model analyses. It emphasizes globally optimized power allocation, implementation constraints, and the transition from idealized to nonlinear amplifier behavior.

  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: Coordinated multi-point analysis derives a closed-form approximation for the SE–EE tradeoff and uses ergodic-rate pre-selection to reduce global user-set search complexity.The pre-selection approach incurs marginal EE performance loss.
  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: Optimal EE precoding is more energy efficient under ideal and linear power-consumption models.A practical policy incorporates circuit power and channel-diagonalizing precoding.
  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: Circuit-power-aware optimization has a unique globally optimal power allocation with a weighted water-filling form and user-specific water levels.The result is associated with the precoding matrix.
  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: MIMO EE schemes include antenna selection, spatial modulation, modulation diversity, cognitive techniques, and polarization, organized by fundamental green tradeoffs.Antenna selection allocates zero power to unselected antenna sets.
  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: The taxonomy connects MIMO schemes with QoS, fairness, high-SE transmission for power-limited users, and delay-aware applications.The approaches are summarized in a taxonomy based on fundamental green tradeoffs.
  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: The current MIMO EE model uses continuous power consumption P/µPA + Pc, where µPA is PA efficiency, enabling quasi-concave formulations.More accurate configurations require accounting for nonlinear and non-continuous amplifier behavior.
  • capacity formula and derive the optimal EE precoding schemes for coordinated multi-point: Nonlinear, non-continuous PA models invalidate EE quasi-concavity and require new convex-approximation methods and deployment analysis.Different PA types and operating regions must be considered before cell-layout design.
  • B. Massive MIMO: Massive MIMO extends conventional MIMO with very large antenna arrays, making channel randomness increasingly deterministic and enabling asymptotic analysis.The theoretical analysis revisits ergodic capacity as the antenna count grows.

2) Energy Efficient Solutions:

Energy-efficient M-MIMO research addresses physical, deployment, and computational scaling constraints. Key priorities include reducing pilot and calibration overhead, handling hardware and PAPR limitations, mitigating pilot contamination, and designing scalable processing and deployment.

  • Energy Efficient Solutions: In FDD M-MIMO, channel-state acquisition requires pilot resources proportional to transmit antennas, while limited resources and estimation accuracy constrain throughput.Pilot beamforming and semi-orthogonal approaches are cited as responses.
  • Energy Efficient Solutions: With fixed 20W air-interface power, increasing antenna count produces linear total-power scaling because static circuit power grows and can dominate consumption.Per-antenna radiated power decreases as power is distributed across more antennas, while overall consumption still increases.
  • Energy Efficient Solutions: M-MIMO can achieve high EE, reported as tens of Mbit/Joule in future cellular networks, but pilot contamination significantly affects EE.The paper therefore identifies active mitigation in multi-cell systems as necessary.
  • Energy Efficient Solutions: Massive arrays make calibration time- and energy-consuming, while residual hardware distortions can accumulate and degrade overall EE.A scalable channel-measuring scheme remains missing.
  • Energy Efficient Solutions: M-MIMO lowers average transmit power per active antenna without proportionally lowering PAPR, requiring expensive and power-inefficient RF components.Low-PAPR waveforms such as FBMC have been proposed for uplink transmission.
  • Energy Efficient Solutions: Energy-efficient M-MIMO also requires scalable processing because channel estimation, equalization, and detection complexity grows with transmit and receive antenna numbers.The paper calls for energy-efficient massive signal-processing techniques.
  • Energy Efficient Solutions: Conventional pilot overhead becomes unaffordable as antenna numbers grow, creating a tradeoff between pilot resources and data-symbol resources.Proposed directions include compressive sensing, incremental estimation, and pilot-contamination control without extra energy.
  • Energy Efficient Solutions: Deployment research must jointly consider strong power-supply needs, power-transmission dissipation, frequency planning, and inter-cell interference.Locating M-MIMO near power stations can reduce transmission-line dissipation.

V. HETEROGENEOUS NETWORKS

The section surveys energy-efficient HetNet solutions through fundamental green tradeoffs, using area spectral efficiency to account for heterogeneous coverage. It reports benefits from small-cell densification alongside deployment and operational challenges.

  • HetNets combine macro-cell coverage with small-cell hot-spot throughput enhancement.
  • Area spectral efficiency averages performance across heterogeneous cell coverage to address macro- and small-cell fairness.It is used instead of conventional spectral efficiency for heterogeneous layouts.
  • HetNets can provide high hot-spot throughput with marginal power, yielding higher area spectral and energy efficiency than homogeneous networks.The same analysis is extended to DE-EE, BW-PW, and DL-PW relations.
  • Small-cell deployment incurs site-maintenance expenditure that reduces deployment-efficiency performance.This practical cost is additional to circuit-power consumption.
  • Proper frequency planning controls inter-cell interference and improves the bandwidth-power tradeoff.
  • The four tradeoff relations show EE dropping and power rising in low-SE/DE and BW/DL regimes.
  • HetNet energy-efficiency research spans resource management, cell cooperation, activation and de-activation, and long-term deployment optimization.These approaches operate across short, medium, and long time scales.
  • Deployment strategies report 30% power reduction for macro-only comparison, 40% to 65% EE improvement, and over 50% network power savings under different settings.These values correspond to distinct deployment studies and conditions.

3) Future Research Issues:

The future-work section identifies practical gaps in HetNet energy-efficiency analysis and deployment. Key boundaries include nonrandom topologies, signaling overhead, synchronization, and computational complexity.

  • Statistical-geometry EE evaluation may fail with limited base-station types and locations because SINR can become discontinuous at cell boundaries.Fig. 7 illustrates this issue for one macro cell and two small cells.
  • Control signaling and system-information broadcasting are often underestimated in heterogeneous networks.Cell selection, reselection, and handover require broadcasting by every cell.
  • EE-optimal deployment with limited base-station types and locations remains computationally difficult because exhaustive search is complexity-prohibited.Mixed-integer programming models the problem, while branch-and-bound and branch-and-cut are suggested directions.
  • Future research should control signaling overhead and facilitate timely cell activation and de-activation.
  • Current no-cell and functionality-separation solutions require perfect synchronization and complicated signaling exchange, limiting large-scale deployment.

B. Ultra Dense Network

The section characterizes ultra-dense networks as massively deployed small-cell systems and examines their green tradeoffs. UDNs improve bandwidth-power and delay-power relations but face saturation, circuit-power, mobility, and cooperation costs.

  • UDNs densely deploy small cells over hot-spot areas, producing small coverage regions and whitened interference from dense neighbors.
  • Under symmetric small-cell assumptions, transmit power is equally divided among small cells after macro-cell power is removed.
  • UDN deployment efficiency drops at high cell density when area spectral efficiency saturates.
  • Flexible frequency reuse and distributed protocol management significantly improve UDN bandwidth-power and delay-power tradeoffs.
  • Network energy efficiency saturates even as area spectral efficiency scales, while circuit-power overhead increases total power consumption.The latter is comparable to the massive-MIMO scenario.
  • Fast-moving users can trigger frequent handovers and significant signaling overhead in UDN regions.Functionality separation keeps mobility within macro cells while using UDNs for throughput boosting.
  • Large-scale cooperation among UDN nodes requires substantial information exchange and processing, motivating distributed interference management.
  • Central-cloud cooperation faces strict front-haul round-trip delay requirements that may become a performance bottleneck.

3) Future Research Issues:

The future-research discussion extends beyond core HetNet and UDN tradeoffs to implementation, computation, mmWave, and cognitive-radio challenges. It emphasizes dynamic control, scalable processing, and the preliminary state of energy-efficient mmWave research.

  • UDN design must address mobility management, large-scale cooperation, and computational-task partitioning across heterogeneous nodes.
  • Legacy medium-timescale cell activation cannot support instantaneous traffic-driven adjustment in 5G UDNs.Fast node switching requires advanced hardware and software control protocols.
  • The paper omits some techniques because of page limits, including LTE-related material and wireless power transfer.
  • Energy-efficient mmWave communications remain at a preliminary stage because existing work emphasizes feasibility and statistical channel models.
  • Sharing RF components and antennas across multiple mmWave bands can prevent total power from scaling as in M-MIMO or UDN systems.
  • Directional beams, fast beam tracking, and alternative waveforms are proposed to address mobility, radio-power waste, high PAPR, and out-of-band emission.
  • Combining mmWave with M-MIMO or UDNs is presented as a route toward energy-efficient coverage and rate boosting.
  • Cognitive-radio designs address energy-efficient sensing, sharing, association, power control, and deployment under heterogeneous channel conditions.

VII. CENTRALIZED OR DISTRIBUTED?

5G energy efficiency requires coordinated choices across centralized and distributed architectures because traffic, delay, connectivity, and energy requirements vary widely. The paper surveys these design tradeoffs and examines heterogeneous computing, protocols, and network structures as responses to those requirements.

  • Motivation: 5G requirements may scale by 100 to 1,000 times, while energy consumption cannot increase at the same rate.The paper therefore treats energy efficiency as increasingly critical for technology evolution.
  • Centralized or Distributed Architecture: Energy-efficient schemes can conflict when combined across waveform, massive-MIMO, and ultra-dense-network designs, diminishing overall EE gains.The paper identifies non-linear interconnections as a challenge when energy-efficient waveform designs are combined with M-MIMO or UDN systems.
  • Centralized or Distributed Architecture: Network heterogeneity centralizes baseband processing while distributing radio-frequency functionality across the network.This decouples information processing from information transportation, allowing computational capability and baseband power to benefit from chipset evolution.
  • Motivation: A single solution cannot simultaneously cover all 5G throughput, delay, and connectivity requirements.Applications can impose sharply different delay targets, including 1 millisecond for vehicle-to-vehicle communication and second-level tolerance for smart metering.
  • Heterogeneous Implementation: The paper proposes heterogeneous implementation with cloud-based centralized computing and separated plug-in accelerators.This framework uses energy-efficient general computing for common applications and specific hardware structures for extreme cases.
  • Conclusion: Fundamental green tradeoffs organize the survey of OFDM, NOA, regular and massive MIMO, HetNets, and UDNs, alongside practical schemes and open technical issues.The paper presents these tradeoffs as important for evaluating future 5G designs under explosive demands for high data rates and massive connections.
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