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On Green Energy Powered Cognitive Radio Networks

Xueqing Huang, Tao Han, Nirwan Ansari

arXiv:1405.5747v2cs.NI

TL;DR

Green-powered cognitive radio addresses spectrum and energy constraints, but its design must accommodate opportunistic spectrum access and intermittent harvested energy. This survey synthesizes energy-efficient CR techniques, green-powered wireless access networks, and research challenges in adapting network operation to those dynamics. It concludes that substantial challenges remain in provisioning green energy powered cognitive radio networks.

  • Problem

    Designing green CR networks requires jointly optimizing dynamic spectrum access and green-energy use despite random, intermittent harvested-energy arrivals and opportunistic spectrum availability.

  • Method

    The paper surveys energy-efficient CR functionality, green-powered wireless access networks, and energy-harvesting-based CR research across sensing, management, sharing, relaying, cooperation, and small cells.

  • Results

    The survey reviews state-of-the-art techniques and elicits recent advances and challenges for energy-harvesting-based green cognitive radios.

  • Takeaways & Limitations

    Green-powered CR networks are presented as a sustainable direction for liberating wireless access networks from spectral and energy constraints, while requiring further research.

Abstract

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Green energy powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, while dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting (EH) through which green energy can be harnessed to power wireless networks. Green energy powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy efficient cognitive radio techniques and the optimization of green energy powered wireless networks. Existing works on energy aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy efficient CR based wireless access network is discussed in various aspects such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing cognitive radio networks which are powered by energy harvesters.

NEW JERSY INSTITUTE OF TECHNOLOGY

Cognitive radio addresses spectrum scarcity through opportunistic access, while green energy and energy harvesting target wireless networks’ energy constraints. The paper surveys energy-efficient CR functionality, green-powered access networks, and research challenges arising from intermittent spectrum and harvested energy.

  • Motivation: Wireless access networks consume escalating power as mobile data traffic grows, motivating greener and more sustainable network operation.Greening is described as a way to reduce operational expenditures and improve ICT sustainability.
  • Cognitive radio foundation: Cognitive radio dynamically senses and accesses available spectrum by adapting transmission parameters to changing radio-frequency conditions.Adaptable parameters include frequency band, modulation mode, and transmission power.
  • Green cognitive radio: Green cognitive radio combines opportunistic spectrum use with renewable energy harvested from ambient sources instead of relying solely on grid or non-rechargeable energy.The approach is motivated by renewable, environmentally friendly energy and can exploit underutilized spectrum without external grid or battery supplementation.
  • Paper scope: The survey covers energy-aware spectrum sensing, management and handoff, spectrum sharing, energy-efficient CR access networks, and green CR optimization.It organizes the review around power-aware CR functionality, energy-efficient wireless access systems, and optimization of green CR networks.
  • Challenges: Green CR design remains difficult because idle spectrum and harvested energy arrive opportunistically, while green-powered networks currently face higher cost per watt than grid energy.The paper presents future research insights despite current deployment limitations.

A. Spectrum Sensing and Analysis

Spectrum sensing enables cognitive radios to detect primary-user activity and identify spectrum opportunities, but sensing architecture and configuration create energy, accuracy, complexity, and infrastructure trade-offs.

  • Purpose and metrics: Spectrum sensing is crucial because it detects primary-user activity and enables opportunistic spectrum access.Detection performance is characterized by detection probability pd and false-alarm probability pf.
  • Purpose and metrics: Higher pd improves primary-user protection, while lower pf exposes more spectrum opportunities for secondary users.The sensing decision is framed as binary hypothesis testing between active and idle primary-user states.
  • Sensing configuration: Channel-specific sensing uses less power and a simpler mechanism, whereas multi-channel sensing offers higher spectrum efficiency with greater power consumption and scheduling complexity.The comparison reflects a direct energy-efficiency versus spectrum-efficiency trade-off.
  • Sensing architecture: Non-cooperative sensing avoids sensing-result sharing but suffers hidden-node and location-diversity problems; cooperative sensing shares results to improve sensing but adds architectural costs.Centralized cooperation offers accurate results with fusion-center overhead, while distributed cooperation avoids infrastructure but has limited sensing quality and efficiency.
  • Energy model: Energy-efficient sensing depends on both sensing performance and power consumed by sensing, reporting, and data transmission.Sensing power increases with sensing time and sample count; reporting power increases with transmission distance and participating-node count, while miss-detection can cause collision and retransmission.

1) Sensing Duration and Frequency Problem:

The survey describes sensing-duration and frequency design as an optimization problem balancing continuous sensing’s energy cost against periodic sensing’s reliability limitations.

  • Sensing Duration and Frequency Problem: Gan et al. optimized sampling rate NS/TS and sensing time TS to minimize total sensing power across multiple potential channels under detection-performance constraints.The approach balances continuous sensing’s high sensing power against periodic sensing’s low pd and high pf.

2) Sensing Architecture Design Problem:

Sensing architecture and scheduling determine how cooperative radios spend energy, share information, and locate useful spectrum. The design must balance sensing quality, reporting overhead, channel opportunity, and channel quality.

  • Sensing Architecture Design Problem: Sleeping and censoring reduce sensing or reporting power by turning sensing devices off probabilistically or transmitting only informative results.Clustering further reduces reporting energy by routing local results through cluster heads, shortening reporting distances.
  • Multi-Channel Scheduling Problem: Cooperative sensing scheduling assigns secondary nodes across channels while balancing improved sensing performance against higher sensing and reporting energy.Assigning more SUs to one channel also creates a performance-opportunity trade-off across channels.
  • Multi-Channel Scheduling Problem: Sequential channel sensing requires an efficient sensing order and stopping rule because channel switching and sensing consume additional time and energy.The objective is to find an idle channel with satisfactory quality rather than merely any idle channel.
  • Multi-Channel Scheduling Problem: Sequential-sensing metrics are tied to transmission throughput and delay because these determine whether a sensed channel is sufficiently good for secondary-user transmission.Spectrum management combines sensing strategy, spectrum analysis, and access decisions to locate high-quality opportunities.

1) Spectrum Access:

Spectrum access in cognitive radio networks must adapt to uncertain channel occupancy while balancing immediate transmission opportunities against sensing and energy costs. Spectrum mobility and handoff add further energy-performance trade-offs.

  • Spectrum Access: Spectrum access decisions must balance immediate access, information gathering, and energy conservation under uncertain primary-user traffic.A POMDP framework models this partially observable operation-mode selection problem.
  • Spectrum Mobility: Spectrum availability varies across time and space, requiring adaptive mobility management when primary traffic changes or primary users reappear.Spectrum mobility requires secondary users to move between spectrum holes to avoid interference.
  • Spectrum Mobility: Spectrum handoff is either reactive, selecting a target channel after a request, or proactive, predetermining the target channel.Both approaches support switching when the current channel becomes busy.
  • Spectrum Mobility: Frequency tuning during handoff consumes additional power, with handoff power related to the frequency difference between target and current channels.The relationship is represented by PHO = FHO(ft −fc), where FHO is increasing.
  • Spectrum Access: Secondary users may wait on the current channel to conserve energy, accepting degraded service quality, so handoff strategies must decide whether to request handoff.Spectrum sharing and allocation also need to account for competing or cooperating secondary users, power budgets, channel conditions, and QoS requirements.
  • Spectrum Sharing: Spectrum sharing coordinates primary and secondary access through spatial, temporal, or hybrid models, with cooperative and non-cooperative secondary-user behavior.Hybrid sharing combines sensing-based access with transmit-power adaptation to limit interference.

1) Spatial Spectrum Sharing:

Spatial spectrum sharing and cooperative relaying address interference, coverage, and energy-efficiency challenges through dynamic resource allocation and cooperative transmission. Relay-based designs can reduce transmission distance and power while supporting communication when direct spectrum access is unavailable.

  • Spatial Spectrum Sharing: Energy-efficient spatial spectrum sharing must jointly consider power budgets, channel conditions, QoS requirements, and interference-temperature constraints.Dynamic resource allocation includes spectrum sharing, power control, bit-rate allocation, and antenna-beam allocation.
  • Spatial Spectrum Sharing: Centralized cooperative resource allocation can maximize bit/Joule efficiency while meeting secondary-user rate demands and primary-user interference constraints.The cited OFDM cognitive-radio formulation models downlink power allocation as a concave fractional program.
  • Green Relaying and Cooperative CR: Cognitive-radio networks must guarantee secondary-user QoS without unacceptable primary-user degradation, while reliable end-to-end transmission can otherwise require substantial power.Relay technology is presented as an enabling approach for improving overall performance while saving energy.
  • Green Relaying and Cooperative CR: Relay-based cognitive-radio networks reduce path loss through shorter transmission ranges and can reduce primary-network interference through lower transmission power.Relays can also establish dual-hop communication when source and destination lack a common available spectrum.
  • Green Relaying and Cooperative CR: Cooperative relaying can improve transmission performance and spectrum sensing, including through relays that forward primary-user signals to help determine occupancy.Cooperative sensing studies optimize sensing samples and amplification gain under detection and false-alarm constraints.

2) Resource Allocation for Cooperative Transmission between SUs:

Cooperative transmission between secondary users introduces relay selection, power allocation, and subcarrier matching problems. Existing approaches optimize rate, energy efficiency, spectrum efficiency, or network lifetime under power, capacity, and interference constraints.

  • Resource Allocation for Cooperative Transmission between SUs: Adding cooperative relays creates resource-allocation problems involving relay selection, power allocation, and subcarrier matching.These decisions must account for the altered transmission structure introduced by relaying.
  • Resource Allocation for Cooperative Transmission between SUs: Relay power allocation can maximize bits/Joule under source and relay power budgets, minimum-capacity requirements, primary-user interference thresholds, and circuit energy consumption.The cited AF-relay formulation includes a constant circuit-energy term.
  • Resource Allocation for Cooperative Transmission between SUs: DF-relay designs jointly optimize power allocation and subcarrier matching to maximize sum rate while keeping interference temperature below a specified threshold.Individual source and relay power constraints are included.
  • Resource Allocation for Cooperative Transmission between SUs: Distributed relay selection can model time-varying channels and spectrum use as a multi-armed restless bandit problem.Other approaches jointly optimize relay selection and power allocation to trade achievable data rate against network lifetime.
  • Resource Allocation for Cooperative Transmission between SUs: In cooperative primary-secondary transmission, primary users can exchange spectrum access opportunities for secondary-user relaying that improves primary throughput, reliability, or energy use.Stackelberg-game formulations model primary users as leaders and secondary users as followers.
  • Green Cognitive Small Cells: Heterogeneous networks combine macrocell coverage with small-cell capacity, but overlapping tiers create cross-tier and intra-tier interference.Cognitive radio provides agile spectrum access intended to address these coexistence issues.

1) Open access architecture:

Cognitive small-cell architectures use sensing, access policies, and energy-harvesting models to manage fluctuating traffic and power availability. Their design spans sleep-mode operation, incentive-based spectrum access, harvesting architectures, and dynamic energy supply.

  • Open access architecture: Small cells can use spectrum sensing to decide whether to enter sleep mode when fluctuating traffic leaves them without users to serve.This supports traffic offloading from macro cells while adapting operation to spatial, temporal, and frequency demand.
  • Open access architecture: Open-access and closed-access cognitive cells differ in which secondary and primary users may communicate through shared subchannels.Closed access restricts communication to registered secondary users, whereas open access permits access by users and cochannel primary users.
  • Open access architecture: Joint power allocation and pricing can model incentives for small cells that grant primary users access in exchange for compensation for extra power consumption.Stackelberg games are used for energy-efficient resource allocation across primary and cognitive macro or femto cells.
  • Green Energy Harvesting Models: Energy harvesting draws renewable energy from ambient sources, while passive and hybrid power architectures address energy-provisioning stability through complementary sources.Hybrid devices retain a non-renewable backup when harvested energy is insufficient.
  • Green Energy Harvesting Models: Harvest-use architectures require instantaneous harvested energy to meet consumption, whereas harvest-store-use architectures store energy for later operation.Sensing, transmission, and reception may occur simultaneously with harvesting or through time switching.
  • Green Energy Harvesting Models: Harvested energy depends on source availability, switching or splitting ratio, and conversion efficiency, with the source commonly modeled as a Markov process.RF harvesting additionally depends on the received signal energy and channel condition.
  • Green Energy Harvesting Models: Energy-harvesting cognitive radios differ from grid-powered systems because opportunistic harvesting makes the energy-arrival rate dynamic rather than constant.Energy harvested in one slot is assumed usable only in subsequent slots under the energy half-duplex constraint.

B. Green Energy Utilization and Optimization

Green energy utilization in cognitive radio networks requires adapting transmission and power management to intermittent harvested energy. Existing work optimizes throughput, completion time, and relay power allocation under different energy-information assumptions.

  • Intermittent harvested energy makes conventional energy-efficiency techniques unsuitable for transmitters and receivers powered by random energy arrivals.Performance maximization is more appropriate for passively powered devices than minimizing current energy consumption alone.
  • Optimal energy allocation uses causal or full side information about harvested energy and time-varying channels to maximize finite-horizon throughput.Related policies also maximize deadline-constrained throughput or minimize transmission completion time.
  • Green-energy management extends to joint source-relay power allocation in three-node decode-and-forward systems and hybrid energy supplies.These approaches account for power drawn from energy-harvesting sources or combined green and on-grid supplies.
  • Packet transmission policies may wait for sufficient harvested energy before using higher transmission rates to reduce completion time.Multi-user diversity in channel conditions can further improve green-energy utilization.

2) Reception Policy with Energy Harvester:

Receiver-side energy harvesting requires choosing how to split or schedule received RF power between harvesting and information reception. These choices affect sensing, transmission, relay forwarding, and overall system performance.

  • 2) Reception Policy with Energy Harvester:: The receiver policy must optimize splitting and switching rules for opportunistic wireless energy harvesting.The relevant architectures are illustrated in Fig. 6.
  • 2) Reception Policy with Energy Harvester:: Co-channel interference can provide useful harvesting energy while degrading data reception, so time switching must account for time-varying channel conditions.Joint transmit-power control and information scheduling can target outage probability, ergodic capacity, and average harvested energy.
  • 2) Reception Policy with Energy Harvester:: Harvested energy determines a relay’s forwarding ability, making receiver harvesting rules especially important in relay networks.The relay uses received energy to forward source information toward the destination.
  • 2) Reception Policy with Energy Harvester:: Energy-aware cognitive functionality links harvesting with sensing, access, and resource allocation decisions.These strategies determine whether to sense, access spectrum, participate cooperatively, and allocate radio resources.
  • 2) Reception Policy with Energy Harvester:: Optimization commonly relies on offline knowledge of energy arrivals or online causal and statistical information, although complete future knowledge is unrealistic.Learning algorithms estimate statistical parameters of intermittent energy-arrival processes.
  • 2) Reception Policy with Energy Harvester:: Power splitting between RF harvesting and data detection remains a research challenge, particularly when harvested energy must support relay transmission.The receiver architecture must balance energy collection against information detection.

3) Challenges on Green Energy Utilization and Optimization:

Green cognitive radio networks must coordinate harvesting, sensing, access, detection, scheduling, and resource allocation under dynamic energy constraints. The survey identifies challenges caused by fluctuating energy availability and its interaction with spectrum decisions.

  • 3) Challenges on Green Energy Utilization and Optimization:: Low residual energy can prevent a complete sensing process or successful data transmission, forcing choices about waiting, sensing, or collecting occupancy information.The appropriate policy depends on accumulated energy thresholds and the energy needed for sensing and transmission.
  • 3) Challenges on Green Energy Utilization and Optimization:: Energy-aware sensing changes detection and false-alarm probabilities because choosing when to sense affects observed channel outcomes.Detection probability may increase when a device waits during busy channels, while false alarms may increase when channels are idle.
  • 3) Challenges on Green Energy Utilization and Optimization:: Unequal residual energy among cooperative devices complicates sensing scheduling and requires online control of each sensor’s active time.Existing scheduling schemes may be insufficient when energy arrival rates are nonzero.
  • 3) Challenges on Green Energy Utilization and Optimization:: Varying energy arrivals can force secondary users to remain idle on unoccupied spectrum when transmission energy is insufficient.Sensing and access policies must incorporate the dynamic energy constraint.
  • 3) Challenges on Green Energy Utilization and Optimization:: Joint sensing and power allocation can maximize secondary-user throughput over multiple slots using dynamic spectrum state, harvested energy, and channel fading.A sub-optimal online algorithm addresses these jointly varying conditions.
  • 3) Challenges on Green Energy Utilization and Optimization:: Green cognitive radio research has also examined smart-grid integration, including renewable generation, electricity pricing, energy-efficient power management, and distributed green-power use.Green power farms and distributed generators are discussed as sources for reducing carbon footprints and supporting networks.

A. Wireless Network Powered by Distributed Green Generators

Distributed green generators support wireless networks through energy sharing, trading, and traffic offloading. Network operation depends on dynamic energy storage, traffic, channel conditions, and cognitive-radio spectrum activity.

  • A. Wireless Network Powered by Distributed Green Generators: Power sharing with neighboring cells can use traffic offloading through user-association or cell-size-adaptation schemes.These schemes can account for green-energy storage and traffic queues across multiple base stations.
  • A. Wireless Network Powered by Distributed Green Generators: Direct power sharing is preferable when traffic users are far from base stations with ample energy storage and offloading is inefficient or infeasible.Power trading may involve traffic offloading, power transmission, or both.
  • A. Wireless Network Powered by Distributed Green Generators: Finite-state Markov channel models discretize continuous average channel gains to predict upcoming channel conditions for packet transmission.Each discrete gain level corresponds to an FSMC state.
  • A. Wireless Network Powered by Distributed Green Generators: Green-network models account for residual battery energy, circuit energy, sensing energy, and transmission energy as distinct resource components.Transmission energy depends on distance and bits, while sensing energy includes listening and processing costs.
  • A. Wireless Network Powered by Distributed Green Generators: Distributed green energy can be shared through power lines, while wireless-network operation remains influenced by storage capacity, traffic demand, and channel conditions.These dynamic processes interact with cognitive-radio operation and spectrum availability.

B. Wireless Network Powered by Green Power Farms

Green-powered wireless networks must balance energy cost, environmental uncertainty, and the differing locations of spectrum, power, and traffic. Proposed approaches include market-based procurement, sensing-result sharing, mobile charging, and grid-based power transfer, each with distinct trade-offs.

  • Energy pricing helps balance surplus green power and storage costs when smart-grid providers supply cellular networks.
  • Green energy provisioning involves a trade-off between centralized farms and distributed generators: farms cost more to build, while distributed generation is less stable.
  • Green cognitive radio networks must coordinate nodes with available spectrum, sufficient power, and data traffic because these resources may reside at different nodes.
  • Sensing-result sharing can balance power by letting nearby cognitive nodes provide sensing information or offload traffic from energy-limited transmitters.
  • Mobile vehicles or robots can periodically deliver stored energy to cognitive devices with insufficient supply, whereas grid transmission suits geographically separated sources and devices.
  • Long-distance mobile charging consumes substantial energy, and sensing-result trading is unreliable because spectrum opportunities are local.
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