Ashok Karmokar and Alagan Anpalagan
© can stock photo/lamnee
Digital Object Identifier 10.1109/MPOT.2013.2245946 Date of publication: 22 July 2013
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Green computing and communication techniques for future wireless systems and networks
ne ependence on electrical and electronic systems in our chi a daily lives is continually increasing at a rapid pace. M People in industrialized countries are now accustomed to broadband wireless services, such as file downloading and Internet browsing on their smartphones, tablets, and netbooks, anywhere and anytime. Cloud computing and machine-to-machine wireless communications are getting increasingly more attention due to their inherent potential of machineindependent data access. That is, the data sensed in a particular system should be available for taking action to © istockphoto/linda bucklin another authorized remote user securely, irrespective of platforms such as the remote computers and handheld devices of the intended user. Developing countries are witnessing the fastest growth of wireless phones and other device penetration due to their lack of a wired infrastructure. This will require an inevitable increase in both installed base stations (BSs) and the demand on power grids and device power usage. While modern electrical systems and devices are providing comfort, security, and ubiquitous communication, they are increasing energy consumption in an exponential manner as well. Increased energy consumption in different sectors is one of the major reasons for greenhouse gas emissions. A recent study by CEA Leti shows that the information and communication technology (ICT) industry sector is responsible for about 3% of the total energy consumption. ICT generates 2% of the worldwide CO2 emissions, which is roughly comparable to one-fourth of the worldwide CO2 emissions by cars. It is also comparable to aviation industries. The consumption of power in the ICT sector is rising at 16–20% each year, and it is doubling every four to five years. Also, the increase in telecommunications data volume is approximately a 10x increase every five years, and the corresponding increase in energy consumption is 16–20% each year. The research on green communication technologies has started to take shape among industry, academia, and government agencies. There are many ongoing green projects internationally, all aimed at reducing the carbon footprint through energy savings. Two notable projects that focus on developing energyefficient mobile communication systems and network technologies are the European Union’s FP7 projects Energy Aware Radio and Network Technologies (EARTH) and Cognitive Radio and Cooperative Strategies for Power Saving (C2POWER). Mobile VCE Green Radio is also aiming to develop new green radio architectures and radio techniques to reduce the overall energy consumption of wireless systems. A consortium of ICT industry, academia, and nongovernmental research experts, called GreenTouch, has an ambitious goal of improving the ICT industry energy efficiency by three orders of magnitude by 2015, compared to the 2010 level. The Green-IT project in Japan aims to develop energy consumption metrics and energy efficiency standards for networking equipment. Some multinational mobile network operators and vendors have already set targets to reduce their carbon emissions significantly within the next ten years. 0278-6648/13/$31.00©2013IEEE
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Green metric Most wireless networks so far have been designed to maximize spectral efficiency (SE), which can be defined as the overall system throughput per unit bandwidth in b/s/Hz. However, modern and future green wireless systems need to be carefully designed so that energy efficiency (EE) is maximized. The EE in general term means that getting the same task done [e.g., the same throughput and the quality of service (QoS)] with minimal energy. Therefore, we call a wireless network green when it uses the least amount of power to transmit a given amount of data while satisfying specified QoS requirements. The EE in b/J is defined as the ratio of the overall system throughput and the total transmit power. Both instantaneous and average EE can be used depending on the scenario. However, these two design objectives (SE and EE) may not always align together and quite often conflict with each other. A balance between SE and EE is important for the future green radio designs.
Green design for wireless networks
various constituent parts and the core radio devices (e.g., power amplifiers and radio transceivers). A typical percentage of power consumption distribution at a BS is given in Fig. 1. It is seen that access and core (backbone) networks should be the prime targets of energy savings to realize a green radio network. It can be made green by designing energy-efficient radio frequency (RF) electronic devices and components (e.g., green power amplifiers), cross-layer adaptive resource allocation algorithms, opportunistic user scheduling and routing protocols, and intelligent interference control techniques by leveraging proper spectrum management, relay nodes and relaying techniques selection, two-tier HetNets, and by using cooperation among BSs for cell zooming, spatial diversity, BSs on/off, and load balancing. Therefore, holistic system-level strategies are needed for realizing future generation green networks. Figure 2 depicts potential energy-efficient technologies.
Adaptation and resource management techniques The EE of a wireless systems increases as the wireless channel gain increases. Because the improved channel gains permit the use of higher-order modulations using the same transmit power and still maintaining the same bit error rate (BER). Link adaptation techniques are used with the random channel gain of the wireless fading channel to transmit a variable number of packets dynamically. In a multiuser scenario, since different users have different channel gains at a particular time instance, opportunistic scheduling where users with the best channels are scheduled can be used to improve the EE of the overall networks provided that the fairness issue is also considered. Using multiple antennas at the transmitter and receiver provides multiple links between
We call a wireless network green when it uses the least amount of power to transmit a given amount of data while satisfying specified QoS requirements. them. Therefore, the combined channel gain at the receiver is improved and that, in turn, gives better EE.
Cooperative relay networks In a traditional wireless network, there is only one hop between the mobile station (MS) and the BS. In order to maintain the link over fading channels, the transmitter needs to use a higher transit power so that the connection does not drop and the quality of the connection is always maintained. In a multihop wireless network, the communication between the MS and the BS is divided into multiple shorter links. Due to a smaller coverage distance, the BSs and relays can use lower transmission power. Thus, the interference to other users is greatly reduced due to lower transmission power. Also, the relay works as another spatially separated antenna, thus providing spatial diversity. Due to this cooperative diversity provided by the spatially diverse multiple antennas, the throughput is improved while the total required power is reduced. One of the major challenges in multihop networks is the selection of relay and relaying strategies (such as amply-and-forward and decodeand-forward). The relay and the associated relaying techniques must be chosen intelligently so that the green metric for the overall end-to-end transmission is optimized as well as other QoS (such as delay and jitter) requirements are satisfied.
The need for green design in future wireless networks is extremely important due to the worldwide growth of mobile subscribers and delay-tolerant data traffic. Modern wireless devices are continually adding support for high data-rate “killer” applications that require a large amount of power at the BS since the bandwidth is limited. According to Shannon’s theorem, the data rate can be increased by increasing either the bandwidth or the power. Cellular network operators are already among the top energy consumers, and the energy consumption growth of cellular networks is much faster than that of the whole ICT industry, according to some studies. Mobile wireless design rules usually ignored EE in downlink communication provides a much higher data rate due to 60 the downloading from the 50 Internet (data and video) serv40 ers. So far, EE has only been 30 considered for uplink communication because of the limited 20 battery power of mobile 10 devices. However, recent stud0 ies have shown that a signifiBase Mobile Core Data Retail cant proportion of the total Station Switching Transmission Center power is consumed by the BSs in wireless networks. The over- Fig. 1 A typical cellular network power consumption distribuall EE of a BS depends on its tion percentage (source: www.vodafone.com).
Cross-layer techniques In order to meet high datarate requirements, several technologies have been proposed and studied in different layers of open system interconnection (OSI) protocol stacks. These layer-specific protocols are usually specified with different simplifying and nonrealistic assumptions on other layers. Due to unnecessary layer-specific design margins, the end-to-end performance of
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Cognitive and Software Radio
adaptive congestion control, adaptive routing, and adaptive user selection techniques can be implemented in order to maximize EE in the transport, network and link layers, respectively.
Green Machine-to-Machine Communications
Interference Mitigation and Management
Cross-Layer Link Adaptation
Base Station Cooperation Two-Tier Cell Design: Femtocell
EE in delay-tolerant applications
Cross-Layer Opportunistic Scheduling
Green Radio
Cross-Layer Adaptive Routing
Energy-Efficient Hardware Design
Energy-Efficient Multihop Communications and Relay
Fig. 2 The potential energy-efficient technology for green networks.
these protocol stacks in wireless networks is typically not energy-optimized. For example, physical layer (PHY) adaptation techniques are based on the assumption that the transmission buffer is of infinite size, the buffer always has a packet to transmit, and such adaptations schemes are designed to react only with channel gains. In the link layer, PHY is viewed as a fixed-rate bit pipe. However, in practice, the buffer has a finite capacity and packet arrivals to the buffer are random. So, the buffer occupancy is random with time. The cross-layer adaptation schemes that consider both the channel and the buffer randomness are shown to improve overall EE.
Application Layer: Source Coding
Figure 3(a) shows a traditional wireless network, where each layer of the OSI stack operates its own protocol to optimize the local objective without interchanging information. Figure 3(b) shows an idealized cross-layer design, where all layers interact with others to get system-level optimized decisions. In practice, all layers’ interaction may be complex due to information exchange burden. However, interaction and information exchange between two and/or three layers, as shown in Figure 3(c), may improve the EE significantly. For example, the application layer may adapt the QoS and source coding with the channel state information (CSI) obtained from the PHY. With CSI information,
Application Layer: Source Coding
Transport Layer: Congestion Control
Network Layer: Routing
Network Layer: Routing
Link Layer: User Scheduling
Link Layer: User Scheduling
Physical Layer: Link Adaptation
Physical Layer: Link Adaptation
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Interference management and mitigation The interference experienced by a wireless terminal determines the rate that it can support for a given transmit power. The lower the total interference from all
Application Layer: Source Coding Traffic Information
Transport Layer: Congestion Control
Most of the emerging wireless data applications are delay-tolerant with specific QoS requirements (such as BER, delay jitter, overflow, and packet loss), which added a degree of freedom to the cross-layer packet scheduler at the PHY for next generation green networks. The scheduler can intelligently and dynamically send packets depending on both the buffer and the traffic information as well as the channel conditions. Suppose that, if the channel is in bad state and the buffer is not full, the scheduler can wait for the good channel by storing the incoming packets and send those opportunistically with much lower power later. Therefore, the same throughput can be obtained with much lower power by utilizing the degrees of freedom provided by the buffer and traffic information from the upper layer via cross-layer interaction and information exchange.
QoS
Central Controller
Channel State Information
Queuing States
Transport Layer: Congestion Control
Network Layer: Routing
Channel State Information
Link Layer: User Scheduling
Physical Layer: Link Adaptation
Traffic Information Queuing States
(c)
Fig. 3 (a) The separation in traditional networks, (b) integrated controlled networks, and (c) cross-layer information exchange between layers to maximize end-to-end system-level EE. 40
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the interferers, the higher the supported rate. To transmit a given amount of data, the transmitter needs more power when the interference is more. Thus, interference cancellation and management (adjacent channel and cochannel interferences) techniques are very important for green radio communications. The key idea of different interference management techniques (such as beamforming and cell sectoring) is to increase the signal-to-interference and noise ratio (SINR) at the receiver so that the BS can save energy. Interference cancellation techniques, on the other hand, use special signal processing techniques that cancel out the interference at the receiver.
BS coordination and cooperation The users at the cell edge are more prone to the interference. The signal quality of the cell edge users can be improved when they are collaboratively served by the surrounding BSs, rather than the best BS. This technique, called a distributed antenna system, requires the BSs to collaborate and transmit simultaneously. Using coordinated BSs, the overall EE of the system can be improved significantly. This becomes more important when the users are mobile and a continuous service with minimum possible power is needed. Also, the study shows that BSs are largely underutilized, most of the time, to minimize the energy consumption. BSs with small or no activity can be turned off by transferring imposed loads to neighboring BSs.
Cell size modulation and overlay networks When the size of the cell is reduced, the overall EE is improved by getting the transmitter and the receiver closer to each other. To reduce energy consumption, the BSs can adjust the cell size dynamically (called cell zooming) depending on traffic load, channel conditions, and user requirements. The key idea of the femtocell (the smaller cell inside a macro cell) is to use the small BSs that use much lower power and provide smaller coverage so that it can serve its closer MS. The savings in power may be huge in femtocell networks since the main BS does not need to use higher power to communicate with a MS. Since the distance between the BS and the MS is much smaller, the femtocell BS can use much less power to maintain the same quality communication links as BS maintains, using higher transmit power.
Recently, the 3GPP LTE-Advanced standard started the provision of a heterogeneous network (HetNet) that contains nodes with different characteristics (such as transmission power and RF coverage area) under the management of the same operator. Low power micro/ pico network nodes with small RF coverage aims to complement macro network nodes with large RF blanket coverage. Intelligent radio resource and interference management needs to be provided to ensure the coverage area of the low power nodes so that energy efficiency promised by the HetNet, via micro/pico/femto network nodes and relay nodes, is realized. In HetNet deployment, the role of backhauling is crucial since if not properly addressed, it nullifies the effects of energy efficient wireless deployment strategies.
Cognitive radio networks Cognitive radio (CR) is a promising technology that is envisioned to cope with the spectrum scarcity problem inherent in consumer wireless networks. Although the cellular band is highly crowded, a study conducted by the U.S. Federal Communications Commission (FCC) shows that most wireless spectra (such as amateur radio and TV band) are severely underutilized. These spectra can be used opportunistically in time, space, and frequency when they are not used by the licensed holders. Therefore, CR provides a green way to use valuable and limited wireless spectrum efficiently. Although extra power is needed at the secondary networks to sense the free spectrum, once acquired, a secondary user (SU) can use adaptive transmission techniques to save the overall transmission power on the additional spectrum. To transmit a given amount of information data that the SU has in the buffer by maintaining the same QoS requirement, the SU resorts to using higher-order modulation and higher transmission power over the fading channels. Therefore, cognitive radio provides additional degrees of freedom at the secondary networks by providing an additional piece of spectrum. Also, the data rate is linearly proportional to bandwidth, whereas it is logarithmically proportional to power. The CR can adapt its modulation order depending on the availability of the number of channels. When more channels are available, the CR can use lower-order modulation to save power since higher-order modulation needs
Although much research has been done to maximize the data rate and save battery power for handheld wireless devices in uplink communications, little attention has been paid to downlink wireless communications. higher power to maintain the same BER, provided other QoS requirements such as delay limit and overflow limit are satisfied as well. Detecting free channels can impose a significant burden and challenge on an individual CR terminal. It has been shown in the literature that cooperative sensing scheduling, where a CR networks decides how many and which channels a particular SU will sense, can be energy efficient. A green design of software-defined radio (SDR) that supports a large variety of wireless spectrum, protocols, and standards on the same reconfigurable radio hardware is required to realize the vision. The major challenge in lowenergy SDR that provides this flexibility is to design a cross-layer radio controller that adapts, at run time, the radio configurations to the system dynamics.
Machine-to-machine communications networks Machine-to-machine (M2M), also called machine-type communication (MTC) in 3GPP, is characterized by intelligent communication among wirelessly connected machines to make collaborative decisions without human intervention. M2M communications are gaining popularity due to its promise to provide ubiquitous solutions for real-time monitoring. The popular examples of M2M communications networks include vehicular ad-hoc communications networks, underwater sensors networks, wireless body-area networks (e.g., for remote e-health care), industrial automation networks, and environmental monitoring networks. In an M2M network, the M2M nodes equipped with multiple sensors (e.g., body sensors in e-health care systems and toxic gas sensors in environmental surveillance) obtain monitoring data and make an intelligent decision. It also transmits the sensed data to an M2M gateway over wireless links. The gateway is
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Energy-efficient hardware design constitutes a major part in energy saving, both in the BS as well as in user equipment. connected to the remote back-end server via wireless/wired networks. One of the major challenges of the fast-growing M2M communications networks is the EE for its successful deployment and for sustainable environments. Different green protocols and techniques need to be implemented such as intelligent cross-layer power adaptations, source coding and routing of the sensed data, scheduling of the nodes, and distributed mechanisms. Efficient sleep scheduling (also called activity scheduling) is very important for M2M, where some nodes can switch to low-power operation (sleeping) mode dynamically, and a subset of connected nodes can remain active while the functionality of the network is maintained.
Hardware design and network architecture Energy-efficient hardware design constitutes a major part in energy saving, both in the BS as well as in user equipment. For example, power amplifiers at the BS consume a significant part of the BS power. Although the efficiency of power amplifiers has increased during last decade by linearizing it, further improved energy-efficient design of hardware can decrease the power consumption in the analog and digital circuitry of the transmission system. The logic design of digital components should be smart and signal-aware. For example, fast deactivation of the base components (i.e., to put them into sleep when there is nothing to transmit) may save the energy. Cloud-oriented architectures have significant energy-saving potential, where baseband processing is done centrally in baseband unit pools instead of distributed processing in each BS. The data center can process baseband data centrally on the basis of the need of individual BSs, and it can be located energy optimally in the network. Energy-aware load balancing between macrocell and femtocell may play an important role in energy saving. The network should be aware of both long- and short-term traffic demand in the geographic area of coverage. Also, instead of fully depending on the electrical power 42
grid, suitable green PHY and MAC layer protocols of wireless networks need to be designed for renewable energy-harvesting sources such as solar energy. Caching, clustering, and data aggregation at an intermediate point for a certain traffic type may also reduce energy consumption by reducing the number of retransmissions of the same data from a content provider.
Conclusions Although much research has been done to maximize the data rate and save battery power for handheld wireless devices in uplink communications, little attention has been paid to downlink wireless communications. Due to the nature of current wireless applications, downlink communications in wireless networks support a major portion of the wireless data and is responsible for a major portion of the total energy consumption. In this article, the need for appropriate green metrics to quantify energy savings has been emphasized. Such metrics have to be taken into account in future wireless network design and performance optimization. We have discussed various downlink green technologies for wireless communication, computing, and networking that can potentially provide overall system-level energy savings for future wireless networks.
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About the author Ashok Karmokar (
[email protected]) is a research fellow in the Department of Electrical and Computer Engineering at Ryerson University, Toronto, Canada. He received his Ph.D. in electrical and computer engineering from the University of British Columbia. Alagan Anpalagan (
[email protected]. ca) is a professor in the Department of Electrical and Computer Engineering at Ryerson University, Toronto, Canada. He directs a research group working on radio resource management and radio access and networking. He earned B.A.Sc., M.A.Sc., and Ph.D. degrees from the University of Toronto. IEEE POTENTIALS
