network energy saving technologies for green wireless access networks

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TECHNOLOGIES FOR GREEEN RADIO C O M M U N I C AT I O N N E T W O R K S

NETWORK ENERGY SAVING TECHNOLOGIES FOR GREEN WIRELESS ACCESS NETWORKS TAO CHEN, VTT TECHNICAL RESEARCH CENTRE OF FINLAND YANG YANG, SHANGHAI RESEARCH CENTER ON WIRELESS COMMUNICATIONS, CAS SHANGHAI INSTITUTE OF MICROSYSTEM AND INFORMATION TECHNOLOGY HONGGANG ZHANG, ZHEJIANG UNIVERSITY HAESIK KIM, VTT TECHNICAL RESEARCH CENTRE OF FINLAND KARI HORNEMAN, NOKIA SIEMENS NETWORKS

ABSTRACT

RBS

PA

Idle Transceivers

Transmission/ aux (optional) (TXM)

The authors provide an overview of network energy saving studies currently conducted in the 3GPP LTE standard body. The aim is to gain a better understanding of energy consumption and identify key EE research problems in wireless access networks. 30

The energy consumption problem in the mobile industry has become crucial. For the sustainable growth of the mobile industry, energy efficiency (EE) of wireless systems has to be significantly improved. Plenty of efforts have been invested in achieving green wireless communications. This article provides an overview of network energy saving studies currently conducted in the 3GPP LTE standard body. The aim is to gain a better understanding of energy consumption and identify key EE research problems in wireless access networks. Classifying network energy saving technologies into the time, frequency, and spatial domains, the main solutions in each domain are described briefly. As presently the attention is mainly focused on solutions involving a single radio base station, we believe network solutions involving multiple networks/systems will be the most promising technologies toward green wireless access networks.

INTRODUCTION The mobile industry faces a critical energy consumption challenge. Anticipated by Gartner [1], by 2013 smartphones will exceed 1.82 billion units and surpass PCs as the most common web access devices. Consequently, more wireless infrastructures have to be deployed with large demands on energy. Meanwhile, data-intensive services are beginning to dominate mobile services. The network data volume is expected to increase by a factor of 10 every five years, associated with a 16–20 percent increase of energy consumption [2]. Applying this rate to mobile communications, which contribute 15–20 percent of the entire information and communications technologies (ICT) energy footprint and 0.3–0.4 percent of global CO2 emissions [2], the mobile

1536-1284/11/$25.00 © 2011 IEEE

industry faces a great sustainable development problem in energy consumption. It is crucial to develop energy-efficient wireless technologies to meet this challenge. We study in this article the energy efficiency (EE) of the wireless access network, which is broadly defined as any wireless system using radio base stations (RBSs) or access points (AP) to interface mobile devices with the core network or Internet. The reasons to focus on wireless access networks are following. First, since wireless access networks are the most widely deployed wireless networks in the world, energy-efficient solutions designed for wireless access networks are expected to significantly improve EE in the ICT sector. Second, as a long tradition, the standards of wireless access networks are mainly focused on throughput performance. Only recently has EE been receiving increasing attention. Significant studies are needed to balance performance and EE. Third, the demand from mobile users for EE is urgent in order to enjoy better mobile services. As shown in Fig. 1, statistics indicate that the RBS is the main source of energy consumption in the network of a mobile operator [3]. Energyefficient solutions for wireless access networks are mainly concentrated on RBSs. Among all components in an RBS, power amplifiers (PAs) drain the most energy. Energy is also dissipated in alternating current/direct current (AC/DC) converting, cabling, and cooling. Various solutions have been proposed to improve EE of the RBS, such as increasing PA efficiency, using non-active cooling techniques, employing masthead PA to reduce feeder loss, exploiting energy efficient backhaul solutions, applying energy-efficient deployment strategies, and introducing energy-efficient protocols. This article overviews soft methods to improve EE of RBSs, with an emphasis on Long Term Evolution (LTE) sys-

IEEE Wireless Communications • October 2011

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tems. Soft methods do not upgrade hardware, but tune parameters in protocols, and apply enhanced architecture and deployment strategies for EE improvement. They enable flexible and cost-efficient solutions with minimum impact on hardware implementation.

UNDERSTANDING ENERGY EFFICIENCY It is worth understanding EE before introducing energy saving techniques. In the field of engineering, a system is usually designed to transform energy to useful work. EE can therefore be defined as the ratio of useful work to the total supplied energy. The useful work in a communication system refers to the effort to deliver modulated signals for information exchange. The definition of EE varies according to measured objects. There are two basic methods to measure EE. One way is to define EE as the ratio of efficient output power/energy to total input power/energy. This definition is widely used by systems and components such as power supply, PAs, and antennas. The other way defines EE as the performance per unit of energy consumption. This is referred to as floatingpoint operations per second (FLOPS) in digital signal processing (DSP), million instructions per second (MIPS) in computer systems, and throughput (bits per second) in communication systems. In a communication system information is transmitted in the form of modulated electrical, electromagnetic, or optical waveforms. Due to the imperfection of electronic components, a significant part of energy turns into heat. Moreover, in a wireless system, due to the open nature of the wireless medium, only part of the radiated energy reaches the receiver. Measurements show a Global System for Mobile Communications (GSM) RBS may only have EE of 3.1 percent [4]. It renders a prominent challenge to improve the EE of wireless systems. EE in a communication system is not a simple problem. Information theory reveals some insights on the complexity. According to the Shannon formula, the EE of a communication system based on the additive white Gaussian noise (AWGN) channel can be written as

ηEE =

⎛ R B P ⎞ = log 2 ⎜1 + ⎟, P P ⎝ BN 0 ⎠

(1)

where R is the bit rate of information, P is the received power, B is the bandwidth, and N 0 is the noise power spectral density. The unit of the EE metric is then bits per joule, which indicates the information units transmitted per one energy unit. Equation 1 shows that if N0 is fixed, EE is the function of power density P/B. There are several observations from Eq. 1: • ηEE does not monotonically increase with B or P. In a practical system where the bandwidth is a less flexible parameter, the maximum EE of a system is hard to achieve. • For a given rate R, using more bandwidth requires less power. If the bandwidth is infinite, the required power is fixed to P = N 0R ln2. This gives a hint to trade bandwidth with energy.


Mobile telephone exchange (MTX) 20% Radio base station (RBS)

Core network 15% 57%

Retail 2%

Data center 6%

Figure 1. Energy consumption composition of a mobile operator [3]. • The objective to optimize throughput performance is normally conflict with that to maximize EE. Balancing these two objectives complicates the system design. Note that Eq. 1 gives an EE model for a generic communication system. For a wireless system, EE also depends on distance, carrier frequency, efficiency of antennas, and so on. Moreover, interference and fading make EE of a wireless system vary according to the radio environment. It should be also remarked that Eq. 1 is an ideal model without considering the hardware implementation of a system. In reality, the circuits of a system will turn a significant amount of energy into heat. Therefore P in Eq. 1 should be replaced by the sum of the supplied power of the RBS and user terminals. Indeed, in a RBS the energy transforming to heat dominates the energy consumption of the RBS. Reducing energy wasted by RBSs becomes the main concern of energy saving in a wireless access system.

RBS ENERGY CONSUMPTION MODEL To understand energy consumption problems in RBSs, it is necessary to establish a generic energy consumption model of the RBS. RBSs conforming to the same standard but produced by different manufacturers are likely implemented differently, and thus show different energy consumption profiles. The generic energy consumption model abstracts the main energy consumption sources in an RBS, which helps to isolate energy consumption problems in and derive EE solutions for RBSs. We use the EE model defined by the European Telecommunications Standards Institute (ETSI) for wireless access network equipment [5]. ETSI has defined the common methods and practices to model, measure, and evaluate the EE of RBSs in various wireless access networks, including GSM, wideband code-division multiple access (WCDMA), Worldwide Interoperability for Microwave Access (WiMAX), and, additionally, LTE systems [5]. As shown in Fig. 2, an RBS site typically comprises RBS equipment and several infra-

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In addition to improve the PA efficiency, which comes with high cost, an alternative is to switch off PAs dynamically when possible, e.g. at the idle time of the RBS. This is a very promising energy saving technique for a RBS.

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Output

RBS site - outdoor system RBS Power interface

AC/DC power supply (PS)

PA

Input Idle Transceivers

RF interface

Climate control (CC)

Transmission/ aux (optional) (TXM)

Battery back-up (excluded in model)

Network

Examples of power consumption values of RBS (unit: watt) Input

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CC

Output

EE

GSM

3802

602

300

2400

500

120

3.1%

3G

300

20

110

150

20

60

20%

*PA of RBS in GSM consumes 1320 watt power

Figure 2. Outdoor energy consumption reference model of a generic RBS site.

structure support systems. RBS equipment is a network component that serves one or more cells and interfaces the mobile device through an air interface and wireless network infrastructure. Radio transceivers, which include radio frequency (RF) PAs, are key components in RBS equipment. Radio transceivers are responsible for transmission and reception of radio signals, as well as sending and reception of signals to/from higher network entities. To emphasize the impact of RF PA on the EE of an RBS, the structure of transceivers is drawn in the model. A PA amplifies the signal for transmission via antenna. In case of no traffic load, the RBS equipment could enter the idle mode by turning off the radio chains of transceivers so that the power of the RBS could be saved. Infrastructure support systems of an RBS include power supply, climate control, transmission module, battery backup, and other auxiliary equipment. The power supply connects to the AC power line or battery, and offers electrical energy to the equipment; the climate control maintains the operating climate of the equipment within a defined range; the transmission module connects the RBS to the core network; the battery backup supplies energy to the RBS when the AC power line is down. Figure 2 shows only the outdoor reference model of a concentrated RBS site. Note that an RBS can be deployed indoors. Moreover, an RBS site may

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be implemented in a distributed form where remote radio heads (RRHs) are collocated with remote antennas. The indoor and distributed models are not depicted since they are similar to the outdoor model. The energy consumption profiles of typical GSM [4] and third-generation (3G) [6] RBSs are provided in Fig. 2. As we can see, the PAs of the GSM RBS consume 35 percent of the feeding power. If the power efficiency of an RBS is defined as the ratio of the radiated power to the feeding power, the overall power efficiency of the GSM RBS is only 3.1 percent. By enhancement, the PA efficiency in a 3G RBS is boosted to 45 percent. The overall power efficiency of a 3G RBS is 20 percent. The PA remains a big problem for the overall EE. The PA is identified as the main cause of the EE problem in an RBS. Limited by material, implementation methods, dynamic operation range, and other constraints, the efficiency of RF power amplifiers normally ranges from 15 to 35 percent. Advanced techniques like Doherty designs and envelope tracking designs are able to boost PA efficiency up to 60 percent [7]. In addition, to improve the PA efficiency, an alternative that comes at a high cost is to switch off PAs dynamically when possible (e.g., at idle times of the RBS). This is a very promising energy saving technique for an RBS. We describe different proposals based on this technique in the next section.


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Network energy saving techniques

Time

Frequency

Extended cell DTX

PA off at signal-free symbol

Space

Bandwidth reduction

MBSFN

Reduce antenna number

Carrier aggregation

Hybrid solutions Switch on/off cell

Layered structure

HCS

Figure 3. Network energy saving techniques for LTE.

NETWORK ENERGY SAVING METHODS Energy saving in an RBS is the key research area to improve EE of a wireless access network [5]. It has great potential to improve EE of an RBS. First of all, as shown in the previous section, the overall EE of an RBS remains low. Second, presently RBSs of all kinds are designed for best performance, where EE is not the primary concern. As a result, the energy consumption of an RBS does not scale with its traffic load but maintains at a high level. As the traffic load of a RBS varies during the day, it gives clues to adapt the power consumption of a RBS with its traffic load, through dynamically tuning the parameters of RBS and mobile devices. This approach is called the network energy saving method. Network energy saving can be done by reducing the operation time of PAs, shrinking the working bandwidth, or even shutting down the RBS site [8]. Network energy saving methods, which are roughly classified into time, frequency, and spatial domains, are summarized in Fig. 3. There are also hybrid solutions that combine methods from different domains. In the rest of the article, the main solutions are introduced. Note that in this article the discussion on network energy saving is focused on the LTE system. This is because: • The maturity of 2G and 3G standards limits the application of network energy saving methods due to the backward compatibility problem. • LTE and WiMAX are selected as the technologies for the International Mobile Telecommunications (IMT)-Advanced system, which is expected to be the mainstream future wireless access system. • Network energy saving studies on LTE are very active in the Third Generation Partnership Project (3GPP) standards body In the following, the term user equipment (UE), which is used in LTE, refers to mobile devices.

TIME DOMAIN Time domain solutions in LTE temporally shut down PAs in a RBS when in given time there is no data traffic in the downlink. Since those solutions are tightly related to the frame structure of the LTE system, it is necessary to provide a brief


introduction on the frame structure and control signals of LTE. The downlink of LTE uses orthogonal frequency-division multiple access (OFDMA) technology. As shown in Fig. 4, the channel access time of an LTE cell served by an RBS is structured into contiguous time frames. Each frame is divided into 10 subframes of 1 ms duration. Each subframe consists of two equal-sized slots. Each slot accommodates 6 or 7 symbols. In the frequency domain, the system bandwidth is divided into a set of subcarriers, each of which has a spacing of 15 kHz. Twelve consecutive subcarriers in one slot are grouped into a physical resource block (PRB), which is the basic resource unit allocated to a unit of UE. A symbol in a PRB is called a resource element of the PRB. The reference signals (RSs) and control signals occupy resource elements of a PRB in a given pattern. They have to be transmitted regularly even when there is no data traffic in a PRB. If there is no or less downlink traffic, the frequency to transmit RSs and control signals can be reduced. This is the principle for time domain energy saving solutions. Accordingly the PAs can be shut down temporally. The energy saving in time domain solutions can be measured by the time fraction where PAs are off during a time period, normally in a frame. There are three basic ways to temporally shut down PAs: turning off a PA in signal-free symbols; using a multicast broadcast single frequency network (MBSFN) subframe to reduce RSs; and introducing the extended cell discontinuous transmission (DTX) approach to further reduce RSs.

There are three basic ways to temporally shut down PAs: turning off a PA in signal-free symbols; using a multicast broadcast singlefrequency network subframe to reduce RSs; and introducing the extended cell discontinuous transmission approach to further reduce RSs.

PA Off at Signal-Free Symbol — The most straightforward approach is to turn off PAs in time periods of a slot where downlink symbols are signal-free. As shown in Fig. 4, in a normal LTE frame, which has 7 symbols per slot, 4 symbols in each of subframes 1–4 and 6–9, 9 symbols in subframe 0, and 6 symbols of subframe 5 cannot be signal-free due to the need to transmit RSs and control signals. Assuming it takes half of a symbol time to turn on a PA but the PA can be immediately turned off, a simple calculation from Fig. 4 shows that at least 47 percent of the time in a frame, PAs have to be on due to the need to transmit RSs and control signals.

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MBSFN — The second approach uses the MBSFN structure to reduce the number of RSs. MBSFN is proposed to deliver services such as mobile TV using the LTE infrastructure. In an MBSFN frame, the symbols for RS in subframes 1–4 and 6–9 are reduced to 1. The PA operating time during a frame is then reduced to 28 percent.

time domain approach is a pure implementationbased approach. MBSFN was included in LTE Release-8, and thus has no backward compatibility problem. The extended cell DTX approach, however, requires changes in the standards. Furthermore, the reduction of RSs in the extended cell DTX approach has a negative impact on the performance of the UE. In LTE, some control procedures are performed with the assistance of RSs. Without enough RSs, some UE may experience unpredictable problems synchronizing with an RBS or decoding control signals. Reducing RSs may also prevent UE from entering into terminal DTX mode and thus shorten its battery life. Those problems have to be solved before applying the extended cell DTX mode. It demands a new adaptive pilot design, in which the pattern of RSs is a function of the downlink traffic load, and the states of UE and RBSs.

Extended Cell DTX — The extended cell discontinuous transmission (DTX) approach [9], which is currently under discussion in 3GPP Radio Access Network (RAN) Working Group (WG) 1, is able to further reduce RSs compared to the MBSFN approach. As shown in Fig. 4, if there is no downlink traffic, in the extended cell DTX mode there is no need to have any transmission in subframes 1–4 and 6–9 of a frame. The PA operating time in a frame is further reduced to 7.1 percent. Time domain solutions are able to significantly reduce the PA operation time when a cell is idle. It is ideal to apply them on RBSs in rural areas. In urban areas the idle traffic condition rarely occurs during a day. Improvements are needed, probably through joint time-frequency domain scheduling, to make them also effective in low traffic conditions. Moreover, time domain solutions have to deal with backward compatibility and UE performance problems [8]. The first

FREQUENCY DOMAIN In the frequency domain there are mainly two approaches to energy saving: bandwidth reduction and carrier aggregation.

Bandwidth Reduction — Since the physical layer specification of LTE supports a set of transmission bandwidths, it is possible for an RBS to change the channel bandwidth if needed. The

1 frame (10ms)

Subframe

1 subframe (1ms)

1

0

1 slot (0.5ms)

2

9

0

Frequency

Slot

Slot

9

Cyclic prefix

0

1

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12 subcarriers

RS

RS

RS

RS

RS

RS

RS

7 OFDM symbols

RS Time Control signal

Subframe 0 On/off of PA

RS

Broadcast channel (BCH) control

Primary sync signal

Secondary sync signal

Subframe 5

Normal Unicast

Subframe 1-4, 6-9

MBSFN

PA on time in a frame

100%

47% 28%

7.1% Extended cell DTX

Normal Unicast

MBSFN

Extended cell DTX

Figure 4. Reduce RSs of LTE frame in idle and low traffic load conditions.

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bandwidth reduction technique adapts the bandwidth with the downlink traffic load. To maintain the same power spectral density (PSD), smaller bandwidth requires less radiated power. If the downlink traffic is low, the channel bandwidth can be shrunk so that less power is required. Moreover, fewer RSs are needed for smaller bandwidth. This further reduces the power budget. For instance, when the channel bandwidth is changed from 10 MHz to 5 MHz, the RF transmission power could be reduced by around 3 dB. There are mainly two ways to reduce bandwidth: shrink bandwidth but keep the carrier frequency; or shrink bandwidth and change the carrier frequency. This approach is suitable for the low traffic load case. It is an implementation-based approach without the need to change standards. However, this approach does not shut down PAs. As an operating PA consumes much more than an inactive PA, the energy saving from this approach may be marginal. Moreover, a PA normally operates at an optimized point with a given output power range. Reducing the output power will degrade the PA efficiency and in turn compromise the energy saving gain.

Carrier Aggregation — In the carrier aggregation approach, it is assumed that in an RBS the carriers are aggregated by groups, and each group is served by individual PAs. The idea is to shut down the associated PAs when the corresponding aggregated carriers are not scheduled for the downlink traffic. In this case this approach heavily relies on the implementation of the RBS. It is only applicable to an RBS that has aggregated carriers and separate PAs attached to each group of carriers. In general, frequency domain solutions have less impact on UE. However, due to the aforementioned constraints, the efficiency of frequency domain solutions is limited. They are normally combined with energy saving methods in other domains.

SPATIAL DOMAIN The aforementioned approaches in the time and frequency domains are employed in a single RBS. In the spatial domain, however, the solutions can be extended to heterogeneous networks, and are therefore more flexible. The main approaches currently used in the spatial domain are reducing antenna number in an RBS and dynamically configuring cells in a multicell scenario. We propose a new approach that takes advantage of traffic offloading in a layered structure to reduce energy consumption of the whole network.

Reduce Antenna Number — Reducing antenna number is the most commonly used energy saving technique in the spatial domain. It works for the situation where the traffic load of a cell is low. For instance, if the branches of antennas are reduced from 4 to 1, energy consumption of transceivers is reduced to 1/4, as the PAs associated with those branches can be switched off. It can combine with the bandwidth reduction technique for the low traffic mode of an RBS. The reduction of antenna branches decreases


the total output power and shrinks the cell size. Therefore, an additional mechanism is needed to maintain the strength of control signals at the cell edge. One straightforward way is to boost the power of RSs and control signals so as to maintain the cell size. The antenna number reduction approach may lead to service degradation or interruption as the antenna reconfiguration is needed. In a normal operation of a RBS, the antenna port number is assumed to be fixed during all active time of the RBS. The change of the antenna number should notify UEs properly. Otherwise it will impact UE’s behaviors. It is suggested to use this approach in the semi-static load case.

Switch On/Off Cell — The cell switch-off approach [10] is a system-level approach that works in an area covered by multiple cells, where those cells may use different radio access technologies (RATs). This approach has no need to modify the low-layer components in the RBS. When the traffic load in a given area is low, some cells can be shut down, and the served UE units are handed over to the remaining cells. Those inactive cells can be turned on during the busy time, signaled by neighboring working cells or the operations, administration, and maintenance (OAM) layer of the system. There are two ways to switch on/off cells. One is signaling directly between RBSs. The other is dedicated control from the OAM layer of the system. A special case of the cell switch-off approach is called the hierarchical cell structure (HCS) approach [11], in which always-on macrocells are deployed for basic coverage and micro/picocells are used for capacity boost. Cells for capacity boost only operate when the traffic load is high in macrocells. Otherwise, they are switched off to save energy if the macrocell is able to provide enough capacity. While the cell switch-off approach tries to make a good balance between performance and energy saving, it has several limitations. First, frequently switching on/off cells affects services in UE. Its usage should be limited to a semi-static manner. Second, switching off cells may reduce the battery life of served UE units as they have to connect with other cells far away. Third, if switching off a cell creates uncovered areas, remaining active cells need to increase their power to cover this area. This may neutralize the energy saving gain.

In general, frequency domain solutions have less impact on UE. However, due to the aforementioned constraints, the efficiency of frequency domain solutions is limited. They are normally combined with energy saving methods in other domains.

Layered Structure — We propose the layered structure approach [12] to further improve EE of wireless access networks. The layered structure is a combination of different systems/networks to serve same mobile devices. Layers are allowed to use different RATs. Normally , a wireless wide area network (WWAN) is deployed in one layer, and wireless local area networks (WLANs) are used in other layers. Assuming a two-layer structure where macrocells form one layer and femtocells another, as femtocells are usually deployed much closer to UE, less transmission energy is needed to maintain equal QoS. It is reasonable for the UE to hand over to a femtocell when feasible for energy saving. Moreover, a macrocell RBS can further reduce its energy consump-

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introduce system information update time and reconfiguration time in an order of 10 ms; changing the carrier frequency will force the UE to hand over to another cell and then back to the new carrier. It may take a long time for a system reconfiguration to reach a stable cell working status. Therefore, hybrid solutions are recommended only in semi-static traffic conditions.

Energy consumption compared to full load mode

100%

80%

60%

PERFORMANCE COMPARISON

40%

20%

0% 1

2

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Network energy saving solutions and reference model 1. Full load, reference model 4. MBSFN for low load 2. Reduce antenna number 5. Extended cell DTX 3. Reduce channel bandwidth 6. MBSFN for idle cell 7. Hybrid solution, reconfiguration based on load variation

Figure 5. Relative energy consumption of different network energy saving techniques. tion by offloading the traffic load to femtocells, and then applying macrocell RBS energy saving techniques, for the components of a macrocell RBS consume much more energy than the counterparts of a femtocell RBS. The layered structure is used to improve capacity, coverage, and EE of the network. It is an extension of HCS. Different from HCS, it has no constraint on deployment but relies on close coordination between layers to achieve coexistence and energy saving. The layered structure approach uses flexible architecture to optimize the use of energy, spectrum and other resources cross systems. It dynamically allocates the traffic load among different layers of the system and reconfigures different layers to meet service requirements and energy saving goals. Traffic offloading algorithms and mobility management are keys to achieve energy saving in the proposed architecture.

HYBRID SOLUTIONS Hybrid solutions combine solutions in different domains to adapt energy consumption of an RBS in different traffic conditions. For instance, to maximize the energy saving gain under very low downlink traffic conditions, an RBS can be configured to use single carrier under the carrier aggregation case, a single antenna, 1.4 MHz instead of 20 MHz bandwidth, and the maximum number of MBSFN subframes in a frame. Preliminary studies have shown it provides significant improvement against a standalone solution. The challenge of hybrid solutions is the processing/interruption time and signaling for system reconfiguration, as well as avoidance of the impact on UE performance. Reducing the cell bandwidth and changing the antenna number

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Performance of different network energy saving approaches discussed in 3GPP RAN WG1 has been actively studied [8, 13]. The preliminary results are shown in this section. The energy saving gains of different energy saving approaches under a given daily traffic pattern are shown in Fig. 5. The daily traffic pattern is assumed in Fig. 6, which includes 7 h low load time and 2 h idle time. It should be noted that different traffic patterns lead to different performance results. The energy saving gains of different approaches is compared with the full load case where no described energy saving technique is applied. In those approaches the hybrid solution dynamically configures the cell bandwidth, carrier number, antenna number, as well as the MBSFN subframe number in a frame according to the load condition of the RBS [13]. Seen from Fig. 5, the energy saving approaches for no load scenarios, which are extended cell DTX and MBSFN for no load, achieve energy saving gain less than 8 percent. This is because the idle time only occupies a very small fraction of time during a day. The energy saving approaches working for the low load scenarios provide better energy saving performance than those only working in the no load scenarios. Among three low load approaches, MBSFN for low load shows highest performance. The bandwidth reduction solution outperforms the antenna number reduction solution. A better energy saving gain can be achieved by the hybrid solution.

RESEARCH CHALLENGES Fundamental research problems remain in the network energy saving research of green wireless communications. First of all, the study of EE in green wireless communications has to be kept in mind a holistic view, which covers probably all aspects in the ecosystem of communications. It is for several reasons. First, in additional to the energy consumed in the use phase of a system, the embodied energy, which is the energy used to manufacture telecom equipment, also plays an important role in the evaluation of any EE solution. The introduction of any new architecture and device for EE improvement should consider the total energy consumed in the life cycle of the system. Second, the optimization of EE at one point of the system may lead to suboptimal results at other points. For instance, the application of a complex signal processing algorithm may reduce the transmission energy for the same amount of data, but drain more power for signal processing and cooling. It is necessary to consider the EE solution from the system perspective. Third, for a communication system the


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improvement of EE should not compromise the required QoS. A good trade-off is needed between performance and energy saving gain. For instance, the aforementioned network energy saving approaches shall avoid any severe impact on the user experience of UE. A holistic approach is needed for comprehensive understanding of the whole system, which further renders challenges on energy consumption modeling, use of EE metrics, and system-level EE design. An EE solution is developed based on certain energy consumption model. A good energy consumption model captures key variables of a system regarding energy consumption while providing sufficient abstraction. The challenge on modeling energy consumption of a system is that energy consumption of telecom equipment is implementation-dependent and load-dependent. Conforming to the same standard, different systems offering similar capacity performance may show significant different energy consumption features. Consequently, a network energy saving solution based on a common model may be far from achieving best energy saving gain for a specific system. It then requires special attention on the energy consumption model before applied it in a specific network energy saving study. The EE metric is normally defined as a performance per unit of energy, and for network energy saving the performance usually refers to throughput. They remain important EE metrics for EE study. For instance, the metric of bit per joule is the widely used EE metric which provides the absolute comparison of EE among different systems. However, the delivery capacity of a system is used to provision services. Conventional EE metrics like bit/joule are insufficient to capture EE from the service perspective. A service provisioned by multicast with a low bit/joule figure may still outperform that by unicast with a high bit/joule figure on EE. Moreover, QoS is normally not considered in the EE metric. For new EE architecture design and cooperative service development for EE, it is required to evaluate EE from a new angle concerning services. New EE metrics are demanded. The main challenge of the system-level EE design lies in the EE improvement of the whole system/network through load balancing, system reconfiguration, multi-domain scheduling, and cross layer adaptation. Given service requirements, the EE problem at the system level can be modeled as a joint optimization problem which takes into account resource allocation in time, frequency and spatial domain. Joint scheduling algorithms enabled by cooperation across multiple RBSs are expected. One of the most promising ways to improve system-level EE of wireless systems may lie in cognitive approaches, in which learning is used for better adaption [14]. Cognitive approaches can be used at the spectrum level for spectrum coexistence and interference management. The use of beamforming through multiple-input multiple-output (MIMO) antennas, powered by intelligent adaptive techniques, has great potential to mitigate interference and save energy. Cognitive approaches can also be applied at the


100% Low Relative traffic load

CHEN LAYOUT

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80% Busy 60% 40% Low 20%

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Figure 6. An example of the daily downlink traffic load pattern of an RBS.

network layer to integrate heterogeneous networks and treat EE as one of the primary optimization targets to provision service across multiple networks.

CONCLUSION This article provides an overview of energy consumption problems in wireless access networks and describes network energy saving techniques proposed for the LTE system. It is identified that the common energy consumption problem in an RBS of a wireless access system is the energy scaling traffic load problem. This problem can be tackled by solutions from the time, frequency, and spatial domains. As most solutions only focus on a single RBS, we believe the most promising solutions are those that apply hybrid techniques cross multiple systems/networks. The energy saving problem cross multiple systems/networks is less understood. More efforts are needed from the modeling to specific solutions. As wireless access networks experience exponential growth worldwide, it is important to make EE a high priority in the design and development of wireless access networks.

ACKNOWLEDGMENT The work from the authors of VTT is supported by the JADE project, which is partially funded by the Finnish Funding Agency for Technology and Innovation under grant DN40474/09. Yang’s work is partially supported by the National Natural Science Foundation of China (NSFC) under grant 60902041, and by the Ministry of Science and Technology (MOST) of China under grants 2009DFB13080 and 2010DFB10410.

REFERENCES [1] B. Gammage et al., “Gartner’s Top Predictions for IT Organizations and Users, 2010 and Beyond: A New Balance,” Gartner Report, Dec. 2009. [2] R. Tafazolli et al., “eMobility Mobile and Wireless Communications Technology Platform: Strategic Applications Research Agenda,” Net!Works European Technology Platform, July 2010, http://www.networksetp.eu [3] C. Han et al., “Green Radio: Radio Techniques to Enable Energy-efficient Wireless Networks,” IEEE Commun. Mag., vol. 49, no. 6, June 2011, pp. 46–54. [4] H. Karl et al., “An Overview of Energy-efficiency Techniques for Mobile Communication Systems,” TKN, Technical University Berlin, Tech. Rep. TKN-03-017, Sept. 2003.

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[5] “Energy Efficiency of Wireless Access Network Equipment,” ETSI TS102706, 2009. [6] U. Barth, “Wireless Networks, EARTH Research Project,” presented at ETSI Green Agenda Seminar, Nov. 2009. [7] S. Cripps, RF Power Amplifiers for Wireless Communications, Artech House, 2006. [8] “Discussion on Network Energy Saving in LTE,” 3GPP R1-101528, Nokia, Feb. 2010. [9] “Extended Cell DTX for Enhanced Energy-efficient Network Operation,” 3GPP R1-095011, Ericsson, Nov. 2009. [10] “Overview to LTE Energy Saving Solutions to Cell Switch Off/on,” 3GPP R1-100162, Huawei, Jan. 2010. [11] “Considerations on Energy Saving Solutions in Heterogeneous Networks,” 3GPP R3-092478, Ericsson, Oct. 2009. [12] T. Chen, I. Harjula, and M. Kiviranta, “Energy Saving Techniques in Layered Structure of LTE Systems,” Mobile VCE Wksp. Green Radio, in conjunction with SDR’11-WInnComm-Europe, Brussels, Belgium, June 2011. [13] “Energy Saving Techniques to Support Low Load Scenarios,” 3GPP R1-101084, Huawei, Feb. 2010. [14] H. Zhang, “Cognitive Radio for Green Communications and Green Spectrum,” COMNETS 2008, in conjunction with CHINACOM 2008, Hangzhou, China, Aug. 2008.

BIOGRAPHIES T AO C HEN [S’05, M’10] ([email protected]) received his B.E. degree from Beijing University of Posts and Telecommunications, China, in 1996, and Ph.D. degree from University of Trento, Italy, in 2007, both in telecommunications engineering. Since 2008, he has been with VTT Technical Research Center of Finland, working as senior researcher and project manager. From 2003 to 2007 he worked as a researcher in CREATE-NET, Italy. From 1996 to 2003, he was an engineer in a national research institute at China. His research interests include dynamic spectrum access, energy efficiency of heterogeneous wireless networks, cooperative communications, and wireless networking. YANG YANG ([email protected]) is currently vice director at Shanghai Research Center for Wireless Communications (WiCO), SIMIT, Chinese Academy of Sciences. Prior to that, he served the Chinese University of Hong Kong, China, and Brunel University and University College London (UCL) in the United Kingdom as an assistant professor, lec-

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turer, and senior lecturer, respectively. His research interests include wireless ad hoc and sensor networks, wireless mesh networks, 4G mobile systems, and cooperative communications. H ONGGANG Z HANG ([email protected]) is a full professor at the Department of ISEE, as well as co-director of the York-Zhejiang Lab for Cognitive Radio and Green Communications, Zhejiang University, China. From October 1999 to March 2002, he was with the Telecommunications Advancement Organization (TAO) of Japan as a TAO Research Fellow. From April 2002 to November 2002 he was at the TOYOTA IT Center. From December 2002 to August 2004 he was with the UWB Research Consortium, the National Institute of Information and Communications Technology (NICT) of Japan. He was the principal author and contributor for proposing DS-UWB in the IEEE 802.15 WPAN standardization task group. From September 2004 to February 2008 he was with CREATE-NET, where he led its wireless group in participating in a number of European FP6 & FP7 projects (EUWB, PULSERS 2). He was Co-Chair of the IEEE GLOBECOM 2008 Symposium on Selected Areas in Communications. He is an Honorary Visiting Professor of the University of York, United Kingdom. He serves as Chair of the Technical Committee on Cognitive Networks (TCCN) of the IEEE Communications Society. HAESIK KIM ([email protected]) received his Ph.D. degree in communication systems from Lancaster University, United Kingdom. He is currently a senior scientist at the VTT Technical Research Centre of Finland, and is involved in cognitive radio and network projects. From 2008 to 2009 he was with NEC Laboratory Europe where he carried out WiMAX projects. From 2002 to 2006 he was with Samsung Advanced Institute of Technology where he focused on UWB and SDR projects. KARI VEIKKO HORNEMAN ([email protected]) received his M.Sc.E.E. degree in 1980 from the University of Oulu, Finland. He is currently with Nokia Siemens Networks, Oulu, where he is working as a program manager for research programs. His current work is related to spectrum and energy efficiency for heterogeneous networks, cognitive radios, and relay networks, mainly for LTE and LTE-A. In the past he has worked on channel estimation, interference cancellation, and suppression for WCDMA, and general signal processing implementations for transmission systems.
