for Future Green Small Cell Wireless Networks. Gaoning He, Shunqing Zhang, Yan Chen, and Shugong Xu. Huawei Technologies, Co. Ltd., Shanghai, China.
IEEE International Conference on Communications 2013: IEEE ICC'13 - The IEEE ICC 2013 2nd International Workshop on Small Cell Wireless Networks (SmallNets)
Architecture Design and Performance Evaluation for Future Green Small Cell Wireless Networks Gaoning He, Shunqing Zhang, Yan Chen, and Shugong Xu Huawei Technologies, Co. Ltd., Shanghai, China Email: {hegaoning, sqzhang, eeyanchen, shugong}@huawei.com
Abstract—To meet the global challenge of reducing ICT environment impact and the exponentially growing data traffic, green design of wireless networks is becoming an important issue for cellular network operators. In order to achieve such goal, green network techniques must be applied to change the current network architecture according to future demand. In this paper, we introduce the cellular network with novel architecture which is called fractional separation green (FSG) networks. We investigate the FSG network system from different aspects and show that the proposed architecture can save the cellular network energy consumption by up to 50%. Index Terms—Data and signaling separation, green architecture, performance evaluation
I. I NTRODUCTION The next generation wireless networks are expected to provide high speed internet access anywhere and anytime. The popularity of iPhone and other types of smartphones doubtlessly accelerates the process and creates new traffic demand, such as mobile video and gaming. The exponentially growing data traffic and the requirement of ubiquitous access have triggered dramatic expansion of network infrastructures and fast escalation of energy demand, which directly results in the increase of greenhouse gas emission and becomes a major threat for environmental protection and sustainable development. European Union has acted as a leading flagship in energy saving over the world and targeted to have a 20% greenhouse gas reduction. China government has also promised to reduce the energy per unit GDP by 20% and the major pollution by 10% by the year of 2020. The pressure from social responsibilities serves as another strong driving force for wireless operators to dramatically reduce energy consumption and carbon footprint. Worldwide actions have been taken. For instance, China Mobile, the largest mobile telecommunications operator in China, has initiated “Green Box Environmental Protection Plan-517 Special Event” and pledged to reduce electricity consumption per unit of telecommunications traffic by 40% by the year of 2010 compared with 2005 . Meanwhile, Vodafone Group has announced to reduce its CO2 emissions by 50% against its 2006/7 baseline of 1.23 million tons, by the year of 2020. To meet the challenges raised by the high demand of wireless traffic and energy consumption, green evolution has become an urgent need for wireless networks today. As has been pointed out in [1], the radio access part of the cellular network is a major energy killer, which accounts for up to more than 70% of the total energy bill for a number of mobile operators . Therefore, increasing
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the energy efficiency of radio networks as a whole can be an effective approach. Vodafone, for example, has foreseen energy efficiency improvement as one of the most important areas that demand innovation for wireless standards beyond LTE [2]. Green Radio (GR) [3], a research direction for the evolution of future wireless architectures and techniques towards high energy efficiency, has become an important trend in both academic and industrial worlds. Before GR, there have been efforts devoted to energy saving in wireless networks, such as designing ultra-efficient power amplifier, reducing feeder losses, and introducing passive cooling. However, these efforts are isolated and thus cannot make a global vision of what we can achieve in five or ten years for energy saving. GR, on the other hand, targets at innovative solutions based on topdown architecture and joint design across all system levels and protocol stacks, which cannot be achieved via isolated efforts. As mentioned in the previous literature, the heterogeneous network topology brings more than 3 times energy efficiency enhancement versus the traditional homogeneous macro networks, which is also regarded as the most energy efficient architecture for future green networks. Recently, GreenTouch consortium has initiated research projects to study the network separation concept for future green network evolution, i.e. to allow separated signaling and data to serve users, see Fig. 1. More specifically, the “Data” in Fig. 1 represents the physical layer data information plus the corresponding data singling, and “Signaling” represents higher layer control signaling. Two immediate advantages can be found when applying the mentioned separation scheme: (1) flexible cell sleeping can be realized since small cells are no longer responsible for control signaling transmission. In this way, small cells can swiftly enter sleep model as long as no active user terminal is asking for data transmission. As a result, the energy consumption of small cell network will tightly scale with the real-time traffic demand as long as the cell size is small enough (e.g. dozens of meters), (2) smooth mobility management can be achieved by letting macro cell transmit signaling and small cells transmit data only. Compared to traditional handover the separation scheme can save significant amount of signaling overhead in the handover process. However, such a separation scheme requires full coverage of small cells since the macro cells are not capable of data transmission. Since a full deployment of small cells over a large area is clearly impractical and inefficient, we proposed in this paper a nouveau scheme called
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Fig. 1.
Illustration of two different transmission schemes: signaling and data coupled transmission (left), signaling and data decoupled transmission (right).
fractional separation green (FSG) architecture. As its name, we fractionally separate the signaling and data transmission, e.g. macro cells keep responsibility of signaling and low-rate transmission and small cells transmit high-rate applications. As a whole, from transmission functionality viewpoint, wireless network architecture can be divided into: • No Separation - Control and data network layers are coupled. This corresponds to current existing wireless systems such as 2G, 3G and 4G. • Data and Signaling Separation - Control and data network layers are decoupled. There are two subclasses: – Complete separation: Coverage cells are responsible for signaling transmission only and small cells are responsible for data transmission only. This idea was proposed in GreenTouch BCG2 project [4]. – Fractional separation: Coverage cells are responsible for signaling and low-rate data transmission, and small cells are responsible for high-rate data only. This scheme corresponds to our FSG architecture. Since the GreenTouch BCG2 architecture can be regarded as a special case of our FSG architecture, we focus on the our FSG architecture to illustrate the benefits of signaling and data decoupled architectures. The rest of the paper is organized as follows. We introduce our models and assumptions in section II. Detailed discussion about the vision of future green wireless networks is given in section III, followed by the an evaluation overview in section IV. Finally, section V concludes the paper. II. M ODELS AND ASSUMPTIONS A. Power model Normally, the power consumption of a BS site includes power losses from circuit power of signal processing, radio frequency, A/D D/A converter, power supply, battery backup, antenna feeder, site cooling consumption, etc. It is shown in [5] that the relation between BS transmit power and BS site power consumption is nearly linear for LTE systems. Thus, the BS site power consumption can be approximated using the following linear model: { T sleep T Ni P(i ) PiT = 0 Pisite = (1) T base T Ni Pi + λi Pi Pi ∈ (0, Pimax ] where PiT is the BS transmit power, Pibase is the power consumption when BS transmits at the minimum non-zero power, λi is the slope of the traffic-dependent power consumption which depends mostly on the power amplifier efficiency,
i.e., BS transmit power and traffic have a near-linear relation, Pisleep is the sleep mode power consumption that is normally smaller than Pibase . Dynamically switching BS into sleep mode (deactivation of components inside BS) when there is nothing to transmit is believed to be a promising solution for energy saving [6]. In this paper, we will analyze the network performance (throughput, energy efficiency and deployment efficiency) taking into account the realistic power model (1) and the impact of dynamic BS sleeping. B. Deployment model Deployment model plays an important role in the network average performance over a large area. Note that the overall wireless network consists of different types of areas, different infrastructure deployment strategies are applied in these areas, respectively, so as to make the overall network operate in a most efficient way. The deployment model shall give the assumption of how different types of base stations (BS) are deployed in different areas so as to satisfy the coverage and capacity requirement of the area. In this paper, we consider a heterogeneous wireless network with two tiers, namely macro tier and small cell tier. Normally, a macro BS has a larger coverage radius while the micro and pico BSs have smaller coverage radius. The macro layer is mainly used for signal coverage while the small cell layer is dedicated for high-speed data offloading. For simplicity of presentation, the area A we consider for the deployment of heterogeneous BSs is within a macro cell region with radius r0 = 500 M. Assume that n = 12 small cells, each with cell radius r1 = 100 M, are non-overlapping distributed in the considered area A (deployment ratio 1 : 12). We further assume that each small cell has two different modes: active mode and sleep mode. A small cell is in active mode if it is serving at least one user. Small cell can enter sleep mode if no active user appears in its coverage. C. Traffic model Traffic model is the most challenging component for the network performance evaluation. This is not only because of the various services types and rates generated by different users, but also the user location and mobility issues. To model all the effects of traffic variations in the network performance evaluation platform is infeasible. However, we can abstract most of the user behaviors based on the following three effects. • User density - User density is the most stable part of the traffic model. Up to the current stage, the penetration
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Fig. 2. Illustration of LTE air-interface energy consumption for data and signaling transmission averaged over 24h traffic profile. The left figure is before ”separation” and the right figure is after ”separation”. The air-interface efficiency of effective data transmission is improved from 56% to 71%.
THE EVALUATION .
rate of the cellular communication has been saturated, e.g. the values of user density may not change for the next 10 years. • Traffic profile - Traffic profile is another factor to be considered in the traffic modeling. In general, different deployment areas will have different traffic profiles and the relationship will be complex. However, according to EARTH [7], we can use the homogeneous user traffic assumption and the traffic profile scales linearly with the user densities as shown in Table 4 and other deployment areas can be directly evaluated by switching the user densities. • Service breakdown - Since different service have different QoS requirement, it is necessary to breakdown the service mix when investigating the traffic models. The system parameters used in the simulation evaluation are listed in Table II. III. FSG D ESIGN AND E VALUATION The demand for higher data rates and the ever-increasing number of wireless users translate to rapidly rising power consumption. At this stage, “green” is becoming a key word for the evolution of next generation wireless networks. However, base stations are fixed devices with fixed characteristics. Turning them into something that can scale to deliver capacity on demand isn’t easy. In this section, we focus on the FSG architecture to illustrate the following three advantages compared with heterogeneous networks, which turns out to be a competitive candidate for future green 5G networks. A. Transmission functionality separation To guarantee the quality of real-time services in the wireless networks, the existing standards, such as GSM and LTE, jointly design the control signaling and the data signaling together, which are adopted in the current HetNet architecture. However, such a fixed pattern inevitably introduces resource redundancy since the amount of signaling cannot automatically adapt to the variation of network status such as traffic load, user mobility, etc. The left figure in Fig. 2 shows the average (over 24h daily traffic profile) energy distribution of different signals in LTE (3GPP release 8) systems. It can be observed that the energy
consumption of real data transmission is only 56% whereas the energy consumption of signaling overhead for transmission control and system information is around 44% dominated by reference signal (RS) - 19% and physical downlink control channel (PDCCH) - 23%. This means that in order to deliver useful data from base station side to the terminal side, the current systems spend nearly half energy in the air-interface for non-data transmission - just for maintaining the wireless link functioning properly. Indeed, the data and signaling transmission functionality in current wireless systems is not optimized and many signaling overheads should not be necessarily transmitted every time. For example, it is wasteful to transmit RS on all resource blocks when base stations are in low load status which is the case for the majority of base stations even in dense urban scenarios. But how to optimize the data transmission efficiency out of signaling overheads? The answer is: to separate their functionalities in the network level, i.e., to maintain the network control capability through control network layer and to provide the high-rate data transmission through data network layer1 , as already shown in Fig. 1. The design of FSG architecture can alleviate the signaling overhead by letting the signaling load adapt to the data traffic. This flexibility is highly related with the user density and the cell sleep strategy inside the FSG scheme. Within the FSG architecture, the static signaling overhead for the network coverage maintenance will be mainly carried by the control network layer. While in the data network layer, the residual signaling overhead comes from the format control and pilot signals, which can also be dynamically switched off based on the traffic profile. Hence, to expand new resources in the data network layer brings few impacts on the static signaling overhead and introduces much less signaling overhead if compared with traditional networks. Fig. 2 illustrates the LTE air-interface energy distribution between data and different signalings. The left figure is for traditional heterogenous architecture without separation and 1 In the heterogeneous architecture, the control network layer is usually composed of macro cells guaranteing full coverage and the data network layer is often made up of small base stations with smaller cell size.
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the right figure is for our FSG architecture with data and signaling separation. As expected, FSG scheme reduces the signaling overhead of RS and PDCCH by 42% (from 19% to 11%) and 30% (from 23% to 16%), respectively. As a result, the FSG scheme improves the air-interface efficiency of effective data transmission from 56% to 71%. This is about 5% energy reduction contribution for the global network2 . B. Flexible node reconfiguration An important advantage of the FSG architecture is the feasibility of configuring or switching off nodes without the conventional coverage issue. Since the control network layer will provide low-power ubiquitous coverage, the data network layer realized by small cells will be switched on ondemand to provide capacity to users. This implies a resource management mechanism for the activity of cells, which is in charge of defining the data network layer configuration with the minimum power consumption for the current user requests. In the FSG architecture, since all the detached and idle UEs can be managed by the control network layer with static network topology, reconfiguration of the data network layer will not trigger any migration events of the idle UEs and the associated delay issue no longer exists. As a result, a more flexible network topology adjustment can be performed under the functionality separation framework, which brings new opportunities to flexibly reconfigure network components (such as base stations or carriers) for green purpose. In this paper, we simulate the cell sleep behavior for different network traffic load. Regarding the cell sleep strategy, we assume that FSG small cell can sleep/active in the minimum resolution of 1 ms (e.g. one TTI in LTE system). We put FSG small cell in sleep mode if no user requires data transmission. We assume that pico can sleep/active in the minimum resolution of one second. Pico is set to sleep mode if “user activity” in the last time interval is lower than certain threshold. Here, “user activity” means the total number of data transmission requests in the last one second. 2 This percentage of energy saving may vary according to the density of small cells.
Fig. 4.
Performance of specific design optimization for FSG systems
In Fig. 3, we show the small cell activation probability before/after data and signaling separation during a 24h daily traffic profile (from 18 Mbps/km2 to 120 Mbps/km2 ) according to EARTH traffic model in dense urban scenario [8]. As expected, after separation the small cell activation probability is significantly decreased especially when the traffic load is low. Also note that when the traffic load is more than 80 Mbps/km2 traditional pico has little chance to enter sleep mode, whereas FSG small cell still has 1/2 chance to sleep. Based on 24-hour daily traffic profile from EARTH traffic model, the proposed FSG scheme decreases 50% (on average) of the pico activities while keeping the same data rate, which is equivalent to 20 ∼ 30% global network energy saving. All the above gains are thanks to the flexibility provided by the data and signaling separation scheme. C. Specific design optimization Another advantage of the FSG architecture is that it offers new opportunities to improve the equipment efficiency (including hardware and software) as well as deployment efficiency that can be specifically optimized for the data and signaling separation scheme. On one hand, this optimization approach is important for coverage macro cells since the average PA efficiency of current macro base station is quite low 20% (due to the dynamic range of its output power). If the output power can stick to 10w for example (thanks to low dynamic range of control signal transmission), the PA efficiency can be raised by at least 3dB and the corresponding average efficiency can be improved to 40%, i.e. 100% energy efficiency improvement for the PA input power. That is about 30% energy saving gain for macro base station alone without considering the hardware improvement of other components. On the other hand, such an optimization approach is also important for the deployment of small cells. That is to say data network layer could be deployed in a certain way so that the global network energy is minimized. Such a topic is related with many interesting research issues such as how to optimize the cell behavior considering various cell abilities (e.g. cell
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sleep [6] and cell zooming [9]) and how to characterize the tradeoff between energy efficiency and deployment efficiency [10] under realistic constraints. The result of Fig. 1 in [10] shows that properly choosing the small cell density can provide up to 30% energy saving gain for their heterogeneous settings. Fig. 4 compares the network area power density (in W/km2 ) of traditional HetNet (without data and signaling separation), FSG (with data and signaling separation but without specific design) and FSG+ (with data and signaling separation and with specific design). In the FSG+ scheme, we assume that the power amplifier of macro base station is improved from 20% to 40% and the density of small cells are optimized for the overall network energy consumption. It can be observed from Fig. 4 that thanks to the hardware specific design FSG+ can further save about 200 W/km2 for the global network on top of FSG scheme, which contributes 15 ∼ 20% total network energy reduction. It is also worth to mention that in Fig. 4 the traditional HetNet enters saturation regime only when the area traffic density is about 40 Mbps/km2 . At this point, the network does not significantly increases power consumption, since macro base stations transmits with their maximal power and all small cells are powered on to serve users. In this case the traffic is still increasing because small cells still have capability to offload traffic to their umbrellas. However, this saturation limit can be pushed beyond 200 Mbps/km2 in the FSG architecture thanks to the node flexibility provided by the data and signaling separation scheme. Under FSG architecture, we can see that the globe network can save significant energy especially for the low and middle traffic regime.
Fig. 5.
V. C ONCLUSION In this paper, we have discussed several candidate architectures for future green cellular networks under the standard network environment. By allowing the signaling and data separation, the proposed FSG architecture provides three main advantages if compared with the traditional heterogeneous networks. As an integrated view, FSG architecture gives a promising evolution way to achieve 50% network energy reduction, which is thus shown to be the competitive candidate for future green cellular networks. ACKNOWLEDGEMENT This paper is partially supported by the National Basic Research Program of China (973 Program 2012CB316000) and the National High Technology Research and Development Program of China (863 Program 2012AA011400). R EFERENCES
IV. A N INTEGRATED VIEW In this section, we give an overview to evaluate the performance of FSG architecture. As already shown in the previous section, the main advantages of FSG architecture can be summarized as follows: • • •
Transmission overhead reduction: −5% (total energy) Flexible topology reconfiguration: −20 ∼ 30% Specific design optimization: −15 ∼ 20%
Note that the transmission overhead reduction, the flexible topology reconfiguration and the specific design optimization corresponds to energy savings in three independent domains including air interface, network topology and circuit power. Hence, we can simply derive the integrated energy saving gain from the equation below X =1−
n ∏
Overview of FSG
(1 − xi ),
i=1
where xi represents the energy reduction of the ith advantage item. As shown in Fig. 5, FSG architecture provides an integrated energy saving gain as much as X = 53.2% (taking x1 = 5%, x2 = 30%, x3 = 20%), which gives a promising evolution roadmap of future green cellular networks.
[1] T. Edler and S. Lundberg, “Energy efficiency enhancements in radio access networks,” in in Erricsson Review, 2004. [2] R. Irmer, “Evolution of lte - operator requirements and some potential solutions,” in in Proc. 5th International FOKUS IMS Workshop, Berlin, Germany, November 2009. [3] Y. Chen, S. Zhang, S. Xu, and G. Y. Li, “Fundemantal Tradeoffs on Green Wireless Networks,” IEEE Commun. Mag., vol. 49, no. 6, pp. 30 – 37, Jun. 2011. [4] “Greentouch 2010-2011 annal report.” [Online]. Available: http://www.greentouch.org [5] G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Ollsson, M. A. Imran, D. Sabella, M. J. Gonzalez, O. Blume, and A. Fehske, “How much energy is needed to run a wireless network?” IEEE Trans. Wireless Commun., vol. 18, no. 5, pp. 40 – 49, Oct. 2011. [6] I. Ashraf, F. Boccardi, and L. Ho, “Sleep mode techniques for small cell deployments,” IEEE Commun. Mag., vol. 49, no. 8, pp. 72 – 79, Aug. 2011. [7] R. Tafazolli, “EARTH - energy aware radio and network technologies,” in Proc. of Next Generation Wireless Green Networks Workshop, Pairs, France, Nov 2009. [8] G. Auer, O. Blume, V. Giannini, I. Godor, M. A. Imran, Y. Jading, E. Katranaras, M. Ollsson, D. Sabella, P. Skillermark, and W. Wajda, “Deliverable d2.3 - energy efficiency analysis of the reference aystems, areas of improvements and target breakdown,” 2012. [Online]. Available: https://www.ict-earth.eu/publications/deliverables/deliverables.html [9] Z. Niu, Y. Wu, J. Gong, and Z. yang, “Cell zooming for cost-efficient green cellular networks,” IEEE Commun. Mag., November 2010. [10] G. He, S. Zhang, Y. Chen, and S. Xu, “Energy efficiency and deployment efficiency tradeoff for heterogeneous wireless networks,” in Proc. of IEEE Global Telecommunications Conference (Globecom), California, USA, December 2012.
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