performance evaluation methodology for future wireless networks from the viewpoint of five basic metrics, i.e., throughput, power consumption, deployment cost ...
Workshop on Smart and Green Communications & Networks (SGCNet)
Fundamental Tradeoffs and Evaluation Methodology for Future Green Wireless Networks Gaoning He, Shunqing Zhang, Yan Chen, and Shugong Xu Huawei Technologies, Co. Ltd., Shanghai, China Email: {hegaoning, sqzhang, eeyanchen, shugong}@huawei.com bandwidth - power (BW-PW) trade-off, and delay - power (DL-PW) trade-off. We have shown that, by means of the framework, the key network performance/cost indicators are stringed together. In this paper, we take a step further to extend the fundamental tradeoff framework from point-to-point links to networks. In particular, we consider the heterogeneous networks, which have stand out as promising solutions for cost-effective and capacity-boosting future wireless architectures, providing many degree-of-freedoms for energy-efficient system design and management. Such a step is not trivial since the network EE, DE, or SE, depends on the performance of multi-link, multi-cell, and their mutual relations (mutual interfering and/or resource sharing). In particular, the density of the small cells, their cell radius, the efficiency of their offloading from the macro cells, the resource partition between different tiers of the network, all affect the network performance. Moreover, as the practical power models are taken into consideration, monotonic performance trends with respect to these parameters usually do not exist. Optimal network configuration, instead, shall be derived from the equilibrium among different factors. In the following, we first illustrate the basic metrics closely related to the efficient wireless network design, followed by the fundamental tradeoff relations elaborated. Detailed investigation under heterogeneous networks is given in section IV, especially the three dimension tradeoff relations of DE, EE. Finally, section V concludes the whole paper.
Abstract—To meet the global challenge of reducing greenhouse gas emissions and the exponentially growing data traffic, green design of cellular network is becoming a urgent issue for wireless network operators. In this paper, we first discuss the performance evaluation methodology for future wireless networks from the viewpoint of five basic metrics, i.e., throughput, power consumption, deployment cost, bandwidth, and latency. We then study the fundamental tradeoffs from Shannon’s perspective based on the five metrics. Furthermore, we extend the tradeoff relation from point-to-point link to a heterogeneous network scenario taking into account realistic base station power model. Our study provides useful insights for the evaluation, modeling and deployment of future green wireless networks. Index Terms—Energy efficiency, Deployment efficiency, fundamental tradeoffs
I. I NTRODUCTION Within the ICT industry, the mobile network is recognized among the biggest energy killers. The exponentially growing data traffic in mobile networks, on the other hand, has made the issue an even grander challenge in the future. As predicted by Cisco [1], the global mobile data traffic will probably increase 18-fold between 2011 and 2016, with a compound annual growth rate of 78%, reaching 10.8 exabytes per month in 2016. In light of this, it is not so hard to imagine that the unprecedented expansion of mobile data traffic will drive the network deployment to be much denser and complex, resulting in a tremendous increase in the overall energy bill of the mobile operators, and at the same time, leaving significant environmental footprint. Therefore, to maintain sustainable capacity growth while limiting the electricity bill is now a demanding challenge in front of the mobile operators. Energy efficient design of the architectures and technologies for mobile networks, as a result, becomes crucial and necessary to meet this challenge. The improvement of energy efficiency and the reduction of energy consumption, however, are usually associated with the cost in other operational metrics, e.g., bandwidth and latency. Therefore, the energy efficiency of the system will have tradeoffs against other desirable operational metrics, such as spectrum efficiency and deployment cost. The key research question to be answered is when and how to tradeoff what while still keeping the whole network service satisfactory to various users. In our earlier work [2], we have proposed a unified green research framework consisted of four fundamental tradeoffs, namely deployment efficiency - energy efficiency (DE-EE) trade-off, spectrum efficiency - energy efficiency (SE-EE) trade-off, 978-1-4673-2997-2/12/$31.00 ©2012 IEEE
II. BASIC M ETRICS OF W IRELESS N ETWORKS From the viewpoint of network design, many metrics have been considered by the engineers for performance evaluation and optimization, among which, the following five are usually of top priorities: Network throughput is a key measure of the performance for wireless networks. It is defined as the summation of the average throughput of all BS sites within the considered network area, where the average throughput of a BS site is the delivered information bits averaged over all mobile terminals served by that BS site. Network power consumption is becoming an important metric for wireless network in recent years. The energy consumption of a wireless network can be measured as the summation of the energy ∑ consumption of different classes of BS sites, i.e., E net = i Ni Eisite , where i is the class index, Ni and Eisite are the number of BS sites and site average
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Intuitively, when considering a certain radio technology the five metrics are closely related with each other: increasing or decreasing the satisfaction level of one metric may affect the satisfaction levels of other metrics. For example, in order to increase the network throughput for a certain area, network operators usually decide to put more base stations which result in the increase of network power consumption and the increase of network deployment cost. For another example, in order to decrease the network latency, it often requires base station to increase the transmit power to compensate the channel degradation due to fading, otherwise base station can save energy by waiting for better channel condition and delivering data only when channel gain becomes good enough. As we can see, the metrics may trade off each other and a high-level framework is needed to guide the network design. Moreover, these metrics all have direct or indirect relation with the network energy consumption. We then propose the tradeoff framework in the following section to facilitate the energy efficient design of future green wireless networks.
Fig. 1. Performance evaluation for LTE and UMTS networks using radar chart based on the satisfaction level of five basic metrics: throughput, energy, latency, spectrum and cost, 100% being “very satisfied” and 0% being “very unsatisfied”.
power consumption in class i, respectively. The latter is the accumulation of power consumption Pisite over a ceratin time duration ∆t. Network deployment cost is the total cost of network deployment, which in general, can be measured as the summation of∑the cost from∑different types of BS sites csite , i.e., i cap op cap N (C + C ). Here, C and = C net = i Ni csite i i i i i i Ciop are BS site’s CapEx and OpEx, which can be further specified as [3]
III. T RADEOFF F RAMEWORK In order to simplify the relation between the throughput metric and the bandwidth/power/cost metrics, we introduces the following notions: • Spectrum efficiency (SE) is defined as the ratio of the average network throughput T over the network bandwidth B, i.e., ηSE = T /B (bit/sec/Hz). • Energy efficiency (EE) is defined as the number of delivered bits over the energy consumption E within a certain period ∆t, i.e., ηEE = T ∆t/E = T /P (bit/joule), where P is the average power consumption. • Deployment efficiency (DE) is defined as the number of delivered bits over the network deployment cost C within a certain period ∆t, i.e., ηDE = T ∆t/C (bit/$ or bit/e).
E Cicap = cBSE + cRN + csite i i i op power trans Ci = ci + clease + ci i E represent the cost of BS equipand cSB , cRN where cBSE i i i ments (BSE), radio network equipments (RNE), and BS site are the annual exbuildout of class i, cpower and clease , ctrans i i i pense of electric power and man power, backhaul transmission lease, and BS site lease of class i. Network bandwidth is important but limited resources for wireless communication systems. The spectrum resource is typically government regulated and, in some cases, is sold or licensed to operators of private radio transmission systems. It is an important metric because the transmission rate scales linearly with the bandwidth (since bandwidth is outside the log of the channel capacity expression). It is therefore one element of what a person perceives as the speed of a network. Network latency is another element that contributes to network speed. The term latency refers to any of several kinds of delays typically incurred in processing of network data. It is a measure of QoS and user experience and is closely related to the upper layer traffic types and statistics. In the light of the above five metric, the network performance can be simply evaluated through a grading system, where the satisfactory grade of a certain metric can be divided into serval levels, e.g., five levels: level-1 represents “very satisfied - 100%”, level-2 represents “satisfied - 75%”, level-3 represents “average - 50%”, level-4 represents “unsatisfied 25%”, and level-5 represents “very unsatisfied - 0%”. Fig. 1 illustrates a simple example of how LTE and UMTS behave in a radar chart characterized by the five-metric grading system.
A. Tradeoffs from Shannon’s perspective Following Shannon’s formular, during a certain period ∆t, assuming the transmit power P and system bandwidth W are fixed, the achievable rate R can be expressed as [4] ( ) P R = W log2 1 + (1) W N0 where N0 represents the power spectral density of the additive white Gaussian noise (AWGN). According ( to the definitions of SE, EE and DE, (we can write ) ) ηSE = log2 1 + WPN0 , ηEE = (W/P ) log2 1 + WPN0 , ( ) and ηDE = (W/C) log2 1 + WPN0 , respectively. Here, C represents the deployment cost of the communication system. From ηEE and ηSE , the SE-EE relation can be written as ηSE ηEE (ηSE ) = ηSE , (2) (2 − 1) N0 and the DE-EE relation can be written as ηDE · C ) ηEE (ηDE ) = ( ηDE ·C . 2 W − 1 W N0
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(3)
B. From theory to practice When putting the previous results into the network scenario, the four tradeoffs (SE-EE, DE-EE, PW-BW, and PW-DL) may not follow the simple relations derived from Shannon’s formula due to many practical reasons. First, for realistic systems the Shannon capacity is not always achieved because of some practical constraints in the implementation of modulation, coding scheme, channel estimation, and resource allocation strategies. Moreover, since the Shannon formula is originally applicable to the point-topoint link, it is no longer suitable for the multi-user network scenario. Second, the circuit power of base station may break the monotonic relation of the four tradeoffs, and turn the SEEE and DE-EE curves to a bell shape [5]. This is because the power consumption of a BS site includes not only the transmit power from the air-interface but also 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 [6] and [7] that the BS site power consumption is nearly linear for LTE systems and it can be approximated using the following linear model: { T sleep T Ni P(i site ) PiT = 0 (6) Pi = base T T Pi ∈ (0, Pimax ] + λi Pi Ni Pi
Fig. 2. Radar chart of SE, EE and DE for two different network configurations
From equation (2) and (3), we find that ηEE is monotonically decreasing with ηSE and ηDE . This means that (for point-topoint link) increasing EE is always at the cost of SE or DE reduction. And equivalently, increasing SE or DE is always at the cost of EE reduction. Fig. 2 illustrates a simple example for the triangle relation among SE, EE, and DE. As we can see, “config 2” gives maximum SE, whereas the EE and DE is quite low. But when the system is operated at “config 1”, although the SE is decreased by 20% the EE and DE are significantly improved. This implies that the traditional blind pursuit of high SE, regardless of energy and cost, is evidently not efficient from a system viewpoint. In the realistic engineering world, the SE should be chosen at an appropriate level so that the energy and cost are spent in an effective manner. Now consider the SE-EE relation (2) as an example, we have { 1/(N0 ln 2) ηSE → 0 ηEE → 0 ηSE → +∞
where PiT is the BS transmit power, Pibase is the power consumption when BS transmits at the minimum non-zero power, NiT is the number of transmit/receive antennas per site, 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 [8]. Third, the DL-PW relation could become much more complicated when considering the traffic dynamics. The transmission delay should include both waiting time in the queue and the transmission time. Moreover, when considering traffic flow, instead of the average delay per bit, the average delay per packet is used, which is closely related with the upper layer protocol. For further detailed analysis, more complex mathematical model is required involving information theory and queueing theory.
The above expression is meaningful to predict the limit and behavior of EE performance for a wireless system. On the one hand, a minimum EE is guaranteed for the case of low SE (whatever how wide is the bandwidth and how low is the transmission power). On the other hand, no positive EE lower bound is guaranteed for the case of high SE. This result implies that for future wireless system with high SE performance, increasing transmit power would have limited contribution to the data rate. Furthermore, for a given transmission rate R, from (1) we can derive the PW-BW relation as ( R ) P (W ) = W 2 W − 1 N0 (4)
IV. F UNDAMENTAL T RADEOFF FOR H ETEROGENEOUS W IRELESS N ETWORKS In this section, we study the tradeoff relations in the scenario of heterogeneous wireless networks. We consider a simplified heterogeneous network model with two layers, namely macro layer and small cell layer. The macro layer is mainly responsible for basic signal coverage and the small cell layer is dedicated for high-speed data offloading.
where the transmit power P is monotonically decreasing with W . Since it takes T = 1/R seconds to transmit one bit, the PW-DL relation can be written as ) ( 1 (5) Pbit (T ) = T 2 T W − 1 W N0
A. System model and assumption
where Pbit is the average power per bit, which is monotonically decreasing with T . Both equation (4) and (5) tell one simple story, that is, transmit power can be saved via trading (more) bandwidth or (longer) transmission delay.
For simplicity of presentation, we focus our analysis within a macro cell coverage A with cell radius r0 . Assume that n small cells, each with cell radius r1 , are non-overlapping
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¯ macro users 1 , i.e., Tm = (K−n ∑K k)R, where n is the number of ¯ deployed small cells, k = k=1 ρk k is the average number of users served by a small cell BS. Therefore, the transmit power of macro BS is given by ( ) ¯ (K−nk)η SE σ2 T (1−ω)K pm = 2 −1 (10) gm Fig. 3.
Heterogeneous wireless network model
We can then write the network EE as KR ηEE = (n¯ ps + pm ) T
placed in A. We assume that K active mobile users, each with rate requirement R bits/sec, are uniformly distributed in the considered area A. We also consider that each small cell has two different modes: (1) active mode and (2) sleep mode. A small cell is in active mode if at least one active user is in its coverage. Small cell can enter sleep mode if no active user appears in its coverage [8]. A fundamental problem in heterogeneous wireless network is how to deal with inter-layer interference. One simple method is “frequency division”, i.e., macro layer and small cell layer use orthogonal spectrum to avoid inter-layer interference. Another method is “frequency sharing”, i.e., macro layer uses the whole bandwidth and small cell layer uses a fraction of the bandwidth. In this paper, we will focus on the first case. The study of the second case would be provided from another paper.
(11)
where p¯s is the average power consumption of small cell BS, pm is the power consumption of the macro BS site. From equation (6), (9) and (10), we can write p¯s and pm as [ )] σ 2 λs ( kΓSE T base ωK p¯s = Ek Ns ps + 2 − 1 +(1 − ρ+ )NsT psleep s gs ] σ 2 λs [ kΓSE = ρ+ NsT pbase + Ek 2 ωK − 1 +(1 − ρ+ )NsT psleep s s gs ) ( ¯ (K−nk)Γ SE σ 2 λm T base pm = Nm pm + 2 (1−ω)K − 1 gm where ρ+ represents the probability of a small cell in the active mode (i.e., serving at least one user in its coverage) ( )K r2 ρ+ = Pr(k > 0) = 1 − 1 − 12 r0
B. Characterizing tradeoffs When the small cells are deployed in the macro cell in a sparse manner, i.e., there is no significant interference between small cells, and given the frequency division spectrum scheme, the throughput of small cells Ts and throughput of macro cell Tm can be expressed as ( ) pTs gs Ts = ωW log 1 + 2 (7) σ ( s ) pT gm Tm = (1 − ω)W log 1 + m2 (8) σm
From equation (11), we can derive the network SE-EE relation, summarized in the following lemma: Lemma 1 (EE-SE relation): For a two-layer heterogeneous wireless network, EE scales with SE in different ways depending on the number of small cells n: { −η 2 SE n is small ηEE ∼ ηSE n is large Proof: We provide a brief proof. From equation (11), we can rewrite the EE expression as
where ω is the fraction of bandwidth allocated to small cells, 2 σs2 = ωW N0 and σm = (1 − ω)W N0 are the noise variance of small cell and macro cell, respectively. pTs and pTm are the transmit power of small cell BS and macro BS, respectively. gs = β1 r1−α and gm = β0 r0−α are the channel gains of macro cell and small cell, respectively. β0 and β1 represent fading coefficients. The throughput of a small cell serving k users is given by Ts = kR. From equation (7), we can derive its transmit power as ) σ 2 ( kηSE pTs (k) = 2 ωK − 1 (9) gs
ηEE =
W ηSE a·2
¯ (K−nk)η SE (1−ω)K
+ b + o(ηSE )
where a, b are just constant parameters. It is apparent to see ¯ (K−nk)η SE
where ηSE = KR/B represents the network SE in bps/Hz, k can take any value from 1 to K with probability ρk , i.e., ( )K−k ( 2 )k r2 r1 k ρk = 1 − 12 CK r0 r02
that the term 2 (1−ω)K begins to dominate the value of ηEE only when n is small enough. When n is large, the numerator W ηSE begins to dominate ηEE . This completes the proof. It is interesting to find that the SE-EE relation for the considered two-layer heterogeneous networks depending on the number of small cells. For the case of sparse deployment, EE scales exponentially with −SE, meaning that in order to achieve high SE the energy consumption needs to be increased exponentially. For the case of dense deployment, EE would increase linearly with SE. This observation may not match our intuition: increasing spectrum efficiency can at the same time increase the energy efficiency.
k where CK represents the k-combinations of set {1, . . . , K}. Similarly, the throughput of macro cell is the sum rate of all the
1 In this paper, a user is associated to small cell if it is located in the small cell’s coverage. Otherwise, the user is associated to macro cell.
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Network energy efficiency (bits/joule)
Energy efficiency (bps/joule)
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1 n=10 n=20 n=30 n=40
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Fig. 4.
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1.8 280/π Mbps/km2 260/π Mbps/km2
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240/π Mbps/km2 220/π Mbps/km2 13 14 15 16 17 Spectrum efficiency (bps/Hz)
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SE-EE tradeoff for different small cell density
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DE-EE tradeoff for different traffic demand
Shannon’s perspective. Furthermore, we have extended the SE-EE and DE-EE tradeoffs from point-to-point link to a two-layer heterogeneous wireless network. Interestingly, in the heterogeneous scenario, we found that the SE-EE and DEEE relations are quite different from what we have obtained from Shannon’s formular. In particular, the SE-EE and DEEE tradeoff behaviors may depend on some parameters such as small cell density, network traffic demand, etc. Future works will consider the extend of BW-PW and DL-PW tradeoff analysis for heterogeneous wireless networks.
Tnet KR ) (12) ( = else Cnet n (¯ ps ϵT + cs ) + pm ϵT + celse m | {z } | {z } cs −cost of small cell BS
2
Fig. 5.
Similar as the energy efficiency expression (11), we can write the network deployment efficiency as ΓDE =
x 10
cm −cost of macro BS
where ϵ represents the unit-price of electricity power, celse s and celse are the CapEx plus OpEx except electricity cost for m small cell BS and macro BS, respectively. Since it is difficult to express the DE-EE relation in an analytical way, we plot the Pareto frontier of DE and EE in Fig. 5, i.e., given one value of DE the curves give the maximum EE value. The simulation parameters are taken as follows: the path loss exponent α = 3.5, the fraction of bandwidth for small cells ω = 0.3, the system bandwidth W = 20M Hz, each user’s rate requirement R = 1M bps, the cell radius of macro and small cells are 1000m and 100m, respectively. It can be observed from Fig. 5 that the maximum EE is a concave function of DE. This observation is completely different from the monotonic decreasing DE-EE relation (3) derived from Shannon formula. The DE-EE curve changes significantly when the network traffic demand varies. This implies that for the considered two-layer heterogeneous network, the DE-EE tradeoff behavior depends on the value of network traffic demand. It is worth to mention that the DEEE tradeoff curves are very useful for network operator to estimate the relation between the network energy efficiency and its deployment cost. For any target network throughput and deployment budget, network operator can first calculate the corresponding DE, from which the maximum achievable EE can be derived on the DE-EE curve.
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 [1] Cisco, “Cisco visual networking index: Global mobile data traffic forecast update, 2011c2016,” Cisco White Paper, Feb. 2012. [2] 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. [3] K. Johansson, “Cost effective deployment strategies for heterogeneous wireless networks,” Ph.D. dissertation, KTH Information and Communication Technology, Stockholm, Sweden, Nov 2007. [4] T. M. Cover and J. A. Thomas, Elements of Information Theory, New York, NY, USA, 1991. [5] G. Miao, N. Himayat, G. Y. Li, and A. Swami, “Cross-layer optimization for energy-efficient wireless communications: A survey,” Wiley Journal Wireless Communications and Mobile Computing, vol. 9, no. unknown, pp. 529–542, Apr. 2009. [6] 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. [7] C. Desset, B. Debaillie, V. Giannini, A. Fehske, G. Auer, H. Holtkamp, W. Wajda, D. Sabella, F. Richter, M. J. Gonzalez, H. Klessig, I. Godor, M. Ollsson, M. A. Imran, A. Ambrosy, and O. Blume, “Flexible power modeling of lte base stations,” in submitted to IEEE Wireless Communications and Networking Conference (WCNC), Paris, France, April 2012. [8] 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.
V. C ONCLUSION In this paper, we have discussed the performance evaluation methodology and characterized the fundamental tradeoffs for future green wireless networks. We showed the four fundamental tradeoffs SE-EE, DE-EE, BW-PW and DL-PW from
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