The Effect of System Architecture on Net Spectral Efficiency for Fixed Services John Naylon [email protected]November 25, 2011 Abstract We consider the net spectral efficiencies of point- to-point and point-to-multipoint fixed service ar- chitectures for mobile broadband backhaul. The properties of mobile broadband backhaul traffic are examined with reference to a network case study. The network case study also provides mea- surements of the improvement in spectral utilisa- tion possible with a point-to-multipoint architec- ture. Considering the projected growth in mobile data consumption, we argue that the gains possi- ble with a point-to-multipoint architecture are in- creasing with trends in RAN design towards high peak rates and small serving cells. Our concluding argument is that technology-neutral, block assign- ment licensing that permits point-to-multipoint architectures for fixed services can improve net spectral efficiency and alleviate frequency band congestion. 1 Fixed Service Architectures Figures 1 and 2 illustrate the simplest point-to- point (P-P) and point-to-multipoint (P-MP) fixed service architectures. The fundamental difference between the two systems relates to the channel ac- cess arrangements. In a P-P system, a given ra- dio frequency (RF) channel, or in the case of a frequency division duplex system, a pair of chan- nels, is statically allocated to a pair of transceivers. There is no channel arbitration in the form of a medium access control (MAC) scheme required, because the channel is only available to this single pair of transceivers. This type of system may be Figure 1: Point-to-point star topology Figure 2: Point-to-multipoint topology 1
We consider the net spectral efficiencies of point-to-point and point-to-multipoint fixed service ar-chitectures for mobile broadband backhaul. Theproperties of mobile broadband backhaul trafficare examined with reference to a network casestudy. The network case study also provides mea-surements of the improvement in spectral utilisa-tion possible with a point-to-multipoint architec-ture. Considering the projected growth in mobiledata consumption, we argue that the gains possi-ble with a point-to-multipoint architecture are in-creasing with trends in RAN design towards highpeak rates and small serving cells. Our concludingargument is that technology-neutral, block assign-ment licensing that permits point-to-multipointarchitectures for fixed services can improve netspectral efficiency and alleviate frequency bandcongestion.
1 Fixed Service Architectures
Figures 1 and 2 illustrate the simplest point-to-point (P-P) and point-to-multipoint (P-MP) fixedservice architectures. The fundamental differencebetween the two systems relates to the channel ac-cess arrangements. In a P-P system, a given ra-dio frequency (RF) channel, or in the case of afrequency division duplex system, a pair of chan-nels, is statically allocated to a pair of transceivers.There is no channel arbitration in the form of amedium access control (MAC) scheme required,because the channel is only available to this singlepair of transceivers. This type of system may be
Figure 1: Point-to-point star topology
Figure 2: Point-to-multipoint topology
1
2 MOBILE BROADBAND BACKHAUL TRAFFIC CHARACTERISTICS 2
conceived of as a circuit-switched network archi-tecture, where the circuit is the statically-allocatedRF channel.In a point-to-multipoint system, by contrast,channel arbitration of some kind is a necessity.The central station broadcasts downstream traf-fic with some kind of additional identifying ad-dressing field. All of the remote terminals (RTs)are able to receive and decode at least the ad-dressing fields and thus can discriminate trafficwhich is destined for them from traffic destinedfor other RTs. A form of medium access controlis used on the upstream direction whereby an RTcan indicate that it has data to transmit and isgranted access to the channel while transmissionby other RTs is suppressed. The upstream anddownstream directions are thus multiplexed, andthe usual multiplexing methodologies may be em-ployed; some form of dynamic time division mul-tiple access is most common. A P-MP system em-bodies a packet-switched architecture.
2 Mobile Broadband Backhaul
Traffic Characteristics
Figure 3 is a representative seven day sampleof downstream traffic destined for a tri-cellular,HSPA+ 21.6Mbps node B1.Figure 3 illustrates one of the key properties ofmobile broadband backhaul traffic, namely thatit is bursty. This characteristic is a result of thebackhaul traffic being the composition of manyindependent data sources which are themselvesbursty. For example, see figure 4, which is a traceof a smartphone’s downstream data demand overthe period of an hour during which the user wasasked to use the handset intensively. We use thepeak-to-mean ratio of such samples as a measure ofthe degree of burstiness of the sample. A samplewith a uniform rate has a peak-to-mean ratio of 1.0by definition and arbitrarily large figures are pos-sible.Figure 5 shows the peak-to-mean ratio of thedownstream backhaul traffic as measured for a
1We will consider downstream traffic here unless otherwisestated, since data rates on the 3GPP downlink are normallyhigher than on the uplink, and consequently the downstreambackhaul demands are higher than upstream. The analysis is,however, equally applicable to traffic in the uplink direction.
group of 922 node Bs (again, tri-cellular HSPA+21.6Mbps). This network-wide analysis illustratesthat the burstiness observed in figure 3 is a univer-sal phenomenon in backhaul over a wide range ofpeak download speeds. There is no particular pat-tern observable in terms of an increase or decreasein peak-to-mean ratio as the peak node B band-width demand rises. However we can observe anaverage peak-to-mean ratio of 3.9 across this broadrange of node B traffic profiles.An immediate consequence of this observedpeak-to-mean ratio is that we can accurately es-timate the utilisation of a point-to-point link car-rying a typical node B’s backhaul traffic. Clearlyfor a given link we will provision at least the peakbackhaul data rate since we do not want the back-haul to constrain the RAN. If we conservatively as-sume that we provision precisely the peak require-ment, then the mean volume of data carried bythe link is related to the peak by the carried data’speak-to-mean ratio. Thus the average utilisation ofsuch a link can be at most the reciprocal of the av-erage peak-to-mean ratio, or approximately 25%given that the average peak-to-mean ratio is 3.9.Considering the range of peak throughput val-ues shown along the horizontal axis of figure 5also begins to illuminate a second property of mo-bile broadband backhaul; namely that backhaulbandwidth demand is not highly correlated be-tween serving cells. Since these are tri-cellularnode Bs, the theoretical instantaneous peak band-width should be 3 × 21.6 = 64.8Mbps, requiredwhen the peak download speed occurs at the sametime in all three cells. In practice, the highest peakobserved is around 27Mbps.It may appear counter-intuitive that backhaulbandwidth demand is not highly correlated giventhe well-known diurnal load pattern (as may bediscerned in figure 3 for example). However, thetiming of the actual peaks in demand, rather thanthe daily ‘swells’, is fundamentally random asthey are driven by the individual locations and ac-tions of a large population of independent users.When considering the cross-correlation betweentwo sources, it is the samples which lie furthestfrom the mean which dominate the sum, i.e. thepeaks and anti-peaks.To quantify the degree of correlation betweennode Bs’ backhaul demands, we examine pairs ofnode Bs that are geographically close to one an-
2 MOBILE BROADBAND BACKHAUL TRAFFIC CHARACTERISTICS 3
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Figure 3: Downstream traffic for a tri-cellular HSPA+ node B over the period of a week.
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Figure 4: Downstream traffic for a smartphone over the period of one hour.
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Figure 5: The peak-to-mean ratio of downstream backhaul traffic plotted against the peak downstreambackhaul traffic demand for a set of 922 HSPA+ tri-cellular node Bs.
2 MOBILE BROADBAND BACKHAUL TRAFFIC CHARACTERISTICS 4
other, and compute the product-moment correla-tion coefficient of each pair of backhaul traces. Byconsidering only node Bs which are close to oneanother, we actually select the pairs which aremostlikely to be well-correlated because they are serv-ing similar user populations (e.g. a set of node Bsserving the central business district of a city can beexpected all to be busy during office hours and allto be quiet at night). Here, our definition of “close”is that the node Bs arewithin the same P-MP sectorin the backhaul network.
A histogram of the resulting coefficients isshown in figure 6 along with a fitted normal distri-bution. A coefficient of 1.0 would indicate perfectcorrelation between two signals; that is all peaksand anti-peaks occurring in perfect synchronicity,while a coefficient of −1.0 would indicate perfectanti-correlation; that is the peaks from the firstsignal being perfectly synchronised with the anti-peaks from the second signal. A zero coefficientindicates no correlation at all. The measuredmeancoefficient of correlation amongst the “close” nodeBs is only 0.16, indicating very weak correlation2.
Such a very low degree of correlation in peakbackhaul requirements is assumed in the NextGeneration Mobile Networks Alliance’s Guidelinesfor LTE Backhaul Traffic Estimation [1] to recom-mend that, when provisioning backhaul forN eN-ode Bs, one should provision a lower bound of:
max(peak,N × busy time mean)
This provisioning rule, shown diagrammaticallyin figure 7, relies on the observation empiricallyverified here, that peak requirements are statisti-cally unlikely to occur simultaneously in nearbyserving LTE cells.
2.1 Summary
This section has illustrated that mobile broadbandbackhaul traffic has the two essential characteris-tics that allow multiple sources to be multiplexedonto a channel and to realise a statistical multi-plexing gain: the sources have a non-uniform ratedistribution, and the sources are uncorrelated to
2Computing the coefficient of correlation amongst all pairsin the network (which lies within a single time-zone) gives amean of 0.06.
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Figure 6: Histogram of the pairwise Pearson’scross-correlation coefficient between the backhauldemands of 2262 pairs of geographically closeHSPA+ tri-cellular node Bs. A figure of 1.0 wouldindicate perfect correlation.
Figure 7: NGMN provisioning guidelines for mul-tiple eNodeB backhaul, reproduced from [1].
3 COMPARATIVE EFFICIENCY CASE STUDY 5
some extent. Best practice in backhaul dimension-ing takes into account these characteristics and thestatistical multiplexing gain (sometimes known as‘trunking gain’) that arises from them. Poor aver-age link utilisation of approximately 25% resultsfrom carrying this bursty packet data over circuit-switched P-P fixed services.
3 Comparative Efficiency Case
Study
The usual measure of spectral efficiency for a mi-crowave link is the number of bits per second perHertz (bps/Hz) achieved by the link. This cap-tures the efficiency of the link’s modulation andcoding scheme, but does not consider the natureof what is being sent. Clearly if there is no data tosend, and we consequently send idle or framingpatterns, the radio channel is still occupied but nouseful work has been done. We therefore definenet spectral efficiency as being the product of spec-tral efficiency in bps/Hz and the mean end-to-endutilisation of the link.
We re-examine data from the same set of HSPA+node Bs previously referenced to quantify the netspectral efficiency gains possible with P-MP. To il-lustrate the methodology, we first consider a singleP-MP sector containing eight node Bs connectedvia Ethernet to P-MP remote terminals (RTs).
We first measure the peak data rate required foreach node B. Logically, since we do not wish toconstrain the throughput of the RAN in the back-haul section of the network, we must provisionat least the peak requirement for each node B. Forthe purpose of this exercise, we will provision ex-actly the peak. If we therefore consider how muchbandwidth in total will be required for a point-to-point system to carry this traffic, the answeris the sum of the peaks of each data set. This isshown graphically in figure 8, and the actual sumis 123.2Mbps.
If instead we envision a point-to-multipoint sys-tem carrying the traffic, at each instant we sumthe combined data requirements and then take thepeak of this summed data set as being the totalbandwidth required. Naturally, the sources be-ing only weakly correlated, some of the peaks andtroughs in the different demands ‘cancel out’, thus
Spectrum Channel Net SpectralRequired Utilisation Efficiency(MHz) (%) (bps/Hz)
P-P 15.4 32.2 2.6P-MP 9.7 51.1 4.1
P-MP 1.6 1.6 1.6Gain
Table 1: Comparison for the example sector.
giving us a reduction in the overall total. This isshown in figure 9, and the actual sum is 77.9Mbps.
Because this data originates from a live networkwhich is actually backhauled using a P-MP sys-tem, we can compare the theoretical, ‘on-paper’multiplexing shown in figure 9 with what is ac-tually achieved in practice. Differences arise be-cause there are some overheads associated withthe channel arbitration (which can lead to theactual total data rate being higher than theory)and also because there are additional multiplex-ing gains occurring below the sampling rate usedto gather the data presented here (which lead tothe actual total data rate being lower than the-ory). Figure 10 shows that the correspondencebetween theory and practice is indeed very close.The actual peak downstream rate in the sector was77.7Mbps.
We may now calculate the amount of spectrumneeded and the mean channel utilisation for the P-P and P-MP architectures in this case. We will as-sume that the RF channel size can be chosen arbi-trarily. In practice this is not usually the case sincethe channels will conform to a preferred channelarrangement recommendation specifying channelwidths of 3.5MHz, 7MHz, 14MHz etc. In addi-tion we will not normally have the a priori knowl-edge of the exact traffic statistics that would benecessary for this precise dimensioning. Never-theless, we make this simplifying assumption forboth the P-P and P-MP case so as not to introduceany bias. We will also assume a spectral efficiencyof 8bps/Hz for both types of system, which is ap-proximately representative of the commercial stateof the art in 2011.
The results are shown in table 1. To computethe spectrum required, we simply take the peak
3 COMPARATIVE EFFICIENCY CASE STUDY 6
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Figure 9: Point-to-multipoint provisioning: The same eight node B backhaul traces multiplexed onto asingle channel sized for the peak of the joint trace. The traces are shown alternately black and grey.
3 COMPARATIVE EFFICIENCY CASE STUDY 7
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Figure 10: The theoretical multiplexing of the eight node Bs’ backhaul requirements (solid grey) andthe actually measured downstream sector data rate (black line). The lower figure shows the theoreticalrate minus the measured rate, showing a mean variance of just 0.01Mbps between theory and practice.
4 RAN TRENDS 8
(123.2Mbps in the P-P case) and divide by thespectral efficiency of 8bps/Hz to give a result inthe P-P case of 15.4MHz. To compute the channelutilisation we divide the mean bandwidth used bythe amount of bandwidth provisioned. The meanbandwidth used in this example is 39.7Mbps inboth the P-P and P-MP case (while statistical mul-tiplexing gain means that the peaks of the individ-ual demands do not sum, the means are simplyadditive). Thus for example the utilisation in theP-MP case here is:
39.7Mbps
77.7Mbps= 51.1%
Finally our net spectral efficiency is the systemspectral efficiency of 8bps/Hz multiplied in eachcase by the utilisation. We use the actualmeasuredP-MPdata rates and not the theoretical summationfor all these calculations.
We can quantify the amount of statistical mul-tiplexing gain achieved by P-MP by dividing anyof these measures. In this case there is a gain of1.6. That is, we require 1.6 times more spectrum tocarry the same data with P-P; or we use the spec-trum 1.6 times more efficiently in the P-MP case;or our net spectral efficiency is 1.6 times higher inthe P-MP case.
3.1 Results
The network in this case study consists of 922HSPA+ node Bs being backhauled via 237 P-MPsectors at 26GHz. For each of these sectors, wecalculate the statistical multiplexing gain as de-scribed above for the example sector. The resultsare shown in figure 11. There are a number of de-generate cases where there is only a single remoteterminal in the P-MP sector. In this case, naturally,there is a unity gain. The modal gain across thenetwork is 1.5.
This is a very significant increase in the effi-ciency of use of spectrum. By way of compar-ison, to achieve an equivalent increase by usinghigher-order modulation, it would be necessaryfor a point-to-point system to move from using256-QAM to 4096-QAM.
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Figure 11: Histogram of the statistical multiplex-ing gain across the network.
4 RAN Trends
The most significant trends forecast for RAN de-ployment in the forthcoming years are the intro-duction of the 3GPP LTE standard and the on-going densification of networks. Both these trendsare driven by a need to increase network capacityto supply an exponentially growing demand formobile broadband data from end users.
4.1 Network Densification
Overall RAN network capacity increases in linearproportion to the number of serving cells. There-fore the use of network densification as a tech-nique to supply capacity to meet an exponentialdemand leads to a super-linear increase in thenumber of node Bs in a unit area if other factorsremain constant. If there are a greater number ofnode Bs per unit area, then the number of node Bsper P-MP sector will increase as long as the P-MPsectors are not capacity limited.If we re-plot the statistical multiplexing gain re-sults from the case study against the number ofremote terminals per P-MP sector on the x-axis, asin figure 12, we observe that the amount of gain
5 CONCLUSION 9
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Figure 12: The statistical multiplexing gain which is achieved by using a P-MP architecture versus a P-P architecture, plotted against the number of remote terminals per P-MP sector to illustrate increasinggain as the network becomes denser.
increases with the number of remote terminals.This is intuitive: the more independent sources wemultiplex together, the greater the likelihood thata peak in one source will be ‘canceled’ by an anti-peak in another. This is extremely strong empiri-cal evidence that the benefits of P-MP in terms ofnet spectral efficiency gain increase with increas-ing network density.
There is a further likely gain in benefit due tonetwork densification. If the number of node Bsincreases then the average number of active UEsper node B will decrease. The result of this is thatthere is less multiplexing of the individual UEs’offered loads occurring in the average node B it-self. This in turn means that the peak-to-meanratio of the backhaul load offered by that cell in-creases (equivalently we can think of this as mean-ing that the backhaul load becomesmore similar toa single UE’s load—figure 4).
Finally, the proliferation of ever more numerousnode Bs with smaller service radii (the ‘small cell’phenomenon) drives the simplification of the nodeB, primarily for cost reasons. Thus while a cur-rent node B will typically be tri-cellular, and oftenmulti-band, a small cell node B is most likely to beuni-cellular and single band. Once again the netresult of this is less multiplexing in the access partof the network—more burstiness in the traffic—and hence a greater need for multiplexing in thenext stage of the network.
4.2 More Sophisticated RANTechnology
The peak spectral efficiency for a 2×2 MIMO LTEsystem is approximately 8.6bps/Hz. Simulationsgive varying results for the mean spectral effi-ciency achieved by a system with a mature real-world user population; for example [2] gives a fig-ure of 1.3bps/Hz. The consensus appears to bein the range 1–1.5bps/Hz which implies that thepeak-to-mean ratio of LTE backhaul traffic couldbe in the range of 5.7–8.6.
As noted in section 2, the mean utilisation of P-P fixed services is at best equal to the reciprocalof the peak-to-mean of the traffic carried, so thesesimulation results imply a degradation in mean P-P efficiency from a current average of 25% to some-where in the range of 11.6–17.5%.
For a P-MP system, as we have seen, the sys-tem’s net spectral efficiency is no longer directlyrelated to the peak-to-mean ratio of an individualoffered load. Therefore if we assume that back-haul demands amongst eNode Bs remain weaklycorrelated as in today’s RAN, we can expect thenet spectral efficiency benefit of P-MP over P-P toincrease.
5 Conclusion
The consumer shift from voice services to mo-bile broadband has changed the characteristics of
REFERENCES 10
the backhaul traffic in mobile networks, with ma-jor implications for backhaul topologies and spec-trum utilisation. Our analysis of an HSPA+ net-work illustrates that backhaul traffic is both highlybursty and very weakly correlated.P-P fixed service topologies popular during thevoice era are not able to carry data of this kind ef-ficiently, since they must be over-provisioned tocope with its peaks, but will then generally oper-ate at amuch lower utilisation. The analysis showsthat a P-MP topology increases spectral utilisationvery significantly, resulting in a modal increase innet spectral efficiency of 50%. Looking to the fu-ture development of the RAN, we foresee an in-crease in the degree of burstiness of backhaul traf-fic and an increase in site density. We observe thatthe benefits of P-MP versus P-P in terms of netspectral efficiency gain increase with both of thesetrends.We therefore consider that, to alleviate conges-tion in fixed service frequency bands, it is ad-vantageous to use P-MP systems because of thereduction in overall spectrum needed to deliverequivalent service. We suggest that further har-monisation of existing frequency bands for P-MPfixed service will be beneficial as the demand forfixed service links for mobile broadband backhaulrises. We consider that opening additional bandsto technology-neutral fixed service with block as-signment would would also be beneficial, allow-ing co-existence with existing P-P services andsmooth migration to more efficient P-MP systems.
References
[1] Next Generation Mobile Networks Alliance,Guidelines for LTE Backhaul Traffic Estimation,July 2011.http://www.ngmn.org/nc/downloads.html
[2] Ofcom, Predicting Areas of Spectrum Shortage,April 2009.http://stakeholders.ofcom.org.uk/
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