InterferenceManagementFor4GCellularStandards IEEE Comm Mag 2010

IEEE Communications Magazine August 201086 0163-6804/10/$25.00 2010 IEEE

INTRODUCTIONMost fourth-generation (4G) systems, includingWiMAX 802.16m [13] and Third GenerationPartnership Program Long Term Evolution(3GPP-LTE) [4], are targeting single-frequencydeployments. Although aggressive frequencyreuse results in a significant increase in systemcapacity, it also severely degrades the perfor-mance experienced by cell edge users due to theincreased interference caused by out-of-celltransmissions. Figure 1 illustrates the degrada-tion in signal-to-interference-plus-noise ratio(SINR) for reuse 1 relative to reuse 3, which isapproximately 10 dB.

While the increase in capacity due to theavailability of increased bandwidth can typicallyoffset the capacity loss due to SINR degrada-tion, the capacity of users with very weak SINR(cell edge users) still degrades. Hence, interfer-ence management schemes are critical toimprove the performance of cell edge users.

Both 802.16m and 3GPP-LTE, therefore,have focused on several interference manage-ment schemes for improving system perfor-

mance. These techniques include semi-staticradio resource management (RRM) throughadaptive fractional frequency reuse (FFR) mech-anisms, power control, and smart antennas tech-niques to null interference from other cells.Together, these techniques aim to address theaggressive requirements of > 2 improvementsin cell edge user throughput and absolute spec-tral efficiency over prior releases [1, 4].

This article describes and evaluates the per-formance of key interference management tech-niques across the 802.16m and 3GPP-LTEstandards. In particular, we focus on RRMschemes, which include FFR and power control.Smart antenna schemes, although extremelyimportant for interference management, will bediscussed elsewhere due to limited space. Wefocus on the standard cellular network deploy-ments, and interference management for multiti-er/heterogeneous network deployments in whichlow-power nodes are placed throughout a macrocellular network [5] is deferred to follow-onpapers.

The organization of the article is as follows.The next section focuses on the downlink (DL)interference management scheme covering adap-tive FFR techniques. We then cover uplink (UL)techniques focusing on power control and ULFFR algorithms. Final conclusions are presentedin the final section.

DL RRMMulticellular RRM efficiently partitionsresources across cells in order to manage perresource interference experienced in each cell.Both 802.16m and 3GPP-LTE have focused onsemi-static RRM techniques, which adapt fre-quency reuse across cells based on user distribu-tion and traffic load. In particular, a mix of highand low reuse frequency resources (e.g., reuse 1and 3, respectively) are allowed in each cell.Resources governed by reuse 1 can be assignedto users that are closer to the center of the celland hence experience less interference fromother cells, while the lower reuse resources areassigned to interference-limited users at the celledge. Allowing a combination of frequency reusepatterns overcomes the capacity limitation inher-ent with lower frequency reuse, while also retain-

ABSTRACT4G cellular standards are targeting aggressive

spectrum reuse (frequency reuse 1) to achievehigh system capacity and simplify radio networkplanning. The increase in system capacity comesat the expense of SINR degradation due toincreased intercell interference, which severelyimpacts cell-edge user capacity and overall sys-tem throughput. Advanced interference manage-ment schemes are critical for achieving therequired cell edge spectral efficiency targets andto provide ubiquity of user experience through-out the network. In this article we compareinterference management solutions across thetwo main 4G standards: IEEE 802.16m(WiMAX) and 3GPP-LTE. Specifically, weaddress radio resource management schemes forinterference mitigation, which include powercontrol and adaptive fractional frequency reuse.Additional topics, such as interference manage-ment for multitier cellular deployments, hetero-geneous architectures, and smart antennaschemes will be addressed in follow-up papers.

WIMAX/LTE UPDATE

Nageen Himayat and Shilpa Talwar, Intel Corporation

Anil Rao and Robert Soni, Alcatel-Lucent

Interference Management for 4G Cellular Standards

HIMAYAT LAYOUT 7/19/10 3:01 PM Page 86

ing a low interference environment to retainthroughput and coverage for cell edge users.Also note that the definition of what constitutescell center vs. cell edge users is an importantpart of FFR design and is typically based onSINR metrics rather than actual user locationwithin the cell.

In the following section we discuss the use ofFFR schemes for interference management inthe DL. UL-FFR is closely tied to power controlmechanisms for interference management andhence is discussed together with UL power con-trol techniques.

DL ADAPTIVE FREQUENCY REUSE IN 802.16M

Soft FFR DL FFR in 802.16m combines reuse1 resource with either reuse 3 or reuse 2resources. Both soft and hard schemes can besupported. Hard reuse refers to the case where ahigher reuse factor (e.g., reuse 2, 3) is achievedby shutting off the interfering base station (BS)on certain resources. In contrast, soft reuserefers to the case where higher reuse factors aresupported by restricting the interfering BS DLtransmit power on certain resources, rather thanturning them off. For all reuse schemes, the totalDL transmission power is kept constant andbelow the maximum allowed value. Soft FFR isbeneficial because these lower-power resourcescan still be used in the cell to service additionalcell center users with good link conditions, with-out causing much interference to cell edge usersin other cells.

Figure 2 illustrates the soft FFR scheme usedin 802.16m, where logical OFDMA resources aredivided into four frequency partitions comprisingreuse 1 and soft reuse 3 resources.

In the figure all cells (sectors) transmit on thereuse 1 partition with equal power, while thetransmit power on the remaining reuse 3 parti-tions is based on the primary partition assignedto the cell for transmission. The actual powerallocation across the frequency partitions is afunction of user distribution across the cell andis optimized cooperatively among cells based onuser feedback.

Normalized Spectral Efficiency Resource(power) allocation across the sectors results inan associated cost for each partition, which cap-tures the spectral efficiency (SE) penalty impliedby lower reuse. For example, a resource belong-ing to a hard reuse 3 partition will use threetimes the cell bandwidth when compared to onein a reuse 1 partition, hence will incur threetimes the cost in terms of lowered spectral effi-ciency. This cost-weighted spectral efficiencyassociated with a resource is referred to as thenormalized spectral efficiency and is computedas

Normalized SE (resource) = Expected SE(resource)/Resource Metric (partition).

The resource metric indicates the cost orspectral efficiency penalty associated with thesoft reuse factor of the partition. The normal-ized spectral efficiency indicates the true spectralefficiency of a given partition, and is used to

determine the preferred FFR partition (PFP),corresponding to the maximum average normal-ized SE, for each user.

Dimensioning of the FFR partitions and theassociated resource metrics is based on coopera-tive sharing of the PFP by all users in the sys-tem. Various optimization schemes may be usedto derive the optimal FFR parameters fromthese reports. The exact algorithm is implemen-tation-dependent.

16m FFR Protocol The initial FFR parti-tions and the corresponding Resource Metricsare available to users as broadcast information.Upon network entry, the user measures the aver-age SINR on each frequency partition and com-putes an average normalized SE. It thencomputes the maximum normalized SE acrossall FFR partitions and reports the correspondingpartition as its PFP. These PFP reports areaggregated across base stations in the system toupdate the FFR configuration, including parti-tions size and power level. A user can periodical-ly update its PFP with changing SINR conditionsacross the partition.

The user will also report channel quality indi-cator (CQI) metrics on the best M resources in

IEEE Communications Magazine August 2010 87

Figure 1. Geometric SINR distribution for a network with multiple frequencyreuse factors (500 m cell).

Geometry SINR (dB)

Empirical CDF

0 -10

0.1

0

F(x)

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10 20 30 40 50 60

Reuse 1Reuser 3/2Reuse 3

Figure 2. Soft FFR reuse partitioning in IEEE 802.16m.

Sector 1

Tx power

Sector 2

Tx power

Sector 3

Tx power

Partition (3)Partition (2)Partition (1)Partition (0)

Reuse 1 partition Reuse 3 partitionsLogic PRU index


IEEE Communications Magazine August 201088

the preferred FFR partition. These metrics areused by the BS for dynamic resource allocationamong users in a cell. The base stations canadjust the resource metrics periodically to ensureadequate use of resources across partitions. Suchupdates may happen locally at the BS levelunless a consistent trend in use of a particularpartition is observed. The BS may report thistrend to the central RRM function for it tomake the necessary changes in the FFR partitionconfiguration. Thus, the FFR configuration maybe managed through faster but localized updatesof the resource metrics coupled with a slowerbut more system-wide change to the FFR parti-tions and configuration.

Performance Results Table 1 illustrates thegains with adaptive FFR for 802.16m systems.The results are based on simulation methodolo-gy compliant with IEEE 802.16 evaluationmethodology [2]. Further details are provided in[6]. The FFR partitions and cost update mecha-nisms are based on the market price iterationalgorithm in [6]. For OFDMA subchannelizationschemes with localized permutations, limitedimprovement is observed with FFR as substan-tial gains are already captured with frequencyselective scheduling. However, adaptive FFRyields significant cell edge throughput (89 per-cent) benefit for the case of distributed sub-car-rier permutations when compared to a baseline16e system. The average cell spectral efficiency

is also improved by 23 percent. The cell edgeperformance may be further improved by tradingoff cell edge gains with gains in average spectralefficiency, by appropriately trading off fairnessof the proportional fair (PF) scheduler througha weighted PF-metric. Note that additional tech-niques such as multi-user multiple-input multi-ple-output (MIMO), and beamforming withnulling may also be used to achieve the 2 gainsin DL performance targets, particularly for thelocalized permutation.

DL FFR IN 3GPP LTEThe 3GPP LTE standard allows for very genericFFR schemes to be implemented in the DL,depending on the distribution of mobiles or traf-fic load. The basic mechanism is the use of a rel-ative narrowband transmit power (RNTP)indicator, which is exchanged between BSs onthe X2 interface [7]. The RNTP is a per physicalresource block (PRB) indicator which conveys atransmit power spectral density mask that will beused by each cell. This feature results in arbi-trary soft reuse patterns being created across thesystem. For instance, the soft FFR pattern shownin Fig. 2 can easily be created.

The idea would be that each cell would havea specific subband for which it will generate lowinterference with its reduced transmit spectraldensity. The DL scheduler can exploit thisinduced frequency selective interference in oneof two ways. First, if frequency selective subbandCQI reporting is used, these CQI reports willinform the scheduler that there is a particularsubband which has low interference and henceimproved CQI. Second, if wideband CQI report-ing is used to reduce the uplink overhead, thescheduler can be made aware of the identity ofthe strongest interfering cell for a particularmobile it is serving. This is done through theEvent A3 reporting mechanism [8]. Based onknowledge of which cell is causing the dominantinterference in the DL, the scheduler can con-sult the RNTP report from this cell to see whichsubband is being transmitted at reduced powerand hence generating less interference, and canchoose to schedule mobile in that subband sothat it experiences higher SINR.

Performance Results We use a simple staticreuse scheme [9] to illustrate FFR performancein Fig. 3. Here seven subbands are utilized ineach cell, of which six are transmitted at the nor-mal power level, but the seventh is transmittedwith 10 dB lower power. We refer to this scheme

Figure 3. Performance of the static DL FFR scheme for frequency selective andwideband CQI feedback.

Sector SE (b/s/Hz)]1.0 0.8

100

0

Cel

l bo

rder

th

rou

gh

pu

t (k

b/s

)

200

300

400

500

600

700

1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

Reuse 7/6, frequency selective CQIReuse 1, frequency selective CQIReuse 7/6, wideband CQIReuse 1, wideband CQI

Table 1. Performance results for IEEE 802.16m adaptive soft FFR.

Scheme ChannelmodelAverage SE(b/s/Hz/cell 2 2) Gain in SE

Cell-edge SE(b/s/Hz/cell)

Gain in cell-edgethroughput

Localized baselineITU PedB-3 km/h

5.84 0.067

Localized AFR-S 5.93 1.6% 0.068 3.3%

Distributed baselineITU PedB-3 km/h

3.68 0.032

Distributed AFR-S 4.52 22.8% 0.060 88.9%



as reuse 7/6. We have used the simulationassumptions for case 1 described in [4] in 10MHz bandwidth with 10 mobiles/sector, andstudied the performance of the case of usingboth frequency selective subband CQI feedbackas well as wideband CQI feedback. The mobilespeed is 3 km/hr, for which the subband CQIfeedback can effectively exploit frequency selec-tive scheduling gains. Different points on thecurve are generated using different degrees offairness in the DL scheduler.

As in the case of 16m, we see that the gainfrom using the static FFR scheme is relativelylow when frequency selective CQI feedback isbeing used. In the case of utilizing widebandCQI feedback, which would be done to reducethe uplink overhead, or with high mobile speed,we see the gain of the FFR scheme is clearer,but only when the scheduler is tuned to be fair.For example, if we consider the case of high fair-ness in reuse 1, which obtains a best cell edgerate of 425 kb/s, the FFR scheme can maintainthis edge rate while improving the sector spec-tral efficiency by 25 percent. Alternatively, theFFR scheme can be used to improve the celledge rate for a given sector spectral efficiency;for the case of proportional fairness we see thecell edge throughput is improved by 30 percentwhile maintaining the same sector spectral effi-ciency.

UL POWER CONTROL AND FFROrthogonal frequency-divison multiple access(OFDMA) systems operate with tight synchro-nization across cells, and the main source ofinterference is intercell interference. Power con-trol is not typically used in the DL in order toavoid dynamic fluctuation in signal power acrossresources. However, uplink power control is crit-ical for managing intercell interference.

UL POWER CONTROL FOR IEEE 802.16MOpen loop power control is used for data trans-mission in 16m. In this case power levels areadjusted to track long-term fading while fast fad-ing variations are tracked through adjustments in

adaptive modulation and coding. Closed looppower control is enabled for control channels.

Open loop uplink power control in 802.16mis designed to manage the average interferencein the system to some desirable interferenceover thermal (IoT) level. Specifically, the powerupdate algorithm is derived based on the con-cept of maximum sector throughput, in whichpower is increased for a user (mobile station,MS) if the gain in spectral efficiency is greaterthan the net spectral efficiency loss in other cellsdue to the increased interference. Several simpli-fying assumptions are used to derive a powerupdate mechanism, that effectively imposes atarget SINR for each user based on its locationwithin the cell [10]. Here users closer to the BSare allowed to maintain a higher SINR target astheir transmissions are less likely to interferewith those of the neighboring cells. Key in deter-mining the target SINR is the reciprocityassumption applicable to time-division duplex(TDD) systems, which implies that the uplinkinterference caused to other BSs by an MS isproportional to the DL interference experiencedby the user. Specifically, the target SINR (in dB)per user is based on the following equation:

Note that the SIRDL serves as an estimate forthe uplink SINR and is a ratio of DL signalpower vs. interference power measured at oneMS receive antenna. The parameter controlsthe level of interference seen by other cells andis set by the BS and broadcast periodically. Nr isthe number of receive antennas at the BS.SINRmin is the minimum SINR in dB corre-sponding to the reliable reception of the lowestmodulation coding scheme. The resulting powerupdate equation per subcarrier is

P(dBm) = L + SINRTarget + NI + Offset.

Here L is the path loss between the MS andthe BS. NI is the average noise plus interferencemeasured at the BS and broadcast periodically.The offset is the additional MS-specific powercorrection used by the BS to make additionalMS specific adjustments.

Performance Results The performance ofuplink power control is shown in Table 2. Theparameter can be used to trade off averagespectral efficiency vs. cell edge performance.The IoT level in the system is a function of .Further details are given in [10]. We note thatwith appropriate choice of , uplink power con-trol alone can effectively meet the uplink sectorand cell edge SE target requirements specifiedin [1] (sector SE = 1.3 b/s/Hz, cell edge SE =0.5 b/s/Hz).

UL FFR IN 802.16MThe 802.16m standard supports uplink FFRoperation [3] by allowing for multiple reuse par-

SINR

SINR

SIRDLTarget

=

10 10

1010

log max

^ ,min

1

Nr

.

Table 2. Sector spectral efficiency and cell edgeperformance trade-off by adapting IoT levelswith gamma.

gamma Sector SE(b/s/Hz)Cell edge SE(b/s/Hz)

0.2 0.9074 0.0596

0.4 1.0404 0.0606

0.6 1.1471 0.0584

0.8 1.2098 0.0528

1.0 1.2752 0.0471

1.2 1.3286 0.0417

1.4 1.3403 0.0375

OFDMA systems

operate with tight

synchronization

across cells, and the

main source of inter-

ference is intercell

interference. Power

control is not typical-

ly used in the DL in

order to avoid

dynamic fluctuation

in signal power

across resources.

However, uplink

power control is

critical for managing

intercell interference.



titions to be configured within a cell. When ULFFR is used, the above power control algorithmis generalized to allow power adjustment perpartition. The power adjustment is determinedbased on the IoT level to be allowed per parti-tion, which is controlled by introducing a per-partition IoT parameter , broadcast by the BS.In our evaluation further application of UL-FFRin addition to uplink power control provides lim-ited additional gain (less than 10 percent). Thisis because the 802.16m power control algorithmis quite powerful in managing intercell interfer-ence and captures most of the performanceimprovement.

UL POWER CONTROL FOR 3GPP-LTEThe uplink power control specification in 3GPPLTE allows for a wide variety of power controlmodes to be utilized. In fact, we show that oneof the modes that is possible is quite similar tothe power control method described for 802.16m.

The baseline uplink power control method fordata transmissions on the physical uplink sharedchannel (PUSCH) in 3GPP LTE is slow, openloop power control. The BS broadcasts a param-eter called P0_PUSCH, which is expressed in dBmand can be set as P0_PUSCH = SINRTarget,Nominal+ I0, where I0 is the total measured uplink inter-ference (thermal noise plus interference fromother cells) level in dBm. The mobile sets itstotal transmit power (in dBm) as [11]

P = 10log10(M) + P0,PUSCH + PL,

where M is the number of scheduled physicalresource blocks (PRBs), which are 180 kHz widein 3GPP LTE, PL is a long-term path loss mea-surement by the mobile in the DL, and 0 1is a fractional path loss compensation factorbroadcast by the BS. Using the expression forP0,PUSCH, the target SINR achieved by themobile will be

SINRTarget = SINRTarget,Nominal (1 )PL.

Since 1, the target SINR always decreaseswith increasing path loss. The parameter allows a flexible trade-off between sectorthroughput (i.e., overall spectral efficiency) andcell edge bit rate; the smaller the value of , thehigher the sector throughput and the smaller thecell edge bit rate, for a fixed IoT level [12]. Itshould be noted that for transmission of controlinformation on the physical uplink control chan-nel (PUCCH), which carries acknowledgment(ACK)/negative ACK (NACK) and CQI to sup-port the DL, is always set to 1 as per the 3GPPstandard, and a separate value called P0_PUCCHis broadcast which the mobile will use instead ofP0_PUSCH. In this way a separate open loop tar-get SINR can be maintain for control channeltransmissions, which is the same for all mobilesregardless of their path loss; this is desired dueto the fact that the QoS is the same for all of thefixed bit rate control channel transmissions.

While the baseline uplink power controlmethod is open loop, aperiodic closed looppower control corrections can be sent by the BSin the uplink scheduling grant which the mobilewill apply on top of the open loop power control

set-point for transmission on the PUSCH. Sepa-rate closed loop power control commands forcontrolling the PUCCH power are sent in theDL scheduling grants. The closed loop powercontrol rate is typically chosen to be much fasterfor the PUCCH due to the tight QoS constraintsand lack of HARQ. The power control modecan be set to accumulate commands receivedover multiple subframes [11]. For example, theuplink scheduler can maintain an internal uplinktarget SINR for mobile, and based on the SINRmeasured on the uplink (on either thePUSCH/PUCCH or the periodic sounding refer-ence signals), the uplink scheduler can sendclosed loop power control corrections in theaccumulated mode to adjust the mobiles trans-mit power to achieve the desired target SINR.

As an example of target SINR settings on thePUSCH, the desired uplink target SINR for aparticular mobile can be based on the long-termDL SINR of that mobile similar to the formula-tion in 802.16m. However, unlike 802.16m, the3GPP-LTE specification does not allow the DLSINR to be used as part of the open loop powercontrol set-point. Instead, a measure of the long-term DL SINR can be inferred from long-termaverages of the CQI, which is fed back in theuplink, and this would be used to set the uplinktarget SINR internally in the uplink scheduler.One advantage of the DL-SINR-based methodover the fractional power control (FPC) methodis that it allows differentiation of mobiles thatmay have low path loss but generate a highamount of interference, such as mobiles locatedto close the BS but near the sector boundary.One disadvantage of the DL-SINR-basedmethod in the 3GPP LTE context is that itdepends on the CQI feedback from the mobile,and there may be notable variability in the CQImeasurement reports for the same radio fre-quency (RF) condition from different mobilemanufacturers.

One formula for uplink target SINR we havesimulated using the DL SINR method takes aform similar to that of the FPC rule, but usesDL SINR instead of path loss as the metric todifferentiate the uplink target SINR betweenusers:

SINRTarget = SINRTarget,Nominal (1 )SINRDL

where SINRDL is the DL SINR experienced bythe mobile (in dB), 0 1 is a factor thatallows a trade-off between sector throughputand cell edge bit rate, and SINRTarget,Nominal isadjusted in order to achieve the desired IoToperating point. When the formula is viewed inlinear scale, it is clear that it is similar to themethod used in 802.16m, although it can only beachieved through closed loop power control in3GPP LTE.

Performance Results In Fig. 4 we illustratethe performance of both the FPC and DL-SINR-based methods in terms of edge of cell user spec-tral efficiency vs. sector spectral efficiency. Thesimulation was performed for a 10 MHz LTEcarrier at a 700 MHz carrier frequency using anoutdoor Hata suburban path loss model and a 2km intersite distance with an extended typical

In our evaluation

further application of

UL-FFR in addition to

uplink power control

provides limited addi-

tional gain (less than

10 percent). This is

because the

802.16m power

control algorithm is

quite powerful in

managing intercell

interference and cap-

tures most of the

performance

improvement.



urban channel model at 3 km/hr mobile speed.We provide results for two different IoT operat-ing points: 6 dB and 10 dB. Because this is aninterference limited deployment, we see both thesector throughput and cell edge bit rate increasewith increasing IoT. The different points on eachcurve have been generated by selecting different and factors for the FPC and DL-SINR-basedmethods, respectively. We used = {1, 0.8, 0.7,0.6} and = {1, 0.8, 0.7, 0.6, 0.5, 0.3, 0.1}. Foreach point, the SINRTarget,Nominal value was cho-sen to achieve the specified IoT target value. Wesee that the DL SINR based method for settingthe uplink target SINR does similarly to the FPCmethod when using high values of and inorder to get high cell edge bit rates at the expenseof sector throughput, but the DL-SINR-basedmethod does better for lower values of and when we desire to trade cell edge bit rate forincreased sector throughput.

UL FFR IN 3GPP-LTEIn 3GPP LTE a high interference indicator(HII), which is defined per PRB, can beexchanged between cells via the X2 interface [9]to implement uplink FFR. When the HII bit isset to 1 for a particular PRB, it signifies that thisPRB has high sensitivity to uplink interferencefor this cell; when the HII bit is set to 0 for aparticular PRB, it signifies that this PRB has lowsensitivity to uplink interference. The exchangeof HII reports between cells allows the creationof fractional reuse patterns through uplinkscheduling and power control. Upon receiving aHII report from a particular neighbor cell, theuplink scheduler in a given cell can intelligentlychoose to schedule mobiles that generate signifi-cant interference to this neighbor cell only inthose PRBs for which the HII report signifieslow sensitivity to uplink interference in thatneighbor cell. Additionally, the uplink schedulercan reduce the power level of mobiles that needto transmit in the PRBs with high sensitivity tointerference for the neighbor cell. The schedulerin a given cell is aware of its mobiles generatingsignificant interference towards a particularneighbor cell because of the Event A3 reportingmechanism, in which a mobile reports to its serv-ing cell the identity of its strongest neighbor cellwhen the DL signal strength of the neighbor cellcomes within a certain range of the DL signalstrength of the serving cell [8].

We evaluate a static uplink FFR algorithmbased on the inverted-reuse pattern described in[9]. In this scheme we designate either one thirdor one ninth of the total PRBs in each sector ofa three-sector system as an interference-bearingzone. The HII bit is set to 0 for these PRBs toinform neighboring cells that interference will beconcentrated in this zone (these PRBs have highsensitivity to uplink interference, while theremaining PRBs have low sensitivity to interfer-ence). The way interference from other cells isconcentrated in this interference-bearing zone isby configuring a frequency-dependent powerrestriction via the uplink scheduler only formobiles located near the cell edge. Mobileslocated toward the interior of the cell do nothave their transmit power level altered from thenormal power control rule.

Cell edge mobiles can only transmit at theircurrent power level as configured by uplinkpower control if the mobile can be scheduledwithin the PRBs of the interference-bearingzone of its strongest neighbor cell. If there areno resources available in this particular zone andthis cell edge mobile must be scheduled outsidethe interference-bearing zone of its strongestneighbor, the scheduler instructs the mobile totransmit with reduced power by issuing an abso-lute power control command in the correspond-ing scheduling grant; a value of 4 dB is allowedby the 3GPP specifications [11]. The basic pro-portional fair scheduling algorithm is not altered;rather, the priority metrics for PRBs located out-side the interference-bearing zone of a particularmobiles strongest neighbor cell will automatical-ly be lower due to the transmit power reduction.Hence, the scheduler will prefer to schedule celledge mobiles in the interference-bearing zone oftheir strongest neighbor.

Performance Results In Fig. 5 we illustratethe performance of the UL FFR scheme usingboth 1/3 and 1/9 inverted-reuse schemes. Forthis simulation we have used the simulation case1 assumptions described in [4], which is an inter-ference limited deployment of a 10 MHz carrierat a 2 GHz carrier frequency. The FPC methodof power control is used, as it is compatible withissuing absolute power control commands need-ed for the UL FFR algorithm. An Event A3threshold of 6 dB is used to classify users as celledge; this classifies approximately 50 percent ofthe mobiles as cell edge in this deployment.

Results are provided for mobile speeds of 3and 120 km/h. At low speeds, frequency selectivescheduling works well as an inherent form of fastinterference coordination between cells, becausethe scheduler obtains information on short-termother-cell interference variations through thechannel sounding provided by its own mobiles.Hence trying to impose additional restrictionsthrough the UL FFR mechanism does not pro-vide any additional gain, and in fact can hurt per-formance as the natural frequency selective

Figure 4. Performance as a function of FPC and DL-SINR-based factors,for IoT levels of 6 dB and 10 dB.

Sector SE (b/s/Hz)0.650.6

0.03

0.025

Cel

l edg

e SE

(b/

s/H

z)

0.035

0.04

0.045

0.05

0.055

0.7 0.75

= 1

= 0.6

= 0.1

= 1

0.8 0.85 0.9 0.95

FPC (loT = 6 dB)DL SINR based (loT = 6 dB)FPC (loT = 10 dB)DL-SINR-based (loT = 10 dB)



scheduler operation is disrupted by power restric-tions for particular mobiles on particular PRBs.This behavior is similar to that observed for802.16m UL FFR. At high speeds the frequencyselective scheduling is not effective, and the ULFFR algorithm can provide some improvement.There is approximately a 1015 percent improve-ment in sector throughput for a given cell edgebit rate when considering FPC = 0.8 or 0.7.Notable gains in cell edge rate are only seen if weoperate with FPC = 0.7 with reuse-1 (the lowcell edge rate region), in which case the UL FFRscheme improves the cell edge bit rate by 35 per-cent while maintaining the sector throughput.

SUMMARY AND CONCLUSIONSThis article has described advanced interferencemanagement schemes across IEEE 802.16m and3GPP-LTE standards for enabling universal fre-quency reuse. Special focus on advanced RRMschemes, including DL/UL FFR and UL powercontrol techniques, has been provided.

It is observed that both IEEE 802.16m and3GPP-LTE standards utilize similar interferencemanagement schemes, and that there are commonelements in how each scheme is used. However,the exact details and the relative emphasis of eachtechnique differ across the two standards. Forexample, 3GPP-LTE uplink power control may beconfigured to give a similar uplink SINR distribu-tion as 802.16m; however, it requires usage of theclosed loop power update mechanism instead ofthe pure open-loop approach taken by 802.16m.Despite differences in certain details, the schemesin both standards are effective in managing inter-ference and substantially improving the cell edgeuser performance to meet 2 improvement targetsset forth by both standards.

ACKNOWLEDGMENTSSeveral colleagues have developed the ideas andresults presented in this article. We especially

acknowledge Clark Chen, Hongmei Sun, HuaYang, Vladimir Kravtsov, Yuval Lomnitz, AliKoc, Ronghzhen Yang, Tolis Papathanasiou,Wendy Wong, Hujun Yin, Christian Gerlach,Andreas Weber, and Micheal Wilhelm.

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col Specification.[9] C. Gerlach et al., ICIC in DL and UL with Network Dis-

tributed And Self Organized Resource Assignment Algo-rithms in LTE, Bell Labs Tech. J., vol. 15, no. 3, Fall2010.

[10] R. Zhang et al., Supporting Material for UL OLPC Pro-posal, IEEE C80216m-09_0845, 2009.

[11] 3GPP TS 36.213, Physical Layer Procedures.[12] A. M. Rao, Reverse Link Power Control for Managing

Intercell Interference in Orthogonal Multiple Access Sys-tems, IEEE VTC-Fall, 2007.

BIOGRAPHIESNAGEEN HIMAYAT ([email protected]) is a seniorresearch scientist with Intel Labs, where she works on sev-eral aspects of cellular system design, covering multitierheterogeneous networks, cross-layer radio resource man-agement, and MIMO-OFDM techniques. Prior to Intel, shewas with Lucent Technologies and General InstrumentCorp, where she developed standards and systems forbroadband access networks. She obtained her B.S.E.Edegree from Rice University and her Ph.D. degree from theUniversity of Pennsylvania in 1989 and 1994, respectively.

SHILPA TALWAR ([email protected]) is a principal engi-neer in the Communications Technology Laboratory atIntel, where she is conducting research on mobile broad-band technologies for increasing cellular capacity and cov-erage. Specifically, she is researching techniques foradvanced interference mitigation, MIMO, and novel cellulartopologies. Prior to Intel, she held several senior technicalpositions in wireless companies over the past 10 years. Shegraduated from Stanford University in 1996 with a Ph.D. inapplied mathematics and an M.S. in electrical engineering.She is the author of numerous technical publications andpatents.

ANIL RAO ([email protected]) is a DistinguishedMember of Technical Staff in Alcatel-Lucents wireless R&Dorganization in Naperville, Illinois. He received his M.S. andPh.D. degrees in electrical engineering from the Universityof Illinois at Urbana Champaign where he held a NationalScience Foundation graduate research fellowship. His workat Alcatel-Lucent has involved various aspects of systemdesign, performance analysis, and algorithm developmentfor UMTS, HSPA/HSPA +, and LTE. He has actively con-tributed to both the standardization and product realiza-tion of these technologies.

ROBERT SONI ([email protected]) is a systemarchitecture manager with Alcatel-Lucent, where he leads ateam developing next-generation cellular technologies andstandards covering physical and MAC layer related tech-niques for MIMO OFDMA and CDMA systems. He receivedhis Ph.D. degree in electrical engineering from the Universi-ty of Illinois at Urbana Champaign in 1998. He has alsoserved as an adjunct professor at Columbia University andNew Jersey Institute of Technology.

Figure 5. Performance of UL FFR for 3km/hr and 120 km/hr using FPC =1.0, 0.8, and 0.7. IoT in non-interference-bearing zone is 6 dB for all points.

Sector SE (bps/Hz)

=0.7

=0.8

=1

0.45 0.4

0.02

0.015

Cel

l edg

e SE

(bp

s/H

z)

0.025

0.03

0.035

0.04

0.045

0.5 0.55 0.6 0.65 0.7 0.75 0.8

no FFR (3 km/hr)FFR inverted reuse-3 (3 km/hr)FFR inverted reuse-9 (3 km/hr)no FFR (120 km/hr)FFR inverted reuse-3 (120 km/hr)FFR inverted reuse-9 (120 km/hr)


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InterferenceManagementFor4GCellularStandards IEEE Comm Mag 2010