Multi-User Resource Allocation for DownlinkControl Channel in LTE Systems

Li Li, Mugen Peng, and Wenbo WangWireless Signal Processing and Network Lab

Key Lab of Universal Wireless Communications, Ministry of EducationBeijing University of Posts & Telecommunications

Beijing, 100876 P. R. China

Abstract—The Physical Downlink Control Channel (PDCCH)is used to signal dynamic resource assignment information in theLong Term Evolution (LTE) system. In the presence of numerousactive users, the system performance is likely to be hindered byshortage of control channel resource. In this paper, several simplealgorithms are brought forward for making efficient PDCCHresource allocation. We first propose a minimum aggregationlevel algorithm (Min-AL) to maximize the total number ofscheduled users by exploiting multi-user diversity gain, followedby algorithms with the purpose of improving the blockingperformance of cell-edge area through co-channel interferenceavoidance (CCI-A) and priority boosting (PB). Simulation resultshave shown that the Min-AL algorithm achieves the best systemperformance at the expense of cell-edge performance, while bothCCI-A and PB algorithms are effective in reaching a compromisebetween system and cell-edge performance.

I. Introduction

In OFDM-based UTRAN Long Term Evolution (LTE) spec-ified by Third Generation Partnership Project (3GPP), dynamicscheduling and resource allocation has been proven to greatlyenhance spectral efficiency [1][2]. However, these gains areachieved at the cost of high signaling overhead which isneeded to inform scheduled users of necessary informationsuch as assigned Physical Resource Blocks (PRBs) and se-lected Modulation and Coding Scheme (MCS).

In LTE systems, the Downlink Control Information (DCI)messages for both uplink grant and downlink resource assign-ment are signaled on the Physical Downlink Control Channel(PDCCH) [3]. During each 1ms Time Transmission Interval(TTI), only the first 1 to 3 OFDM symbols are reserved to beused by PDCCH and several other downlink control channels,e.g. Physical Control Format Indicator Channel (PCFICH).

The key objective of PDCCH resource allocation is to max-imize the supportable number of scheduled User Equipments(UEs) in each TTI while satisfying user Quality of Service(QoS) requirements, given such small subset of time andfrequency resource available. Several algorithms for effectivePDCCH scheduling are proposed in [4]. According to previouswork, data scheduler is assumed to have higher priority overPDCCH scheduler, i.e., the priorities of UEs in acquiringcontrol resource follow those generated by data scheduler. It istrue that maintenance of priorities chosen by data scheduler isof great importance for dynamic resource allocation. However,when there is an inadequate amount of control resource, which

is often the case in a densely populated system with numeroussmall-packet active users, the overall system performance interms of blocking probability is likely to be hampered by thepassive manner of scheduling in PDCCH manager.

In order to efficiently pack users into limited amount ofcontrol resource, this paper allows more flexibility for PDCCHresource manager to find its best way to assign control resourceto users chosen by data scheduler. Several types of PDCCHresource allocation algorithms with feasible complexities areintroduced in this work. We start with one aggressive algorithmattempting to maximize the number of UEs served by PDCCHby taking advantage of multi-user diversity, i.e. allocatingresource to UEs in an order determined by the size of theirrequired resource. However, a large group of cell-edge userscould be rejected by this aggressive algorithm since moreresource is needed to compensate for serious signal attenuationand Co-Channel Interference (CCI). We then go farther bylooking for means to decrease the blocking probability of cell-edge users without causing much system loss. Two algorithmsare subsequently presented on basis of aggressive algorithmfor this purpose, one by improving cell-edge user channelquality through CCI avoidance, and the other by increasingthe likelihood of cell-edge users to get control resource.

This paper is organized as follows: in the next sectionwe describe basic assumptions and constraints involved inPDCCH resource allocation; several algorithms for PDCCHresource allocation are proposed in Section III; in Section IVsimulation results are shown to compare the performance ofthese algorithms; we conclude this paper in Section V.

II. Assumptions and ConstraintsA. Basic Assumptions

We consider a LTE cellular system featuring typical hexag-onal cell layouts [5], in which a site consists of one basestation (BS) and three cells, and the overall J cells operate onthe same frequency bandwidth. A large number of UEs areuniformly distributed across the system, and serving BS/celleach UE connected to is selected based on Reference SignalReceived Power (RSRP). UEs bound to the inner 2/3 area ofa site are categorized as cell-center users, the remaining UEsnear site border as cell-edge users. Since UE density per areais equal over the entire system, cell-center and cell-edge UEsapproximately comprise 2/3 and 1/3 of total users in each cell.

2010 IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications

978-1-4244-8016-6/10/$26.00 ©2010 IEEE 1499

A greedy traffic source is assumed for all users in thesystem, such that there is a perpetual need for each user to bescheduled. In each TTI, UEs selected by data scheduler consistof both uplink and downlink users. PDCCH resource allocationfor these UEs, either uplink or downlink, is implemented ina similar way, except for different DCI messages involved inthe user-related transmission. For convenience, the followingdiscussion is in terms of downlink UEs only. Let vectorU(k)

j =[u(k)

j,1, . . . , u(k)j,N

]denote a prioritized list of total UEs

passed by data scheduler in cell j and TTI k, where 1 ≤ j ≤ J,0 ≤ k < 10, and N is the average number of active UEs percell. U(k),center

j and U(k),edgej represents cell-center and cell-edge

UE sublist derived from U(k)j . Relative order of UEs in cell-

center/edge sublist complies with that in U(k)j .

B. PDCCH Resource Allocation Constraints

As mentioned above, PDCCH is multiplexed with otherdownlink control channels within the first M(k)

j (1 ≤ M(k)j ≤ 3)

OFDM symbols in TTI k and cell j. Control region, namely theResource Elements (REs) dedicated to PDCCH are groupedinto Control Channel Elements (CCEs) and numbered in away depicted in [3]. The number of total CCEs available maydiffer among cells and vary over TTIs, and a specific CCE isnot formed by a fixed group of REs. In this paper, however, weassume a constant M(k)

j in all cells and TTIs, leading to a totalnumber of CCEs per cell NCCE . In addition, CCE of a givenindex is of the same place in time and frequency domain.

Different amount of CCEs (1, 2, 4 or 8) are aggregatedto enable various coding rates for UEs according to channelconditions. For example, for a cell-edge UE with a poorchannel, 4 or even 8 CCEs are required to achieve sufficientrobustness; while 1 CCE may be adequate for a UE close to theBS. The UE has no prior knowledge about the exact numberand index/indices of CCE(s) used for its related controlinformation transmission. In such cases, the UE shall attemptto decode control information by monitoring a set of PDCCHcandidates. Here, we mainly focus on PDCCH candidates inUE-specific search space. For a particular Aggregation Level(AL) of CCEs L ∈ {1, 2, 4, 8} in TTI k, the corresponding setof CCEs originates from the one with index of W (k)

L as

W (k)L = L · {Yk mod �NCCE/L�} (1)

where Yk = (X · Yk−1) mod Z, X = 39827, Z = 65537 andY−1 is initialized with a Radio Network Temporary Identifier(RNTI) for the intended UE. To limit the complexity of blinddecoding, the number of PDCCH candidates is predefined, i.e.,6 for 1-CCE and 2-CCE, 2 for 4-CCE and 8-CCE, respectively.Fig. 1 shows an example of PDCCH candidates for a given UEin two successive TTIs, in the case of Yk = 1 and NCCE = 30.

C. Link-to-System Mapping

The Exponential Effective SINR Mapping (EESM) is usedto translate Signal to Interference Plus Noise Ratio (SINR)per subcarrier to effective SINR. Block Error Rate (BLER) ofeach UE is then read from a BLER versus SINR curve withthe given effective SINR, where such curve can be obtained

28 290 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

CCE Index

8

4

2

1

AL

Candidates in TTI-k Candidates in TTI-(k+1)

Fig. 1. An illustrative example for PDCCH candidates

through link level simulation for a certain combination of ALand DCI format. Under realistic control channel constraints,downlink cell throughput in TTI k is expressed by

C(k)j =

∑u(k)

j,n∈U(k)j

Φ(γ(k)

j,n

)· S(η(k)

j,n

), (2)

where UE throughput S(η(k)

j,n

)is approximated by an attenuated

and truncated form of the Shannon bound given in [5] usingreceived data channel SINR η(k)

j,n. Binary function Φ(γ(k)

j,n

)related with effective control channel SINR γ(k)

j,n equals 1only if control channel resource is assigned to u j,n and DCIdecoding is successful; otherwise Φ

(γ(k)

j,n

)is zero. The state of

DCI decoding (successful/failing) is determined by drawing arandom number based on user’s BLER.

III. PDCCH Resource Allocation AlgorithmsTo facilitate our discussion, the process of PDCCH resource

allocation presented below is divided into four major steps,namely 1) power shaping, 2) AL selection, 3) user schedulingand 4) physical resource allocation. “Power shaping” dealswith the power allocation for control subcarriers. “AL selec-tion” determines the number of CCEs that shall be assignedfor each user to guarantee reliable DCI message delivery.In “user scheduling”, the prioritized lists of active users aregenerated through some PDCCH-oriented strategies, followedby “physical resource allocation” to sequentially assign CCEsto UEs from the ordered lists. For simplicity, TTI index isomitted, and resource allocation in cell j is given as anexample in the remainder of this paper.

A. Minimum Aggregation Level Algorithm (Min-AL)The target of Min-AL algorithm is for UEs as many as

possible to be multiplexed in PDCCH during one TTI whensubject to a certain BLER target. Notice that allowing UEs of4- or 8-CCEs to access first could cause greater blocking ratedue to the fact that the resource collision for a UE tends tooccur more frequently with an increasing amount of occupiedresource. Therefore, we allocate resource in an order from UEswith lower ALs to those with higher ones, so that more UEswould have better chances to find empty PDCCH candidates.The algorithm is formally presented below.

Algorithm: Min-AL1) Power shaping: Assign the total amount of power avail-

able for PDCCH (Pmax) evenly among all CCEs, and theaverage power per CCE is

PCCE = Pmax/NCCE . (3)

1500

Next, estimate the worst-case SINR per CCE by takinginto account all potential CCI from neighboring cells forterminal u j,n with

γ j,n =PCCE · h j,n

J∑l=1l� j

PCCE · hl,n + N0

≈ h j,n

J∑l=1l� j

hl,n

, (4)

where h j,n and hl,n respectively denotes u j,n’s averagechannel gain against serving cell and other co-channelcells. In interference-limited systems, noise power perCCE N0 is small enough to be ignored, and γ j,n isindependent of actual transmit power, making it anobjective prediction of channel quality.

2) AL selection: The suitable aggregation level for a par-ticular UE is typically the one achieving the highestcoding rate while satisfying 1% BLER target [6]. Theaforementioned BLER versus SINR link level curvesare adopted to assist our decision, and the chosenaggregation level Σ j,n for user u j,n is the one that satisfies

γ j,n ≥ γThΣ j,n, (5)

where γThΣ j,n

is SINR corresponding to 1% BLER on linklevel curve associated with aggregation level Σ j,n anduser u j,n related DCI format. Σ j,n is set to 0 if even thehighest AL is unable to meet the target BLER.

3) User scheduling: Sort U j =[u j,1, . . . , u j,N

]in

an ascending order in terms of UE aggregationlevel to form a new prioritized user list U j ={u j,1, . . . , u j,N

∣∣∣Σ j,n ≤ Σ j,n′ , 1 ≤ n ≤ n′ ≤ N}, so that users

with lower ALs are given higher priorities to choosecontrol resource within their search space than userswith higher ALs. If two users have identical ALs, theirrelative order is kept the same as in U j.

4) Physical resource allocation: Allocate CCEs to UEsfollowing an order defined by U j, one UE at a time.For each user u j,n (1 ≤ n ≤ N ) of Σ j,n � 0, an attempt ismade to find an empty PDCCH candidate based on (1).If vacant PDCCH candidates are found available in itssearch space, the first one is assigned to u j,n and thenmarked “occupied”; otherwise, u j,n is blocked. Sinceblocking of one user does not necessarily mean blockingfor another under PDCCH constraints, every user in U jshould be given a try until all CCEs are exhausted.

The AL-based scheduling (step 3) in Min-AL) exploits thebenefits of multi-user diversity by allocating the current CCEsto UEs who can best utilize them. In general, UEs assignedwith low ALs on PDCCH are likely to have good downlinkchannel for high data rate. The Min-AL, therefore, is also ableto improve system throughput by passing a large number of“good” UEs. However, Min-AL dose not treat all users equally,especially those cell-edge UEs suffering from serious CCI. Inorder to find a good compromise between system and cell-edgeperformance, two algorithms are proposed afterwards.

Primary CCE Set Secondary CCE Set

13

4 5

6

7

Site 2, 4 and 6

S1

S2 S3

CCE Index

Site 3, 5 and 7

S1

S2

S3

Power Site 1

S1 S2

S3

2

Fig. 2. Co-channel interference avoidance algorithm

B. Co-Channel Interference Avoidance Algorithm (CCI-A)

One method to enhance the competitiveness of cell-edgeusers in AL-based scheduling is to improve their receivedSINRs so that lower ALs can be supported. Inspired by thetechnique of Fractional Frequency Reuse (FFR) [7] used indownlink data channel, we apply similar approach to PDCCHfor CCI mitigation. The fundamental idea of CCI-A is illus-trated in Fig. 2. First, the whole set of CCEs are evenly dividedinto three segments (S 1, S 2 and S 3), and then grouped intoprimary CCE set (PCS) and secondary CCE set (SCS), denotedrespectively by S P

j and S Sj . PCSs for cell-edge users are kept

orthogonal among neighboring sites. Less power is loaded forSCSs for cell-center users, thus lowering CCI in cell edge area.

Algorithm: CCI-A1) Power shaping: Load power for every CCE in SCS by

PSCCE = θ j · PCCE , (6)

where θ j ∈ (0, 1] is a configurable parameter chosenindependently in each site. CCEs in PSCs are loadedwith nominal transmit power per CCE PCCE (see (3)).Calculate worst-case SINR γP

j,n′ corresponding to S Pj for

each cell-edge user u j,n′ ∈ Uedgej according to power

pattern in neighboring cells (refer to (4)). Note that twoCCE segments in S S

j may experience different CCI, aset of SINRs γs

j,n′′ =[γs,1

j,n′′ , γs,2j,n′′]

are then calculated foreach cell-center user u j,n′′ ∈ Ucenter

j .2) AL selection: Determine AL Σ j,n′ for cell-edge user

u j,n′ as the minimum one whose PDCCH candidates(at least one) locate within S P

j and with which thetarget BLER can be met by γP

j,n′ (see (5)). SupportableALs Σ1

j,n′′ and Σ2j,n′′ for cell-center user u j,n′′ are chosen

likewise on SCS-related CCE segments, and then letΣ j,n′′ = min(Σ1

j,n′′ ,Σ2j,n′′ ). Set Σ j,n′ and Σ j,n′′ 0 if no suitable

aggregation level is found.3) User scheduling: Use AL-based scheduling given in 3)

of Min-AL to obtain updated scheduling sublists Uedgej

and Ucenterj respectively with Σ j,n′ and Σ j,n′′ .

4) Physical resource allocation: The procedure resembles 4)in Min-AL except for an additional restriction imposed

1501

Cell-center UE list

...

Cell-edge UE list

...

HeadTail

SelectorUpdated UE list

...

HeadTail

ˆ edge

jU

ˆ center

jU

W1

W2

ˆj

U

Fig. 3. Priority boosting algorithm

on available resource for different types of UEs, that is,S P

j for Uedgej and S S

j for Ucenterj .

Note that the UE-specific search space changes over time,a temporarily declined cell-edge/center UE due to a lackof suitable PDCCH candidates within PCS/SCS could stillbe a competitive applicant for control resource in next TTI.Thus the static resource partition involved in CCI-A exerts anegligible effect on user fairness in the long term.

C. Priority Boosting Algorithm (PB)An alternative for cutting down blocking probability of cell-

edge users is to raise their ranks in the order list produced byAL-based scheduling, thus permitting them to choose theirfavorable resource with higher priorities. This idea can befurther developed into a priority boosting algorithm illustratedin Fig. 3, where a selector functions as a bi-directional switchturning on each of the two prioritized lists with a certainprobability (W1 versus W2 for cell-center and cell-edge UElist) to build the final UE list for resource allocation.

Algorithm: PB1) Power shaping: This is the same as 1) for Min-AL.2) AL selection: Use the same procedure as for Min-AL.3) User scheduling: First, implement AL-based scheduling

for Uedgej and Ucenter

j and get two updated prioritized sub-lists, i.e., cell-edge sublist Uedge

j and cell-center sublistUcenter

j . A bi-directional selector is then used to mergethese two sublists into a new UE list U j, in a mannerof picking UEs one-by-one from the top of either Uedge

j

or Ucenterj with probability W1 versus W2. One simple

way to carry out this process is through Bernoulli trialswhich takes value 1 (cell-center list) with probability W1and value 0 (cell-edge list) with probability W2.

4) Physical resource allocation: Same as 4) for Min-AL.To quantitatively analyze how much the cell-edge users are

favored, define preferential factor as follows

μ =W2/W1∣∣∣∣Uedge

j

∣∣∣∣/∣∣∣∣Ucenter

j

∣∣∣∣, s.t.W1 +W2 = 1, (7)

where the function of |·| denotes the size of the correspondingvector. In the regime of μ > 1, the likelihood that cell-edge UEs are picked by selector is greater in proportion tothat of cell-center UEs. The impact of such biased selectionwill become more conspicuous with a larger μ. Once thepreferential factor is determined towards the desirable systemperformance, probability W1 and W2 can be derived from (7).

TABLE ISimulation Parameters and Assumptions

Parameter Values and Assumptions

Cell layouts 64 sites-3cells/site (only central 16 sites are

observed), ISD =500m

Propagation model Urban Area [5]

Shadowing deviation 10dB

Antenna configuration 2Tx*2Rx SFBC (antenna radiation pattern

and gain for BS and UE in [5] )

Multi-path model Extended Pedestrian A 5Hz (EPA5)

BS transmit power 46dBm

System bandwidth 10MHz (50RB)

System loading Full buffer services

Number of downlink UEs 50 UEs per cell-or otherwise stated

Data scheduling Round Robin

Number of CCEs per cell 30 (3 OFDM symbols)

CCE Aggregation levels 1, 2, 4, 8 CCE

PDCCH modulation QPSK

PDCCH BLER target 1% BLER

PDCCH payload 46bits

1000 2000 3000 4000 5000

2000

4000

6000

12

34

56

78

910

1112

1314

1516

(a) RS

x (m)

y (m

)1000 2000 3000 4000 5000

2000

4000

6000

12

34

56

78

910

1112

1314

1516

(b) Min-AL

x (m)

y (m

)

1000 2000 3000 4000 5000

2000

4000

6000

12

34

56

78

910

1112

1314

1516

(c) CCI-A, � = 0.5

x (m)

y (m

)

1000 2000 3000 4000 5000

2000

4000

6000

12

34

56

78

910

1112

1314

1516

(d) PB, � = 2

x (m)

y (m

)

0

1

2

3

4

5

6

7

8

Fig. 4. Aggregation level distribution over 16 central statistical sites, activeusers are marked by dots of different color corresponding to various ALs(blocked UEs with 0-AL are marked by blue dots).

IV. Simulation Results

In this section, simulation results are shown for variousalgorithms presented above to evaluate their impact on thedownlink system performance. The quasi-static system-levelsimulation is done in accordance with PDCCH descriptionsin 3GPP LTE Release 8 and link-to-system model givenin Section II. More details of simulation parameters andassumptions can be found in Tab. 1. To witness the perfor-mance gain yielded by PDCCH-oriented scheduling, randomscheduling (RS), an approach whose UE priorities are decidedby data scheduling instead of PDCCH scheduling, is adoptedfor comparisons in the following discussion. Other resourceallocation procedures of RS are the same with Min-AL.

Fig. 4 plots aggregation level distribution pattern of dif-ferent algorithms, i.e., each user’s geographic location andits assigned number of CCE(s). Compared with RS, whosescheduled UEs spreading over the entire system in a randommanner, the Min-AL favors more the 1- and 2-CCE cell-centerusers, leaving the majority of cell-edge users unattended. Bycontrast, CCI-A and PB have relative larger coverage thanMin-AL since more users in cell edge area are accommodated.

Fig. 5 shows the Probability Density Functions (PDFs) ofthe number of total and cell-edge scheduled users for eachalgorithm. All our proposed algorithms, i.e., Min-AL, CCI-

1502

5 10 15 20 25 300

0.1

0.2

0.3

Pro

babili

ty D

ensity F

unction (a)

0 2 4 6 8 10 120

0.1

0.2

0.3

0.4

Number of scheduled users

Pro

babili

ty D

ensity F

unction (b)

RS

Min-AL

CCI-A, =0.5

PB, =2

RS

Min-AL

CCI-A, =0.5

PB, =2

Fig. 5. PDFs of the number of (a) cell scheduled UEs, and (b) cell-edgescheduled UEs.

30 35 40 45 500.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Number of processed users

CC

E U

tiliz

atio

n

RSMin-AL

CCI-A, � =0.5

PB, � =2

Fig. 6. CCE utilization versus the number of processed users

A and PB, outperform RS concerning the total number ofscheduled UEs, among which Min-AL passes the greatestnumber of scheduled UEs (avg. 19.1), with growth nearlyas much as 50% than RS, followed by 40% gain producedby PB (avg. 17.9) and 27% by CCI-A (avg. 16.2). When itcomes to cell-edge performance, however, the aggressive Min-AL carries the least cell-edge users, only 2.7 UEs on average;while an average of 5.4 and 3.9 cell-edge users are supportedby PB and CCI-A, representing 100% and 45% improvementover Min-AL. Analysis given above confirms that CCI-A andPB are effective in improving cell-edge performance, withoutcausing significant loss to system performance.

In Fig. 6, the CCE utilization is shown against the numberof processed users. We notice that those algorithms that areable to pass more users tend to have lower CCE utilization.The potential explanation for CCI-A demonstrating lower CCEutilization than PB is that the resource partition employedin CCI-A brings obstacles for users to find empty PDCCHcandidates of larger ALs, thus resulting in less 4-CCE and 8-CCE scheduled users (see Fig. 4), and inferior cell/cell-edgeblocking performance to PB (see Fig. 5).

The influence of PDCCH resource allocation on averagedownlink throughput is illustrated in Fig. 7. By and large, thedownlink throughput increases proportionally to the averagenumber of scheduled users. An exception lies in that CCI-A

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Ave

rage

cel

l-edg

e th

roug

hput

(Mbp

s)

CCI-A, ����� PB, ���

0123456789

10

Ave

rage

cel

l thr

ough

put (

Mbp

s)

RS Min-AL

12.8 UEs

19.1 UEs

16.2 UEs17.9 UEs

4.4 UEs

3.9 UEs

2.7 UEs

5.4 UEs

Fig. 7. Average downlink cell throughput (left) and average cell-edgethroughput (right), with the average number of cell/ cell-edge scheduled UEsgiven on top of each bar corresponding a specific algorithm.

exhibits higher cell-edge throughput than RS although it hasfewer scheduled users. This is mainly contributed to AL-basedscheduling used in CCI-A which picks a larger percentage ofUEs with good channel quality.

V. ConclusionIn this paper we consider PDCCH resource allocation prob-

lem in systems where the limited PDCCH resource is over-loaded with numerous active users. Several simple algorithmsfollowing PDCCH constraints are proposed to address theproblem of efficient control resource allocation among mul-tiple users. The effectiveness of the proposed algorithms areproven through simulations. It is shown that the proposed Min-AL algorithm achieves significant system gain over randomscheduling with regard to the number of scheduled UEs anddownlink throughput. Meanwhile, both of CCI-A and PB algo-rithms work well in improving the cell-edge performance andsimultaneously maintaining acceptable system performance.

AcknowledgmentThis work was supported in part by the Fok Ying Tong

Education Foundation Application Research Projects (GrantNo. 122005), and the State Major Science and TechnologySpecial Projects (Grant No. 2009ZX03002-012-01).

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