JOURNAL OF TELECOMMUNICATIONS, VOLUME 22, ISSUE 2, NOVEMBER 2013 1

Joint Power Control and Interference Coordination for LTE-Advanced Het-Nets

Irfan Ahmed

Abstract— In this research paper, we present the joint resource allocation and power control for LTE-Advanced cellular system. Hetrogeneous networks (HetNets) combined with Orthogonal Frequency Division Multiplexing Access (OFDMA) technology has been widely recognized as a promising candidate for future cellular infrastructure due to the performance enhancement by flexible resource allocation schemes. Most of the existing schemes aim to optimize single cell performance gain. However, the higher frequency reuse factor and smaller cell size requirement lead to severe inter-cell interference problem. Therefore, the multi-cell resource allocation of subchannel, time scheduling and power have been jointly considered to alleviate the severe inter-cell interference problem. Simulation results show the efficacy of proposed scheme as compared to the round robin and proportional fairness schemes.

Index Terms—Long Term Evalution (LTE), Radio resource allocation, OFDMA, relay

—————————— u ——————————

1 INTRODUCTIONhe Global System for Mobile communications (GSM) family of technologies including GSM, GPRS, EDGE

and UMTS/HSPA played a vital role in accounting for more than 3.6 million subscribers till March 2009 throughout the world. The demand of higher data rates in support of wide range of multimedia applications, inter-net services has gained a significant attraction around the globe from mobile researchers and industries [1]. According to Informa Telecoms & Media in 2008, there were almost 162 million smartphones sold, surpassing notebook sales for the first time, according to Informa Telecoms & Media. Informa forecasts sales of new smartphones in 2009 will grow more than 30% to 211.2million units, driven by innovative new devices and operator subsidies designed to promote mobile data con-sumption, so that by 2013 almost four in every ten hand-sets sold worldwide will be a smartphone. While voice will always be important, mobile data is taking the driv-er’s seat as mobile operators develop their near and long-term technology strategies [2]. The emerging smart phones/similar devices (Smart de-vices) and the multimedia applications used by these smart phones have caused enormous increase in wireless mobile data traffic. When the HSPA has been emerged, wireless mobile broadband traffic exceeded voice traffic with more than 50% of traffic used by smart devices. A single Smart device generates as much traffic as 30 voice/SMS type mobile phones, while a laptop aircard generates as much traffic as 450 such devices. By the end of 2013 it is estimated that mobile broadband will con-quer more than 80% of all mobile traffic. In order to sur-

vive in this new environment, operators need to decrease or keep operating expenses (OPEX) constant with in-creased data rate and traffic volumes. To build a business model that decouples network cost from traffic volume, operators are rapidly upgrading their networks to highly-efficient all-IP (Internet protocol) packet switched matri-ces. Up to a year ago, 4G was still referenced as a set of four competing technologies: LTE, WiMAX, Unlicensed Mo-bile Access (UMA) and Ultra Mobile Broadband (UMB). In the meantime the smoke has cleared and LTE has emerged as the winning technology. UMA and WiMAX will remain as recess technologies. UMB was stopped by Qualcomm in November 2008. With a clear evolution path from both OFDMA and CDMA based technologies, LTE provides a universal 4G technology that will result in a single, compatible global communication infrastructure within the foreseeable future. Third Generation Partnership Project (3GPP) a group of telecommunication associations is working on Long-Term Evolution Advanced (LTE-Advanced) in order to achieve the requirements of next generation technology. The key goals for this evolution are increased data rate, improved spectrum efficiency, improved coverage and reduced la-tency. Despite the improvements in peak data rate and system capacity provided by MIMO in LTE and LTE-Advanced, it was recognized that there was a possibility for further performance improvement by having coordi-nated transmission and reception between multiple points [3], 3GPP, TR 36.814 “Further Advancements for E-UTRA; Physical Layer Aspects.” Layered OFDMA radio access is used to attain high system performance and full backward compatibility. Moreover, key radio access tech-nologies such as fast inter-cell radio resource manage-ment, multi-antenna transmissions with more antennas for coverage, and enhanced techniques are employed to achieve a high level of cell-edge spectrum efficiency. Radio resource allocation can greatly affect the perfor-mance and spectrum efficiency of LTE networks. In LTE,

———————————————— • I. Ahmed is with the Department of Computer Engineering, College of

Computers and Information Technology, Taif University, Taif 21974, Sau-di Arabia.

T  

2

l,0,k

scheduling decisions are made at the base station for both downlink and uplink radio transmissions. Radio resource management includes transmission power management, mobility management, and scheduling of radio resources. An intelligent radio resource management is at the heart of LTE to make it a robust technology to meet the broad-band needs of upcoming years. 3GPP standardizing body has determined the required technical specifications of signal, frame, packet and the required user capacity/throughput and leaves the door open for manufacturer and designer to devise scheduling and resource allocation schemes to achieve the best trade-off between throughput and fairness in uplink and down-link radio transmissions. Throughout the world 4G network systems using LTE are being deployed by many operators to attract more and more subscribers by offering faster access with more effi-ciency and lower latency than 3G/3.5G. But the anticipat-ed future growth of data traffic especially at high traffic areas is so tremendous that it is necessary to increase the network space using small cells (network densification). These small cells should be enhanced and need to be bal-anced with existing macrocells so as to optimize perfor-mance and provide cost/energy efficient operation. Network densification techniques such as coordinated multipoint (CoMP) transmission/reception and enhanced intercell interference coordination (eICIC) [4] were of great interest in 3GPP after Rel-10. CoMP, eICIC and technical requirements of small cell enhancements along with other techniques are outlined in [5].

Recently, in [6] authors have formulated the resource allocation and scheduling design for multi-cell OFDMA systems with DF relaying as a mixed non-convex and combinatorial optimization problem. They have incorpo-rated a time slot allocation per subcarrier based strategy into the problem formulation for interference mitigation. In the presented scheme, during first time slot, eNB transmits to relays and in second time slot relays transmit to cell edge users. In this paper, authors have only con-sidered the users in outer cell area.

In another paper [7], the resource allocation problem is heuristically divided into subchannel allocation at cell lvel and subchannel restriction at central controller level for interference coordination. This paper does not account for the optimal solution and only suboptimal solution has been provided. Another difference from our proposed scheme is that the relays transmit to users in second time slot, while our proposed solution allows both eNB and relay to transmit in second time slot with intra-cell inter-ference coordination.

Impact of in-band backhaul on radio resource alloca-tion in a relay-assited OFDMA downlink has been inves-tigated in [8]. Radio resource allocation framework is proposed with the objective to ensure proportional fair-ness among the UEs. An asymptotically optimal solution is derived by applying the gradient-based scheduling scheme and the Karush- Kuhn-Tucker (KKT) conditions for optimality. Again their work does not cater the intra-cell interference between eNB and relay's associated UEs.

2 SYESTEM MODEL We consider a multi-cell relay-assisted LTE-A net-

work as shown in Fig. 1. Each cell has Ns number of sectors and Ml number of relays are placed at 3/4 distance from the eNodeB. Let l ∈    L    = {1, ..., L}, m ∈    Ml = {0, ..., Ml}, k ∈  Km = {1, ..., Km}, n ∈  N   = {1, ..., N }   denote the macrocell, relay/eNodeB (m = 0 means eNodeB) in macrocell l, UE associated with re-lay/eNodeB m, and resource blocks (RBs), respec-tively. Since relay receives data in one time slot and transmits in other time slot, the transmission com-pletes in two time slots t = {t1, t2}. The channel state information (CSI) of all wireless links in a cell is assumed to be perfectly known to the eNodeB of the same cell. We assume that the relay selection is done before the resource allocation scheme on the basis of long term CSI, such as in [9]. The fading chan- nels between the network nodes and the UEs are frequency- selective across different RBs while the channels are frequency flat within the same RB. We model the short-term fading due to multipaths be

Relay

UE

eNodeB

Figure 1: System Model

tween network node and UE as Rayleigh fading and backhaul fading channel as Rician fading because re-lays are owned and managed by operators and are usually placed at a possible line of sight (LOS) loca-tion. The channel gains also count both long-term path loss and shadowing. We denote the channel gain of RB n between eNodeB (m = 0) and UE k in mac-rocell l as h(n)and between the relay node (m ≠ 0) and UE k in macrocell l as h(n)

l,m,k . We consider in-band type 1 relays which use same carrier frequency in backhaul (Un) and access (Uu) interfaces. We assume that RBs pairing has been done at each relay according to sorted ordered pair [10] such that (n,n') represents the RB ordered pair for backhaul and access links. The received signal-to-interference-plus-noise ratio (SINR) !(n)

l,0,k at UE k associated with macrocell l eNodeB in

3

   ( , ) ( , ) ( , ) 2,0, ,0, ,0,( , )

,0,( ) 2 ( ) 2,0, , , , , , , 0

{ } 1 1

| |

| | | | | | | |l

n t n t n tl k l k l kn t

l k MLn nl k l l k l m l m k m l k l k

l l l m

x P h

h P a b h P a a Nγ

ʹ′ ʹ′ ʹ′ ʹ′ ʹ′ ʹ′ʹ′∈ − = =

=

− + − +∑ ∑∑L

    (1)  

 ( ) ( ) 2, , , ,( , )

, ,( ) 2 ( ) 2,0, , , , , , , 0

1 1 { }

| |

| | | | | | | |l

n nl m k l m kn t

l m k L Ln nl k l l k l m l m k m l k l k

l l m m

P h

h P a b h P a a Nγ

ʹ′ ʹ′ʹ′

ʹ′ ʹ′ʹ′ ʹ′ ʹ′ ʹ′ ʹ′ ʹ′

ʹ′= = ∈ −

=− + − +∑ ∑ ∑

M

    (2)  

 ( , ) ( , ) ( , ) 2, ,0 , ,0 , ,0( , )

, ,0( ) 2 ( ) 2, ,0 , , , , , , 0

{ } 1 { }

| |

| | | | | | | |l

n t n t n tl m l m l mn t

l m Ln nl m l l k l m l m k m l k l m

l l l m m

x P h

h P a b h P a b Nγ

ʹ′ ʹ′ ʹ′ ʹ′ ʹ′ ʹ′ ʹ′ʹ′ ʹ′∈ − = ∈ −

=− + − +∑ ∑ ∑

L M

  (3)  

RB n is given by (1), where ( , ),0,n tl kx is the binary decision

variable for RB allocation such taht it is equal to one if RB n is allocated to directly connected UE k in macro-cell l and ( , )

,0,n tl kP is the transmit power in RB n allocat-

ed to UE k in direct transmission. Similarly, the re-

ceived SINR '( , ), ,n tl m kγ at UE associated with relay m in

macrocell l is given by (2), and the received SINR ( , ), ,0n tl mγ at relay m during backhaul transmission from

eNodeB in macrocell l is (3).

3 PROPOSED SOLUTION The interference coordination and power control schemes are described in the following sub-sections:

3.1 Interference Coordination LTE-A type 1 relaying transmissions in the downlink con-sists of two phases in sequence (i.e., first from eNodeB to RS and then from RS to UE). Considering this characteris-tic, the subframes allocated to different transmission phases can be coordinated among cells in the time do-main. In the first phase the same carrier carries the RBs for directly connected UEs i,e., in first phase/slot, fre-quency resources are orthogonally divided between RS and directly connected UEs in frequency domain. In the second phase, RS and eNodeB transmit in whole frequen-cy band because their associated UEs are orthogonal in space except small percentage in interference region. The intra-cell interference can be locally managed by serving eNodeB through orthogonal RB allocation to UEs in this region. According to the channel path loss model defined in [11], the strongest interference to the remote users served by an RS mostly comes from their neighboring RSs, and it is advisable to ensure that the resources for RS-UE links of the neighboring cells are kept orthogonal in the time/frequency domain to mitigate the interfer-ence. We achieve orthogonality among RS-UE links in all neighboring sectors of macrocells. Our scheme is more dynamic and flexible than [12] which presents time-domain resource allocation with dedicated time slots for eNB-RS, RS-UE, and eNB-UE links which can be ineffi-

cient, e.g., if there are few active UEs associated with RS, it will allocate slot 1 with whole frequency resources to underload link eNB-RS, however, our scheme can shrink the frequency domain resource of eNB-RS in in slot 1 and decreases the frequency resource in slot 2 proportionally for RS-UE link.

3.2 Power Control We consider the power allocation problem with the al-

location granularity up to the resource block level. If the channel is known to the transmitter and the receiver, the OFDMA based multiuser access technique with adaptive subchannel and power allocation is superior to other mul-tiuser techniques [13]. Similar to the RRM schemes [14] ,[15], our algorithm first assigns the subchannels to users and then the power allocation algorithm determines the power for each subchannel . Given a set of feasible binary

,n kx 's , each user independently allocates its own power to different subchannels to maximize the utility function. As the utility function is a strictly increasing function of user throughput, maximizing utility is analogous to max-imizing throughput. It has been proven that the optimal power allocation for user k is \emph{water-filling} over the subchannels with , 1n kx = [14]. In our proposed scheme, there is an upper bound on water-filling power allocation determined by the target BER and can be ex-plained by the following BER and transmit power relation for M-QAM in additive white Gaussian noise (AWGN) channel [16] (we choose AWGN for the sake of simplicity, since the BER-SNR trends are the same for AWGN and fading channels, except the gain):

( )1.50.2exp1

k

k

PBERM P

ηη⎡ ⎤−≤ ⎢ ⎥

−⎣ ⎦                                (4)  

where ( ) ( )k kP P p dη η η= ∫ . It can be seen from the above expression that for a fixed BER requirement, if the received SNR increases for a particular UE, then its as-signed modulation scheme is upgraded (e.g. from 4QAM to 16QAM). But if an UE is already transmitting with highest modulation scheme (64QAM) then the increase in received SNR results in the reduction of the transmit

4

power in order to maintain the required target BER. The "Water-filling principle" can be obtained by max-

imizing the individual user information rate under the power constraint:

,

, ,RB k

n k k maxn

P P k Kν∈

= ∀ ∈∑I

                     (5)  

Following the method of Lagrange multipliers, we get the lagrangian L :

( ), ,

2 , , ,log 1RB k RB k

n k k k max n kn n

L P Pλ∈ ∈

⎛ ⎞= + + −⎜ ⎟⎜ ⎟

⎝ ⎠∑ ∑I I

G (6)  

The unknown transmit powers ,n kP are determined by

setting the partial derivatives of $L$ to zero, which results in the following,

1, 0 , ,,n k n k RB kP iω

+−⎡ ⎤= − ∈⎣ ⎦G I                    (7)  

where 0ω is a constant, given by 1/ 2klnλ ., with kλ being the lagrange multiplier associated with user k . 0ω can be determined from the power constraint and ,i kG is the channel gain to noise ratio, defined as

2,

, ,2

| |,n k

n k RB kn

Hi

σ= ∈

ΓG I                              (8)  

4 SIMULATION RESULTS We consider LTE-A cellular network with 7 macrocells

with three sectors per macrocell. Simulations are per-formed according to the latest 3GPP LTE-Advanced spec-ifications [17]. Results are provided for both urban (Case 1) scenario with inter-site-distances (ISDs) of 500 m. For this case, carrier frequency of 2GHz has been specified. We use frequency division duplex (FDD) multiplexing for uplink and downlink transmission orthogonality with 10MHz for each link. The eNB-only deployment is con-sidered as a reference to determine the performance gains of interference coordination when relay cell expansion is applied. One relay is placed at 2/3 radius distance from the donor eNodeB in each sector. Simulation scenario is shown in Fig. 2

As a baseline, we have evaluated the UEs throughput and throughput distribution in a macrocell with relays but without power control in Fig. 3 and. Two scheduling schemes, round robin (RR) and proportional fairness (PF) have been used to allocate the radio resources to UEs. It can be seen in Fig. 3 that channel-aware PF outperforms the flat RR resource allocation scheme. We have com-pared our proposed 3D resource allocation scheme with RR time domain interference coordination (RR-TD-IC) and PF time domain interference coordination (PF-TD-IC) schemes. TD-IC has been presented in [12] which utilizes three time slots. First time slot for eNB to directly con-nected UEs transmission, second time slot for eNB to re-

lay transmission and third time slot for relay to relay's associated UEs. Fig. 4 shows that 3D schemes provides higher throughput per UE as compared to the RR-TD-IC and PF-TD-IC schemes with power control. It obvious that though, three time slot scheme eliminates all possible intercell interferences but it suffers from extra time delay occurred in successive time slots. While our 3D scheme uses two time slots for intercell interference minimization by rendering time domain orthogonality from major sources of interference.

Fig. 5 shows the cumulative distribution function

(CDF) graphs of RR-TD-IC, PF-TD-IC and proposed 3D without power control. It can be seen that the proposed scheme outferms the RR and PF schemes. When power control is applied as shown in Fig. 6, the probability of getting higher throughput has been increased.

The sum throughput as a function of increasing num-

ber of UEs with and without power control is shown in Fig. 7. This figure shows that the sum throughput in-creases with the increasing number of UEs, but this in-crease is not linear because of limited number of radio resources.

Figure 2: Simulation scenario

Figure 3: Individual user throughput without power control

-10 -5 0 5 10 15 20 25-10

-5

0

5

10

15

20

25

30

distance scale 1:50

dist

ance

sca

le 1

:50

3GPP Case 1 (macrocell radius 500m, 25UEs/macrocell)

UERelayeNodeB

5

Figure 4: Individual user throughput with power control Figure 5: User throughput distribution without power

control

Figure 6: User throughput distribution power control

Figure 7: Macrocell throughput Vs number of users

5 CONCLUSION In this paper we have presented a joint radio resource allocation and power control scheme to mitigate the interfenece in LTE-A Het-Nets. The proposed frequen-cy-time domain resource allocation scheme when combined with water-filling power control provides a substantial gain in terms of individual throughput as well as sytem throughput. Simulation results have been provided to show the quantitative analysis be-tween few existing schemes as compared to the pro-posed scheme.

ACKNOWLEDGMENT This work was supported in part by a grant from King Abdulaziz City of Science and Technology (KACST) grant No. P-C-11-555.

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6

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Irfan Ahmed received the B.E. Electrical Engineering degree from University of Engineering and Technology, Taxila, Pakistan, in 1999, the M.S. Computer Engineering degree from CASE, Islamabad, Pakistan, in 2003, and the PhD degree in Telecommunication Engineering from Bei-jing University of Posts and Telecommunications, Beijing, China, in 2008. Currently, he is working as assistant professor in Taif Uni-versity, KSA. He was post-doctoral fellow with Qatar Uni-versity from April 2010 to March 2011, where he worked on two research projects, wireless mesh networks with Purdue University, USA, and radio resource allocation for LTE with Qtel. He has also been involved in National ICT Pakistan funded research project “design and develop-ment of MIMO and Cooperative MIMO test-bed” at Iqra

University, Islamabad, Pakistan, during 2008 to 2010. His research interests include wireless LAN (WLAN) medium access control (MAC) protocol design and analysis, coop-erative communications, MIMO communications, perfor-mance analysis of wireless channels, energy constrained wireless networks, cognitive radio networks, and radio resource allocation. He is an author of more than 25 In-ternational publications.