3GPP LTE Downlink System Performance

Abstract— In this paper we quantify 3rd Generation

Partnership Project (3GPP) Long Term Evolution (LTE) Release 8 downlink system performance for a macro cell hexagonal grid scenario. The system performance is analyzed for a closed loop Single User Multi-Input Multi Output (SU-MIMO) mode and compared with Single Input Multiple Output (SIMO) and Multi-User (MU) MIMO modes, for a full buffer scenario and static users.

In addition to the full buffer scenario, traffic models are considered for SIMO mode to evaluate impact of partial loading and handover. Voice over Internet Protocol (VoIP) system performance is quantified for static users. Mobility simulations are performed for Video Telephony (VT) users and compared to the static case.

Index Terms—3GPP LTE, MIMO, VoIP.

I. INTRODUCTION HE 3rd Generation Partnership Project (3GPP) completed standardization of Long Term Evolution (LTE) Release 8 air interface in March of 2009 (See [1][2][3]). LTE is

considered leading standard for worldwide deployment in future 4th generation mobile cellular networks. On downlink, LTE air interface utilizes Orthogonal Frequency Division Multiple Access (OFDMA) waveform. High spectral efficiency, high data rates with a peak of just over 300 Mbps for 20 MHz bandwidth, and low latency makes LTE suitable for best effort data application and multi-media traffic.

In this paper, we evaluate LTE downlink system performance under three different scenarios. In the first scenario we investigate maximum downlink spectral efficiency and consider closed loop Single User Multi Input Multi Output (SU-MIMO) mode, as defined in LTE. We compare it to Single Input Multi Output (SIMO) and Multi User (MU) MIMO mode, as defined in LTE. In this simulation scenario all users are static and full buffers are assumed because the focus is to establish maximum spectral efficiency LTE.

The remaining two scenarios consider the impact of traffic models, and simulation study is conducted for SIMO mode only. The second simulation scenario establishes LTE downlink performance for Voice over Internet Protocol (VoIP). In case of VoIP, regular dynamic scheduling cannot be effectively utilized. That is due to high control channel overhead because VoIP packets are relatively small in size.

Manuscript received March 31 2009. The authors are with QUALCOMM

Incorporated, 5775 Morehouse Drive, San Diego, CA 92121.

Dynamic scheduling of VoIP packets creates a scenario where system performance is bounded by the number of grants the control channel region can support. In this paper, we consider semi-persistent scheduling for VoIP traffic that significantly reduces the required number of grant messages and mitigates the control channel overhead issue. The third scenario evaluates the impact of partial loading and handover. Partial loading creates channel mismatch between Reference Symbols (RS) data tones. Since channel estimation is conducted on the RS tones and data traffic is sent over the data tones, channel estimation mismatch results in performance loss. In this paper, we estimate the loss due to channel estimation, and propose methods how to cope with partial loading in order to maximize downlink system performance. Mobility simulations in this paper consider regular backward handover, where serving cell prepares a target ahead of handover based on the received measurement reports.

The paper is organized as follows. In Section II, we describe system modeling, including system simulation assumptions and scheduler algorithm. Section III presents the simulation results for the system simulation with static users, while in Section IV we describe how mobility is modeled and present results. Conclusions are given in Section V.

II. SYSTEM MODELLING

A. Link to System Interface Link curves are utilized to generate packet errors in the

system simulation. Additive While Gaussian Noise (AWGN) link curves are used along with the corresponding payload adjustment. In order to account for varying channel conditions, the constrained capacity Effective Signal to Noise ratio (ESNR) method to calculate supportable data rate and Packet Error rate (PER).

B. Channel and Interference Estimation Loss Channel estimation is performed on a common RS, defined

for up to 4 antenna ports in LTE [1]. CRS in used for measurements as well as demodulation purposes. RS from different cells can be colliding or non-colliding depending on the cell specific RS offset. Depending on the offset values of RS for neighbor cells, it is possible that the Carrier to Interference (C/I) seen on RS tones is different from data tones. This is particularly true for partially loaded scenarios, bursty traffic sources, or power controlled traffic, e.g. VoIP. Therefore, the channel estimation losses depends on the RS tones C/I and the data C/I. There is also a mismatch in the interference estimate obtained from RS and the interference observed by data.

3GPP LTE Downlink System Performance

Amir Farajidana, Wanshi Chen, Aleksandar Damnjanovic, Taesang Yoo, Durga Malladi, and Chris Lott

T

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE "GLOBECOM" 2009 proceedings.978-1-4244-4148-8/09/$25.00 ©2009

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In the system level, these losses and mismatches are modeled by applying a backoff values to the combined effective Signal to Interference and Noise Ratio (SINR) in dB domain. These backoff values are obtained from link-level simulations with different operating points for RS tone C/I, Traffic to Pilot ratio (T/P), and packet format. The T/P ratio essentially shows the difference between RS C/I and data C/I. The back-off value is the performance difference between the ideal and non-ideal case that is obtained for each operating point (RS C/I, T/P, packet format).

At the beginning of the system simulations, RS offset values are assigned to different cells randomly. Based on RS offset values, cells with colliding pilot patterns are identified.

At time of decoding based on the combined ESINR of data tone, the RS C/I and chosen packet format the back-off value is read from the table. Based on the link-level simulations it is observed that back-off value for a given RS C/I and T/P value is similar for different packet formats that are relevant for the operating data C/I.

Also the feedback computation for Channel Quality Indicator (CQI)/ Precoding Matrix Index (PMI)/ Rank Indicator (RI) is done based on sampled RS tones that correspond with the chosen RS offset value. Therefore, if the two cells have the same RS offset value their sampled tones for feedback computation are the same and exhibit interference from RS tones and not data tones of the other cell.

C. Control and Overhead Modeling Bandwidth and power overhead of control channels and

signals in the simulation are modeled for both the traffic region and control region. The overhead in the traffic region is considered to be fixed during the simulation. This overhead accounts for RS tones, primary and secondary synchronization signals, control symbols and Physical Broadcast Channel (PBCH). The total overhead depends on the number of control symbols and number of transmit antennas. For instance the orthogonal resource overhead for 3 control symbols and 2 transmit antennas is 29.9%. These overheads are accounted for in the reported results.

The fixed overhead in the control region is related to the Physical Control Format Indicator Channel (PCFICH) and Physical Hybrid automatic repeat request Indicator Channel (PHICH). The control channel performance is dynamically modeled in the simulator. The power and bandwidth requirements for sending grant to each User Equipment (UE) is taken into account and each Physical Downlink Control Channel (PDCCH) grant is power-controlled. There is also an error model for the grants, where the decoding probability of the grant depends on the decoding C/I, and it is read from the link curves. In these simulations, the power control targets 1% probability of error for PDCCH. Since the PDCCH region is shared between the uplink and downlink, we reserve power and bandwidth resources for uplink grants in the following way:

Statistics of uplink grant usage are compiled during uplink simulation runs. Distribution of the number of uplink grants used in each subframe is created. At each scheduling instance on downlink, a value for the number of uplink grants is chosen from this distribution. A set of UEs is randomly chosen from among UEs in the cell are assumed to require uplink grants.

The orthogonal resource and power used by the uplink grants with the orthogonal resources used by PHICH are subtracted from the available control region resources. The grants for downlink scheduling need to fit into the remaining orthogonal and power resources after the fixed overhead, PHICH, and dynamic uplink grants are accounted for.

D. Channel Feedback Modeling In these simulations, the CQI/PMI/RI feedback is subband

based. The CQI reporting period is 5ms, with 2ms processing delay. The CQI report is quantized and map to 16 values according to [3]. Errors or erasures on feedback channel are not modeled.

E. Scheduler Description The scheduler used for the full buffer simulations is based

on the proportional fair metric. Subband scheduling is assumed. The scheduler considers PDCCH limitations and uses the channel quality per subband for computing the priority metrics. A multi-flow scheduler is employed for other sources that takes into account the delay and buffer status of different flows in the system along with channel quality (possibly per subband) for scheduling different flows.

F. Modulation and Coding Scheme (MCS) Selection and Rate Prediction

The initial rate prediction thresholds correspond to 10% PER points given by AWGN curves for each packet format. An outer loop per UE is maintained by the scheduler to ensure that all the transmitted codewords (in case MIMO two codewords are transmitted in one subframe) are decoded within the first transmission with more than 90% probability.

G. System Setup The simulation assumptions are in line with the evaluation

methodology given in [4]. TABLE I summarizes main simulation assumptions.

TABLE I

SIMULATION PARAMETERS

Parameter Explanation/Assumption Cellular layout 19 eNode-B, 3-cell sites wraparound

Inter-site distance 500, 1732 (meters)

Speed 3km/h Carrier Frequency 2GHz Penetration Loss 20 dB

Number of UEs/cell 10 UE / cell site Antenna horizontal pattern 70 deg (-3 dB) with 20 dB front-to-

back ratio for Macro Antenna Gain 14 dB BS total Tx power 46 dBm Bandwidth 10 MHz Sampling frequency 15.36 MHz Number of RBs 50 Number of control symbols 2, 3 Number of subbands 5 Antennas Configurations 1x2, 1x4: SIMO

2x2, 4x2, 4x4: SU-MIMO 4x2, 4x4 : MU-MIMO



Antenna Spacing 0.5λ at UE Single User setup: 10λ at cell-site Multi User setup: 0.5λ at cell-site

Specific fast fading model Urban Macro SCM specified modeling [5]

III. RESULTS This section provides simulation results for the considered

scenarios. We first show SU-MIMO performance for full buffer scenario and compare it to SIMO and MU-MIMO schemes. Then we consider traffic models and show VoIP results for the static UEs and consider VT to illustrate the impact of mobility and handover.

A. SU-MIMO Performance TABLE II and TABLE III show the system performance for

D1 and D3 scenarios (see [4]), with the assumption that 2 OFDM symbols are used for the control region. UE throughput cumulative distribution functions (CDF) for D1, D3 scenarios are shown in Fig. 1 and Fig. 2, respectively.

TABLE II

PERFORMANCE FOR D1 SCENARIO, 2 CONTROL SYMBOLS

Configuration 1x2 2x2 4x2 1x4 4x4

Cell Tput[b/s/Hz] 1.50 1.51 1.63 2.04 2.37

5% Tput[b/s/Hz] 0.0329 0.0502 0.0481 0.0790 0.0709

TABLE III



Cell Tput[b/s/Hz] 1.41 1.45 1.52 1.84 2.20

5% Tput[b/s/Hz] 0.0134 0.0329 0.0381 0.0616 0.0575

Fig. 2. UE Throughput CDF, D3, n=2.

B. N = 3 Control Symbols TABLE IV and TABLE V show the system performance for

D1 and D3 scenarios, assuming 3 OFDM symbols are used for the control region assumptions. UE throughput distributions across the system for D1, D3 scenarios are shown in Fig. 3 and Fig. 4, respectively. Similar to the case with 2 control symbols, we observe small gains for SU-MIMO operation. Note that part of the gain vanishes because of the higher overhead associated to larger number of transmit antennas deployed at the eNodeB e.g., the common reference signal (CRS) overhead is doubled going to 2 transmit antenna scenario.Also comparing the case with 2 and 3 control symbols, we can observe loss in the cell edge performance due to smaller number of scheduling grants available with 2 control symbols. Also looking at the plots of Fig. 2 - Fig. 4 we observe the performance in 1x4 configuration is saturated at high end because of packet format limitations.

TABLE IV



Cell Tput[b/s/Hz] 1.36 1.41 1.49 1.80 2.14

5% Tput[b/s/Hz] 0.0475 0.0485 0.0451 0.0708 0.0644

TABLE V



Cell Tput[b/s/Hz] 1.27 1.39 1.40 1.68 1.98

5% Tput[b/s/Hz] 0.0356 0.0393 0.0375 0.0567 0.0518

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Fig. 3. UE Throughput CDF, D1, n =3.


C. MU-MIMO Performance So far we examined the performance benefit of SU-MIMO

systems. LTE supports MU-MIMO, as well. However, there are not enough supporting mechanisms in the standard for MU-MIMO operation. The CQI mechanisms are not optimized for MU-MIMO and the control signaling to help the UE to cancel the interference of other multiplexed UEs is not provided. Also the precoding operation is codebook based and the codebook is optimized for SU-MIMO operation. The designed codebook does not provide the low cross-correlation and interference leakage desired for MU-MIMO operation.

Given all this, the MU-MIMO operatio is beneficial only for the cases with small antenna spacing at the eNodeB, so that healthy beams can be formed for each UE.

In this section, we provide performance for MU-MIMO with 0.5λ antenna spacing at the eNB site. The CQI mechanism is the same as rank 1 SU-MIMO report. Each UE report a precoding index corresponding to the preffered precoing matrix and the corresponding CQI value to the eNodeB. Subband scheduling is used along with MU-MIMO

scheduling in these simulations. The scheduler at eNodeB multiplexes UEs, whose reported precoding vectors are orthogonal to each other.

TABLE VI shows the cell edge and the average cell throughput comparison between a 1x2, 1x4 SIMO and 2x2, 4x2, and 4x4 MU-MIMO with 0.5λ antenna spacing at the cell site. N=3 control symbols are assumed in these simulations. At we can see compared to the SIMO case, MU-MIMO can provide significant benefits in cell edge and average cell throughput for closely spaced antenna systems at transmitter.

TABLE VI

D1 SCENARIO, 0.5Λ ANTENNA SPACING AT CELL SITE, N=3

SIMO MU-MIMO


cell Tput[b/s/Hz] 1.36 1.86 1.38 1.78 2.55

5% Tput[b/s/Hz] 0.0474 0.0707 0.0589 0.0610 0.0992

D. Bursty Traffic Performance

In this section we study system performance for bursty traffic sources. Two types of traffic sources are considered here: VoIP and VT. UE is defined to be in outage if 98th percentile of the tail latency of the packets of the source type is greater than the maximum latency considered for the source. The system performance for the particular traffic source is defined as maximum number of UEs with that traffic source in the system (or per cell) such that at most 5% are in outage. The simulation assumptions for this part are summarized in TABLE VII.

TABLE VII

SIMULATION ASSUMPTIONS FOR BURSTY TRAFFIC

Parameter Explanation/Assumption Cellular layout 7 eNode-B, 3-cell sites

wraparound Inter-site distance 500 (meters)

Speed 3km/h, 30km/h BS total Tx power 43 dBm Bandwidth 5 MHz Number of control symbols 3 Antennas Configuration 1x2

Shadowing Parameters

Standard deviation 8 dB

correlation 0.5 inter-site, 1 intra-site

1) VoIP Simulations

VoIP simulations utilize Adaptive Multi Rate (AMR) vocoder with 12.2 kbps average rate. Details of the source model can be found in TABLE VIII. The delay target considered for VoIP simulations is 50ms.

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CDF

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1x2

2x24x2

4x4

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1x4

4x4



eNode B employs a delay sensitive scheduling algorithm. Because of the control channel region limitations, semi-persistent allocation of resources is utilized at the beginning of UE’s talkspurt. Resources for the first transmission of every packet are pre-allocated and have a periodicity of 20ms. Re-transmissions are dynamically scheduled.

eNodeB employs power control for transmission of VoIP packets. The power control is based on wideband CQI. The CQI transmission periodicity is assumed to be 20ms, accounting for reasonable overhead on uplink.

TABLE VIII

VOIP SOURCE ASSUMPTIONS

Codec AMR12.2

Activity factor 50%

Frame length 20ms

VoIP payload size 320 bits

SID payload size 120 bits

Modulation QPSK

Allocation size for VoIP packet 2RB

Fig. 5 shows the percentage of UEs in outage for different

load.

Fig. 5. Percentage of VoIP UEs in outage vs. Load for D1, 3km/h, 5MHz

scenario.

E. VT Simulations The VT source that we consider in this paper is non-rate

adaptive and is comprised of the voice source that is modeled as AMR 12.2kbps source outlined in previous section, and a video source with average rate of 48kbps. The video source is the trace of a real video source and is provided as an input file to the simulator. The delay targets for voice and video portion of the VT source are considered to be 60ms and 130ms in these simulations. In this system, the voice packets

The scheduler used for the VT source is based on a multi-flow scheduler that performs dynamic scheduling of the VT UEs in the system and tries to achieve the delay requirements.

The performance of VT source is also considered with mobility modeling and handover. The handover modeling is inline with backward handover of Release 8 LTE, where serving cell prepares a target ahead of handover based on the received measurement reports. The mobility model is a line model with random direction, where a UE chose a random direction and move along that direction with the given speed. A time-varying shadowing with correlation distance of 50m is also modeled. Other details on the parameters related to mobility and handover is given in TABLE IX. The backward-handover flow modeled in the simulations is shown in Fig. 6.

TABLE IX

HANDOVER AND MOBILITY RELATED PARAMETERS

Parameter Explanation/Assumption Measurements Narrow band Reference Signal

Received Power (RSRP) based on antenna port 0

Measurement period 10ms

Measurement report Once every 7ms Measurement filter Finite Impulse Response (FIR)

filter of window length 200ms Hysteresis 2dB Report Time to Trigger 20ms Back-haul delay Truncated-exponential with

mean 20ms, (min, max) of (10,100)ms

Random Access Channel (RACH) delay

18ms

Radio Link Failure (RLF) delay 60ms

Cell acquisition threshold -9dB

Lognormal Shadowing

Time varying shadowing as defined in [6]

Shadowing correlation distance 50m

220 230 240 250 260 2702

3

4

5

6

7

8

9

10

Number of Loaded VoIP users

Per

cent

age

of U

Es

in O

utag

e

Talk-Spurt Scheduling

Fig. 6. Backward Handover Flow.



Fig. 7 shows the performance of a VT source for D1 scenario under different number of VT users per cell. The simulations are static in these cases. The figures show the 98% tail delay for both voice and video source.

Fig. 8 and Fig. 9 show the tail delay CDF with mobility and handover modeled for video and voice traffic respectively. TABLE X summarizes VT results for different scenarios.

TABLE X

VT PERFORMANCE FOR D1 SCENARIO, 5MHZ

3 km/h 30 km/h Mobility Static Mob. Static Mob.

Max # UEs for 5% outage

45 40 45 30

IV. SUMMARY In this paper we have evaluated system performance of LTE

Release 8 system. In the static system simulation setting that we consider, spectral efficiency of 4x4 MIMO in LTE Release 8 is 17 % and 60% higher than 1x4 SIMO and 1x2 SIMO configurations, respectively. Mobility simulations conducted for VT source indicate that system performance degrades only marginally for 3 km/h, while degradation is about 30% for the 30 km/h case.

ACKNOWLEDGMENTS The authors would like to thank Yongbin Wei and Xiaoxia Zhang for thorough review and valuable comments.

REFERENCES [1] 36.211 3GPP E-UTRA Physical Channels and Modulation, Release 8

(2009-03).

[2] 36.212 3GPP E-UTRA Multiplexing and channel coding, Release 8 (2009-03).

[3] 36.213 3GPP E-UTRA Physical layer procedures Release 8, (2009-03).

[4] 25.814 3GPP Physical layer aspect for evolved Universal Terrestrial Radio Access (UTRA), Release 7.

[5] 25.996 3GPP Spatial channel model for Multiple Input Multiple Output (MIMO) simulations

[6] Gudmundson, “Correlation Model for Shadow Fading in Mobile Radio Systems” Electronics. Letters, Vol. 27, 23, 2145-2146, November, 1991.

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Fig. 8. CDF of 98% video tail delay, D1 Case, mobility.

Fig. 7. CDF of 98% tail delay, D1 Case, no mobility.

Fig. 9. CDF of 98% voice tail delay, D1 Case, mobility.



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3GPP LTE Downlink System Performance