Performance Evaluation of Codebooks Proposed for IEEE 802.16m Amendment IEEE 802.16 Presentation Submission Template (Rev. 9)

Document Number: IEEE C80216m-09_0344r1 Date Submitted: 2009-01-07 Source: David Mazzarese, Bruno Clerckx, Kwanhee Roh, Wang Zhen, Heewon Kang Keun Chul Hwang, Sungwoo Park, Soon-Young Yoon, Hokyu Choi, Jerry Pi Kaushik Josiam, Sudhir Ramakrishna, Farooq Khan Samsung Electronics [email protected]

Venue: IEEE 802.16m Session#59, San Diego, US IEEE 802.16m-08/053r1, “Call for Contributions for P802.16m Amendment Text Proposals”. Topic: “DL MIMO and UL MIMO”.

Base Contribution: IEEE C80216m-09_0344r1 Purpose: Discussion and approval Notice:

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Introduction • Several codebooks have been proposed in the base contribution C80216m-09_0279.

• This contribution provides supporting simulations and analysis of base codebooks.

Codebook-based feedback • Codebooks are used in uplink feedback to the BS for supporting downlink precoding • 3 codebook-based feedback modes in SDD – Standard: base codebook – Adaptive: correlation matrix transformation – Differential: differential PMI feedback

Base Codebook Analysis

Base Codebook Candidates

Reference 806.16e

806.16e

C80216m-09_0279 C80216m-09_0279 C80216m-09_0056 C80216m-09_0056 C80216m-09_106r1 C802.16m-08_983 C80216m-MIMO-08/69 C80216m-08_1101 C80216m-MIMO-08/74 C80216m-MIMO-08_067 C80216m-08/916 C80216m-08/1264r1 Author 802.16e

802.16e

David Mazzarese David Mazzarese Guangjie Li Guangjie Li Yang Tang Jaewan Kim Bishwarup Mondal Shaohua li Ron Porat Label used in figures 16e_3bit 16e_6bit 09/0279_4bit 09/0279_6bit 09/0056_4bit 09/0056_6bit 09/0106_6bit 08/983_4bit 08/1101_4bit 08/067_6bit 08/916_4bit

Measures of Codebook Goodness

Measure

Throughput Feedback overhead DFT structure Block-diagonal matrices Full nested property Constant modulus matrix elements QPSK alphabet Avoidance of rank deficiency weakness

Description

First and foremost measure Codebook size (number of bits) Best for calibrated correlated linear arrays Adapted for dual polarized arrays at BS Ranks 2, 3 and 4 matrices are composed of rank 1 precoders: CQI computation complexity reduction Good for power amplifier transmit power balance, good for PAPR in precoded systems CQI computation complexity reduction by avoiding numerous complex multiplications Any combination of 2 to Nt rank 1 precoders produces an invertible matrix: desirable for simple implementation complexity of ZFBF

Comparison of 4Tx Codebooks

Feedback overhead Performance (SLS) DFT structure Block-diagonal matrices Full nested property Constant modulus 4 bits C: good U: good Yes No Full Yes 6 bits C: good U: good Yes No Full No 3/6 bits C: bad U: good No No No No 4 bits C: good U: good Yes No No No 6 bits C: good U: good Yes No No No QPSK alphabet Avoidance of rank deficiency 09/0279 No Yes 09/0279 No Yes 802.16e

No Yes 09/0056 No Yes 09/0056 No No 09/0106 6 bits C: good U: good Yes Yes 08/983 4 bits C: good U: bad Pure No Yes (rank 1 and 2) No Yes Yes No No No Yes 08/1101 08/067 4 bits C: bad U: bad Yes No No Yes Yes No 6 bits C: good U: bad No No No No No No 08/916 4 bits C: bad U: bad No No Yes Yes Yes No C: correlated channels U: uncorrelated channels

Performance Evaluation

Simulation Environments • SU MIMO SLS in DL 4x2 – ULA: uncorrelated, correlated channels • MU MIMO (ZFBF) SLS in DL 4x2 – ULA: uncorrelated, correlated channels

16e_6bit Transformed 16e_3bit Transformed 09/0106_6bit 09/0279_6bit 09/0279_4bit 09/0056_6bit 09/0056_4bit 16e_6bit 16e_3bit 80.00

SU MIMO SLS in DL 4x2 Uncorrelated Channel (4 lambdas)

SU CL MIMO SLS (uncorrelated channel)

95.19

92.27

85.00

91.00

90.00

95.00

Sector Throughput relative to the best codebook

97.00

98.18

98.26

100.00

99.76

99.84

100.00

105.00

SU MIMO SLS in DL 4x2 Correlated Channel (1/2 lambda)

SU CL MIMO SLS (correlated channel)

16e_6bit Transformed 16e_3bit Transformed 09/0106_6bit 09/0279_6bit 09/0279_4bit 09/0056_6bit 09/0056_4bit 16e_6bit 16e_3bit 80.00

98.75

98.68

99.49

100.37

100.37

100.00

99.71

85.00

94.78

88.91

90.00

95.00

Sector Throughput relative to the best codebook

100.00

105.00

SU MIMO SLS in DL 4x2

• 16e vs. DFT-based codebooks – 16e codebooks lose 6% and 11% throughput in correlated channels in reference to the best 6-bit codebook – DFT-based codebooks are robust in all scenarios • 6-bit DFT-based codebooks – 6-bit DFT-based codebooks ≥ 16e codebooks in uncorrelated channels – All 6-bit DFT-based codebooks are within 0.5% of each other • 4-bit DFT-based codebooks – All 4-bit DFT-based codebooks are within 1% of each other – The 4-bit DFT-based codebooks lose only 1% and 3% to the 6-bit DFT based codebooks in correlated and uncorrelated channels, respectively • Transformation – The transformation brings the performance of small codebooks to the same level as the 6-bit DFT-based codebooks in correlated channels – The transformation is ineffective in uncorrelated channels

SU MIMO SLS in DL 4x2

 Given the lower computational complexity and feedback overhead requirements of a 4-bit codebook over a 6-bit codebook, a 4-bit codebook seems like a reasonable choice

MU MIMO SLS in DL 4x2 Uncorrelated Channel (10 lambdas)

MU MIMO (uncorrelated channel)

09/0106_6bit Transformed 09/0279_6bit Transformed 09/0279_4bit Transformed 09/0056_4bit Transformed 09/0056_4bit Transformed 16e_6bit Transformed 08/916_4bit 08/067_6bit 08/1101_4bit 08/983_4bit 09/0106_6bit 09/0279_6bit 09/0279_4bit 09/0056_6bit 09/0056_4bit 16e_6bit 90.00

99.81

100.76

94.88

100.66

94.31

100.57

91.94

96.30

92.13

94.02

99.05

99.81

93.93

100.00

93.26

99.15

92.00

94.00

96.00

98.00

Sector throughput relative to the best codebook without transformation

100.00

102.00

09/0106_6bit Transformed 09/0279_6bit Transformed 09/0279_4bit Transformed 09/0056_4bit Transformed 09/0056_4bit Transformed 16e_6bit Transformed 08/916_4bit 08/067_6bit 08/1101_4bit 08/983_4bit 09/0106_6bit 09/0279_6bit 09/0279_4bit 09/0056_6bit 09/0056_4bit 16e_6bit 60.00

MU MIMO SLS in DL 4x2 Correlated Channel (1/2 lambda)

MU MIMO (correlated channel)

100.95

103.39

103.69

103.69

101.37

103.87

67.02

65.00

100.00

66.85

97.80

97.92

98.15

91.07

94.76

90.18

77.02

70.00

75.00

80.00

85.00

90.00

95.00

100.00

Sector throughput relative to the best codebook without transformation

105.00

110.00

MU MIMO SLS in DL 4x2

• 16e vs. DFT-based codebooks – 16e codebooks lose 1% and 22% throughput in correlated channels in reference to the best 6-bit codebook – DFT-based codebooks are robust in all scenarios • • • 6-bit DFT-based codebooks – 6-bit DFT-based codebooks are as good or better than 16e in uncorrelated channels – All 6-bit DFT-based codebooks are within 1% of each other in uncorrelated channels – All 6-bit DFT-based codebooks are within 1% of each other in correlated channels, except 09/0056_6bit which loses 4.5% to 09/0279_6bit 4-bit DFT-based codebooks – 4-bit DFT-based codebooks are within 1% of each other, except 08/1101 and 08/916 – The 4-bit DFT-based codebooks lose only 7% and 8% to the 6-bit DFT-based codebooks in correlated and uncorrelated channels, respectively Transformation (benefits and drawbacks also shown in C80216m-08_1285r1) – The transformation allows the performance of 4-bit codebooks to exceed the 6-bit DFT-based codebooks by 3% in correlated channels – The transformation is ineffective in uncorrelated channels

MU MIMO SLS in DL 4x2

 The average sector throughput with MU MIMO in correlated channels is about 1.5 times higher than in uncorrelated channels. This fact stresses the importance of 1. Optimizing the codebook in the correlated channel 2. Calibrating the antenna array at the BS • to avoid random phase effects • to benefit from the DFT-based structure  Thus a 4-bit codebook seems like a reasonable choice, since it requires lower computational complexity and feedback overhead than a 6-bit codebook

Feedback Overhead

• Penalty of 6-bit codebook vs. 4-bit codebook – It depends on feedback channel design • CQICH or feedback header used for PMI feedback?

• How many PMIs carried in one CQICH?

• Subband info carried in same CQICH as PMI?

• How many best subbands?

– It also depends on the number of users that feedback PMI • More knowledge of feedback channel design and analysis of feedback procedure is necessary before finally choosing between a 4-bit and a 6-bit base codebook – Up to 33% feedback overhead could be saved with a 4-bit codedook

Appendix Simulation Assumptions

Number of Antennas Antenna configuration MIMO Scheme Channel Model Channel correlation Scenario PAPR Antenna Calibration 2 transmitter, 2 receiver [2Tx, 2Rx] 4 transmitter, 2 receiver [4Tx, 2Rx] 4 transmitter, 4 receiver [4Tx, 4Rx] ULA: 0.5 lambda; 4 lambda, 10 lambda Split Linear Array, Dual Polarized Array 1.

Closed-loop single user with dynamic rank adaptation 2.

Zero-forcing multiple user MIMO Schedule from 1 to 2 users dynamically based on the same rank-1 PMI feedback. No SU/MU mode adaptation.

Modified Ped-B 3km/h 1. Uncorrelated Channel : 4 lambda antenna spacing, angular spread of 15 degrees 2. High correlated channel: 0.5 lambda antenna spacing, angular spread of 3 degree 1. No constraint on per-antenna power imbalance 2. Limitation of per-antenna power imbalance by scaling in every subframe 1.

2.

  Ideal antenna calibration (mandatory) Uncalibrated antennas (optional) Random phase on each transmit antenna + Random delay between each pair of adjacent transmit antennas (uniformly distributed between 0 and N samples) Fixed for one drop

OFDM parameters OFDM symbols per subframe Permutation Number of total RU in one subframe Scheduling Unit Number of RU for PMI and CQI calculation CQI, PMI feedback period Feedback delay Link Adaptation (PHY abstraction) 10 MHz (1024 subcarriers) 6 Localized 48 Whole band (48 PRUs) 12 subbands 1 subband = 4 consecutive PRUs 1 PMI and 1 CQI feedback per subband 4 which is same as in IEEE 802.16e

Every 1 frame (5ms) 1 frame (5ms) QPSK 1/2 with repetition 1/2/4/6, QPSK 3/4, 16QAM 1/2, 16QAM 3/4, 64QAM 1/2, 64QAM 2/3, 64QAM 3/4, 64QAM 5/6

HARQ Scheduling MIMO receiver Data Channel Estimation Feedback Channel Measurement Cellular Layout Distance-dependent path loss Inter site distance Shadowing standard deviation Antenna pattern (horizontal) (For 3-sector cell sites with fixed antenna patterns) Users per sector Scheduling Criterion Feedback channel error rate Chase combining, non-adaptive, asynchronous. HARQ with maximum 4 retransmissions, 4 subframes ACK/NACK delay, no error on ACK/NACK.

HARQ retransmission occurs no earlier than the eighth subframe after the previous transmission.

No control overhead, 12 subbands of 4 PRUs each, latency timescale 1.5s

Linear Minimum Mean Squared Error (LMMSE) Perfect data channel estimation Perfect feedback channel measurement Hexagonal grid, 19 cell sites, wrap-around, 3 sectors per site L=130.19 + 37.6log

10 (.R), R in kilometers 1.5km

8 dB  3

dB A

    min    12     3

dB

  2 ,

A m

   = 70 degrees,

A m

= 20 dB 10 (EMD) Proportional Fair (PF for all the scheduled users) No error

Power fluctuation among antennas

• Constant modulus property – Definition: Every elements of codebook vector has same magnitude – Good for per-antenna peak power limit – DFT-based codebooks have a constant modulus property, while 16e-based do not Power [watt] Power [watt]

Sum power is limited to 20W Per-ant power limited to 5W

5 5 Ant-1 Ant-2 Ant-3 Ant-4

Total power limitation

Ant-1 Ant-2 Ant-3 Ant-4

Per-ant. Peak power limitation Power adjustment subframe by subframe

23/#NN