SpaceTime Fronthaul Compression of Complex Baseband Uplink LTE

Space-Time Fronthaul Compression of Complex Baseband Uplink LTE Signals Jinseok Choi, Brian L. Evans and *Alan Gatherer Embedded Signal Processing Laboratory Wireless Networking & Communications Group The University of Texas at Austin *Huawei Technologies, Plano, Texas http: //www. wncg. or

Fronthaul Link and Challenge Network Features • • • Separation of RRH and BBU Physical link between RRH and BBU Multiple RRH support at one BBU RRH: Remote radio head BBU: Baseband processing unit UE: User. B equipment RRH S BBU UE Challenge • Rapidly growing data traffic Very expensive fronthaul links DL + UL Monthly Data Traffic [Ericsson, Akamai, 2013] Fronthaul Links B S B S ex. Cloud Radio Access Network 2

Key Intuition § Dimensionality reduction for massive multiple receive antennas § Adaptive quantization of dimension-reduced signals Our Contributions § Space-time fronthaul compression method for uplink LTE signals • High compression ratio • Noise reduction: communication performance improvement § Numerically validate the proposed compression method 3

System Model Network Model Signal Model RRH Process Single-Antenna UEs • • RRHs - equipped with massive antennas • RRHs - serve multiple users • Users - equipped with a single antenna • M Antennas/RRH RRHs: M Rx antennas User: single antenna Channel: frequency selective No inter-cell interference 4

Compression Process Proposed compression method § Compression at the RRH § Time domain compression for complex baseband LTE uplink signals § Decompression at the BBU in a reverse order 5

M: # antennas at RRH N: # samples per stream L: rank of the matrix Y Principal Component Analysis (PCA) PCA § Low-rank approximation Compression Block Linear Transform § Dimension reduction (DR) by Q 1 Q 2 § SNR gain due to denoising QL Compression Rate with DR Original # of samples Rank Search SVD Reduced # of samples Bit. Allocatio n 6

M: # antennas at RRH N: # samples per stream L: rank of the matrix Y b. SD: standard quantization bi bi: quantization bits for ith p, Transform Coding with Bit Allocation (BA) • Performs individual quantization pi and vi • Minimizes overall weighted mean-squared quantization Mean-squared error of Q(pi) error Transform Coding Compression Block Linear Transform Q 1 i-th eigenvalue Q 2 • Uses a simple greedy algorithm QL Compression Rate with BA Original # of total quantization bits Rank Search SVD Bit. Allocation Compressed # of total quantization bits 7

Compression Rates Compression Rate with DR PCA Compression Block Transform Coding Linear Transform Q 1 Q 2 Compression Rate with BA QL Compression Rate with DR+BA Rank Search SVD M: # antennas at RRH N: # samples per stream L: rank of the matrix Y b. SD: standard quantization bits bi: quantization bits for ith p, v Bit. Allocation Quantization side information 8

Validation – Link Level Simulation Parameters for LTE Transmission Simulation Setting • 64 -QAM modulation • 64 antennas • {4, 8} users • Resource blocks per user : {12, 6} blocks each • Compression block length N = 1096 ; (1024+CP) Transmission BW [MHz] 1. 4 3 5 10 15 20 Occupied BW [MHz] 1. 08 2. 7 4. 5 9. 0 13. 5 18. 0 Guardband [MHz] 0. 32 0. 3 0. 5 1. 0 1. 5 2. 0 Sampling Frequency [MHz] 1. 92 3. 84 7. 68 15. 36 23. 04 30. 72 FFT size 128 256 512 1024 1536 2048 # of occupied subcarriers 72 180 300 600 900 1200 # of resource blocks 6 15 25 50 75 100 # of CP samples (normal) 9 x 6 10 x 1 18 x 6 20 x 1 36 x 6 40 x 1 72 x 6 80 x 1 108 x 6 120 x 1 144 x 6 160 x 1 32 64 128 256 384 512 • Pedestrian A channel model # of CP samples : Four delay paths (extended) [Fundamentals of LTE, Arunabha Ghosh, Jun Zhang, Jeffery G. Andrews, Rias Muhame 9

Validation: Error Vector Magnitude 6 d. B Gain 3 d. B Gain Numerical Results • Achieves 8. 0 x / 5. 0 x compression for 64 -antenna cases with 4 / 8 users • Achieves 6 d. B / 3 d. B SNR gain – EVM improvement • Satisfies error vector magnitude (EVM) requirement for 64 -QAM (< 8%) 10

Validation: Bit Error Rate 6 d. B Gain 3 d. B Gain Numerical Results • Achieves 8. 0 x / 5. 0 x compression for 64 -antenna cases with 4 / 8 users • Achieves 6 d. B / 3 d. B SNR gain – BER improvement 11

Conclusion Space-time fronthaul compression method § Achieves 8. 0 x / 5. 0 x compression for 64 -antenna cases with 4 / 8 users § Achieves 6 d. B / 3 d. B SNR gain – BER improvement § Satisfies EVM Requirement for 64 -QAM (< 8%) Limitations § Compression ratio depends on # antennas, # users & channel state dimen § Low rank matrix is necessary for high compression ratio Future work § Analyze error performance § Develop downlink space-time compression method 12