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.org

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

Key Intuition Our Contributions 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

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

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 To resolve the difficulties, key math tools PPP: explains the distribution of these points. Assuming UE at the origin, pdf of the closest base station: Distance r r is a random variable

Principal Component Analysis M: # antennas at RRH N: # samples per stream L: rank of the matrix Y Principal Component Analysis (PCA) Low-rank approximation Dimension reduction (DR) by SNR gain due to denoising Compression Block Bit-Allocation Q2 QL PCA Rank Search Q1 Linear Transform SVD Compression Rate with DR Original # of samples Reduced # of samples 6

Transform Coding Transform Coding with Bit Allocation (BA) M: # antennas at RRH N: # samples per stream L: rank of the matrix Y bSD: standard quantization bits bi: quantization bits for ith p, v Transform Coding with Bit Allocation (BA) Performs individual quantization pi and vi Minimizes overall weighted mean-squared quantization error Uses a simple greedy algorithm Mean-squared error of Q(pi) Compression Block Bit-Allocation Q2 QL Transform Coding Rank Search Q1 Linear Transform SVD i-th eigenvalue Compression Rate with BA Original # of total quantization bits Compressed # of total quantization bits 7

Compression Rates Compression Rate with DR Compression Rate with BA Compression Block Bit-Allocation Q2 QL PCA Transform Coding Rank Search Q1 Linear Transform SVD Compression Rate with DR Compression Rate with BA Compression Rate with DR+BA M: # antennas at RRH N: # samples per stream L: rank of the matrix Y bSD: standard quantization bits bi: quantization bits for ith p, v Specifying the multi-user jt beamforming, we employ the distributed zero-forcing. The signal model for this is is shown here, and we can see that the inter-user interference is nullified by zero-forcing beamforming, and the desired channel gain is modified by the beamformer. We assume the sum-power constraint. Quantization side information 8

Validation – Link Level Simulation Simulation Setting Parameters for LTE Transmission 64-QAM modulation 64 antennas {4, 8} users Resource blocks per user : {12, 6} blocks each Compression block length N = 1096 ; (1024+CP) Pedestrian A channel model : Four delay paths 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 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 # of CP samples (extended) 32 64 384 We performed link-level simulation for the validation of our algorithm for 10MHz case the simulation is set as 64QAM Modulation with 8, 16, 32 and 64 antenna cases. And the number of users is fixed as 4. Assigned Resource blocks per user is 12 block, total 48 block out of 50 blocks (which is 96% of full usage). Compression Block Length is 1096 and Pedestrian A channel model is used. [Fundamentals of LTE, Arunabha Ghosh, Jun Zhang, Jeffery G. Andrews, Rias Muhamed, 2010] 9

Validation: Error Vector Magnitude 3 dB Gain 6 dB Gain Achieves 8.0x / 5.0x compression for 64-antenna cases with 4 / 8 users Achieves 6 dB / 3 dB SNR gain – EVM improvement Satisfies error vector magnitude (EVM) requirement for 64-QAM (< 8%) Numerical Results 10

Validation: Bit Error Rate 6 dB Gain 3 dB Gain Achieves 8.0x / 5.0x compression for 64-antenna cases with 4 / 8 users Achieves 6 dB / 3 dB SNR gain – BER improvement Numerical Results 11

Conclusion Space-time fronthaul compression method Limitations Achieves 8.0x / 5.0x compression for 64-antenna cases with 4 / 8 users Achieves 6 dB / 3 dB SNR gain – BER improvement Satisfies EVM Requirement for 64-QAM (< 8%) Limitations Compression ratio depends on # antennas, # users & channel state dimension Low rank matrix is necessary for high compression ratio Future work Analyze error performance Develop downlink space-time compression method