Jingon Joung , Yeow Khiang Chia, Sumei Sun Modulation and Coding Department

1. Energy-Efficient, Large-scale Distributed Antenna System (L-DAS)under revision for JSTSP Parts of this work have been presented at the IEEE GLOBECOM, Atlanta, GA, USA, Dec. 2013 Jingon Joung, Yeow Khiang Chia, Sumei Sun Modulation and Coding Department Institute for Infocomm Research, A*STAR Internal Meeting with Prof. Moe Z. Win 14 January 2014

2. Motivation • To achieve high spectral efficiency (SE) and energy efficiency (EE) • For high SE • MU-MIMO: LTE-A beyond Re-7 • Distributed systems: e.g., coordinated multi-point operation (CoMP), LTE-A Re-11 • Massive (large) MIMO: recent trend • For high EE • Power control (PC): efficient-power transmission

3. L-DAS System BBU: baseband unit (signal processing center) IAD: intra-ant distance U users M antennas H: U-by-M MU-MIMO ch. matrix S: M-by-U binary AS matrix W: M-by-Uprecoding matrix P: U-dim diagonal PC matrix x: U-by-1 symbol vector n: U-by-1 AWGN vector

4. Objectives & Contribution • Study an L-DAS • Provide a practical power consumption model • Formulate an EE maximization problem • Resolve issue on huge signaling, complexityrequirement: • Antenna selection(AS) method • Threshold-based user-clustering method • MU-MIMO precoding method • Optimal and heuristic power control methods • Verify the EE merit of L-DAS

5. Power Consumption Model Power consumption TPI(transmit power independent) term TPD (transmit power dependent) term eRF (electric RF) oRF (optical RF)

6. Cont. • TPD term • TPI term Pcc1: eRF Pcc2: per unit-bit-and-second of oRF Ru: target rate of user u β>=0: implies overhead power consumption of MU processing compared to SU-MIMO

7. EE Maximization Problem

8. Splitting Problem • Channel-gain-based greedy AS: RSSI • Min-dist-based greedy AS: localization Info.

9. Cont. • SINR-threshold-(γ)-based clustering • SINR btw users in the same cluster < γ • SINR btw users in diff clusters > γ γ = 25dB γ = 32dB

10. Per-Cluster Optimization • Now, AS matrix is given • For fixed PC matrix, • ZF-MU-MIMO precoding matrix

11. Cont. • Now, AS and precoding matrices are given • Assumption: ICI is negligible • Optimal for MU: using bisection algo, convex feasibility test • Heuristic for MU and optimal for SU:

12. Numerical Results Single cell Single antenna for each user No adaptation for - # of antennas for each user - clustering threshold

16. Remaining Issues for L-DAS • Deployment, implementation, and operation • Cell planning, • Regular/irregular deployment of antennas • Synchronization for large cluster • Robustness against CSI error • Infrastructure cost for wired optical fronthaul • Comparative, quantitative study of L-DAS and L-CAS considering Capex and Opex

17. Cont. • Iteration for • # of antennas • clustering threshold

18. Cont. • Example at cell boundary of two cells • Outage  increase # of active DAs Circle: Non-outage user Circle color stands for the cluster/DA X: Deactivated DA Colored Square: Active distributed antenna (DA) Colored Thick Circle: Active DA allocated to the outage user Black Dot: outage user Circle color stands for the cluster/DA

19. Cont. • Outage 

20. Cont. • Increase clustering threshold γ  outage 

21. Cont. • Increase # of active DAs  outage 

22. Cont. • Increase clustering threshold γ  outage 

23. Cont. • Increase # of active DAs  outage 

24. Cont. • No outage: threshold update (2,3) times

25. Cont. • Demo • cell_no_outage

26. Cont. • Demo • cell_outage