Post on 21-Jan-2016
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
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
L-DAS System
BBU: baseband unit (signal processing center)
IAD: intra-ant distance
U usersM antennas
H: U-by-M MU-MIMO ch. matrixS: M-by-U binary AS matrixW: M-by-U precoding matrixP: U-dim diagonal PC matrixx: U-by-1 symbol vectorn: U-by-1 AWGN vector
Objectives & Contribution
• Study an L-DAS
• Provide a practical power consumption model
• Formulate an EE maximization problem
• Resolve issue on huge signaling, complexity requirement:– 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
Power Consumption Model
Power consumption TPI (transmit power independent) termTPD (transmit power dependent) term
eRF (electric RF)oRF (optical RF)
Cont.
• TPD term
• TPI term
Pcc1: eRFPcc2: per unit-bit-and-second of oRFRu: target rate of user uβ>=0: implies overhead power consumption of MU processing compared to SU-MIMO
EE Maximization Problem
Splitting Problem
• Channel-gain-based greedy AS: RSSI
• Min-dist-based greedy AS: localization Info.
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
Per-Cluster Optimization
• Now, AS matrix is given
• For fixed PC matrix,
– ZF-MU-MIMO precoding matrix
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:
Numerical Results
Single cell
Single antenna for each user
No adaptation for - # of antennas for each user- clustering threshold
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
Cont.
• Iteration for – # of antennas– clustering threshold
Cont.
• Example at cell boundary of two cells
• Outage increase # of active DAs
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=-Inf
1=-Inf
Colored Square:Active distributed antenna (DA)
Circle: Non-outage userCircle color stands for the cluster/DA
Black Dot: outage userCircle color stands for the cluster/DA
Colored Thick Circle: Active DAallocated to the outage user
X: Deactivated DA
Cont.
• Outage
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=-Inf
1=-Inf
Threshold Update
Cont.
• Increase clustering threshold γ outage
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=1e-005
1=1e-005
Cont.
• Increase # of active DAs outage
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=1e-005
1=1e-005
Threshold Update
Cont.
• Increase clustering threshold γ outage
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=4
1=4
Cont.
• Increase # of active DAs outage
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=4
1=4
Threshold Update
Cont.
• No outage: threshold update (2,3) times
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0=4
1=8
Cont.
• Demo
• cell_no_outage
Cont.
• Demo
• cell_outage