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

Energy-Efficient, Large 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. Tan Chee Wei

23 December 2013

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

Objectives & Contribution

• study an L-DAS

• provide a practical power consumption model

• formulate an EE maximization problem

• propose a suboptimal strategy including – Threshold-based user-clustering method – Antenna selection (AS) method– MU-MIMO precoding method– Optimal and heuristic power control methods

• clarify the EE merit of L-DAS

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

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

Problem Decomposition

• Channel-gain-based greedy antenna selection

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– For SU cluster,

– Optimal PC

Cont.– For MU cluster,

– Optimal and heuristic PC methods

Numerical Results

Single cell

Single antenna for each user

No adaptation for - # of antennas for each user- clustering threshold

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

Remaining Issues for L-DAS

• Deployment issues – Regular / irregular DAs– Cost

• Synchronization issue

• Signaling overhead

• Outage reduction for collocated users

• Asymptotic analysis