May 20, 2016 1 / 17

Joint In-Band Backhauling and InterferenceMitigation in 5G Heterogeneous Networks

Trung Kien Vu, Mehdi Bennis, Sumudu Samarakoon,Me’rouane Debbah†, and Matti Latva-aho

Centre for Wireless Communications, University of Oulu, Oulu,Finland, and †Mathematical and Algorithmic Sciences Lab, Huawei

France R&D, Paris, France.. Email: vkien@ee.oulu.fi.

May 20, 2016

May 20, 2016 2 / 17

Outline

Introduction

System Model

Problem Formulation

Lyapunov Framework

Numerical Results

Conclusions

May 20, 2016 3 / 17

IntroductionI To meet the massive mobile data demand1:

I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands

Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020

12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.

May 20, 2016 3 / 17

IntroductionI To meet the massive mobile data demand1:

I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands

Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020

12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.

May 20, 2016 3 / 17

IntroductionI To meet the massive mobile data demand1:

I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands

Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020

12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.

May 20, 2016 3 / 17

IntroductionI To meet the massive mobile data demand1:

I Advanced spectral-efficiency technique (Massive MIMO)I Dense deployment of small cellsI High frequency bands

Figure: Cisco Forecasts 30.6 Exabytes per Month of Mobile Data Traffic by 2020

12020: Beyond 4g radio evolution for the gigabit experience,” White Paper, Noikia SiementsNetworks, 2011.

May 20, 2016 4 / 17

Solutions

I The interplay between Massive MIMO and a densedeployment of self-backhaul small cells(SCs)

I The problem of joint scheduling, interference mitigation,and in-band wireless backhauling

I A network utility maximization problem subject todynamically varying wireless backhaul and network stability

May 20, 2016 4 / 17

Solutions

I The interplay between Massive MIMO and a densedeployment of self-backhaul small cells(SCs)

I The problem of joint scheduling, interference mitigation,and in-band wireless backhauling

I A network utility maximization problem subject todynamically varying wireless backhaul and network stability

May 20, 2016 4 / 17

Solutions

I The interplay between Massive MIMO and a densedeployment of self-backhaul small cells(SCs)

I The problem of joint scheduling, interference mitigation,and in-band wireless backhauling

I A network utility maximization problem subject todynamically varying wireless backhaul and network stability

May 20, 2016 5 / 17

ToolsI Random Matrix Theory2

I Large number of antennas N , number of UEs K

I Stochastics optimization3

I Large number of variables and constraints, and dynamicload.

I Success approximation convex method4

I Convert the non-convex program by its solvable convexupper bound.

2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.

3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.

4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.

May 20, 2016 5 / 17

ToolsI Random Matrix Theory2

I Large number of antennas N , number of UEs K

I Stochastics optimization3

I Large number of variables and constraints, and dynamicload.

I Success approximation convex method4

I Convert the non-convex program by its solvable convexupper bound.

2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.

3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.

4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.

May 20, 2016 5 / 17

ToolsI Random Matrix Theory2

I Large number of antennas N , number of UEs K

I Stochastics optimization3

I Large number of variables and constraints, and dynamicload.

I Success approximation convex method4

I Convert the non-convex program by its solvable convexupper bound.

2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.

3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.

4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.

May 20, 2016 5 / 17

ToolsI Random Matrix Theory2

I Large number of antennas N , number of UEs K

I Stochastics optimization3

I Large number of variables and constraints, and dynamicload.

I Success approximation convex method4

I Convert the non-convex program by its solvable convexupper bound.

2S. Wagner, R. Couillet, M. Debbah, and D. Slock, “Large system analysis of linearprecoding in correlated MISO broadcast channels under limited feedback,” IEEE Transactionson Information Theory, vol. 58, no. 7, pp. 4509–4537, 2012.

3 M. J. Neely, “Stochastic network optimization with application to communication andqueueing systems,” Synthesis Lectures on Communication Networks, vol. 3, no. 1, pp. 1–211,2010.

4A. Beck, A. Ben-Tal, and L. Tetruashvili, “A sequential parametric convex approximationmethod with applications to nonconvex truss topology design problems,” Journal of GlobalOptimization, vol. 47, no. 1, pp. 29–51, 2010.

May 20, 2016 6 / 17

System Model

MBSFD-SC

MUE

Massive MIMO Antennas

beamforming

D: Queue buffer

Dataflow

ISD: 250, 125, 100, ..., 35 m

Q: Network Queue

Data

FD-SC

SUEwir

eless b

ackhau

l

data access

MUE served by MBS

and interfered by SCs

SUE served by SC only

and interfered by MBS

Figure: Network Scenario.

May 20, 2016 7 / 17

Network Assumptions

I N number of antennas, M number of macro users (MUEs),S number of SCs

I N ≥ (K = M + S) ≥ 1

I Dense deployment of SCsI Full-duplex capacityI Two antennas, small cell user (SUE) per each

I Co-channel time-division duplexing (TDD) protocol

I Imperfect channel state information (CSI)

May 20, 2016 8 / 17

Queue Buffer at SC

I rs(t) and rm(t) be the data rates from MBS to SC andMUE, respectively.

I rfws (t) be the data rate of from SC to SUE.

I D = (D1(t), D2(t), ...) as a data queue at SCs,

Ds(t + 1) = max[Ds(t) + rs(t)− rfws (t), 0] (1)

May 20, 2016 9 / 17

Network Utility MaximizationNetwork utility maximization problem:

max f(r̄) (2)subject to r̄ ∈ R (3)

D̄ <∞ (4)network stability (5)

I The network utility function f =∑k f(r̄k), f(r̄k) is assumed to be twice differentiable,

concave.I The rate region R

I r̄ , limt→∞

1t

t−1∑τ=0

E[r(τ)] denotes the time average expectation of the Ergodic data rate*.

I D̄ , limt→∞ 1t

∑t−1τ=0 E

[|D(τ)|

]

May 20, 2016 10 / 17

How to apply the Lyapunov framework

Convert to a queuing network

Convert all constraints by introducing new variables

Replace them by virtual queue

Write the Lyapunov function including

all network queues and virtual queue

Use the Lyapunov plus penalty technique to solve the original problem

by minimizing the Lyapunov function and the objective function

Decouple into several subproblems which are solvable and independence.

Figure: Lyapunov Framework

May 20, 2016 11 / 17

Flowchart of Proposed Algorithm

Algorithm 1: Inband Scheduling

& FD Mode Control

Beamforming Design

Power Allocation

Network Queue Update

Tim

eIn

dic

es

Alg

ori

thm

1

Qu

eu

eU

pd

ate

Pow

erA

lloca

tion

DL

Tra

nsm

issi

on

DL Transmission

Network Queue

& Channel Initialization

CSI ReportUniform Power Assign

May 20, 2016 12 / 17

Simulation Setup

I Different frequency bands: 2.4, 10, and 28 GHz.

I HomNet: where the MBS with Massive MIMO will serve all UEs.

I Number of SCs is increased by reducing the inter-site-distance.

I Number of antennas is twice as number of users, which ensuringthe closed-form expression of user rate is hold.

May 20, 2016 13 / 17

Average UE ThroughputI 28 GHz achieves more than 60× gain as compared to 2.4 GHz.

Number of Small Cells per km216 64 144 400 900A

chie

vab

le A

ver

age

UE

Th

rou

gh

pu

t [G

bp

s][B

lue]

0

1

2

3

4

28 GHz

HetNet-HybridHomNetArrival Mean Rate

0

20

40

Aver

age

Queu

e L

ength

[G

b][

Red

]

0

10

20

30

40

16 64 144 4000

0.2

0.410 GHz

0

2

4

16 64 100 1440

0.12.4 GHz

0

0.2

0.4

ISD=80mISD=100m

ISD=125m

ISD=250m

ISD=50m

ISD=33m

Figure: Achievable Average UE throughput and Network Queue length versus number of

Small Cells at 28 GHz, 10 GHz, and 2.4 GHz.

May 20, 2016 14 / 17

Cell-Edge UE ThroughputI 1 Gbps and 0.73 Gbps in case of HetNet-Hybrid and HomNet, respectively.

Number of Small Cells per km2

16 64 144 400 900

Ach

iev

able

Cel

l-E

dg

e U

E T

hro

ug

hp

ut

[Gb

ps]

0

1

2

3

28 GHz

HetNet-HybridHomNetArrival Mean Rate

16 64 144 4000

0.2

0.410 GHz

16 64 100 144

0.03

0.062.4 GHz

ISD=33mISD=50m

ISD=80m

ISD=125m

ISD=100m

ISD=250m

Figure: Achievable 5th% UE throughput versus number of Small Cells at 28 GHz, 10 GHz,

and 2.4 GHz

May 20, 2016 15 / 17

Utility-Queue length tradeoffI There exists an [O(1/ν),O(ν)] utilityqueue backlog tradeoff, which leads to an

utility-delay tradeoff.

ν 10 5×105 10

6 1.5×10

6 2×10

6 2.5×10

6

Aver

age

Net

work

Uti

lity

[G

bps]

[B

lue]

6

8

10

12

14

16

18HetNet-Hybrid

HomNet

Aver

age

Queu

e B

acklo

g [

Gb][

Red

]

6

8

10

12

14

16

18

Figure: Impact of control parameter ν on the Utility and Network Backlogs at 28 GHz when

K = 16, N = 64.

May 20, 2016 16 / 17

Conclusions

I Joint inband scheduling and interference mitigationoptimization.

I Lyapunov framework solution to decouple the problem.

I Performance gains of three combined techniques in 5GHetNets.

May 20, 2016 17 / 17

Thank You!