inter-cell interference coordination for backhaul-aware...

Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016

INTER-CELL INTERFERENCE COORDINATION FOR BACKHAUL-AWARE SMALL CELL DTX

A. De Domenico and D. Kténas

Contact: [email protected]

[email protected]

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OUTLINE

Motivation• Future RAN architectures• Heterogeneous BH constraints

Joint RAN/BH Optimization• Key Concept• Network Wide Energy Optimization

Joint RAN/BH Discontinuous Transmission• ICIC for Discontinuous Transmissions• Fuzzy Q-Learning

Conclusion and Outlook


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D-RAN C-RAN

S-GW/MME

SC

SC

MBS

S-GW/MME

RRH

RRHRRH

BBU

Core

Network

Het.

Backhaul

RAN

Core

Network

Backhaul

RAN

Fronthaul

Towards the RAN Cloudification…


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D-RAN C-RAN

S-GW/MME

SC

SC

MBS

Inter-cell

Interference

Energy

Consumption

S-GW/MME

RRH

RRHRRH

BBU

Simple

Architecture

Low cost BH

Site Rental

Centralization

Gains

High end-user

Performance

Low-latency

High-capacity

FH

Large

investments

Backhaul/Fronthaul is a key issue to design future wireless networks

Towards the RAN Cloudification…


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BH sets constraints in terms of Capacity and Latency

Due to BH network technology and topology

RAN lower layer functionalities (data plane) have stringent

requirements

Upper layer mainly affected by latency (but relaxed constraints)

BH technologyTotal Latency

(one-way)Per-hop Latency

Throughput

Ideal fiber access 2.5 µs 5 µs/km 10 Gbps

Fiber Access 1 10 – 30 ms 1 ms 10 Mbps – 10 Gbps

Fiber Access 2 5 – 10 ms 1 ms 100 Mbps – 1 Gbps

DSL Access 5 – 35 ms 5 – 35 ms 10 Mbps – 100Mbps

Cable 25 – 35 ms 25 – 35 ms 10 Mbps – 100Mbps

Sub-6 GHz Wireless 5 – 10 ms5 ms

50 Mbps – 1Gbps

Microwave < 1 ms 200 µsec 100 Mbps – 1Gbps

mmW radio < 1 ms 200 µsec 500 Mbps – 2Gbps

BH Constraints

UE FP7 iJOIN, D5.3, ”Final definition of iJOIN architecture,” April, 2015Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016

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KEY CONCEPT: JOINT RAN/BH OPTIMIZATION

3GPP considers the BH underlying the mobile network as out of

scope

As we have seen, RAN & BH are inter-dependent

BH can be a bottleneck towards RAN optimization and user

performance

5G will require joint RAN/BH optimization, more flexibility and

scalability

Co-designing and optimizing needs to consider requirements and

constraints

New interfaces/functionalities are required for RAN/BH

interworking


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NETWORK WIDE ENERGY OPTIMIZATION

BH has a larger impact on energy consumption

than small cells

Joint optimization brings notable gains (>50%)

𝑃𝑇𝑜𝑡 = 𝑃𝐵𝐻 +

𝑖=1

𝑁𝑆𝐶

𝑃𝑒𝑁𝐵,𝑖

0 max

eNB

sleep

if 0 1

if 0

pP y P yP

P y

𝑃𝐵𝐻 =

𝑖=1

𝑁𝑆𝐶

𝑃𝑠𝑤𝑖𝑡𝑐ℎ,𝑖 𝑦𝑖 +𝑁𝑚𝑤,𝑖 ∙ 𝑃𝑙𝑖𝑛𝑘,𝑖(𝑦𝑖)

𝑃𝑖𝑑𝑙𝑒

A. De Domenico et al. “Backhaul-Aware Small Cell DTX based on Fuzzy Q-Learning in Heterogeneous

Cellular Networks,” IEEE ICC 2016Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016

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ARCHITECTURE FOR ICIC AWARE RAN/BH DTX


Control the small cell duty cycle to avoid concurrent tx of nearby cells

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DISCONTINUOUS TRANSMISSIONS (DTX)

Adapt network configuration to the fast traffic variations (packet level)

Fast BH and small cell switch on/off

Some HW still active

To support vital functionalities such as synchronization and reference signals

transmission

To enable fast activation

Store data at the RANaaS and transmit only when necessary

It can lead to further gains but also to packet loss

Trade off latency for energy saving

No impact on the UE QoE Avoid simultaneous activation of nearby small cells

When to transmit data and when to sleep?

Stochastic environment (packet arrival, fading, interference)

Describe the network status is complex

Many variables (buffer status, packet TTL, cell capacity, interference, BH

latency)

Very large size of the variable space

Estimate the perceived interference is challengingJournées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016

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Q-LEARNING

Reinforcement Learning technique

Based on the state-value function 𝑄𝜋 𝑠, 𝑎 = 𝐸 σ𝑡=0∞ 𝛾𝑘𝑟 𝑠𝑡,𝑎𝑡 |𝑠0 = 𝑠, 𝑎0 = 𝑎

r is a cost function

𝛾 is a discount factor that weights future cost on istantaneous decisions

Our problem:

Find the policy 𝜋∗ = 𝑎𝑟𝑔𝑚𝑖𝑛 𝑄𝜋 𝑠, 𝑎

Where 𝑟 𝑠𝑡,𝑎𝑡 = 𝑃𝑇𝑜𝑡 + 𝛽 ∙ 𝐿𝑃𝑘𝑡 ,

𝛽 trades off power consumption and packet loss

𝐿𝑃𝑘𝑡 considers latency requirements and interference effects


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Q-LEARNING

Optimal policy is learned by interacting with the environment

Exploration vs Exploitation tradeoff

However, in our problem the number of state-action pairs is to large

Hence memory required to store Q 𝑠, 𝑎 as well as the learning time are

not acceptable

𝛼 is a learning rate


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FUZZY Q-LEARNING

Extension of Q-learning based on fuzzy logic

State space represented by linguistic terms like ‘small’ and ‘large’

𝑆 = 𝐵𝑢𝑓𝑓, 𝐶𝑎𝑝, 𝑄𝑜𝑆, 𝐼𝑛𝑡𝑒𝑟𝑓. , 𝐵𝐻 𝑙𝑎𝑡.• (𝑇 𝐵𝑢𝑓𝑓 , 𝑇 𝐶𝑎𝑝 , 𝑇 𝐼𝑛𝑡𝑒𝑟𝑓. , 𝑇(𝐵𝐻 𝑙𝑎𝑡. )) = 𝑉𝑒𝑟𝑦 𝐿𝑜𝑤, 𝐿𝑜𝑤,𝑀𝑖𝑑, 𝐻𝑖𝑔ℎ• 𝑇(𝑄𝑜𝑆) = 𝑁𝑜𝑡 𝑈𝑟𝑔𝑒𝑛𝑡,𝑀𝑜𝑑𝑒𝑟𝑎𝑡𝑒𝑙𝑦 𝑈𝑟𝑔𝑒𝑛𝑡, 𝑈𝑟𝑔𝑒𝑛𝑡, 𝑉𝑒𝑟𝑦 𝑈𝑟𝑔𝑒𝑛𝑡

The mapping is done through membership functions

Only a limited number of state action-pairs has to be visited to find the optimal policy

1

0100 [ms]80604020

VU U MU NU


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ICIC AWARE FUZZY Q-LEARNING-BASED RAN/BH DTX


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SYSTEM MODEL [3GPP TR 36.782]

• 1 small cell cluster per macro sector

• 4 small cells per cluster

• 40 UEs per sector

• Small cells operate on dedicated channel

• 6 dBi cell range expansion

• Near real time traffic (100 ms lat. req.)

Macro Node

Distance between cluster and

macro node

R1

Cluster 1

Dmacro-cluster

R2

R1: radius of small cell dropping within a cluster;

R2: radius of UE dropping within a cluster


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SIMULATION RESULTS


• No additional improvements on energy efficiency side

• No impact on the perceived latency

• Large gains in terms of Packet Error Rate

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CONCLUSIONS & OUTLOOK

Joint RAN/BH optimization is a new paradigm

• It leads to new flexible architecture

• It increases the overall resource utilization efficiency

Network wide Energy Efficiency

• BH to be taken into account

• Joint RAN/BH DTX

Interference is still a challenge

• Optimal selection of small cell to activate is combinatorial

• Lower layer coordination may be required (ABS, joint scheduling)

• UE multi-node connectivity can improve performance

| 17Nom événement | Nom Prénom | Date

REFERENCES

1. P. Rost, C.J. Bernardos, A. De Domenico, M. Di Girolamo, M. Lalam, A. Maeder,

D. Sabella, D. Wübben: Cloud Technologies for Flexible 5G Radio Access

Networks, 5G Special Issue of IEEE Communications Magazine, Vol. 52, No. 5,

pp. 68-76, Mai 2014

2. E.Pateromichelakis, A. Maeder, A. De Domenico, R. Fritzsche, P. de Kerret,”

Joint RAN/Backhaul Optimization in virtualized 5G RAN,” submitted to EUCNC

2015.

3. J. Bartelt, P. Rost, D. Wübben, J. Lessmann, B. Melis, G. Fettweis, “Fronthaul

and Backhaul Requirements of Flexible Centralization in Cloud Radio Access

Networks,” submitted to IEEE Wireless Communication Magazine

4. D. Sabella, A. De Domenico, E. Katranaras, M. Imran, M. Di Girolamo, U. Salim,

M. Lalam, K. Samdanis, A. Maeder, ‘Energy Efficiency benefits of RAN-as-a-

Service concept for a cloud-based 5G mobile network infrastructure’, IEEE Open

Access 2014.

5. A. De Domenico et al. “Backhaul-Aware Small Cell DTX based on Fuzzy Q-

Learning in Heterogeneous Cellular Networks,” IEEE ICC 2016

6. UE FP7 iJOIN, D5.3, ”Final definition of iJOIN architecture,” April, 2015

7. 3GPP TSG RAN, “TR 36.872, Small cell enhancements for E-UTRA and E-

UTRAN-Physical layer aspects (Release 12),” V12.1, Dec. 2013.

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