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]
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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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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…
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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…
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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ICIC AWARE FUZZY Q-LEARNING-BASED RAN/BH DTX
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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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
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
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SIMULATION RESULTS
Journées scientifiques 2016 d’URSI-France| De Domenico Antonio| 16/03/2016
• 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
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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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