How to Decouple?

• Signaling Separation in LTE systems

– 5 typical signaling: Cell discovery; paging; random access; RRC connection setup;

and data service setup

Logic Channels Transmission Channels Physical Channels CBS TBS

- - PSS、SSS、RS On Off

BCCH BCH、DL-SCH PBCH、PDSCH On Off

PCCH PCH PDSCH On Off

- RACH PRACH On Off

CCCH DL-SCH、UL-SCH PDSCH、PUSCH On Off

DCCH DL-SCH、UL-SCH PDSCH、PUSCH Off On

DTCH DL-SCH、UL-SCH PDSCH、PUSCH Off On

MCCH MCH PMCH、PDSCH On On

MTCH MCH PMCH、PDSCH On On

X. Xu, G. He, S. Zhang, Y. Chen and S. Xu, “On Functionality Separation for Future

Green Mobile Network: Concept Study over LTE”，IEEE Commun. Mag., 201218

Testbed for OAI-based HCA

图8 Octoclock时钟源实物图

19T. Zhao, P. Yang, H. Pan, R. Deng, S. Zhou(周盛), and Z. Niu(牛志升), “Software Defined Radio Implementation of Signaling Splitting in

Hyper-Cellular Network,”ACM SIGCOMM Workshop of Software Radio ImplementationForum (SRIF 2013), Hong Kong, Aug. 2013.

Cloud-based Software-defined HCA

• BS Virtualization and Software-defined Fronthaul Network

VM

LTE vBSLTE vBSTBS

VM

3G vBS3G vBSCBS

VM

3G vBS3G vBS

Air Interface Control

VM

3G vBS3G vBSService

Analysis and

Aggregation

Low Rate Traffic

High Rate Traffic

Multicast and Broadcast

Traffic

Control Signaling

Traffic Coverage Layer II

Traffic Coverage Layer I

Control Coverage

Multicast and Broadcast Coverage

RRH Network

SoftwareDefined

FronthaulFronthaul Data Plane

Fonthaul Switch

Virtual BS

Cloud

Fonthaul Network Physical Representation

Logical Representation

VM

3G vBS3G vBS

Fronthaul Control

[1] J. Liu, T. Zhao, S. Zhou, Y. Cheng, Z. Niu., “CONCERT: A Cloud-Based Architecture for

Next-generation Cellular Systems,” IEEE Wireless Commun. Mag., Dec 2014

[2] S. Zhou, T. Zhao, Z. Niu, and S. Zhou, “Software-Defined Hyper-Cellular Architecture for

Green and Elastic Wireless Access,” IEEE Commun. Mag., Jan. 2016 20

• Smart Fronthaul for Device-Centric and Latency-Sensitive

Communication (Typical baseband processing structure of a LTE BS)

Redesign Fronthaul for HCA

J. Liu, S. Xu, S. Zhou, Z. Niu, “Redesigning Fronthaul for Next-Generation Networks: Beyond

baseband samples and Point-to-Point Links”, IEEE Wireless Comm. Mag., Oct. 201521

Container-based VBS and Lab Demo

• Container virtualization

ContainerContainer

lightweight virtual machine

No guest OS: less performance degradation

high level programming language API

22

How much compute resource needed？

Compute-Aware Energy Model

BS:

RRH:

BBU: 𝑁c CPU cores,

T. Zhao, J. Wu. S. Zhou, Z. Niu, “Energy-Delay Tradeoffs of Virtual Base Stations With a Computational-

Resource-Aware Energy Consumption,” 14th IEEE Intl. Conf. Commun. Sys. (ICCS'14), Macau, Nov. 2014

Optimal rate!

Extra compute resources can greatly reduce average delay

23

What’s the Optimal Size of VBS Pool?

• Optimal VBS pool size for tradeoff between pooling

gain and fronthaul cost

Spectrum Resource limited

ComputingResource limited

J. Liu, S. Zhou, Z. Niu., “On the Statistical Multiplexing Gain of Virtual Base Station Pools,” IEEE

GLOBECOM’14. 24

Dynamic BS Sleeping wrt Traffic Dynamics

• Optimal BS Density in Dense Urban Scenario (EARTH Model)

– CM = 780 + 28.2PM , Cm = 112 + 5.2Pm

– PM = 20W, Pm =2.42W

– Reference model: macro-only homogeneous network with no BS sleeping:

total energy consumption=3.26 KW/Km2

0.82 (average)(75% saving)

26

Impact of Traffic Burstness (temporal)

Energy-Delay Tradeoff under N-policy

Interrupted Poisson Process (IPP) and Switched Poisson Process (SPP)

N-policy with ESW

27

1. J. Wu, Z. Niu, S. Zhou, "Traffic-Aware Base Station Sleeping Control and Power Matching for

Energy-Delay Tradeoffs in Green Cellular Networks“, IEEE Trans. Wireless Comm., Aug. 2013

2. J. Wu J, Y. Bao, G. Miao, S. Zhou, Z. Niu, “Base Station Sleeping Control and Power Matching for

Energy-Delay Tradeoffs with Bursty Traffic”, IEEE Trans. Vehicular Tech., May 2016.

Burstiness can bring energy saving gain!

Energy and Delaynot always trade-off!

But, the optimality of N-policy not guaranteed

burstiness

Impact of Traffic Burstness (spatial)

Dynamic Programing Approach for BS Sleeping Control Simulation study with non-uniform traffic

28

1. S. Zhou, J. Gong, Z. Niu, “Green Mobile Access Network with Dynamic Base Station Energy

Saving”, ACM MobiCom’09

2. J. Gong, S. Zhou, Z. Niu, “A Dynamic Programming Approach for Base Station Sleeping in

Cellular Networks,” IEICE Trans. Commun., Vol.E95-B, No.2, pp.551-562, Feb. 2012

Non-uniformity brings energy saving gain and reduces blocking!

(0.88 0.63 0.50) (0.83 0.50 0.33) (0.81 0.44 0.25) (0.80 0.40 0.20) (0.79 0.38 0.17)60

61

62

63

64

65

66

67

68

69

70

Ave

rag

e N

o. o

f A

ctive

BS

s

(0.88 0.63 0.50) (0.83 0.50 0.33) (0.81 0.44 0.25) (0.80 0.40 0.20) (0.79 0.38 0.17)10

-4

10-3

10-2

10-1

Ave

rag

e B

lockin

g P

rob

ab

ility

(1

2

3)

Uniform alg. ave. active BSs

DP alg. ave. active BSs

DP alg. ave. blocking

Uniform alg. ave. blocking

Uniform, Energy Consumption

Uniform, Blocking Prob.

Non-Uniform, Blocking Prob.

Non-uniform, Energy Consumptionx-axis (m)

y-a

xis

(m

)

500 1000 1500 2000 2500 3000

500

1000

1500

2000

2500

High Load

Medium

Low Load

Hotspot center:

1st tier:

2nd tier:

3rd tier:

( )h t

1 ( )h t

2 ( )h t

3 ( )h t

Non-uniformity

Two-threshold control with wait-and-see (W/S) property

No W/S property for Poisson arrival

18

Optimal Sleeping Control

Arrival phase changes from ON to OFF

Shorter Q but arrival phase changes from ON to OFF or longer Q but arrival in ON

Qactive

Qsleep

Poisson

Wake-up W/S

Period

Poisson

Active threshold

Sleep threshold

Active SleepStay

1. B. Leng, X. Guo, X. Zheng, B. Krishnamachari and Z. Niu, “A Wait-and-See Two-Threshold Optimal Sleeping

Policy for a Single Server With Bursty Traffic”, IEEE Trans. on Green Comm. Networking., vol.1, no.4, 2017

2. Z. Jiang, B. Krishnamachari, S. Zhou, Z. Niu, “Optimal Sleeping Mechanism for Multiple Servers with

MMPP-Based Bursty Traffic Arrival”, IEEE Wireless Comm. Lett., Dec. 2017

Data-Driven BS Sleeping Control

with Deep Reinforcement Learning

• Learn optimal sleeping patterns from data, without models of E (Traffic Profile) or S (Base Station)– Formulate BS sleeping control as a Reinforcement Learning task and

combine Q-learning with deep neural network

* DQN: Deep Q-Network

Liu J, Krishnamachari B, Zhou S, Niu Z. DeepNap: Data-Driven Base Station Sleeping Operations

through Deep Reinforcement Learning. submitted to IEEE IoT Journal, 2017

30

DeepNap: Data-Driven Base Station Sleeping

Operations through Deep Reinforcement Learning

• Formulate BS sleeping management as a Reinforcement Learning task and combine Q-learning with deep neural network

• Data from a Chinese University Campus

31

Data-Driven BS Sleeping Control with DQN

• Compared with Model-based Policies

– Approach the best N-based policy in Poisson traffic

– Beat best N-based policy in realistic traffic

Due to burstiness, non-stationarity…

32

Summary

• What’s 5G/5G+?

– 5G should be a paradigm shift of cellular architecture for Green and Smart

• A novel Hyper Cellular architecture for 5G

– Decoupling signaling functions from data services to make cellular more

adaptive and intelligent

– Always-on hyper cells for coverage guarantee and on-demand data cells

• Enabling technologies for 5G/5G+

– Separation of control and data coverage

– Resource/network virtualization and network dimensioning

– Traffic adaptation technologies, including cell zooming, BS sleeping,

coverage extension, ……

– Energy-delay tradeoff can help to shift the peak and therefore save energy

33

Faster, Greener, Smarter

Future Communication & Networking

34