WINLAB

Dynamic Spectrum Access in 5G

Narayan B. Mandayam

WINLAB, Rutgers Universitynarayan@winlab.rutgers.eduwinlab.rutgers.edu/~narayan

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WINLAB

What is 5G ?

2

Wide range of spectrum choices

100s of MHz to 100 GHz,

Flexible BW, Licensed, Unlicensed

Wide range of application choices

IoT, M2M, D2D

V2V

Wide range of QoS requirements

Ultra low latency

Very high data rate, Best effort

Wide range of device choices

Low power, Mid-to-high power

Low complexity, High complexity

Wide range of networking choices

Mesh, Capillary, Phantom, HetNets

5G: Anything you want it to be!

5G: Academic’s dream!

Wide range of networking paradigms

ICN, MF, NOM, User-centric

WINLAB

5G DSA: What’s out there ?

Three distinct approaches to DSA have been proposed

Agile/cognitive radio – autonomous sensing at radio devices to avoid interference

Spectrum Access System (SAS) – centralized Database to provide visibility of

potentially interfering networks and/or global assignment

Distributed inter-network collaboration – peering protocols to support decentralized

spectrum assignment algorithms

AP/ BS

A

AP/ BS

B

Net ARF sensing

RF sensing

Spectrum

Server

Net B

Net C

Distributed

Algorithm

1. AGILE RADIO

Internet

Query/

Assignment

2. SPECTRUM SERVER

3. DECENTRALIZED

NETWORK COLLABORATION

(Collocated Networks)

WINLAB4

5G DSA: Agile radio

Cognitive radio networks require a large of amount of network (and channel) state information to enable efficient

Discovery, Self-organization

Resource Management

Cooperation Techniques

PHY A

PHY B

PHY C

Control

(e.g. CSCC)

Multi-mode radio PHY

Ad-Hoc Discovery

& Routing Capability

Functionality can be quite

challenging!

Scalability?

Cost of Cooperation?

WINLAB

5G DSA: Spectrum Access System (SAS)

5

Primarily in 3.5 GHz spectrum

Small Cells for Cellular

Coexistence with Navy Radar

Internet

Query/

Assignment

SPECTRUM SERVER

Design Principles and Architecture

Registration with Spectrum Server/Database Tiering and Prioritization of users Protect Incumbents Wide range of technical issues related to access

Licensed Shared Access Generalized Authorized Access

Control and Network State Information Radio and Network parameters exposed

Coordination across databases Monitoring and Enforcement

WINLAB

5G DSA: Network Cooperation

Net A

Net B

Net C

Distributed

Algorithm

Radio MAP

Information

Exhange

SAVANT: Spectrum Access Via Inter-Network Cooperation

Focus on decentralized architecture for sharing spectrum info

Parallels with BGP exchange of route information between peers

Architecture enables regional visibility for setting radio parameters

Further, networks may collaborate to carry out logically centralized

optimization for max throughput subject to policy/technology constraints

Local Adaptation to

Observed Spectrum UseCooperative Regional

Optimization of Radio

Parameters

*Supported by NSF EARS grant CNS 1247764

WINLAB/Princeton Project

WINLAB

SAVANT: Inter-Network Protocol

Architecture involves two protocol interface levels between

independent wireless domains:

• Lower layer for sharing aggregate radio map using technology neutral

parameters

• Higher layer for negotiating spectrum use policy, radio resource

management (RRM) algorithms, and controller delegation

WINLAB

Elephant in Room: WiFi

8

Smart Phone growth is the U.S. from 2013 to 2015 is ~300% Smartphone data consumption in 2015 ~10 GB/user/month

~85% over WiFi and ~15% over Cellular

WiFi AP density in cities ~100-200 per sq km

01/2009 01/2010 01/2011 01/2012 01/20130

5

10

15

20

25

Date

% o

f E

nte

rpri

se

/SP

AP

s

San Francisco

New York

Chicago

Boston

Licensed Assisted Access (LAA) and other cooperative methods including aggregation/integration with WiFi

WINLAB

5G DSA: Technical Challenges

9

Noncontiguous Spectrum Transmission TX power is no longer “King”!

Control Plane Design Scalability, Performance

Distributed/Hybrid Algorithms for Spectrum Coordination

Stability, Convergence of Algorithms

Case for Noncontiguous Transmission - I

10

1

2

3

A

B

C

X

• Three available channels

• Node A transmits to node C via node B.

• Node B relays node A’s data and transmits itsown data to node C.

• Node X, an external and uncontrollableinterferer, transmits in channel 2.

2

If we use max-min rate objective and allocate channels, node B requires two channels; node A requires one channel

Scheduling options for Node A and Node B?

?

?

Case for Noncontiguous Transmission - II

11

2

A

C

3

B

• Transmission in link BC suffers interference in channel 2

1 2

#1: Contiguous OFDM

X

2

A

C

B

• Spectrum fragmentation limited by number of radio front ends

1 3

2

#2: Multiple RF front ends

X

11

2

A

C

B

2

1 3

#3: Non-Contiguous OFDM (NC-OFDMA)

Nulled Subcarrier

X

NC-OFDM accesses multiple fragmented spectrum chunks

with single radio front end

12

2

A

AP

B

2

1 3

Non-Contiguous OFDM

Nulled Subcarrier

Serial toParallel

IFFTParallelto Serial

D/A

X

X[1] X[3]X[1]

X[3]

0

x[1]x[2]x[3]

X[2] =

NC-OFDM accesses multiple fragmented spectrum chunks

with single radio front end

• Node B places zero in channel 2 and avoids interference

• Node A, far from the interferer node X, uses channel 2.

• Both nodes use better channels.

• Node B spans three channels, instead of two.• Sampling rate increases.

Modulation

NC-OFDM Operation

Resource Allocation in Noncontiguous Transmission

Benefits: Avoids interference, incumbent users

Uses better channels

Each front end can use multiple fragmented spectrum chunks

13

Challenges: Increases sampling rate

Increases ADC & DAC power Increases amplifier power

Increases peak-to-average-power-ratio (PAPR)

Multiple RF Front Ends vs Single RF Front End ?

Centralized, Distributed and Hybrid algorithms for carrier and forwarder selection, power control ?

Spectrum Allocation under Interference and Spectrum Span Constraints

Radio nodes

Interference nodes

Available channels

Controller

How to allocate noncontiguous channels subject to ADC/DAC power constraints?

Maxmin Rate Allocation (Integer Linear Program)

n1 n2 n3

n4 B n5

n6 n7 n8

CA

L1 n1-n2

L2 n3-n4

L3 n5-n7

L4 n6-n8

WINLAB

Control Plane Design: Noncontiguous Transmission

16

CDMA is back!

Short PN-seq Control Channel DataLong PN-seq

Experimental Results from ORBIT testbed

Network Setup:

• Multiple p2p secondary links operating in the presence of a primary transmission

• 1 MHz BW, 64-subcarrier NC-OFDM with CDMA-based underlay (spreading sequence length 40-160)

• Underlay to noise ratio ~ 0 dB, primary transmission to noise ratio ~ 10 dB

ORBIT testbed

USRP

Result 1: Spectrum assignment while minimizing spanof assigned subcarriers (reduces ADC/DAC powerconsumption)

Reassigned subcarriers with minimal loss (< 10%)of throughput

Result 2: Reliable timing and frequency recovery fromunderlay control channel in the presence of primarytransmissions Result 3: Control channel BER as a

function of primary signal strength withunderlay to noise ratio set to 0 dB;Control channel rate = 30 kbps

Primary Signal SNR BER

3 dB < 1e-3

6 dB 6.3*1e-3

7.7 dB 2.6*1e-2

9.2 dB 9.2*1e-2

correct timing instance

peak indicating timing instance

detection

peak detection threshold

WINLAB

Conventional LTE Conventional Wi-Fi

Spectrum Exclusive licensed Shared unlicensed

Operation

technique

OFDMA: channel hopping over

time to exploit good channel

condition

CSMA/CA: Channel sensing

before transmission to avoid

packet collision

Controller

entity

A single licensed carrier No common controller

Advantage Packet efficient Cost effective, fair sharing

Network Coordination: LTE/WiFi

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WINLAB

Formulating LTE/WiFi Cooperation as an Optimization problem

19

LWiP

LWiPP

W, i NGPGP

LrS

WirS

SS

i

i

C

Lj

ijj

M

ikk

jjll

iiww

Lj

jl

Wi

ba

iw

bi

liiw

, , : variablesgControllin

, , 0

,

,j ,)log1(

, ,)log1( subject to

11 maximize

max

0

k

min,2

min,2

Objective: Downlink power control optimization using Geometric

ProgrammingMaximize sum-throughput across Wi-Fi and LTE

Minimum SINR requirement for data rate transmission

CCA threshold requirement at Wi-Fi

Range of Tx power

Tx power

WiiMMb

WiiMMa

S

b

i

b

ii

a

i

a

ii

i

, : ,||11

, : ,||11

:

i l ink at SINR

where

Set of Wi-Fi APs in the CSMA range of AP

Set of Wi-Fi APs in the interference range of AP

WINLAB

LTE/WiFi Scenario

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Interfering

APj

Associated

APi

Interfering

APj

dA

-| dI| +|

dI|

(0,0)

UEi

+x-axis- x-axis

• UE – Associated AP: either Wi-Fi or LTE link, interfering AP is of

other technology

• Varying parameters:

• dA = distance(UE, Associated AP)

• dI = distance(UE, interfering AP)

• Assuming UE at (0,0): if interfering AP on the (1) –X axis, dI = -| dI|,

(2) +X axis, dI = +| dI|

• Reason: inter-AP distance matters due to WiFi clear channel assessment

WINLAB

Example LTE/WiFi Coordination Results –Performance of LTE

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20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

No coordinationPower control optimization Time division channel access optimization

Sagari, Baystag, Saha, Seskar, Trappe & Raychaudhuri, “Coordinated Dynamic Spectrum Management of LTE-U and WiFi Networks”

IEEE Dyspan 2015 (to apear)

WINLAB

20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

Example LTE/WiFi Coordination Results: Performance of WiFi

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20 40 60 80 100-100

-50

0

50

100

AP-UE dist [m]

Inte

rfering A

P-U

E d

ist [m

]

10

20

30

40

50

60

No coordinationPower control optimization Time division channel access optimization

Sagari, Baystag, Saha, Seskar, Trappe & Raychaudhuri, “Coordinated Dynamic Spectrum Management of LTE-U and WiFi Networks”

IEEE Dyspan 2015 (to apear)

WINLAB

End-User Behavior and Radio Resource Management

2

Differentiated Pricing

How does uncertainty in the service affect end-user decisions and the network?

Increasing significance of end-user decisions

Can we influence end-user behavior and improve RRM?

Higher speed

Low

er g

uara

nte

e

Figure from www.fcc.gov Measuring Broadband America

WINLAB

Prospect Theory: An Alternative to Expected Utility Theory for Modeling Decision Making

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Losses usually “loom larger” than gains

Probability Weighting Effect

Framing Effect “Overweigh” low probabilities

“Underweigh” moderate and high

probabilities

WINLAB

Prospect Pricing for Radio Resource Management

User preferences, biases can be

“mitigated” by pricing

Can be used to improve RRM

Under EUT, loss is 0

Deviation from EUT results in

loss, pricing reduces loss

25Yang, Park, Mandayam, Seskar, Glass and Sinha “Prospect Pricing in Cognitive Radio Networks” IEEE Trans. on Cognitive Communication Networks, To Appear

Psychophysics Experiments

Measured Probability Weighting

Function for video QoS

WINLAB

Rural Broadband: LTE-U based Backhaul in TVWS with Local WiFi Access

WiFi Coverage Area

WiFi Coverage Area

WiFi Coverage Area

WiFi Coverage Area

WiFi Coverage Area

Backhaul Tower with

WS Radio and WiFi AP for

local distribution

Backhaul Tower with

WS Radio and WiFi AP for

local distribution

WiFi Coverage Area

WiFi Coverage Area

Tower with Fiber Access

LTE-U Link

BS 5

BS 1

BS 2 BS 3

BS 4

BS 6

BS 7

LTE-U BS 1 Coverage

Area

LTE-U BS 6 Coverage Area

WiFi Coverage Area

BS 8

LTE-U BS 4 Coverage

Area

LTE in TVWS: FCC Guidelines

LTE Attributes FCC TVWS Rules for 6 MHz Channel

LTE eNodeB DL Transmitter Power

2W EIRP for LTE FDD 3 MHz

LTE eNodeB UL Transmit Power

2W EIRP for LTE FDD 3 MHz

LTE eNodeB Transmitter Height

30 meters HAAT

LTE eNodeB Antenna Gain 0 dBi

LTE in TVWS: Simulation throughputs with multiple channels

0

50

100

150

0 5 10 15 20

Thro

ugh

pu

t (M

bp

s)

Distance (km)

LTE FDD Throughput with multiple TVWS channels vs Inter-Tower Distance DL TP @ 1 TVWS

ChanDL TP @ 2 TVWSChanDL TP @ 3 TVWSChanDL TP @ 4 TVWSChanDL TP @ 5 TVWSChanDL TP @ 6 TVWSChanDL TP @ 7 TVWSChan18 Mbps Load

35 Mbps Load

Estimated RuralDemandMean Estimate ofRural Demand

Generic Scenario : E.g. Wichita, KS

• Area: 423 square km2

• Population: 385,577 (2012 Census) [1]

• Available white space for fixed devices [2]

57 79 85 491 527533 671

Location(MHz)

Maxmin Rate Backhaul

11.72

26.36

46.86

15.98 15.98 15.98

36.52 36.52 36.52

60.87 60.87

54.78

91.31 91.31

85.02

0

10

20

30

40

50

60

70

80

90

100

lnter-tower distance = 2 Km lnter-tower distance = 3 Km lnter-tower distance = 4 Km

Dat

a R

ate

(Mb

ps)

Throughput vs Demand for Various Cell Size

Traffic Demand A = {5} A = {1,9} A = {1,5,9} A={1,3,7,9}

3 Fiber BS can cover 144 sq km

References• R. Kumbhkar, T. Kuber, G. Sridharan, N. B. Mandayam, I. Seskar,

“Opportunistic Spectrum Allocation for Max-Min Rate in ” DySPAN 2015, October 2015

• S. Sagari, Baystag, D. Saha, I. Seskar, W. Trappe, D. Raychaudhuri, “Coordinated Dynamic Spectrum Management of LTE-U and WiFi Networks” DySPAN 2015, October 2015

• S. Pattar, N. B. Mandayam, I. Seskar, J. Chen, Z. Li, “Rate Optimal Backhaul and Distribution using LTE in TVWS” SCTE Cable-Tec Expo’15, October 2015

• R. Kumbhkar, M. N. Islam, N. B. Mandayam, I. Seskar, “Rate Optimal design of a Wireless Backhaul Network using TV White Space,” COMSNETS 2015, January 2015

Y. Yang, L. Park, N. B. Mandayam, I. Seskar, A. Glass and N. Sinha “Prospect Pricing in Cognitive Radio Networks” IEEE Trans. on Cognitive Communication Networks, To Appear

31

Acknowledgments

32

• U.S. National Science Foundation

• Office of Naval Research

• WINLAB Collaborators: Ratnesh Kumbhkar, Gokul Sridharan, Neel Krishnan, Ivan Seskar, Dipankar Raychaudhuri, Arnold Glass

• Qualcomm: Nazmul Islam

• NRL: Sastry Kompella