Dynamic Spectrum Access in 5G

Narayan B. Mandayam WINLAB, Rutgers University [email protected] winlab.rutgers.edu/~narayan

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WINLAB

What is 5G ? 

Wide range of spectrum choices 100s of MHz to 100 GHz, Flexible BW, Licensed, Unlicensed 





Wide range of device choices Low power, Mid-to-high power Low complexity, High complexity

Wide range of networking choices Mesh, Capillary, Phantom, HetNets







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 networking paradigms ICN, MF, NOM, User-centric

5G: Anything you want it to be! 

5G: Academic’s dream! 2

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 2. SPECTRUM SERVER 3. DECENTRALIZED NETWORK COLLABORATION (Collocated Networks)

1. AGILE RADIO Spectrum Server RF sensing

Net B

Internet RF sensing

Net A

Query/ Assignment

AP/ BS A

Distributed Algorithm

Net C

AP/ BS B

WINLAB

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

Cost of Cooperation?

Scalability? PHY A

PHY C

PHY B

Control (e.g. CSCC)

Multi-mode radio PHY Ad-Hoc Discovery & Routing Capability

Functionality can be quite challenging! 4

WINLAB

5G DSA: Spectrum Access System (SAS) SPECTRUM SERVER

  

Internet

Query/ Assignment

Primarily in 3.5 GHz spectrum Small Cells for Cellular Coexistence with Navy Radar

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    

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WINLAB

5G DSA: Network Cooperation 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 Use

Cooperative Regional Optimization of Radio Parameters

Net B

Net A

Distributed Algorithm

Radio MAP Information Exhange

Net C

*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  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 % of Enterprise/SP APs

25 San Francisco New York Chicago Boston

20 15 10 5 0

01/2009

01/2010

01/2011

01/2012

01/2013

Date

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

WINLAB

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5G DSA: Technical Challenges 

Noncontiguous Spectrum Transmission 



Control Plane Design 



TX power is no longer “King”!

Scalability, Performance

Distributed/Hybrid Algorithms for Spectrum Coordination 

Stability, Convergence of Algorithms

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WINLAB

Case for Noncontiguous Transmission - I C

?

• Three available channels

1 23 • Node A transmits to node C via node B. • Node B relays node A’s data and transmits its own data to node C. • Node X, an external and uncontrollable interferer, transmits in channel 2.

?

B

X

A

 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? 10

2

Case for Noncontiguous Transmission - II #1: Contiguous OFDM

#2: Multiple RF front ends

C

C

1 3

1 3

A • Transmission in link BC suffers interference in channel 2

B

B

2 X

3

Nulled Subcarrier

C

12

B

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

2

2 X

2

X

2 A

A

NC-OFDM accesses multiple • Spectrum fragmentation limited by number of radio fragmented spectrum chunks with single radio front end front ends 11

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NC-OFDM Operation Non-Contiguous OFDM Nulled Subcarrier

X[2] = 0 X[1] X[1] X[3]

Serial to Parallel

IFFT

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

AP

1 3 Parallel to Serial

D/A

Modulation

B

X[3]

• 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.

2 X

2 A

NC-OFDM accesses multiple fragmented spectrum chunks with single radio front end

• Node B spans three channels, instead of two. • Sampling rate increases. 12

Resource Allocation in Noncontiguous Transmission Benefits:  Avoids interference, incumbent users  Uses better channels  Each front end can use multiple fragmented spectrum chunks

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 ? 13

Spectrum Allocation under Interference and Spectrum Span Constraints Controller

Available channels

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

Radio nodes Interference nodes

Maxmin Rate Allocation (Integer Linear Program)

A

n1

n2

n3

n4

B

n5

n6

n7

n8

L1

n1-n2

L2

n3-n4

L3

n5-n7

L4

n6-n8

C

Control Plane Design: Noncontiguous Transmission CDMA is back!

Short PN-seq

Long PN-seq

Control Channel Data

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WINLAB

Experimental Results from ORBIT testbed Result 1: Spectrum assignment while minimizing span of assigned subcarriers (reduces ADC/DAC power consumption)

USRP ORBIT testbed

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

Result 2: Reliable timing and frequency recovery from underlay control channel in the presence of primary transmissions correct timing instance peak indicating timing instance detection peak detection threshold

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

Result 3: Control channel BER as a function of primary signal strength with underlay 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

Network Coordination: LTE/WiFi 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

WINLAB

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Formulating LTE/WiFi Cooperation as an Optimization problem Objective: Downlink power control optimization using Geometric Programming Maximize sum-throughput across Wi-Fi and LTE

maximize

 ab      1   S 1   S   l j w i w i i

iW

l

jL

subject to  w (1  log 2  w Si )  ri ,min , i  W ,

Minimum SINR requirement for data rate transmission

 l (1  log 2  l S j )  rj ,min , j  L,

 Pk Gik  Pj Gij  N 0  C , i W ,

CCA threshold requirement at Wi-Fi

jL

kM ib

0  Pi  Pmax , Controlling variables : Pi ,

i W , L

Range of Tx power

i W , L

Tx power

where Si : SINRatlinki

  b  1 1   | M |, M

ai  1 1 | M ia | , M ia : i

b i

b i

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

:

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

i, i  W i, i  W

WINLAB

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LTE/WiFi Scenario • 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 +| -| dI| dI | dA

+x-axis

- x-axis Interfering APj

(0,0) UEi

Interfering APj

Associated APi

WINLAB

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Example LTE/WiFi Coordination Results – Performance of LTE 100

100

100 100 60

50

0

-50

-100

20

40

60

80

AP-UE dist [m] No coordination

100

Interfering AP-UE dist [m] Interfering AP-UE dist [m]

Interfering AP-UE dist [m]

Interfering AP-UE dist [m]

60 5050 40

0

20 -50 10

-100

505050

5050

40

4040

30

30

20

40

60

80

AP-UE dist [m] Power control optimization

100

6060

0 0

3030

20 -50-50

2020

10

1010

-100 -10020 20 4040 6060 8080 100 100

AP-UE dist [m] AP-UE dist [m]

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

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Example LTE/WiFi Coordination Results: Performance of WiFi 100 100

100

100

0

-50

-100

20

40

60

80

AP-UE dist [m] No coordination

100

Interfering AP-UE dist [m] Interfering AP-UE dist [m]

Interfering AP-UE dist [m]

Interfering AP-UE dist [m]

50

6060

60

60 5050 40

0

5050

40

4040

30

30 20 -50 10

-100

505050

20

40

60

80

AP-UE dist [m] Power control optimization

100

0 0

3030

20 -50-50

2020

10

1010

-100 -10020 20 4040 6060 8080 100 100

AP-UE dist [m] AP-UE dist [m]

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

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End-User Behavior and Radio Resource Management  Differentiated Pricing Higher speed

 Increasing significance of end-user decisions  Can we influence enduser behavior and improve RRM?

Lower guarantee

 How does uncertainty in the service affect enduser decisions and the network?

Figure from www.fcc.gov Measuring Broadband America 2

WINLAB

Prospect Theory: An Alternative to Expected Utility Theory for Modeling Decision Making Probability Weighting Effect Framing Effect

 “Overweigh” low probabilities  “Underweigh” moderate and high probabilities



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Losses usually “loom larger” than gains

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



Psychophysics Experiments 

Measured Probability Weighting Function for video QoS

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

WINLAB

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

LTE-U BS 1 Coverage Area

BS 1 WiFi Coverage Area

BS 4 WiFi Coverage Area

BS 2 WiFi Coverage Area

BS 5 WiFi Coverage Area

BS 3 WiFi Coverage Area

BS 6 WiFi Coverage Area

BS 7 WiFi Coverage Area LTE-U BS 6 Coverage Area

LTE-U BS 4 Coverage Area

BS 8 WiFi Coverage Area

Backhaul Tower with WS Radio and WiFi AP for local distribution

Tower with Fiber Access

LTE-U Link

WINLAB

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 LTE FDD Throughput with multiple TVWS channels vs InterDL TP @ 1 TVWS Tower Distance

Chan DL TP @ 2 TVWS Chan DL TP @ 3 TVWS Chan DL TP @ 4 TVWS Chan DL TP @ 5 TVWS Chan DL TP @ 6 TVWS Chan DL TP @ 7 TVWS Chan 18 Mbps Load

Throughput (Mbps)

150

100

50

35 Mbps Load

0 0

5

10 Distance (km)

15

20

Estimated Rural Demand Mean Estimate of Rural Demand

Generic Scenario : E.g. Wichita, KS

• Area: 423 square km2 • Population: 385,577 (2012 Census) [1] • Available white space for fixed devices [2] Location (MHz)

57

79 85

491 527 533 671

Maxmin Rate Backhaul

3 Fiber BS can cover 144 sq km

Data Rate (Mbps)

Throughput vs Demand for Various Cell Size 100 90 80 70 60 50 40 30 20 10 0

91.31

91.31 85.02

60.87

60.87 54.78 46.86

36.52

36.52

36.52

26.36 15.98

15.98

15.98

11.72

lnter-tower distance = 2 Km

Traffic Demand

A = {5}

lnter-tower distance = 3 Km

A = {1,9}

lnter-tower distance = 4 Km

A = {1,5,9}

A={1,3,7,9}

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

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Acknowledgments • 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

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