Dynamic Spectrum Access in 5G Narayan B. Mandayam WINLAB, Rutgers - - PowerPoint PPT Presentation

WINLAB

Dynamic Spectrum Access in 5G

Narayan B. Mandayam

WINLAB, Rutgers University narayan@winlab.rutgers.edu winlab.rutgers.edu/~narayan

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WINLAB

What is 5G ?

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 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 A RF 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)

WINLAB

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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)

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 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

• ptimization for max throughput subject to policy/technology constraints

Local Adaptation to Observed Spectrum Use Cooperative 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

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 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 01/2013 5 10 15 20 25

Date % of Enterprise/SP APs

San Francisco New York Chicago Boston

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

WINLAB

5G DSA: Technical Challenges

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 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

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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 its
• wn data to node C.
• Node X, an external and uncontrollable

interferer, transmits in channel 2.

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 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

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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

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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

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2 A AP B 2 1 3

Non-Contiguous OFDM Nulled Subcarrier

Serial to Parallel IFFT Parallel to Serial D/A

X

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

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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 C A

L1 n1-n2 L2 n3-n4 L3 n5-n7 L4 n6-n8

WINLAB

Control Plane Design: Noncontiguous Transmission

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CDMA is back!

Short PN-seq Control Channel Data Long 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 span

• f assigned subcarriers (reduces

ADC/DAC power consumption)

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 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 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

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 

 

L W i P L W i P P W , i N G P G P L r S W i r S S S

i i C L j ij j M ik k j j l l i i w w L j j l W i b a i w

b i l i i w

, , : variables g Controllin , , , , j , ) log 1 ( , , ) log 1 ( subject to 1 1 maximize

max k min , 2 min , 2

               

   

   

      

 

Objective: Downlink power control optimization using Geometric Programming

Maximize 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

   

W i i M M b W i i M M a S

b i b i i a i a i i i

      , : , | | 1 1 , : , | | 1 1 : i link 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
• ther 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

50 100

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

20 40 60 80 100

• 100
• 50

50 100

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

20 40 60 80 100

• 100
• 50

50 100

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

10 20 30 40 50 60

No coordination Power 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

50 100

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

20 40 60 80 100

• 100
• 50

50 100

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

20 40 60 80 100

• 100
• 50

50 100

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

Example LTE/WiFi Coordination Results: Performance of WiFi

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10 20 30 40 50 60

No coordination Power 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

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 Differentiated Pricing  How does uncertainty in the service affect end- user decisions and the network?  Increasing significance

• f end-user decisions

 Can we influence end- user behavior and improve RRM? Higher speed Lower guarantee

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

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Yang, 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

50 100 150 5 10 15 20 Throughput (Mbps) Distance (km) LTE FDD Throughput with multiple TVWS channels vs Inter- Tower Distance

DL TP @ 1 TVWS 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 35 Mbps Load 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]

57 79 85 491 527 533 671

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

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

Data Rate (Mbps) 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
• f 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

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• 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