Vehicular Technology Society
Connecting the Mobile World
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Scope and Focus
Mobile Radio Land Transportation Motor Vehicles
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Conferences – Vehicular Technology Conference
“VTC”, our biannual flagship conference with 500 to 700 attendees
Ve h i c u l a r Te c h n o l o g y S o c i e t y
free to members society news and tutorial papers
Publications –Vehicular Technology Magazine
Ve h i c u l a r Te c h n o l o g y S o c i e t y
40+ active chapters throughout the world
Ve h i c u l a r Te c h n o l o g y S o c i e t y
62 Distinguished Lecturers
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Member connection
“VTS Mobile World” Monthly e-newsletter – With industry news – Society news and
events
Ve h i c u l a r Te c h n o l o g y S o c i e t y
Educational Activities
Video Lectures on technical topics of interest to members
– Distributed on DVD with the VT magazine
Example topics: – Grounding of Hybrid Vehicles – Thermal Stress Failures in Electronic and Photonic Systems – In Vehicle Networking – Hybrid and Plug-In Electric Vehicle Systems – Hybrid Powertrain Design
ì
Opportunistic Spectrum Sharing in LTE-‐U with Lyapunov Optimization based Auction
Shiwen Mao Samuel Ginn Dis-nghuished Professor
Director: Wireless Engineering Research and Educa-on Center (WEREC) Auburn University, Auburn, AL
hEp://www.eng.auburn.edu/~szm0001
IEEE VTS San Diego Chapter, Dec. 11, 2015
Auburn University
2
12/11/15 Shiwen Mao, Auburn University, Auburn, AL
Auburn University
3
� Taken from a poem “The Deserted Village” by Oliver Goldsmith: � “Sweet Auburn! Loveliest village of the plain ...”
� Chartered 1856 � 27,287 students � 5, 501 graduate students � 140 degrees and 13 schools/colleges
� 36th among public universi-es na-onwide (US News and World Report)
12/11/15 Shiwen Mao, Auburn University, Auburn, AL
Auburn University
4
12/11/15 Shiwen Mao, Auburn University, Auburn, AL ISMB April 2013 4 © Shiwen Mao
2010 national championship
Samford Hall
Toomer’s corner oak trees
rolling the corner
War Eagle!
Auburn Tigers
We are the Tigers who say 'War Eagle'
Hargis Hall
Langdon Hall
Auburn University – Toomer’s Corner
5
12/11/15 Shiwen Mao, Auburn University, Auburn, AL
Electrical and Computer Engineering
6
� Established in 1891 � 26 faculty members
� 14 Fellows of IEEE � 6 fellows of other professional
socie-es � 10 presidencies of technical
socie-es � 3 ABET evaluators � 11 editors of technical journals
� 196 graduate students � 567 undergraduate students
� EE, CE, and BWE
� Over 7,000 alumni: Vincent Poor, Ed Knightly, Tim Cook, …
12/11/15 Shiwen Mao, Auburn University, Auburn, AL
WEREC
ì Ini-ated by a $25 million gih by Dr. Samuel L. Ginn ì $3 million from Vodafone-‐US Founda-on for scholarship and facility
ì Mainly involves facul-es from ECE and CSSE
ì Developed the ABET-‐accredited Bachelor of Wireless Engineering program (BWE), star-ng Fall 2002
7
Research Capability ì RFIC and low-‐power
IC design for broadband access & applica-ons
ì Wireless cyber security
ì Wireless system design, modeling and simula-ons
ì Wireless networks
ì Wireless applica-ons in robo-cs
8
Sponsors
ì NSF, MDA, Cisco, NRL, Landis+Gyr, Verizon, …
9
Facility
10
ì RFIC/VLSI Design and Tes-ng ì Over $3M of precision device and circuit measurement required for RF device and IC research (e.g., a
50GHz vector network analyzer, a 20GHz signal generator, a 26GHz spectrum analyzer, a 26GHz noise figure analyzer, etc.)
ì A 250MHz Advantest T2000GS automa-c test equipment (ATE)
ì Assembly and Packaging ì Can do IC packaging and tes-ng in house
ì A Techno CNC rou-ng & drilling system, an MPM TF100 stencil printer, a Carver lamina-on press and Fisher Scien-fic programmable firing furnace for H-‐2 processing mul-layer Si3N4 substrates and packages, and a state-‐of-‐the-‐art, high volume SMT and flip chip produc-on line, among others
ì Clean room ì 5 cleanrooms totaling 6000 sq. h., Cleanroom Class 10 – Cleanroom Class 10000
ì Virtual Symmetric Mul-processing High Performance Compute Cluster
ì Access to Alabama Supercompu-ng Authority facility in Huntsville, AL ì MRI center
ì Siemens Verio open-‐bore 3T MRI scanner
ì One of the first ac-vely-‐shielded whole-‐body 7T MRI scanners in the US
12/11/15 Shiwen Mao, Auburn University, Auburn, AL
11
Shiwen Mao’s Research Topics Sponsors: National Science Foundation (CNS, DUE, ECCS, IIP, EAGER), DoD, Cisco, TranSwitch
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Opportunistic Spectrum Sharing in LTE-U withLyapunov Optimization based Auction
Zhefeng Jiang and Shiwen Mao
Department of Electrical and Computer EngineeringAuburn University, Auburn, AL, USA
HOME: http://www.eng.auburn.edu/∼szm0001/
December 11, 2015
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Synopsis
Problem: opportunistic sharing of unlicensed bands amongLTE-unlicensed base stations (BS), while:
Guaranteeing the QoS of user equipments (UE)Minimizing the packet drop rate
Approach:
Lyapunov optimization based algorithm for valuationDistributed auction to maximize the social welfare
In this paper:
Online distributed auction; BS’s bid truthfully, spatial reuseGuarantee UEs’ QoS requirements on delay and packet droprate with bounded optimality gapsDemonstrate the trade-off between delay and packet drop rateValidated with simulations and comparisons
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Introduction
Unprecedented growth in wireless data
Cisco Visual Networking Index (VNI) reportQualcomm: The 1000x data challenge report
Potential solutions
Spectrum expansionNetwork densificationSpectrum efficiency enhancement
“Moore’s Law” for wireless (Martin Cooper)
More spectrum: 25; Frequency division: 5; Modulation andcoding: 5; Spectrum reuse: 1600
Qualcomm “The 1000x data challenge report”
Viral, Ad-hoc Deployed Small Cells (indoor): 20% householdpenetration + 10x more Spectrum
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
LTE in Unlicensed Bands
A new solution initiated by industry: LTE-Unlicensed
Deploy LTE in the 5GHz unlicensed bandCoexistence of the two principal wireless technologies
The Unlicensed National Information Infrastructure (UNII)radio band
Band Freq. Range Bandwidth Max Power
U-NII Low / U-NII-1 / U-NII Indoor 5.150-5.250 GHz 100 MHz 50 mWU-NII Mid / U-NII-2 / Power 5.2505.350 GHz 100 MHz 250 mWU-NII WorldWide / U-NII-2e 5.470-5.725 GHz 255 MHz .U-NII Upper / U-NII-3 5.725-5.825 GHz 100 MHz 1 W
Using 4% of the 5GHz band, LAA can provide up to a 150Mbps gain [Ericsson Report]
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
License Assisted Access (LAA) scenario
Supplemental Downlink (SDL) operation: downlink only, themost basic form of LTE-U
Full TDD LTE-U operation: both uplink and downlink
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
LTE-LAA Timeline
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Deployment Scenarios
Pico cell configuration: Licensed LTE, unlicensed 5GHz LAA, WiFi (opt.)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Coexistence Scenarios and Challenges
Coexistence with existing unlicensed band users (i.e., WiFi)LTE: continuous frames, channels always onWiFi: Channel occupied only when need to transmit
Interference among LTE-unlicensed users themselvesSignificant throughput degradations due to inter-operatorinterference [Huawei’14]
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
WiFi vs. LTE
Coexistence of the two major wireless technologies, which are verydifferently designed
WiFI LTE
Available bandwith 40 or 20 MHz/channel 20 MHz/100 RBsMax. Spectral Eff. 15 b/s/Hz 16 b/s/HzHighest PHY rate 600 Mb/s 373 Mb/sPeak uplink MAC throughput 228 Mb/s 75.6 M/sInterference Hidden/exposed terminal, collision co-tier/cross-tier co-channel InterferenceChannel access Contention based DCF Centralized on DL/ULChannel usage On-demand, on and off Continuous frames, always on
Goal: not to impact WiFi more than an additional WiFi networkdeployed on the same carrier
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Proposed Solutions
Coexistence with WiFi usersLTE-LAA Duty Cycling (US, Korean, and China)
Turning LTE off to release channel to WiFiMinimal changes to Release 10/11 PHY/MAC standardsFor example, CSAT [Qualcomm] and ABS [Zhang’15]
Listen-Before-Talk (LBT) (Europe and Japan)
A simplified version of DCFClear channel assessment (CCA): carrier senseChange to the LTE air interface (3GPP Release 13)
Coexistence of LTE-unlicensed users
An agreement for the operators to allocate the unlicensedspectrum (may not be practical)Enable opportunistic access to unlicensed channels.
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Coexistence Without LBT
Three mechanisms[Qualcomm’14]:
Channel selectionCarrier-Sensing AdaptiveTransmission (CSAT)Secondary Carrier Off
CSAT:
Senses medium for longerduration (10s of - 200 ms)Gates off LTEtransmissionproportionally
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Coexistence with LBT
Clear Channel Assessment (CCA): detect the channel energylevel at a designated time for a duration of CCA period
If clean, transmit for a duration of Channel Occupancy Time
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Background and MotivationRelated Works
Related Works
Technical reports and white papers
Benefit and challenges [Qualcomm’14, Chen’15]Deployment scenarios [Huawei’14]
Simulation studies
The WiFi performance could be significantly degraded, whileLTE was only slightly affected [Cavalcante’13, Nihtila’15]LBT is necessary for coexistence with WiFi [Ratasuk’14,Chen’15]
QoS guarantees and distributed auction
ε-persistence queue was introduced to guarantee a worst casedelay [Neely’11]Credit-token-based spectrum bidding [Gao’14] and Vickreyauctions [Wu,’09][R.J’03]
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Network ModelTransmission and Queueing ModelSpectrum Auction and LBT on Unlicensed BandUtility Function and Social Welfare
LTE-unlicensed Network Model
Assumptions
Consider the LAA scenario, in which licensed and unlicensedcarrier bands are integrated and usedFocus on the downlink transmission of LAA with FDDNon-co-site deployment of licensed and unlicensed bandsA high speed backhaul for coordinating the BS operation
Scenario
A set of BS’s M = {1, 2, · · · ,M}A set of orthogonal, unlicensed channels C = {1, 2, · · · ,C}Interference among BS’s can be denoted as:
Ii,j =
{1, if BS i and j interfere with each other0, otherwise
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Network ModelTransmission and Queueing ModelSpectrum Auction and LBT on Unlicensed BandUtility Function and Social Welfare
Transmission and Queueing Model
Transmission model
Consider UEs covered by LTE licensed and unlicensedLBT: a BS should wait for a clear period before bidding fortransmission in an unlicensed bandFor UE i , BS m provides a data rate on licensed bands withrate Rm
i (t) in frame tBS m can provide an extra data rate for UE i if it wins atransmission opportunity on an unlicensed channel c in framet, denoted as Rm
ic (t) = ϕmic (t)em
ic (t)
ϕmic (t): number of Resource Blocks (RBs) assigned to UE i ;
emic (t): expected data rate an RB provides
Arrival: Ami (t); excess traffic may be dropped: dm
i (t)
Traffic queueing model
Qmi (t + 1) = max{Qm
i (t)−Rmic (t)−Rm
i (t)−dmi (t), 0}+Am
i (t)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Network ModelTransmission and Queueing ModelSpectrum Auction and LBT on Unlicensed BandUtility Function and Social Welfare
Spectrum Auction and LBT on Unlicensed Band
A channel bidding mechanism to avoid collision amongLTE-unlicensed BS’s
Each LTE-unlicensed BS can bid for a transmissionopportunity on an unlicensed band after LBT
The auction process is held at the auction holder and involvesBS’s interfering the auction holder; the auction holder is thefirst BS claims interest in transmission on the channel
WiFi occupation WiFi idleWiFi
LTE
LTE-U
LTE frame on licensed band
CCABidding window
LTE-U transmissions LTE-U idle
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Network ModelTransmission and Queueing ModelSpectrum Auction and LBT on Unlicensed BandUtility Function and Social Welfare
Spectrum Auction and LBT on Unlicensed Band (cont’d)
Notation and parameters
Um: the set of UEs served by BS mSm∗
c (t): the set of BS’s participated in the auctionBS m∗: the auction holderb̃m
c (t): the value of transmitting on channel c in frame tbm
c (t): BS m’s bid for transmitting on channel c in frame tαm∗
c (t): channel assignment decision
Auction procedure
Step 1: BS’s submit their bids to the auction holderStep 2: The auction holder makes channel assignmentdecisions and payment arrangementsStep 2: The winner BS’s make decision on transmitting ordropping packets
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Network ModelTransmission and Queueing ModelSpectrum Auction and LBT on Unlicensed BandUtility Function and Social Welfare
Utility Function and Social Welfare
If BS m participates in an auction of channel c that is available atframe t, the utility function is defined as
φmc (t) =
∑i∈Um
{−βm
i dmi (t)− b̂m
c (t)}, (1)
The social welfare of auction Sm∗c (t) is defined as follows.∑
m∈Sm∗c (t)
φmc (t) =
∑m∈Sm∗
c (t)
∑i∈Um
{−βmi dm
i (t)} . (2)
βmi : penalty of dropping a UE i packet at BS m
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Virtual Queue and Delay Bound
We adopt the ε-persistence queue [Neely’11] to guarantee themaximum delay requirement
Zmi (t + 1) = max
{Zm
i (t) + εmi · 1{Qm
i (t)>0} − Rmi (t) −
Rmic (t)− dm
i (t)− Zmi (t) · 1{Qm
i (t)=0}, 0}
(3)
Fact I [Neely’11]: Suppose Qmi (t) and Zm
i (t) are bounded for allframes t ∈ {0, 1, · · · }, as
Qmi (t) ≤ (Qm
i )max and Zmi (t) ≤ (Zm
i )max (4)
⇒ the delay bound can be written as
(Wmi )max = d((Qm
i )max + (Zmi )max )/εm
i e (5)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Lyapunov Optimization
We define the Lyapunov function L(Θm(t)) as
L(Θm(t)).
=1
2
∑i∈Um
{(Qmi (t))2 + (Zm
i (t))2} (6)
... and a 1-step sample path Lyapunov drift as
∆1(Θm(t)).
= L(Θm(t + 1))− L(Θm(t)) (7)
The penalty is defined as payment plus the cost of dropped packets
−Vmφm(t).
= Vmbmc (t) + Vm
∑i∈Um
βmi dm
i (t) (8)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Drift-Plus-Penalty
The problem of minimizing the drift-plus-penalty can be defined as
min : ∆1(Θm(t)) + Vmbmc (t) +
∑i∈Um
Vmβmi dm
i (t) (9)
s.t.∑
i∈Um
ϕmic (t) = ϕ, for c ∈ C (10)
ϕmic (t) ≥ 0, for i ∈ Um, c ∈ C (11)
Rmic (t) + Rm
i (t) + dmi (t) ≤ Qm
i (t), for i ∈ Um, c ∈ C (12)
εmi ≥ (Am
i )max , for i ∈ Um (13)
(dmi )max ≥ (Am
i )max , dmi (t) ≥ 0, for i ∈ Um (14)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Reformulation
We can reformulate the drift-plus-penalty function to derive its upperbound as
∆1(Θ(t)) + Vmbmc (t) +
∑i∈Um
Vmβmi dm
i (t) (15)
≤ Bm − 1
2(Zm
i (t))21{Qmi (t)=0} +
∑i∈Um
Zmi (t)εm
i 1{Qmi (t)>0} − Φm
(1)(t)− Φm(2)(t)
whereΦm
(1)(t) =∑
i∈Um (Rmic (t) + Rm
i (t))(Qmi (t) + Zm
i (t))− Vmbmc (t)
Φm(2)(t) =
∑i∈Um dm
i (t)(Vmβmi − Qm
i (t)− Zm(t))
Bm .= 1
2
∑i∈Um{[(Rm
ic + Rmi + dm
i )max ]2 + 2[(Ami )max ]2+
[(εm − Rmic − Rm
i − dmi )max ]2}
(16)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Valuation and Scheduling
Maximizing Φm(1)(t) and Φm
(2)(t), we can obtain the valuation ofchannel as well as scheduling decisions
True Value of Channel
b̃mc (t) =
1
Vmmax
{∑i∈Um
Rmic (t)(Qm
i (t)+Zmi (t))
}s.t. Constraints (10), (11), (12)
Packets to Drop
dmi (t) =
{(dm
i )max , Qmi (t) + Zm
i (t) > Vmβmi
0, Otherwise(17)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Maximum Delay
Lemma
With the drop decision (17) and assuming 0 ≤ εmi ≤ (dm
i )max and0 ≤ (Am
i )max ≤ (dmi )max , the proposed resource allocation and
dropping policies ensure the following upper bounds on the realand virtual queues.
(Qmi (t) + Zm
i (t))max = Vmβmi + (Am
i )max + εmi (18)
(Zmi )max = Vmβm
i + εmi . (19)
⇒ The real and virtual queues can be stabilized with the dropdecision (17)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Virtual Queue and Delay BoundLyapunov OptimizationResource AllocationGuarantee on Maximum Delay
Maximum Delay(cont’d)
Theorem
With the proposed resource allocation and packet dropping policesand the FIFO service discipline, the queueing delay is upperbounded by (Wm
i )max . That is, any packet is either transmitted ordropped within (Wm
i )max , given by
(Wmi )max = 2 + (2Vmβm
i + (Ami )max )/εm
i . (20)
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Determine the Auction WinnerPricing MechanismProposed AlgorithmsPerformance of the Proposed Algorithms
Auction Winner
In each auction, the auction holder determines the biddingsubset αm∗
c (t) that wins the auction, i.e., obtaining thetransmission opportunity on channel c in frame t
max{αm∗
c (t)}: Gc (t)|{Sm∗
c (t)}.
=∑
m∈αm∗c (t)
bmc (t) (21)
s.t. Ii ,j = 0, for all i , j ∈ αm∗c (t) (22)
A maximum weighted independent set problem, which isNP-hard
... but, we proved that the size of the problem is small; wecan solve the problem recursively
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Determine the Auction WinnerPricing MechanismProposed AlgorithmsPerformance of the Proposed Algorithms
Pricing Mechanism
We introduce the second-price strategy in second-price sealedbid auctions (i.e., Vickrey auctions)
The winner BS set αm∗c (t) pays the maximum sum bids of the
non-interfering bidding sets among the losers
... the payment is split among the winners as
b̂mc (t) =
bm
c (t)Gc (t)|{Sm∗
c (t)\αm∗c (t)}
Gc (t)|{Sm∗c (t)}
, m ∈ αm∗c (t)
−bmc (t), m ∈ αm∗
c (t)′
0, otherwise
(23)
αm∗
c (t)′: the optimal non-interfering set of loser BS’s
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Determine the Auction WinnerPricing MechanismProposed AlgorithmsPerformance of the Proposed Algorithms
The Proposed Algorithms
To solve problem (21), with
complexity is O(n(k − 1)!).
The proposed LMWA algorithm ⇒
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Determine the Auction WinnerPricing MechanismProposed AlgorithmsPerformance of the Proposed Algorithms
Algorithm Performance
Theorem (Truthful Bidding)
The pricing scheme in (23) guarantees the truthfulness of bidding, i.e., bmc (t) = b̃m
c (t).
Theorem (Utility Maximization for Individual BS)
If the compound process {Ami (t), em
ic (t)} is i.i.d. over frames and for any UE i servedby BS m, the proposed LMWA algorithm achieves the following lower bound on theutility of BS m.
E{φmc (t)} ≥ {φm
c }opt − Bm/V m, (24)
where φmc (t) is the utility of BS m defined in (1), Bm is defined in (16), and (φm
c )opt is
the maximum utility BS m can achieve without knowing the bids of others in an
auction.
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Determine the Auction WinnerPricing MechanismProposed AlgorithmsPerformance of the Proposed Algorithms
Algorithm Performance (cont’d)
Theorem (Weighted Dropping Minimization)
If V m .= V is a constant for all BS’s, and the compound process {Am
i (t), emi (t)} is
i.i.d. over frames, for BS m ∈ Sm∗c (t) and UE i ∈ Um, then for each auction the
following inequality holds true.
∑m∈Sm∗
c (t)
∑i∈Um
E{βmi dm
i (t)} ≤ E
∑
m∈Sm∗c (t)
∑i∈Um
[βmi dm
i (t)]
opt
+ B/V , (25)
where E{∑
m∈Sm∗c (t)
∑i∈Um [βm
i dmi (t)]}opt is the expected minimum weighted
dropping penalty that can be achieved in an auction, and B =∑
m∈Sm∗c (t) Bm (Bm is
given in (16))
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Simulation SettingsSimulation Results
Simulation Settings
Evaluation with MATLAB simulations
Benchmark schemes
Single-Winner: selects only one winner during an auctionRandom Access: randomly selects a winner during the biddingstage
εmi = 8 and (dm
i )max = 8 for all UEs, normalized to the timescale of one second, and βm
i = β
The network area of 200× 200 m2 is covered with LTE macrocells in licensed bands, with average rate 4 MB/s for all UEs
Total spectrum resource is B = 20 MHz
A truncated Poisson traffic model, with average arrival rate λand maximum arrival rate 2λ
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Simulation SettingsSimulation Results
Simulation Results: Arrival Rate versus Average Drop Rate
Vβ = 20 for all UEs
LMWA achieves aconsiderably smallerdropping rate
The dropping rate ofSingle-Winner is alsoconsiderably lowerthan that of RandomAccess
Auction lets thehigh-utitlity BS’s winthe unlicensedspectrum
3 3.2 3.4 3.6 3.8 40
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Arrival Rate
Avera
ge D
rop R
ate
Proposed LMWA
Single−Winner
Random Access
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Simulation SettingsSimulation Results
Simulation Results: Arrival Rate versus Average Delay
Vβ = 20 for all UEs
Approximately linearrelationship,validating Theorem 2
LMWA guarantees amaximum delay
LMWA outperformsthe two benchmarkswith considerablegains 3 3.2 3.4 3.6 3.8 4
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2
Arrival Rate
Av
era
ge D
ela
y (
s)
Proposed LMWA
Single−Winner
Random Access
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Simulation SettingsSimulation Results
Simulation Results: Vβ versus Average Drop Rate
Ami = 3.5 for all UEs
Confirms theO(1/Vβ) bound ofdrop rate
Demonstrates thetradeoff betweendropping rate anddelay
The proposed schemeoutperforms the twobenchmarks
10 15 20 25 300
0.1
0.2
0.3
0.4
0.5
Value of Vβ
Av
era
ge D
rop
Rate
Proposed LMWA
Single−Winner
Random Access
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Simulation SettingsSimulation Results
Simulation Results: Vβ versus Average Delay
Ami = 3.5 for all UEs
Confirms the delaybound as function ofVβ
LMWA outperformsthe two benchmarkswith considerablegains
10 15 20 25 301
1.2
1.4
1.6
1.8
2
2.2
2.4
Value of Vβ
Av
era
ge D
ela
y (
s)
Proposed LMWA
Single−Winner
Random Access
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
Conclusions
Distributed online auction for opportunistic sharing ofunlicensed bands among LTE-unlicensed BS’s, which
allows spatial reuseminimizes the expected packet drop rateguarantees a maximum delay bound
Lyapunov optimization based schemes to
evaluate the true value of unlicensed spectrumallocate RBs on unlicensed bands, anddecide when to drop packets
A truthful auction mechanism
maximize the overall social welfare
Simulation and comparison studies
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
References
Qualcomm, “Extending the benefits of LTE Advanced to unlicensed spectrum,” Technical Report, [online]Available: https://www.qualcomm.com, Apr.2014
Huawei, “U-LTE: Unlicensed spectrum utilization of LTE,” Technical Report, [online] Available:http://www.huawei.com/ilink/en/download/_hw327803, 2014
T. Chen, “Licensed Assisted Access: Operation principles,” in Ericsson Research Blog, [online] Available:http://www.ericsson.com/research-blog/lte/license-assisted-access/.
Qualcomm, “Making the best use of unlicensed spectrum for 1000x,” Technical Report, [online] Available:https://www.qualcomm.com/, May 2015.
R. Zhang, M. Wang, L. Cai, Z. Zheng, X. Shen, and L.-L. Xie, “LTEunlicensed: the future of spectrumaggregation for cellular networks,” IEEE Wireless Commun., vol.22, no.3, pp. 150–159, June 2015.
R. Ratasuk, N. Mangalvedhe, and A. Ghosh, “LTE in unlicensed spectrum using licensed-assisted access,”IEEE Trans. Commun., vol. 57, no. 10, pp.3059–3068, 2009.
J. Xiang, Y. Zhang, T. Skeie, and L. Xie, “Downlink spectrum sharing for cognitive radio femtocellnetworks,” in Proc. IEEE GLOBECOM’14, Austin, TX, Dec. 2014, pp.746–751.
A. Al-Dulaimi, S. Al-Rubaye, Q. Ni, and E. Sousa, “5G communications race: Pursuit of more capacitytriggers LTE in unlicensed band,” IEEE Veh. Technol. Mag, vol.10, no.1, pp.43–51, Mar. 2015.
C. Chen, R. Ratasuk, and A. Ghosh, “Downlink performance analysis of LTE and WiFi coexistence inunlicensed bands with a simple listen-before-talk scheme,” in Proc. IEEE VTC-Spring’15, Glasgow,Scotland, May 2015, pp. 1–5.
A. Cavalcante, E. Almeida, R. Vieira, F. Chaves, R. Paiva, F. Abinader, S. Choudhury, E. Tuomaala, andK. Doppler, “Performance evaluation of LTE and Wi-Fi coexistence in unlicensed bands,” in Proc. IEEEVTC-Spring’13, Dresden, Germany, June 2013, pp. 1–6.
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
IntroductionSystem Model
Valuation and SchedulingAuction and Pricing
Performance EvaluationConclusions
References(cont’d)
T. Nihtila, V. Tykhomyrov, O. Alanen, M. Uusitalo, A. Sorri, M. Moisio, S. Iraji, R. Ratasuk, and N.Mangalvedhe, “System performance of LTE and IEEE 802.11 coexisting on a shared frequency band,” inProc. WCNC’15, New Orleans, LA, Apr. 2013, pp. 1038–1043.
F. Teng, D. Guo, and M. Honig, “Sharing of unlicensed spectrum by strategic operators,” in Proc. IEEEGlobalSIP’14, Atlanta, GA, Dec. 2014, pp. 288–292.
A. Bhorkar, C. Ibars, and P. Zong, “On the throughput analysis of LTE and WiFi in unlicensed band,” inProc. 2014 IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov. 2014,pp. 1309–1313.
A. B. Gao, Y. Yang, and J.-M. Park, “A credit-token-based spectrum etiquette framework for coexistenceof heterogeneous cognitive radio networks,” in Proc. IEEE INFOCOM’14, Toronto, Canada, Apr. 2014, pp.2715–2723.
J. Jia, Q. Zhang, Q. Zhang, and M. Liu, “ARevenue generation for truthful spectrum auction in dynamicspectrum access,” in Proc. ACM MobiHoc’09, New Orleans, LA, USA, May 2009, pp.3–12.
H. Li, C. Wu, and Z. Li, “Socially-optimal online spectrum auctions for secondary wirelesscommunication,” in Proc. IEEE INFOCOM’15, Hong Kong, China, Apr. 2015, pp.2047–2055.
M. Neely, “Opportunistic scheduling with worst case delay guarantees in single and multi-hop networks,” inProc. IEEE INFOCOM’11, Shanghai, China, Apr. 2011, pp.1728–1736.
Y. Wu, B. Wang, K. Liu, and T. Clancy, “A scalable collusion-resistant multi-winner cognitive spectrumauction game,” IEEE Trans. Commun, vol.57, no.12, pp.3805–3816, Dec. 2009.
R. J. Weber, “Auction Theory: By Vijay Krishna,” Games and Economic Behavior, vol.45, no.2,pp.488–497, Nov. 2003.
3GPP, “Further advancements for E-UTRA physical layer aspects, V9.0.0,” Tech. Rep. TR 36.814, Mar.2010.
Zhefeng Jiang and Shiwen Mao Opportunistic Spectrum Sharing in LTE-U
Acknowledgments
ì Research sponsors ì NSF ì CERDEC, US Army, US NRL ì Cisco ì TranSwitch
ì Students and collaborators
ì This work is supported in part by the NSF under Grant CNS-0953513, the NSF I/UCRC BWAC Auburn Site, and the Wireless Engineering Research and Education Center (WEREC) at Auburn University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the foundation.
12/11/15 Shiwen Mao, Auburn University, Auburn, AL
12
Thank You!
Questions & comments?
hEp://www.eng.auburn.edu/~szm0001
12/11/15
13
Shiwen Mao, Auburn University, Auburn, AL