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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

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�  Taken from a poem “The Deserted Village” by Oliver Goldsmith: �  “Sweet Auburn! Loveliest village of the plain ...”

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Auburn University

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12/11/15 Shiwen Mao, Auburn University, Auburn, AL ISMB April 2013 4 © Shiwen Mao

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ì  Mainly involves facul-es from ECE and CSSE

ì  Developed the ABET-‐accredited Bachelor of Wireless Engineering program (BWE), star-ng Fall 2002

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Research Capability ì  RFIC and low-‐power

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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.)

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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




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





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






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]






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






LTE-LAA Timeline






Deployment Scenarios

Pico cell configuration: Licensed LTE, unlicensed 5GHz LAA, WiFi (opt.)






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]






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






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.






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






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






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]





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






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)






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






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






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





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)






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)






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)






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)






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)






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)






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)





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






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






The Proposed Algorithms

To solve problem (21), with

complexity is O(n(k − 1)!).

The proposed LMWA algorithm ⇒






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.






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))





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






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






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






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






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





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





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.


https://www.qualcomm.com

http://www.huawei.com/ilink/en/download/_hw 327803

http://www.ericsson.com/research-blog/lte/license-assisted-access/

https://www.qualcomm.com/




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.


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

Thank You!

Questions & comments?

hEp://www.eng.auburn.edu/~szm0001

12/11/15

13

Shiwen Mao, Auburn University, Auburn, AL

Documents

Vehicular Technology Society - Amazon S3s3.amazonaws.com/sdieee/1903-VTSDL_SanDiego_talk_LTE-U+all.pdf · Vehicular Technology Society Scope and Focus Mobile Radio Land Transportation