non-orthogonal multiple access for 5g and beyond

Non-Orthogonal Multiple Access for 5G andBeyond

Proceedings of the IEEE, Dec. 2017Tutorials of VTC2018-Fall, Chicago

Lajos Hanzo and Yuanwei Liu

Aug. 27th, 2018

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Outline

1 Overview and Motivation: OMA vs NOMA

2 Power-Domain NOMA Basics

3 Sustainability of NOMA Networks

4 Compatibility of NOMA in 5G Networks

5 Security Issues in NOMA Networks

6 Other Research Contributions on NOMA

7 Research Opportunities and Challenges for NOMA

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Brief History of Wireless Standardization

MIMO

Sq.BF Close

OVSF-CDMA St.

OMA/

NOMA

Sq.

Turbo St. FEC

Sq.LDPC St.

BIC

M-I

D S

t.

MFAA St.

4G

Sq.

HetNets

CR

SDN

Sq.

UL/

DL

de

cou

plin

g S

t.

MFAA

LS-MIMO

Terrace

5G

Place

Te

lep

r. A

ve

.M

PE

G S

t.

[1] Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, “Non-Orthogonal Multiple Access for

5G”, Proceedings of the IEEE ; Dec 2017. 3 / 118

Orthogonal multiple access: FDMA, TDMA and CDMA

Code

Time

Freq

uenc

y

Freq

uenc

y

Freq

uenc

y

TimeTime

User 1

User 2 Use

r 1

Use

r 2

21

User 3

4 / 118

Intentional DS-CDMA Spreading

A/SF

Signal

B

A

SF B

Spreading code

A/SF

Interferer

B

A

SF B

Spreading code

Despreading code

A/SF

A

5 / 118

Unintentional Spreading in the FD

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Capacity of OMA vs. NOMA in AWGN channel: (a)Uplink; (b) Downlink.

A

B

C

Rate of user 1

Rate of user 2

Rate of user 1Rate of user 2

OMA

NOMA

OMA

NOMA

(a) (b)

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Diverse NOMA contributions

R. Zhang and L. Hanzo, “A unified treatment of superposition coding aidedcommunications: Theory and practice,” IEEE Commun. Surveys Tutorials,vol. 13, no. 3, pp. 503–520, Mar. 2011.P. Botsinis, D. Alanis, Z. Babar, H. Nguyen, D. Chandra, S. X. Ng, andL. Hanzo, “Quantum-aided multi-user transmission in non-orthogonal multipleaccess systems,” IEEE Access, vol. PP, no. 99, pp. 1–1, 2016.A. Wolfgang, S. Chen, and L. Hanzo, “Parallel interference cancellation basedturbo space-time equalization in the SDMA uplink,” IEEE TWC, vol. 6, no. 2,pp. 609–616, Feb. 2007.L. Wang, L. Xu, S. Chen, and L. Hanzo, “Three-stage irregular convolutionalcoded iterative center-shifting K-best sphere detection for soft-decisionSDMA-OFDM,” IEEE TVT, vol. 58, no. 4, pp. 2103–2109, May 2009.S. Chen, L. Hanzo, and A. Livingstone, “MBER space-time decision feedbackequalization assisted multiuser detection for multiple antenna aided SDMAsystems,” IEEE TSP, vol. 54, no. 8, pp. 3090–3098, Aug. 2006.L. Hanzo, S. Chen, J. Zhang, and X. Mu, “Evolutionary algorithm assisted jointchannel estimation and turbo multi-user detection/decoding forOFDM/SDMA,” IEEE TVT, vol. 63, no. 3, pp. 1204–1222, Mar. 2014.S. Chen, A. Wolfgang, C. J. Harris, and L. Hanzo, “Symmetric RBF classifier fornonlinear detection in multiple-antenna-aided systems,” IEEE TNN, vol. 19,no. 5, pp. 737–745, May 2008.

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S. Chen, A. Livingstone, H. Q. Du, and L. Hanzo, “Adaptive minimum symbolerror rate beamforming assisted detection for quadrature amplitude modulation,”IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1140–1145, Apr. 2008.J. Zhang, S. Chen, X. Mu, and L. Hanzo, “Turbo multi-user detection forOFDM/SDMA systems relying on differential evolution aided iterative channelestimation,” IEEE Trans. Commun., vol. 60, no. 6, pp. 1621–1633, Jun. 2012.J. Zhang, S. Chen, X. Mu, and L. Hanzo, “Joint channel estimation andmulti-user detection for SDMA/OFDM based on dual repeated weightedboosting search,” IEEE Trans. Veh. Technol., vol. 60, no. 7, pp. 3265–3275,Jun. 2011.C.-Y. Wei, J. Akhtman, S.-X. Ng, and L. Hanzo, “Iterativenear-maximum-likelihood detection in rank-deficient downlink SDMA systems,”IEEE Trans. Veh. Technol., vol. 57, no. 1, pp. 653–657, Jan. 2008.A. Wolfgang, J. Akhtman, S. Chen, and L. Hanzo, “Iterative MIMO detectionfor rank-deficient systems,” IEEE Signal Process. Lett., vol. 13, no. 11, pp.699–702, Nov. 2006.L. Xu, S. Chen, and L. Hanzo, “EXIT chart analysis aided turbo MUD designsfor the rank-deficient multiple antenna assisted OFDM uplink,” IEEE Trans.Wireless Commun., vol. 7, no. 6, pp. 2039–2044, Jun. 2008.

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A. Wolfgang, J. Akhtman, S. Chen, and L. Hanzo, “Reduced-complexitynear-maximum-likelihood detection for decision feedback assisted space-timeequalization,” IEEE Trans. Wireless Commun., vol. 6, no. 7, pp. 2407–2411,Jul. 2007.J. Akhtman, A. Wolfgang, S. Chen, and L. Hanzo, “Anoptimized-hierarchy-aided approximate Log-MAP detector for MIMO systems,”IEEE TWC, vol. 6, no. 5, pp. 1900–1909, May 2007.

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NOMA Beamforming Example

NOMA Beamforming Example

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Uplink/Downlink Beamforming

Why?Increase ofcapacityHow?Spatially separatedinterfering signalsare suppressed

weight calculation

y = wHx

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MMSE Based Beamforming

Weights are calculated in orderto minimize:

ε(t)2 =(

wH x(t)− r(t))2

w: Beamformer weightsx(t): Channel outputr(t): Reference symbolFor AWGN channels MMSEweights can be calculated usinga closed form expression

Realizations: LMS, RLS, SMI reference sequence

calculate weights to minimize MSE

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MSE and BER Surfaces at the Output of a [5 x 2] NOMABeamformer

Error surfaces at the re-ceiver’s output calculatedfor five BPSK modulatedsources having equal receivedpower and communicatingover AWGN channels at S-NR=10 dB.

-2-1.5

-1-0.5

0 0.5

1 1.5

2

Rew1

-2-1.5-1-0.5 0 0.5 1 1.5 2

Rew2

0 2 4 6 8

10 12 14

MSE

-0.5 0 0.5

1 1.5

2 2.5

Rew1

-0.5 0

0.5 1

1.5 2

2.5

Rew2

-6

-5

-4

-3

-2

-1

0

log10(BER)

The imaginary part of both weights of the 2-element array was fixed.

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MMSE vs MBER NOMA Beamforming

Test case: BPSK modulatedsources having equalreceived power andcommunicating over AWGNchannelsMMSE solution calculatedanalytically

MBER solution obtainedwith the aid of conjugategradient algorithm

1e-20

1e-15

1e-10

1e-05

1e+00

0 5 10 15 20

BE

R

SNR [dB]

MMSE 2el MMSE 4el MBER 2el MBER 4el

Scenario S(2el.)

70o

15o

80o

30o

60o

Scenario U(4el.)

70o

26o

80o

4o15

o

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NOMA SDMA Example

NOMA SDMA Example

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Evolution from CDMA-NOMA to SDMA-NOMA

0 16 32 48 64 80 96 112 128Symbol Index

0.0

0.2

0.4

0.6

0.8

1.0

Ampli

tude

0 16 32 48 64 80 96 112 128Symbol Index

0.0

0.2

0.4

0.6

0.8

1.0

Ampli

tude

0 16 32 48 64 80 96 112 128Symbol Index

0.0

0.2

0.4

0.6

0.8

1.0

Ampli

tude

0 16 32 48 64 80 96 112 128Symbol Index

0.0

0.2

0.4

0.6

0.8

1.0

Ampli

tude

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Quantum-Search Aided MUD in NOMA

Multiple Access SDMA-OFDMNumber of Users U = 3Number of AEs at the BS P = 1Normalized User-Load UL = Uq/P = 3Modulation 8-PAM M = 8Eb/N0 0 dBChannel Code Turbo Convolutional Code,

8 trellis states,R = 1/2

Channel Model Extended Typical Urban (ETU)Mobile Velocity v = 130 km/hCarrier Frequency fc = 2.5 GHzSampling Frequency fs = 15.36 GHz (77 delay taps)Doppler Frequency fd = 70 HzNumber of Subcarriers Q = 1024Cyclic Prefix CP = 128Interleaver Length 10 240 bits per userChannel Estimation Perfect

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Quantum-Search Aided MUD in NOMA

There are 83 = 512 symbols in the full constellation, while 53and 46 symbols are obtained by the randomly-initialized andZF-initialized DHA, respectively.The purple circle denotes the random initial input, or the ZFdetector’s output, which may be used as an initial input. TheZF is as bad as the random one in this rank-deficient scenario.By using the DHA, we find symbols better than the previouslyfound symbols, which are denoted by the yellow circles in the3D figure.But we also find symbols that are ”worse” than the previouslyfound symbols, as represented by the blue circles in the 3Dfigure.The red square is the optimal symbol which is eventuallyfound.

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Durr-Høyer MUD for CDMA/SDMA NOMA - Userload=2

2

User 1

Full Constellation

0

-2-2

0

User 2

2

1

0

-1

-2

2

Use

r 3

2

User 1

Randomly Initialized DHA

0

-2-2

0

User 2

2

1

0

-1

-2

2U

se

r 3

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Quantum Computing Meets MUD

NOMA CDMA vs SDMA

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DS-CDMA vs SDMA NOMA Systems

System 1 System 2 System 3 System 4Number of Users U = 14 U = 14 U = 15 U = 15Multiple Access Scheme DS-CDMA SDMA DS-CDMA SDMANumber of AEs at the BS P = 1 P = 7 P = 1 P = 15Spreading Factor SF = 7 N/A SF = 15 N/ASpreading Codes m-sequences N/A Gold Codes N/ANormalized User Load UL = 2 UL = 2 UL = 1 UL = 1Bit-based Interleaver Length 42 000 42 000 40 000 40 000Number of AEs per User NTx = 1Modulation BPSK M = 2

Channel Code Turbo Code, R = 1/2, 8 Trellis statesIinner = 4 iterations

Channel Uncorrelated Rayleigh ChannelChannel Estimation Perfect

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Durr-Høyer CDMA/SDMA NOMA AT Userload=2

10−5

2

5

10−4

2

5

10−3

2

5

10−2

2

5

10−1

2

5BER

3 4 5 6 7 8 9 10 11 12

Eb/N0 per Receive Antenna (dB)

ML MUDDHA QMUD

U = 15, P = 15U = 14, P = 7U = 15, SF = 15U = 14, SF = 7

SDMA DS-CDMA

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Iterative Joint Channel & Data Estimation Turbo-Receiversfor NOMA

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Future 5G network architecture.

Macro cell

……

Massive MIMO

Small cells

D2D

f

• Ultra Wideband

(cmWave, mmWave)

• NOMA

Power

fV2V

M2M

IoT

Fronthaul

...

Cloud RAN

Forwarding

Virtualization Software defined networking controller

Applications...IoT Health Safety

Telco API

Radio access unit

VR

[1] Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, “Non-Orthogonal Multiple Access for

5G”, Proceedings of the IEEE ; Dec 2017. 25 / 118

From OMA to NOMA

1 Question: What is multiple access?2 Orthogonal multiple access (OMA): e.g., FDMA, TDMA,

CDMA, OFDMA.3 New requirements in 5G

High spectrum efficiency.Massive connectivity.

4 Non-orthogonal multiple access (NOMA): to breakorthogonality.

5 Standard and industry developments on NOMAWhitepapers for 5G: DOCOMO, METIS, NGMN, ZTE, SKTelecom, etc.LTE Release 13: a two-user downlink special case of NOMA.Next generation digital TV standard ATSC 3.0: a variationof NOMA, termed Layer Division Multiplexing (LDM).

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Introduction to NOMA Systems

The non-orthogonal nature of a multiple access system maymanifest itself in the time-, frequency-, code- orspatial-domains as well as in their arbitrary combinations;Even if originally an OMA scheme is used, the deleteriouseffects of the wireless channel may erode the orthogonality.For example, the channel-induced dispersion may ’smear’ theoriginally orthogonal time-slots of a TDMA system into eachother, because the transmitted signal is convolved with thedispersive channel’s impulse response (CIR).Similarly, the Orthogonal Variable Spreading Factor (OVSF)codes of the 3G systems rely on orthogonal Walsh-Hadamardcodes, but upon transmission over the dispersive channel theirorthogonality is destroyed.

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Introduction to NOMA Systems

This realization has then led to the concept of NOMA basedon the Spatial Division Multiple Access (SDMA) philosophy,where the unique, user-specific non-orthogonal channelimpulse responses are used for distinguishing the uplinktransmissions of the users - provided that their CIR isestimated sufficiently accurately.In simple tangible terms this implies that a NOMA system iscapable of supporting more users than the number of distincttime-, frequency-, code-domain resources, provided that theirchannels can be sufficiently accurately estimated even underthese challenging interference-contaminated conditions.Naturally, this challenging channel estimation anduser-separation process typically imposes an increased signalprocessing complexity.Many of these NOMA-user-separation techniques are surveyedin this paper, with a special emphasis on the power-domainNOMA techniques, which have found favour in the 5Gstandardization body known as 3GPP.

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Power-Domain NOMA Basics

User m detection

User n detection

User n

Subtract user m’s signal

0

BS

User m

User m detection

Superimposed signal of User m and n

SIC

Pow

er

FrequencyUser n

User m

Time

1 Supports multiple access within a given resource block(time/frequecy/code), using different power levels fordistinguishing/separating them [1].

2 Apply successive interference cancellation (SIC) at thereceiver for separating the NOMA users [2].

3 If their power is similar, PIC is a better alternative.[1] Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, “Non-Orthogonal Multiple Access for5G”, Proceedings of the IEEE ; Dec 2017.

[2] Z. Ding, Y. Liu, J. Choi, Q. Sun, M. Elkashlan, Chih-Lin I, and H. V. Poor (2017), “Application of

Non-orthogonal Multiple Access in LTE and 5G Networks”, IEEE Communication Magazine;(Web of Science Hot

paper, Top 5 Most Popular Article on Commun. Mag.).29 / 118

NOMA Basics

1 Question: Why NOMA is a popular proposition for 5G?2 Consider the following two scenarios.

If a user has poor channel conditionsThe bandwidth allocated to this user via OMA cannot be usedat a high rate.NOMA - improves the bandwidth-efficiency.

If a user only needs a low data rate, e.g. IoT networks.The use of OMA gives the IoT node more capacity than itneeds.NOMA - heterogeneous QoS and massive connectivity.

[1] Z. Ding, Y. Liu, J. Choi, Q. Sun, M. Elkashlan, Chih-Lin I, and H. V. Poor (2017), “Application of

Non-orthogonal Multiple Access in LTE and 5G Networks”, IEEE Communication Magazine;(Web of Science Hot

paper, Top 5 Most Popular Article on Commun. Mag.).

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Research Contributions in NOMA

NOMA for 5G

Security

Compatibility

Sustainability

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Sustainability of NOMA Networks

1 Transmission reliability - cooperative NOMA.2 Energy consumption - radio signal energy harvesting.

Base Station

User A

User B

Direct Information flowCooperative information flow

SIC Procedure

Energy flow

3 Propose a wireless powered cooperative NOMA protocol [1].4 The first contribution on wirelessly powered NOMA networks.

[1] Y. Liu, Z. Ding, M. Elkashlan, and H. V. Poor (2016), “Cooperative Non-orthogonal Multiple Access withSimultaneous Wireless Information and Power Transfer”, IEEE Journal on Selected Areas in Communications(JSAC). (Web of Science Hot Paper, Top 15 Most Popular Article on JSAC)

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Network Model

Direct Transmission Phase with SWIPT

Cooperative Tansmission Phase

... ...

iAh iBhig

CDR BDRADR... ...

C BD DR R≫

A1

S

A6

A3

A5

A2

A4

Ai

B1

B2

B6

B5B4

B3

Bi

Illustration of a downlinkSWIPT NOMA systemwith a base station S (bluecircle). The spatialdistributions of the nearusers (yellow circles) andthe far users (green circles)obey a homogeneousPoisson Point Process(PPP).

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Network Model

The locations of the near and far users are modeled ashomogeneous PPPs Φκ (κ ∈ A,B) with densities λΦκ .The near users are uniformly distributed within the disc andthe far users are uniformly distributed within the ring.The users in Bi are energy harvesting relays that harvestenergy from the BS and forward the information to Ai usingthe harvested energy as their transmit powers.The DF strategy is applied at Bi and the cooperativeNOMA system consists of two phases.It is assumed that the two phases have the same transmissionperiods.

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Non-Orthogonal Multiple Access with User Selection

A natural question arises: which specific near NOMA usershould help which particular far NOMA user?To investigate the performance of a specific pair of selectedNOMA users, three opportunistic user selection schemes maybe considered, based on the particular locations of users toperform NOMA as follows:

random near user and random far user (RNRF) selection,where both the near and far users are randomly selected fromthe two groups.nearest near user and nearest far user (NNNF) selection, wherea near user and a far user closest to the BS are selected fromthe two groups.nearest near user and farthest far user (NNFF) selection, wherea near user which is closest to the BS is selected and a far userwhich is farthest from the BS is selected.

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Outage Probability of the Near Users of RNRF

An outage of Bi can occur for two reasons.1 Bi cannot detect xi1.2 Bi can detect xi1 but cannot detect xi2.

Based on this, the outage probability of Bi can be expressedas follows:

PBi = Pr(

ρ|hBi |2|pi1|2

ρ|hBi |2|pi2|2 + 1 + dαBi

< τ1

)

+ Pr(

ρ|hBi |2|pi1|2

ρ|hBi |2|pi2|2 + 1 + dαBi

> τ1, γxi2S,Bi

< τ2

). (1)

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Outage Probability of the Far Users of RNRF

Outage experienced by Ai can occur in two situations.1 Bi can detect xi1 but the overall received SNR at Ai cannot

support the targeted rate.2 Neither Ai nor Bi can detect xi1.

Based on this, the outage probability can be expressed as follows:

PAi = Pr(γxi1

Ai,MRC < τ1, γxi1S,Bi

∣∣∣βi =0

> τ1

)+ Pr

(γxi1

S,Ai< τ1, γ

xi1S,Bi

∣∣∣βi =0

< τ1

). (2)

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Diversity Analysis of RNRF—Near Users

The diversity gain is defined as follows:

d = − limρ→∞

log P (ρ)

log ρ . (3)

Near users: When ε→ 0, a high SNR approximation with1− e−x ≈ x is given by

FYi (ε) ≈ 12

N∑n=1

ωN

√1− φn

2cnεAi (φn + 1). (4)

Substituting (4) into (3), we obtain that the diversity gain forthe near users is one, which means that using NOMA withenergy harvesting will not decrease the diversity gain.

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Diversity Analysis of RNRF—Far Users

Far users: For the far users, we obtain

d =− limρ→∞

log(− 1ρ2 log 1

ρ

)log ρ

=− limρ→∞

log log ρ− log ρ2

log ρ = 2. (5)

Remarks:This result indicates that using NOMA with an energyharvesting relay will not affect the diversity gain.At high SNRs, the dominant factor for the outage probabilityis 1

ρ2 ln ρ.The outage probability of using NOMA with SWIPT decays ata rate of ln SNR

SNR2 . However, for a conventional cooperativesystem without energy harvesting, a faster decreasing rate of

1SNR2 can be achieved.

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NNNF Selection Scheme and NNFF Selection Scheme

Advantage of NNNF: it can minimize the outage probabilityof both the near and far users.Advantage of NNFF: NOMA can offer a larger performancegain over conventional MA when user channel conditions aremore distinct.

Following a procedure similar to that of RNRF, we can obtain theoutage probability, diversity gain, and the throughput of NNNFand NNFF.

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Numerical Results

10 15 20 25 30 SNR (dB)

35 40 45 5010−4

10−3

10−2

100

Ou

tag

e p

rob

abil

ity

of

the

nea

r u

sers

10−1

Simulation

NNN(F)F analytical (α = 2)

RNRF analytical (α = 2)

RNRF analytical-appro (α = 3)

RNRF analytical-appro (α = 4)

NNN(F)F analytical-appro (α = 3)NNN(F)F analytical-appro (α = 4)

Incorrect choice of rate

NNN(F)F RNRF

R1 = R2 = 1(BPCU)

R1 = 0.5, R2 = 1(BPCU)

Lower outage probability isachieved than with RNRF.All curves have the sameslopes, which indicates thesame diversity gains.The incorrect choice ofrate results in an outageprobability for the nearusers, which is always one.

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Numerical Results

NNN(F)FRNRF

R1 (BPCU)R2 (BPCU)

0.5

1.0

000.20.40.60.81.010−3

10−2

10−1

100

Ou

tag

e p

rob

abil

ity

of

the

nea

r u

sers

The outage of the nearusers occurs morefrequently as the rate ofthe far user, R1, increases.For the choice of R1, itshould satisfy the condition(|pi1|2 − |pi2|2τ1 > 0).For the choice of R2, itshould satisfy thecondition that the splitenergy for detecting xi1 isalso sufficient to detect xi2(εAi ≥ εBi ).

42 / 118

Numerical Results

10 15 20 25 30 SNR (dB)

35 40 45 50

10−3

10−2

10−1

100

10−6

10−5

10−4

Ou

tag

e p

rob

abil

ity

of

the

far

use

rs

RNRF simulation

NNNF simulation NNFF simulation

RNRF analytical-appro

NNNF analytical-approNNFF analytical-appro

α = 2

α = 3

NNNF achieves the lowestoutage probability.NNFF achieves loweroutage than RNRF, whichindicates that the distanceof the near users has moreimpact than that of the farusers.All of the curves have thesame slopes, whichindicates that the diversitygains of the far users arethe same.

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Numerical Results

10 15 20 25 30 SNR (dB)

35 40 45 50

10−2

10−1

100

10−3

Ou

tag

e p

rob

abil

ity

of

the

far

use

rs

10−4

10−5

RNRF Cooperative NOMA

NNNF Cooperative NOMANNFF Cooperative NOMA

RNRF Non-cooperative NOMANNNF Non-cooperative NOMANNFF Non-cooperative NOMA

Cooperative NOMA has asteeper slope than that ofnon-cooperative NOMA.NNNF achieves the lowestoutage probability.NNFF has higher outageprobability than RNRF innon-cooperative NOMA,however, it achieves loweroutage probability thanRNRF in cooperativeNOMA.

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NOMA in 5G Networks—HetNets

1 Heterogenous networks (HetNets): meet the requirementsof high data traffic in 5G.

Question: How to support massive connectivity in HetNets?Question: How to further improve the spectral efficiency ofHetNets?

Pico BS

Marco BSFemto BS

OMA

2 New framework: NOMA-enabled HetNets.3 Challenge: Complex co-channel interference environment.

[1] Z. Qin, X. Yue, Y. Liu, Z. Ding, and A. Nallanathan (2017),“User Association and Resource Allocation in

Unified NOMA Enabled Heterogeneous Ultra Dense Networks”, IEEE Communication Magazine;

45 / 118

NOMA in 5G Networks—HetNets

1 Heterogenous networks (HetNets): meet the requirementsof high data traffic in 5G.

Question: How to support massive connectivity in HetNets?Question: How to further improve the spectral efficiency ofHetNets?

Pico BS

Marco BSFemto BS

NOMA

2 New framework: NOMA-enabled HetNets.3 Challenge: Complex co-channel interference environment.

[1] Z. Qin, X. Yue, Y. Liu, Z. Ding, and A. Nallanathan (2017),“User Association and Resource Allocation in

Unified NOMA Enabled Heterogeneous Ultra Dense Networks”, IEEE Communication Magazine;

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NOMA in HetNets I — Resource Allocation

Fig.: System model.

K-tier HetNets: One macro base station (MBS), B small base stations(SBSs)M macro cell users (MCUs), M RBs, K small cell users (SCUs) served byeach SBSEach SBS serves K SCUs simultaneously on the same RB via NOMA

[1] J. Zhao, Y. Liu, K. K. Chai, A. Nallanathan, Y. Chen and Z. Han (2017),“Spectrum Allocation and Power

Control for Non-Orthogonal Multiple Access in HetNets”, IEEE Transactions on Wireless Communications

(TWC).

46 / 118

Channel Model

Received signal at the k-th SCU, i.e., k ∈ 1, ...,K, served bythe b-th SBS, i.e., b ∈ 1, ...,B, on the m-th RB is given by

ynb,k = f m

b,k√pbab,kxm

b,k︸︷︷︸desired signal

+ f mb,k∑K

k′=k√pbab,k′xm

b,k′︸︷︷︸interference from NOMA users

+ ζmb,k︸︷︷︸

noise

+∑M

m=1λm,bhm,b,k

√pmxm︸︷︷︸cross-tier interference

+∑

b∗6=bλb∗,bgm

b∗,b,k√pb∗xm

b∗︸︷︷︸co-tier interference

.

(6)Received SINR:

γmb,k,k =

∣∣∣f mb,k

∣∣∣2pbamb,k

Ik,kN + Ik

co + Ikcr + σ2

, (7)

where Ik,kN = |f m

b,k |2pb∑K

i=k+1 amb,i

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Problem Formulation

maxλ,a

B∑b=1

M∑m=1

Uα (Rmb (λ, a)), (8a)

s.t.B∑

b=1λm,bpb |tb,m|2 ≤ Ithr

m ∀m, (8b)

∆(λ) ≥ 0, ∀m, b, (8c)λm,b ∈ 0, 1 , ∀m, b, (8d)∑

mλm,b ≤ 1, ∀b, (8e)∑

bλm,b ≤ qmax , ∀m, (8f)

ab,k ≥ 0, ab,j ≥ 0, ∀b, (8g)ab,k + ab,j ≤ 1, ∀b. (8h)

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Matching Model

Solution:NP-hard =⇒ High complexitySolution: Many-to-one matching theory

Matching Model:Two-sided matching between SBSs and RBs: “Preference” based on players’ utilitySBSs’ utility: sum-rate of all the serving SCUs minus its cost foroccupying RB m

Ub =

K∑k=1

Rmb,k − βpb |gb,m|2 , (9)

RBs’ utility: sum-rate of the occupying SCUs

Um =

B∑b=1

λm,b

(K∑

k=1

Rmb,k + βpb |gb,m|2

), (10)

49 / 118

Matching Algorithm

Step 1: Initialization: GS algorithm to obtain initial matching state

Step 2: Swap operations: keep finding swap-blocking pairs —- until noswap-blocking pair exists;

Flag SRa,b to record the time that SBS a and b swap their allocatedRBs=⇒ prevent flip flop

Step 3: Final matching result

50 / 118

Numerical Results

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 176

8

10

12

14

16

18

20

22

Number of iterations

Sum

rat

e of

SC

Us

(bits

/(s*

Hz)

)

CentralizedSOEMAIA

B=10, M=5

B=7, M=5

Fig.: Convergence of the proposed algorithms for different number of RBs and SBSs.

51 / 118

Numerical Results (cont’)

10 12 14 16 186

8

10

12

14

16

18

Number of SBS (B)

Sum

rat

e of

SC

Us

(bits

/(s*

Hz)

)

SOEMAIASOEMA−OMAIA −OMA

Fig.: Sum-rate of the SCUs for different number of small cells, with M = 10.

52 / 118

Summary

NOMA-enabled HetNets

Novel resource allocation algorithm based on matchingtheory

Complexity: O(B2)Performance: near-optimal performance

NOMA-enabled HetNets outperform OMA-based one

53 / 118

NOMA in HetNets II — Large-Scale Analysis

Massive MIMO

User 1

SIC of User m signal

User n signal detection

User n

User m signal detection

User mNOMA

User 2User N

……

Pico BS

Marco BS

Fig.: System model.

High spectrum efficiencyLow complexity: The complex precoding/cluster design forMIMO-NOMA systems can be avoided.Fairness/throughput tradeoff: allocating more power to weak users.

[1] Y. Liu, Z. Qin, M. Elkashlan, A. Nallanathan, JA McCann (2017),“Non-orthogonal Multiple Access in

Large-Scale Heterogeneous Networks”, IEEE Journal on Selected Areas in Communications (JSAC).

54 / 118

Network Model

K-tier HetNets model: the first tier represents the macrocells and the other tiers represent the small cells such as picocells and femto cells.Stochastic Geometry: the positions of macro BSs and allthe k-th tier BSs are modeled as homogeneous poisson pointprocesses (HPPPs).Hybrid access: massive MIMO transmissions in macro cellsand NOMA transmissions in small cells.Flexible User association: based on the maximum averagereceived power.

55 / 118

Information Signal Model

The signal-to-interference-plus-noise ratio (SINR) that atypical user experiences at a macro BS is

P1/Nho,1L (do,1)

IM,1 + IS,1 + σ2 . (11)

The SINR that user n experiences at the k-th tier small cell is

γkn =an,kPkgo,kL (do,kn )

IM,k + IS,k + σ2 . (12)

The SINR experienced by user m in the k-th tier small cell is

γkm∗ =am,kPkgo,kL (Rk)

Ik,n + IM,k + IS,k + σ2 . (13)

56 / 118

User Association Probability

The user association probability of a typical user connectingto the NOMA-enhanced small cell BSs in the k-th tier and tothe macro BSs can be calculated as:

Ak =λk

K∑i=2

λi(

Pik Bik)δ

+ λ1(

P1kGMNan,kBk

)δ , (14)

and

A1 =λ1

K∑i=2

λi

(an,i Pi1Bi N

GM

)δ+ λ1

, (15)

Remark 4.1By increasing the number of antennas at the macro cell BSs, theuser association probability of the macro cells increases and theuser association probability of the small cells decreases.

57 / 118

Coverage Probability

A typical user can successfully transmit at a target data rate of Rt .1 Near User Case: successful decoding when the following

conditions hold.The typical user can decode the message of the connected userserved by the same BS.After the SIC process, the typical user can decode its ownmessage.

Pcov ,k (τc , τt , x0)|x0≤rk= Pr γkn→m∗ > τc , γkn > τt , (16)

2 Far User Case: successful decoding when the followingcondition holds

Pcov ,k (τt , x0)|x0>rk= Pr

go,km >

εft xαi

0(Ik + σ2)

Pkη

. (17)

58 / 118

Spectrum Efficiency

The spectral efficiency of the proposed hybrid Hetnet is

τSE,L = A1Nτ1,L +∑K

k=2Akτk , (18)

where Nτ1 and τk are the lower bound spectrum efficiency ofmacro cells and the exact spectral efficiency of the k-th tiersmall cells.

59 / 118

Energy Efficiency

The energy efficiency is defined as

ΘEE =Total data rate

Total energy consumption . (19)

The energy efficiency of the proposed hybrid Hetnets is asfollows:

ΘHetnetsEE = A1Θ1

EE +∑K

k=2AkΘk

EE, (20)

Here, Ak and A1 are the user association probability of thek-th tier small cells and macro cell, respectively.Θk

EE = τkPk,total

and Θ1EE =

Nτ1,LP1,total

are the energy efficiency ofk-th tier small cells and macro cell, respectively.

60 / 118

Numerical Results—User Association Probability

50 100 150 200 250 300 350 400 450 500

M

0.2

0.3

0.4

0.5

0.6

0.7

Use

r as

soci

atio

n pr

obab

ility

Marco cells

Pico cells

Femto cells

Simulation

B2=20

B2=10

Fig.: User association probability versusantenna number with different biasfactor.

As the number of antennasat each macro BSincreases, more users arelikely to associate to macrocells — larger array gain.Increasing the bias factorcan encourage more usersto connect to the smallcells — an efficient way toextend the coverage ofsmall cells or control theload balance among eachtier of HetNets.

61 / 118

Numerical Results — Coverage Probability

Rt (BPCU)

Rc (BPCU)

6

405

0.2

24

0.4

3

Cov

erag

e pr

obab

ility

0.6

21

0.8

00

1

am

=0.9, an=0.1

am

=0.6, an=0.4

Fig.: Successful probability of typicaluser versus targeted rates of Rt and Rc .

Observe the cross-over ofthese two surfaces —optimal power sharing forthe target-rate considered.For inappropriate powerand target-rate selection,the coverage probability isalways zero.

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Numerical Results — Spectrum Efficiency

5 10 15 20 25 30

B2

0

0.5

1

1.5

2

2.5

3

3.5

Spe

ctru

m e

ffici

ency

(bi

t/s/H

z)

Analytical NOMA, P2= 20 dBm

Analytical NOMA, P2=30 dBm

SimulationOMA,P2=30 dBm

OMA,P2=20 dBm

NOMA

OMA

Fig.: Spectrum efficiency comparison ofNOMA and OMA based small cells.

NOMA-based small cellsoutperform theconventional OMA basedsmall cells.The spectral efficiency ofsmall cells is reduced asthe bias factor is increased— larger bias factor resultsin associating more macrousers having a low SINR tosmall cells.

63 / 118

Numerical Results — Energy Efficiency

5 10 15 20 25 30

B2

0

0.5

1

1.5

2

2.5

3

Ene

rgy

effic

ienc

y (b

its/H

z/Jo

ule)

Macro cells M=200NOMA small cells M=200HetNets M=200Macro cells M=50NOMA small cells M=50HetNets M=50 OMA small cells M=200OMA small cells M=50

HetNets

Macro cells

OMA small cells

NOMA small cells

Fig.: Energy efficiency of the proposedframework.

The energy efficiency ofthe macro cells is reducedas the number of antennasis increased owing to thepower consumption of thebaseband signal processingof massive MIMO.NOMA-assisted small cellsmay achieve higher energyefficiency than the massiveMIMO aided macro cellsas a benefit of denselydeploying the BSs inNOMA-aided small cells.

64 / 118

Security in NOMA Networks

1 Question: Is NOMA still secure when there are eavesdroppersin the networks?

Main Channel

Alice

Bob n

Bob m

Eve

Wiretap Channel for Bob m & Bob n

2 Propose to use Artificial Noise to enhance the security ofNOMA [1].

3 The first work of considering the security in NOMA.

[1] Y. Liu, Z. Qin, M. Elkashlan, Y. Gao, and L. Hanzo(2017), “Enhancing the Physical Layer Security ofNon-orthogonal Multiple Access in Large-scale Networks”, IEEE Transactions on Wireless Communications(TWC). (Web of Science Highly Cited Paper, Top 2 Most Popular Article on TWC)

65 / 118

Security in NOMA Networks

1 Question: Is NOMA still secure when there are eavesdroppersin the networks?

Main Channel

Alice

Bob n

Bob m

Eve

Wiretap Channel for Bob m & Bob n

Artificial noise

2 Propose to use Artificial Noise to enhance the security ofNOMA [1].

3 The first work of considering the security in NOMA.

[1] Y. Liu, Z. Qin, M. Elkashlan, Y. Gao, and L. Hanzo(2017), “Enhancing the Physical Layer Security ofNon-orthogonal Multiple Access in Large-scale Networks”, IEEE Transactions on Wireless Communications(TWC). (Web of Science Highly Cited Paper, Top 2 Most Popular Article on TWC)

65 / 118

Network Model

DR

∞

User Eavesdropper

pr

Base station

Network model for theNOMA transmissionprotocol under maliciousattempt of eavesdroppers inlarge-scale networks, whererp, RD, and ∞ are theradius of the protected zone,NOMA user zone, and aninfinite two dimensionalplane for eavesdroppers,respectively.

66 / 118

Network Model—SINR for NOMA users

Based on the aforementioned assumptions, the instantaneoussignal-to-interference-plus-noise ratio (SINR) for the m-th user andsignal-to-plus-noise ratio (SNR) for the n-th user can be given by

γBm =am|hm|2

an|hm|2 + 1ρb

, (21)

and

γBn = ρban|hn|2, (22)

respectively. We denote ρb = PAσ2

bas the transmit SNR, where PA is

the transmit power at Alice and σ2b is the variance of additive

white Gaussian noise (AWGN) at Bobs.

67 / 118

Network Model—SNR for the Eavesdroppers

The instantaneous SNR for detecting the information of the m-thuser and the n-th user at the most detrimental Eve can beexpressed as follows:

γEκ = ρeaκ maxe∈Φe ,de≥rp

|ge |2L (de)

. (23)

It is assumed that κ ∈ m, n, ρe = PAσ2

eis the transmit SNR with

σ2e is the variance of AWGN at Eves.

In this paper, we assume that Eves can be detected if they areclose enough to Alice. Therefore, a protect zone with radiusrp is introduced to keep Eves away from Alice.

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Secrecy Outage Probability

The secrecy rate of the m-th user and the n-th user can beexpressed as

Im = [log2(1 + γBm )− log2(1 + γEm )]+, (24)

and

In = [log2(1 + γBn )− log2(1 + γEn )]+, (25)

respectively, where [x ]+ = maxx , 0.

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Exact Secrecy Outage Probability

Given the expected secrecy rate Rm and Rn for the m-th and n-thusers, a secrecy outage is declared when the instantaneous secrecyrate drops below Rm and Rn, respectively. Based on (24), thesecrecy outage probability for the m-th and n-th user is given by

Pm (Rm) = Pr Im < Rm

=

∫ ∞0

fγEm (x) FγBm

(2Rm (1 + x)− 1

)dx . (26)

and

Pn (Rn) = Pr In < Rn

=

∫ ∞0

fγEn (x) FγBn

(2Rn (1 + x)− 1

)dx , (27)

respectively.70 / 118

Secrecy Diversity Analysis

The secrecy diversity order can be given by

ds = − limρb→∞

log (P∞m + P∞n − P∞m P∞n )

log ρb= m, (28)

The asymptotic secrecy outage probability for the user pair can beexpressed as

P∞mn =P∞m + P∞n − P∞m P∞n ≈ P∞m Gm(ρb)−Dm . (29)

Remarks: It indicates that the secrecy diversity order and theasymptotic secrecy outage probability for the user pair aredetermined by the m-th user.

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Numerical Results

ρb (dB)

0 10 20 30 40 50

Sec

recy

out

age

prob

abili

ty

10-4

10-3

10-2

10-1

100

asymptotic, m=1, n=3exact, m=1 n=3simulation, m=1, n=3asymptotic, m=1, n=2exact, m=1, n=2simulation, m=1, n=2asymptotic, m=2, n=3exact, m=2, n=3simulation, m=2, n=3

α=3

α=4

The red curves and theblack curves have the sameslopes. While the bluecurves can achieve a largersecrecy outage slope.It is due to the fact that thesecrecy diversity order of theuser pair is determined bythe poor one m.This phenomenon alsoconsists with the obtainedinsights in Remark 1.

72 / 118

Numerical Results

rp (m)

1 2 4 6 8 10 12 14 16 18 20

Sec

recy

out

age

prob

abili

ty

10-3

10-2

10-1

100

RD

= 5 m, λe= 10-3

RD

= 10 m, λe= 10-3

RD

= 5 m, λe= 10-4

RD

=10 m, λe= 10-4

λe= 10-3

λe= 10-4

The secrecy outageprobability decreases as theradius of the protected zoneincreases, whichdemonstrates the benefits ofthe protected zone.Smaller density λe of Evescan achieve better secrecyperformance, becausesmaller λe leads to lessnumber of Eves, which lowerthe multiuser diversity gainwhen the most detrimentalEve is selected.

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Multi-antenna Aided Security Provisioning for NOMA

(a) PLS of NOMA with External Eves (b) PLS of NOMA with Internal Eves

Main Channel

Alice

Bob n

Bob m

Alice

Bob n

Bob m & Eve

Eve

Wiretap Channel for Bob m & Bob n Wiretap Channel for Bob n

1 Artificial Noise for enhancing the security [1].2 Multi-antenna to create channel differences [2].

[1] Y. Liu, Z. Qin, M. Elkashlan, Y. Gao, and L. Hanzo(2017), “Enhancing the Physical Layer Security ofNon-orthogonal Multiple Access in Large-scale Networks”, IEEE Transactions on Wireless Communications(TWC).[2] Z. Ding, Z. Zhao, M. Peng, and H. V. Poor (2017), “On the Spectral Efficiency and Security Enhancements ofNOMA Assisted Multicast-Unicast Streaming”, IEEE Transactions on Communications (TCOM).

74 / 118

Other Research Contributions on NOMA

1 MIMO-NOMA design.2 NOMA in mmWave Networks.3 Interplay between NOMA and cognitive radio networks.4 Cross layer design for NOMA — a QoE perspective.5 NOMA in UAV networks.6 Full-duplex design for NOMA.7 Relay-selection for NOMA.8 A Unified NOMA Network.

75 / 118

MIMO-NOMA Design - Beamformer Based Structure

1 Centralized Beamforming.2 Coordinated Beamforming.

BS

User n

mw

nw

Subtract user m’s signal

SIC

User m detectionwith m mR →

User n detectionwith n nR →

User m detection with n mR →

User m

[1] Y. Liu, H. Xing, C. Pan, A. Nallanathan, M. Elkashlan, and L. Hanzo, “Multiple Antenna AssistedNon-Orthogonal Multiple Access”, IEEE Wireless Communications.

76 / 118

MIMO-NOMA Design - Beamformer Based Structure

1 Centralized Beamforming.2 Coordinated Beamforming.

Centric Far Cell Edge User

BS Near User

BSNear User

BSNear UserUnserved

User

Unserved User

Unserved User

Coordinated

beamforming link Data link for

near user Interference link


77 / 118

MIMO-NOMA Design - Cluster Based Structure

1 Inter-Cluster Interference Free Design.2 Inter-Cluster Interference Contaminated Design.

BSUser 1

User 1User 2

User L1……

User 1

User 2

User LM

……

User 2……

User Lm

……

……


78 / 118

MmWave-NOMA Networks

1 MotivationDirectional beams in mmWave communication withlarge-scale arrays bring large antenna array gains and smallinter-beam interference.Support massive connections with high user-overloadscenarios.Meet the diversified demands of users while enhancing thespectral efficiency by using SIC techniques

2 ChallengesAccurate channel estimation and CSI feedback to the basestation (BS) induce heavy system overhead particularly inmulti-user mmWave downlink systems.The inter-beam and intra-beam interference in mmWaveNOMA systems affects the decoding order of NOMA.

[1] Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, “Non-Orthogonal Multiple Access for5G”, Proceedings of the IEEE ; vol. 105, no. 12, pp. 2347-2381, Dec. 2017.

79 / 118

MmWave-NOMA System Model

Baseband processing

...

M superposed data stream

s

...

Partial channel information feedback

K users

...

M beams

...User jerer UsUsUsUsUsererererer ererererer UsUsUsUsUsererererer jjjjjjjjjjjjjjjjjjUser j

User ker UsUsUsUsUserererer erererer kkkkkkkkkkkkkUsUsUsUsUsererererer kkkkkUser k

jjjjjjjjjjjjjjjj

w1

wM

SIC Procedure

Desired signal

detectionDesired signal

detection

Small-cell BS

1 Construct M orthogonal beams at BS in spatial domain.2 Realize NOMA transmission in each beam and apply

successive interference cancellation (SIC) at users.[1] J. Cui, Y. Liu, Z. Ding, P. Fan, and A. Nallanathan, “Optimal User Scheduling and Power Allocation forMillimeter Wave NOMA Systems,” to appear in IEEE Trans. Wireless Commun.,vol. 17, no. 3, pp. 1502-1517,Mar. 2018.

80 / 118

Received Signal Model

1 Based on the NOMA principle, the received SINR of user k todecode user j on beam m is given by

SINRmj→k =

gmk β

mj

gmk∑π(i)>π(j) β

mi +

∑n 6=m gn

k βn + σ2 (30)

2 Note that the achievable SINR for user j on beam m can beobtained with k = j .

3 The corresponding decoding rate isRm

j→k = log2(1 + SINRmj→k), for any π(k) ≥ π(j), j , k ∈ Cm.

4 SIC condition of success: Rmj→k ≥ Rm

j→j for π(k) ≥ π(j),j , k ∈ Cm.

81 / 118

Optimization Problem

1 The considered sum rate maximization problem:

maxc,β

M∑m=1

qm∑j=1

Rmj→j (31a)

s.t. Rmj→k ≥ Rm

j→j ,M∑

m=1

∑j∈Cm

βmj ≤ Ptot , (31b)

K∑k=1

cmk = qm,

M∑m=1

cmk ≤ 1, Rm

j→j ≥ Rj , (31c)

πm ∈ Π, π(k) > π(j), j , k ∈ Cm, m ∈M. (31d)

c denotes the index set, where term cmk indicates the indicators

for user k on beam m, cmk ∈ 0, 1.

Π denotes the set of all possible SIC decoding orders.82 / 118

Overview of Proposed Solutions

1 Difficulties:Intra-beam andinter-beam interferenceare jointly considered.The decoding order ofNOMA is affected bythe inter-beam powerallocation.Joint user schedulingand power allocation isNP-hard.

2 Solutions: Divide thecomplicated problem intosome ease of subproblems.

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Overview for Power Allocation Algorithm

Intra-beam and inter-beaminterference is jointlyconsidered.The decoding order ofNOMA is affected by theinter-beam powerallocation.Joint user scheduling andpower allocation isNP-hard.

84 / 118

An example for Branch and Bound (BB) Algorithms

1 Construct a boxconstraint:

Consider atwo-dimension spacedenoted by Γ1 and Γ2.G is the feasible set. D0is the constructed initialrectangle.Point A and point Bcorrespond to theminimum and maximumboundary point in D0,respectively.

Let f be the objective function with monotonically decreasing.The optimal objective f ? belongs to the interval between f (A)and f (B).

85 / 118

An Example for Branch and Bound (BB) Algorithms

2 Branch operations:Split D0 into D1 and D2along the longest edge.(A,C) and (D,B) denotethe boundary point ofD1 and D2, respectively.Calculate the upper andlower bounds over D1and D2, respectively.

86 / 118


3 Bound operations:The lower boundL = minf (A), f (D).The upper boundU = minf (C), f (B).Note thatU − L ≤ f (A)− f (B),the potential interval forf ? decreases.

87 / 118


4 Pruning operations:Split D1 and D2 alongits longest edge,respectively.Remove D5, which willnot affect theoptimality.

88 / 118

Subproblem 1: Power Allocation Problem

1 For given the selected users and the corresponding decodingorder, the power allocation subproblem can be formulated asfollows.

minβ,Γ−

M∑m=1

qm∑jm=1

log2(1 + Γm

jm→jm)

(32a)

s.t.Γmjm→jm ≤

gmjmβ

mjm

gmjm∑qm

im=jm+1 βmim +

∑n 6=m gn

jmβn + σ2 , (32b)

M∑m=1

qm∑jm=1

βmjm ≤ Ptot , Rm

jm→jm ≥ Rjm , (32c)

∑n 6=m

(gm

km gnjm − gm

jm gnkm

)βn +

(gm

km − gmjm)σ2 ≥ 0, (32d)

km > jm, jm, km ∈ Cm, m ∈M. (32e)

89 / 118

Key Steps for Branch and Bound (BB) Algorithms

1 Construct box constraint sets:The objective function and the feasible set of (32) can berewritten as

U(Γ) =−M∑

m=1

qm∑jm=1

log2

(1 + Γm

jm→jm

),G =

Γ|(32b)− (32e)

.

The equivalent reformulation of power allocation problem is givenby

minΓ

U(Γ) s.t. Γ ∈ G. (33)

90 / 118


2 Construct boundfunctions:

The lower bound function:

g(Γ) =

U(Γ), Γ ∈ G0, o.w.,

,

The upper bound function:

g(Γ) =

U(Γ), Γ ∈ G0, o.w..

Observations:g(C/G/H) = U(C/G/H), and g(F/A/D) = U(F/A/D), forD3,D4,D6, respectively.g(G) = 0 and g(G) = 0 for D5.

91 / 118


Question: How to express the observations in mathematicalproblem?

3 Check the feasibility: Givena set of SINR values, testingif it is achievable isequivalent to solving thefollowing feasibility problem:

Find PA coefficientss.t. Γ ∈ G.

(34)

Observations:Problem (34) is feasible forA, D and F.One cannot find a feasiblePA coefficients for D5.

92 / 118

Subproblem 2: Matching Theory for User Selection

1 Given the user power allocation coefficients, the userselection problem can be transformed into

maxc

H =M∑

m=1

qm∑j=1

Rmj→j

s.t.K∑

k=1cm

k = qm,M∑

m=1cm

k ≤ 1,

πm ∈ Π, π(k) > π(j), j , k ∈ Cm, m ∈M.

(35)

Problem (35) is a combinational problem.Exhaustive search provides an optimal approach but it surfers acumbersome computational complexity.There two objects: users and beams, which motivates usbuild a matching model.

93 / 118

Subproblem 2: Matching Theory for User Selection

1 Preference lists:The preference value for the user k on beam m is theachievable rate of user k on beam m:

Hmk = log2

(1 + Γm

k

). (36)

The preference value of beam m is the sum rate of all users onbeam m:

Hm =∑

k∈ϕ(m)

log2

(1 + Γm

k

). (37)

The inter-beam interference and the intra-beam interferenceexist for each user’s rate.Users and beams compose a many-to-one matching withexternalities.

94 / 118

Overview for Matching Algorithms

EDA denotes the extend deferredacceptance.The users first propose to theBSs based on its preference list.Then each BS accepts the userswith prior preferences.The goal of swap operationprocedure is to further enhancethe system sum rate.Two-sided exchange-stablematching provides the stopcriteria.

95 / 118

Simulation Results

200 400 600 800 10000

2

4

6

8

10

Iteration Number

Sum

rat

e (b

its/s

/Hz)

BB: Lower boundBB: Upper bound

SNR = 5 dB

SNR = −5 dB

The proposed BBalgorithm is converged fordifferent SNR.the convergence becomeslow when the SNRincreases.

96 / 118

Simulation Results

0 5 10 15 200

2

4

6

8

10

SNR (dB)

Sum

rat

e (b

its/s

/Hz)

Exhaust+BB: NOMAExhaust+BB: OMAMatching+BB: NOMAMatching+BB: OMARandom+BB: NOMARandom+BB: OMA

Matching+BB achieves agood balance between theperformance and thecomputational complexity.The application of NOMAinto mmWave can furtherimprove the spectralefficiency by appropriatepower and user selectionpolicies.

97 / 118

Conclusions

The problem to maximize the sum rate for the mmWaveNOMA system by designing of user selection and powerallocation algorithms has been considered.BB technique was applied for solving the power allocationproblem optimally.For the integer optimization of the user selection, a lowcomplexity algorithm based on matching theory wasdeveloped.

98 / 118

Exploiting Multiple Access in Clustered Millimeter WaveNetworks: NOMA or OMA?

Base station with

multi-antennas

NOMA users

Near user

Far user SIC

procedureNear user

signal detection

Far user signal

detection

Layout of PCP

Beamforming

Fig.: Illustration of the clustered NOMA networks with mmWave communications.The spatial distributions of the NOMA users follow the Poisson Cluster Processes.

99 / 118

Interplay between NOMA and cognitive radio networks

PT BS

(a) Conventional CR (b) CR Inspired NOMA

Transmission link Interfernce link

PR

SR PR (User m)

SR (User n)

ST

PT (user m)+ST (user n)

1 Cognitive radio inspired NOMA [1].2 NOMA in cognitive radio networks [2].

[1] Z. Ding, P. Fan, and H. V. Poor (2016), “Impact of User Pairing on 5G Nonorthogonal Multiple-AccessDownlink Transmissions”, IEEE Trans. Veh. Technol. (TVT).[2] Y. Liu, Z. Ding, M. Elkashlan, and J. Yuan, “Non-orthogonal Multiple Access in Large-Scale Underlay CognitiveRadio Networks”, IEEE Trans. Veh. Technol. IEEE Trans. Veh. Technol. (TVT).

100 / 118

D2D Enabled NOMA

BS

Cellular User

Reuse

Subchannel

Reuse

Subchannel

D2D Group Dn

DRk

DR1

DRLn

DTn

D2D Group D1

DT1DR1DRLn

…

...

......

Fig.: System model.

Single-cell uplink scenarioSet of traditional cellular users: C = C1, ...,CMSet of D2D groups: D = D1, . . . ,Dn, . . . ,DN

[1] J. Zhao, Y. Liu, K. K. Chai, Y. Chen, and M. Elkashlan (2017),“Joint Subchannel and Power Allocation for

NOMA Enhanced D2D Communications”, IEEE Transactions on Communications (TCOM), 2017.101 / 118

Cross layer design for NOMA — a QoE perspective

1 QoE-Aware NOMA Framework [1].2 Multi-cell Multi-carrier QoE aware resource allocation [2].

Content

Codec, bitrate

Clustering,scheduling

Superposition coding/non-orthogonal multi-carrier design

Context

Application display

Decoding

Buffering

Transmitter Receiver

212 1

Packet queue

2 1

1 Subtract 1 221

1FrequencyPo

wer

\cod

e

UserN

User1User2

...

[1] W. Wang, Y. Liu, L. Zhiqing, T. Jiang, Q. Zhang and A. Nallanathan, “Toward Cross-Layer Design forNon-Orthogonal Multiple Access: A Quality-of-Experience Perspective”, IEEE Wireless Communications.

[2] J. Cui, Y. Liu, Z. Ding, P. Fan, and A. Nallanathan, “QoE-based Resource Allocation for Multi-cell NOMA

Networks”, IEEE Transactions on Wireless Communications (TWC).102 / 118

Multiple antenna aided NOMA for UAV networks

z

x

h

Origin

Rm

Rd

Beamforming

directions

[1] T. Hou, Y. Liu, Z. Song, X. Sun, Y. Chen, “Multiple Antenna Aided NOMA in UAV Networks: A StochasticGeometry Approach”, IEEE Transactions on Communications, arXiv available.

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HD/FD Relay Selection for NOMA

0h

1h

2h

LIh

1D

2D

1 Network model for the NOMA transmission consisting of onebase station (BS), K relays and two users (i.e., the nearbyuser D1 and distant user D2).

2 Assuming that the BS is located at the origin of a disc andthe location of the relays are modeled as homogeneouspoisson point processes (HPPPs).

[1] X. Yue, Y. Liu, S. Kao, A. Nallanathan, and Z. Ding„ “Spatially Random Relay Selection for Full/Half-Duplex

Cooperative NOMA Networks”, IEEE Transactions on Communications.104 / 118

Two-Way Relay NOMA

1 Two way relay (TWR) technique is capable of boostingspectral efficiency, where the information is exchangedbetween two nodes with the help of a relay.

2 The existing treaties on cooperative NOMA are all based onone-way relay scheme, where the messages are delivered inonly one direction, (i.e., from the BS to the relay or userdestinations). Hence the application of TWR to NOMA isa possible approach to further improve the spectral efficiencyof systems.

3 A two-way relay non-orthogonal multiple access(TWR-NOMA) system is investigated, where two groups ofNOMA users exchange messages with the aid of onehalf-duplex (HD) decode-and-forward (DF) relay.

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Two-Way Relay NOMA

Relay

2A1A

1D

2D

3D

4D1G 2G

2h

1h 3h

4h

1 System model for TWR-NOMA communication scenarioconsisting of one relay R, two pairs of NOMA usersG1 = D1,D2 and G2 = D3,D4.

2 The exchange of information between user groups G1 and G2is facilitated via the assistance of a (DF) relay with twoantennas, namely A1 and A2.

3 Assume that the direct links between two pairs of users areinexistent due to the effect of strong shadowing.

[1] X. Yue, Y. Liu, S. Kao, A. Nallanathan, and Y. Chen (2018),“Modeling and Analysis of Two-Way RelayNon-Orthogonal Multiple Access Systems”, IEEE Transactions on Communications;

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SINRs for NOMA signals

During the first slot, the pair of NOMA users in G1 transmitthe signals to R just as uplink NOMA. Applying the NOMAprotocol, R first decodes Dl ’s information xl by the virtue oftreating xt as IS. Hence the receivedsignal-to-interference-plus-noise ratio (SINR) at R to detect xlis given by

γR→xl =ρ|hl |2al

ρ|ht |2at + ρ$1(|hk |2ak + |hr |2ar ) + 1, (38)

where ρ = PuN0

denotes the transmit SNR. $1 ∈ [0, 1] denotesthe impact levels of interference signal (IS) at R.(l , k) ∈ (1, 3) , (3, 1), (t, r) ∈ (2, 4) , (4, 2).

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After SIC is carried out at R for detecting xl , the receivedSINR at R to detect xt is given by

γR→xt =ρ|ht |2at

ερ|g |2 + ρ$1(|hk |2ak + |hr |2ar ) + 1, (39)

where ε = 0 and ε = 1 denote the pSIC and ipSIC employedat R, respectively. The residual IS is modeled as Rayleighfading channels denoted as g with zero mean and variance ΩI .In the second slot, the information is exchanged between G1and G2 by the virtue of R.

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According to NOMA protocol, SIC is employed and thereceived SINR at Dk to detect xt is given by

γDk→xt =ρ|hk |2bt

ρ|hk |2bl + ρ$2|hk |2 + 1, (40)

where $2 ∈ [0, 1] denotes the impact level of IS at the usernodes. Then Dk detects xl and gives the corresponding SINRas follows:

γDk→xl =ρ|hk |2bl

ερ|g |2 + ρ$2|hk |2 + 1. (41)

Furthermore, the received SINR at Dt to detect xr is given by

γDr→xt =ρ|hr |2bt

ρ|hr |2bl + ρ$2|hr |2 + 1. (42)

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Outage probability

Outage Probability of xl

In TWR-NOMA, the outage events of xl are explained as follow: i)R cannot decode xl correctly; ii) The information xt cannot bedetected by Dk ; and iii) Dk cannot detect xl , while Dk can firstdecode xt successfully. The complementary events of x1 areemployed to express its outage probability and is given by

P ipSICxl =1− Pr (γR→xl > γthl )

× Pr (γDk→xt > γtht , γDk→xl > γthl ) , (43)

where ε = 1. γthl = 22Rl − 1 with Rl being the target rate at Dkto detect xl and γtht = 22Rt − 1 with Rt being the target rate atDk to detect xt .

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Outage probability

Outage probability of xt

Based on NOMA principle, the complementary events of outage forxt have the following cases. One of the cases is that R can firstdecode the information xl and then detect xt . Another case is thateither of Dk and Dr can detect xt successfully. Hence the outageprobability of xt can be expressed as

P ipSICxt =1− Pr (γR→xt > γtht , γR→xl > γthl )

× Pr (γDk→xt > γtht ) Pr (γDr→xt > γtht ) , (44)

where ε = 1.

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Diversity analysis

To gain more insights for TWR-NOMA in the high SNR region,the diversity order analysis is provided according to the derivedoutage probabilities. The diversity order is defined as

d = − limρ→∞

log(P∞xi (ρ)

)log ρ , (45)

where P∞xi denotes the asymptotic outage probability of xi .Remarks:

1 Due to impact of residual interference, the diversity order of xlwith the use of ipSIC is zero.

2 The communication process of the first slot similar to uplinkNOMA, even though under the condition of pSIC, diversityorder is equal to zero as well for xl .

3 The diversity orders of xt with ipSIC/pSIC are also equal tozero. This is because residual interference is existent in thetotal communication process.

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Numerical Results

0 10 20 30 40 5010

−4

10−3

10−2

10−1

100

SNR (dB)

Outa

ge P

robabili

ty

Simulation

Error floor

x1 − TWR−OMA

x2 − TWR−OMA

x1 − Exact − ipSIC

x1 − Exact − pSIC

x2 − Exact − ipSIC

x2 − Exact − pSIC

pSIC gain

As can be observed from the figure, theoutage behaviors of x1 and x2 forTWR-NOMA are superior toTWR-OMA in the low SNR regime.This is due to the fact that the influenceof IS is not the dominant factor at lowSNR.It can be seen that the outage behaviorsof x1 and x2 converge to the error floorsin the high SNR regime. The reason canbe explained that due to the impact ofresidual interference by the use of ipSIC,x1 and x2 result in zero diversity orders,which verifies the conclusion in Remark3.

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Numerical Results

0 10 20 30 40 5010

−3

10−2

10−1

100

SNR (dB)

Out

age

Pro

babi

lity

Simulationx1 − Exact pSICx2 − Exact pSICx1 − Exact ipSICx2 − Exact ipSIC

Ω I = − 20, 10, 0 (dB)

It can be seen that the different valuesof residual IS affects the performance ofipSIC seriously.

As the values of residual IS increases,the preponderance of ipSIC is inexistent.The outage behaviors of users’ signalsfor TWR-NOMA become more worse.

When ΩI = 0 dB, the outage probabilityof x1 and x2 will be in close proximity toone.

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Numerical Results

0 10 20 30 40 500

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

SNR (dB)

Dela

y−

limited T

hro

ughput (B

PC

U)

TWR−OMA

pSIC − TWR−NOMA

ipSIC − TWR−NOMA

Ω I = − 20, −15, −10 (dB)

One can observe that TWR-NOMA iscapable of achieving a higherthroughput compared to TWR-OMA inthe low SNR regime, since it has a loweroutage probability.

It is worth noting that ipSIC consideredfor TWR-NOMA will further degradethroughput with the values of residualIS becomes larger in high SNR regimes.

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A Unified NOMA Framework

1 1 0 1 0

1 1 1 0 0

0 0

1 0 1 0 1K M

! "# $# $# $# $% &

! ! ! ! ! !

User n User m

RE 1

RE 2

RE K

User n

User m

BS

[1] Z. Qin, X. Yue, Y. Liu, Z. Ding, and A. Nallanathan (2017),“User Association and Resource Allocation inUnified Non-Orthogonal Multiple Access Enabled Heterogeneous Ultra Dense Networks”, IEEE CommunicationMagazine;

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Research Opportunities and challenges for NOMA

1 Error Propagation in SIC.2 Imperfect SIC and limited channel feedback.3 Synchronization/asynchronization design for NOMA.4 Different variants of NOMA.5 Novel coding and modulation for NOMA.6 Hybrid multiple access7 Efficient resource management for NOMA8 Security provisioning in NOMA9 Different variants of NOMA10 Massive NOMA in IoT Networks

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Thank you!

Thank you!

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non-orthogonal multiple access for 5g and beyond

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