Intelligent Massive NOMA towards 6G: what will be different?yuanwei/slides/NOMA_BUAA.pdf · 2020. 11. 23. · Power NOMA Basics 1 Question: Why NOMA is a popular proposition for beyond

Intelligent Massive NOMA towards 6G:what will be different?

Dr. Yuanwei Liu

Queen Mary University of London, UK

[email protected]

Nov. 4th, 2020

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Outline

1 Power-Domain NOMA Basics

2 Signal Processing Advances for NOMA: A Machine LearningApproach

3 Semi-Grant-Free NOMA

4 Emerging Applications/Techniques for NOMA towards 6G

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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 beyond 5G

Ultra-high spectrum efficiency.Ultra-massive connectivity.Heterogeneous QoS and mobility requirement.

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

5 Standard and industry developments on NOMAWhitepapers: 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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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 et al., “Non-Orthogonal Multiple Access for 5G”, Proceedings of the IEEE ; Dec 2017. (Web of ScienceHot paper)

[2] Z. Ding, Y. Liu, et al. (2017), “Application of Non-orthogonal Multiple Access in LTE and 5G Networks”, IEEE

Communication Magazine;(Web of Science Hot paper).4 / 38

Power NOMA Basics

1 Question: Why NOMA is a popular proposition for beyond5G?

2 Consider the following two scenarios.If a user has poor channel conditions

The 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.


Communication Magazine;(Web of Science Hot paper).

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Power NOMA Basics







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Power NOMA Basics







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Power NOMA Basics







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What will NOMA for 6G be?

Intelligent (AI) + Massive ((Semi-)Grant-Free) + Nonorthogonal(Power/Code Domain)+ Compatibility (New techniques/scenarios)

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My Previous Research Contributions in NOMA

NOMA for 5G

Security

Compatibility

Sustainability

http://www.eecs.qmul.ac.uk/∼yuanwei/Publications.html

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Signal Processing Advances for NOMA: A MachineLearning Approach

Raw Data Sets

Live streaming

data

Social media

data

Proposed Unified Machine Learning Framework

Feature

extraction

Features

Neural networks Reinforcement learning

Data

modelling

Prediction/

online

Refinement

Data

modelling

Prediction/

online

Refinement

Periodically

update

Applications

Raw

input

UAV comunication

AD control

MENs provisioning

Predicted

behaviors

Fig.: Artificial intelligent algorithms for wireless communications.

[1] Y. Liu, S. Bi, Z. Shi, and L. Hanzo, “When Machine Learning Meets Big Data: A Wireless Communication

Perspective”, IEEE Vehicular Communication Magazine, vol. 15, no. 1, pp. 63-72, March 2020,

https://arxiv.org/abs/1901.08329 .

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Discussions for Applying Machine Learning in WirelessCommunications

� Two most successful applications for MLComputer Vision and Natural Language Processing

� Why and what are the key differences?Dataset: CV and NLP are data oriented/driven and exist richdatasetWell established mathematical models in wirelesscommunications

� Before Problem formulationCan this problem be solved by conventional optimizationapproach?If yes, what is the key advantages of using machine learning?

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Motivation and challenge of AI for NOMA networks

� MotivationUser behavior/peculiarity is consideredDynamic scenario with heterogenous QoS requirements andheterogenous user mobilityLong-term benefitsInteractive with environmentMixed-integer, and non-convex optimization problem

� ChallengesThe hidden relationship between history and future informationhas no concrete mathematical expressions.Resource allocation for massive user and base station (BS)connection has high computational complexity.

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Case Study: Cache-Aided NOMA MEC

MEC server

Task computation

results caching storage

Step 2: Task

computing

Step 3: Task computation

results caching

Step 1: Task

offloading decision

AP

User 1

User 2

User Nu-1

User Nu

2x

1x

NOMA uplink

1 2, , ,tN

zzZ zé ù= ë ûùû,tNtùù,

Nz,

1uNx -

uNx

1 2, , ,uN

Y y y yé ù= ë ûùûuNuy, , ùùNy, ,1 2, , ,

uNX x x xé ù= ë ûùû,

uNux, ùùNx,

Fig.: An illustration of a multi-usercache-aided MEC.

Multiple users are served byone MEC server.The computation tasks arecapable of being computedlocally at the mobile devicesor in the MEC server.The computation results areselectively cached in thestorage of the MEC server.

[1] Z. Yang, Y. Liu, Y. Chen, N. Al-Dhahir, “Cache-Aided NOMA Mobile Edge Computing: A Reinforcement

Learning Approach”, IEEE Transactions on Wireless Communications, https://arxiv.org/abs/1906.08812 .

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System Model: Communication Model

The user with higher channel gain is decoded first, thesignal-to-interference-plus-noise ratio (SINR) for user i at time tcan be given by

Ri (t) = Blog2

1 +ρi (t) |hi (t)|2

Nup∑l=i+1

ρl (t) |hl (t)|2 + σ2

, (1)

Accordingly, the offloading time for task j with input size πj attime t is

Toffloadi,j (t) =

πjRi (t)

. (2)

Meanwhile, the transmit energy consumption of offloading at timet is given by

Eoffloadi,j (t) = ρi

πjRi (t)

. (3)

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System Model: Computation Model

Local Computing: The computing time Tloci,j and energy

consumption Eloci,j for task j with computational requirement

ωj areTloc

i,j =ωjωloc

i. (4)

Eloci,j = P loc

iωjωloc

i. (5)

ωloci : the local computing

capability,

P loci : the energy consumption

per second.MEC Computing: The computing time Tmec

i,j (t) and energyconsumption Emec

i,j (t) are

Tmeci,j (t) =

ωjyi (t) CMEC

. (6)

Emeci,j (t) = Pmec ωj

yi (t) CMEC. (7)

yi : the proportion of thecomputing resources allocatedfrom the MEC server,

Pmec : the energy consumptionper second at MEC server.

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Problem Formulation

The sum energy consumption isE (t, xi (t) , yi (t) , zj (t)) =(

Prji (1− zj(t))

(xi (t)Eloc

i,j + (1− xi (t)) Eoffloadi,j (t) + (1− yi (t)) Emec

i,j (t)))

.

(8)The optimization problem is

(P1) minX ,Y ,Z

T∑t=1

Nu∑i=1

E (t, xi (t) , yi (t) , zj (t)), (9a)

s.t. C1 : xi (t) ∈ {0, 1} ,∀i ∈ [1,Nu] , t ∈ [1,T ] , (9b)C2 : yi (t) ∈ [0, 1] ,∀i ∈ [1,Nu] , t ∈ [1,T ] , (9c)C3 : zj (t) ∈ {0, 1} ,∀j ∈ [1,Nt ] , t ∈ [1,T ] , (9d)

C4 :Nu∑i=1

yi (t) = 1,∀t ∈ [1,T ] , (9e)

C5 :Nt∑

j=1zj (t) ≤ Ccache,∀t ∈ [1,T ] , (9f)

C6 : Toj (t) ≤ T ,∀j ∈ [1,Nt ] , t ∈ [1,T ] . (9g)

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Resource allocation: From the Formulated Problem toReinforcement Learning Model

A Markov decision process (MDP) model is a tuple 〈S,A,R〉.1 Objective: maximize the sum reward

Vπ(s) = Eπ[ ∞∑

t=0γtrt |s0 = s

]of a trajectory

s0a1|r1→ s1

a2|r2→ s2 · · ·an|rn→ sn.

2 State space (S):s (t) = [x (t) , y (t) , z (t)] ∈ S = X × Y × Z .

3 Action space (A): a (t) = [∆x (t) ,∆y (t) ,∆z (t)] ∈ A.4 Reward function (r): the sum energy consumption of taking

an action on a statert =

∑Nui=1 Ei (t − 1, st−1)−

∑Nui=1 Ei (t, st).

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How to define State and Action Space? From Maze to theProposed Framework

State Space: High dimensional matrix related to theparameters in the objective function.Action Space: Moving granularity in each element of thestate space.

Maze game UAV trajectory Proposed Problem

State Space

Action Space

2 Dimensional 3 Dimensional 3 Dimensional

( ) ( ) ( ),s t x t y t= é ùë ûÎ = ´S X Y

( ) ( ) ( ) ( ), ,s t x t y t z t= é

=

ùëÎ ´

û´S X Y Z

ùû

( ) ( ) ( ) ( ), ,a t x t y t z t= D D Dé ùë û( ) ( ) ( ),a t x t y t= D Dé ùë û

ÎS =X ´Y´Z

a (t ) = éëDx(t ) ,Dy(t ) ,Dz (t)ùû

s (t ) = éëx (t ) , y (t) , z (t )

Fig.: Setting of state and action space.

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Reinforcement Learning Model

The goal of reinforcement learning is to find an optimal policy thatmaximize the long-term sum rewards:

π∗ = arg maxπ

E[ ∞∑

t=0γtrt |π

]. (10)

Policy π: a function fromstate to action that specifieswhat action to take in eachstate.

The Q-value function is adopted to measure the performance ofthe policy.

Q∗ (s, a) = maxπ

E[ ∞∑

t=0γtrt |s = s0, a = a0, π

]. (11)

The optimal Q-value function satisfies the Bellman Equation

Q∗ (s, a) = Es′∼ε

[r + γmax

a′Q∗(s ′, a′

)|s, a

]. (12)

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How does the Intelligent Agent Learn?

st+1 st+2stat+1a1t at+2

rt rt+1 rt+1

… …Q3(st,at)=0

Q1(st,at)=2

Q2(st,at)=1a2t

a3t

Fig.: Q-learning flow.

The agent takes action a1t , because the corresponding Q value

Q1 (st , at) is max.

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The Learning Results: A Maze Case Example

Random policy before learning Optimal policy after training

Fig.: Q-learning expected result (star represents the treasure).

After learning, we obtain the optimal action for each state.

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Resource Allocation: The Proposed ReinforcementLearning for Cache-Aided NOMA MEC

Action 1

Action 2

Action N

BLA based

MAQ-learning

in cache-aided

NOMA-MEC

networks

Agent 1

(User 1)

Agent 2

(User 2)

Agent N

(User N)

State 1

State 2

State N

Reward 1

Reward 2

Reward N

Reward 1

Reward 2

Reward N

BLA based action

selection scheme

BLA based action

selection scheme

BLA based action

selection scheme

BLA based action

selection scheme

BLA based action

selection scheme

BLA based action

selection scheme

Fig.: Bayesian learning automata basedmulti-agent Q-learning for resourceallocation.

� Each mobile user is set as aintelligent agent.

� Bayesian Learning automata(BLA) is capable ofobtaining optimal action fortwo action case.

� The multiple intelligentagents operate in a selflishmanner.

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Numerical Results: Resource Allocation (the proposedReinforcement Learning Algorithm

Fig.: Total energy consumption vs. thecomputation capacity of the AP.

Fig.: Total transmit energy consumptionvs. cache capacity of the AP.

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Grant-Based Versus Grant-Free

Grant-BasedSimple processing at BSFinite orthogonal preamble due to the short coherence time,which results in high failure due to collision.High overhead

Grant-FreeActive devices first send their unique preambles to the BS andthen transmit the data signals directlydue to the massive number of devices and the use of shortpackets, the preamble sequences are not orthogonalShift detection tasks to the BS.

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Massive NOMA: Grant-Free NOMA

Grant-FreeLow latency uplink transmission.Removing the uplink scheduling requests and dynamicscheduling grants compared to grant based transmission.

Grant-Free NOMAFrequent collision situation.NOMA for reducing the collisions of users with multi-userdetection.Low-latency and high reliability.

[1] M. Fayaz, W. Yi, Y. Liu, and A. Nallanathan “Transmit Power Pool Design for Uplink IoT Networks withGrant-free NOMA” IEEE ICC 2021, under review.

[2] Y. Zou, Z. Qin, and Y. Liu, “Joint User Activity and Data Detection in Grant-Free NOMA using Generative

Neural Networks” IEEE ICC 2021, under review.

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Massive NOMA: from Grant-Free to Semi-Grant-FreeTransmission (SGF)

What is SGF?Grant-free (GF) and grant-based (GB) users share theresource blocks.

MotivationSpectral efficiency enhancement: The extra spectrumresources are utilized by newly connected GF users.Few collision scenarios: Exploiting power-domain NOMAto reduce the collisions of users.Stability enhancement: Employing SGF protocol to avoidtoo many GF users.

[1] C. Zhang, Y. Liu, Z. Qin and Z. Ding, “Semi-Grant-Free NOMA: A Stochastic Geometry Model,” IEEE Trans.Commun., https://arxiv.org/abs/2006.13286.

[2] C. Zhang, Y. Liu, W. Yi, Z. Qin and Z. Ding, “Semi-Grant-Free NOMA: Ergodic Rates Analysis with Random

Deployed Users,” IEEE Wireless Communications Letters, accepted to appear, https://arxiv.org/abs/2010.15169.

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Dynamic Protocol

Dynamic protocol?Define a channel quality threshold PGB|hGB|2d−αGB , which isthe instantaneous channel gain of the GB user.

PGB : transmit power|hGB |2: small scale fadingd−α

GB : large scale fadingThe GF users complicate a comparison between their channelgains and thresholds.The GF user with lower or higher channel gain will access intothe NOMA pair.

latency-sensitive GB user: Choose GF users with lowerchannel gains - Avoid GB user with SIC process.latency-tolerant GB user: Choose GF users with higherchannel gains - Further reduce the latency of GF users.

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Handshakes Comparison

Handshakes Comparison among conventional GF,conventional GB and SGF transmission schemes.SGF have one more handshake than the conventional GFtransmission but still handshake-reduced scheme compared toconventional GB transmission.

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Stochastic Geometry Based Analysis: Why do we needStochastic Geometry?

1 Limitations of Conventional Analysis [1]Ignore the density and mobility of nodes.Mathematical modelling and optimization for large-scalenetworks are intractable.

2 Advantages of Stochastic geometryCapture the spatial randomness of the networks.Provide tractable analytical results for the average networkbehaviors according to some distributions.

[1] Y. Liu et al., “Non-Orthogonal Multiple Access for 5G and Beyond”, Proceedings of the IEEE ; Dec 2017.

(Web of Science Hot paper)

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System Model: Stochastic Geometry

Time Block

Location

DistributionhGB hGF

R1

dGB dGF

Time

Arrival

UL

Grant Data

Time

Arrival Data Arrival Data

gGF < τth

Open-loop

ProtocolDynamic

Protocol

Time

PGFgGF <PGBgGB

R2

hGBhGF

R1

dGBdGF

Time

Arrival

UL

Grant Data

Time


gGF > τth

Open-loop

ProtocolDynamic

Protocol

Time

PGFgGF >PGBgGB

R2

Scenario I Scenario II

nhGB hhhGFGFGF

R1

dGBdd dGFdd

Time

AArrivalA

UL

Grant Data

Time


gGF th

Open-loop

ProtocolDynamic

Protocol

T TimeTT

PGFgFF GF PGBgBB GB

R2

The GB users

The GF users

hhhhGBGBGBhhGF

R1

dGBdddGFdd

Time

AArrivalA

UL

Grant Data

h

Time


gGF th

Open-loop

ProtocolDynamic

Protocol

TT TimeTT

PGFgFF GF PGBgBB GB

R2

Grant-based

transmission

Scenarios: 1) Latency-sensitive GB user as near userdeployed in the circle as Scenario I; 2) Latency-tolerantGB user as far user deployed in the ring as Scenario II.Performance analysis: Outage probability and ergodic ratesof two paired NOMA users in two scenarios.

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Numerical Results on Outage Probability

90 95 100 105 110 115 120 125 13010

−5

10−4

10−3

10−2

10−1

100

Transmit SNR of GB User ρGB = PGB/σ2 (dB)

OP

Simullation resultsAnalysis: the GF users under open-loop protocolAnalysis: the GF users under dynamic protocolAnalysis: the GB users under open-loop protocolAnalysis: the GB users under dynamic protocolError floors for the GF usersAsymptotic results for the GB users

105 110 115

10−2

10−1

GB

GF

60 70 80 90 100 110 12010

−5

10−4

10−3

10−2

10−1

100

Transmit SNR of GF users ρGF = PGF/σ2 (dB)

OP

Traditional GF transmission

Traditional GB transmission

Semi−GF transmission

PGB = −20 dB

PGB = 0 dB

PGB = 20 dB

Consistent diversity gains: 1) one for near users (linearrelationship) and 2) zero for far users (error floors).Outage performance comparison: the outage performanceof SGF protocol is better than conventional GF transmissionschemes but worse than conventional GB transmissionschemes.

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Numerical Results on Ergodic Rate

90 100 110 120 1300

1

2

3

4

5

6

7

Transmit SNR of the GB users ρGB (dB)

Erg

odic

Rate

(B

PC

U)

Simulation resultsApproximated reasultsAnalytical results when PGF = 20 dBmAnalytical results when PGF = 30 dBmAnalytical results when PGF = 40 dBm

PGF = 20, 30, 40 dBm

80 90 100 110 120 1300

2

4

6

8

10

12

Transmit SNR of the GF users ρGF (dB)

Erg

odic

Rate

(B

PC

U)

Simulation resultsApproximated reasultsAnalytical results when PGB = 20 dBmAnalytical results when PGB = 30 dBmAnalytical results when PGB = 40 dBm

PGB = 20, 30, 40 dBm

The GB user: the ergodic rate of the GB user is proportionalto the transmit power of the GB user PGB but inverselyproportional to the transmit power of GF users PGF .The GF user: 1) the ergodic rate of the GF user is inverselyproportional to PGB; 2) the ergodic rate has the maximumvalue when PGF increases.

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Interplay Between RIS/IRS and NOMA Networks

MotivationsOne the one hand, intelligent reflecting surface (IRS) to NOMA: 1)enhance the performance of existing NOMA networks; 2) Provide highflexibility for NOMA networks, from channel quality based NOMA toQoS based NOMA; 3) reduce the constraints for MIMO-NOMA designas IRS provides additional signal processing ability [1].One the other hand, NOMA to IRS: NOMA can provide more efficientmultiple access scheme for multi-user IRS aided networks.

ChallengesFor multi-antenna NOMA transmission, additional decoding rateconditions need to be satisfied to guarantee successful SIC.Both the active and passive beamforming in IRS-NOMA affect thedecoding order among users.

[1] T, Hou, Y. Liu, Z. Song, X. Sun, and Y. Chen “MIMO-NOMA Networks Relying on Reconfigurable Intelligent

Surface: A Signal Cancellation Based Design”, IEEE Transactions on Communications,

https://arxiv.org/abs/2003.02117 .

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UAV Communications based on NOMA

MotivationsOne the one hand, NOMA to UAV: enhance the performance/efficiency/and improve the connectivity of existing UAV networks.One the other hand, UAV to NOMA: The distinct channel conditions canbe realized (e.g., to pair one static user with one moving UAV user) [1].

ChallengesHeterogeneous mobility profiles and Heterogenous QoS requirements.New techniques like OTFS may require to exploit the heterogeneousmobility profiles.The new mobility models need to be exploited.

[1] Y. Liu et al., “UAV Communications Based on Non-Orthogonal Multiple Access”’, IEEE Wireless

Communications, vol. 26, no. 1, pp. 52-57, Feb. 2019 .

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Why RIS and UAV Communications are good applicationscenarios for NOMA?

Common PartsFlexible decoding order: in conventional NOMA transmission, the SICdecoding orders among users are generally determined by the “dumb”channel conditions.UAVs and IRSs are both “channel changing” technologies: The channelconditions of users can be enhanced or degraded by exploiting UAVsŠmobility and/or adjusting IRS reflection coefficients, thus enabling a“smart” NOMA operation to be carried out.More distinct channel differences can be created

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Selected Research Contributions for NOMA-UAV

[1] Y. Liu et al., “UAV Communications Based on Non-Orthogonal Multiple Access”’, IEEE WirelessCommunications, vol. 26, no. 1, pp. 52-57, Feb. 2019 .

Stochastic Geometry Based Analysis[2] T. Hou, Y. Liu, Z. Song, X. Sun, Y. Chen, “Multiple Antenna Aided NOMA in UAV Networks: A StochasticGeometry Approach”, IEEE Transactions on Communications, vol. 67, no. 2, pp. 1031-1044, Feb. 2019 .[3] T. Hou, Y. Liu, Z. Song, X. Sun, Y. Chen, “Exploiting NOMA for Multi-UAV Communications in Large-ScaleNetworks”, IEEE Transactions on Communications; vol. 67, no. 10, Oct. 2019 .[4] T. Hou, Y. Liu, Z. Song, X. Sun, Y. Chen, “NOMA Enhanced Terrestrial and Aerial IoT Networks with PartialCSI”, IEEE Internet of Things, vol. 7, no. 4, pp. 3254-3266, April 2020, https://arxiv.org/abs/1907.05571 .[5] T. Hou, Y. Liu, Z. Song, X. Sun, Y. Chen, “UAV-to-Everything (U2X) Networks Relying on NOMA: AStochastic Geometry Model’, IEEE Transactions on Vehicular Technology; vol. 69, no. 7, pp. 7558-7568, July2020,https://arxiv.org/abs/1907.05571 .

Convex Optimization/Machine Theory for Trajectory Design and Resource Allocation[6] X. Mu, Y. Liu, L. Guo, and J. Lin, “Non-Orthogonal Multiple Access for Air-to-Ground Communication”, IEEETransactions on Communications; vol. 68, no. 5, pp. 2934-2949, May 2020, https://arxiv.org/abs/1906.06523 .[7] T. Zhang, Y. Wang, Y. Liu, W. Xu and A. Nallanathan, “Cache-enabling UAV Communications: NetworkDeployment and Resource Allocation”„ IEEE Transactions on Wireless Communications,https://arxiv.org/abs/2007.11501 .[8] T. Zhang, Z. Wang, Y. Liu, W. Xu and A. Nallanathan, “Caching Placement and Resource Allocation for CacheEnabling UAV NOMA Networks”, IEEE Transactions on Vehicular Technology,https://arxiv.org/abs/2008.05168 .

A Machine Learning Approach[9] J. Cui, Y. Liu, A. Nallanathan, “Multi-Agent Reinforcement Learning Based Resource Allocation for UAVNetworks’, IEEE Transactions on Wireless Communications; accept to appear..[10] X. Liu, Y. Liu, Y. Chen, and L. Hanzo, ”Trajectory Design and Power Control for Multi-UAV Assisted WirelessNetworks: A Machine Learning Approach”, IEEE Transactions on Vehicular Technology; vol. 69, no. 7, pp.7558-7568, July 2020, https://arxiv.org/abs/1812.07665 .

[11] X. Liu, Y. Liu, and Y. Chen, ”Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement

Design”, IEEE Transactions on Vehicular Technology; accept to appear, https://arxiv.org/abs/1904.05242 . 34 / 38

Selected Research Contributions for NOMA-RIS (1/2)

[1] Y. Liu, et. al. “Reconfigurable Intelligent Surfaces: Principles and Opportunities”, IEEE CommunicationsSurvey and Tutorial, under revision, https://arxiv.org/abs/2007.03435 .

Conventional Performance Analysis and Stochastic Geometry Based Analysis[1] J, Xu and Y. Liu, “A Novel Physics-based Channel Model for Reconfigurable Intelligent Surface-assistedMulti-user Communication Systems ”, IEEE Transactions on Wireless Communications, under review.https://arxiv.org/abs/2008.00619[2] Y. Cheng, K. H. Li, Y. Liu, K. C. Teh, and H. V. Poor, “Downlink and uplink intelligent reflecting surface aidednetworks: NOMA and OMA”, IEEE Transactions on Wireless Communications,major revision.https://arxiv.org/abs/2005.00996[3] X. Yue and Y. Liu, “Performance Analysis of Intelligent Reflecting Surface Assisted NOMA Networks”, 2020.[Online]. Available: https://arxiv.org/abs/2002.09907v2.[4] T, Hou, Y. Liu, Z. Song, X. Sun, Y. Chen and L. Hanzo, “Reconfigurable Intelligent Surface Aided NOMANetworks”, IEEE Journal on Selected Areas (JSAC) in Communications, accept to appear .[5] Y. Cheng, K. H. Li, Y. Liu, K. C. Teh, and G. K. Karagiannidis, “Non-orthogonal multiple access (NOMA) withmultiple intelligent reflecting surfaces”, IEEE Transactions on Wireless Communications, under review.https://arxiv.org/abs/2005.00996[6] T, Hou, Y. Liu, Z. Song, X. Sun, and Y. Chen “MIMO-NOMA Networks Relying on Reconfigurable IntelligentSurface: A Signal Cancellation Based Design”, IEEE Transactions on Communications, accept to appearhttps://arxiv.org/abs/2003.02117 .[7] T, Hou, Y. Liu, Z. Song, X. Sun, and Y. Chen “MIMO Assisted Networks Relying on Large Intelligent Surfaces:A Stochastic Geometry Model”, IEEE Transactions on Vehicular Technology, under revision,https://arxiv.org/abs/1910.00959 .[8] C. Zhang, W. Yi and Y. Liu, “Reconfigurable Intelligent Surfaces Aided Multi-Cell NOMA Networks: AStochastic Geometry Model,” IEEE Trans. Wireless Commun., https://arxiv.org/abs/2008.08457.

Capacity Characterization, Beamforming and Resource Allocation[9] X. Mu, Y. Liu, L. Guo, J. Lin, N. Al-Dhahir “Capacity and Optimal Resource Allocation for IRS-assistedMulti-user Communication Systems”, IEEE Transactions on Communications, major revision,https://arxiv.org/abs/2001.03913 .

[10] Y. Guo, Z. Qin, Y. Liu, N. Al-Dhahir “Intelligent Reflecting Surface Aided Multiple Access Over Fading

Channels”,IEEE Transactions on Communications, major revision, https://arxiv.org/abs/2006.07090. 35 / 38

Selected Research Contributions for NOMA-RIS (2/2)

[11] X. Mu, Y. Liu, L. Guo, J. Lin, N. Al-Dhahir “Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint BeamformingOptimization”, IEEE TWC, accept https://arxiv.org/abs/1910.13636 .[12] J. Zuo, Y. Liu, E. Basar and O. A. Dobre, ”Intelligent Reflecting Surface Enhanced Millimeter-Wave NOMASystems”, IEEE Communications Letters, Accepted .[13] J. Zuo, Y. Liu, Z. Qin and N. Al-Dhahir, ”Resource Allocation in Intelligent Reflecting Surface AssistedNOMA Systems”, IEEE Transactions on Communications, Accepted .

Deployment and Multiple Access

[14] X. Mu, Y. Liu, L. Guo, J. Lin, R. Schober “Joint Deployment and Multiple Access Design for IntelligentReflecting Surface Assisted Networks”, IEEE Transactions on Wireless Communications, under review,https://arxiv.org/abs/2005.11544 .

A Machine Learning Approach

[15] X. Liu, Y. Liu, Y. Chen, and V. Poor “RIS Enhanced Massive Non-orthogonal Multiple Access Networks:Deployment and Passive Beamforming Design”, IEEE Journal of Selected Areas in Communications (JSAC),accept to appear, https://arxiv.org/abs/2001.10363 .

[16] X. Liu, Y. Liu, and Y. Chen, “Machine Learning Empowered Trajectory and Passive Beamforming Design in

UAV-RIS Wireless Networks”, IEEE JSAC, major revision ,https://arxiv.org/pdf/2010.02749.pdf.

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Research Opportunities and challenges for NOMA

1 Joint MIMO-NOMA-RIS design.2 NOMA in Heterogenous Mobility Networks3 Massive NOMA in IoT Networks4 Grant/Semi-Grant Free NOMA5 Error Propagation in SIC.6 Imperfect SIC and limited channel feedback.7 NOMA for UAV and areal-to-ground communications8 Different variants of NOMA.9 Novel coding and modulation for NOMA.10 Hybrid multiple access11 Security provisioning in NOMA

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Thank you!

Thank you!

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Documents

Intelligent Massive NOMA towards 6G: what will be different?yuanwei/slides/NOMA_BUAA.pdf · 2020. 11. 23. · Power NOMA Basics 1 Question: Why NOMA is a popular proposition for beyond