Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens - - PowerPoint PPT Presentation

Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Arrays

Bichai Wang†, Linglong Dai†, Xiqi Gao#, and Lajos Hanzo∗

†Tsinghua University, Beijing, China #Southeast University, Nanjing, China ∗University of Southampton, Southampton, U.K.

• Sep. 2017

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Outline

Technical Background System Model of Beamspace MIMO Proposed Beamspace MIMO-NOMA Simulation Results Conclusions

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Technical Background

5G key performance indicators (KPIs) defined by ITU

M.2083-03

User experienced data rate (Mbit/s) 100 Spectrum efficiency

IMT-2020

3 500 1 10

6

10 20 100 Mobility (km/h) Latency (ms) Connection density (devices/km )

2

Network energy efficiency Area traffic capacity (Mbit/s/m )

2

Peak data rate (Gbit/s) 10 1 400 350 10 10

5

10 1 1 0.1 1

IMT-advanced

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Technical Background

Three technical directions for 5G

Higher/Wider Frequency Bands More Antennas

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Technical Background

MmWave massive MIMO can combine the roadmaps of 5G in an unified form

mmWave High frequency Short wavelength Serious path-loss Spectrum expansion Large antenna array Small cell 1000x data rates increase!

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Technical Background

Challenges of mmWave massive MIMO

• Traditional MIMO: One dedicated RF chain for one antenna
• Enormous number of RF chains due to large antenna array
• Unaffordable energy consumption (250 mW per RF chain at 60

GHz)

How to reduce the number of required RF chains?

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Outline

Technical Background System Model of Beamspace MIMO Proposed Beamspace MIMO-NOMA Simulation Results Conclusions

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System Model of Beamspace MIMO

Basic idea

• Concentrate the signals from different directions (beams) on

different antennas by lens antenna array

• Transform conventional spatial channel into beamspace

channel

• Limited scattering at mmWave → beamspace channel is sparse
• Select dominant beams to reduce the dimension of MIMO

system

• Negligible performance loss → significantly reduced number of

RF chains

High- dimensional digital signal processing RF chain

Conventional MIMO Beamspace MIMO

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System Model of Beamspace MIMO

Sparsity

• ˜

H =

• ˜

h1, ˜ h2, · · · , ˜ hK

• = UH = [Uh1, Uh2, · · · , UhK]
• ˜

hk with a small number of dominant elements

• Approximately sparse

Beam selection

• Select a small number of dominant beams
• Only a small number of RF chains

 H UH 

Spatial channel Beamspace channel

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System Model of Beamspace MIMO

Fundamental limit of beamspace MIMO

• A single beam can only support a single user in existing

beamspace MIMO systems

• The maximum number of users that can be supported cannot

exceed the number of RF chains

• Massive users cannot be supported with limited number of RF

chains

Lens Dimension- Reduced Digital Precoding RF Chains Selecting Network

...

User 1 User K User 2 10 / 24

Outline

Technical Background System Model of Beamspace MIMO Proposed Beamspace MIMO-NOMA Simulation Results Conclusions

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Proposed Beamspace MIMO-NOMA

Non-Orthogonal Multiple Access (NOMA)

• Superposition coding at the transmitter
• Successive interference cancellation (SIC) at the receiver
• Multiple users can be supported at the same time-frequency

resources

SIC of User 2 signal User 1 signal detection User 2 signal detection Frequency Power User 2 User 1 User 2 User 1

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Proposed Beamspace MIMO-NOMA

Basic principle

• Selecting one beam for each user using beam selection

algorithms, such as the maximum magnitude (MM) selection and SINR maximization based selection

• Interfering users can be simultaneously served within the same

beam

• The number of supported users can be larger than the number
• f RF chains
• Spectrum efficiency and connectivity density can be improved

User 1,1 User ,1 User 2,1 User 1,2 User 2,2 User 2,3

RF

N User 1 User K User 2 13 / 24

Proposed Beamspace MIMO-NOMA

System model

• NRF beams, K users
• The set of users in the nth beam is Sn (Si ∩ Sj = Φ)
• Beamspace channel vector between the BS and the mth user

in the nth beam is denoted by hm,n

• Uniform precoding vector for users in the nth beam is wn
• We assume that
• hH

1,nwn

• 2 ≥
• hH

2,nwn

• 2 ≥ · · · ≥
• hH

|Sn|,nwn

• 2
• After intra-beam SIC, the remaining signal received at the mth

user in the nth beam beam can be written as ˆ ym,n = hH

m,nwn√pm,nsm,n

• desired signal

+ hH

m,nwn m−1

• i=1

√pi,nsi,n

• intra−beam interferences

+ hH

m,n

• j=n

|Sj|

• i=1

wj√pi,jsi,j

• inter−beam interferences

+ vm,n

• noise

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Proposed Beamspace MIMO-NOMA

System model

• The SINR the mth user in the nth beam can be represented as

γm,n =

• hH

m,nwn

• 2

2 pm,n

ξm,n where ξm,n =

• hH

m,nwn

• 2

2 m−1

• i=1

pi,n +

j=n

• hH

m,nwj

• 2

2 |Sj|

• i=1

pi,j + σ2

• The achievable rate of the mth user in the nth beam

Rm,n = log2 (1 + γm,n)

• Achievable sum rate

Rsum =

NRF

• n=1

|Sn|

• m=1

Rm,n

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Proposed Beamspace MIMO-NOMA

Precoding

• Challenge:

* The number of users is higher than the number of beams, which means that this system is underdetermined * Conventional linear precoding cannot be directly used

• Solution:

* An equivalent channel can be determined for each beam to generate the precoding vector * The beamspace channel vectors of different users in the same beam are highly correlated * we use the beamspace channel vector of the first user in each beam as the equivalent channel vector ˜ H = [h1,1, h1,2, · · · , h1,NRF]

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Proposed Beamspace MIMO-NOMA

Precoding

• Precoding matrix:

˜ W = [˜ w1, ˜ w2, · · · , ˜ wNRF] =

• ˜

H † = ˜ H

• ˜

HH ˜ H −1

• After normalizing the precoding vectors, the precoding vector

for the nth beam can be written as wn = ˜ wn ˜ wn2

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Proposed Beamspace MIMO-NOMA

Power allocation

• Problem formalization:

max

{pm,n} NRF

• n=1

|Sn|

• m=1

Rm,n s.t. C1 : pm,n ≥ 0, ∀n, m, C2 :

NRF

• n=1

|Sn|

• m=1

pm,n ≤ P C3 : Rm,n ≥ Rmin, ∀n, m

• The objective function is non-convex

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Proposed Beamspace MIMO-NOMA

Power allocation

• Theorem 1:

Rm,n = max

cm,n max am,n>0

• −am,nem,n

ln 2 + log2am,n + 1 ln 2

• where em,n = E
• |sm,n − cm,nˆ

ym,n|2

• The optimization problem can be reformulated as

max

{pm,n} NRF

• n=1

|Sn|

• m=1

max

cm,n max am,n>0(− am,nem,n ln 2

+ log2am,n +

1 ln 2)

s.t. C1, C2, C3

• Iteratively optimize {cm,n}, {am,n}, {pm,n} (All of the three
• ptimization problems are convex)

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Outline

Technical Background System Model of Beamspace MIMO Proposed Beamspace MIMO-NOMA Simulation Results Conclusions

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Simulation Results

Simulation parameters

• N = 256, K = 32
• Channel: Saleh-Valenzuela multipath channel (1 LoS + 2

NLoS)

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Outline

Technical Background System Model of Beamspace MIMO Proposed Beamspace MIMO-NOMA Simulation Results Conclusions

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Conclusions

Propose the beamspace MIMO-NOMA to break the fundamental limit of beamspace MIMO The equivalent channel vector was determined for each beam for ZF-based precoding Propose to jointly optimize the power allocation of all users by maximizing the achievable sum rate An iterative optimization algorithm was developed for power allocation The proposed beamspace MIMO-NOMA achieves better performance than beamspace MIMO in terms of spectrum and energy efficiency

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