Energy Efficient ACE-SI-based Hybrid Precoding for SWIPT-Enabled Massive MIMO-NOMA Systems

1. Energy Efficient ACE-SI-based Hybrid Precoding
for SWIPT-Enabled Massive MIMO-NOMA
Systems
Deeptanu Datta
Roll No. :- 1811EE05
Guided by :- Dr. Sudhir Kumar
Department of Electrical Engineering
Indian Institute of Technology, Patna
June 25, 2020 1 / 52

2. Outlines
Literature Survey
Motivation
Contributions
Classification of Precoding
Hybrid Precoding
Structures
Model Description
SI-operation
Optimum A and D
ACE Algorithm
Updating Formula
Simulation Results and
Discussions
Optimum Precoding
Structure
Conclusions
Future Scope of Work
References
Publication
2 / 52

3. Literature Survey
In Beamspace MIMO [1], dominant beams are selected to
reduce RF chains.
IA-based [2], sparsity masks-based [1], SINR-based [2]
methods are commonly used in literature to select beams.
RF chains are also reduced by hybrid precoding - can be
realized by analog phase shifters or switches [1].
[3, 2, 3, 1, 2] uses APSs to implement hybrid precoding.
[1, 3] uses switches and/or inverters in hybrid precoding.
3 / 52

4. Literature Survey (contd...)
Energy-efficient SIC-based HP is proposed for mmWave
massive MIMO systems in [3], where sum-rate problem is
splitted into sub-rate problems for each array.
Adaptive HP is proposed in [1], where phases of all APSs are
jointly optimized to maximize spectral efficiency.
In [3], ML-estimated AoDs are used to design precoding
vectors in mmWave MIMO systems.
PZF-HP is proposed in [2] for MU massive MIMO systems
4 / 52

5. Literature Survey (contd...)
In [1], analog precoder is designed for MIMO-OMA systems
using SIs, whose parameters are updated by ACE algorithm.
In [3], switches are used along with APSs to reduce RF chain
in mmWave massive MIMO systems.
In [2], hybrid precoding is realized by APSs and RF adders in
SWIPT-enabled mmWave massive MIMO-NOMA systems.
5 / 52

6. Motivation
Ordinary MIMO needs large hardwares.
High complexity, cost, and power consumption [1].
Hybrid Precoding : simplifies structure : reduces RF chain
count : makes it more energy-efficient [2].
Current HP techniques uses APSs - consumes considerable
amount of power.
APSs needs to be replaced with switches and inverters to
guarantee best performance.
6 / 52

7. Contributions
Novel energy-efficient hybrid precoding is designed for
mmWave massive MIMO-NOMA systems with SWIPT.
Probability vector is updated by ACE algorithm using
smoothed updating procedure to generate both ±
1
√
N
randomly [3].
Its performance is validated by extensive simulation study of
spectral efficiency and energy efficiency against SNR.
7 / 52

8. Classification of Precoding
8 / 52

9. Digital Precoding
Each antenna is connected
to individual RF chains.
Full control of amplitude
and phase of signals from
each individual antennas.
Most inefficient for massive
MIMO due to high cost and
power consumption [1].
Generally used in
conventional MIMO.
Figure: Digital Precoding
9 / 52

10. Analog Precoding
RF chains are connected
to antenna by phase
shifters [2].
Signal phases are
adjusted in RF domain to
supress interference and
increase SINR.
Boost in antenna array
gain overcomes high
pathloss at mmWave
frequency [3].
Used in sonar, radar,
IEEE 802.11 ad [1].
10 / 52

11. Hybrid Precoding
Both analog and digital precoding are combined to extract the
advantages of each.
2-stage process : fully-digital precoder is decomposed into
high-dimensional analog precoder and low-dimensional digital
precoder.
Analog Precoding is first applied across all RF paths per RF
chain to extract antenna array gain [3].
Digital Precoding is then applied across all RF chains to
supress interuser interference [3].
Widely used in mmWave massive MIMO systems.
Physically realized by a number of configurations.
11 / 52

12. Hybrid Precoding Structures
12 / 52

13. Fully-connected architecture
Transmitted Signal on each RF
chain traverses through N RF
paths [3].
Each RF chain extracts
full-array gain, so maximum
spectral efficiency.
NNRF APSs and N RF adders :
high complexity, cost, power
consumption, so poor energy
efficiency [3].
Analog precoding matrix :
A = [a1 a2 · · · aNRF
]
ai = array steering vector of all
N antennas on ith RF chain. 13 / 52

14. Sub-connected architecture
Transmitted Signal on each RF
chain traverses through only
M =
N
NRF
RF paths [3].
Array gain per RF chain reduces
NRF times.
Only N APSs : lower complexity,
cost, power consumption, so better
energy efficiency [3].
Block diagonal analog precoding
matrix A = diag(ai ) ∀ i = 1 : NRF;
ai = array steering vector of all M
antennas connected to ith RF
chain.
14 / 52

15. Switch-based architecture
Recasting of sub-connected
architecture by switches.
APSs replaced with switches to
enhance energy efficiency [1].
Only NRF switches, so NRF active
antennas : array gain reduces
drastically [2].
Block diagonal
A = diag(ai ) ∀ i = 1 : NRF; all ai
has elements from set
1
√
N
{0, 1}
randomly.
15 / 52

16. SI-based architecture
Recasting of sub-connected
architecture with switches and
inverters.
One inverter and M switches
for each RF chain.
All antennas involved to extract
full-array gain - most optimum
energy-efficient structure [1].
Block diagonal
A = diag(ai ) ∀ i = 1 : NRF; all
ai has one of two elements
±
1
√
N
randomly.
16 / 52

17. System Model
MU-downlink mmWave massive MIMO-NOMA system is
considered with SI-based sub-connected architecture [3].
Users possess power splitting receiver for SWIPT [2].
To extract full multiplexing gain, G = NRF is assumed [2, 1].
NOMA enables each beam to serve multiple users K ≥ G [3].
Ki = number of users accomodated in the ith beam.
Signal received by mth user in the gth beam is
yg,m = hH
g,mA
G
i=1
di pT
i xi + ng,m
xi = transmitted signal vector of ith beam, s.t. E(xi xH
i ) = IKi
17 / 52

18. Desired, Interference, and Noise components
Expanding above equation
yg,m = hH
g,mAdg
√
pg,mxg,m + hH
g,mAdg pT
g[m]xg[m]
+ hH
g,mA
G
i=1
i=g
di pT
i xi + ng,m
First term - Desired signal component of the mth user in the
gth beam.
Second term - Interference from users of same beam -
Intrabeam Interference.
Third term - Interference from users of remaining beams -
Interbeam Interference.
Fourth term - AWGN Noise introduced by channel.
18 / 52

19. SWIPT-Enabled NOMA
Effective channel gains are sorted in descending order for all
beams i.e., |hH
i,j Adi | ≥ |hH
i,j+1Adi | ∀ j = 1 : Ki , i = 1 : G
User transmit power follows reverse order for all beams i.e.,
pi,j ≤ pi,j+1 ∀ j = 1 : Ki , i = 1 : G
Applying SIC for NOMA at the receiver [3]
yg,m = hH
g,mAdg
√
pg,mxg,m + hH
g,mAdg pT
g,{1:m−1}xg,{1:m−1}
+ hH
g,mA
G
i=1
i=g
di pT
i xi + ng,m
Received signal at ID output of mth user in gth beam is
ˆyID
g,m = yg,m
√
γg,m + nPS
g,m
19 / 52

20. Achievable sum-rate
SINR for mth user in gth beam is
(SINR)g,m =
γg,m|hH
g,mAdg |2pg,m
(NI)g,m
(NI)g,m = γg,m(|hH
g,mAdg |2 S pT
g,{1 : (m−1)} +
G
i=1
i=g
|hH
g,mAdg |2 S(pT
i ) + σn
2) + σn
2
PS
Achievable rate of mth user in gth beam
Rg,m = log2(1 + (SINR)g,m)
Spectral efficiency of the system : Rsum =
G
i = 1
(beam)
Ki
j = 1
(user)
Ri,j
20 / 52

21. Channel Model
Due to channel sparsity [1] and low SINR, Saleh-Valenzuela
geometric channel model is used [2, 1, 1, 2].
hg,m =
N
Lg,m
Lg,m
l=1
(paths)
αl
g,ma(ϕl
g,m, θl
g,m)
Net array steering vector a(ϕl , θl ) = aaz(ϕl ) ⊗ ael(θl ) ∀ l
aaz(ϕ) =
1
√
Naz
ej2πnaz
daz
λ
sin(ϕ)
T
is array steering vector in
azimuthal direction.
Equi-spaced antennas at mmWave freq. daz = del =
λ
2
[3].
21 / 52

22. SI-operation
For SI-operation, each element in a(ϕ, θ) must be ±
1
√
N
.
Net ASV for equi-spaced antennas daz = del =
λ
2
[3] is given
by a(ϕ, θ) =
1
√
N
ejπ(naz sin(ϕ)+nel sin(θ))
T
[2].
{sin(ϕ) , sin(θ)} ∈ {0, ±1}
{ϕ , θ} ∈ 0, ±
π
2
, ±π
22 / 52

23. Problem Formulation
max
A,D
Rsum
s.t. C1 : pi,j ≥ 0, ∀ i, j
C2 : pi,j ≤ pi,j+1, ∀ i, j
C3 :
G
i = 1
(beam)
Ki
j = 1
(user)
pi,j ≤ Ptr
C4 : Ri,j ≥ Rmin
i,j , ∀ i, j
C5 : PEH
i,j ≥ Pmin
i,j , ∀ i, j
C6 : ai{j} = ±
1
√
N
, ∀ i, j
23 / 52

24. Optimum A and D
Probabilistic model-based ACE algorithm is used [3].
For SI-operation, N non-zero elements obey constraint C6 in
block-diagonal A of sub-connected architecture.
Initialization :
= [aT
 aT
2 . . . aT
G ]T
f = [f1 f2 . . . fN]T
∀ j = 1 : N, j is a Bernoulli random variable, such that
fj = Pr j =
1
√
N
Initially, f(itr = 0)
=
1
2
× 1N×1
24 / 52

25. ACE Algorithm
Generate E random data samples and reshape them as
matrices A.
Calculate achievable sum-rate Rsum for each sample.
Rearrange the achievable sum-rates in descending order.
Rsum(A[1]
) ≥ Rsum(A[2]
) ≥ · · · ≥ Rsum(A[E]
)
Select the elites as {A[1]
, A[2]
, A[3]
, · · · , A[Eelite]
}.
Calculate weight we of each elite ∀ e = 1 : Eelite.
Update f for next iteration using smoothed procedure.
Repeat all the above steps till f becomes binary vector [3].
25 / 52

26. Updating formula
Each elite is allocated a weight based on spectral efficiency
achieved by it.
Weight alloted to eth elite is we =
EeliteRsum(A[e])
Eelite
e=1
(Rsum(A[e]
))
These weights are used to update f in next iteration using
smoothed updating procedure as [3]
f(itr+1)
=
ξ
√
N
Eelite
Eelite
e=1
we
e(itr)
+ (1 − ξ)f(itr)
0 ≤ ξ ≤ 1 is smoothing parameter; e(itr) is vectored A of eth
elite at itrth
iteration.
26 / 52

27. Simulation Results
Table: Simulation Setup
Parameter Value
N 64
NRF 4
K 6, 10, and 12
Lg,m 3
α1
g,m (LoS) CN(0, 1)
αl
g,m ∀l = 1 (NLoS) CN(0, 0.1)
ϕl
i,j , θl
i,j 0, ±π
2 , ±π
Ptr 30 mW [2]
SNR
Ptr
σ2
n
[1]
E 100 [1]
Eelite 20 [1]
27 / 52

28. Spectral Efficiency
Figure: Spectral Efficiency against SNR for K = 6
28 / 52

29. Spectral Efficiency (contd...)
Figure: Spectral Efficiency against SNR for K = 10
29 / 52

30. Spectral Efficiency (contd...)
Figure: Spectral Efficiency against SNR for K = 12
30 / 52

31. Trends in Spectral Efficiency
MIMO-NOMA systems has higher spectral efficiency than
MIMO-OMA systems due to higher spectral efficiency of
NOMA [3].
Fully digital system has highest spectral efficiency as all N RF
chains are used to serve K users concurrently to extract full
multiplexing gain [1].
Fully-connected architecture achieves higher spectral efficiency
than sub-connected architecture since each RF chain extracts
full-array gain.
The proposed ACE-SI-based sub-connected HP-NOMA
architecture has almost similar trend as APS-based
sub-connected HP-NOMA as spectral efficiency depends only
on number of users using the same resources concurrently.
31 / 52

32. Energy Efficiency
Figure: Energy Efficiency against SNR for K = 6
32 / 52

33. Energy Efficiency (contd...)
Figure: Energy Efficiency against SNR for K = 10
33 / 52

34. Energy Efficiency (contd...)
Figure: Energy Efficiency against SNR for K = 12
34 / 52

35. Trends in Energy Efficiency
The proposed ACE-SI-based sub-connected HP-NOMA
architecture has highest energy efficiency due to use of only
energy-efficient switches and inverters in analog precoder [1].
Fully digital system has the least energy efficiency due to its
tremendous cost energy consumption [1].
Sub-connected architecture is more energy-efficient than fully
connected architecture due to fewer number of APSs.
MIMO-NOMA systems has also higher energy efficiency than
MIMO-OMA systems with the same total power consumption
due to higher spectral efficiency of NOMA [3].
EE =
Rsum
Pcons
35 / 52

36. Power Consumption Analysis
Total power consumed by a precoding structure is
Pcons = Ptr + PDP + Px
Px = power consumed by internal components of structure,
which can be calculated by Table 2.
Px relies on internal circuitry of a particular architecture.
Inverters are designed by chip with similar power rating as
switches, so PINV ≈ PSW [2].
Table: Power Consumption of different precoder components [1]
Component Notation Power Consumed (in mW)
RF chain PRF 250
Phase Shifter PPS 40
Digital precoder PDP 200
Switch PSW 5
Inverter PINV 5 36 / 52

37. Power Consumption Comparison
Table: Power Consumption of different precoding schemes
Architecture Px Pcons (in Watts)
Fully Digital NPRF 16.23
Fully-connected NRFPRF + NNRFPPS 11.47
Sub-connected NRFPRF + NPPS 3.79
Switch-based NRFPRF + NRFPSW 1.25
ACE-SI-based NRFPRF + NPSW + NRFPINV 1.57
(Proposed)
37 / 52

38. Optimum Precoding Design
As per Table 3, switch-based architecture consumes least
power to yield highest energy efficiency.
But, it can not extract full-array gain as only NRF antennas
are active [2].
The next structure consuming least power is SI-based
architecture, which extracts full-array gain [1].
So, the optimum precoding architecture is SI-based
architecture.
38 / 52

39. Conclusions
Energy-efficient ACE-SI-based hybrid precoding scheme is
proposed for SWIPT-Enabled massive MIMO-NOMA systems.
ACE algorithm is leveraged to update probability parameter
vector at each iterations to obtain better performance.
Proposed ACE-SI-based HP scheme attains near-optimal
sum-rate performance, but highest energy efficiency than
existing schemes.
39 / 52

40. Future Scope of Work
Variation of spectral and energy efficiencies against number of
RF chains can be investigated.
Variation of spectral and energy efficiencies against number of
multipath components can also be studied.
T-R separation can be optimized to design more
energy-efficient system.
40 / 52

41. Publication
Deeptanu Datta and Sudhir Kumar, ”Energy Efficient
ACE-SI-based Hybrid Precoding for SWIPT-Enabled Massive
MIMO-NOMA Systems,” IEEE Communication Letters, 2020.
(Current Status : Reject and Resubmitted)
41 / 52

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52. THANK YOU
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