Interference management in spectrally and energy efficient wireless networks

Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Interference Management in Spectrally
and Energy Efficient Wireless Networks
Mohamed Seif, BSc
Wireless Intelligent Networks Center (WINC), Nile University, Egypt
August 10, 2016
Thesis Committee:
Prof. Mohamed Nafie
Prof. Amr Elkeyi
Prof. Karim G. Seddik
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 1

Data Transmission
Storage
Energy

1 Interference Management with Limited CSI
2 Sparse Spectrum Sensing in CRNs
3 D2D Communications
4 M2M Communications

The Big Problem in Wireless Communications
Figure: An illustrative example for a heterogeneous network.

Interference is a fundamental bottleneck in many wireless
systems

systems
Interference management is getting convoluted
Homogeneous → Heterogeneous

systems
Interference management is getting convoluted
Homogeneous → Heterogeneous
How to manage interference in an efﬁcient manner?

Coding Against Interference
1
Channel state information at transmitter.

Coding Against Interference
Interference shaping using CSIT 1
is a key enabler for mitigating
interference
1
Channel state information at transmitter.

Closed Loop Systems
Figure: Illustration of the CSIT feedback and sharing process.

Challenges in Obtaining Global and Accurate CSIT
Tx
Channel Feedback
User1
User2
User3

Tx
Channel Feedback
User1
User2
User3
Possible error sources in the CSI feedback process
Channel estimation error
Quantization error (e.g., Compressed channel feedback)
Feedback delay (Maddah Ali et al.’12)

Tx
Channel Feedback
User1
User2
User3
Possible error sources in the CSI feedback process
Channel estimation error
Quantization error (e.g., Compressed channel feedback)
Feedback delay (Maddah Ali et al.’12)
CSIT sharing via backhaul links (e.g., CoMP in LTE)

Degrees of Freedom (DoF)
DoF notion:

DoF notion:
1 Lizhong and Tse in IEEE IT Trans. 2003

DoF notion:
1 Lizhong and Tse in IEEE IT Trans. 2003
2 Rigorous approximation to the network capacity in the high
SNR regime.
Mathematically,
C∑(P) = DoFlog(P) + o(log(P)) (1)
where limP→∞
o(log(P))
log(P) = 0.
Alternatively,
It represents the number of interference-free signalling
dimensions in the network.

System Model
The received signal at the ith receiver
is given by
Yi (t) = Hi (t)X(t) + Ni (t), i = 1,...,K
(2)
The total DoF of the network is deﬁned
as
DΣ(K) = max
(d1,d2,...,dK )∈D
d1 + d2 + ⋅⋅⋅ + dK
(3)
UE3
UE1 UE2
UE4
UEi
)(tHi
K-antenna Tx
UEK
Figure: Network Model

CSI Model
Perfect and global CSIR.
Three states of the availability of CSIT
about each receiver:
Perfect CSIT (P): instantaneous
and without error.
Delayed CSIT (D): delay greater
than or equal one time slot
duration (coherence time) and
without error.
No CSIT (N): not available to
transmitter at all.
UE3
UE1 UE2
UE4
UEi
)(tHi
K-antenna Tx
UEK
Introduced by Tandon and Shamai in IEEE IT Trans. 2012 for the 2-user BC

Alternating CSIT Model
The fraction of time associated with
CSIT state S,
λS =
∑
n
t=1 ∑
K
i=1 I(Si (t) = S)
nK
(4)
where n is the number of channel
uses,
∑
S∈{P,D,N}
λS = 1. (5)
UE3
UE1 UE2
UE4
UEi
)(tHi
K-antenna Tx
UEK

ICR Scheme
Phase I: Interference Creation
UE2
UE1
Tx
UE3
1u
2u
3u
),,( 321
1
1 uuuL
),,( 321
1
2 uuuI
),,( 321
1
3 uuuI
N
D
D
Figure: ICR scheme t = 1.

ICR Scheme
UE2
UE1
Tx
UE3
1v
2v
3v
),,( 321
1
1 vvvI
),,( 321
1
2 vvvL
),,( 321
1
3 vvvI
N
D
D

ICR Scheme
UE2
UE1
Tx
UE3
1p
2p
3p
),,( 321
1
1 pppI
),,( 321
1
2 pppI
),,( 321
1
3 pppL
D
D
N

ICR Scheme
Phase II: Interference Resurrection (Based on orthogonal projection pre-coding and PNC)
UE2
UE1
Tx
UE3
),,( 321
2
1 uuuL
),,( 321
2
2 vvvL
),,( 321
2
3 pppL
Old interference
terms from UE3
P
N
P
Based on orthogonal projection pre-coding and PNC

ICR Scheme
Phase II: Interference Resurrection (Based on orthogonal projection pre-coding and PNC)
UE2
UE1
Tx
UE3
),,( 321
3
1 uuuL
),,( 321
3
2 vvvL
),,( 321
3
3 pppL
Old interference
terms from UE2
N
P
P
Based on orthogonal projection pre-coding and PNC

ICR Scheme
UE2
UE1
Tx
UE3
1u 2u 3u
1v 2v 3v
1p 2p 3p
Figure: D∑ = 9
5
, S5
123 = {NDD,DND,DDN,PPN,PNP}.

Synergistic Alternating CSIT
Phase I: Creation Phase II: Resurrection
(NDD,DND,DDN) (PPN,PNP)
(NDD,DDN,DND) (PNP,PPN)
(DND,DDN,DDN) (PPN,NPP)
(DND,DDN,NDD) (NPP,PPN)
(DDN,DND,NDD) (NPP,PNP)
(DDN,NDD,DND) (PNP,NPP)
Table: All synergistic CSIT patterns for the 3-user BC.
Synergy Deﬁnition
Synergy is the interaction of multiple elements in a system to
produce an effect greater than the sum of their individual
effects.

Synergy Deﬁnition
Consider: S5
123 = (NNN,DDD,DDD,DDD,PPP).

Synergy Deﬁnition
Consider: S5
123 = (NNN,DDD,DDD,DDD,PPP).
D∑(3) = 1 × 3
15 + 18
11 × 9
15 + 3 × 3
15 = 98
55 < 9
5

Upper Bound on the DoF for the K-user BC
Bounds were introduced by Tandon et al.’13
DΣ(K) = d1 + d2 + ⋅⋅⋅ + dK ≤
K2
+ (K − 1)∑K
i=1 γi
2K − 1
(6)
where,
γi =
∑n
t=1 I(Si(t) = P)
n
≤ γ,∀i = 1,...,K (7)

DoF Region Characterization for the 3-user BC
Given perfect CSIT distribution (γ1,γ2,γ3),
P1: max
d1,d2,d3
d1 + d2 + d3
s.t. 3d1 + d2 + d3 ≤ 3 + 2γ1 (8)
d1 + 3d2 + d3 ≤ 3 + 2γ2 (9)
d1 + d2 + 3d3 ≤ 3 + 2γ3 (10)
0 ≤ di ≤ 1, ∀i = 1,2,3 (11)

P1: max
d1,d2,d3
d1 + d2 + d3
s.t. 3d1 + d2 + d3 ≤ 3 + 2γ1 (8)
d1 + 3d2 + d3 ≤ 3 + 2γ2 (9)
d1 + d2 + 3d3 ≤ 3 + 2γ3 (10)
0 ≤ di ≤ 1, ∀i = 1,2,3 (11)
Closed form solution,
d∗
i =
3 + 4γi − ∑3
j=1,j≠i γj
5
, ∀i = 1,2,3 (12)

P1: max
d1,d2,d3
d1 + d2 + d3
s.t. 3d1 + d2 + d3 ≤ 3 + 2γ1 (13)
d1 + 3d2 + d3 ≤ 3 + 2γ2 (14)
d1 + d2 + 3d3 ≤ 3 + 2γ3 (15)
0 ≤ di ≤ 1, ∀i = 1,2,3 (16)
Solution,
Given: (γ1,γ2,γ3) = (2
5 , 1
5 , 1
5 )
Optimal DoF tuple: d∗
= (0.84,0.64,0.64)

P1: max
d1,d2,d3
d1 + d2 + d3
s.t. 3d1 + d2 + d3 ≤ 3 + 2γ1 (13)
d1 + 3d2 + d3 ≤ 3 + 2γ2 (14)
d1 + d2 + 3d3 ≤ 3 + 2γ3 (15)
0 ≤ di ≤ 1, ∀i = 1,2,3 (16)
Solution,
Given: (γ1,γ2,γ3) = (2
5 , 1
5 , 1
5 )
= (0.84,0.64,0.64)
Achievable DoF tuple: d = (0.6,0.6,0.6).

P1: max
d1,d2,d3
d1 + d2 + d3
s.t. 3d1 + d2 + d3 ≤ 3 + 2γ1 (13)
d1 + 3d2 + d3 ≤ 3 + 2γ2 (14)
d1 + d2 + 3d3 ≤ 3 + 2γ3 (15)
0 ≤ di ≤ 1, ∀i = 1,2,3 (16)
Solution,
Given: (γ1,γ2,γ3) = (2
5 , 1
5 , 1
5 )
= (0.84,0.64,0.64)
Achievable DoF tuple: d = (0.6,0.6,0.6).
Conjecture: The outer bound can be achieved by adding
multi-cast messaging in the network.

ICR Scheme vs MAT Scheme
Achievable DoF for this network is given by
DΣ(K) =
K2
2K − 1
>
K
1 + 1
2 + ⋅⋅⋅ + 1
K
Delayed CSIT - MAT scheme
(17)
and the distribution of fraction of time of the different states
{P,D,N} required for our proposed scheme is
λP =
(K − 1)2
2K2 − K
,λD =
K − 1
2K − 1
,λN =
1
K
. (18)

Results
1 2 3 4 5 6 7 8 9 10
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
K (users)
DoF
sum
(K)
CSIT with alternation
CSIT with all delayed
Figure: DoF comparison for broadcast channel between all delayed
and alternating CSIT models.

Results
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
K (users)
D
Σ
(K)
Upper bound on the K−user BC, γ=1
Upper bound on alternating CSIT for the K−user BC
Achievable DoF based on ICR scheme
Figure: DoF comparison for the K-user BC.

Sampling Theory
Shannon/Nyquist sampling theorem:
No information loss if we sample
at 2x signal bandwidth
Storage/processing problem

Sampling Theory
Solution?

Sampling Theory
Solution?
Yes, Compressive Sensing/Sampling

Compressive Sensing

Compressive Sensing
Pioneered by E. Candes, T.Tao and D. Donoho

Compressive Sensing
Signal acquisition and compression in one step

Compressive Sensing
Signal acquisition and compression in one step
Sparsity in a certain transform domain (e.g., frequency
domain)

Compressive Sensing Formulation

RIP Condition:
(1 − δ) x 2
2 ≤ Φx 2
2 ≤ (1 + δ) x 2
2 . (19)

Figure: Random measurements by φ (Gaussian).

Figure: Random measurements by φ (Gaussian).
Signal Recovery ( 1 norm recovery):
min
x∈RN
x 1 s.t. y − φx 2 ≤ (20)

CS for Spectrum Sensing
frequency
N channel sub-bands
Empty sub-band Occupied sub-band

frequency
N channel sub-bands
Empty sub-band Occupied sub-band
Sparsity in PU occupation

CR3
CR1 CR2
CR4
CRi
Fusion Center
Figure: Fusion based CRN.
Decision making: Majority-Rule, AND-Rule

CS for Spectrum Sensing in CRNs
Secondary network:
G(M,E)
Adjacency matrix A(k) ∈ RM×M
:
aij (k) =
⎧⎪⎪
⎨
⎪⎪⎩
1 if ¯τij (k) >= τ, i ≠ j
0 otherwise
(21)
aij modeled as a Bernoulli R.V. with prob.
of success p
CR3
CR1 CR2
CR4
CRi
Figure: Infrastructure-less
CRN.

CS for Spectrum Sensing in CRNs
1 1 norm recovery
2 Vector Consensus algorithm
bj (k) = (
1
M
(b(0) +
1
Kp
K−1
∑
t=0
B(t)¯aT
j (t)))
(22)
Convergence will be achieved
lim
k→∞
bj (k) = b∗
(23)
Majority-Rule asymptotic behavior
lim
K→∞
Pd (K) =
N
∑
j=1
M
∑
i=⌈ M
2 ⌉
(
M
i
)(1−π11)M−i
πi
11
(24)
CR3
CR1 CR2
CR4
CRi
Figure: Infrastructure-less
CRN.

Simulation Parameters
Parameter Symbol Realization
No. channels N 200
No. measurements T 30
No. PU nodes P 4
No. SU nodes M 12
Minimum Distance dmin 10 (m)
Area A 1000 (m) ×1000(m)
Pathloss Exponent α 2

Results
0 5 10 15 20 25
0.9
0.95
1
SNR (dB)
P
d
0 5 10 15 20 25
0
2
4
6
8
x 10
−3
SNR (dB)
P
fa
Centralized − Majority Rule
Infrasturcture−less, K=20
Centralized − Majority Rule
Figure: Performance comparison

Results
0 5 10 15 20 25
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
SNR (dB)
P
d
Centralized− Majority Rule
Infrastructure−less, p=1
Infrastructure−less, p=0.8
Figure: Effect of link quality

Results
0 5 10 15 20 25
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SNR (dB)
P
d
Centralized − Majority Rule, T=50
Infrasturcture−less, T=50
Figure: Effect of number of measurements

Results
1 2 3 4 5 6 7 8 9 10
0.7
0.75
0.8
0.85
0.9
0.95
1
k (iterations)
P
d
(k)
Good connectivity, p=0.8, SNR=10 dB
Poor connectivity, p=0.3, SNR =10 dB
Good connectivity, p=0.8, SNR =5 dB
Poor connectivity, p=0.3, SNR =5 dB
Figure: The convergence of consensus algorithm in terms probability of
detection

Motivation
eNB
Figure: Traditional Cellular Network.

Motivation
eNB
Applications are hungry!

Motivation
eNB
Multimedia services

Motivation
eNB
Multimedia services
Existing infrastructure

Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Figure: D2D Communications.

Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Advantages:
Ofﬂoading the cellular system → high data rates

Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Advantages:
Reliable communications/Instant communications

Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Advantages:
Reliable communications/Instant communications
Proximity effect → power saving

System Model
BS
CUE
D1
D2
Figure: Network Model: Cellular network with D2D network (shaded
area).

Cooperative Scheme
BS
CUE
D1
D2
Figure: Cooperative System, t = 1.

Cooperative Scheme
BS
CUE
D1
D2

Cooperative Scheme
BS
CUE
D1
D2
xCT
D1
=
√
αPCT
D1
xC +
√
(1 − α)PCT
D1
xD2
(25)

Problem Formulation
P1:max
ρ,α
RCT
C
s.t. PCT
D1
≤ PT,max(EH constraint) (26)
RB,D1
≥ RCT
C (Decoding at D1) (27)
RCT
D2
≥ ¯RD2
(Target rate for D2D pair) (28)
ρ,α ∈ [0,1]. (29)

Simulation Parameters
Table: List of symbols.
Symbol Description Value
PB BS TX power 41 dBm
N0 Noise power −100 dBm
L Pathloss Exponent 1.8 − 3.8
dB,D1
Distance between B and D1 50 − 500 m
dD1,C Distance between D1 and C 10 − 20 m
dD1,D2
Distance between D1 and D2 5 − 20 m
dB,C Distance between B and C 200 − 1000 m
dD1,D2
Distance between D1 and D2 5 − 20 m
R Cell radius 500 m

Results
0 )1(log
2
1
21,2
CT
DDR 

2DR
UB
Cooperative Transmission
(CT)
Direct Transmission
(DT)
Figure: α vs ¯RD2
.

Results
Pathloss Exponent
2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8
RC
8
9
10
11
12
13
14
15
16
Without cooperation
With cooperation
Figure: RC vs Pathloss Exponent: PT,max = 29 dBm.

Results
dD1,D2(max)
20 25 30 35 40 45 50 55 60 65 70
Prob.ofsucc.cancelation
0.2
0.3
0.4
0.5
0.6
0.7
0.8
CUE
D2D-Rx
Figure: Probability of SIC vs dD1,D2
(max) at D2

Motivation

System Model
Central Aggregator
1 t
2 t
3 t
K t
RN : Receive Antennas ix :Sparse signal of activity
Trafﬁc Nature:
1 Low data rate
2 Sporadic → Sparse

Proposed Solutions
Figure: QPSK Constellation with threshold contour..

Proposed Solutions
Detect Activity

Proposed Solutions
Detect Activity
Decode the data from the modulation alphabet A (e.g.,
QPSK modulation)

Proposed Solutions
1 norm recovery

Proposed Solutions
1 norm recovery
MAP
ˆx = min
x∈A0
y − Hx 2
2 + 2σ2
n x 0 log(
(1 − pa) A
pa
) (30)

Proposed Solutions
1 norm recovery
MAP
ˆx = min
x∈A0
y − Hx 2
2 + 2σ2
n x 0 log(
(1 − pa) A
pa
) (30)
MMSE
ˆxMMSE = (HH
H + σ2
nI)−1
HH
y (31)

Thank You!

Interference management in spectrally and energy efficient wireless networks

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