interference management in spectrally and energy efficient wireless networks
TRANSCRIPT
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Interference Management in Spectrallyand Energy Efficient Wireless Networks
Mohamed Seif, BSc
Wireless Intelligent Networks Center (WINC), Nile University, Egypt
August 10, 2016
Thesis Committee:
Prof. Mohamed NafieProf. Amr Elkeyi
Prof. Karim G. SeddikMohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 1
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Data Transmission
Storage
Energy
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 2
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
1 Interference Management with Limited CSI
2 Sparse Spectrum Sensing in CRNs
3 D2D Communications
4 M2M Communications
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 3
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
1 Interference Management with Limited CSI
2 Sparse Spectrum Sensing in CRNs
3 D2D Communications
4 M2M Communications
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 4
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
The Big Problem in Wireless Communications
Figure: An illustrative example for a heterogeneous network.
Interference is a fundamental bottleneck in many wirelesssystemsInterference management is getting convoluted
Homogeneous → Heterogeneous
How to manage interference in an efficient manner?
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 5
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
The Big Problem in Wireless Communications
Figure: An illustrative example for a heterogeneous network.
Interference is a fundamental bottleneck in many wirelesssystems
Interference management is getting convolutedHomogeneous → Heterogeneous
How to manage interference in an efficient manner?
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 5
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
The Big Problem in Wireless Communications
Figure: An illustrative example for a heterogeneous network.
Interference is a fundamental bottleneck in many wirelesssystemsInterference management is getting convoluted
Homogeneous → Heterogeneous
How to manage interference in an efficient manner?
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 5
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
The Big Problem in Wireless Communications
Figure: An illustrative example for a heterogeneous network.
Interference is a fundamental bottleneck in many wirelesssystemsInterference management is getting convoluted
Homogeneous → Heterogeneous
How to manage interference in an efficient manner?
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 5
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Coding Against Interference
Interference shaping using CSIT 1 is a key enabler for mitigatinginterference
1Channel state information at transmitter.
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Coding Against Interference
Interference shaping using CSIT 1 is a key enabler for mitigatinginterference
1Channel state information at transmitter.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 6
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Closed Loop Systems
Figure: Illustration of the CSIT feedback and sharing process.
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Challenges in Obtaining Global and Accurate CSIT
Tx
Channel Feedback
User1
User2
User3
Possible error sources in the CSI feedback processChannel estimation errorQuantization error (e.g., Compressed channel feedback)Feedback delay (Maddah Ali et al.’12)
CSIT sharing via backhaul links (e.g., CoMP in LTE)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 8
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Challenges in Obtaining Global and Accurate CSIT
Tx
Channel Feedback
User1
User2
User3
Possible error sources in the CSI feedback processChannel estimation errorQuantization error (e.g., Compressed channel feedback)Feedback delay (Maddah Ali et al.’12)
CSIT sharing via backhaul links (e.g., CoMP in LTE)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 8
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Challenges in Obtaining Global and Accurate CSIT
Tx
Channel Feedback
User1
User2
User3
Possible error sources in the CSI feedback processChannel estimation errorQuantization error (e.g., Compressed channel feedback)Feedback delay (Maddah Ali et al.’12)
CSIT sharing via backhaul links (e.g., CoMP in LTE)Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 8
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Degrees of Freedom (DoF)
DoF notion:
1 Lizhong and Tse in IEEE IT Trans. 20032 Rigorous approximation to the network capacity in the high
SNR regime.
Mathematically,
C∑(P) = DoF log(P) + o(log(P)) (1)
where limP→∞o(log(P))
log(P) = 0.
Alternatively,It represents the number of interference-free signallingdimensions in the network.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 9
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Degrees of Freedom (DoF)
DoF notion:1 Lizhong and Tse in IEEE IT Trans. 2003
2 Rigorous approximation to the network capacity in the highSNR regime.
Mathematically,
C∑(P) = DoF log(P) + o(log(P)) (1)
where limP→∞o(log(P))
log(P) = 0.
Alternatively,It represents the number of interference-free signallingdimensions in the network.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 9
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Degrees of Freedom (DoF)
DoF notion:1 Lizhong and Tse in IEEE IT Trans. 20032 Rigorous approximation to the network capacity in the high
SNR regime.
Mathematically,
C∑(P) = DoF log(P) + o(log(P)) (1)
where limP→∞o(log(P))
log(P) = 0.
Alternatively,It represents the number of interference-free signallingdimensions in the network.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 9
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
System Model
The received signal at the i th receiveris given by
Yi(t) = Hi(t)X(t) +Ni(t), i = 1, . . . ,K(2)
The total DoF of the network is definedas
DΣ(K ) = max(d1,d2,...,dK )∈D
d1 + d2 + ⋅ ⋅ ⋅ + dK
(3)
UE3
UE1 UE2
UE4
UEi
)(tHi
K-antenna Tx
UEK
Figure: Network Model
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 10
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
CSI Model
Perfect and global CSIR.
Three states of the availability of CSITabout each receiver:
Perfect CSIT (P): instantaneousand without error.Delayed CSIT (D): delay greaterthan or equal one time slotduration (coherence time) andwithout error.No CSIT (N): not available totransmitter 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
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Alternating CSIT Model
The fraction of time associated withCSIT state S,
λS = ∑nt=1∑
Ki=1 I(Si(t) = S)
nK(4)
where n is the number of channeluses,
∑S∈{P,D,N}
λS = 1. (5)
UE3
UE1 UE2
UE4
UEi
)(tHi
K-antenna Tx
UEK
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR SchemePhase 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.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 13
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR SchemePhase I: Interference Creation
UE2
UE1
Tx
UE3
1v
2v
3v
),,( 321
1
1 vvvI
),,( 321
1
2 vvvL
),,( 321
1
3 vvvI
N
D
D
Figure: ICR scheme t = 2.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 14
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR SchemePhase I: Interference Creation
UE2
UE1
Tx
UE3
1p
2p
3p
),,( 321
1
1 pppI
),,( 321
1
2 pppI
),,( 321
1
3 pppL
D
D
N
Figure: ICR scheme t = 3.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 15
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR SchemePhase 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
Figure: ICR scheme t = 4.
Based on orthogonal projection pre-coding and PNCMohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 16
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR SchemePhase 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
Figure: ICR scheme t = 5.
Based on orthogonal projection pre-coding and PNCMohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 17
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR Scheme
UE2
UE1
Tx
UE3
1u2u 3u
1v2v 3v
1p2p 3p
Figure: D∑ = 95 , S5
123 = {NDD,DND,DDN,PPN,PNP}.
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
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 toproduce an effect greater than the sum of their individualeffects.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 19
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
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
Consider: S5123 = (NNN,DDD,DDD,DDD,PPP).
D∑(3) = 1 × 3
15 +1811 ×
915 + 3 × 3
15 = 9855 < 9
5
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
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
Consider: S5123 = (NNN,DDD,DDD,DDD,PPP).
D∑(3) = 1 × 3
15 +1811 ×
915 + 3 × 3
15 = 9855 < 9
5
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Upper Bound on the DoF for the K-user BC
Bounds were introduced by Tandon et al.’13
DΣ(K ) = d1 + d2 + ⋅ ⋅ ⋅ + dK ≤K 2 + (K − 1)∑K
i=1 γi
2K − 1(6)
where,
γi =∑n
t=1 I(Si(t) = P)n
≤ γ,∀i = 1, . . . ,K (7)
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
DoF Region Characterization for the 3-user BC
Given perfect CSIT distribution (γ1,γ2,γ3),
P1: maxd1,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)
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
DoF Region Characterization for the 3-user BC
Given perfect CSIT distribution (γ1,γ2,γ3),
P1: maxd1,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)
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
DoF Region Characterization for the 3-user BC
Given perfect CSIT distribution (γ1,γ2,γ3),
P1: maxd1,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 ,15 ,
15)
Optimal DoF tuple: d∗ = (0.84,0.64,0.64)
Achievable DoF tuple: d = (0.6,0.6,0.6).Conjecture: The outer bound can be achieved by addingmulti-cast messaging in the network.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 23
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
DoF Region Characterization for the 3-user BC
Given perfect CSIT distribution (γ1,γ2,γ3),
P1: maxd1,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 ,15 ,
15)
Optimal DoF tuple: d∗ = (0.84,0.64,0.64)Achievable DoF tuple: d = (0.6,0.6,0.6).
Conjecture: The outer bound can be achieved by addingmulti-cast messaging in the network.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 23
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
DoF Region Characterization for the 3-user BC
Given perfect CSIT distribution (γ1,γ2,γ3),
P1: maxd1,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 ,15 ,
15)
Optimal DoF tuple: d∗ = (0.84,0.64,0.64)Achievable DoF tuple: d = (0.6,0.6,0.6).
Conjecture: The outer bound can be achieved by addingmulti-cast messaging in the network.
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
ICR Scheme vs MAT Scheme
Achievable DoF for this network is given by
DΣ(K ) = K 2
2K − 1> K
1 + 12 + ⋅ ⋅ ⋅ +
1K
´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶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
2K 2 −K, λD = K − 1
2K − 1, λN = 1
K. (18)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 24
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
1 2 3 4 5 6 7 8 9 101
1.5
2
2.5
3
3.5
4
4.5
5
5.5
K (users)
DoF
sum
(K)
CSIT with alternationCSIT with all delayed
Figure: DoF comparison for broadcast channel between all delayedand alternating CSIT models.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 25
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
1 2 3 4 5 6 7 8 9 101
2
3
4
5
6
7
8
9
10
K (users)
DΣ(K
)
Upper bound on the K−user BC, γ=1Upper bound on alternating CSIT for the K−user BCAchievable DoF based on ICR scheme
Figure: DoF comparison for the K-user BC.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 26
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
1 Interference Management with Limited CSI
2 Sparse Spectrum Sensing in CRNs
3 D2D Communications
4 M2M Communications
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 27
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Sampling Theory
Shannon/Nyquist sampling theorem:
No information loss if we sampleat 2x signal bandwidthStorage/processing problem
Solution?
Yes, Compressive Sensing/Sampling
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 28
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Sampling Theory
Shannon/Nyquist sampling theorem:
No information loss if we sampleat 2x signal bandwidthStorage/processing problem
Solution?
Yes, Compressive Sensing/Sampling
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 28
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Sampling Theory
Shannon/Nyquist sampling theorem:
No information loss if we sampleat 2x signal bandwidthStorage/processing problem
Solution?
Yes, Compressive Sensing/Sampling
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 28
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing
Pioneered by E. Candes, T.Tao and D. DonohoSignal acquisition and compression in one stepSparsity in a certain transform domain (e.g., frequencydomain)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 29
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing
Pioneered by E. Candes, T.Tao and D. Donoho
Signal acquisition and compression in one stepSparsity in a certain transform domain (e.g., frequencydomain)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 29
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing
Pioneered by E. Candes, T.Tao and D. DonohoSignal acquisition and compression in one step
Sparsity in a certain transform domain (e.g., frequencydomain)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 29
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing
Pioneered by E. Candes, T.Tao and D. DonohoSignal acquisition and compression in one stepSparsity in a certain transform domain (e.g., frequencydomain)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 29
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing Formulation
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 30
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing Formulation
RIP Condition:
(1 − δ) ∥x∥22 ≤ ∥Φx∥2
2 ≤ (1 + δ) ∥x∥22 . (19)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 31
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing Formulation
RIP Condition:
(1 − δ) ∥x∥22 ≤ ∥Φx∥2
2 ≤ (1 + δ) ∥x∥22 . (19)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 31
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing Formulation
Figure: Random measurements by φ (Gaussian).
Signal Recovery (`1 norm recovery):
minx∈RN
∥x∥1 s.t.∥y − φx∥2 ≤ ε (20)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 32
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Compressive Sensing Formulation
Figure: Random measurements by φ (Gaussian).
Signal Recovery (`1 norm recovery):
minx∈RN
∥x∥1 s.t.∥y − φx∥2 ≤ ε (20)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 32
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
CS for Spectrum Sensing
frequencyN channel sub-bands
Empty sub-band Occupied sub-band
Sparsity in PU occupation
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 33
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
CS for Spectrum Sensing
frequencyN channel sub-bands
Empty sub-band Occupied sub-band
Sparsity in PU occupation
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 33
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
CS for Spectrum Sensing
CR3
CR1 CR2
CR4
CRi
Fusion Center
Figure: Fusion based CRN.
Decision making: Majority-Rule, AND-Rule
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 34
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
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 ≠ j0 otherwise
(21)
aij modeled as a Bernoulli R.V. with prob.of success p
CR3
CR1 CR2
CR4
CRi
Figure: Infrastructure-lessCRN.
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
CS for Spectrum Sensing in CRNs
1 `1 norm recovery
2 Vector Consensus algorithm
bj(k) = ( 1M
(b(0) + 1Kp
K−1
∑t=0
B(t)aTj (t)))
(22)
Convergence will be achieved
limk→∞
bj(k) = b∗ (23)
Majority-Rule asymptotic behavior
limK→∞
Pd(K ) =N
∑j=1
M
∑i=⌈ M
2 ⌉
(Mi )(1−π11)M−iπi
11
(24)
CR3
CR1 CR2
CR4
CRi
Figure: Infrastructure-lessCRN.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 36
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Simulation Parameters
Parameter Symbol RealizationNo. channels N 200No. measurements T 30No. PU nodes P 4No. SU nodes M 12Minimum Distance dmin 10 (m)Area A 1000 (m) ×1000(m)Pathloss Exponent α 2
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 37
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
0 5 10 15 20 250.9
0.95
1
SNR (dB)
Pd
0 5 10 15 20 250
2
4
6
8x 10
−3
SNR (dB)
Pfa
Centralized − Majority RuleInfrasturcture−less, K=20Infrasturcture−less, K=10Infrasturcture−less, K=1000
Centralized − Majority RuleInfrasturcture−less, K=20Infrasturcture−less, K=10Infrasturcture−less, K=1000
Figure: Performance comparison
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 38
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
0 5 10 15 20 250.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
SNR (dB)
Pd
Centralized− Majority RuleInfrastructure−less, p=1Infrastructure−less, p=0.8Infrastructure−less, p=0.3Infrastructure−less, p=0.1
Figure: Effect of link quality
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 39
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
0 5 10 15 20 250.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SNR (dB)
Pd
Centralized − Majority Rule, T=50Infrasturcture−less, T=50Infrasturcture−less, T=40Infrasturcture−less, T=30Infrasturcture−less, T=20
Figure: Effect of number of measurements
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
1 2 3 4 5 6 7 8 9 100.7
0.75
0.8
0.85
0.9
0.95
1
k (iterations)
Pd(k
)
Good connectivity, p=0.8, SNR=10 dBPoor connectivity, p=0.3, SNR =10 dBGood connectivity, p=0.8, SNR =5 dBPoor connectivity, p=0.3, SNR =5 dB
Figure: The convergence of consensus algorithm in terms probability ofdetection
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 41
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
1 Interference Management with Limited CSI
2 Sparse Spectrum Sensing in CRNs
3 D2D Communications
4 M2M Communications
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 42
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
eNB
Figure: Traditional Cellular Network.
Applications are hungry!
Multimedia services
Existing infrastructure
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 43
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
eNB
Figure: Traditional Cellular Network.
Applications are hungry!
Multimedia services
Existing infrastructure
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 43
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
eNB
Figure: Traditional Cellular Network.
Applications are hungry!
Multimedia services
Existing infrastructure
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 43
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
eNB
Figure: Traditional Cellular Network.
Applications are hungry!
Multimedia services
Existing infrastructure
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Figure: D2D Communications.
Advantages:Offloading the cellular system → high data ratesReliable communications/Instant communicationsProximity effect → power saving
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Figure: D2D Communications.
Advantages:Offloading the cellular system → high data rates
Reliable communications/Instant communicationsProximity effect → power saving
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Figure: D2D Communications.
Advantages:Offloading the cellular system → high data ratesReliable communications/Instant communications
Proximity effect → power saving
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
CUE
CUE
CUE
eNB
CUE
D2D Pair
D2D Pair
D2D Pair
Figure: D2D Communications.
Advantages:Offloading the cellular system → high data ratesReliable communications/Instant communicationsProximity effect → power saving
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
System Model
BS
CUE
D1
D2
Figure: Network Model: Cellular network with D2D network (shadedarea).
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Cooperative Scheme
BS
CUE
D1
D2
Figure: Cooperative System, t = 1.
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Cooperative Scheme
BS
CUE
D1
D2
Figure: Cooperative System, t = 2.
xCTD1
=√αPCT
D1xC +
√(1 − α)PCT
D1xD2 (25)
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Cooperative Scheme
BS
CUE
D1
D2
Figure: Cooperative System, t = 2.
xCTD1
=√αPCT
D1xC +
√(1 − α)PCT
D1xD2 (25)
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Problem Formulation
P1: maxρ,α
RCTC
s.t. PCTD1
≤ PT,max(EH constraint) (26)
RB,D1 ≥ RCTC (Decoding at D1) (27)
RCTD2
≥ RD2(Target rate for D2D pair) (28)ρ,α ∈ [0,1]. (29)
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Simulation Parameters
Table: List of symbols.
Symbol Description Value
PB BS TX power 41 dBmN0 Noise power −100 dBmL Pathloss Exponent 1.8 − 3.8
dB,D1 Distance between B and D1 50 − 500 mdD1,C Distance between D1 and C 10 − 20 mdD1,D2 Distance between D1 and D2 5 − 20 mdB,C Distance between B and C 200 − 1000 m
dD1,D2 Distance between D1 and D2 5 − 20 mR Cell radius 500 m
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
0 )1(log2
121,2
CT
DDR
2DR
UB
Cooperative Transmission(CT)
Direct Transmission(DT)
Figure: α vs RD2 .
Mohamed Seif, BSc Nile University
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
Pathloss Exponent2 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 cooperationWith cooperation
Figure: RC vs Pathloss Exponent: PT,max = 29 dBm.
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 51
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Results
dD1,D2(max)20 25 30 35 40 45 50 55 60 65 70
Pro
b. o
f suc
c. c
ance
latio
n
0.2
0.3
0.4
0.5
0.6
0.7
0.8
CUED2D-Rx
Figure: Probability of SIC vs dD1,D2(max) at D2
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 52
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
1 Interference Management with Limited CSI
2 Sparse Spectrum Sensing in CRNs
3 D2D Communications
4 M2M Communications
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 53
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Motivation
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 54
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
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 rate2 Sporadic → Sparse
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 55
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Proposed Solutions
Figure: QPSK Constellation with threshold contour..
Detect ActivityDecode the data from the modulation alphabet A (e.g.,QPSK modulation)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 56
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Proposed Solutions
Figure: QPSK Constellation with threshold contour..
Detect Activity
Decode the data from the modulation alphabet A (e.g.,QPSK modulation)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 56
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Proposed Solutions
Figure: QPSK Constellation with threshold contour..
Detect ActivityDecode the data from the modulation alphabet A (e.g.,QPSK modulation)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 56
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Proposed Solutions
`1 norm recovery
MAP
x = minx∈A0
∥y −Hx∥22 + 2σ2
n∥x∥0 log((1 − pa)∣A∣pa
) (30)
MMSExMMSE = (HHH + σ2
nI)−1HHy (31)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 57
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Proposed Solutions
`1 norm recoveryMAP
x = minx∈A0
∥y −Hx∥22 + 2σ2
n∥x∥0 log((1 − pa)∣A∣pa
) (30)
MMSExMMSE = (HHH + σ2
nI)−1HHy (31)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 57
![Page 88: Interference management in spectrally and energy efficient wireless networks](https://reader031.vdocument.in/reader031/viewer/2022021813/5881f5eb1a28abb4748b5783/html5/thumbnails/88.jpg)
Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Proposed Solutions
`1 norm recoveryMAP
x = minx∈A0
∥y −Hx∥22 + 2σ2
n∥x∥0 log((1 − pa)∣A∣pa
) (30)
MMSExMMSE = (HHH + σ2
nI)−1HHy (31)
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 57
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Thank You!
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 58
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Interference Management with Limited CSI Sparse Spectrum Sensing in CRNs D2D Communications M2M Communications
Thank You!
Mohamed Seif, BSc Nile University
Interference Management in Spectrally and Energy Efficient Wireless Networks 58