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Interference Management in Wireless Networks
Aly El Gamal
Department of Electrical and Computer EngineeringPurdue University
Venu Veeravalli
Coordinated Science LabDepartment of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
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Just Published - Cambridge University Press
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Part 1: Introduction
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Explosion in Wireless Data Traffic
How to accommodate exponential growth without new useful spectrum?
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Through Improved PHY?
• Point-to-Point wireless technology mature• Modulation/demodulation• Synchronization• Coding/decoding (near Shannon limits)• MIMO
• Centralized (in-cell) multiuser wireless technology also mature• Orthogonalize users when possible• Otherwise use successive interference cancellation
Spectral efficiency gains from further improvements in PHY are limited!
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By Adding More Basestations?
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Through Improved Interference Management
Several useful engineering solutions for managing interference
But...
What are fundamental limits?
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References
1 T.S. Rappaport. Wireless communications: Principles andpractice. New Jersey: Prentice Hall 1996.
2 A. J. Viterbi. CDMA: Principles of spread spectrumcommunication. Addison Wesley, 1995.
3 D. Tse and P. Viswanath. Fundamentals of wirelesscommunication. Cambridge University Press, 2005.
4 E. Biglieri et al. Principles of Cognitive Radio. CambridgeUniversity Press, 2012.
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Part 2: Degrees of Freedom Characterization ofInterference Channels
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Information Theory for IC: State-of-the-art
• Exact characterization• Very hard problem, still open even after > 30 years
• Approximate characterization• Within constant number of bits/sec• Provides some architectural insights
• Degrees of freedom (or multiplexing gain)
DoF = limSNR→∞
sum capacity
log SNR
• Pre-log factor of sum-capacity in high SNR regime• Number of interference free sessions per channel use• Simplest of the three, but can provide useful insight
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Degrees of Freedom
Advantages:
1 Simplicity
2 Captures the interference effect (without noise)
3 Highlights the combinatorial part of the problem
Drawbacks:
1 Insensitive to Gaussian noise
2 Insensitive to varying channel strengths
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K-user (SISO) Interference Channel
W1 W1Tx1 Rx1
W2 W2Tx2 Rx2
W3 W3Tx3 Rx3
How many Degrees of Freedom (DoF)?
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Degrees of Freedom with Orthogonalization
• One active user per channel use• Every user gets an interference free channel once every K channel
uses• DoF per user is 1/K; total DoF equals 1
• Special Case: K = 2• Can easily show that outer bound on DOF equals 1
=⇒ TDMA optimal from DoF viewpoint for K = 2
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Degrees of Freedom for general K
• Outer Bound on DoF [Host-Madsen, Nosratinia ’05]
• There are K(K− 1)/2 pairs and each user appears in (K− 1) pairs• Thus DoF ≤ K/2 or per user DoF ≤ 1/2
• Amazingly, this outer bound is achievable via linear interferencesuppression!
Interference Alignment [Cadambe & Jafar ’08]
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Linear Transmit/Receive Strategies
Interference Channel with Tx/Rx Linear CodingU1 0 00 U2 00 0 U3
†︸ ︷︷ ︸
Receive Beams
H1,1 H1,2 H1,3
H2,1 H2,2 H2,3
H3,1 H3,2 H3,3
︸ ︷︷ ︸
Channel
V 1 0 00 V 2 00 0 V 3
︸ ︷︷ ︸
Transmit Beams
End-to-End matrix is Diagonal =⇒ No Interference!
# streams = Size of the Diagonal matrix
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DoF of Linear Strategies
U1 0 00 U2 00 0 U3
† H1,1 H1,2 H1,3
H2,1 H2,2 H2,3
H3,1 H3,2 H3,3
V 1 0 00 V 2 00 0 V 3
H i,j : NT ×NT block-diagonal matrix
• (Symmetric) MIMO:N = # antennas
• Symbol Extensions (Time or Frequency)T = # symbol extensions
DoF (T ) = (#streams)/T
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Complexity of asymptotic Interference Alignment
# symbol extensions 0 20 40 60 80 100
0.44
0.45
0.46
0.47
0.48
0.49
0.5
PUDoF
PUDoF of 0.5 is achieved asymptotically
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Complexity of asymptotic Interference Alignment
# symbol extensions
PUDoF
0 200 400 600 800 1000 1200 14000.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.53 User
4 User
[Choi, Jafar, and Chung, ’09]
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Interference Alignment Summary
+ Achieves optimal PUDoF for fully connected channel
- Requires global channel state information (CSI)
- Requires large number of symbol extensions
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References
1 V.R. Cadambe and S.A. Jafar. “Interference Alignment andDegrees of Freedom of the K-User Interference Channel.” IEEETrans. Inform. Theory, August 2008.
2 S. A. Jafar. “Interference Alignment – A New Look at SignalDimensions in a Communication Network.” In Foundations andTrends in Communications and Information Theory, NOWPublications, 2010.
3 A. Host-Madsen and A. Nosratinia, “The multiplexing gain ofwireless networks,” in Proceedings of ISIT, 2005.
4 S.W. Choi, S.A. Jafar, and S.-Y. Chung. “On the beamformingdesign for efficient interference alignment.” IEEE CommunicationsLetters, 2009.
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Part 3: Coordinated Multi-Point Transmission
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K-User Interference Channel
Channel State Information known at all nodes.
W1 W1Tx1 Rx1
W2 W2Tx2 Rx2
W3 W3Tx3 Rx3
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Coordinated Multi-Point (CoMP) Transmission
Messages are jointly transmitted using multiple transmitters.
W1 W1Tx1 Rx1
W2 W2Tx2 Rx2
W3 W3Tx3 Rx3
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CoMP Transmission
• Each message is jointly transmitted using M transmitters
• Message i is transmitted jointly using the transmitters in Ti
• For all i ∈ [K], |Ti| ≤M
• We consider all message assignments that satisfy the cooperationconstraint
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Degrees of Freedom (DoF)
DoF = limSNR→∞
sum capacity
log SNR
Objective: Determine the DoF as a function of K and M
PUDoF(M) = limK→∞
DoF(K,M)
K
Is PUDoF(M)>PUDoF(1) for M > 1?
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Example: Two-user Interference Channel
W1 W1Tx1 Rx1
W2 W2Tx2 Rx2
No Cooperation, DoF=1, Time Sharing
Full Cooperation, DoF=2, ZF Transmit Beamforming
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No Cooperation (M = 1)
• For M = 1, outer bound = K/2
• The outer bound can be achieved by jointly coding across multipleparallel channels [Cadambe & Jafar ’08]:
DoF(K,M = 1) = limL→∞
DoF(K,M = 1, L)
L= K/2
where L is the number of parallel channels
Corollary
Without cooperation, the Per User DoF number is given by
PUDoF(M = 1) =1
2
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Full Cooperation (M = K)
• In this case, the channel is a MISO Broadcast channel.
• Each message is available at K antennas, and hence, can becanceled at K − 1 receivers.
• Each user achieves 1 DoF,
DoF(K,M = K) = K.
What happens with partial cooperation (1 < M < K)?
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Clustering
W1 W1Tx1 Rx1
W2 W2Tx2 Rx2
W3 W3Tx3 Rx3
W4 W4Tx4 Rx4
No Degrees of Freedom Gain
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Spiral Message Assignment
Ti = {i, i+ 1, . . . , i+M − 1}
W1 W1Tx1 Rx1
W2 W2Tx2 Rx2
W3 W3Tx3 Rx3
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Spiral Message Assignment: Results
Theorem
The DoF of interference channel with a spiral message assignmentsatisfies
K +M − 1
2≤ DoF(K,M) ≤
⌈K +M − 1
2
⌉
Proof of Achievability: First M − 1 users are interference-free, andinterference occupies half the signal space at each other receiver
Generalizes the Asymptotic Interference Alignment scheme
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Outline of the Achievable Scheme
OriginalChannel
ZFEncoder
IAEncoder
IADecoder
Derived Channel
Approach:
1 ZF Step: Exploit cooperation to transform the interferencechannel into a derived channel (with single-point transmission)
2 IA Step: Use the known IA techniques to design beams forderived channel
3 Prove that the asymptotic IA step works for generic channelcoefficients
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DoF Outer Bound: Results
Definition
We say that a message assignment satisfies a local cooperationconstraint if and only if ∃r(K) = o(K), and for all K−user channels,
Ti ⊆ {i− r(K), i− r(K) + 1, . . . , i+ r(K)},∀i ∈ [K]
Theorem
With the restriction to local cooperation,
PUDoFloc(M) =1
2
Local cooperation cannot achieve a scalable Dof gain
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DoF Outer Bound: Results
Theorem
For M ≥ 2,
PUDoF(M) ≤ M − 1
M
Corollary
PUDoF(2) =1
2
Assigning each message to two transmitters cannot achieve a scalableDoF gain
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References
1 P. Marsch and G. P. Fettweis “Coordinated Multi-Point in MobileCommunications: from theory to practice,” First Edition,Cambridge, 2011.
2 A. Host-Madsen and A. Nosratinia, “The multiplexing gain ofwireless networks,” in Proc. IEEE International Symp. Inf. Theory(ISIT), 2007.
3 V. Cadambe and S. A. Jafar, “Interference alignment and degreesof freedom of the K-user interference channel,” IEEE Trans. Inf.Theory, 2008.
4 V. S. Annapureddy, A. El Gamal, and V. V. Veeravalli, “Degreesof Freedom of Interference Channels with CoMP Transmission andReception,” IEEE Trans. Inf. Theory, 2012.
5 A. El Gamal, V. S. Annapureddy, and V. V. Veeravalli, “OnOptimal Message Assignments for Interference Channels withCoMP Transmission,” in Proc. CISS, 2012
6 C. Wilson and V. Veeravalli, “Degrees of Freedom for theConstant MIMO Interference Channel with CoMP Transmission,”IEEE Trans. Comm., 2014
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Part 4: Locally Connected Networks
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Locally Connected Model
Tx i is connected to receivers {i, i+ 1, . . . , i+ L}.
Wyner Model: L =1
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
Rx4 Tx4
Tx5 Rx5
L = 2
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
Rx4 Tx4
Tx5 Rx5
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Justifying Choices: Network Topology
Local Connectivity:
• Reflects path loss
• Simplifies problem, only consider local cooperation
Large Networks:
• Understand scalability
• Derive insights
Solutions generalize to cellular network models
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Justifying Choices: Network Topology
Cellular Network Model
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Justifying Choices: Network Topology
Solutions for L = 2 are applicableECE Illinois & Purdue
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Results for Wyner Model [Lapidoth-Shamai-Wigger ’07]
Rx2 Tx2
Rx3 Tx3
Rx4 Tx4
Tx1 Rx1
Rx5 Tx5
Rx6 Tx6
W2
W3
W4
W1
W5
W6
Backhaul load factor =1
PUDoF (L=1,M=2) = 2/3 > 1/2
W1
W2
W4
W5
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Example: No Cooperation
PUDoF(L =1,M =1) = 1
2 PUDoF(L =1,M =1) = 23
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
W1
W2
W3
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
W1
W2
W3
Interference-aware message assignment + Fractional reuse
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Locally Connected IC with CoMP: Main Result
Theorem
Under the general cooperation constraint |Ti| ≤M, ∀i ∈ {1, 2, . . . ,K},
2M
2M + L≤ PUDoF(L,M) ≤ 2M + L− 1
2M + L
and the optimal message assignment satisfies a local cooperationconstraint.
Corollary
PUDoF(L = 1,M) =2M
2M + 1
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DoF Achieving Scheme
Rx2 Tx2
Rx3 Tx3
Rx4 Tx4
Tx1 Rx1
Rx5 X5
W2
W4
W1
W2
W4
W1
W5
W3
W5
Backhaul load factor =6/5 PUDoF (L=1,M=2) = 4/5 > 2/3
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DoF Outer Bound
Have to consider all possible message assignments satisfying|Ti| ≤M, ∀i ∈ [K]
1 First simplify the combinatorial aspect of the problem byidentifying useful message assignments
2 Then derive an equivalent model with fewer receivers and sameDoF
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DoF Outer Bound: Useful Message Assignments
An assignment of a message Wx to a transmitter Ty is useful only ifone of the following conditions holds:
1 Signal delivery: Ty is connected to the designated receiver Rx
2 Interference mitigation: Ty is interfering with anothertransmitter Tz, both carrying the message Wx
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DoF Outer Bound: Useful Message Assignments
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Tx4 Rx4
W3 W3
Assigning W3 to Tx1 is not useful.
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DoF Outer Bound: Useful Message Assignments
Corollary
An assignment of a message Wx to a transmitter Ty is useful only ifthere exists a chain of interfering transmitters carrying Wx thatincludes Ty and another transmitter Tz that is connected to Rx
Proves optimality of local cooperation
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CoMP Transmission for IC: Summary
• Local Cooperation• no PUDoF gain for fully connected channel• is optimal for locally connected channel
• Interference aware message assignments allow for higherthroughput
• Fractional reuse and zero-forcing transmit beam-forming aresufficient to achieve PUDoF gains, without need for symbolextensions and interference alignment
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Uplink: Achieving Full DoF
M1 M1BS1 MT1
M2 M2BS2 MT2
M3 M3BS3 MT3
Associating each MT with two BSs connected to it
Message Passing Decoding: Interference-free Degrees of Freedom
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Results: Uplink1
PUDoFZFU (N) =
1 L+ 1 ≤ NN+1L+2
L2 ≤ N ≤ L
2N2N+L 1 ≤ N ≤ L
2 − 1
≥ 12 ,∀N ≥
L2
Higher than Downlink
Is Cooperation useful for N < L2 ?
1M. Singhal, A. El Gamal, “Joint Uplink-Downlink Cell Associations forInterference Networks with Local Connectivity,” Allerton ’17ECE Illinois & Purdue
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Average Uplink-Downlink DoF
M1 M1BS1 MT1
M2 M2BS2 MT2
M3 M3BS3 MT3
M4 M4BS4 MT4
M5 M5BS5 MT5
Downlink Associations Uplink Associations
N = 3 PUDoF =1+ 4
52 = 9
10ECE Illinois & Purdue
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Average Uplink-Downlink DoF
M1 M1BS1 MT1
M2 M2BS2 MT2
M3 M3BS3 MT3
M4 M4BS4 MT4
M5 M5BS5 MT5
PUDoFUD(N,L = 1) = 4N−34N−2
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Average Uplink-Downlink DoF
For L ≥ 2:
PUDoFZFUD(N) ≥
12
(1 +
(dL2 e+δ+N−(L+1)
N
))L+ 1 ≤ N
2N2N+L 1 ≤ N ≤ L
where δ = (L+ 1) mod 2.
For L+ 1 ≤ N , scheme is different from both downlink and uplink
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Summary of Insights
• Local cooperation is optimal for locally connected networks
• Significant DoF gains achieved with ZF and message passingdecoding
• Limited cell associations ⇒ Same for downlink and uplink
• Limited associations and no delay constraint ⇒ CoMP useful?
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Further Questions
1 General network topologies
2 When to simplify into optimizing for uplink / downlink only
3 Constrain average number of cell associations
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References
1 V. S. Annapureddy, A. El Gamal, and V. V. Veeravalli, “Degreesof Freedom of Interference Channels with CoMP Transmission andReception,” IEEE Trans. Inf. Theory, 2012.
2 A. Lapidoth, N. Levy, S. Shamai (Shitz) and M. A. Wigger“Cognitive Wyner networks with clustered decoding,” IEEE Trans.Inf. Theory 2014
3 A. Wyner, “Shannon-theoretic approach to a Gaussian cellularmultiple-access channel,” IEEE Trans. Inf. Theory, 1994.
4 S. Shamai and M. A. Wigger, “Rate-limited TransmitterCooperation in Wyner’s Asymmetric Interference Network,” inProc. IEEE Int. Symp. Inf. Theory, 2014
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Part 5: Cellular Network and Backhaul Load Constraint
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Backhaul Load Constraint
More natural cooperation constraint that takes into account overallbackhaul load: ∑
i∈[K] |Ti|K
≤ B
Solution under transmit set size constraint |Ti| ≤M, ∀i ∈ [M ], can beused to provide solutions under backhaul load constraint
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Wyner’s Model with Backhaul Load Constraint
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
Theorem
Under cooperation constraint∑i∈[K] |Ti| ≤ BK,
PUDoF(B) =4B − 1
4B
Recall that |Ti| ≤M,∀i ∈ [K]⇒ PUDoF(M) = 2M2M+1
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Coding Scheme: B = 1
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
W1
W2
W3
B = 2
3 PUDoF = 2
3
B = 6
5 PUDoF = 4
5
3K
8users
5K
8users
PUDoF (B =1) = 3
4
Rx2 Tx2
Rx3 Tx3
Rx4 Tx4
Tx1 Rx1
Rx5 X5
W2
W4
W1
W3
W5
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Application in Denser Networks
Tx i is connected to receivers {i, i+ 1, . . . , i+ L}.
Rx1 Tx1
Rx2 Tx2
Rx3 Tx3
Rx4 Tx4
Tx5 Rx5
L = 2
Result: Using only zero-forcing transmitbeamforming and fractional reuse:
PUDoF(L,B = 1) ≥ 1
2,∀L ≤ 6.
without need for interference alignmentand symbol extensions
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Application in Denser Networks
PUDoF(M = 1) = 12 PUDoF(B = 1) ≥ 5
9
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Interference in Cellular Networks
Locally (partially) connected interference channel!
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Interference Graph for Single Tier
Tx,Rx pair
Each node represents a Tx-Rx pair
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No Intra-sector Interference
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No Extra Backhaul Load
B = 1, PUDoF= 12
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Discussion: Cloud-based Communication
Global Knowledge / Control available at Central nodes
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Discussion: Understanding Network Topologies
• Centralized Solution Benchmark for Distributed Algorithms
• Enables Multi-RAT Networks
• Enables AI and Blockchain2
2A. El Gamal, H. El Gamal, “A Blockchain Example for Cooperative InterferenceManagement”, submitted to WComm Letters.ECE Illinois & Purdue
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Summary
• Infrastructure enhancements in backhaul can be exploited throughcooperative transmission to lead to significant rate gains
• Minimal or no increase in backhaul load• Fractional reuse and zero-forcing transmit beam-forming are
sufficient to achieve rate gains• No need for symbol extensions and interference alignment
• Open Questions:• Partial/unknown CSI• Network dynamics and robustness to link erasures• Joint design with message passing schemes for uplink
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References
1 A. El Gamal and V. V. Veeravalli, “Flexible Backhaul Design andDegrees of Freedom for Linear Interference Channels,” in Proc.IEEE Int. Symp. Inf. Theory, 2014.
2 M. Bande, A. El Gamal, and V. V. Veeravalli, “Flexible BackhaulDesign with Cooperative Transmission in Cellular InterferenceNetworks,” in Proc. IEEE Int. Symp. Inf. Theory, 2015
3 V. Ntranos, M. A. Maddah-Ali, and G. Caire, “CellularInterference Alignment,” arXiv, 2014.
4 V. Ntranos, M. A. Maddah-Ali, and G. Caire, “OnUplink-Downlink Duality for Cellular IA,” arXiv, 2014.
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Part 6: Dynamic Interference Management
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Application: Vehicle-to-Infrastructure (V2I) Networks
Network with Dynamic Nature
Delay Sensitive - Simple Coding Schemes Desired
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Application: Vehicle-to-Infrastructure (V2I) Networks
Associations between On-Board-Units and Road Side Units
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Extensions
1 Interference Networks with Block Erasures
2 Interference Management with no CSIT
3 Fast Network Discovery
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Deep Fading Block Erasures3
Communication takes place over blocks of time slots.
• Link block erasure probability p (long-term fluctuations).
• Non-erased links are generic (short-term fluctuations).
Maximize average performance
3 A. El Gamal, V. Veeravalli, “Dynamic Interference Management,”Asilomar ’13ECE Illinois & Purdue
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Dynamic Linear Interference Network
Tx i can only be connected to receivers {i, i+ 1}
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Each of the dashed links can be erased with probability p
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Average Degrees of Freedom (DoF)
DoF(K,N) = limSNR→∞
sum capacity(K,N, SNR)
log SNR
PUDoF(N) = limK→∞
DoF(K,N)
K
• For dynamic topology: PUDoF is a function of p and N
PUDoF(p,N) = Ep [PUDoF(N)]
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Cell Association (N = 1)
Theorem
For the Cell Association problem in dynamic Wyner’s linear model,
PUDoF(p,N = 1) = max{
PUDoF(1)(p),PUDoF(2)(p),PUDoF(3)(p)}
PUDoF(1)(p): Optimal at high values of p
PUDoF(2)(p): Optimal at low values of p
PUDoF(3)(p): Optimal at middle values of p
Achievable through TDMA
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Cell Association (N = 1): Results
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.5
0.6
0.7
0.8
0.9
1
p
PU
DoF
p(M
=1)
/(1−
p)
PUDoFp(1)
PUDoFp(2)
PUDoFp(3)
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Cell Association (N = 1): High Erasure Probability
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Tx4 Rx4
Tx5 Rx5
M1 M1
M2 M2
M3 M3
M4 M4
M5 M5
Maximize probability of message delivery
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Cell Association (N = 1): Low Erasure Probability
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
M1 M1
M2 M2
M3 M3
Avoiding Interference
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Cell Association (N = 1): Low Erasure Probability
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
M1 M1
M2 M2
M3 M3
Avoiding Interference
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Cell Association (N = 1)
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Tx4 Rx4
M1 M1
M2 M2
M3 M3
M4 M4
Optimal at middle values of p
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CoMP Transmission (N = 2): No Erasures
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Tx4 Rx4
Tx5 Rx5
M1 M1
M2 M2
M4 M4
M5 M5
PUDoF(p = 0, N = 2) = 45
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Interference-Aware Message Assignment
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Tx4 Rx4
Tx5 Rx5
M1 M1
M2 M2
M3 M3
M4 M4
M5 M5
Note that limp→1PUDoF(p,N=2)
1−p = 85
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High Erasure Probability: Ignoring Interference
Tx1 Rx1
Tx2 Rx2
Tx3 Rx3
Tx4 Rx4
Tx5 Rx5
M1 M1
M2 M2
M3 M3
M4 M4
M5 M5
Note that limp→1PUDoF(p,N=2)
1−p = 2
Role of Cooperation: CoverageECE Illinois & Purdue
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CoMP Transmission in Dynamic Linear Network
Definition
A message assignment is universally optimal if it can be used toachieve PUDoF(p,N) for all values of p.
Theorem
For any value of N , there is no universally optimal messageassignment.
Knowledge of p is necessary to design the optimal scheme
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CoMP Transmission (N = 2)4
1 Identified optimal zero-forcing associations
2 As p goes from 1 to 0, role of cooperation shifts tointerference management
3 As p goes from 0 to 1, role of cooperation shifts tocoverage extension
Knowledge of p is necessary
Needed level of accuracy?
4Y. Karacora, T. Seyfi, A. El Gamal, “The Role of Transmitter Cooperation inLinear Interference Networks with Block Erasures,” Asilomar ’17ECE Illinois & Purdue
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Wyner’s Interference Networks
M1 M1Tx1 Rx1
M2 M2Tx2 Rx2
M3 M3Tx3 Rx3
Is Transmitter Cooperation with no CSIT useful?
ECE Illinois & Purdue
91 / 100
Results: Full Transmitter Cooperation with no CSIT
Wyner’s Asymmetric Network:
PUDoF =2
3
Wyner’s Symmetric Network:
PUDoF =1
2
Achieved with no Cooperation and TDMA!
ECE Illinois & Purdue
92 / 100
TDMA: Asymmetric Model
M1 M1Tx1 Rx1
M2 Tx2 Rx2
M3 M3Tx3 Rx3
Last transmitter inactive ⇒ No inter-subnetwork interference
ECE Illinois & Purdue
93 / 100
Converse: Asymmetric Model
Tx1 Rx1
M2 Tx2 Rx2
Tx3 Rx3
Knowing Rx3, we obtain a statistically equivalent version of Tx2 as Rx2
Knowing Rx1, we obtain a statistically equivalent version of Tx1 as Rx2
ECE Illinois & Purdue
94 / 100
Converse: Asymmetric Model
Tx1 Rx1
M2 Tx2 Rx2
Tx3 Rx3
Knowing Rx1, Rx3, Rx4, Rx6, ... , we reconstruct all messages
PUDoF ≤ 23
ECE Illinois & Purdue
95 / 100
Next Tasks
• Can transmitter cooperation help in any network topology?
• Characterize DoF for general network topologies
• Extend to Dynamic Interference Networks
Coordinated Multi-Point can still improve Coverage
ECE Illinois & Purdue
96 / 100
Coordinated Learning of Network Topology
• Earlier work for the broadcast problem5
• Cloud communication can enable some of these ideas
5Noga Alon, Amotz Bar-Noy, Nathan Linial, David Peleg, “On the complexity ofradio communication”, 1987,1991ECE Illinois & Purdue
97 / 100
Coordinated Learning of Network Topology
Lemma
Let x1, · · · , xL ≤ K be L distinct integers, then for every 1 ≤ i ≤ L,there exists a prime p ≤ L logK such that,
xi 6= xj mod p,∀j ∈ {1, · · · , L}, j 6= i
L : Connectivity parameter K : Number of users
ECE Illinois & Purdue
98 / 100
Coordinated Learning of Network Topology
Lemma
Let x1, · · · , xL ≤ K be L distinct integers, then for every 1 ≤ i ≤ L,there exists a prime p ≤ L logK such that,
xi 6= xj mod p,∀j ∈ {1, · · · , L}, j 6= i
1 Let p1, · · · , pm be the prime numbers in {1, · · · , L logK}
2 m phases of transmission
3 in ith phase, xj transmits in slot xj mod pi
ECE Illinois & Purdue
99 / 100
Coordinated Learning of Network Topology
Lemma
Let x1, · · · , xL ≤ K be L distinct integers, then for every 1 ≤ i ≤ L,there exists a prime p ≤ L logK such that,
xi 6= xj mod p,∀j ∈ {1, · · · , L}, j 6= i
1 Let p1, · · · , pm be the prime numbers in {1, · · · , L logK}
2 m phases of transmission
3 in ith phase, xj transmits in slot xj mod pi
O(L2 log2K) Communication rounds
ECE Illinois & Purdue
100 / 100
Summary
• Exploiting infrastructure enhancements in backhaul to achieverate gains
• No delay requirements (ZF Transmit Beamforming)
• No or minimal backhaul load
• Promising results for cellular networks
ECE Illinois & Purdue