The document discusses the practical challenges of implementing interference alignment (IA) in wireless networks. It outlines some key challenges including dimensionality issues that cause the number of dimensions needed for IA to grow exponentially with the number of users. Other challenges include requiring high SNR to perform well, overhead from channel state information estimation and feedback, and needing tight synchronization between nodes. The document also discusses potential solutions such as iterative algorithms to compute IA solutions while accounting for low SNR, and methods for obtaining channel state information like using channel reciprocity or limited feedback which have their own tradeoffs. It provides some numerical results demonstrating how factors like limited scattering, channel state information mismatch, and user grouping can impact IA performance.

1. The Practical Challenges of Interference Alignment
Daniel Tai
10/28/2013
Daniel Tai
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10/28/2013
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2. Reference
1
O El Ayach et al., “The Practical Challenges of Interference Alignment”, IEEE Wireless
Communications, Issue 1, Vol. 20, 2012
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3. Intoduction to Interference Alignment
Outline
1
Intoduction to Interference Alignment
2
Challenges of IA
3
Solutions
4
Some Numerical Results
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4. Intoduction to Interference Alignment
Interference Alignment
The idea of interference alignment (IA) is to use channel’s multiplexing gain (degree of
freedom) to cancel out interference at different users.
Specifically, IA allow users to cooperatively design precoder/decoder to project (“align”)
all interferences onto the same space different than signal space.
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5. Intoduction to Interference Alignment
IA Formulation
To be even more specific,
Consider a system with K users with Si datastreams to transmit to each.
Transmitting symbol si ∈ CSi ×1 is encoded using precoder F
Hi,j denotes channel matrix from the system intended for user j to user i
Received signal at user i:
yi = Hi,i Fi si +
Hi,l Fl sl + ni
l=i
where ni is noise
Hi,j and Fi ’s dimensions and structures depend on the type of systems (for example Hi,j
is diagonal for TDD/FDD system)
The goal of IA is to design Fi 1 such receivers are able to cancel out all interferences. (For
example, if linear decoder Wi is used, Wi Hi,l Fl = 0∀l)
1
(my interp.) decoders might as well, but this paper focuses on precoder design
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6. Challenges of IA
Outline
1
Intoduction to Interference Alignment
2
Challenges of IA
3
Solutions
4
Some Numerical Results
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7. Challenges of IA
Challenges of IA
Dimensionality and Scattering
The # of dim. need for IA grows exponentially with # of users.
Relatively milder for MIMO systems which we can add antennas
(This paper mainly focuses on MIMO IA)
SNR
IA performs well at high-SNR
In moderate-SNR, IA may not reach theoretical channel capacity. (discussed later)
CSI Estimation and Feedback
CSI need causes overhead/quality trade-off and other performance degradations in
transmission.
Synchronization
IA technique needs tight synchronization (no CFO, CTO) between cooperating nodes.
Insufficient synchronization causes additional noise.
Currently GPS solution is used.
Network Organization
Nodes needs to share system parameters, CSI... etc.
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8. Solutions
Outline
1
Intoduction to Interference Alignment
2
Challenges of IA
3
Solutions
4
Some Numerical Results
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9. Solutions
Computing IA Solutions I
Iterative algorithms are generally used to compute IA
Earliest method 2 :
At each iteration, leakage is minimized.
Ideally, Wi Hi,l Fl = 0 at convergence point.
Good performance in high-SNR.
Problem: Oblivious to desired signal power, so it performs far from optimal in low-SNR or
bad channels.
Improvements:
2
Changing the goal to max. per-stream SINR. (Performs also well in low-SNR)
Maximizing sum rate3 : The paper gives the equivalence between max sum rate and min sum
MSE. Hence MMSE solution is developed.
Other limitations
Allows users to cooperate generate lots of uncoordinated (colored) noise:
Algorithm enhanced in 4
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10. Solutions
Computing IA Solutions II
CSI sharing might causes large overhead:
5
uses game theory to replace matrix feedback by scalar feedback.
2
K. Gomadam, V. Cadambe, and S. Jafar, “A Distributed Numerical Approach to Interference Alignment
and Applications to Wireless Interference Networks,” IEEE Trans. Info. Theory, vol. 57, no. 6, June 2011, pp.
3309-22.
3
Q. Shi et al., “An Iteratively Weighted MMSE Approach to Distributed Sum Utility Maximization for a
MIMO Interfering Broadcast Channel,” IEEE Trans. Signal Proc., vol. 59, no. 9, Sept. 2011, pp. 433140.
4
S. W. Peters and R. W. Heath, Jr., “Cooperative Algorithms for MIMO Interference Channels,” IEEE
Trans. Vehic. Tech., vol. 60, no. 1, Jan. 2011, pp. 206218.
5
C. Shi et al., “Local Interference Pricing for Distributed Beamforming in MIMO Networks,” Proc. IEEE
MILCOM, Oct. 2009.
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11. Solutions
Obtaining CSI I
2 Mainstream methods: Channel reciprocity and feedback
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12. Solutions
Obtaining CSI II
Problems of reciprocity method
Iterative causes overhead (uses lots of timeslots)
Reciprocity might not stand.(ex. uncoordinated interference observation at rx and tx are
not reciprocal)
Does not work for FDD systems
Problems of feedback method
Quality(accuracy)/overhead tradeoff
Limited feedback is low-overhead, but efficient quantization such as Grassmannian
codebooks grows exponentially with accuracy requirement in high SNR ⇒ hard to
generate and encode.
Limited feedback cannot be applied to systems without structured CSI.
Analog feedback: prone to thermal noise (bad for uplink)
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13. Solutions
Obtaining CSI III
Other problems
CSI data grows with network size: overhead may cancel possible gain
BS backhaul link requirement: high capacity and low latency
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14. Solutions
IA in large scale networks
6
Shows that in partially connected networks, a finite number of antennas can be used to
IA in infinitely large network.
Issues: Realistic? How to set the threshold of interference?
7
has a more complicated model: a finite channel coherence time and different path loss
to each link. CSI acquisition is also considered.
⇒ TDMA performs better for fast fading channels. The paper also providing partitioning
algorithm to make IA/TDMA hybrid systems.
6
M. Guillaud and D. Gesbert, “Interference Alignment in the Partially Connected K-User MIMO Interference
Channel,” Proc. Euro. Signal Proc. Conf., Barcelona, Spain, Sept. 2011, pp. 15.
7
S. W. Peters and R. W. Heath, Jr., “User Partitioning for Less Overhead in MIMO Interference Channels,”
IEEE Trans. Wireless Commun., vol. 11, no. 2, 2012, pp. 592603.
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15. Some Numerical Results
Outline
1
Intoduction to Interference Alignment
2
Challenges of IA
3
Solutions
4
Some Numerical Results
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16. Some Numerical Results
Effects of Limited Scattering
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17. Some Numerical Results
Effects of CSI Mismatch
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18. Some Numerical Results
Effects of User Grouping
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