CSI Feedback Reduction for MIMO Interference Alignment
TL;DR: This paper develops a novel IA precoder/decorrelator design and establishes new IA feasibility conditions, and introduces a novel metric, namely the feedback dimension, which serves as a first-order measurement of CSI feedback overhead.
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Abstract: Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder/decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.
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Figures
Fig. 7. An example of constructed max-flow graph for a K = 3 user MIMO network under feedback profile parameters: Nsi = M s i = 2, di = 1, ∀i, ΩIVj = {1, · · · j − 1, j + 1, · · ·K}, ΩIj = ΩIIj = ΩIIIj = ∅, ∀j, i, j 6= i. The value f/c near each edge denotes the flow (f ) and capacity (c). ![Fig. 4. Throughput versus SNR under a K = 4, [N1, · · ·N4] = [5, 4, 4, 3], [M1, · · ·M4] = [4, 3, 2, 4], [d1, · · · d4] = [2, 1, 1, 1] MIMO interference network and the sum feedback bit constraint is 400.](/figures/figure4-1-2o64f77u1q6v.png)
Fig. 4. Throughput versus SNR under a K = 4, [N1, · · ·N4] = [5, 4, 4, 3], [M1, · · ·M4] = [4, 3, 2, 4], [d1, · · · d4] = [2, 1, 1, 1] MIMO interference network and the sum feedback bit constraint is 400. 
Fig. 1. Example of feedback topology design 
Fig. 5. Throughput versus sum feedback bits under a K = 4, Ni = Mi = 3, di = 1, ∀i MIMO interference network and the average transmit SNR is 25 dB. 
Fig. 6. Throughput versus SNR under a K = 4, Ni = Mi = 3, di = 1, ∀i MIMO interference network and the sum feedback bits constraint is 200. 
Fig. 3 plots the network throughput versus the sum limited feedback bits under transmit SNR 25 dB. The proposed scheme outperforms all the baselines. This is because the proposed scheme significantly reduces the CSI feedback dimension while preserving the IA feasibility, and hence more feedback bits can
![Fig. 4. Throughput versus SNR under a K = 4, [N1, · · ·N4] = [5, 4, 4, 3], [M1, · · ·M4] = [4, 3, 2, 4], [d1, · · · d4] = [2, 1, 1, 1] MIMO interference network and the sum feedback bit constraint is 400.](/figures/figure4-1-2o64f77u1q6v.webp)
Citations
Interference Alignment and Its Applications: A Survey, Research Issues, and Challenges
TL;DR: In this review, a survey on IA and its applications is provided, some fundamental aspects of IA are discussed, including feasibility condition, performance metrics, iterative algorithms, and CSI, and some research challenges are identified.
219
•Posted Content
Interference Alignment with Incomplete CSIT Sharing
Paul de Kerret,David Gesbert +1 more
TL;DR: This paper investigates the notion of IA feasibility for CSIT configurations being as incomplete as possible, as this leads to feedback overhead reductions in practice, and shows conditions for which IA is feasible in strictly incomplete CSIT scenarios, even in tightly-feasible settings.
40
Adaptive Modulation and Coding for Interference Alignment With Imperfect CSIT
TL;DR: The statistics of the imperfect CSI are analyzed and an adaptive scheme based on the available imperfect CSI is designed and the accuracy of the approximations in the regimes with practical interest are provided.
29
To Align or Not to Align: Topology Management in Asymmetric Interference Networks
TL;DR: A spectrum-efficient topology management (TM) scheme is proposed for asymmetric interference networks where the location of each user is randomly distributed, and results show that the proposed TM scheme is much more spectrum efficient than the conventional IA scheme in asymmetric interfered networks.
21
Minimization of CSI Feedback Dimension for Interference Alignment in MIMO Interference Multicast Networks
Xiongbin Rao,Vincent K. N. Lau +1 more
TL;DR: This paper considers IA in MIMO interference multicast networks under partial CSI feedback, and attempts to minimize the CSI feedback cost subject to IA feasibility constraints with a given degree of freedom (DoF) requirements, formulated as a combinatorial optimization problem.
21
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TL;DR: For the fully connected K user wireless interference channel where the channel coefficients are time-varying and are drawn from a continuous distribution, the sum capacity is characterized as C(SNR)=K/2log (SNR)+o(log( SNR), which almost surely has K/2 degrees of freedom.
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A Distributed Numerical Approach to Interference Alignment and Applications to Wireless Interference Networks
TL;DR: Examples of iterative algorithms that utilize the reciprocity of wireless networks to achieve interference alignment with only local channel knowledge at each node are provided, providing numerical insights into the feasibility of interference alignment that are not yet available in theory.