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Distributed Interference Alignment for MIMO Interference Networks

Distributed Interference Alignment for MIMO Interference Networks. Gill Jul. 15, 2010. System Model. Rx. Tx. nTx × nRx L links Rayleigh fading channel Algorithms compared Min Leakage DIA (ML-DIA) MaxSNR DIA (MS-DIA) Distributed IA with MMSE Rx (DIA-MMSE)

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Distributed Interference Alignment for MIMO Interference Networks

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  1. Distributed Interference Alignment for MIMO Interference Networks Gill Jul. 15, 2010

  2. System Model Rx Tx nTx×nRx L links Rayleigh fading channel Algorithms compared Min Leakage DIA (ML-DIA) MaxSNR DIA (MS-DIA) Distributed IA with MMSE Rx (DIA-MMSE) MMSE at Tx&Rx, MaxSINR (MMSE)

  3. Sum-rate & convergence 4×4 3 links Convergence • 2000 fading channels are simulated, if the algorithm converges in 500 iterations for a channel, the converged value will be kept. If not, the value at 500th iteration will be kept. • MS-DIA has comparable performance in sum-rate due to diversity gain. It also converges fast.

  4. Sum-rate & convergence (Cont.) 3×3 3 links Convergence • L >= M= N, DIA-MMSE performs better than MS-DIA in sum-rate, but it has slower convergence. • The performance of MS-DIA is the same as that of ML-DIA since no extra degrees of freedom can be used to maximize desired signal power.

  5. Sum-rate & convergence (Cont.) 2×2 3 links Convergence • MS-DIAis modified by choosing eigenvector associated with smallest eigenvalue when nullspace does not exist in the case of L > M = N. • 2×2 3 links with fast convergence is a special case in DIA-MMSE and MMSE algorithms.

  6. Convergence of DIA-MMSE L>M=N Notes: 2000 fading channels are simulated. The result of each channel is obtained at the 500th iteration regardless of whether the algorithm converges or not. The case of 2×2 3 links, where DIA-MMSE converges fast, is a special one. Usually, DIA-MMSE does not converge fast.

  7. Convergence of DIA-MMSE L<M=N L=M=N • L< M= N, DIA-MMSE converges fast. • L= M= N, DIA-MMSE converges slower.

  8. Discussion • Larry’s Q1: why two x’s are different? Why their power different? • The 2 x-vectors should be 2 x-scalars since each pair has a single stream. • Different power might be allocated to x_j and x_j-bar to achieve the same set of SINRs in both networks. • In our simulation, uniform power allocation is assumed for simplicity. This might not guarantee monotonicity of SINR in analytical aspect, but average SINR seems to increase monotonically in numerical results. • Larry’s Q2: What is the criterion of convergence used in simulation? • Gill: |SINR(k) - SINR(k+1)| <= 10^(-6) • Xiantao: |10*log SINR(k) - 10*log SINR(k+1)| <=  0.1 ( in dB)

  9. Discussion (Cont.) • Contents of convergence table? • The percent of successful convergences within 500 iterations. • The median time to converge. • Should we keep DIA-MMSE in the paper? • compared with MS-DIA and ML-DIA, DIA-MMSE does not always converge fast; • compared with MMSE, DIA-MMSE is suboptimal in terms of sum-rate. • Letter title and Xiantao’s J#3 as reference? • Title: emphasize MS-DIA which maximizes SNR. • Reference: how to refer J#3 since MS-DIA is based on Coordinated ZF algorithm.

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