PROJECT TITLE :
Distributed Low-Overhead Schemes for Multi-Stream MIMO Interference Channels
Our aim during this paper is to propose fully distributed schemes for transmit and receive filter optimization. The novelty of the proposed schemes is that they solely need a few forward-backward iterations, so causing minimal communication overhead. For that purpose, we tend to relax the well-known leakage minimization problem, and then propose two totally different filter update structures to resolve the resulting nonconvex problem: though one leads to standard full-rank filters, the other ends up in rank-deficient filters, that we have a tendency to exploit to gradually scale back the transmit and receive filter rank, and greatly speed up the convergence. Furthermore, galvanized from the decoding of turbo codes, we tend to propose a turbo-like structure to the algorithms, where a separate inner optimization loop is run at every receiver (additionally to the most forward-backward iteration). In that sense, the introduction of this turbo-like structure converts the communication overhead needed by standard methods to computational overhead at every receiver (a cheap resource), permitting us to achieve the desired performance, under a minimal overhead constraint. Finally, we tend to show through comprehensive simulations that both proposed schemes massively outperform the relevant benchmarks, particularly for large system dimensions.
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