Efficient Capacity Computation and Power Optimization for Relay Networks
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Publication:2986500
DOI10.1109/TIT.2013.2295099zbMATH Open1360.68169arXiv1111.4244OpenAlexW2002190550MaRDI QIDQ2986500
Raúl Hernán Etkin, F. Parvaresh
Publication date: 16 May 2017
Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)
Abstract: The capacity or approximations to capacity of various single-source single-destination relay network models has been characterized in terms of the cut-set upper bound. In principle, a direct computation of this bound requires evaluating the cut capacity over exponentially many cuts. We show that the minimum cut capacity of a relay network under some special assumptions can be cast as a minimization of a submodular function, and as a result, can be computed efficiently. We use this result to show that the capacity, or an approximation to the capacity within a constant gap for the Gaussian, wireless erasure, and Avestimehr-Diggavi-Tse deterministic relay network models can be computed in polynomial time. We present some empirical results showing that computing constant-gap approximations to the capacity of Gaussian relay networks with around 300 nodes can be done in order of minutes. For Gaussian networks, cut-set capacities are also functions of the powers assigned to the nodes. We consider a family of power optimization problems and show that they can be solved in polynomial time. In particular, we show that the minimization of the sum of powers assigned to the nodes subject to a minimum rate constraint (measured in terms of cut-set bounds) can be computed in polynomial time. We propose an heuristic algorithm to solve this problem and measure its performance through simulations on random Gaussian networks. We observe that in the optimal allocations most of the power is assigned to a small subset of relays, which suggests that network simplification may be possible without excessive performance degradation.
Full work available at URL: https://arxiv.org/abs/1111.4244
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