18,969 research outputs found
Linear Precoding and Equalization for Network MIMO with Partial Cooperation
A cellular multiple-input multiple-output (MIMO) downlink system is studied
in which each base station (BS) transmits to some of the users, so that each
user receives its intended signal from a subset of the BSs. This scenario is
referred to as network MIMO with partial cooperation, since only a subset of
the BSs are able to coordinate their transmission towards any user. The focus
of this paper is on the optimization of linear beamforming strategies at the
BSs and at the users for network MIMO with partial cooperation. Individual
power constraints at the BSs are enforced, along with constraints on the number
of streams per user. It is first shown that the system is equivalent to a MIMO
interference channel with generalized linear constraints (MIMO-IFC-GC). The
problems of maximizing the sum-rate(SR) and minimizing the weighted sum mean
square error (WSMSE) of the data estimates are non-convex, and suboptimal
solutions with reasonable complexity need to be devised. Based on this,
suboptimal techniques that aim at maximizing the sum-rate for the MIMO-IFC-GC
are reviewed from recent literature and extended to the MIMO-IFC-GC where
necessary. Novel designs that aim at minimizing the WSMSE are then proposed.
Extensive numerical simulations are provided to compare the performance of the
considered schemes for realistic cellular systems.Comment: 13 pages, 5 figures, published in IEEE Transactions on Vehicular
Technology, June 201
Exponential Lower Bounds for Polytopes in Combinatorial Optimization
We solve a 20-year old problem posed by Yannakakis and prove that there
exists no polynomial-size linear program (LP) whose associated polytope
projects to the traveling salesman polytope, even if the LP is not required to
be symmetric. Moreover, we prove that this holds also for the cut polytope and
the stable set polytope. These results were discovered through a new connection
that we make between one-way quantum communication protocols and semidefinite
programming reformulations of LPs.Comment: 19 pages, 4 figures. This version of the paper will appear in the
Journal of the ACM. The earlier conference version in STOC'12 had the title
"Linear vs. Semidefinite Extended Formulations: Exponential Separation and
Strong Lower Bounds
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Networks are a unifying framework for modeling complex systems and network
inference problems are frequently encountered in many fields. Here, I develop
and apply a generative approach to network inference (RCweb) for the case when
the network is sparse and the latent (not observed) variables affect the
observed ones. From all possible factor analysis (FA) decompositions explaining
the variance in the data, RCweb selects the FA decomposition that is consistent
with a sparse underlying network. The sparsity constraint is imposed by a novel
method that significantly outperforms (in terms of accuracy, robustness to
noise, complexity scaling, and computational efficiency) Bayesian methods and
MLE methods using l1 norm relaxation such as K-SVD and l1--based sparse
principle component analysis (PCA). Results from simulated models demonstrate
that RCweb recovers exactly the model structures for sparsity as low (as
non-sparse) as 50% and with ratio of unobserved to observed variables as high
as 2. RCweb is robust to noise, with gradual decrease in the parameter ranges
as the noise level increases.Comment: 8 pages, 5 figure
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