References of "Palomar, Daniel"
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See detailA parallel decomposition method for nonconvex stochastic multi-agent optimization problems
Yang, Yang UL; Scutari, Gesualdo; Palomar, Daniel et al

in IEEE Transactions on Signal Processing (2016), 64(11), 2949-2964

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See detailAn online parallel algorithm for spectrum sensing in cognitive radio networks
Yang, Yang UL; Zhang, Mengyi; Pesavento, Marius et al

in Proc. 48th Asilomar Conference on Signals, Systems and Computers (2014, November)

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See detailRobust MIMO cognitive radio systems under interference temperature constraints
Yang, Yang UL; Scutari, Gesualdo; Song, Peiran et al

in IEEE Journal on Selected Areas In Communications (2013), 31(11), 2465-2482

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See detailMulti-portfolio optimization: A potential game approach
Yang, Yang UL; Rubio, Francisco; Scutari, Gesualdo et al

in IEEE Transactions on Signal Processing (2013)

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See detailParallel stochastic decomposition algorithms for multi-agent systems
Yang, Yang UL; Scutari, Gesualdo; Palomar, Daniel

in Proc. IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (2013, June)

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See detailRobust MIMO cognitive radio systems under interference temperature constraints
Yang, Yang UL; Song, Peiran; Scutari, Gesualdo et al

in Proc. 2013 IEEE International Conference on Acoustics, Speech and Signal (2013, May)

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See detailMulti-portfolio optimization: A potential game approach
Yang, Yang UL; Rubio, Francisco; Scutari, Gesualdo et al

in Game Theory for Networks, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 75. Springer, Berlin, Heidelberg (2011, April)

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See detailJoint Bit Allocation and Precoding for MIMO Systems with Decision Feedback Detection
Bergman, Svante; Palomar, Daniel; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2009), 57(11), 4509-4521

This paper considers the joint design of bit loading, precoding and receive filters for a multiple-input multiple-output (MIMO) digital communication system employing decision feedback (DF) detection at ... [more ▼]

This paper considers the joint design of bit loading, precoding and receive filters for a multiple-input multiple-output (MIMO) digital communication system employing decision feedback (DF) detection at the receiver. Both the transmitter as well as the receiver are assumed to know the channel matrix perfectly. It is well known that, for linear MIMO transceivers, a diagonal transmission (i.e., orthogonalization of the channel matrix) is optimal for some criteria. Surprisingly, it was shown five years ago that for the family of Schur-convex functions an additional rotation of the symbols is necessary. However, if the bit loading is optimized jointly with the linear transceiver, then this rotation is unnecessary. Similarly, for DF MIMO optimized transceivers a rotation of the symbols is sometimes needed. The main result of this paper shows that for a DF MIMO transceiver where the bit loading is jointly optimized with the transceiver filters, the rotation of the symbols becomes unnecessary, and because of this, also the DF part of the receiver is not required. The proof is based on a relaxation of the available bit rates on the individual substreams to the set of positive real numbers. In practice, the signal constellations are discrete and the optimal relaxed bit loading has to be rounded. It is shown that the loss due to rounding is small, and an upper bound on the maximum loss is derived. Numerical results are presented that confirm the theoretical results and demonstrate that orthogonal transmission and the truly optimal DF design perform almost equally well. [less ▲]

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