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Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization (2003)
| Venue: | IEEE TRANS. SIGNAL PROCESSING |
| Citations: | 296 - 19 self |
BibTeX
@ARTICLE{Palomar03jointtx-rx,
author = {Daniel Pérez Palomar and John M. Cioffi and Miguel Angel Lagunas},
title = {Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization},
journal = {IEEE TRANS. SIGNAL PROCESSING},
year = {2003},
volume = {51},
number = {9},
pages = {2381--2401}
}
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Abstract
This paper addresses the joint design of transmit and receive beamforming or linear processing (commonly termed linear precoding at the transmitter and equalization at the receiver) for multicarrier multiple-input multiple-output (MIMO) channels under a variety of design criteria. Instead of considering each design criterion in a separate way, we generalize the existing results by developing a unified framework based on considering two families of objective functions that embrace most reasonable criteria to design a communication system: Schur-concave and Schur-convex functions. Once the optimal structure of the transmit-receive processing is known, the design problem simplifies and can be formulated within the powerful framework of convex optimization theory, in which a great number of interesting design criteria can be easily accommodated and efficiently solved, even though closed-form expressions may not exist. From this perspective, we analyze a variety of design criteria, and in particular, we derive optimal beamvectors in the sense of having minimum average bit error rate (BER). Additional constraints on the peak-to-average ratio (PAR) or on the signal dynamic range are easily included in the design. We propose two multilevel water-filling practical solutions that perform very close to the optimal in terms of average BER with a low implementation complexity. If cooperation among the processing operating at different carriers is allowed, the performance improves significantly. Interestingly, with carrier cooperation, it turns out that the exact optimal solution in terms of average BER can be obtained in closed form.
Keyphrases
design criterion joint tx-rx beamforming design convex optimization unified framework mimo channel average ber optimal structure schur-convex function multiple-input multiple-output linear processing carrier cooperation design problem simplifies minimum average bit error rate optimal beamvectors processing operating additional constraint different carrier peak-to-average ratio transmit-receive processing multilevel water-filling practical solution communication system signal dynamic range great number closed form reasonable criterion convex optimization theory linear precoding powerful framework exact optimal solution objective function low implementation complexity joint design closed-form expression separate way
