TY - JOUR
T1 - Joint Tx-Rx beamforming design for multicarrier MIMO channels
T2 - A unified framework for convex optimization
AU - Palomar, Daniel Pérez
AU - Cioffi, John M.
AU - Lagunas, Miguel Angel
PY - 2003/9
Y1 - 2003/9
N2 - 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.
AB - 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.
KW - Array signal processing
KW - Beamforming
KW - Joint transmit-receive equalization
KW - Linear precoding
KW - MIMO channels
KW - Space-time filtering
KW - Water-filling
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000184861900012
UR - https://openalex.org/W2165350939
UR - https://www.scopus.com/pages/publications/0042283724
U2 - 10.1109/TSP.2003.815393
DO - 10.1109/TSP.2003.815393
M3 - Journal Article
SN - 1053-587X
VL - 51
SP - 2381
EP - 2401
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 9
ER -