Matrix computations (3rd ed.)
Microwave Mobile Communications
Microwave Mobile Communications
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
A new two-step precoding strategy for closed-loop MIMO systems
IEEE Transactions on Communications
Capacity of fading channels with channel side information
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels
IEEE Transactions on Information Theory
Antenna selection in MIMO systems
IEEE Communications Magazine
A simple transmit diversity technique for wireless communications
IEEE Journal on Selected Areas in Communications
From theory to practice: an overview of MIMO space-time coded wireless systems
IEEE Journal on Selected Areas in Communications
Capacity limits of MIMO channels
IEEE Journal on Selected Areas in Communications
A new two-step precoding strategy for closed-loop MIMO systems
IEEE Transactions on Communications
An approach to secure wireless communications using randomized eigenvector-based jamming signals
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Securing wireless communications in transmit-beamforming systems by precoding jamming noise signals
Security and Communication Networks
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In this paper, we present a low-complexity method to generate a transmit beamforming vector for multiple-input-multiple-output (MIMO) systems. We begin by introducing new definitions regarding orthogonality between two complex-valued vectors and then present new expressions of complex rotation matrices for the complex vector orthogonalization. The rotation matrices are utilized to derive the weight vector for the maximum-norm combining (MNC) process of two complex vectors, which provides a constructive basis for a new beamforming method. The proposed transmit beamforming method uses successive column combining of MIMO channel matrices based on MNC, and as a result, an approximate solution to the optimum beamforming vector is obtained. The proposed method offers a good tradeoff between complexity and performance. Simulation results demonstrate that the proposed beamforming method achieves the near-optimal performance with much reduced computational complexity, compared to the optimal beamforming scheme using singular-value decomposition (SVD) of the channel matrix.