Vector quantization and signal compression
Vector quantization and signal compression
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Channel Feedback Quantization for High Data Rate MIMO Systems
IEEE Transactions on Wireless Communications
On the achievable throughput of a multiantenna Gaussian broadcast channel
IEEE Transactions on Information Theory
Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality
IEEE Transactions on Information Theory
On beamforming with finite rate feedback in multiple-antenna systems
IEEE Transactions on Information Theory
Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels
IEEE Transactions on Information Theory
Grassmannian beamforming for multiple-input multiple-output wireless systems
IEEE Transactions on Information Theory
On the capacity of MIMO broadcast channels with partial side information
IEEE Transactions on Information Theory
Dirty-paper coding versus TDMA for MIMO Broadcast channels
IEEE Transactions on Information Theory
Efficient use of side information in multiple-antenna data transmission over fading channels
IEEE Journal on Selected Areas in Communications
On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming
IEEE Journal on Selected Areas in Communications
Multi-Antenna Downlink Channels with Limited Feedback and User Selection
IEEE Journal on Selected Areas in Communications
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This paper addresses the problem of linear beamforming design in MIMO broadcast channels. An iterative optimization method for unitary beamforming is proposed, based on successive optimization of Givens rotations. Under the assumption of perfect channel state information at the transmitter (CSIT) and for practical average signal-to-noise ratios (SNR), the proposed technique provides higher sum rates than zeroforcing (ZF) beamforming while performing close to minimummean-squared-error (MMSE) beamforming when the number of transmit antennas equals the number of scheduled users. Moreover, it is shown to achieve linear sum-rate growth with the number of transmit antennas. Interestingly, the proposed unitary beamforming approach proves to be very robust to channel estimation errors. In the simulated scenarios, it provides better sum rates than ZF beamforming and even MMSE beamforming as the variance of the estimation error increases. When combined with simple vector quantization techniques for CSIT feedback in systems with multiuser scheduling, the proposed technique proves to be well suited for limited feedback scenarios with practical number of users, exhibiting performance gains over existing techniques.