This paper considers an application of blind identification to beamforming. The key point is to use estimates of directional vectors rather than resorting to their hypothesized value. By using estimates of the directional vectors obtained via blind identification i.e. without knowing the arrray

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We analyze the effect of finite rate feedback on CDMA (code-division multiple access) signature optimization and MIMO (multi-input-multi-output) beamforming vector selection. In CDMA signature optimization, for a particular user, the receiver selects a signature vector from a codebook to best avoid

beamforming vector which are not always realizable in practice. In this correspondence, a quantized maximum signal-to-noise ratio (SNR) beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter. By using

Abstract—We analyze the effect of finite rate feedback on code-division multiple-access (CDMA) signature optimization and multiple-input multiple-output (MIMO) beamforming vector selection. In CDMA signature optimization, for a particular user, the receiver selects a signature vector from a

between any two beamforming vectors in the beamformer codebook, and is equivalent to the problem of designing unitary space time codes under certain conditions. Finally, we show that good beamformers are good packings of 2-dimensional subspaces in a 2t-dimensional real Grassmannian manifold with chordal

Limited feedback enables the practical use of channel state information in multiuser multiple-input multiple-output (MIMO) wireless communication systems. Using the limited feedback concept, channel state information at the receiver is quantized by choosing a representative element from a codebook known to both the receiver and transmitter. Unfortunately, achieving the high resolution required with multiuser MIMO communication is challenging due to the large number of codebook entries required. This paper proposes to use a progressively scaled local codebook to enable high resolution quantization and reconstruction for multiuser MIMO with zero-forcing precoding. Several local codebook designs are proposed including one based on a ring and one based on mutually unbiased bases; both facilitate efficient implementation. Structure in the local codebooks is used to reduce search complexity in the progressive refinement algorithm. Simulation results illustrate sum rate performance as a function of the number of refinements. Copyright © 2009 Robert W. Heath Jr. et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.

the optimal source and relay beamforming vectors. Next, we consider the beamforming problem with receiver CSI only and provide a beamforming vector quantization scheme. Based on the statistics of the optimal beamforming vectors, we show that Grassmannian codebooks minimize the upper bound for SNR loss caused

Abstract—Joint power control and beamforming schemes are proposed for cellular systems where adaptive arrays are used only at base stations. In the uplink, mobile powers and receiver diversity combining vectors at base stations are calculated jointly. The mobile transmitted power is minimized

technique, the quantized beamforming (QB) technique was proposed by using only some feedback bits instead of full channel information to calculate the suitable beamforming vectors. Unfortunalely, the complexity of finding the beamforming vectors is the limitation of the QB technique. In this paper, we