Adaptive channel direction quantization based on spherical prediction

In this paper, we present algorithms for the quantization of a correlated unit norm random vector process. Such algorithms are important, e.g., for channel vector quantization in wireless communication systems. Starting from a quantization codebook that uniformly quantizes the unit sphere, we propose to iteratively adapt the codebook to the channel statistics, in order to improve the quantization accuracy. This is achieved by increasing the density of the quantization code vectors in that area of the unit sphere where the next realization of the random process is expected to lie, without increasing the codebook size. Additionally, we propose spherical prediction algorithms for the considered random process. Combining the codebook adaptation techniques with this prediction leads to improved quantization accuracy. The performance of the algorithms is demonstrated by employing them in an LTE system for providing accurate transmitter channel knowledge used for multiuser beamforming.