Channel Quantization Based on the Statistical Characterization of Spatially Correlated Fading

Abstract

Multiuser multiple-input multiple-output (MU-MIMO) techniques, such as scheduling and precoding, have shown to improve the spectral efficiency of wireless communication systems. However, these techniques require an accurate knowledge of the channel of the different users at the transmitter. In frequency division duplex systems, this information has to be provided by the different users, motivating the research of efficient limited feedback schemes. This paper presents a novel statistical characterization of the spatial multiple-input single-output (MISO) channel. In this characterization, one antenna is selected as the reference and the channel fading experienced from this antenna is also considered as reference. The conditional probability density functions (CPDF) of the envelope and phase of the channel fading coefficients from the rest of the antennas (denoted as non-reference channel fading and nonreference antennas) are obtained given the reference one. Based on this statistical characterization, this paper proposes a channel quantization scheme that individually quantizes the channel fading coefficient of each transmit antenna that is seen by each user. The envelope and phase of the reference channel fading are quantized considering a Rayleigh distribution and a uniform distribution, respectively. The non-reference channel fading coefficients are quantized according to their respective CPDFs, which in turn depend on the spatial correlation between each channel fading and the reference channel fading.

Enabling Covariance-Based Feedback in Massive MIMO: A User Classification Approach

“…Numerous works have been dedicated to reducing feedback overhead, such as trellis-extended codebook design [4], compressive sensing-based channel feedback reduction [5], channel covariance-based feedback representation [6]- [9]. In [9], a two-stage precoding structure was introduced to reduce downlink pilot and channel feedback overhead.…”

Effects of non‐uniform quantisation on the interference mitigation using multi‐cell multiple‐input and multiple‐output‐coordinated beamforming

“…15 levels for amplitude and 38 levels for phase). Concerning the high computational complexity, Domene et al [14] proposed uniform quantisation methods for multiuser MIMO systems based on the spatially‐correlated fading statistics and quantisation theory in [13]. Frequency correlation can be considered in addition to spatial correlation.…”