Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems

Abstract

This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications.

Three-Dimensional Wideband Non-Stationary Channel Modeling for Unmanned Aerial Vehicle Communication under Flight-Induced Jitter Effects

“…UAVs have emerged as a promising solution to meet these demands due to their agility, flexibility, and ability to operate in challenging environments [1][2]. MIMO (Multiple-Input Multiple-Output) technology, with its ability to use multiple antennas for transmitting and receiving signals simultaneously, is a key enabler for achieving high data rates and improved spectral efficiency [3][4][5]. Integrating MIMO technology with UAVs opens up new possibilities for enhanced communication capabilities, enabling applications such as real-time video streaming, Internet of Things (IoT) connectivity, and mission-critical communications [6,7].…”

IUG‐based beam selection for wideband millimetre wave massive MIMO systems

“…Millimetre wave (mmWave) massive multiple-input multipleoutput (MIMO) has become one of the key technologies for future wireless communication systems [1][2][3]. Generally, each antenna is required to connect to one dedicated radio-frequency (RF) chain for realizing the fully digital precoding, but this will result in the huge hardware complexity and energy consumption, especially in the massive MIMO systems [4][5][6] Fortunately, the beamspace MIMO system based on a lens antenna array (LAA) is developed [7], where the conventional spatial channel can be transformed into the beamspace channel [8,9].…”

“…Instead, all users are brought into the iterative evolution process without distinction. In addition, the fitness (e.g., sum-rate) of all evolutionary individuals need to be calculated frequently, which requires relatively high computational complexity, especially for wideband systems 1 . To select multiple beams for each user, a beam selection method following signal to interference plus noise ratio (SINR) maximization is proposed [16], which effectively improves the system sum-rate.…”