Statistical Beamforming for FDD Downlink Massive MIMO via Spatial Information Extraction and Beam Selection

Abstract

In this paper, we study the beamforming design problem in frequency-division duplexing (FDD) downlink massive MIMO systems, where instantaneous channel state information (CSI) is assumed to be unavailable at the base station (BS). We propose to extract the information of the angle-of-departures (AoDs) and the corresponding large-scale fading coefficients (a.k.a. spatial information) of the downlink channel from the uplink channel estimation procedure, based on which a novel downlink beamforming design is presented. By separating the subpaths for different users based on the spatial information and the hidden sparsity of the physical channel, we construct near-orthogonal virtual channels in the beamforming design. Furthermore, we derive a sum-rate expression and its approximations for the proposed system. Based on these closed-form rate expressions, we develop two low-complexity beam selection schemes and carry out asymptotic analysis to provide valuable insights on the system design.Numerical results demonstrate a significant performance improvement of our proposed algorithm over the state-of-the-art beamforming approach.

Low-complexity Beam Selection algorithms based on SVD for MmWave Massive MIMO Systems

“…where Ĥ ∈ C M ×K is the beamspace channel matrix obtained by (7), F ∈ R M ×K is the beam selection matrix whose entries f ij are either 0 or 1 , and n ∼ CN 0, σ 2 I K is a K × 1 additive white Gaussian noise (AWGN) vector. Then the SINR of user k can be expressed as…”

“…We can only select a few energyfocused beams with negligible performance losses [6], and the required number of RF chains can be drastically fewer than traditional MIMO. However, it has been proved in [7] that the beam selection problem is NP-hard.…”

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

“…In appreciation of the current learning models availed in different machine learning-based problem analysis, there is existing models of single and 2D only. Based on the most recurrent studies of beam selection for instance, various effective algorithms to find the active beam set and the user power allocation have been proposed in search of efficiency in communication, using the DL-centered mmWave beam selection for fifth-generation applicability of NR and 6Gbased access to an unlicensed spectrum with sub-6 gigahertz channel information for proper algorithmic and prototypic validations, propagation of the high speed transport category of the railway searching for the lower complexity of beam selection, increased performance and evaluation of adaptable receiver beam selections, intersecting index-based joint beam selection for mmWave multiuser multiple-input and multiple-output systems, and simplified spatial data mining and beam selection [13][14][15][16][17][18]. This paper is proposing an alternative way to attack the problem to network issue analysis to the acknowledged medical network situations based on the prevailing readings, prototypes, and designs, which are clarified on the medical application illustrated above in 3D.…”

Structured Channel Covariance Estimation from Limited Samples for Large Antenna Arrays

“…Massive multiple-input multiple-output (MIMO) communication system, where the number of base station (BS) antennas M is much larger than the number of single antenna users, has been shown to achieve high spectral efficiency in wireless cellular networks and to enjoy various system level benefit, such as energy efficiency, inter-cell interference reduction, and dramatic simplification of user scheduling (e.g., see [2,3]). In a large number of papers on the subject, the knowledge of the uplink (UL) and downlink (DL) channel covariance matrix, i.e., of the correlation structure of the channel antenna coefficients at the BS array, is assumed and used for a variety of purposes, such as minimum mean square error (MMSE) UL channel estimation and pilot decontamination [4][5][6], efficient DL multiuser precoding/beamforming design, especially in the frequency division duplexing (FDD) case [7][8][9][10][11], statistical channel state information (CSI) based transmission design, and statistical beamforming [12][13][14].…”