Reconfigurable Massive MIMO: Precoding Design and Channel Estimation in the Electromagnetic Domain

Abstract

Reconfigurable massive multiple-input multiple-output (RmMIMO), as an electronically-controlled fluid antenna system, offers increased flexibility for future communication systems by exploiting previously untapped degrees of freedom in the electromagnetic (EM) domain. The representation of the traditional spatial domain channel state information (sCSI) limits the insights into the potential of EM domain channel properties, constraining the base station's (BS) utmost capability for precoding design. This paper leverages the EM domain channel state information (eCSI) for antenna radiation pattern design at the BS. We develop an orthogonal decomposition method based on spherical harmonic functions to decompose the radiation pattern into a linear combination of orthogonal bases. By formulating the radiation pattern design as an optimization problem for the projection coefficients over these bases, we develop a manifold optimization-based method for iterative radiation pattern and digital precoder design. To address the eCSI estimation problem, we capitalize on the inherent structure of the channel. Specifically, we propose a subspace-based scheme to reduce the pilot overhead for wideband sCSI estimation. Given the estimated full-band sCSI, we further employ parameterized methods for angle of arrival estimation. Subsequently, the complete eCSI can be reconstructed after estimating the equivalent channel gain via the least squares method. Simulation results demonstrate that, in comparison to traditional mMIMO systems with fixed antenna radiation patterns, the proposed RmMIMO architecture offers significant throughput gains for multi-user transmission at a low channel estimation overhead.

Figures (16)

• Figure 1: Schematic diagram of RmMIMO-based multi-user downlink transmission in a single cell.
• Figure 2: SHOD of four typical radiation patterns, where the original shape of the Hertz dipole antenna pattern, the 3GPP 38.901 pattern, a downtilt pattern, and a split pattern are compared with their respective reconstructed versions. Furthermore, the respective NMSEs and weight coefficients are shown.
• Figure 3: a) Complexity versus the array size $M$, b) complexity versus the number of SH functions $K$, c) complexity versus the number of UEs $U$, d) complexity versus the number of subcarriers $G$.
• Figure 4: Transmission frame structure of the proposed uplink channel estimation and combing scheme.
• Figure 5: a) Complexity versus the pilot overhead $J$, b) complexity versus the array size $M$, c) complexity versus the multipath number $L_u$.