Broad Beam Reflection for RIS-Assisted MIMO Systems with Planar Arrays

While reconfigurable intelligent surface (RIS)-aided user-specific beamforming has been vastly investigated, the aspect of utilizing RISs for assisting cell-specific transmission has been largely unattended. Aiming to fill this gap, we study a downlink broadcasting scenario where a base station (BS) sends a cell-specific signal to all the users located in a wide angular area with the assistance of a dual-polarized RIS. We utilize the polarization degree of freedom offered by this type of RIS and design the phase configurations in the two polarizations in such a way that the RIS can radiate a broad beam, thereby uniformly covering all azimuth and elevation angles where the users might reside. Specifically, the per-polarization configuration matrices are designed in such a way that the total power-domain array factor becomes spatially flat over all observation angles implying that the RIS can preserve the broad radiation pattern of a single element while boosting its gain proportionally to its aperture size. We validate the mathematical analyses via numerical simulations.

Dual-Polarized Reconfigurable Intelligent Surface Assisted Broad Beamforming

A reconfigurable intelligent surface (RIS) consists of a large number of low-cost elements that can control the propagation environment seen from a transmitter by intelligently applying phase shifts to impinging signals before reflection. This paper studies an RIS-assisted communication system where a transmitter wants to transmit a common signal to many users residing in a wide angular area. To cover this sector uniformly, the RIS needs to radiate a broad beam with a spatially flat array factor, instead of a narrow beam as normally considered. To achieve this, we propose to use a dual-polarized RIS consisting of elements with orthogonal polarizations and show that the RIS can produce a broad beam if the phase shift configuration vectors in the two polarizations form a so-called Golay complementary sequence pair. By utilizing their properties, we also present a method for constructing configuration for large RISs from smaller ones, while preserving the broad radiation pattern of the smaller RIS. The numerical results corroborate the mathematical analyses and highlight the greatly improved coverage properties.

Deep-Learning Based Linear Precoding for MIMO Channels with Finite-Alphabet Signaling

This paper studies the problem of linear precoding for multiple-input multiple-output (MIMO) communication channels employing finite-alphabet signaling. Existing solutions typically suffer from high computational complexity due to costly computations of the constellation-constrained mutual information. In contrast to existing works, this paper takes a different path of tackling the MIMO precoding problem. Namely, a data-driven approach, based on deep learning, is proposed. In the offline training phase, a deep neural network learns the optimal solution on a set of MIMO channel matrices. This allows the reduction of the computational complexity of the precoder optimization in the online inference phase. Numerical results demonstrate the efficiency of the proposed solution vis-\`a-vis existing precoding algorithms in terms of significantly reduced complexity and close-to-optimal performance.

Efficient Cell-Specific Beamforming for Large Antenna Arrays

We propose an efficient method for designing broad beams with spatially flat array factor and efficient power utilization for cell-specific coverage in communication systems equipped with large antenna arrays. To ensure full power efficiency, the method is restricted to phase-only weight manipulations. Our framework is based on the discovered connection between dual-polarized beamforming and polyphase Golay sequences. Exploiting this connection, we propose several methods for array expansion from smaller to larger sizes, while preserving the radiation pattern. In addition, to fill the gaps in the feasible array sizes, we introduce the concept of $\epsilon$-complementarity that relaxes the requirement on zero side lobes of the sum aperiodic autocorrelation function of a sequence pair. Furthermore, we develop a modified Great Deluge algorithm (MGDA) that finds $\epsilon$-complementary pairs of arbitrary length, and hence enables broad beamforming for arbitrarily-sized uniform linear arrays. We also discuss the extension of this approach to two-dimensional uniform rectangular arrays. Our numerical results demonstrate the superiority of the proposed approach with respect to existing beam-broadening methods.