In line-of-sight (LoS) environments, point-to-point (P2P) multiple-input multiple-output (MIMO) channel matrix turns out to be rank deficient such that spatial multiplexing becomes unattainable. In this paper, we propose the deployment of distributed intelligent reflecting surfaces (IRSs) to act as artificial scatterers and synthesize a sort of multi-path propagation such that additional degrees of freedom are created. We show that given the far-field deployment of the IRS, it simply resembles a full-duplex relay with a single effective reflection coefficient. However, to maximize the channel capacity both the effective reflection coefficients of all IRSs and the transmit covariance matrix should be jointly optimized, which is a nonconvex optimization problem. Thus, we develop an alternating optimization algorithm to iteratively find a sub-optimal solution. Moreover, we propose different schemes to enhance the composite channel power which would result in an improvement to the achievable rate. Our simulation results show that the deployment of distributed IRSs with P2P MIMO systems in LoS environments increases the rank of the channel matrix, and improves the achievable rate by making spatial multiplexing possible.

We study the positional impact of an intelligent reflecting surface (IRS) on the achievable rate for single and multiple antenna systems. We show that in IRS-aided single antenna systems, it is always best to place the IRS as close as possible to the transmitter or receiver since the large-scale fading for IRS-reflected links is the main factor that characterizes the performance gain. However, for IRS-aided multiple antenna systems, the propagation environment has an important role in characterizing the efficient regions of IRS placement. In the case of a line-of-sight environment, the channel matrix turns out to be rank-deficient. Thus, both far and near IRS placements result in significant achievable rate improvements where the former provides a substantial additional degree-of-freedom, while the latter results in a power gain. Furthermore, as the wireless channel becomes richer with multipath, the rank of the channel matrix increases. Thus, the efficient far placement regions gradually shrink until they disappear in the case of a Rayleigh fading channel where IRS near placements become more efficient than far placements as they result in higher power gains.

We propose a novel binary polarization shift keying modulation scheme for a line-of-sight environment by exploiting the polarization control ability of the reconfigurable intelligent surface (RIS). The RIS encodes the information data in terms of the polarization states of either the reflected wave from the RIS or the composite wireless channel between an RF source and receiver. In the first case, polarization mismatch correction becomes essential at the receiver. In the second case, the RIS pre-codes the reflected wave to compensate for the polarization mismatch which allows non-coherent demodulation at the receiver.

We propose a joint polarization and spatial modulation (JPSM) scheme using reconfigurable intelligent surface (RIS). In this scheme, a RIS equipped with dual-polarized (DP) reflecting elements is used to assist the communication between a transmitter of a single polarized antenna and a receiver equipped with a uniform linear array of DP antennas, while additionally encoding the reflected waves on the RIS to perform JPSM scheme. The information data is encoded in terms of the receiver DP antenna index as well as the polarization state of the received signal. Furthermore, we develop exhaustive and heuristic RIS phase shift design solutions to enable the RIS-JPSM scheme. Moreover, both an optimum maximum likelihood and a low complexity greedy detectors are formulated. The proposed scheme enhances the data rate by operating higher-order polarization modulation in comparison to the conventional RIS-based spatial modulation scheme.

Polarization is a fundamental property of electromagnetic radio signals but often neglected in localization studies. In this paper, we study the potential benefits of integrating the polarization dimension into localization applications. We develop a three-dimensional (3D) geometric channel model between a base station (BS) and user equipment (UE), both equipped with dual-polarized (DP) antennas, which offers fundamental insights into the angles of departure (AoD) from the BS to the UE as well as the 3D orientation of the UE. From the model, we identify the degrees of freedom (DoF) provided by the polarization dimension for localization solutions by evaluating the rank of the equivalent Fisher information matrix. Subsequently, we leverage these DoF to introduce three distinct localization applications: (i) 3D orientation estimation, (ii) 2D AoD estimation, and (iii) mixed 2D position and 1D orientation estimation for vehicular scenarios. Furthermore, for the three localization applications we identify their regions of operation in terms of the ranges of the angles of interest, to avoid any ambiguity occurrence through the estimation process, thereby guaranteeing unique solutions. Finally, we derive the Cramér-Rao lower bounds and numerically establish the efficiency of the proposed estimators.