Terahertz Massive MIMO With Holographic Reconfigurable Intelligent Surfaces

Abstract

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing subwavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closedloop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular and delay domain. Simulation results demonstrate the superiority of holographic RISs over the nonholographic ones, and the effectiveness of the proposed CE scheme.

Intelligent Reflecting Surfaces at Terahertz Bands: Channel Modeling and Analysis

“…Although there are still many critical questions about the operation of THz IRSs, there is a dearth of related literature. From related work, we distinguish [10], where the authors showed that the far-field beampattern of a holographic IRS can be well approximated by that of an ultra-dense IRS, and then proposed a channel estimation scheme for THz massive multiple-input multiple-output (MIMO) aided by a holographic IRS. However, due to the high propagation losses and the short wavelength, a THz IRS is expected to consist of a massive number of passive reflecting elements, resulting in a radiating near-field, i.e., Fresnel zone, of several meters.…”

Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

“…Content may change prior to final publication. Derivation of exact end-to-end SNR expressions for the RISaided system and AF relay system [68] Provide a two-stage positioning with dual RIS in mmWave MIMO systems [69] Introduce the cross-entropy method to maximize the sum-rate performance of RIS-aided indoor THz transmission [70] Discuss the coverage of an RIS-assisted large-scale mmWave cellular network using stochastic geometry [71] Present a robust technique for mitigating quantization errors in low resolution mmWave RISs under plane-wave illumination using random phase delays [72] Investigate an IRS-aided multi-user THz MIMO system with OFDMA, where the sparse radio frequency chain antenna structure is adopted for reducing the power consumption [73] Formulate the closed-form expression of uplink achievable rate expression of RIS-aided mmWave systems [74] Study the enhancement of network reliability and connectivity of mmWave RISs-aided communication system in the presence of random blockages [75] Discuss Geometric Mean Decomposition (GMD)-based beamforming for RIS-aided wideband hybrid mmWave MIMO systems [76] Study the ranging performance of RIS-aided mmWave systems in the presence of blockages [77] Present a design of a practical beam alignment and channel parameter estimation method for RIS-aided mmWave MIMO networks via atomic norm minimization [78] Discuss a holographic version of an RIS and its use in THz mMIMO systems [79] Present the optimal RIS placement in mmWave networks reduction, channel acquisition, beamforming design, and user localization strategies [67].…”

“…To design a practical beam alignment and channel parameter estimation method for single-user RIS-aided mmWave MIMO networks, the results in [77] show that the proposed method outperforms the standard beam alignment in terms of mean square error and spectrum efficiency. To reduce the pilot overhead, Compressive Sensing (CS)-based algorithm is developed that exploits the dual sparsity of THz MIMO channels in both the angular and delay domain [78]. In [79], the authors presented an optimal RIS placement in mmWave networks, that maximizes the SNR performance of highly directional single-user RIS-aided mmWave links of fixed topology under blockage.…”

Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

“…But different from wireless relays, RIS is generally implemented by low-cost hardware, such as meta-material antennas, positive intrinsicnegative diodes, field effect transistors, and can passively reflect the signal in the RF frontend without any baseband signal processing capabilities. The RIS introduces additional channel links between the transmitter and the RIS as well as those between the RIS and the receiver [23][24][25]. As a result, the CSI acquisition in the RIS-assisted mmWave massive MIMO system becomes much more complicated and is different from that in mmWave systems.…”