Reconfigurable intelligent surface (RIS) is a recent low-cost and energy-efficient technology with potential applicability for future wireless communications. Performance gains achieved by employing RIS directly depend on accurate channel estimation (CE). It is common in the literature to assume channel reciprocity since it minimizes channel feedback, simplifies the beamforming design, and reduces the overall latency. However, in practice, due to hardware limitations at the RIS and transceivers, the channel non-reciprocity may occur naturally, so such behavior needs to be considered. In this paper, we focus on the CE problem in a non-reciprocal RIS-assisted multipleinput multiple-output (MIMO) wireless communication system. Making use of a novel closed-loop three-phase protocol for non-reciprocal CE estimation, we propose a two-stage fourthorder Tucker decomposition-based CE algorithm. In contrast to classical time-division duplexing (TDD) and frequency-division duplexing (FDD) approaches the proposed method concentrates all the processing burden for CE on the base station (BS) side, thereby freeing hardware-limited user terminal (UT) from this task. Our simulation results show that the proposed method has satisfactory performance in terms of CE accuracy compared to benchmark FDD LS-based and tensor-based techniques.

Recent research has focused on advanced architectures for reconfigurable intelligent surfaces (RIS) incorporating sensing capabilities. A notable development is the hybrid simultaneously sensing and reflecting RIS (HRIS), which integrates both sensing and reflecting meta-atoms. This design endows HRIS with signal processing abilities to tackle the channel estimation challenge. This work develops a closed-form semi-blind receiver pair for HRIS-assisted multiple-input multiple-output wireless communications, leveraging HRIS for joint symbol detection and channel estimation under a tensor space-time coding. By transmitting information symbols instead of pilots during the CE stage, our data-aided method reduces decoding delay while estimating symbols at a low computational cost. Simulation results show competitive performance in terms of normalized mean square error and symbol error rates compared to existing solutions, highlighting the effectiveness of enabling semi-blind estimation at both the HRIS and BS. Furthermore, data estimation directly at the HRIS opens new opportunities and use cases for HRIS/RISassisted communications.

Intelligent reconfigurable surface IRS are becoming an attractive component of cellular networks due to their ability to shape the propagation environment and thereby improve coverage. While IRS nodes incorporate a great number of phase-shifting elements and a controller entity, the phase shifts are typically determined by the cellular base station (BS) due to its computational capability. Since controlling a large number of phase shifts may become prohibitive in practice, it is important to reduce the control overhead between the BS and the IRS controller. To this end, in this paper, we propose a low-rank modeling approach for the IRS phase shifts. The key idea is to represent the IRS phase shift vector using a low-rank tensor approximation model, where each rank-one component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Our simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase shifts, but with significantly lower feedback overhead.

In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum intelligent reconfigurable surface (IRS) phase-shift vector for reducing the feedback overhead. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off between achievable data rate and feedback reduction by controling the factorization parameters. Our simulations show that the proposed phase-shift factorization drastically reduces the feedback overhead, while improving the data rate in some scenarios, compared to the state-of-the-art schemes.

In this work, we propose a two-stage tensor-based receiver for joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems. First, we cast the received signal at the pilot subcarriers as a third-order PARAFAC model. Based on this model, we propose a closed-form algorithm based on the LS-KRF (Least Squares - Khatri-Rao Factorization) that estimates the channel gains and the phase-noise terms through multiple rank-one factorizations. From the estimated channel, the second stage of the receiver consists of data estimation based on a ZF (Zero-Forcing) receiver that capitalizes on the tensor structure of the received signal at the data subcarriers via a Selective Kronecker Product (SKP) approach. Our numerical simulations show that the proposed receiver achieves an improved performance compared to the state-of-art receivers.

In this paper, we address the problem of joint downlink (DL) and uplink (UL) channel estimation for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Assuming a closed-loop and multifrequency-based channel training framework in which pilot signals received by multiple antenna mobile stations (MSs) are coded and spread in the frequency domain via multiple adjacent subcarriers, we propose two tensor-based semiblind receivers by capitalizing on the multilinear structure and sparse feature of the received signal at the BS equipped with a hybrid analog-digital beamforming (HB) architecture. As a first processing stage, the joint estimation of the compressed DL and UL channel matrices can be obtained in an iterative way by means of an alternating least squares (ALS) algorithm that capitalizes on a parallel factors model for the received signals. Alternatively, for more restricted scenarios, a closed-form solution is also proposed. From the estimated effective channel matrices, the users’ channel parameters such as angles of departure (AoD), angles of arrival (AoA), and path gains are then estimated in a second processing stage by solving independent compressed sensing (CS) problems (one for each MS). In contrast to the classical approach in the literature, in which the DL and UL channel estimation problems are usually considered as two separate problems, our idea is to jointly estimate both the DL and UL channels as a single problem by concentrating most of the processing burden for channel estimation at the BS side. Simulation results demonstrate that the proposed receivers achieve a performance close to the classical approach that is applied on DL and UL communication links separately, with the advantage of avoiding complex computations for channel estimation at the MS side as well as dedicated feedback channels for each MS, which are attractive features for massive MIMO systems.

In this paper, we consider a wireless communication scenario where M sources simultaneously transmit towards a base station equipped with an array of K sensors. A new method is proposed to solve the spatial signature estimation problem without resorting to training sequences and without knowledge of sources' covariance structure. By assuming that the sources' amplitudes vary between successive time blocks, a fourth-order tensor decomposition of the multimode spatio-temporal data covariance is proposed, from which an iterative algorithm is formulated to estimate sources' spatial signatures. A distinguishing feature of the proposed tensor method is its efficiency in treating the case where the sources' covariance matrix is non-diagonal and unknown, which generally happens when working with sample data covariances computed from a reduced number of snapshots.