Parametric Sparse Channel Estimation for RIS-Assisted Terahertz Systems

The attenuation of signals due to propagation environment is the major challenge of wireless communication which results in frequent call drops, reduced signal strength and low transmission rates. The propagation channel often degrades the signal quality at the receiver due to channel effects including fading, shadowing, path loss and other overhead. The propagation channel and its successful estimation is very important for ensuring communication reliability in next generation wireless systems. This paper presents an intelligent mechanism based on deep learning to estimate the wireless channel such that the system spectral efficiency is enhanced. The impact of signal distortion due to hardware effects is also considered. Further, the proposed scheme is compared with the conventional LMMSE channel estimation scheme. Also, to extract the data using the estimated channel, the performance of proposed scheme is evaluated using three receivers namely, RZF, MMSE and modified MMSE. It is observed that the proposed scheme outperforms the LMMSE scheme in terms of normalised mean square error (NMSE) by 14.43% and by 27.16% in the absence of distortion. In the end, the performance comparison with the system with known perfect channel state information (CSI) is also performed.

This paper investigates a hybrid framework that combines free space optics (FSO) and terahertz (THz) technologies for a backhaul network. In this hybrid configuration, the FSO and THz systems are empowered by reconfigurable intelligent surface (RIS) and are combined at the receiver using either maximal ratio combining (MRC) or selection combining (SC). The performance of the RIS-assisted hybrid FSO/THz system is evaluated under multiple impairments for FSO and THz links. The exact closed-form expressions for probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) of the output signal-to-noise ratio (SNR) of both RIS-assisted FSO and THz links are derived. Leveraging these SNR statistics, we compute the outage probability and the average symbol error rate (SER) of the proposed hybrid FSO/THz system with MRC and SC schemes. Additionally, this paper conducts an asymptotic analysis at high SNR levels, yielding asymptotic expressions for outage and average SER, which not only reduce computational complexity but also provide insights into the system’s diversity gain. In the numerical results, the performance of the proposed hybrid FSO/THz system is compared against other state-of-the-art wireless systems, including direct link FSO without RIS, RIS-aided FSO, and the hybrid FSO/THz systems without RIS using diversity combining techniques.

Recently, technological developments have enhanced, the use of Millimeter-wave (mm-Wave) Multiple Input Multiple Output (MIMO) system in various communication applications and wireless sensor networks as channel estimation efficiency can be immensely improved with the help of this technological developments in Millimeter-wave MIMO system and wireless sensor network as well. Moreover, they can improve quality of communication services to a great extent. However, cell interference in Millimeter-wave (mm-Wave) MIMO system can produce a massive impact on spectral efficiency. Therefore, a Routing Enabled Channel Estimation (RECE) technique is presented in this article to minimize interference between cells. The proposed Channel Estimation technique improves channel capacity as well as spectral efficiency. Moreover, Normalized Mean Square Error (NMSE) is minimized heavily using proposed RECE technique. Here, main aim of this article is to reduce cell interference and channel estimation inside a cell by using route selection, beam selection, and spatial frequency estimation. Here, different scenarios and parameters are considered to evaluate performance efficiency of proposed RECE technique in terms of spectral efficiency, NMSE and SNR and compared against varied traditional channel estimation techniques. Moreover, it is clearly evident from performance results that the proposed channel estimation technique performs better than the other two methods.

Downlink channel estimation with low pilot overhead is an important and challenging problem in massive multiple‐input–multiple‐output (MIMO) systems due to the substantially increased MIMO channel dimension. A block iterative support detection (block‐ISD)‐based algorithm for downlink channel estimation to reduce the pilot overhead is proposed, which is achieved by fully exploiting the block sparsity inherent in the block‐sparse equivalent channel derived from the spatial correlations of MIMO channels. Furthermore, unlike conventional compressive sensing (CS) algorithms that rely on prior knowledge of the sparsity level, block‐ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block‐ISD yields better normalised mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 84% pilot overhead compared with conventional channel estimation techniques.

Reconfigurable intelligent surface (RIS) has shown its potential in terahertz (THz) communications, due to its capability to expand coverage and compensate for the severe attenuation of THz signals. This paper investigates a large-scale RIS-assisted THz communications system in the near-field. Nevertheless, the beam squint effect of RIS, caused by the frequency-independent phase shifting circuit, results in severe array gain loss across the wide bandwidth. While true time delays (TTDs) that generate frequency-dependent phase shifts can mitigate beam squint, they often suffer from high power consumption. To address the drawback, we introduce a set of fixed true time delays (FTTDs) with low power consumption and low insertion loss for the RIS. These FTTDs, shared by the elements of RIS, can generate frequency-dependent phase shifts, thereby addressing the beam squint effect. To overcome the limitation of FTTDs being unable to change delays, we propose a dynamic architecture that consists of a switch network and two-layer phase shifters, allowing the elements of RIS to select the FTTDs. Subsequently, we analyze the theoretical array gain of the proposed FTTD-equipped RIS and determine the minimum number of FTTDs required for effective mitigation. Then, we formulate a problem of maximizing the achievable rate and propose a two-stage algorithm. Specifically, in the first stage, we obtain the optimal wideband beamforming design for both the BS and the RIS. In the second stage, we approximate this optimal design with the beamforming design from our proposed architecture. Finally, simulation results demonstrate that our proposed RIS design, with a small number of FTTDs, can achieve a near-optimal achievable rate and higher energy efficiency.

Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating blockages in millimeter wave (mmWave) and terahertz (THz) communications networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Tra-ditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for extracting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.

In this paper, we propose a location-aware channel estimation based on the atomic norm minimization (ANM) for the reconfigurable intelligent surface (RIS)-aided millimeter-wave multiple-input-multiple-output (MIMO) systems. The beam training overhead at the base station (BS) is reduced by the direct beam steering towards the RIS with the location of the BS and the RIS. The RIS beamwidth adaptation is proposed to reduce the beam training overhead at the RIS, and also it enables accurate channel estimation by ensuring the user equipment receives all the multipath components from the RIS. After the beam training, the cascaded effective channel of the RIS-aided MIMO systems is estimated by ANM. Depending on whether the beam training overhead at the BS or at the RIS is reduced or not, the channel is represented as a linear combination of either 1D atoms, 2D atoms, or 3D atoms, and the ANM is applied to estimate the channel. Simulation results show that the proposed location-aware channel estimation via 2D ANM and 3D ANM achieves superior estimation accuracy to benchmarks.

Transportation infrastructures integrate advanced Information Technology to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept that improve the efficiency and safety of road traffic are being built and will be commercially available through Intelligent Transport Systems (ITS) technology and services. To support ITS technology, Vehicle-To-Everything (V2X) is needed, and in the 3rd Generation Partnership Project, many studies have focused on Long Term Evolution (LTE)-based vehicle communications. In order to realize reliable and optimized communications performance in vehicle communications, which move in propagation environments at high speed, in this study, we propose a novel channel estimation scheme suited for LTE sidelink-based Vehicle-To-Vehicle systems. Conventional channel estimation schemes can be categorized as Decision-Directed Channel Estimation, spectral temporal averaging, and smoothing methods. In this study, unlike conventional channel estimation schemes, we propose a Novel Interference Cancellation of Channel Estimation (NICCE) using Quadratic Smoothing of the pilot symbols, which estimates a channel with greater accuracy, and a novel interference cancellation of channel estimation in data symbols. In simulation results, the proposed NICCE scheme shows improved overall performance in terms of the Normalized Mean Square Error and uncoded Bit Error Rate relative to conventional schemes.

Inherent inter-symbol and inter-carrier interference elimination ability of cyclic prefixed OFDM transmission fails for the case of multipath fading channels when the channel impulse response (CIR) length exceeds the duration of cyclic prefix (CP). Conventional channel estimation and equalization schemes, if applied to this case of insufficient CP, suffer significant performance degradation. We propose, in this paper, a channel estimation scheme that enables estimation of the complete CIR even beyond the CP length. We then design an optimal MMSE based equalizer for the suppression of insufficient CP generated interference. A robust and low complexity version of this equalizer is also derived. Simulation results for the proposed schemes show significant performance gain at low SNRs and drastic reduction of the error floors at high SNRs and more importantly, as opposed to earlier schemes, without any loss in transmission efficiency.

Recently, using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems has received much attention from academia and industry. RIS is a uniform planar array composed of a large number of low-cost, passive, and tunable reflecting elements, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. However, due to a large number of passive RIS elements without signal processing capability, which will result in a sharp increase of the pilot overhead for Channel Estimation (CE). To tackle this challenge, we propose a holographic version of RIS based on the holographic principle and Hall effect to accomplish communication without CE, which can simplify the complicated CE in RIS to the invisible CE containing holographic principle, avoiding the complicated CE. The scheme accomplished communication by interference patterns to restore the communication information required by the user. We loaded the Hall sensor on the RIS to fulfill the electromagnetic recording about the pattern. Finally, simulation results illustrate the effectiveness of the proposed scheme.

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate-energy Pareto frontier by 30-75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber-physical systems.

Millimeter wave communications are sensitive to complex scattering environments (e.g., to the presence of obstacles), which can be mitigated by reconfigurable intelligent surfaces (RISs). Traditional nearly passive RISs lack the ability to perform signal processing operations, which makes channel estimation in RIS-aided communications more challenging. Hence, in this paper, we focus on a hybrid RIS architecture, which is equipped with a small number of active elements. These active elements can be connected with baseband processing units through radio frequency (RF) chains. We estimate the whole channel, including the channel between the base station (BS) and the RIS, that between the users and the RIS, and that between the BS and the users. The whole channel is acquired at the hybrid RIS through the transmission of several segments of training pilots. In order to decrease the hardware cost, the BS and the hybrid RIS are equipped with RF chains with low-resolution analog-to-digital converters (ADCs). Since the numbers of BS antennas and RIS elements are very large, the estimation of the full-space channels is not straightforward. To tackle this problem, we propose a channel extrapolation scheme based on a joint selection model. Specifically, we select a BS antenna subset and an RIS element subset to be connected to the RF chains and to estimate the partial-space channels related to these subsets. Then, a reference-based variational auto-encoder model is used to implement the extrapolation from the partial-space channels to the full-space channels. Besides, the optimal joint selection pattern is acquired through a selection network to improve the channel extrapolation performance. Moreover, to overcome the quantization error caused by the use of low-resolution ADCs, we propose a two-stage repair scheme for channel estimation. Simulation results are provided to demonstrate the effectiveness of the designed channel extrapolation scheme.

A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell’s reflection law. However, the optimal control of the RIS requires perfect channel state information (CSI) of the individual channels that link the base station (BS) and the mobile station (MS) to each other via the RIS. Thereby super-resolution channel (parameter) estimation needs to be efficiently conducted at the BS or MS with CSI feedback to the RIS controller. In this paper, we adopt a two-stage channel estimation scheme for RIS-aided millimeter wave (mmWave) MIMO systems without a direct BS-MS channel, using atomic norm minimization to sequentially estimate the channel parameters, i.e., angular parameters, angle differences, and the products of propagation path gains. We evaluate the mean square error of the parameter estimates, the RIS gains, the average effective spectrum efficiency bound, and average squared distance between the designed beamforming and combining vectors and the optimal ones. The results demonstrate that the proposed scheme achieves super-resolution estimation compared to the existing benchmark schemes, thus offering promising performance in the subsequent data transmission phase.

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglected the inherent difficulty of channel estimation (CE) for RIS-assisted systems, and the discrete phase shift constraint in practical deployment. In this paper we design a new element-wise RIS architecture and propose a distributed location-aided transmission scheme with low complexity to enhance the reflected gain for channel state information (CSI)-limited RIS-assisted near-field communications. Specifically, the new element-wise RIS provides dynamic element selection capability with low hardware resources. Based on Fresnel diffraction theory, we construct the mapping from locations in space-domain to phase distributions of waves in phase-domain and reveal the priority of elements for harvesting and reflecting. Then, the distributed beamforming design with the phase of determine-then-align is proposed, where the estimation overhead reduction stems from exempted requirements of RIS-associated CE at base station (BS). The asymptotic analysis indicates that the proposed scheme can achieve the optimal gain with a fixed proportion of reflective elements when RIS is large, followed by simulations to verify its superiority to other protocols.

Channel estimation (CE) plays a key role in reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication systems, while it poses a challenging task due to the passive nature of RIS and the cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (PDANM) framework is proposed for CE of RIS-aided MIMO systems, which exploits the three-dimensional angular sparsity of the channel. In particular, PDANM partially decouples the differential angles at the RIS from other angles at the base station and user equipment, reducing the computational complexity compared with existing methods. A reweighted PDANM (RPDANM) algorithm is proposed to further improve CE accuracy, which iteratively refines CE through a specifically designed reweighting strategy. Building upon RPDANM, we propose an iterative approach named RPDANM with adaptive phase control (RPDANM-APC), which adaptively adjusts the RIS phases based on previously estimated channel parameters to facilitate CE, achieving superior CE accuracy while reducing training overhead. Numerical simulations demonstrate the superiority of our proposed approaches in terms of running time, CE accuracy, and training overhead. In particular, the RPDANM-APC approach can achieve higher CE accuracy than existing methods within less than 30 percent training overhead while reducing the running time by tens of times.