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The directions and gains of the paths with significant power can reconstruct mm-wave channel estimation, attributed to spatial sparsity resulting from severe propagation loss. Leveraging this feature, the two-dimensional beamspace method of direction estimation (2-D BMODE) is developed to estimate the path directions, including angles of departure and arrival. The least-squares(LS) method is then employed to estimate the path gains. Different from the beamspace two-dimensional multiple signal classification (MUSIC) method, the method proposed in this paper significantly ruduces computational complexity without spectral search. Moreover, the DODs and DOAs can be paired automatically by reduced-dimension(RD) MUSIC. Meanwhile, the performance of this method deteriorates when the covariance matrix of gains isn’t of full column rank. This paper also enhances the performance using spatial smoothing technique and oblique projecting to overcome the influence of rank deficiency. The 2-D BMODE method, as illustrated in this paper, outperforms in terms of both unity in computational complexity and estimation accuracy. Numerical simulations validate the advantages and demonstrate the method can serve as a superior alternative.

The latest discussions on upcoming 6G wireless communications are envisioning future networks as a unified communications, sensing, and computing platform. The recently conceived concept of the smart radio environment, enabled by reconfigurable intelligent surfaces (RISs), contributes toward this vision, offering programmable propagation of information-bearing signals. Typical RIS implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways. However, this solely reflective operation induces significant challenges for RIS optimization from the wireless network orchestrator. For example, RISs lack information to locally tune their reflection pattern, which can be acquired only by other network entities and then shared with the RIS controller. Furthermore, channel estimation, which is essential for coherent RIS-empowered communications, is challenging with the available RIS designs. This article reviews the emerging concept of hybrid reflecting and sensing RISs (HRISs), which enables metasurfaces to reflect the impinging signal in a controllable manner while simultaneously sensing a portion of it. The sensing capability of HRISs facilitates various network management functionalities, including channel parameter estimation and localization, while giving rise to potentially computationally autonomous and self-configuring metasurfaces. We discuss a hardware design for HRISs and detail a full-wave electromagnetic (EM) proof of concept. The distinctive properties of HRISs, in comparison to their solely reflective counterparts, are highlighted, and a simulation study evaluating HRISs’ capability for performing full and parametric channel estimation is presented. Future research challenges and opportunities arising from the HRIS concept are also included. [ABSTRACT FROM AUTHOR]

Supporting high mobility is an important feature of the 6G mobile communication systems, which puts forward a great challenge for conventional orthogonal frequency division multiplexing (OFDM) systems due to the severe Doppler effects. Recently, orthogonal time frequency space (OTFS) has been proposed as a promising waveform for high-mobility scenarios, due to its excellent capability of exploiting delay-Doppler (DD) diversity to significantly improve the system performance. However, in multicell OTFS systems, intercell interference (ICI) is one of the major challenges, especially for cell-edge users (CEUs). In this article, the impacts of different types of interference on OTFS systems are first studied. Then, interference coordination and cooperative communications-based multicell OTFS systems are studied to overcome ICI, respectively. Next, simulation results are presented for the performance evaluation of these schemes. Finally, the main challenges are discussed and future research directions for multicell OTFS systems are highlighted. The performance evaluations demonstrate the effectiveness of interference coordination and cooperative communications on multicell OTFS systems. [ABSTRACT FROM AUTHOR]

Visible Light Communication (VLC) system, a wireless system that utilizing the visible light spectrum to transmit data has been considered to become a complementary technology to radio frequency (RF) wireless technology in overcoming spectrum limitations. Recently, numerous VLC research projects have been conducted. However, many of these experiments were performed under ideal conditions, and only a few demonstrated real-time results with networking capabilities. To address this limitation, this paper proposes and details the design of a real-time VLC prototype that supports TCP/IP networking. First, we propose a system-on-chip (SoC) architecture for a VLC system. We design Register-Transfer-Level (RTL) modules using orthogonal frequency-division multiplexing (OFDM) for time synchronization, channel estimation, and equalization. Additionally, we optimize the Sampling Frequency Offset (SFO) estimation and compensation in the receiver to address the mismatch between TX and RX clock oscillators. The optimization of the SFO module is done by eliminating division operations. To realize full-stack design we integrate our design, consisting baseband processor in an FPGA as physical layer device, Linux’s kernel TCP/IP stack and the analog output to the VLC analog front-end. This full system design are evaluated over real channel at 1 m distance of transmitter and receiver. Experimental evaluations show that our VLC system can do network tests in an end-to-end system with commercial-off-the-shelf (COTS) networking devices and real applications such as internet browsers. Our VLC system offers a maximum real-time net TCP data rate of 6.65 Mb/s as measured using the iperf tool. Theses experimental results show that our VLC system achieves a $6\times $ data rate improvement as compared to the state-of-the-art work. Specifically, our SFO compensator results demonstrate an improvement in TCP data rate of $8.25\times $ and a reduction in frame loss of 22.42%. Moreover, our system also provides design features in terms of flexibility and scalability for future development.

Utilizing unmanned aerial vehicle (UAV) technology in communication holds promise for meeting the increasing data rate demands in future wireless systems due to its flexibility. Meanwhile, reconfigurable intelligent surface (RIS) has garnered increased attention for their potential to enhance wireless communication performance through intelligent control of the transmission environment. In this paper, we first combine the UAV and the RISs to construct an Internet of Things (IoT) uplink transmission system, where the UAV serves as an aerial relay to collect data from IoT terminal (IT) and forward it to base stations (BS), while RISs assist communication to reduce congestion. Then, a parallel factor (PARAFAC) tensor model is formulated at the BS. At last, the iterative alternating least squares (ALS) algorithm and the closed-form singular value decomposition (SVD) algorithm are derived to fit the constructed tensor model for joint channel estimation and symbol detection. Compared with the competitive algorithms, the two proposed algorithms offer lower computational complexity and superior channel estimation performance. Furthermore, the proposed algorithms exhibit good symbol detection capabilities even at higher transmission rates. The numerical results demonstrate the effectiveness of the proposed algorithms.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is emerging as a robust technology, especially valued in vehicular communication scenarios for its resilience. In such dynamic environments, reliable channel estimation becomes critical to maintain system performance due to the fast time-varying and double dispersion channel nature. Despite the harsh conditions, OFDM-IM enhances system performance by leveraging inactive sub-carriers to convey additional information. However, these idle sub-carriers complicate channel estimation compared to traditional OFDM systems. In response to these challenges, this paper introduces an enhanced channel estimation scheme tailored for the IEEE 802.11p standard, which supports cooperative intelligent transport systems (C-ITS). Our proposed method, the enhanced time-domain reliable test frequency-domain interpolation with index pilots (E-TRFI-IP), strategically places pilots and utilizes the positions of inactive sub-carriers to facilitate accurate detection and prevent performance degradation from inadequate channel tracking. Additionally, to further improve channel estimation performance, a comparison threshold methodology for reliable sub-carriers test is introduced by utilizing the structure of IM and joint interpolation method. Extensive simulation results demonstrate that E-TRFI-IP significantly surpasses existing channel estimation methods for both OFDM and OFDM-IM in terms of channel estimation performance. Notably, OFDM-IM systems employing the E-TRFI-IP estimation scheme also exhibit superior performance in bit error rate (BER) compared to those using conventional OFDM-IM and other channel estimation techniques.

The massive MIMO approach presents an exciting prospect for the upcoming generation of wireless transmission systems. However, the adoption of actual massive MIMO scenarios is hindered by high hardware expenses and increased energy usage, particularly as the quantity of RF modules expands. To address this issue and make massive MIMO more commercially viable, the design of 1-bit analog-to-digital converters (ADCs) has been considered as a solution. Various deep learning (DL) techniques for channel estimation (CE) with 1-bit ADCs have been developed in the literature. Nonetheless, most of these methods demonstrate limited performance in CE regarding pilot lengths and noise levels. In this paper, an efficient DL model known as bi-directional long short-term memory (BiLSTM) is proposed. This model enhances CE performance with limited pilot signals by training on long input sequence data within a bi-directional framework. The bi-directional (forward and backward) tasks in the hidden layers of BiLSTM contribute to its enhanced training ability, thereby enriching the CE of the proposed system. Moreover, in this paradigm, BiLSTM is utilized in conjunction with previous channel estimation data to learn the complex mapping from quantized incoming evaluations to channels. Consequently, the proposed model demonstrates superior CE efficiency for the same size of pilot sequencing as it deduces the necessary length and configuration of the pilot sequencing to ensure the existence of this mapping function. Therefore, lower pilot signals are needed with additional antennas for identical CE capability. Simulation outcomes verify that the proposed model exhibits satisfactory CE accuracy. It is confirmed that the increase of the number of antennas improves CE concerning the acquired signal-to-noise ratio per antenna and the normalized mean squared error.

Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling intelligent wireless networks in the data-driven society of the future. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, offering a controllable means for increasing signal diversity and reshaping transmission channels, without implementation constraints traditionally associated with multi-antenna systems. In this paper, we present a comprehensive survey of recent works on the applications of FL to RIM-aided communications. We first review the fundamental basis of FL with an emphasis on distributed learning mechanisms, as well as the operating principles of RIMs, including tuning mechanisms, operation modes, and deployment options. We then proceed with an in-depth survey of literature on FL-based approaches recently proposed for the solution of three key interrelated problems in RIM-aided wireless networks, namely: channel estimation (CE), passive beamforming (PBF) and resource allocation (RA). In each case, we illustrate the discussion by introducing an expanded FL (EFL) framework in which only a subset of active users partake in the distributed training process, thereby allowing to reduce transmission overhead. Lastly, we discuss some current challenges and promising research avenues for leveraging the full potential of FL in future RIM-aided extremely large-scale multiple-input-multiple-output (XL-MIMO) networks.

Extreme large-scale multiple-input multiple-output (XL-MIMO) is one of the key technologies for future 6G communications. Channel estimation plays a crucial role in XL-MIMO systems, as accurate Channel State Information (CSI) is essential for effective signal transmission. The existing channel estimation methods mainly distinguish between far-field channel estimation and near-field channel estimation. In the case where the sparsity of the channel is known, the traditional Orthogonal Matching Pursuit (OMP) algorithm is relied upon to estimate the hybrid-field channel in XL-MIMO systems.To overcome these limitations, in this paper, we propose a joint hybrid-field channel estimation scheme and adopt the Simultaneous Weighted Orthogonal Matching Pursuit (SWOMP) algorithm to effectively address these issues. Specifically, to more effectively estimate the hybrid-field channel, we propose a joint channel estimation approach that no longer distinguishes between far-field and near-field channel estimation methods. In the case where the sparsity of the hybrid-field channel is unknown, we employ the SWOMP algorithm to accurately estimate the channel state information. Furthermore, we substitute the Sherman-Morrison-Woodbury transform for the matrix inversion operation in the SWOMP algorithm, which does not reduce computational complexity but provides a novel approach to matrix inversion. Based on this, finally, we further propose a low-complexity SWOMP algorithm based on the Gauss-Seidel method transformation. Simulation results demonstrate that the proposed approach can obtain more accurate channel state information compared to traditional methods in XL-MIMO systems.

While reconfigurable intelligent surface (RIS) technology has been shown to provide numerous benefits to wireless systems, in the hands of an adversary such technology can also be used to disrupt communication links. This paper describes and analyzes an RIS-based attack on multi-antenna wireless systems that operate in time-division duplex mode under the assumption of channel reciprocity. In particular, we show how an RIS with a non-diagonal (ND) phase shift matrix (referred to here as an ND-RIS) can be deployed to maliciously break the channel reciprocity and hence degrade the downlink network performance. Such an attack is entirely passive and difficult to detect and counteract. We provide a theoretical analysis of the degradation in the ergodic sum rate that results when an arbitrary malicious ND-RIS is deployed and design an approach based on the genetic algorithm for optimizing the ND structure under partial knowledge of the available channel state information. Our simulation results validate the analysis and demonstrate that an ND-RIS channel reciprocity attack can dramatically reduce the downlink throughput.