The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical intelligent reflecting surfaces (IRSs). In this paper, we model the impact of the IRS-induced delay dispersion and derive the channel impulse response (CIR) of IRS-assisted FSO links. The proposed model takes into account the characteristics of the incident and reflected beams' wavefronts, the position of transmitter and receiver, the size of the IRS, and the incident beamwidth on the IRS. Our simulation results reveal that a maximum effective delay spread of 0.7 ns is expected for a square IRS with area 1 $\mathrm{m}^2$, which induces inter-symbol interference for bit rates larger than 10 Gbps. We show that the IRS-induced delay dispersion can be mitigated via equalization at the receiver.
Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracking (UT) algorithm for IRS-assisted mmWave communication, allowing suitable reconfiguration of the IRS unit cell phase shifts, resulting in a high reflection gain. The presented algorithm acquires the direction information of the user based on a peak likelihood-based direction estimation. Using the direction information, the user's trajectory is extrapolated to proactively update the adopted codeword and adjust the IRS phase shift configuration accordingly. Furthermore, we conduct a theoretical analysis of the direction estimation error and utilize the obtained insights to design a codebook specifically optimized for direction estimation. Our numerical results reveal a lower direction estimation error of the proposed UT algorithm when employing our designed codebook compared to codebooks from the literature. Furthermore, the average achieved signal-to-noise ratio (SNR) as well as the average effective rate of the proposed UT algorithm are analyzed. The proposed UT algorithm requires only a low overhead for direction and channel estimation and avoids outdated IRS phase shifts. Furthermore, it is shown to outperform two benchmark schemes based on direct phase shift optimization and hierarchical codebook search, respectively, via computer simulations.
Multiple access techniques are cornerstones of wireless communications. Their performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize MA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS. In particular, we design a dedicated neural network (NN) architecture RISnet with good scalability and desired symmetry. Moreover, we combine ML-enabled RIS configuration and analytical precoding at BS since there exist analytical precoding schemes. Furthermore, we propose another variant of RISnet, which requires the channel state information (CSI) of a small portion of RIS elements (in this work, 16 out of 1296 elements) if the channel comprises a few specular propagation paths. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and symmetry.
Efficient beam training is the key challenge in the codebook-based configuration of reconfigurable intelligent surfaces (RISs) because the beam training overhead can have a strong impact on the achievable system performance. In this paper, we study the performance tradeoff between overhead and achievable signal-to-noise ratio (SNR) in RIS beam training while taking into account the size of the targeted coverage area, the RIS response time, and the delay for feedback transmissions. Thereby, we consider three common beam training strategies: full search (FS), hierarchical search (HS), and tracking-based search (TS). Our analysis shows that the codebook-based illumination of a given coverage area can be realized with wide- or narrow-beam designs, which result in two different scaling laws for the achievable SNR. Similarly, there are two regimes for the overhead, where the number of pilot symbols required for reliable beam training is dependent on and independent of the SNR, respectively. Based on these insights, we investigate the impact of the beam training overhead on the effective rate and provide an upper bound on the user velocity for which the overhead is negligible. Moreover, when the overhead is not negligible, we show that TS beam training achieves higher effective rates than HS and FS beam training, while HS beam training may or may not outperform FS beam training, depending on the RIS response time, feedback delay, and codebook size. Finally, we present numerical simulation results that verify our theoretical analysis. In particular, our results confirm the existence of the proposed regimes, reveal that fast RISs can lead to negligible overhead for FS beam training, and show that large feedback delays can significantly reduce the performance for HS beam training.
Liquid crystal (LC) technology offers a cost-effective, scalable, energy-efficient, and continuous phase tunable realization of extremely large reconfigurable intelligent surfaces (RISs). However, LC response time to achieve a desired differential phase is significantly higher compared to competing silicon-based technologies (RF switches, PIN diodes, etc). The slow response time can be the performance bottleneck for applications where frequent reconfiguration of the RIS (e.g., to serve different users) is needed. In this paper, we develop an RIS phase-shift design that is aware of the transition behavior and aims to minimize the time to switch among multiple RIS configurations each serving a mobile user in a time-division multiple-access (TDMA) protocol. Our simulation results confirm that the proposed algorithm significantly reduces the time required for the users to achieve a threshold signal quality. This leads to a considerable improvement in the achievable throughput for applications, where the length of the TDMA time intervals is comparable with the RIS reconfiguration time.
In this chapter, we investigate the mathematical foundation of the modeling and design of reconfigurable intelligent surfaces (RIS) in both the far- and near-field regimes. More specifically, we first present RIS-assisted wireless channel models for the far- and near-field regimes, discussing relevant phenomena, such as line-of-sight (LOS) and non-LOS links, rich and poor scattering, channel correlation, and array manifold. Subsequently, we introduce two general approaches for the RIS reflective beam design, namely optimization-based and analytical, which offer different degrees of design flexibility and computational complexity. Furthermore, we provide a comprehensive set of simulation results for the performance evaluation of the studied RIS beam designs and the investigation of the impact of the system parameters.
The high directionality of millimeter-wave (mmWave) communication systems has proven effective in reducing the attack surface against eavesdropping, thus improving the physical layer security. However, even with highly directional beams, the system is still exposed to eavesdropping against adversaries located within the main lobe. In this paper, we propose \acrshort{BSec}, a solution to protect the users even from adversaries located in the main lobe. The key feature of BeamSec are: (i) Operating without the knowledge of eavesdropper's location/channel; (ii) Robustness against colluding eavesdropping attack and (iii) Standard compatibility, which we prove using experiments via our IEEE 802.11ad/ay-compatible 60 GHz phased-array testbed. Methodologically, BeamSec first identifies uncorrelated and diverse beam-pairs between the transmitter and receiver by analyzing signal characteristics available through standard-compliant procedures. Next, it encodes the information jointly over all selected beam-pairs to minimize information leakage. We study two methods for allocating transmission time among different beams, namely uniform allocation (no knowledge of the wireless channel) and optimal allocation for maximization of the secrecy rate (with partial knowledge of the wireless channel). Our experiments show that \acrshort{BSec} outperforms the benchmark schemes against single and colluding eavesdroppers and enhances the secrecy rate by 79.8% over a random paths selection benchmark.
The reconfigurable intelligent surface (RIS) is a promising technology that enables wireless communication systems to achieve improved performance by intelligently manipulating wireless channels. In this paper, we consider the sum-rate maximization problem in a downlink multi-user multi-input-single-output (MISO) channel via space-division multiple access (SDMA). Two major challenges of this problem are the high dimensionality due to the large number of RIS elements and the difficulty to obtain the full channel state information (CSI), which is assumed known in many algorithms proposed in the literature. Instead, we propose a hybrid machine learning approach using the weighted minimum mean squared error (WMMSE) precoder at the base station (BS) and a dedicated neural network (NN) architecture, RISnet, for RIS configuration. The RISnet has a good scalability to optimize 1296 RIS elements and requires partial CSI of only 16 RIS elements as input. We show it achieves a high performance with low requirement for channel estimation for geometric channel models obtained with ray-tracing simulation. The unsupervised learning lets the RISnet find an optimized RIS configuration by itself. Numerical results show that a trained model configures the RIS with low computational effort, considerably outperforms the baselines, and can work with discrete phase shifts.
The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical relays or optical intelligent reflecting surfaces (IRSs). In this paper, we show that the power reflected from FSO IRSs and collected at the receiver (Rx) lens may scale quadratically or linearly with the IRS size or may saturate at a constant value. We analyze the power scaling law for optical IRSs and unveil its dependence on the wavelength, transmitter (Tx)-to-IRS and IRS-to-Rx distances, beam waist, and Rx lens size. We also consider the impact of linear, quadratic, and focusing phase shift profiles across the IRS on the power collected at the Rx lens for different IRS sizes. Our results reveal that surprisingly the powers received for the different phase shift profiles are identical, unless the IRS operates in the saturation regime. Moreover, IRSs employing the focusing (linear) phase shift profile require the largest (smallest) size to reach the saturation regime. We also compare optical IRSs in different power scaling regimes with optical relays in terms of the outage probability, diversity and coding gains, and optimal placement. Our results show that, at the expense of a higher hardware complexity, relay-assisted FSO links yield a better outage performance at high signal-to-noise-ratios (SNRs), but optical IRSs can achieve a higher performance at low SNRs. Moreover, while it is optimal to place relays equidistant from Tx and Rx, the optimal location of optical IRSs depends on the phase shift profile and the power scaling regime they operate in.