A Tunable Reflection Surface with Independently Variable Phase and Slope

A reconfigurable intelligent surface (RIS) is an essential component in the architecture of the next generation of wireless communication systems. An RIS is deployed to provide a controllability to the multi-path environment between the transmitter and the receiver, which becomes critical when the line-of-sight signal between them is blocked. In this work, we design an electrically tunable linearly polarized RIS at 2.5 GHz that yields a controllable reflection phase and phase-frequency slope; in other words, we add tunability of the phase-frequency slope to the tunability of the resonance center frequency. The proposed design consists of two layers of unit cells placed over a ground plane, with dog-bone-shaped elements in the top layer and patch elements in the bottom layer. Each patch and dog-bone element is loaded with a varactor, whose reverse bias voltage is controlled to provide a phase-frequency profile with a slope value of 9 degrees/MHz or 0.95 degrees/MHz, and a phase shift range of 320 degrees.

Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities

Next-generation communication networks are expected to exploit recent advances in data science and cutting-edge communications technologies to improve the utilization of the available communications resources. In this article, we introduce an emerging deep learning (DL) architecture, the transformer-masked autoencoder (TMAE), and discuss its potential in next-generation wireless networks. We discuss the limitations of current DL techniques in meeting the requirements of 5G and beyond 5G networks, and how the TMAE differs from the classical DL techniques can potentially address several wireless communication problems. We highlight various areas in next-generation mobile networks which can be addressed using a TMAE, including source and channel coding, estimation, and security. Furthermore, we demonstrate a case study showing how a TMAE can improve data compression performance and complexity compared to existing schemes. Finally, we discuss key challenges and open future research directions for deploying the TMAE in intelligent next-generation mobile networks.

Hybrid Digital-Wave Domain Channel Estimator for Stacked Intelligent Metasurface Enabled Multi-User MISO Systems

Stacked intelligent metasurface (SIM) is an emerging programmable metasurface architecture that can implement signal processing directly in the electromagnetic wave domain, thereby enabling efficient implementation of ultra-massive multiple-input multiple-output (MIMO) transceivers with a limited number of radio frequency (RF) chains. Channel estimation (CE) is challenging for SIM-enabled communication systems due to the multi-layer architecture of SIM, and because we need to estimate large dimensional channels between the SIM and users with a limited number of RF chains. To efficiently solve this problem, we develop a novel hybrid digital-wave domain channel estimator, in which the received training symbols are first processed in the wave domain within the SIM layers, and then processed in the digital domain. The wave domain channel estimator, parametrized by the phase shifts applied by the meta-atoms in all layers, is optimized to minimize the mean squared error (MSE) using a gradient descent algorithm, within which the digital part is optimally updated. For an SIM-enabled multi-user system equipped with 4 RF chains and a 6-layer SIM with 64 meta-atoms each, the proposed estimator yields an MSE that is very close to that achieved by fully digital CE in a massive MIMO system employing 64 RF chains. This high CE accuracy is achieved at the cost of a training overhead that can be reduced by exploiting the potential low rank of channel correlation matrices.

Unit Cell Phase-Frequency Profile Optimization in RIS-Assisted Wide-Band OFDM Systems

The reflection characteristics of a reconfigurable intelligent surface (RIS) depend on the phase response of the constituent unit cells, which is necessarily frequency dependent. This paper investigates the role of an RIS constituting unit cells with different phase-frequency profiles in a wide-band orthogonal frequency division multiplexing (OFDM) system to improve the achievable rate. We first propose a mathematical model for the phase-frequency relationship parametrized by the phase-frequency profile's slope and phase-shift corresponding to a realizable resonant RIS unit cell. Then, modelling each RIS element with $b$ control bits, we propose a method for selecting the parameter pairs to obtain a set of $2^b$ phase-frequency profiles. The proposed method yields an RIS design that outperforms existing designs over a wide range of user locations in a single-input, single-output (SISO) OFDM system. We then propose a low-complexity optimization algorithm to maximize the data rate through the joint optimization of (a) power allocations across the sub-carriers and (b) phase-frequency profile for each RIS unit cell from the available set. The analysis is then extended to a multi-user multiple-input single-output (MISO) OFDM scenario. Numerical results show an improvement in the coverage and achievable rates under the proposed framework as compared to single-slope phase-frequency profiles.

Enhancing Secret Key Generation in Block Fading Channels using Reconfigurable Intelligent Surfaces

Physical layer security (PLS) is superior to classical cryptography techniques due to its notion of perfect secrecy and independence to an eavesdropper's computational power. One form of PLS arises when Alice and Bob (the legitimate users) exchange signals to extract a common key from the random common channels. The drawback of extracting keys from wireless channels is the ample dependence on the dynamicity and fluctuations of the radio channel. However, some radio channels are constant such as line of sight (LoS) and can be estimated by Eve (an illegitimate user), or can be quite static in behaviour due to the presence low-mobility users thus restricting the amount of randomness. This in turn lowers the secret key rate (SKR) defined as the number of bits of key generated per channel use. In this work, we aim to address this challenge by using a reconfigurable intelligent surface (RIS) to produce random phases at certain carefully curated intervals such that it disrupts the channel in low entropy environments. We propose an RIS assisted key generation method, study its performance, and compare with benchmarks to observe the benefit of using an RIS while considering various important metrics such as key mismatch rate and average secret key throughput. Simulations are made to validate our theoretical findings showing an improvement in performance when an RIS is deployed.

Reconfigurable Intelligent Surfaces Assisted Communication Under Different CSI Assumptions

This work studies the net sum-rate performance of a distributed reconfigurable intelligent surfaces (RISs)-assisted multi-user multiple-input-single-output (MISO) downlink communication system under imperfect instantaneous-channel state information (I-CSI) to implement precoding at the base station (BS) and statistical-CSI (S-CSI) to design the RISs phase-shifts. Two channel estimation (CE) protocols are considered for I-CSI acquisition: (i) a full CE protocol that estimates all direct and RISs-assisted channels over multiple training sub-phases, and (ii) a low-overhead direct estimation (DE) protocol that estimates the end-to-end channel in a single sub-phase. We derive the asymptotic equivalents of signal-to-interference-plus-noise ratio (SINR) and ergodic net sum-rate under both protocols for given RISs phase-shifts, which are then optimized based on S-CSI. Simulation results reveal that the low-complexity DE protocol yields better net sum-rate than the full CE protocol when used to obtain CSI for precoding. A benchmark full I-CSI based RISs design is also outlined and shown to yield higher SINR but lower net sum-rate than the S-CSI based RISs design.

Improving UAV Communication in Cell Free MIMO Using a Reconfigurable Intelligent Surface

Communication with unmanned aerial vehicles (UAVs) in current terrestrial networks suffers from poor signal strength due to the down-tilt of the access points (APs) that are optimized to serve ground users ends (GUEs). To solve this, one could tilt the AP antenna upwards or allocate more power to serve the UAV. However, this negatively affects GUE downlink (DL) rates. In this paper, we propose to solve this challenge using a reconfigurable intelligent surface (RIS) to enhance the UAV communication while preserving the 3GPP- prescribed downwards antenna tilt and potentially improving the DL performance of the GUE. We show that under conjugate beamforming (CB) precoding and proper power split between GUEs and the UAV at the APs, an RIS with phase-shifts configured to reflect radio signals towards the UAV can significantly improve the UAV DL throughput while simultaneously benefiting the GUEs. The presented numerical results show that the RIS- aided system can serve a UAV with a required data rate while improving the GUEs DL performance relative to that in a CF- MIMO system without a UAV and an RIS. We support this conclusion through simulations under a varying numbers of RIS reflecting elements, UAV heights, and power split factor.

User Association in User-Centric Hybrid VLC/RF Cell-Free Massive MIMO Systems

A continuous goal in all communication systems is to enhance the users experience and provide them with the highest possible data rates. Recently, the concept of cell-free massive MIMO (CF-mMIMO) systems has been considered to enhance the performance of systems that operate merely with Radio Frequency (RF) or visible light communication (VLC) technologies. In this paper, a hybrid VLC/RF cell-free massive MIMO system is proposed where an RF cell-free network and a VLC cell-free network coexist to serve users. The idea is to utilize the benefits of each network and balance the load aiming at maximizing the system's sum-rate. The system is evaluated using zero-forcing (ZF) precoding scheme. Two user association algorithms are proposed to assign users to either the VLC or the RF networks. In addition, two user-centric clustering approaches are proposed and evaluated. Simulation results show that the proposed association algorithms significantly outperform a random network association of users in terms of sum-rate. Results also show great potential for the proposed system compared to standalone cell-free networks.

Alternating Channel Estimation and Prediction for Cell-Free mMIMO with Channel Aging: A Deep Learning Based Scheme

In large scale dynamic wireless networks, the amount of overhead caused by channel estimation (CE) is becoming one of the main performance bottlenecks. This is due to the large number users whose channels should be estimated, the user mobility, and the rapid channel change caused by the usage of the high-frequency spectrum (e.g. millimeter wave). In this work, we propose a new hybrid channel estimation/prediction (CEP) scheme to reduce overhead in time-division duplex (TDD) wireless cell-free massive multiple-input-multiple-output (mMIMO) systems. The scheme proposes sending a pilot signal from each user only once in a given number (window) of coherence intervals (CIs). Then minimum mean-square error (MMSE) estimation is used to estimate the channel of this CI, while a deep neural network (DNN) is used to predict the channels of the remaining CIs in the window. The DNN exploits the temporal correlation between the consecutive CIs and the received pilot signals to improve the channel prediction accuracy. By doing so, CE overhead is reduced by at least 50 percent at the expense of negligible CE error for practical user mobility settings. Consequently, the proposed CEP scheme improves the spectral efficiency compared to the conventional MMSE CE approach, especially when the number of users is large, which is demonstrated numerically.

Performance Analysis under IRS-User Association for Distributed IRSs Assisted MISO Systems

Distributed intelligent reflecting surfaces (IRSs) deployed in multi-user wireless communication systems promise improved system performance. However, the signal-to-interference-plus-noise ratio (SINR) analysis and IRSs optimization in such a system become challenging, due to the large number of involved parameters. The system optimization can be simplified if users are associated with IRSs, which in turn focus on serving the associated users. We provide a practical theoretical framework for the average SINR analysis of a distributed IRSs-assisted multi-user MISO system, where IRSs are optimized to serve their associated users. In particular, we derive the average SINR expression under maximum ratio transmission (MRT) precoding at the BS and optimized reflect beamforming configurations at the IRSs. A successive refinement (SR) method is then outlined to optimize the IRS-user association parameters for the formulated max-min SINR problem which motivates user-fairness. Simulations validate the average SINR analysis while confirming the superiority of a distributed IRSs system over a centralized IRS system as well as the gains with optimized IRS-user association as compared to random association.