Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications

Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aided multiuser multiple-input single-output (MISO) systems with hybrid architectures. Combining the structure of model driven and data driven deep learning approaches, a hybrid driven learning architecture is devised for joint estimation and learning the properties of the channels. For a passive IRS aided system, we propose a residual learned approximate message passing as a model driven network. A denoising and attention network in the data driven network is used to jointly learn spatial and frequency features. Furthermore, we design a flexible hybrid driven network in a hybrid passive and active IRS aided system. Specifically, the depthwise separable convolution is applied to the data driven network, leading to less network complexity and fewer parameters at the IRS side. Numerical results indicate that in both systems, the proposed hybrid driven channel estimation methods significantly outperform existing deep learning-based schemes and effectively reduce the pilot overhead by about 60% in IRS aided systems.Comment: 30 pages, 8 figures, submitted to IEEE transactions on wireless communications on December 13, 202

Ergodic Achievable Rate Maximization of RIS-assisted Millimeter-Wave MIMO-OFDM Communication Systems

Reconfigurable intelligent surface (RIS) has attracted extensive attention in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of millimeter-wave (mmWave) (including sub-Terahertz) spectrum is a major trend in next-generation wireless communications, and statistical CSI is more practical to obtain in realistic systems. Thus, we {consider} the ergodic achievable rate of RIS-assisted mmWave multiple-input multiple-output orthogonal frequency division multiplexing communication systems. The widely used Saleh-Valenzuela channel model is adopted to characterize the mmWave channels and only the statistical CSI is available. We first derive the approximations of the ergodic achievable rate by means of the majorization theory and Jensen's inequality. Then, an alternating optimization based algorithm is proposed to maximize the ergodic achievable rate by jointly designing the transmit covariance matrix at the base station and the reflection coefficients at the RIS. Specifically, the design of the transmit covariance matrix is transformed into a power allocation problem and solved by spatial-frequency water-filling. The reflection coefficients are optimized by the Riemannian conjugate gradient algorithm. Simulation results corroborate the effectiveness of the proposed algorithms.Comment: submitted for possible publication. in IEEE Transactions on Wireless Communications, 202

A survey on reconfigurable intelligent surfaces: wireless communication perspective

Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed

Joint Beamforming Design for RIS-enabled Integrated Positioning and Communication in Millimeter Wave Systems

Integrated positioning and communication (IPAC) system and reconfigurable intelligent surface (RIS) are both considered to be key technologies for future wireless networks. Therefore, in this paper, we propose a RIS-enabled IPAC scheme with the millimeter wave system. First, we derive the explicit expressions of the time-of-arrival (ToA)-based Cram\'er-Rao bound (CRB) and positioning error bound (PEB) for the RIS-aided system as the positioning metrics. Then, we formulate the IPAC system by jointly optimizing active beamforming in the base station (BS) and passive beamforming in the RIS to minimize the transmit power, while satisfying the communication data rate and PEB constraints. Finally, we propose an efficient two-stage algorithm to solve the optimization problem based on a series of methods such as the exhaustive search and semidefinite relaxation (SDR). Simulation results show that by changing various critical system parameters, the proposed RIS-enabled IPAC system can cater to both reliable data rates and high-precision positioning in different transmission environments

Fundamental Limits of Intelligent Reflecting Surface Aided Multiuser Broadcast Channel

Intelligent reflecting surface (IRS) has recently received significant attention in wireless networks owing to its ability to smartly control the wireless propagation through passive reflection. Although prior works have employed the IRS to enhance the system performance under various setups, the fundamental capacity limits of an IRS aided multi-antenna multi-user system have not yet been characterized. Motivated by this, we investigate an IRS aided multiple-input single-output (MISO) broadcast channel by considering the capacity-achieving dirty paper coding (DPC) scheme and dynamic beamforming configurations. We first propose a bisection based framework to characterize its capacity region by optimally solving the sum-rate maximization problem under a set of rate constraints, which is also applicable to characterize the achievable rate region with the zero-forcing (ZF) scheme. Interestingly, it is rigorously proved that dynamic beamforming is able to enlarge the achievable rate region of ZF if the IRS phase-shifts cannot achieve fully orthogonal channels, whereas the attained gains become marginal due to the reduction of the channel correlations induced by smartly adjusting the IRS phase-shifts. The result implies that employing the IRS is able to reduce the demand for implementing dynamic beamforming. Finally, we analytically prove that the sum-rate achieved by the IRS aided ZF is capable of approaching that of the IRS aided DPC with a sufficiently large IRS in practice. Simulation results shed light on the impact of the IRS on transceiver designs and validate our theoretical findings, which provide useful guidelines to practical systems by indicating that replacing sophisticated schemes with easy-implementation schemes would only result in slight performance loss

IRS-Aided Wideband Dual-Function Radar-Communications with Quantized Phase-Shifts

peer reviewedIntelligent reflecting surfaces (IRS) are increasingly considered as an emerging technology to assist wireless communications and target sensing. In this paper, we consider the quantized IRS-aided wideband dual-function radar-communications system with multi-carrier signaling. Specifically, the radar receive filter, frequency-dependent transmit beamforming and discrete phase-shifts are jointly designed to maximize the average signal-to-interference-plus-noise ratio (SINR) for radar while guaranteeing the communication SINR among all users. The resulting optimization problem has a fractional quartic objective function with difference of convex and discrete phase constraints and is, therefore, highly non-convex. Thus, we solve this problem via the alternating maximization framework, in which the alternating direction method of multipliers and Dinkelbach's algorithm are integrated to tackle the related subproblems. Numerical results demonstrate that the proposed method, even with the low-resolution IRS, achieves better sensing performance compared with non-IRS system