Intelligent reflecting surface (IRS) emerges as a promising technology to enhance wireless communication in recent years. However, the applications of deep learning algorithms within RIS-aided communication systems often suffer performance degradation under extreme conditions owing to a reliance on manual trial-and-error attempts. In this paper, the proposed beamforming neural network architecture search (BNAS) framework automates the design of of neural networks for the joint optimization of precoding vectors and IRS phase shift vectors. To improve robustness and performance, a specialized search space, incorporating two cascading supernets with selectable channel routes, diverse topological connections, and varied operations, is meticulously crafted for beamforming tasks. Meanwhile, the integration of neural tangent kernel theory, supported by alternative optimization guidance and bayesian optimization, not only enhances interpretability but also improves efficiency, thus enabling a more systematic and insightful search process compared to conventional approaches. Extensive numerical simulations confirm the applicability of BNAS, demonstrating superior performance compared to existing deep learning-based methods and traditional algorithms, particularly in challenging scenarios.

This paper investigates an intelligent reflecting surface (IRS)-aided wireless-powered communication network (WPCN) for maximizing the weighted sum rate (WSR). To reduce the complexity of traditional model-driven algorithms and improve convergence in data-driven deep learning approaches, a novel hybrid block coordinate descent (BCD) algorithm motivated by the dilation extraction and context attention (DECA) neural network (NN) is proposed. Specifically, the WSR maximization problem is firstly reformulated as a more tractable form, enabling the BCD algorithm to efficiently optimize the decoupled variables within the constraints. Meanwhile, at each BCD iteration, the DECA NN accelerates IRS phase shift optimization by facilitating the majorization-minimization (MM) algorithm to solve the computationally intensive fractional programming problem. Moreover, by leveraging dilation convolution and high-speed attention mechanisms, the DECA NN significantly outperforms existing deep learning benchmarks in both precision and convergence speed. Numerical results show that the proposed hybrid framework delivers performance comparable to the traditional BCD algorithm with dramatically reduced time consumption, while consistently maintaining robust performance under imperfect CSI and exhibiting strong transferability across diverse communication scenarios.

As a revolutionary paradigm for intelligently controlling wireless channels, intelligent reflecting surface (IRS) has emerged as a promising technology for future sixth-generation (6G) wireless communications. While IRS-aided communication systems can achieve attractive high performance gain, existing schemes require plenty of IRS elements to mitigate the “multiplicative fading” effect in cascaded channels, leading to high complexity for real-time beamforming and high signaling overhead for channel estimation. In this paper, the concept of sustainable intelligent element-grouping IRS (IEG-IRS) is proposed to overcome those fundamental bottlenecks. Specifically, based on the statistical channel state information (S-CSI), the proposed grouping strategy intelligently pre-divide the IEG-IRS elements into multiple groups based on the beam-domain grouping method, with each group sharing the common reflection coefficient and being optimized in real time using the instantaneous channel state information (I-CSI). Then, we further analyze the asymptotic performance of the IEG-IRS to reveal the substantial capacity gain in an extremely large-scale IRS (XL-IRS) aided single-user single-input single-output (SU-SISO) system. In particular, when a line-of-sight (LoS) component exists, it demonstrates that the combined cascaded link can be considered as a “deterministic virtual LoS” channel, resulting in a sustainable squared array gain achieved by the IEG-IRS. Finally, we formulate a weighted-sum-rate (WSR) maximization problem for an IEG-IRS-aided multiuser multiple-input single-output (MU-MISO) system and a two-stage algorithm for optimizing the beam-domain grouping strategy and the multi-user active-passive beamforming is proposed. Simulation results validate the superiority of our proposed two-stage algorithm in low pilot overhead conditions and show that in the context of an XL-IRS aided MU-MISO system, the proposed IEG-IRS can achieve a significant WSR gain, thus overcoming this performance drawback associated with high complexity and signaling overhead.

Integrating wireless sensing capabilities into base stations (BSs) has become a widespread trend in the future beyond fifth-generation (B5G)/sixth-generation (6G) wireless networks. In this paper, we investigate intelligent reflecting surface (IRS) enabled wireless localization, in which an IRS is deployed to assist a BS in locating a target in its non-line-of-sight (NLoS) region. In particular, we consider the case where the BS-IRS channel state information (CSI) is unknown. Specifically, we first propose a separate BS-IRS channel estimation scheme in which the BS operates in full-duplex mode (FDM), i.e., a portion of the BS antennas send downlink pilot signals to the IRS, while the remaining BS antennas receive the uplink pilot signals reflected by the IRS. However, we can only obtain an incomplete BS-IRS channel matrix based on our developed iterative coordinate descent-based channel estimation algorithm due to the “sign ambiguity issue”. Then, we employ the multiple hypotheses testing framework to perform target localization based on the incomplete estimated channel, in which the probability of each hypothesis is updated using Bayesian inference at each cycle. Moreover, we formulate a joint BS transmit waveform and IRS phase shifts optimization problem to improve the target localization performance by maximizing the weighted sum distance between each two hypotheses. However, the objective function is essentially a quartic function of the IRS phase shift vector, thus motivating us to resort to the penalty-based method to tackle this challenge. Simulation results validate the effectiveness of our proposed target localization scheme and show that the scheme’s performance can be further improved by finely designing the BS transmit waveform and IRS phase shifts intending to maximize the weighted sum distance between different hypotheses.

The cell-free network (CFN) is a promising technology capable of delivering high-reliability, high-data rate wireless communication services for Metaverse communication. This paper studies a joint optimization problem of user scheduling (US) and beamforming (BF) in CFN, where constraints of per access point (AP) power and the limited number of the scheduled users per AP are considered. In order to reduce the interaction overhead, this problem is investigated using local channel state information (CSI). Since this problem is a mixed-integer nonlinear programming (MINP) program with non-convexity and high complexity, we propose an alternating optimization framework to solve this problem. Specifically, we first adopt the weighted$l_{1}$-norm approximation to transform the discrete variables into the continuous variables. Then, we solve the rest of the problem by fractional programming, and solve the subproblems alternatively. The analysis of complexity and convergence analysis validate the efficiency and accuracy of the proposed algorithm. Numerical results show that the cross-layer design of the US&BF scheme is superior to the separate design of US&BF schemes. In addition, the proposed algorithm with local CSI achieves a comparable data rate to the algorithms with global CSI.

Intelligent reflecting surface (IRS) is a promising new paradigm for enhancing wireless information transmission (WIT) and wireless power transfer (WPT) cost-effectively in the future. In this paper, we study an IRS-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid node (HN) operating in FD mode sends information signals to multiple devices in the downlink (DL), and meanwhile receives energy signals from a power station (PS) in the uplink (UL), both of which are assisted by an IRS. Our objective is to boost the weighted sum throughput by jointly optimizing the active transmit beamformer at the PS and HN, along with the passive reflection coefficients of the IRS. To deal with the formulated non-convex optimization problem with intricately coupled design variables, most of existing works employ the alternating optimization (AO) method, whose performance, however, is closely related to parameter initialization. In contrast, we develop two novel penalty-based algorithms for the single-device and multi-device cases, respectively. In particular, our proposed rank-one constraint reformulation method of matrix proves to be efficient, especially for the case where the objective function is a higher-order function of the IRS phase shifts. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and resource allocation for achieving better WPCN performance. Moreover, we draw useful insights into the fine-tuning of IRS deployment location in the studied WPCN.

Acquiring the wireless channel state information (CSI) is an essential task to reap the wireless system performance gain brought by intelligent reflecting surface (IRS). In this paper, we study an IRS-assisted wireless energy transfer (WET) network, where an energy receiver (ER) harvests the wireless energy transmitted from an energy transmitter (ET) with the help of an IRS. Different from the commonly adopted wireless CSI acquisition approaches such as pilot or codebook based methods, we propose a novel channel learning method that requires only one-bit feedback information from the ER. Specifically, each feedback bit indicates whether the increase or decrease of the harvested energy amount at the ER within the present interval as compared to the previous one. Based on the feedback information, the ET continually adjusts its transmit beamforming in subsequent channel learning intervals to help infer the cascaded ET-IRS-ER CSI. It is worth noting that an optimization technique named analytic center cutting plane method (ACCPM) is applied in the channel learning phase. Numerical results unveil that our proposed one-bit feedback based channel estimation method is able to effectively estimate the cascaded ET-IRS-ER channel, and greatly reduce the requirement on the hardware complexity of the ER simultaneously.

In this paper, we consider an intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD mode sends information signals to a device in the downlink (DL) and meanwhile receives energy signals from a power station (PS) in the uplink (UL) with the help of an IRS. Our objective is to maximize the achievable data rate from the HAP to the device by jointly optimizing the transmit covariance matrix at the PS, the transmit beamforming vector at the HAP, and the phase shift vector at the IRS. The optimal transmit beamformer at the HAP is derived in closed from, and the joint optimization of the transmit covariance matrix at the PS and the phase shift vector at the IRS results in an intractable non-convex problem. To tackle this challenge, we propose an efficient penalty-based algorithm consisting of two layers. In the inner layer, we iteratively increase the device's signal-to-interference-plus-noise ratio (SINR) by applying the Dinkelbach's transform. While in the outer layer, we gradually decrease the penalty parameter. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and active beamforming for achieving better WPCN performance.

Accurate channel estimation is an indispensable prerequisite for intelligent reflecting surface (IRS) aided communication systems to achieve huge system performance gains. Current works show that deep neural network-based channel estimation is a promising solution to achieve competitive performance compared with the conventional methods. However, neural network-based approaches generally realize the channel estimation by manually designing network architectures in a trial-and-error manner which need complex neural network domain knowledge and tremendous computation resource. This paper automatically constructs a high-performance neural network architecture to obtain dedicated channel estimation schemes intelligently. Specifically, we propose a channel estimation neural network architecture search (CENAS) method based on gradient alternatively search strategy to search a channel estimation neural network. With the search space designed meticulously for the channel estimation task, the network searched by the proposed method outperforms the conventional and deep learning-based channel estimation algorithms.

In this paper, we propose a robust max-min fairness transmission design for intelligent reflecting surface (IRS)-aided wireless network in the presence of user location uncertainty. In particular, the non-isotropic reflection property for the IRS element is considered. We investigate the joint design of the active transmit beamformer at the base station (BS) and the passive phase shift matrix along with the deployment orientation (facing/pointing direction) of the IRS for maximizing the worst-case minimum signal-to-interference-plus-noise ratio (SINR) received by the users. In order to show the potential gains obtained by adjusting the deployment orientation of the IRS, a single-input-single-output (SISO) system is studied where a closed-form signal-to-noise ratio (SNR) of the user is obtained. For the multi-user case, to solve the resulting non-convex problem, an inexact-alternating-optimization algorithm consisting of a double-loop iteration is proposed. Specifically, in the inner loop, an optimization problem with semi-infinite constraints needs to be solved to increase the worst-case min-SINR compared to the given SINR reference value. We first transform the semi-infinite constraints into linear matrix inequality (LMI) constraints with finite form by applying the Taylor expansion approximation method, the general S-procedure, and the general sign-definiteness lemma. Then an efficient alternating optimization (AO) algorithm based on the two-dimensional search method, negative square penalty (NSP) method, and successive convex approximation (SCA) technique is proposed. While in the outer loop, we update the given SINR reference value as the worst-case minimum SINR obtained after each inner loop iteration. The whole algorithm terminates when the updated SINR reference values converge. Simulation results demonstrate the effectiveness of the proposed algorithm, and also show the additional system performance gain brought by the optimization of the IRS deployment orientation compared to its counterpart with fixed IRS deployment orientation, especially for a smaller IRS element number and a more prominent non-isotropic reflection property of the IRS element.

Deep learning (DL) is an emerging paradigm for accurate channel estimation for intelligent reflecting surface (IRS)-aided wireless communication systems. It has been proven to be a promising way to achieve better channel estimation performance for the IRS-aided wireless communication system than traditional methods (e.g., least-square algorithm). However, existing DL-based methods rely on ground truth (labels of the true channels) which is difficult to obtain in real networks. In this paper, we propose a self-supervised learning (SSL) method for the IRS channel estimation problem. No ground truth channel is needed in the training, while a simple and novel self-supervised denoising formula without a clean reference signal is presented. Particularly, in the training phase, the self-supervised signal and the input are the received signal vector and its noisy version, respectively. While in the inference phase the input is the estimated channel by using the least-square method and the output is the refined channel estimation. That is, our neural network-based channel estimation algorithm is not reciprocal for training and testing. We demonstrate that the proposed SSL solution has good convergence performance and generalization ability through numerical simulations. Interestingly, we find a “double descent” phenomenon in the learning curve during the test phase, i.e., when we gradually increase the number of training epochs, the performance first gets better, then becomes worse, and further gets better again. Besides, we propose to analyze SSL using the loss landscape and centered kernel alignment method. The results show that the self-supervised model has a similar loss landscape and representational similarity to the supervised model. We explored the effects of different signal-to-noise ratios (SNRs), different neural network sizes, and different training data volumes on our algorithm through numerical simulations. Extensive numerical simulation results show that our SSL algorithm is still competitive without ground truth. We also show that the developed scheme exhibits robustness to SNR ratio mismatch.

Abstract This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes.