Adaptive Pilot Interval Optimization for Intelligent Reflecting Surface-Aided Communication Systems

In many intelligent reflecting surface (IRS) aided communication systems, the IRS is deployed to provide a line-of-sight (LOS) link between the transmitter and receiver. In order to maximize the efficiency of the system, deploying the IRS at the correct position and orientation is important. We propose a method to find the optimal IRS orientation that maximizes the received power or achievable rate at locations within a desired geographical area. As shown through both analytical and simulation results, the proposed methodology on optimizing IRS orientation based on the receiver or transmitter locations significantly improves the overall system performance. For example, there is an approximately 8.1 bps/Hz achievable rate improvement by installing the IRS at the optimal orientation compared to an arbitrary orientation in a typical deployment scenario.

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the potential gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser systems with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Then, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users' weighted sum-rate. Simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase shift) on the system performance under imperfect CSI.

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system. However, due to the complex channel models of coal mines, the optimization problem of IRS deployment position is non-convex. To solve this problem, two auxiliary variables along with logarithmic operations and Taylor approximation are introduced. On this basis, a three-step joint rate optimization involving the transmit power, IRS phase shifts and IRS deployment position is proposed to maximize the effective achievable rates at the user station. The simulation results show that compared with other rate optimization schemes, the effective achievable rates at the user station using the proposed joint rate optimization scheme can be improved by approximately 12.32% to 54.17% for different parameter configurations. It is also pointed out that the deployment position of the IRS can converge to the same optimal position independent of the initial deployment position. Moreover, we investigate the effects of the roughness of the tunnel walls in a coal mine on the effective achievable rates at the user station, and the simulation results indicate that the proposed three-step joint rate optimization scheme performs better in the coal mine scenario regardless of the roughness.

Intelligent reflecting surface (IRS) composed of unit cells with tunable reflection coefficients, amplitude, phase, and frequency has received great interest from wireless communication communities. In this paper, a non-stationary three-dimensional (3-D) wideband IRS-assisted channel model, including spatial and temporal non-stationarity, is proposed for high altitude platform (HAP) multiple-input multiple-output (MIMO) communication systems. The proposed channel model considers the propagation environment around the receiver (Rx) and that around both the IRS and Rx, which are modeled using multiple 3-D cylinders and confocal elliptic-cylinders, respectively. In the proposed model, the optimal reflection phases of the IRS reflection units are proposed, and the number and the size of the IRS reflection units are also considered, which are used to investigate received signal strength, multipath fading effect, and spatial non-stationarity of IRS-aided communication systems. The numerical results show that the IRS reflection units can improve the received signal strength by adjusting the reflection phases and multipath fading effect can be effectively reduced by increasing the number and the size of IRS reflection units. Also, it is found that the spatial correlation significantly decreases with the increase of the number and size of the IRS reflection units, and the proposed IRS-aided channel model presents spatial non-stationarity. These observations can provide guidelines and references for systems design and performance analysis of IRS-aided HAP-MIMO communication systems.

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Intelligent reflecting surface (IRS) has emerged as a promising technique to enhance wireless communication performance cost-effectively. The existing literature has mainly considered IRS being deployed near user terminals to improve their performance. However, this approach may incur a high cost if IRSs need to be densely deployed in the network to cater to random user locations. To avoid such high deployment cost, in this paper we consider a new IRS aided wireless network architecture, where IRSs are deployed in the vicinity of each base station (BS) to assist in its communications with distributed users regardless of their locations. Besides significantly enhancing IRSs’ signal coverage, this scheme helps reduce the IRS-associated channel estimation overhead as compared to conventional user-side IRSs, by exploiting the nearly static BS-IRS channels over short distance. For this scheme, we propose a new two-stage transmission protocol to achieve IRS channel estimation and reflection optimization for uplink data transmission efficiently. In addition, we propose effective methods for solving the user-IRS association problem based on long-term/statistical channel knowledge and the selected user-IRS-BS cascaded channel estimation problem. Finally, all IRSs’ passive reflections are jointly optimized with the BS’s multi-antenna receive combining to maximize the minimum achievable rate among all users for data transmission. Numerical results show that the proposed co-site-IRS empowered BS scheme can achieve significant performance gains over the conventional BS without co-site IRS and existing schemes for IRS channel estimation and reflection optimization, thus enabling an appealing low-cost and high-performance BS design for future wireless networks.

This paper proposes a novel framework for optimizing beamforming gain in multicarrier non-orthogonal multiple access (MC-NOMA) systems that are assisted by intelligent reflecting surfaces (IRS).The framework includes channel estimation based on deep learning, dynamic IRS assignment, and beamforming optimization.First, a convolutional neural network-long short-term memory (CNN-LSTM) model was implemented to estimate the channel state information (CSI) accurately.After that, a Qlearning agent utilizes this estimated CSI to allocate IRS elements to user clusters in a dynamic manner, with the objective of optimizing the use of IRS units according to the existing channel conditions.Using the allocated IRS elements and the estimated CSI, a deep Q-network (DQN) is developed to find the best beamforming vectors that save the most power and get the optimum signal-to-interference-plus-noise ratio (SINR).When compared to random IRS assignment and traditional heuristic beamforming optimization methods, the results show that the proposed framework shows significant improvements in SINR, power efficiency, and total system capacity.Simulation results show that the integration of deep learning and reinforcement learning techniques in the IRS-assisted MC-NOMA system can significantly improve the performance, indicating that the proposed framework is a potential solution for wireless communication in the future.

Intelligent reflecting surfaces (IRSs) have been regarded as promising enablers for future wireless communications thanks to their ability to customize favorable propagation environments. Meanwhile, the solution of large-scale antenna arrays with low-resolution analog-to-digital converters (ADCs), is supposed to achieve a good performance-complexity trade-off. In this paper, we investigate the channel estimation issue of IRS-aided systems with one-bit ADCs. By utilizing the Bussgang decomposition, we reformulate the non-linear one-bit quantization operation as a statistically equivalent linear model and propose a linear minimum mean square error (LMMSE) channel estimator. Simulation results reveal that the proposed LMMSE estimator can effectively reduce the impact of the quantization distortion, and therefore significantly outperforms the conventional least square estimator.

To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.

To solve the blockage effect in millimeter wave (mmWave) communication, intelligent reflecting surface (IRS) is introduced to create additional links and enhance the system performance, by properly optimizing the IRS phase shifts based on the channel state information (CSI). However, channel estimation in IRS-enhanced mmWave system is challenging, since IRS is unable to perform signal processing and the large number of reflecting elements of IRS leads to high complexity. To reduce the overhead and obtain accurate CSI, we propose a channel estimation scheme based on least square (LS) estimation with partial on-off and super-resolution (SR) network. Specifically, we switch on part of the reflecting elements and estimate the cascaded channel matrix, which can be considered as a low-resolution (LR) image with low-precision. Then it is expanded to a high-resolution (HR) image with low-precision by linear interpolation. Furthermore, we feed this HR image into an SR network to improve the estimation accuracy. Numerical results demonstrate the advantages of our proposed SR channel estimation compared with benchmark schemes.

An intelligent reflecting surface (IRS) assisted wireless communication system, with statistically correlated channels between the transmitter and the IRS pair and between the IRS and the receiver pair, is studied in the absence of channel state information (CSI). Assuming the phases added by IRS elements to be deterministic, the closed-form analytical expression for the symbol error probability (SEP) and the upper bound on the channel capacity are derived, with the transmitter employing binary phase-shift keying (BPSK) modulation for data transmission. The asymptotic SEP expression is derived and the achievable diversity order of the system is shown to be independent of the number of IRS elements. A search based algorithm is proposed to obtain the optimal phase at IRS minimizing the average SEP, as a equal phase configuration. The optimal equal phase is also found to maximize the average channel capacity. Numerical results are presented for the case of channels following a practical IRS correlation model.

In this work, we consider an intelligent reflecting surface (IRS)-assisted covert wireless detection system, where a transmitter aims to improve the detection accuracy of its transmission at a legitimate receiver, but avoid this transmission being detected by a warden. Considering Rician fading channels and a bounded noise uncertainty model, the detection performance at the legitimate receiver and warden is thoroughly analyzed. Then, the optimal design of the transmit power and IRS reflection matrix is proposed for maximizing the detection performance at the legitimate transceiver subject to a covertness constraint (i.e., the detection accuracy at the warden is less than a specific tolerable level). Our design is solely based on the line-of-sight (LoS) channel components and thus partially eliminates the need for acquiring the IRS's instantaneous channels, which takes a step towards the practical deployment of IRS. Our examination shows that IRS can still improve the covert detection performance even when only the LoS channel components are known and the improvement increases as the LoS components become more dominant.

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob’s covert transmission from a warden (Willie), which cost-effectively exploits the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.

Intelligent reflecting surfaces (IRSs) improve both the bandwidth and energy\nefficiency of wideband communication systems by using low-cost passive elements\nfor reflecting the impinging signals with adjustable phase shifts. To realize\nthe full potential of IRS-aided systems, having accurate channel state\ninformation (CSI) is indispensable, but it is challenging to acquire, since\nthese passive devices cannot carry out transmit/receive signal processing. The\nexisting channel estimation methods conceived for wideband IRS-aided\ncommunication systems only consider the channel's frequency selectivity, but\nignore the effect of beam squint, despite its severe performance degradation.\nHence we fill this gap and conceive wideband channel estimation for IRS-aided\ncommunication systems by explicitly taking the effect of beam squint into\nconsideration. We demonstrate that the mutual correlation function between the\nspatial steering vectors and the cascaded two-hop channel reflected by the IRS\nhas two peaks, which leads to a pair of estimated angles for a single\npropagation path, due to the effect of beam squint. One of these two estimated\nangles is the frequency-independent `actual angle', while the other one is the\nfrequency-dependent `false angle'. To reduce the influence of false angles on\nchannel estimation, we propose a twin-stage orthogonal matching pursuit\n(TS-OMP) algorithm.\n

Intelligent reflecting surfaces (IRSs) can actively adjust the wireless environment. However, accurate channel estimation on IRS-aided communication systems is difficult to obtain. Therefore, we study a robust beamforming design for an IRS-aided integrated data and energy transfer (IDET) with imperfect channel state information (CSI). Against the uncertain channel estimation error, we robustly design the transmit beamformers of the transmitter and the passive reflecting beamformer of the IRS to minimize the transmit power by satisfying both the wireless data transfer (WDT) and wireless energy transfer (WET) requirements for realising energy-sustainability in 6G. A successive target migration optimization (STMO) algorithm is proposed to obtain a robust design. The transmit covariance matrices are optimized by relaxing rank-one constraints, when a passive reflecting beamformer is given. Then, the target to minimize the transmit power is migrated to maximize the QoS requirements of energy users due to the fixed transmit power. A local optimal reflecting beamformer is obtained for improving the attainable WET performance, when the transmit covariance matrices are given. Finally, we prove that the rank-one transmit beamformers can always be found, which have the same WET and WDT performance as the transmit covariance matrices. The numerical results demonstrate the advantage of our design.