Coverage Analysis of Intelligent Reflecting Surface assisted Cellular Networks

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.

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 reflective surfaces (IRSs) are invoked for improving both the spectral efficiency (SE) and energy efficiency (EE). Specifically, an IRS-aided multiple-input multiple-output network is considered, where the performance of randomly roaming users is analyzed by utilizing stochastic geometry tools. As such, to distinguish the superposed signals at each user, the passive beamforming weight at the IRSs and detection weight vectors at the users are jointly designed. As a benefit, by adopting a zero-forcing-based design, the intra-cell interference imposed by the IRS can be suppressed. In order to evaluate the performance of the proposed network, we first derive the approximated channel statistics in the high signal-to-noise-ratio (SNR) regime. Then, we derive the closed-form expressions both for the outage probability and for the ergodic rate of users. Both the high-SNR slopes of ergodic rate and the diversity orders of outage probability are derived for gleaning further insights. The network's SE and EE are also derived. Our numerical results are provided to confirm that: i) the high-SNR slope of the proposed network is one; ii) the SE and EE can be significantly enhanced by increasing the number of IRS elements.

In this paper, we consider an intelligent reflecting surface (IRS) enabled mobile network, where a fixed access point (AP) communicates with a mobile user (MU) via the aid of an IRS. We assume that the MU moves from one elementary square to another following a Markov random walk within a grid, and propose a maximum a posteriori (MAP) criterion to track the movement of the MU by leveraging the line-of-sight component of the IRS-MU link. Since it is infeasible to derive an explicit expression for the average probability of estimation error (APEE) for the proposed MAP criterion, we derive a closed-form upper bound for the APEE, which is used as the cost function to optimize the phase shifts of the IRS units. Considering the unit modulus constraints incurred by the IRS units, a manifold optimization (MO) method is firstly employed to gain a favorable solution to the formulated optimization problem, followed by a low-complexity codebook based solution to circumvent the high computational cost of the MO method. Our numerical results demonstrate the superior performance of the proposed IRS phase shift designs over the benchmark method.

Satisfying the users’ increasing demand and reducing the networks’ energy consumption are among the most critical requirements of future cellular networks. In this paper, we exploit Intelligent Reflecting Surfaces (IRSs) to reduce the bandwidth required by users, which will allow more users to be served and/or reduce the energy footprint of cellular base stations. In contrast to most of the existing studies that focus on configuring the phase shifts of IRSs and/or the active beamforming of the base station, we consider that the IRS consists of blocks of resources that can be shared by several users. We formulate the problem of managing these resources as nonlinear integer problem. Then, we solve the optimization problem using exhaustive search, and propose two low complexity heuristic algorithms. The performance of the system is evaluated considering variable number of users, position of IRS, required bit rate and radius of the cell. Results show that using IRS can achieve significant bandwidth savings and important energy demand reduction when the IRS resources are well managed.

This article investigates physical-layer security (PLS) of a typical wireless-powered communication (WPC) system with the aid of intelligent reflecting surface (IRS) in the presence of a passive eavesdropper for Internet of Things (IoT), and proposes three IRS-aided secure WPC modes. Specifically, in mode-I, the IRS is deployed between hybrid access point (HAP) and wireless user (U) for co-located power station (PS) and access point (AP). In mode-II, the IRS is deployed between AP and U for separate PS and AP, while in mode-III, the IRS is deployed between PS and U for separate PS and AP. For each mode, the optimal phase shift is designed to maximize the reception of energy and information at the legitimate receiver. We comprehensively analyze the performance of each mode, and derive the closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) for each mode, respectively. The theoretical analysis and simulation results reveal that from the perspective of reliability, mode-I can achieve the best COP with the increased number of IRS elements, while mode-II and mode-III have a similar COP. From the perspective of security, mode-II can achieve the best SOP with the increased number of IRS elements, while mode-I and mode-III have a similar SOP. Moreover, under the condition of small transmission power at HAP/PS or small number of IRS elements, mode-I has the best EST, while as the power or the number increases, the EST of mode-II becomes the best one.

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.

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS–UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.

Prior studies on intelligent reflecting surface (IRS) have mostly considered wireless communication systems aided by a single passive IRS, which, however, has limited control over wireless propagation environment and suffers product-distance path-loss. To address these issues, we propose in this paper a new hybrid active/passive IRS aided wireless communication system, where an active IRS and multiple passive IRSs are deployed to assist the communication between a base station (BS) and a remote user in complex environment, by establishing a multi-hop reflection path across active/passive IRSs. In particular, the active IRS enables signal reflection with power amplification, thus effectively compensating the severe path-loss in the multi-reflection path. To maximize the achievable rate at the user, we first design the optimal beamforming of the BS and selected (active/passive) IRSs for a given multi-reflection path, and then propose an efficient algorithm to obtain the optimal multi-reflection path by using the path decomposition method and graph theory. We show that the active IRS should be selected to establish the beam routing path when its amplification power and/or number of active reflecting elements are sufficiently large. Last, numerical results demonstrate the effectiveness of the proposed hybrid active/passive IRS beam routing design as compared to the benchmark scheme with passive IRSs only.

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 article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

The use of intelligent reflecting surfaces (IRS) in next‐generation mobile networks is currently a hot topic. At the same time, next‐generation mobile networks are going airborne as heavily discussed in this book. Since the base stations and users both are meant to be mobile in this setting, this chapter explores the possibility of using IRS in airborne networks (ANs) for localization of users and base stations. Positioning is an important aspect in present and future wireless networks, where it augments the network operations and assists in multiple localization‐based applications. This chapter outlines the use of IRS in AN in different settings and gives an overview of potential positioning using pilot symbols in beyond‐5G (B5G) mobile networks employing IRSs.

An intelligent reflecting surface (IRS) is an emerging technology in the next-generation (B5G and 6G) wireless communications with the aim of improving the spectral/energy efficiency of the wireless networks. In this paper, we focus on multiple IRS-aided multiuser multiple-input single-output (MISO) downlink network, where each IRS is deployed near to the cell boundary of the cellular network in order to help the downlink transmission to the cell-edge users. To achieve the significant performance benefits provided by the rate-splitting (RS) transmission in multiuser scenario, two-layer hierarchical rate splitting (2L-HRS) technique is deployed at the base station (BS) to serve all the users. Furthermore, we propose an On-Off scheme for controlling the IRSs with practical phase shifts. In order to analyze the performance of the users, we derive the closed-form expressions of outage probability for both cell-edge users and near users. By extensive Monte Carlo simulations, we demonstrate that the proposed design framework outperforms the corresponding one-layer RS (1L-RS), the multiuser linear precoding (MU-LP) and 2L-HRS without IRS. In addition, we reveal the advantages of introducing RS-based IRS in improving the cell-users’ performance. The impact of channel estimation errors due to imperfect channel state information, and the various network’s parameters, such as the number of reflecting elements and the number of cell-users, on the network performance is demonstrated.

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.