Intelligent Reflecting Research Articles

Artificially intelligent reflection? Smoke and mirrors and a tale of two perspectives

Artificially intelligent reflection? Smoke and mirrors and a tale of two perspectives

Simultaneous Transmit Diversity and Passive Beamforming With Large-Scale Intelligent Reflecting Surface

Intelligent reflecting surface (IRS) has emerged as a cost-effective solution to enhance wireless communication performance via passive signal reflection. Existing works on IRS have mainly focused on investigating IRS’s passive beamforming/reflection design to boost the communication rate for users assuming that their channel state information (CSI) is fully or partially known. However, how to exploit IRS to improve the wireless transmission reliability without any CSI, which is typical in high-mobility/delay-sensitive communication scenarios, remains largely open. In this paper, we study a new IRS-aided communication system with the IRS integrated to its aided access point (AP) to achieve both functions of transmit diversity and passive beamforming simultaneously. Specifically, we first show an interesting result that the IRS’s passive beamforming gain in any channel direction is invariant to the common phase-shift applied to all of its reflecting elements. Accordingly, we design the common phase-shift of IRS elements to achieve transmit diversity at the AP side without the need of any CSI of the users. In addition, we propose a practical method for the users to estimate the CSI at the receiver side for information decoding. Meanwhile, we show that the conventional passive beamforming gain of IRS can be retained for the other users with their CSI known at the AP. Furthermore, we derive the asymptotic performance of both IRS-aided transmit diversity and passive beamforming in closed-form, by considering the large-scale IRS with an infinite number of elements. Numerical results validate our analysis and show the performance gains of the proposed IRS-aided simultaneous transmit diversity and passive beamforming scheme over other benchmark schemes.

Secure computation offloading assisted by intelligent reflection surface for mobile edge computing network

As an novel paradigm, computation offloading in the mobile edge computing (MEC) system can effectively support the resource-intensive applications for the mobile devices (MD) equipped with limited computing capability. However, the hostile radio transmission and data leakage during the offloading process may erode the MEC system’s potential. To tackle these hindrances, we investigate an IRS-assisted secure MEC system with eavesdroppers, where the intelligent reflecting surface (IRS) is deployed to enhance the communication between the MD and the AP equipped with edge servers and the malicious eavesdroppers may attack the wireless data offloaded by MD. The MD opt for offloading part of the tasks to the edge server for execution to support the computation-intensive applications. Moreover, the relevant latency minimization problem is formulated by optimizing the offloading ratio, the allocation of edge server computing capability, the multiple-user-detection (MUD) matrix and the IRS phase shift parameters, subject to the constraints of edge computation resource and practical IRS phase shifts. Then, the original problem is decouple into two subproblem, and the computing and communication subproblems are alternatively optimized by block coordinate descent (BCD) method with low complexity. Finally, simulation results demonstrate that the proposed scheme can significantly enhance the performance of secure offloading in the MEC system.

Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.

Intelligent Reflecting Surface-Assisted Low-Latency Federated Learning Over Wireless Networks

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

Deep Non-Orthogonal Multiple Access Network Assisted by Intelligent Reflecting Surface

The intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) symbiotic communication technology is expected to enhance the access performance, energy efficiency and spectrum efficiency of the communication network, and is regarded as an important candidate technology to support the evolution of the sixth-generation (6G) towards large-scale, high-capacity and sustainable development. However, the relevant research of this technology is still at the initial stage, and many key challenges have not been fully studied. Therefore, it is urgent to open up relevant research ideas and methods to promote its development and early implementation, so as to make it an effective 6G technology. In view of this, this paper intends to carry out the research on the theory and method of IRS assisted NOMA symbiotic transmission, starting from the analysis of the active and passive symbiotic mechanism of NOMA transmission protocol. Based on this, we further study the efficient symbiotic modulation transmission technology and multi-dimensional resource optimization allocation method. The research content of this paper is to explore the transmission theory and technology of high energy efficiency and high frequency spectrum efficiency for 6G, and break through the bottleneck problem of spectrum and energy consumption encountered by wireless communication, which has important practical significance for the wireless communication.

Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications

In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network’s sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D transmitter and the associated D2D receiver. Our objective is to jointly optimise the transmit power at the D2D transmitter and the phase shift matrix at the IRS to maximise the network sum-rate. We formulate a Markov decision process and then propose the proximal policy optimisation for solving the maximisation game. Simulation results show impressive performance in terms of the achievable rate and processing time.

Super-tough, self-sensing and shape-programmable polymers via topological structure crosslinking networks

Shape-memory polymers (SMPs) are smart materials capable of deforming actively under external excitation. Owing to their characteristics, including variable stiffness, programmable deformation, composability, and self-sensing, and its behavior is similar to the intelligent reflection of life, these materials are highly valuable for applications in many fields. We have developed a molecular engineering strategy based on topological cross-linking networks, which form hydrogen bonds, hyperbranching, ring network structure, etc. to obtain shape memory epoxy resin (SMEP) with super toughness, high-temperature resistance and triple shape memory effect (TSME). Additionally, the internal relationship between the molecular network structure and material performance is simulated by molecular dynamics. It has super toughness (1288 %) and high impact energy (216 MJ/m3) above its transition temperature, breaking through the previously reported thermosetting resin. Importantly, the developed SMPs system exhibited outstanding fatigue resistance and was successfully loaded repeatedly for more than 100 cycles. Finally, the intramolecular cyclization effect led to the generation of different linked structures in situ, which endowed SMEP with TSME, thereby rendering such materials potentially applicable in engineering fields, such as aerospace, smart furniture, and soft robotics.

Cognitive Complexity and Communicative Context: Reflection of User Intelligence in Social Media Texts

The focus of this article is the reflection of intelligence as ability in social media texts. The authors have developed a model of communicative environments according to which the manifestation of intelligence in a message depends on the communicative situation in which the information is transmitted. Thus, the cognitive complexity of texts is a consequence not only of the author's intellectual capacity, but also of his willingness and ability to adapt the complexity of messages to the characteristics of the recipient. This paper analyses data from individual social media profiles in relation to user intelligence test scores, as well as similar data obtained at a regional level. The study involved 438 subjects who took an intelligence test and provided access to their social media profiles. Facebook users were found to be, on average, more intelligent than VKontakte users, posting more posts containing text, posting less often overall. No significant differences in post characteristics were found between the two social networks. However, differences in the nature of the relationship between intelligence and cognitive complexity of messages were demonstrated for different social networks and for male and female subsamples of users. Correlations were found to be higher at the regional level compared to the individual level, higher for Facebook compared to VKontakte and higher for men compared to women. It is concluded that indicators of texts’ cognitive complexity from social media do reflect the intelligence of their authors, but the extent of this reflection depends on the characteristics of the communicative situation

Intelligent Reflecting Surface-Aided Wireless Powered Hybrid Backscatter-Active Communication Networks

Wireless powered hybrid backscatter-active communications are recognized as the promising technique to provide the sustainable communication service for wireless devices. In this paper, we consider an intelligent reflecting surface-aided wireless powered hybrid backscatter-active communication network, where multiple sensors devices powered by a power beacon, transmit their information to an access point through backscatter and active communications. In particular, an intelligent reflecting surface is properly deployed to improve the communication quality between sensor devices and access point by utilizing the composite channel gain and phase beamforming gain. Furthermore, we formulate a system energy efficiency maximization problem under the minimum throughput requirements of sensor devices, to jointly optimize the phase shifts of intelligent reflecting surface, time allocation for backscatter communications and active transmissions, and transmit power of sensor devices and power beacon. To solve the formulated non-convex problem, we first unveil the optimal phase shifts of intelligent reflecting surface, and then utilize the variable substitutions and Dinkelbach-based method to develop an iterative method to solve the energy efficiency maximization problem. Simulation results reveal that the proposed method outperforms the existing several benchmark methods in terms of system energy efficiency.

Transmit and Reflect Beamforming for Max-Min SINR in IRS-Aided MIMO Vehicular Networks

6G vehicular networks will require massive connectivity among vehicles and Road Side Units (RSUs) to enable number of safety and non-safety applications. Intelligent Reflecting Surface (IRS) will be a vital part of 6G vehicular networks, providing features such as enhanced line of sight communication, extended coverage range and high data rate transmission. In this paper, we consider a multiple IRSs assisted multi-vehicle Multiple-Input Multiple-Output (MIMO) communication system and optimize the transmit beamforming vectors at the transmitter and reflect beamforming (phase shifts) at each IRS to achieve max-min Quality of Service (QoS) in the form of weighted Signal-to-Interference-Plus-Noise Ratio (SINR). In particular, we divide the optimization problem into two Semidefinite Relaxation (SDR) sub-problems and solve them iteratively using the alternate optimization technique. Simulation results show that the proposed technique significantly increases the sum-rate and SINR of the vehicular network.

Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Cooperative Jamming Model Design

Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Cooperative Jamming Model Design

Resource Configuration for Throughput Maximization in UAV-WPCN With Intelligent Reflecting Surface

UAV-based wireless powered communication network is a promising method of power supply for battery-free IoT devices, but the limited wireless transmission capability of the UAV constrains the coverage area and transmission throughput. This paper aims to address this issue by exploring intelligent reflecting surfaces with optimized configurations, including the number of reflective elements, transmission power, and the UAV’s altitude. The scheme design is challenging because such a throughput maximization problem is essentially a non-convex optimization problem due to the random wireless channel state and the unknown probability distribution of the objective function. By sequentially applying alternating optimization, successive convex approximation, penalty function, and difference-convex optimization, the schemes proposed in this paper can transform the original non-convex optimization problem into a convex one. Extensive evaluation proves the efficacy of the proposed scheme. The paper further compares two settings of the intelligent reflecting surface, namely dynamic phase shift and static phase shift, and provides their performance gap.

Outage Behaviors of Active Intelligent Reflecting Surface Enabled NOMA Communications

Active intelligent reflecting surface (IRS) is a novel and promising technology that is able to overcome the multiplicative fading introduced by passive IRS. In this paper, we consider the application of active IRS to non-orthogonal multiple access (NOMA) networks, where the incident signals are amplified actively through integrating amplifier to reflecting elements. More specifically, the performance of active/passive IRS-NOMA networks is investigated over large and small-scale fading channels. Aiming to characterize the performance of active IRS-NOMA networks, the exact and asymptotic expressions of outage probability for a couple of users, i.e., near-end user and far-end user are derived by exploiting a 1-bit coding scheme. Based on approximated analyses, the diversity orders of user and user are obtained for active IRS-NOMA. In addition, the system throughput of active IRS-NOMA is discussed in the delay-sensitive transmission. The simulation results are carried out to verify that: i) The outage behaviors of active IRS-NOMA networks are superior to that of passive IRS-NOMA networks; ii) As the reflection amplitude factors increase, the active IRS-NOMA networks are capable of furnishing the enhanced outage performance; and iii) The active IRS-NOMA has a larger system throughput than passive IRS-NOMA and conventional communications.

Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.

Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment

In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model is a reliable approach to realizing the system as long as the hardware impairment level is controlled. The results show that the proposed scheme can greatly improve achievable rates, obtain optimal performance at one of two devices and exhibit a small performance gap compared with the other benchmark scheme.

Distributed Intelligent Reflecting Surfaces-Aided Communication System: Analysis and Design

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment with reconfigurable passive elements, which can achieve high spectral and energy efficiency. In this paper, we investigate a distributed IRS-assisted multi-user communication system. Different from most previous works relying on instantaneous channel state information (CSI) for beamforming design, this paper proposes a low-complexity two-timescale beamforming scheme using both statistical CSI (S-CSI) and instantaneous CSI (I-CSI), where closed-form expressions of IRS phase shifts and the base station (BS) beamforming vectors are obtained. Moreover, a closed-form expression of the achievable rate is derived, which enables the evaluation of the impact of key parameters on system performance. The analytical results show that the achievable rate can be significantly improved by increasing the number of IRS elements. In addition, the performance of the distributed IRS-assisted multi-user communication system is better than that of the centralized IRS-assisted system. Numerical results show that the beamforming algorithm proposed in this paper is superior to the beamforming algorithm using I-CSI under low and moderate user rate requirements. In addition, the phase shift design scheme has almost the same performance as the genetic algorithm based on S-CSI, but with much lower complexity. Finally, a power allocation scheme utilized S-CSI has been proposed to improve the system performance.

Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels

The application of intelligent reflecting surface (IRS) depends on the knowledge of channel state information (CSI), and has been hindered by the heavy overhead of channel training, estimation, and feedback in fast-changing channels. This paper presents a new two-timescale beamforming approach to maximizing the average achievable rate (AAR) of IRS-assisted MIMO systems, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the base station precoder and power allocation are updated frequently based on quickly outdated instantaneous CSI (I-CSI). The key idea is that we first reveal the optimal small-timescale power allocation based on outdated I-CSI yields a water-filling structure. Given the optimal power allocation, a new mini-batch sampling (mbs)-based particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS configuration with reduced channel samples. Another important aspect is that we develop a model-driven PSO algorithm to optimize the IRS configuration, which maximizes a lower bound of the AAR by only using the S-CSI and eliminates the need of channel samples. The model-driven PSO serves as a dependable lower bound for the mbs-PSO. Simulations corroborate the superiority of the new two-timescale beamforming strategy to its alternatives in terms of the AAR and efficiency, with the benefits of the IRS demonstrated.

Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems.

This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV. Since the problem is non-convex, an alternating optimization (AO) algorithm is proposed to solve it. Specifically, the TPC matrix and AN covariance matrix are derived by the Lagrange dual method. The alternating direction method of multipliers (ADMM), majorization-minimization (MM), and Riemannian manifold gradient (RCG) algorithms are presented, respectively, to solve the IRS phase shift matrix, and then the performance of the three algorithms is compared. Based on the proportional integral (PI) control theory, a secrecy rate gradient (SRG) algorithm is proposed to iteratively search for the UAV position by following the direction of the secrecy rate gradient. The theoretic analysis and simulation results show that our proposed AO algorithm has a good convergence performance and can increase the SR by 40.5% compared with the method without IRS assistance.

Covert Communications in Intelligent Reflecting Surface-Assisted Two-Way Relaying Networks

Covert communications is critical in many application scenarios for ensuring transmission security and privacy. Two-way protocols are widely adopted in intelligent reflecting surface (IRS) assisted relaying networks to enable covert communications and prevent wireless signals from being overheard. To quantify the performance of covert communications in this special scenario, we derive the closed-form expression of outage probability and its asymptote in this paper. Considering the worst-case of covert communications, the optimal normalized power threshold of warden's detector is analyzed under a complex Gaussian distribution approximation. To meet the requirement of the detection error probability, the ratio of the transmission power between the covert nodes is investigated. Simulation results are provided to validate the theoretical expressions as well as the asymptotic analysis with a small number of reflecting elements.