Intelligent reflecting surface (IRS) is an emerging technology that is able to significantly improve the performance of wireless communications, by smartly tuning signal reflections at a large number of passive reflecting elements. On the other hand, with ubiquitous wireless devices and ambient radio-frequency signals, wireless sensing has become a promising new application for the next-generation/6G wireless networks. By synergizing low-cost IRS and fertile wireless sensing applications, this article proposes a new IRS-aided sensing paradigm for enhancing the performance of wireless sensing cost-effectively. First, we provide an overview of wireless sensing applications and the new opportunities of utilizing IRS for overcoming their performance limitations in practical scenarios. Next, we discuss IRS-aided sensing schemes based on three approaches, namely, passive sensing, semi-passive sensing, and active sensing. We compare their pros and cons in terms of performance, hardware cost and implementation complexity, and outline their main design issues including IRS deployment, channel acquisition and reflection design, as well as sensing algorithms. Finally, numerical results are presented to demonstrate the great potential of IRS for improving wireless sensing accuracy and the superior performance of IRS active sensing compared to other schemes.
Intelligent reflecting surface (IRS) has emerged as a promising technology to realize smart radio environment for future wireless communication systems. Existing works in this line of research have mainly considered the conventional passive IRS that reflects wireless signals without power amplification, while in this article, we give an overview of a new type of IRS, called active IRS, which enables simultaneous signal reflection and amplification, thus significantly extending the signal coverage of passive IRS. We first present the fundamentals of active IRS, including its hardware architecture, signal and channel models, as well as practical constraints, in comparison with those of passive IRS. Then, we discuss new considerations and open issues in designing active-IRS-aided wireless communications, such as the reflection optimization, channel estimation, and deployment for active IRS, as well as its integrated design with passive IRS. Finally, numerical results are provided to show the potential performance gains of active IRS as compared to passive IRS and traditional active relay.
Intelligent reflecting surface (IRS) is a new promising technology that is able to reconfigure the wireless propagation channel via smart and passive signal reflection. In this paper, we investigate the capacity region of a two-user communication network with one access point (AP) aided by $M$ IRS elements for enhancing the user-AP channels, where the IRS incurs negligible delay, thus the user-AP channels via the IRS follow the classic discrete memoryless channel model. In particular, we consider two practical IRS deployment strategies that lead to different effective channels between the users and AP, namely, the distributed deployment where the $M$ elements form two IRSs, each deployed in the vicinity of one user, versus the centralized deployment where all the $M$ elements are deployed in the vicinity of the AP. First, we consider the uplink multiple-access channel (MAC) and derive the capacity/achievable rate regions for both deployment strategies under different multiple access schemes. It is shown that the centralized deployment generally outperforms the distributed deployment under symmetric channel setups in terms of achievable user rates. Next, we extend the results to the downlink broadcast channel (BC) by leveraging the celebrated uplink-downlink (or MAC-BC) duality framework, and show that the superior rate performance of centralized over distributed deployment also holds. Numerical results are presented that validate our analysis, and reveal new and useful insights for optimal IRS deployment in wireless networks.
In this paper, we propose a new multiple-input multiple-output (MIMO) communication system with movable antennas (MAs) to exploit the antenna position optimization for enhancing the capacity. Different from conventional MIMO systems with fixed-position antennas (FPAs), the proposed system can flexibly change the positions of transmit/receive MAs, such that the MIMO channel between them is reconfigured to achieve higher capacity. We aim to characterize the capacity of MA-enabled point-to-point MIMO communication systems, by jointly optimizing the positions of transmit and receive MAs as well as the covariance of transmit signals. First, we develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix and the position of each transmit/receive MA with the other variables being fixed. Next, we propose alternative algorithms of lower complexity for capacity maximization in the low-SNR regime and for the multiple-input single-output (MISO) and single-input multiple-output (SIMO) cases. Numerical results show that our proposed MA systems significantly improve the MIMO channel capacity compared to traditional FPA systems as well as various benchmark schemes, and useful insights are drawn into the capacity gains of MA systems.
The rate maximization for the K-user interference channels (ICs) has been investigated extensively in the literature. However, the dual problem of minimizing the error probability with given signal constellations and/or data rates of the users is less exploited. In this paper, by utilizing the additional degrees of freedom attained from the improper signaling (versus the conventional proper signaling), we optimize the precoding matrices for the K-user single-input single-output (SISO) ICs to achieve minimal transmission symbol error rate (SER). Compared to conventional proper signaling as well as other state-of-the-art improper signaling designs, our proposed improper signaling scheme is shown to achieve notable SER improvement in SISO-ICs by simulations. Our study provides another viewpoint for optimizing transmissions in ICs and further justifies the practical benefit of improper signaling in interference-limited communication systems.
Massive multiple-input multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless networks. However, the use of very large antenna arrays brings new issues, such as the significantly increased hardware cost and signal processing cost and complexity. In order to reap the enormous gain of massive MIMO and yet reduce its cost to an affordable level, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed electromagnetic lens antenna (ELA). An ELA has the capability of focusing the power of any incident plane wave passing through the EM lens to a small subset of the antenna array, while the location of focal area is dependent on the angle of arrival (AoA) of the wave. As compared to conventional antenna arrays without the EM lens, the proposed system can substantially reduce the number of required radio frequency (RF) chains at the receiver and hence, the implementation costs. In this paper, we investigate the proposed system under a simplified single-user uplink transmission setup, by characterizing the power distribution of the ELA as well as the resulting channel model. Furthermore, by assuming antenna selection used at the receiver, we show the throughput gains of the proposed system over conventional antenna arrays given the same number of selected antennas.
As power quality disturbances become increasingly complex, it is imperative that the speed and accuracy of the inspection robot at the converter station be improved. For this purpose, this study designs a power quality disturbance feature extraction method based on the fast‐adaptive S‐transform. This method preserves the main feature information and eliminates redundant calculations on the basis of adaptive transform. On this basis, a power quality disturbance identification model built on a multi label lightweight gradient elevator is constructed. In the experimental results, compared to the generalized S‐transform, adaptive S‐transform, and S‐transform, the total extraction time of the proposed method was reduced by 96.09%, 91.56%, and 91.22%, respectively. The average accuracy of extracting features for a single disturbance was 99.56%, while for complex disturbance, it was 98.24%, both of which outperformed the comparison algorithms. It is verified that the proposed method can improve the extraction accuracy of power quality disturbance signal. In the recognition of a single disturbance signal, the constructed model exhibited a high accuracy of over 99%. In recognizing composite disturbance signals, the model demonstrated high accuracy and strong stability. Its effectiveness has been confirmed through experiments. The paper aims to enhance the speed and accuracy of power quality disturbance recognition algorithms. This will assist inspection robots working in converter stations, and ensure stable and safe operation of the power grid.
It is a significant challenge to construct reconfigurable electromagnetic metamaterials that can precisely manipulate broadband megahertz electromagnetic waves. Herein, we report a reconfigurable electromagnetic metamaterial (REMM) composed of a two-dimensional periodic array of spiral copper-clad unit cells, each paralleled with a micro-tunable capacitor, which has nearly linear voltage-controlled properties. Moreover, the on-board integrated sample-and-hold modules, linked to all the REMM unit cells, are activated sequentially to perform precise voltage regulation of micro-tunable capacitors for controlling the electromagnetic properties of each unit cell. The experiment results demonstrate that the REMM sample has a maximum frequency adjustment range of 2.1 MHz, ranging from 8.7 MHz to 10.9 MHz with less than 0.1 MHz adjustment step. Furthermore, in a wireless power transfer system, the proposed REMM can achieve the desirable magnetic-field manipulation by precisely adjusting the permeability distribution compared with the traditional metamaterial slab merely capable of full-negative permeability. As a result, the power transfer efficiency (PTE) can be increased from 9.53% to 11.51% (1.69% for the case without the metamaterial slab), and approximately 3.5-fold improvement (from 0.28% to 0.98%) can be achieved when coils are misaligned. This work lays the foundation for the control of electromagnetic waves through using broadband and precise reconfiguration of megahertz electromagnetic metamaterials.
No abstract is provided for this article.
No abstract is provided for this article.
Consider a wireless network of two tiers with different priorities: a primary tier and a secondary tier, which is an emerging network scenario with the advancement of cognitive radio technologies. The primary tier is constructed over static nodes of density n, which are randomly distributed and have an absolute priority to access the spectrum. The secondary tier contains mobile nodes of density m = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ß</sup> with ß ¿ 2, which can only access the spectrum opportunistically to limit the interference to the primary tier. By allowing the secondary tier to relay the packets for the primary tier, we show that the achievable per-node throughput scaling for the primary tier can be improved to ¿ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n) = ¿(1/log n). In the associated delay analysis, two mobility models are considered for the secondary nodes: an i.i.d. mobility model and a random walk model. We show that the primary tier can achieve delay scaling laws of ¿(1) and ¿(1/S) with the two mobility models, respectively, where S is the random walk step size. Furthermore, we show that the primary tier can achieve a delay-throughput tradeoff of D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n) = O (n¿ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n)) with ¿ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n) = O(1/log n) for the random walk model. The throughput and delay scaling laws for the secondary tier are also established, which are the same as those for a stand-alone mobile network.
Consider a static wireless network that has two tiers with different priorities: a primary tier vs. a secondary tier. The primary tier consists of randomly distributed legacy nodes of density n, which have an absolute priority to access the spectrum. The secondary tier consists of randomly distributed cognitive nodes of density m = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beta</sup> with beta ges 2, which can only access the spectrum opportunistically to limit the interference to the primary tier. By allowing the secondary tier to route the packets for the primary tier, we show that the primary tier can achieve a throughput scaling of lambda <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n) = Theta(1/log n) per node and a delay-throughput tradeoff of D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n) = Theta (radic(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beta</sup> log nlambda <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n))) for lambda <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> (n) = O (1/log n), while the secondary tier still achieves the same optimal delay-throughput tradeoff as a stand-alone network.
Conventional wireless security assumes wireless communications are legitimate, and aims to protect them against malicious eavesdropping and jamming attacks. However, emerging infrastructure- free mobile communication networks can be illegally used (e.g., by criminals or terrorists) but are difficult to monitor, thus imposing new challenges in public security. To tackle this issue, this article presents a paradigm shift of wireless security to the surveillance and intervention of infrastructure-free suspicious and malicious wireless communications, by exploiting legitimate eavesdropping and jamming jointly. In particular, proactive eavesdropping (via jamming) is proposed to intercept and decode information from suspicious communication links for the purpose of inferring their intentions and deciding further measures against them. Cognitive jamming (via eavesdropping) is also proposed to disrupt, disable, and even spoof the targeted malicious wireless communications to achieve various intervention tasks.
The newly emerging wireless powered communication networks (WPCNs) have recently drawn significant attention, where radio signals are used to power wireless terminals for information transmission. In this paper, we study a WPCN where one multi-antenna access point (AP) coordinates energy transfer and information transfer to/from a set of single-antenna users. A harvest-then-transmit protocol is assumed where the AP first broadcasts wireless power to all users via energy beamforming in the downlink (DL), and then the users send their independent information to the AP simultaneously in the uplink (UL) using their harvested energy. To optimize the users' throughput and yet guarantee their rate fairness, we maximize the minimum throughput among all users by a joint design of the DL-UL time allocation, the DL energy beamforming, and the UL transmit power allocation plus receive beamforming. We solve this non-convex problem optimally by two steps. First, we fix the DL-UL time allocation and obtain the optimal DL energy beamforming, UL power allocation and receive beamforming to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. This problem is shown to be in general non-convex; however, we convert it equivalently to a spectral radius minimization problem, which can be solved efficiently by applying the alternating optimization based on the non-negative matrix theory. Then, the optimal time allocation is found by a one-dimension search to maximize the minimum rate of all users. Furthermore, two suboptimal designs of lower complexity are proposed, and their throughput performance is compared against that of the optimal solution.
To realize the efficient transmission under ultra-reliable and low-latency communication (URLLC), cooperative non-orthogonal multiple access (C-NOMA), in which the two-phase cooperative transmission is considered and non-orthogonal multiple access (NOMA) is implemented during the cooperation, has gained much attention in recent years. When the cooperation is implemented in the first phase of relaying, the C-NOMA scheme has superb performance only when the relay (R) is close to the source, since NOMA gain comes from the channel quality difference. To expand the versatility of C-NOMA in the case where R is close to the destination (D), we propose a packet re-management-based C-NOMA transmission scheme, in which the cooperation is implemented in the second phase of relaying. Further, considering the importance of power consumption in 5G, we also propose an efficient algorithm to minimize the power consumption, subject to URLLC requirements. Specifically, to tackle the above non-convex and implicit problem, we first provide a tight upper bound approximation of channel dispersion to make the problem explicit. Then, on the basis of block coordinate descent and successive upper bound approximation frameworks, a recursion method is proposed to solve the joint optimization problem. Simulation results verify the effectiveness of the proposed scheme in the case where R is close to D.
Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network.
