Energy efficiency has become increasingly pivotal for sustainable wireless communications, driving the exploration of innovative technologies to enhance performance while minimizing energy consumption. Movable antenna (MA) technology emerges as a promising paradigm in this pursuit, introducing enhanced spatial degrees of freedom by dynamically adjusting antenna positions at the base station (BS). In this paper, we investigate the energy-efficient design problem for downlink communication systems, where the BS is equipped with MAs and serves multiple single-antenna users.We develop a comprehensive energy efficiency model that integrates the communication sum rate with the power consumption associated with both MA movements and signal transmissions. We aim to maximize the energy efficiency by jointly optimizing the transmit beamforming and antenna positions at the BS, subject to practical constraints including the transmit power budget, minimum inter-antenna distance, and maximum movement range. To address this non-convex problem, we propose an efficient alternating optimization algorithm that iteratively solves the beamforming and MA position optimization subproblems using successive convex approximation and particle swarm optimization methods, respectively. Extensive simulations show that the proposed MA-aided system achieves significantly higher energy efficiency than conventional fixed-position antenna systems and hybrid analog/digital array systems with the same number of radio frequency chains.

In recent years, physical layer security (PLS) techniques have been paid considerable attention due to its high-security level and strong compatibility. However, the request of the superior legitimate channel is the Achilles’ Heel of PLS. A significant challenge exists in ensuring information confidentiality when the eavesdropper locates at user’s direction in the multi-antenna system. To overcome this limitation, we propose a novel framework to achieve secure communication via distributed split single sideband (SSB) time modulated array (TMA). By strategically dividing the I/Q paths of the transmitted signals into geographically separated subarrays, we establish the non-aliasing zone to achieve error-free communication for legitimate user (LU), while the eavesdropper positioned in the aliasing zone receives irrecoverably disturbed waveform. To assess performance, we adopt the encoder-decoder-based deep neural network to optimize the time-switching sequence, ensuring the subarrays’ spatial radiation areas overlap exclusively at the LU. A specialized loss function is formulated to enhance the interference-to-signal ratio by increasing the −1stto the +1stharmonic power ratio in non-LU regions. Furthermore, the joint optimization improves security by focusing energy on the LU while generating interference in non-LU areas. The bit error rate (BER) is used as the metric, and the simulation results validate the effectiveness of the proposed method, ensuring reliable transmission and increasing Eve’s BER to approximately 0.5, thereby compromising her ability to intercept the communication.

For two-dimensional direction-of-arrival (2-D DOA) estimation, the uniform planar arrays (UPAs) can offer satisfactory estimation performance among various sensor array configurations, but is prone to be affected by the unknown mutual coupling effects. Existing 2-D DOA estimation algorithms accounting for the mutual coupling effects calibration either suffer from low estimation resolution or high computational complexity. To deal with this problem, we propose a tensor-based 2-D DOA estimation algorithm for UPAs in the presence of unknown mutual coupling. Specifically, by exploiting the block banded symmetric Toeplitz structure of the mutual coupling matrix, we construct a calibration matrix to relieve the mutual coupling effect. Then, the received signals are reformulated into a tensor format admitting the canonical polyadic decomposition, where the factor matrices incorporate the azimuth and elevation angles. By exploiting the inherent Vandermonde structure of the equivalent steering matrix, we develop an algebraic-based factor matrix estimation method without the necessity of iteration, followed by the azimuth and elevation angles extraction from the estimated factor matrices. In addition, the closed-form solutions of the mutual coupling coefficients are obtained based on the estimated angles. On this basis, we mathematically investigate the uniqueness condition of the tensor decomposition and the maximum number of resolvable targets. Moreover, we derive the Cramér-Rao bound to evaluate the performance limit for the considered 2-D DOA estimation problem with mutual coupling effects, and the computational complexity. Simulation results corroborate the superiority of the proposed tensor-based 2-D DOA estimation algorithm over competing methods in terms of complexity and resolution.

Movable Antenna (MA) technology is emerging as a promising advancement with the potential to significantly enhance the performance of future wireless communication and sensing systems. In this paper, we address two-dimensional (2D) direction of arrival (DOA) estimation via joint shape-position optimization. Specifically, we formulate an optimization problem aimed at minimizing the Cram´er-Rao Bound (CRB) based on a 2D DOA estimation model for MA systems. To tackle the highly non-convex nature of this CRB minimization, we investigate the spatial utilization of the movable region (MR) under minimum antenna spacing constraints. By demonstrating that an equilateral triangle yields the minimum overlap area, we strategically design an equilateral triangular MR. This specific geometric configuration enables the exploitation of structural symmetry to simplify the geometric constraints, which effectively reduces the complexity of solving the optimization problem. Subsequently, we derive the optimal MA positions by selecting the candidate locations farthest from the centroid of MR. The results demonstrate that the proposed joint shape-position optimization substantially enhances 2D DOA estimation performance.

Movable antenna (MA)-enabled multiple-input multiple-output (MIMO) systems offer a promising enhancement for integrated sensing and communication (ISAC) applications. Unlike conventional MIMO systems with fixed-position antenna (FPA) arrays, MAs can flexibly adjust their positions within a given region, enabling reconfiguration of both communication and sensing channels with additional spatial degrees of freedom. In this paper, we propose a unified mutual information (MI) framework for MA-enabled MIMO ISAC systems, where MI characterizes communication performance as reliably conveyable information and sensing performance as extractable target information in cluttered environments. We formulate an optimization problem to maximize the weighted sum of communication and sensing MI by jointly optimizing the transmit beamforming matrix under a transmit power constraint and the MA positions under practical constraints, with a weighting coefficient characterizing their trade-off. To tackle the non-convexity arising from the log-det objective, position constraints, and the nonlinear coupling between optimization variables, we develop an alternating optimization-based algorithm that iteratively updates the transmit beamforming matrix and the MA positions. Specifically, with the fixed MA positions, we optimize the beamforming by approximating the objective function using weighted mean square error and majorization-minimization methods, yielding a closed-form solution. Moreover, with fixed beamforming, the MA positions are sequentially refined by decomposing the position optimization into simpler subproblems, resulting in an efficient suboptimal solution. Numerical results show that the unified MI framework with MAs significantly outperforms conventional FPA systems in both communication and sensing. Channel amplitude heatmap visualizations further illustrate how MA positioning strategies exploit spatial flexibility in array geometry to enhance overall system performance.

Phased array (PA) has received considerable attention as a representative multiantenna technique due to its inherent advantages of superior directionality, spatial multiplexing capabilities, and robust anti-jamming characteristics. However, PA suffers from relatively high hardware complexity and power consumption. As a low-complexity array technology with excellent beamforming capability, time modulated array (TMA) has attracted much attention in recent years. In this article, we exploit a TMA for enhancing the Age of Information (AoI) of covert communication. Specifically, we first propose the transmitter structures and the corresponding beamforming methods for the TMA scheme and the PA scheme as a benchmark. Subsequently, the closed-form expressions of the Kullback-Leibler (KL) divergence is derived to serve as the quantitative measure of communication covertness under both schemes, based on which the average covert AoI (CAoI) is derived to jointly characterize the covertness and timeliness performance. Then, to minimize the average CAoI, the optimization problems of the block-length and beamforming parameters for both the TMA and PA schemes are formulated and solved. Finally, the numerical results are provided to show that the proposed TMA scheme surpasses the PA scheme in terms of both the convergence rate and the average CAoI.

Integrated sensing and massive multiple-input-multiple-output (MIMO) communication (mMIMO-ISAC) at terahertz (THz) bands can provide vast spatial degrees of freedom and abundant bandwidth resources. However, the employment of a massive number of antennas will pose prominent challenges to both target sensing and channel training in THz-mMIMO-ISAC. In this article, our goal is to integrate the target sensing functionality into the channel estimation stage and develop a channel-training-aided target sensing framework to facilitate the efficient resource sharing of THz-mMIMO-ISAC. Specifically, by exploiting the sparse characteristics of THz mMIMO channels, we build up the intrinsic connection between the channel parameters and the target parameters in angular, delay, and Doppler dimensions. Then, we propose a shared channel training pattern accommodating the hybrid architecture constraints of THz transceiver. Both the channel estimation and the target sensing can be formulated as two structured tensor decomposition problems and then concurrently addressed at the UE and BS sides, respectively. Next, we propose a tensor-based parameter estimation algorithm to acquire the target and channel parameters, where the associated angles of arrival/departure, time delays, Doppler shifts, and coefficients can be extracted from the estimated factor matrices. In addition, we present the detailed derivation of the Cramér-Rao bound (CRB) for the considered parameter estimation problem in THz-mMIMO-ISAC. Numerical results demonstrate that the proposed algorithm can achieve the target parameters estimation performance close to their corresponding CRB, and recover the high-dimensional THz mMIMO channels with substantially reduced training overhead.

A wideband active single-sideband time modulator (STM) is proposed in this paper, which achieves high-resolution frequency-independent phase shifting performance through high-precision time delay, eliminating the need for calibrations. The analysis starts with N-step time modulation sequences for the active STM, followed by discussions on enhancing the sideband suppression ratio (SSR) and the effects of quadrature mismatch on SSR. The proposed active STM is based on a periodically controlled active vector modulator with regularly scalable gate-widths, and its timing sequences for control bits feature identical duty cycles and modulation frequency ($f_{\mathrm {P}}$). For verification, a wideband active STM is implemented using 0.13-$\mu $m CMOS technology, which is composed of an input balun and quadrature generator, a periodically controlled 4-bit active vector modulator and a variable-gain stage. Measurement results indicate root mean square (RMS) phase and gain errors ranging from 0.1 to 0.4° and less than 0.2 dB respectively, within a 3-dB frequency range of 13.0 to 20.6 GHz at the maximum gain state. The active STM provides a peak gain of −0.1 dB, an equivalent 10-bit phase control across a 360° range, and a 3-bit gain control spanning 21.0 dB. The measured SSR is below −23.3 dBc, and the instantaneous bandwidth is expanded to$16f_{\mathrm {P}}$. Additionally, the input 1-dB compression point (IP$_{\mathrm {1dB}}$) ranges from 6.1 to 9.3 dBm. The chip occupies a 2.4 mm2 area and consumes 58.8 mW from a 1.2 V supply voltage.

This article presents a low-phase noise (PN) trifilar transformer-based Class-F23voltage-controlled oscillator (VCO) with noise-circulating technology. The topology minimizes the side effect of the parasitic capacitors that incorporates an uncontrollable common-mode (CM) path in the conventional noise-circulating VCOs, and reduces the magnitude of the noise sources injected into the tank by simultaneously switching on/off the source degeneration. Additionally, the PN is reduced because the proposed trifilar transformer-based tank can provide extra drain-to-gate gain. The proof-of-concept prototype is a 5.2–6.1GHz VCO in 65 nm CMOS. It exhibits a −146.6 dBc/Hz PN at 10 MHz offset, consuming 7.92 mW from a 0.9 V power supply at 5.9 GHz. The corresponding figure of merit (FoM) at 10MHz offset is 193 dBc/Hz. It achieves a slight supply frequency pushing of 13.3 and −18.7 MHz/V at the lowest and highest frequency, respectively. The occupied compact core area is 0.12mm2.

Extremely large-scale multiple-input multiple-output (XL-MIMO) with orthogonal frequency division multiplexing (OFDM) transmission can provide unprecedented improvement in spectral efficiency and data rate. Dynamic metasurface antennas (DMAs) have been proposed as a cost-effective and power-efficient solution for realizing XL-MIMO systems. However, the extremely large number of antennas in XL-MIMO-OFDM with DMAs poses critical challenges in acquiring accurate channel state information. To address this issue, we propose in this paper a tensor-based channel estimation method for frequency-selective XL-MIMO-OFDM systems with DMAs. We first characterize the configurable property of DMAs and propose a microstrip-sequential channel training method with quasi-dynamically adjustable metamaterial elements, by representing the received frequency-domain training signals as a fourth-order tensor which admits the canonical polyadic decomposition. Then, by exploiting the sparsity of XL-MIMO channels, we propose a two-stage tensor decomposition-based channel estimation algorithm, where the four coupling factor matrices are obtained without the need of iterative refinement, and the channel multipath parameters can be extracted for reconstructing the entire high-dimensional channel matrix. In addition, we analyze the uniqueness condition for the proposed tensor-based channel estimation method, which reveals that the required channel training overhead is only proportional to the number of channel multipaths, instead of that of metamaterial elements and microstrips. Numerical results demonstrate the superior performance of our proposed design with significantly reduced training overhead as compared to various benchmark schemes.

The massive number of wirelessly connected devices interacting in the increasingly crowded Internet of Things (IoT) environment places high demands on electromagnetic (EM) wave manipulation in a compact size. Since the limits of the reciprocity theorem, it remains challenging to independently achieve the transmitting, receiving, and scattering field control on the same array in the in-band co-polarization scenarios. To address this challenge, this paper presents a novel tri-functional multiplexed linear array (TMLA), which can multiplex the transmitting, receiving, and backscattering control functions within a single array. Firstly, a co-linearly polarized dual-port linear antenna array (CDLA) with joint in-unit and inter-unit decoupling is proposed, where isolation between all ports enables the independent implementation and design of radiation and scattering; secondly, a feeding network based on the self-interference cancellation (SIC) technique is designed to realize transmit-receive (Tx-Rx) isolation; thirdly, an active scattering control circuit is designed to achieve enhanced scattering control capabilities, including flexible enhancement or reduction of backscattering. A prototype is fabricated and measured. Measured results show that the proposed array achieves multifunction multiplexing with well-isolated transmitting, receiving, and scattering ports. The proposed array can be applied to IoT scenarios, especially in intelligent transportation systems.

Since respiration rate is a crucial indicator of the health state, respiration monitoring for a long duration is critical for assessing the physical health of in-vehicle passengers, especially in the cases of seniors, patients, or children. Microwave radar sensor has been shown to be suitable for detecting and monitoring respiration rates without any physical contact. However, there are two main challenges to overcome for the in-vehicle scenarios: 1) the respiration monitoring of multiple closely spaced subjects and 2) the impact of strong stationary clutters and multipath reflection from in-vehicle environments. To tackle these challenges, a time-division multiple tags (TDMT)-based frequency-modulated continuous wave (FMCW) radar is proposed to detect the phase changes caused by the movements of the human chests, which are captured by novel surface-circuit metasurfaces (SCMs) tags attached to the seat belts worn by passengers. On the one hand, the TDMT scheme can separate respiration signals from two closely spaced passengers. On the other hand, the innovative SCM tags can simultaneously accomplish spatial filtering, retro-reflection, and polarization rotation to combat the strong clutters and multipath reflection in in-vehicle environments. The miniaturized radar prototype and tags are analyzed and implemented. Its accurate and robust performance is demonstrated with extensive experiments conducted under real in-vehicle scenarios.

In the cellular Internet of Things (CIoT), it is crucial to ensure the information freshness for status update applications. Considering the centralized access methods could cause large access delay and hamper timely status updates, this paper exploits the random access method and studies decentralized status update schemes to minimize the average age of information (AoI) for CIoT. However, due to the non-cooperation among machine type communication devices (MTCDs) in random access, packet collisions are inevitable, which makes it tricky to improve the AoI performance. In this regard, we design novel age-based status update schemes to control the transmission behavior of MTCDs, where the AoI at the MTCDs and the base station (BS) is used. We first model the AoI minimization problem as a Markov decision process. Then, through variable substitution and linear programming, we get a slightly more computationally complex status update scheme, where the dual threshold structure of the scheme is proved theoretically. To facilitate system design and reduce computational complexity, we further design a low-complexity scheme, where the age thresholds at both the MTCDs and BS are optimized. Simulation results verify that the proposed schemes significantly outperform the common access scheme.

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde structure. Then, we develop a unified tensor-based algorithm to estimate the parameters including angles, time delays, Doppler shifts, and reflection/path coefficients of the targets/channels. In addition, we propose a segment-based shared training pattern to facilitate the channel and target parameter estimation for the case with significant beam squint effects. Simulation results verify our theoretical analysis and the superiority of the proposed unified algorithms in terms of estimation accuracy, sensing resolution, and training overhead reduction.

Integrated sensing and communications (ISAC), supported by massive multiple-input multiple-output (MIMO), can provide simultaneously improvement of sensing capability and communication capacity. However, employing the conventional fully digital beamforming architecture with a large-scale antenna array will incur the prohibitively high hardware cost and power consumption. In this paper, we propose a hybrid beamforming design with the overlapped subarrays (OSA)-based hybrid architecture for massive MIMO-ISAC systems. We design the analog and digital beamformers by jointly optimizing the spectral efficiency of communication and beampattern mean squared error of sensing under the specific constraints of OSA structures, power budget, and constant modulus. To tackle the resulting non-convex problem, we relax it as a weighted summation minimization problem, where the Euclidean distance between the designed hybrid beamformers and the optimal communication/desired sensing beamformers is minimized. We further decompose the formulated problem into three subproblems and develop an effective alternating minimization algorithm. Numerical simulations demonstrate the effectiveness and flexibility of the proposed OSA-based hybrid beamforming design in terms of spectral efficiency and sensing beampattern performance.

Abstract Time‐modulated arrays (TMAs) have been widely studied owing to their convenient control mode and simple structure. Although direction‐of‐arrival (DOA) estimation based on TMA has garnered considerable attention, underdetermined DOA estimation in TMA is yet to be studied. In this study, a novel method is proposed for underdetermined DOA estimation based on the higher‐order cumulants of harmonic signal in a TMA. The periodic modulation of radio‐frequency switches leads to a TMA generating harmonics in space. Cumulants of harmonic statistics can be used for DOA estimation, and with higher‐order harmonics cumulants, underdetermined DOA estimation can be realised with a TMA using the multiple signal classification method, and the degree of freedom can be improved further. Another advantage of the proposed method is its simple structure, which only requires one receiver with several filters. Numerical simulations show that the proposed method can achieve good resolution and precision performance.

Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave) can provide unprecedented performance improvement for realizing future wireless communications. However, acquiring accurate channel state information in wideband mmWave massive MIMO systems with hybrid transceiver architectures is even challenging, especially in high-mobility scenarios with severe Doppler effects. In this paper, we propose a tensor decomposition-based method to estimate the time-varying and frequency-selective (TVFS) mmWave MIMO channels. Specifically, by exploiting the sparse scattering nature of TVFS channels, we model the frequency-domain received signal as a third-order tensor that admits a canonical polyadic (CP) decomposition format. Then, we analyze the uniqueness condition of the proposed CP decomposition-based channel estimation problem and propose a novel estimator to acquire TVFS channel parameters including angle of departure/arrival (AoD/AoA), time delay, path gain, and the Doppler shift. To address the sophisticated coupling among unknown parameters, we further propose a joint AoD and Doppler shift estimation (JADE) algorithm that provides reliable initial and iteratively refined estimates. The derived analysis and simulation results verify that the proposed JADE algorithm achieves higher estimation accuracy and guarantees the superiority of the proposed TVFS channel estimator over existing schemes.

This paper presents a new approach to design filtering power dividers (FPD) with regular triangle patch resonator (RTPR). According to the electric-field (E-field) distributions of the resonator modes, the TM10and TM11modes have been selected to realize two- and three-way power dividers respectively, showcasing the flexible characteristic of the RTPR. Afterwards, an in-phase multiway excitation scheme for the RTPR is developed to well excite the designated modes and suppress multiple spurious modes, simultaneously. Subsequently, to further reject the harmonics so as to extend the upper stopband greatly, shorting posts are deployed at the locations with null electric field distribution for the specified operation mode. Ultimately, to validate the design concept, a two-way and a three-way second-order FPD employed at TM10($f_{10}= 1.27$GHz) with fractional bandwidth (FBW) of 7% and at TM11($f_{11}= 2.2$GHz) with FBW of 4.5% are designed and measured. The measured results of the two fabricated circuits well agree with the simulated ones. Results indicate that both of the two FPDs exhibit properties of good port-to-port isolation, high selectivity with transmission zeros near both sides of the passband, as well as wide stopband extended to 3.35 and 2.9 times of fundamental operation frequencies, respectively.

This paper proposes a new structural design to excite surface plasmonic polaritons to enhance the double‐ring interference structure. The double‐ring structure was etched into a thin film to form fundamental interference patterns, and periodic concentric‐ring grooves were employed to gather energy from the surrounding regions through the excitation of surface plasmonic polaritons. Accordingly, the energy of the incident light can be concentrated at the center. The surface plasmon modulates the interference pattern and the transmission spectra. The transmission peak position and its intensity can be tuned by changing the alignment of the grooves. The proposed structure can be applied for designing plasmonic devices as useful components of the plasmonic toolbox.

Network slicing is treated as a key technology of the rapidly developing 5G system. Nevertheless, the environment of the users is extremely complex, leading to a great challenge for allocating the slices in an optimal manner. In this paper, we propose a dynamic slice allocation scheme with two- tier paradigm in consideration of the quality of experience (QoE). In the first tier, called local tier, we employ linear programming aided by a penalty function to allocate the radio resources in the slices to services for user equipments aiming at the best QoE. In the second tier, called edge tier, we design a deep reinforcement learning algorithm to dynamically allocate the computing resources to the edge networks, to achieve the best QoE and highest resource utilization rate. Simulation results demonstrate that the proposed paradigm can achieve better throughput and QoE in comparison with the traditional network slicing paradigms.

A single‐layer, dual‐port, dual‐band, and dual circularly polarized (CP) microstrip array is designed for satellite communication in this paper. The operating frequencies are 8.2 and 8.6 GHz with a very low ratio of 1.05. First, a rectangular patch element is fed through microstrip lines at two orthogonal edges to excite two orthogonal dominant modes of TM01 and TM10. The very low frequency ratio can be realized with high polarization isolations. Then, a 2‐by‐2 dual‐band dual‐CP subarray is constructed by two independent sets of sequentially rotated (SR) feed structures. An 8‐by‐8 array is designed on the single‐layer thin substrate. Finally, by utilizing one‐to‐four power dividers and semirigid coaxial cables, a 16‐by‐16 array is developed to achieve higher gain. Measured results show that the 16‐by‐16 array has 15 dB return loss (RL) bandwidths of 4.81% and 6.75% and 3 dB axial ratio (AR) bandwidths of 2.84% and 1.57% in the lower and the upper bands, respectively. Isolations of 18.6 dB and 19.4 dB and peak gains of 25.1 dBic and 25.6 dBic are obtained at 8.2 and 8.6 GHz, respectively.