Large Language Models (LLMs) have demonstrated remarkable capabilities in machine translation.However, maintaining discourse coherence and terminological consistency remains a persistent challenge in documentlevel translation (DocMT).Existing solutions, such as memory-based agents, predominantly rely on explicit context concatenation.This paradigm treats historical context as a static external resource, which often leads to context dilution, high inference latency, and superficial knowledge integration.To address these limitations, we propose AdaDPI, an adaptive agentic framework that shifts the DocMT paradigm from static retrieval to dynamic parametric internalization.Specifically, we design a linguistic uncertainty monitor (LUM) to actively detect critical discourse discontinuities by the model's epistemic uncertainty.Upon detection, a context-to-parameter integrator (CPI) compiles retrieved external constraints directly into the model's intrinsic state via an online parameter adaptation mechanism.Through the online parameter adaptation on a lightweight adapter, AdaDPI internalizes document-specific norms into the model's intrinsic representations, enabling a progressive evolution of the translation strategy as the discourse unfolds.Extensive experiments on the discourse-rich GuoFeng and IWSLT2017 datasets demonstrate that AdaDPI significantly outperforms the SoTA baselines by more than 5 points on the consistency metric.

In this paper, we propose a hybrid simultaneous wireless information and power transfer (SWIPT)–enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional radio-frequency (RF) transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signals from Internet of Things (IoT) devices. To fully exploit this integration, we jointly design the transmission scheme and the power-splitting strategy to maximize the weighted sum rate, which leads to a non-convex problem. To address this challenge, we first derive closed-form lower bounds on the uplink achievable rates for maximum ratio combining (MRC) and zero-forcing (ZF), as well as on the downlink rate and harvested energy for maximum ratio transmission (MRT) and ZF precoding. Building upon these bounds, we propose an iterative algorithm relying on the best monomial approximation and geometric programming (GP) to solve the non-convex problem. Finally, simulations validate the tightness of our derived lower bounds and demonstrate the superiority of the proposed algorithm over benchmark schemes. Importantly, by integrating RAQR with SWIPT-enabled MIMO, the BS can reliably detect weak uplink signals from IoT devices powered only by harvested energy, enabling battery-free IoT networks.

This paper investigates near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems with sparse uniform planar arrays (UPAs). Based on the Green’s function-based channel model, the paper derives closed-form expressions for the signal beam power when the distance coordinate or the angular coordinates varies with respective to the focused position. Based on that, closed-form expressions for the lobe length and the suppressing ratio are obtained, indicating that both the distance-focusing property and the grating lobe behavior can be enhanced as the focal distance decreases or the antenna spacing increases. Furthermore, the paper introduces a crucial constraint on system parameters, under which effective degrees-of-freedom (EDoF) of XL-MIMO systems with sparse UPAs can be precisely estimated. Then, the paper proposes an algorithm to obtain a closed-form expression, which can estimate EDoF with high accuracy and low computational complexity. The numerical results verifies the correctness of the main results of this paper.

This paper investigates a two-timescale uplink transmission framework for a fluid antenna-enabled multiuser multi-input multi-output system (MIMO-FAS). Antenna positions are optimized based on statistical channel state information (CSI), while beamforming vectors at the base station (BS) adapt to instantaneous CSI. Under a Rician fading channel with imperfect CSI, we establish a linear minimum mean square error (LMMSE)-based channel estimation approach and derive a closed-form expression for the achievable uplink rate using a low-complexity maximal-ratio-combining (MRC) detector. The optimization problem is formulated as a minimum user rate maximization problem by optimizing the fluid antenna positions, subject to the feasible region and the minimum spacing distance constraints. To address this non-convex problem, a genetic algorithm (GA) method is proposed, encoding antenna configurations as population individuals. Additionally, an accelerated gradient ascent algorithm is proposed to enhance computational efficiency. Numerical results validate the mathematical derivations and demonstrate that the proposed two-timescale transmission strategy significantly outperforms traditional FPA systems, with both algorithms achieving enhanced gains.

Leveraging inherent advantages of large bandwidth, high carrier frequency, and fine resolution, millimeter-wave (mmWave) technology is poised to play a pivotal role in integrated sensing and communication (ISAC) applications envisioned for sixth-generation (6G) networks. This paper proposes a stochastic geometry-based analytical framework to evaluate the performance of hybrid ISAC networks integrating sub-6 GHz and mmWave base stations (BSs). The framework explicitly incorporates band-specific propagation characteristics. Each mmWave BS is equipped with a large-scale antenna array to compensate for high-frequency propagation loss. Based on received signal power, we propose the maximum received echo signal power (Max-RESP) and maximum received average signal power (Max-RASP) association schemes to ensure that the target and user are associated with the BS providing better link conditions, respectively. Using stochastic geometry and probability theory, we derive analytical expressions for sensing distance accuracy and the communication achievable rate. The analytical results are validated via extensive Monte Carlo simulations. Numerical results show that hybrid sub-6 GHz and mmWave ISAC networks significantly outperform conventional pure sub-6 GHz networks and can approach the performance of pure mmWave networks by appropriately tuning the deployment density ratio. The appropriate density ratio provides practical guidance for balancing cost efficiency with performance enhancement. Moreover, the results reveal a performance bottleneck at higher density ratios, primarily due to the saturation of the signal-to-interference-plus-noise ratio (SINR). These findings highlight the crucial role of selecting an appropriate density ratio in hybrid sub-6GHz and mmWave ISAC networks.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

In this paper, we investigate an integrated sensing and communication (ISAC) system assisted by a dynamic metasurface antenna (DMA), where the base station (BS) simultaneously communicates with multiple users and performs target sensing. Specifically, this paper aims to maximize the radar signal-to-noise ratio (SNR) at the BS by jointly optimizing the beamforming matrix and the DMA weight matrix, subject to signal-to-interference-plus-noise ratio (SINR) constraints for the users, the maximum transmit power at the BS and the Lorentzian constraint associated with the DMA elements. To tackle this non-convex optimization problem, an alternating optimization (AO) algorithm is proposed. In this algorithm, semidefinite relaxation (SDR) is employed to optimize the beamforming matrix, while sequential rank-one constraint relaxation (SRCR) is used to optimize the DMA weight matrix. Additionally, the penalty dual decomposition (PDD) and successive convex approximation (SCA) techniques are utilized as an alternative approach to solve the DMA weight matrix. Simulation results demonstrate that the DMA-assisted ISAC system achieves favorable results. The impact of different parameters on the objective value is analyzed, which shows that the PDD method outperforms the SRCR method.

In this paper, we propose a three-stage unified channel estimation strategy for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels, where the base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the developed three-stage strategy stems from the careful design of both the pilot signal sequence of the users and the vectors of RIS. Specifically, in Stage I, the cascaded channel components are eliminated by configuring the RIS phase shift vectors with a π difference to estimate the direct channels for all users. The orthogonal subspace projection is employed in Stage II to obtain equivalent signal matrices, enabling the estimation of angles of departure (AoDs) of the user-RIS channel for all users. In Stage III, we combine the signals of the time slots with the same pilots and project obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and remaining parameters of the cascaded channel for all users by exploiting the sparsity and correlation in the obtained equivalent matrices. Simulation results demonstrate that the proposed method yields better estimation performance than the existing methods.

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the farfield case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network– Generative Pre-trained Transformer 2 (CNN–GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner. A pretraining–finetuning strategy is further adopted, where the model is first pretrained via masked prediction and then finetuned for the downstream beam prediction task, significantly improving training efficiency and accuracy. Simulation results under the 3GPP TR 38.901 channel model demonstrate that the proposed method achieves higher beam prediction accuracy than conventional recurrent models, while maintaining competitive performance in normalized beam-forming gain. These results confirm the feasibility of employing large-model AI for robust and low-overhead near-field beam management in future 6G systems.

This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering parameter multiport network theory. In Stage~I, a dimension-reduced subspace-based method is proposed to estimate the common angle of arrival (AoA) at the BS using the received signals across all users. In Stage~II, MC-aware cascaded channel estimation is performed for a typical user. The equivalent measurement vectors for each cascaded path are extracted and the reference column is reconstructed using a compressed sensing (CS)-based approach. By leveraging the structure of the cascaded channel, the reference column is rearranged to estimate the AoA at the RIS, thereby reducing the computational complexity associated with estimating other columns. Additionally, the common angle of departure (AoD) at the RIS is also obtained in this stage, which significantly reduces the pilot overhead for estimating the cascaded channels of other users in Stage~III. The RIS phase shift training matrix is designed to optimize performance in the presence of MC and outperforms random phase scheme. Simulation results validate that the proposed method yields better performance than the MC-unaware and existing approaches in terms of estimation accuracy and pilot efficiency.

This paper investigates the two-timescale design for access point (AP) mode selection and power allocation to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode selection and power allocation are adapted to the slowly-changing statistical CSI. Firstly, the minimum mean square error (MMSE) estimator is applied to estimate the channels between the APs and the channels between the APs and the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and maximum ratio combining (MRC) detector, and derive the closed-form expressions of the ergodic rate of the UEs and the sensing signal-to-interference-plus-noise-ratio (SINR). A non-convex mix integer optimization problem is formulated to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex mix integer optimization problem. Extensive simulation results demonstrate the analytical accuracy of the closed-form expressions and validate the convergence and effectiveness of the proposed AP mode selection and power allocation scheme.

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability, radar information rate and coverage probability for sensing. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

In this paper, we investigate the channel estimation for mmWave multiple-input multiple-output-(MIMO) orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios. By leveraging the low-rank nature of mmWave channels and the multidimensional characteristics of MIMO-OFDM signals across space, time, and frequency, the received signals are structured as a fourth-order tensor that fits a low-rank CANDECOMP/PARAFAC (CP) model. We propose an estimation of signal parameters via rotational invariance techniques (ESPRIT)type decomposition-based method to solve the CP decomposition, which exploits the Vandermonde structure of the factor matrix. The channel parameters are then estimated from the factor matrices. Simulation results show that our method outperforms existing benchmarks.

Projection-type receivers have recently emerged as a promising approach for uplink integrated sensing and communications (ISAC) systems, facilitating simultaneous uplink signal detection and target sensing. However, the signal detection problem within projection-type receivers faces challenges due to the high dimensionality and rank-deficiency of the equivalent channel matrix. To address this rank-deficiency issue, we introduce two novel variations to reconfigure the equivalent channel matrix: the Projection-Tikhonov (PT) receiver and the Projection-Orthogonal Multiple Access (P-OMA) transceiver. The PT receiver is specifically designed to mitigate the impact of rank-deficiency through regularization, while the P-OMA transceiver utilizes the independent columns of the equivalent channel matrix to transmit a reduced number of communication symbols. For signal detection with the reconfigured channel matrix, we demonstrate that while the linear decoding algorithm can be effectively computed for various projection-type receivers, it yields poor performance. Given the high dimensionality of the equivalent channel matrix, we propose an efficient iterative algorithm based on the extreme point pursuit (EXPP) framework, using a low-complexity linear detector as the initial point. Finally, simulation results validate the effectiveness of the proposed design.

In this paper, a three-phase joint direct and cascaded channel estimation strategy is proposed for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multipleoutput (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels. The base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the devised three-phase strategy is contingent upon the meticulous design of the pilot signal sequence and the RIS phase shift vectors. Specifically, in Phase I, by reversing the RIS phase shift vectors, we remove the cascaded channel components to estimate the direct channels. In Phase II, we employ the orthogonal subspace projection to obtain equivalent signal matrices for the estimation of angles of departure (AoDs) of the user-RIS channel. In Phase III, we combine the signals of time slots with the same pilots and project the obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and the remaining parameters of the cascaded channels. Simulation results show that the proposed method outperforms existing methods.

In most existing works focusing on channel estimation for reconfigurable intelligent surface (RIS)-Aided MU-MIMO mmWave systems, Simultaneous Orthogonal Matching Pursuit (SOMP) method is used to estimate the angle of departure (AoD) at the users to convert the overall MIMO cascaded channel into multiple MISO cascaded channels for further estimation. However, it has poor performance when the number of antennas is small due to the strong correlation of atoms in the dictionary. This paper proposes a novel uplink two-phase channel estimation scheme with high accuracy in angle domain. Specifically, in the first phase, by carefully designing the precoding matrix, the 1-th sub-cascaded channel related to the 1 -th antenna is separated and estimated. In the second phase, by utilizing the invariance of angles and the linear correlation of gains, AoDs can be estimated based on the one-dimensional search method with high accuracy. Thus, all the remaining sub-cascaded channels can be calculated and combined into the overall MIMO cascaded channel. Simulation results demonstrate the superiority of the proposed algorithm.

This paper proposes an uplink two-timescale transmission scheme for a fluid antenna-enhanced multi-user multiinput multi-output system (MU-MIMO-FAS), where antenna positions are optimized based on statistical channel state information (CSI), and beamforming at the base station (BS) adapts to rapidly-varying instantaneous CSI. Using a Rician channel model with imperfect CSI, we employ the linear minimum mean square error (LMMSE) method for channel estimation and a maximal ratio combining (MRC) detector to derive a closed-form expression for the achievable rate. Subsequently, we formulate a minimum user rate maximization problem for antenna position design, subject to movement and spacing constraints, and utilize a genetic algorithm (GA) to solve this non-convex problem. Numerical results demonstrate that the proposed two-timescale MU-MIMO-FAS design significantly outperforms the traditional fixed-position antenna (FPA) system.

This paper investigates the resource allocation problem for multi-user wideband cooperative integrated sensing and communication (ISAC) networks based on orthogonal frequency-division multiplexing (OFDM) waveforms. In order to balance sensing and communication performance with limited spectrum resources in this wideband cell-free system, we aim to maximize the sum rate, encompassing both communication and radar rates, while adhering to constraints related to access point (AP) power and spectrum resources. We utilize alternate optimization (AO) methods to optimize power and spectrum resources separately. For power optimization, we employ the fractional programming (FP) algorithm to convert the problem into a convex one, which can be quickly solved by the primal-dual subgradient (PDS) method. As for subcarrier allocation optimization, we derive its closed-form solution. Simulation results indicate that the communication and sensing performance of the cell-free ISAC system outperforms that of the conventional centralized ISAC system.

This paper proposes a novel channel estimation protocol for a reconfigurable intelligent surface (RIS) aided multi-user (MU) multi-input multi-output (MIMO) millimeter wave (mmWave) system under the hybrid architecture where the direct channels between the base station (BS) and user equipment (UE) exist. There are two stages respectively estimating the direct and cascaded channels. In Stage I, besides the direct channels, the angles of arrival (AoA) and the angles of departure (AoD) of the cascaded channels are also estimated. Stage II is divided into two sub-stages and the overall cascaded channels are estimated. In sub-stage I, the cascaded channel of a typical UE is estimated. In sub-stage II, the cascaded channels of all the remaining UEs are estimated. Simulation results demonstrate that the proposed method has lower pilot overhead and achieves higher accuracy than the existing benchmark approaches.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. Firstly, new near-field channel models and near-field XL-MIMO transmit beamforming (TBF) codebooks have to be adopted due to the dramatic increase in the number of antennas, which results in an excessive pilot overhead for beam training. Secondly, when the user density is high, the wireless propagation environments of the adjacent users are similar and hence the pilot signals received by the BS from different users appear to be interrelated, which is potentially beneficial but difficult to exploit. Thirdly, different users might share the same beam-direction, which causes excessive inter-user interference. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the best available near-field codeword for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme significantly improves beam training accuracy and reduces pilot overhead compared to traditional neural network-based benchmarks. Hence it is more suitable for multiuser XL-MIMO systems. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.

Combining millimetre-wave (mmWave) communications with an extremely large-scale antenna array (ELAA) presents a promising avenue for meeting the spectral efficiency demands of future sixth-generation (6G) mobile communications. This technology achieves a high data rate and establishes high-gain directional transmission links. However, beam training for mmWave ELAA systems is challenged by excessive pilot overheads as well as insufficient accuracy, as the huge near-field codebook has to be accounted for. In this paper, inspired by the similarity between far-field sub-6 GHz channels and near-field mmWave channels, we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the optimal near-field mmWave codeword, which aims to reduce pilot overhead and bypass the channel estimation. Moreover, we adopt deep learning to perform this dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and a novel neural network structure called NMBEnet is designed to enhance the precision of beam training. Specifically, when considering the orthogonal frequency division multiplexing (OFDM) communication scenarios with high user density, correlations arise both between signals from different users and between signals from different subcarriers. Accordingly, the convolutional neural network (CNN) module and graph neural network (GNN) module included in the proposed NMBEnet can leverage these two correlations to further enhance the precision of beam training. To better evaluate the performance of the proposed algorithm, we employ state-of-the-art system simulation software to obtain realistic channel data. Simulation results demonstrate the superior performance of the proposed strategy compared to the exhaustive search scheme and existing deep learning-based schemes.

In this paper, we propose a three-stage high-accuracy uplink channel estimation scheme that utilizes super-resolution algorithms for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems. The proposed protocol enhances both estimation accuracy and pilot overhead efficiency. In Stage I, we derive the covariance matrix of the received signal and estimate the common angles-of-arrival (AoAs) at the base station (BS) using super-resolution algorithms. In Stage II, we construct an equivalent multi-snapshot received signal matrix to estimate the cascaded angles-of-departure (AoDs) at the RIS for a typical user. This takes advantage of the invariance of angle information across multiple channel coherence blocks while accounting for varying channel gains. To further reduce noise impact, we apply the minimum mean square error (MMSE) criterion. The full channel state information (CSI) of the typical user is then estimated using a combination of super-resolution algorithms for angle estimation and the least squares (LS) method for gain estimation. In Stage III, we reconstruct the common BS-RIS channel and use the results from Stages I and II to estimate the full CSI for other users, significantly reducing pilot overhead. Simulation results demonstrate that the proposed method outperforms the existing approaches in terms of both angle estimation accuracy and overall performance, while maintaining the same pilot overhead.

This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system’s physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix and the MAs’ positions under the constraints of the maximum transmit power and the minimum spacing between MAs. To solve this non-convex problem with highly coupled optimization variables, the block coordinate descent (BCD) method is applied to alternately update the variables. Specifically, we first reformulate the SR into a tractable form, and derive the optimal TPC matrix and the AN covariance matrix with fixed MAs’ positions by applying the Lagrangian multiplier method in semi-closed forms. Then, the majorization-minimization (MM) algorithm is employed to iteratively optimize each MA’s position while keeping others fixed. We also extend this work to the more general multicast scenario. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms and the significant advantages of the MAs over conventional fixed position antennas (FPAs) in enhancing system’s security.

This paper studies a comprehensive framework for reconfigurable intelligent surface (RIS)-assisted integrated communication, sensing, and computation (ICSC) systems. To satisfy the critical need for low-latency sensing, we formulate a weighted latency minimization problem encompassing both multi-user equipment (UE) and simplified single-UE scenarios. To address the formulated non-convex problem in the multi-UE scenario, we decouple the original problem into two subproblems, where the computational and beamforming settings are optimized alternately. Specifically, for the computational settings, we derive a closed-form solution for the offloading volume and propose a low-complexity algorithm based on the bisection search method to optimize the edge computing resource allocation. Additionally, we employ two equivalent transformations to address the challenge posed by the non-convex sum-of-ratios form in the objective function (OF) of the subproblem related to active and passive beamforming. Several techniques are then combined to address these subproblems. To bridge the gap between theoretical assumptions and practical deployments, a robust design extension accounting for imperfect channel state information (CSI) is developed using statistical error modeling. Furthermore, a low-complexity algorithm that offers closed-form solutions is developed for the simplified single UE scenario. Finally, simulation results substantiate the effectiveness of the proposed framework.

Time-varying channels are significantly influenced by the Doppler effect, which leads to rapid changes in channel gain and requires Doppler frequency estimation to compensate for channel phase shifts and improve communication quality. In this paper, we propose a novel fifth-generation (5G) new radio (NR) orthogonal frequency division multiplexing (OFDM)-based transmission structure for time-varying channel estimation in high-mobility scenarios. By designing an appropriate subcarrier spacing and slot format, we ensure that the pilot signals remain nearly invariant within a single slot and exhibit rotational invariance between different slots. Leveraging this rotational invariance, we introduce a novel algorithm based on Vandermonde-structured tensor decomposition, which is non-iterative and has lower computational complexity and higher robustness than other tensor-based algorithms. Moreover, we provide a theoretical analysis of the uniqueness condition of tensor decomposition, proving that the proposed algorithm has strong feasibility and requires low pilot overhead. We also analyze the mean square errors (MSEs) of the parameter estimates and present a concise derivation of the Cramér-Rao Bound (CRB). The results demonstrate that the proposed algorithm significantly outperforms compressed sensing (CS)-based methods and other tensor-based methods in terms of parameter estimation performance at medium to high SNR. Furthermore, the proposed algorithm, based on the instantaneous channel model, offers higher channel estimation accuracy than the Kalman filtering-based algorithm, which relies on statistical channel models. Simulation results with channel data generated by Wireless InSite, which constructs a real-world scattering environment, demonstrate the high estimation accuracy of the proposed algorithm, validating its effectiveness in practical scenarios.

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a two-dimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-conditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters (ADCs). Frequency-selective Rician fading channels are considered, and the OFDM data transmission process is presented in time domain. This paper derives the closed-form approximate expression of the uplink achievable rate, based on which the asymptotic system performance is analyzed when the number of the antennas at the base station and the number of reflecting elements at the RIS grow to infinity. Besides, the power scaling laws of the considered system are revealed to provide energy-saving insights. Furthermore, this paper proposes a gradient ascent-based algorithm to design the phase shifts of the RIS for maximizing the minimum user rate. Finally, numerical results are presented to verify the correctness of analytical conclusions and draw insights.

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for low-altitude sensing scenario, aiming at estimating the parameters of the uncrewed aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs’ parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across the multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs’ parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to conventional techniques.

In this paper, we investigate a double-active-reconfigurable intelligent surface (RIS)-aided downlink wireless communication system, where a multi-antenna base station (BS) serves multiple single-antenna users with both double reflection and single reflection links. Due to the signal amplification capability of active RISs, they can effectively mitigate the multiplicative fading effect. However, this also induces signal bouncing between the two active RISs that cannot be ignored. This phenomenon is termed as the “inter-excitation” effect and is characterized in the received signal by proposing a feedback-type model. Based on the signal model, we formulate a weighted sum rate (WSR) maximization problem by jointly optimizing the beamforming matrix at the BS and the reflecting coefficient matrices at the two active RISs, subject to power constraints at the BS and active RISs, as well as the maximum amplification gain constraints of the active RISs. To solve this non-convex problem, we first transform the problem into a more tractable form using the fractional programming (FP) method. Then, by introducing auxiliary variables, the problem can be converted into an equivalent form that can be solved by using a penalty dual decomposition (PDD) algorithm. Furthermore, the power scaling order of the signal-to-noise ratio (SNR) in double-active-RIS-aided system considering inter-excitation effect is derived. Finally, simulation results indicate that the proposed scheme outperforms benchmark schemes with single active RIS and double passive RISs in terms of achievable rate. Furthermore, the results demonstrate that the proposed scheme can enhance the WSR by 30% compared to scenarios that do not take this effect into account when the maximum amplification gain is 40 dB. Additionally, the proposed scheme is capable of achieving high WSR performance at most locations where double active RISs are deployed between the BS and the users, thereby providing greater flexibility in their deployment.

This paper investigates the performance of simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with direct links. We apply the two-timescale scheme to design the base station (BS) beamforming and the phase shifts of the STAR-RIS. Specifically, we derive the closed-form expression of the average achievable rate. Based on the derived rate, we theoretically draw insights from comparing STAR-RIS and conventional RIS under the same condition. Then, we optimize the phase shifts of the STAR-RIS using accelerated gradient ascent algorithm. Finally, numerical results are provided to validate our theoretical insights that STAR-RIS outperforms the conventional RIS under some special cases.

In this paper, we investigate an reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. Our objective is to maximize the achievable sum rate of the multi-antenna communication users through the joint active and passive beamforming. Weighted minimum mean-square error (WMMSE) method is used to reformulate the original problem into an equivalent one. Then, we utilize an alternating optimization (AO) approach to separate the optimization variables and decompose this challenging problem into two subproblems. Given reflecting coefficients, a penalty-based algorithm is utilized to deal with the transmit power and the non-convex radar signal-to-noise ratio (SNR) constraints. For the given beamforming matrix of the BS, we apply majorization-minimization (MM) to transform the problem into a quadratic constraint quadratic programming (QCQP) problem, which is ultimately solved using a semidefinite relaxation (SDR)-based algorithm. Simulation results illustrate the advantage of deploying RIS in the considered multi-user MIMO (MU-MIMO) ISAC systems.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as one of the key technologies for future 6G networks, which can further improve spectral efficiency by deploying far more antennas than conventional massive MIMO systems. However, beam training in multiuser XL-MIMO systems is challenging. To tackle this issue, we propose a graph neural network (GNN)-based beam training scheme for the multiuser XL-MIMO system, in which only the far-field wide beams need to be tested for each user. Specifically, the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user, where the information of the surrounding users can also be utilized by the GNN to further improve the accuracy of the beam training. Simulation results show that the performance of the proposed scheme can approach that of the exhaustive scheme but has more than a 93 % reduction in the pilot overhead.

Reconfigurable intelligent surfaces (RISs) have emerged as a disruptive technology that can reconfigure wireless communication environments cost-effectively. In order to fully unveil the potential of RIS-aided wireless communications, some existing contributions considered the double cooperative passive RISs to achieve a higher capacity scaling orders. However, due to the multiplicative fading effect, the double cooperative passive RISs performs poorly when deployed far from the BS and user respectively. To address this issue, we investigate double cooperative active RISs which are equipped with amplifiers and can overcome the severe path loss caused by the multiplicative fading. Specifically, we aim to maximize the downlink achievable rate subject to the transmit power constraints of the base station (BS) and the double active RISs. The formulated problem is solved by using an alternating optimization (AO) algorithm based on the majorization-minimization (MM) algorithm and the fractional programming (FP) method. Simulation results demonstrate that much better rate performance can be achieved by adopting active RIS compared to passive RIS in the double RIS-aided systems. Meanwhile, deploying them appropriately far away from the BS and user and more elements assigned to the active RIS near the user will achieve better performance.

This paper presents a comprehensive framework for jointly analyzing the angle estimation error and designing a three-dimensional (3D) positioning algorithm for an Internet of Things (IoT) millimeter wave (mmWave) positioning system. Initially, the azimuth and elevation angles of arrival (AoAs) at the anchors are estimated by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. The angle estimation error is then analyzed in terms of probability density functions (PDF) by utilizing the properties of the 2D-DFT algorithm and employing challenging derivations and linear approximations. The analysis reveals that the resulting angle estimation error is non-Gaussian, distinguishing it from previous studies. Next, the complex expression of the PDF for the AoA estimation error is simplified using the first-order linear approximation of triangle functions. Subsequently, a complex expression for the variance is derived based on the obtained PDF. Specifically, the variance for the azimuth estimation error is integrated separately according to the different non-zero intervals of the obtained PDF. Additionally, the closed-form expressions of the variances are formulated using generalized hypergeometric series. Finally, the two-stage weighted least square (TSWLS) algorithm is employed to estimate the 3D position of the mobile user (MU) using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm the non-Gaussian nature of the derived angle estimation error and demonstrate the superiority of the proposed framework.

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

This study proposes a two-timescale transmission scheme for extremely large-scale reconfigurable intelligent surface aided (XL-RIS-aided) massive multi-input multi-output (MIMO) systems in the presence of visibility regions (VRs). The beamforming of base stations (BSs) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase shifts of RIS are configured based on slowly varying statistical CSI. Specifically, we first formulate a system model with spatially correlated Rician fading channels and introduce the concept of VRs. Then, we derive a closed-form approximate expression for the achievable rate and analyze the impact of VRs on system performance and computational complexity. Then, we solve the problem of maximizing the minimum user rate by optimizing the phase shifts of RIS through an algorithm based on accelerated gradient ascent. Finally, we present numerical results to validate the performance of the considered system from different aspects and reveal the low system complexity of deploying XL-RIS in massive MIMO systems with the help of VRs.

This paper investigates the performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with Rician fading channels and channel estimation errors. We adopt the two-timescale scheme to design the systems, namely, applying the instantaneous channel state information (CSI) to design the base station (BS) beamforming and leveraging the statistical CSI to design the phase shifts of the STAR-RIS. Specifically, we estimate the overall channels based on the linear minimum mean-squared error (LMMSE) estimator and derive the closed-form expression of the average achievable rate. Based on the derived rate, we analyze the power scaling laws in which the transmit power is respectively reduced inversely proportional to the number of BS antennas and STAR-RIS elements. Besides, we draw insights from the comparison between STAR-RIS and conventional RIS under the same condition and the power scaling laws of STAR-RIS and optimize the phase shifts of the STAR-RIS to maximize the sum rate using an accelerated gradient ascent-based algorithm. Finally, numerical results are provided to validate our theoretical insights. In particular, we also compare the two-timescale scheme with the instantaneous CSI scheme in the simulation. We show that STAR-RIS outperforms conventional RIS, and the two-timescale-based scheme outperforms the instantaneous CSI-based scheme. Furthermore, we draw insight into this phenomenon.

This paper considers an intelligent reflecting surface (IRS) aided secure communication with direct communication links where a legitimate receiver (Bob) served by a base station (BS) is overheard by multiple eavesdroppers (Eves), meanwhile the artificial noise (AN) is incorporated to confuse Eves. Since Eves are not legitimate users, their channels cannot be estimated perfectly. We investigate two scenarios with partial channel state information (CSI) error of only cascaded BS-IRS-Eve channel and full CSI errors of both cascaded BS-IRS-Eve channel and direct BS-Eve channel under the statistical CSI error model. To ensure the security performance under CSI errors, the transmit beamforming, AN spatial distribution at the BS, and phase shifts at IRS are jointly optimized to minimize the transmit power constrained by the minimum data rate requirement of Bob and the outage probability of maximum data rate limitation of Eves. In contrast to existing works, the direct link considered in our work makes the optimization of phase shifts at IRS much more challenging, thus we propose a series of novel and artful mathematical manipulations to tackle this issue. Moreover, the proposed algorithm can be applied for both uncorrelated and correlated CSI errors. Simulations confirm the superiority of our proposed algorithm.

This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at a dual-function radar-communication (DFRC) base station (BS) and the reflecting coefficients at an active RIS subject to the quality of service (QoS) constraints of communication user equipments (UEs) and the transmit power constraints of active RIS and DFRC BS. To tackle the optimization problem, the majorization-minimization (MM) algorithm is applied to address the nonconvex radar SINR objective function, and the resulting quartic problem is solved by developing an semidefinite relaxation (SDR)-based approach. Moreover, we derive the scaling order of the radar SINR with a large number of reflecting elements. Next, the transmit power allocation problem and the deployment strategy of the active RIS are studied with a moderate number of reflecting elements. Finally, we validate the potential of the active RIS in ISAC systems compared to passive RIS. Additionally, we deliberate on several open problems that remain for future research.

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the weighted sum rate while considering the spatially correlation and channel estimation errors. Firstly, the relationship between the channel estimation error and spatially correlated RIS’s elements is revealed by using the linear minimum mean square error. Secondly, based on the maximum-ratio transmission precoding, a tight lower bound of the rate under short packet transmission is derived. Finally, the NP-hard problem is decomposed into two optimization problems, where the transmission power is obtained by geometric programming and phase shifts are designed by using gradient ascent method. Besides, we have rigorously proved that the proposed algorithm can rapidly converge to a sub-optimal solution with low complexity. Simulation results confirm the tightness between the analytic results and Monte Carlo simulations. Furthermore, the two-timescale scheme provides a practical solution for the short packet transmission.

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual channel state information (CSI) of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization–minimization (MM) algorithm framework, and, in each iteration low-complexity solutions for the BS and RIS beamforming are found based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

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.

In this paper, the reconfigurable intelligent surface (RIS)-aided massive multiple-input-multiple-output (mMIMO) system with ultra-reliability and low latency communications (URLLC) is investigated. Specifically, the spatial correlation and imperfect channel estate information (CSI) are considered, where the phase shifts of the RIS and the transmission power of the base station (BS) are jointly optimized to maximize the weighted sum rate. Firstly, the aggregated channel is estimated relying on the linear minimum mean square error (LMMSE) method, and the normalized mean square error (NMSE) is analyzed. Secondly, the lower bound for the achievable data rate is derived for maximum-ratio transmission (MRT). Finally, the non-convex problem is separated into two optimization problems. Then, based on the statistical CSI, geometric programming and gradient descent are adopted to optimize the transmission power of the BS and the phase shifts of the RIS, respectively. Simulation results confirm the accuracy of the analytic results and the superiority of our proposed algorithm.

In this work, we propose low-complexity designs for XL-MIMO in the near-field with spatial non-stationarities. We first introduce a notion of visibility region (VR) and propose a VR detection algorithm. Then, we exploit the acquired VR information to design a low-complexity detection scheme for XL-MIMO systems. To further reduce the complexity, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Then, partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the effectiveness of the proposed algorithms which approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

In this paper, an efficient two-phase channel estimation scheme with reduced pilot overhead and error propagation is proposed for a uniform planar array (UPA)-type reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. In Phase I, based on the carefully designed RIS phase shift matrix, all users jointly transmit the pilot signals to estimate the correlation factors between different propagation paths of the common RIS-base station (BS) channel, which facilitates a significant MU diversity gain. Then, in Phase II, with the constructed ambiguous RIS-BS channel composed of the correlation factors obtained in the previous phase, each user independently sends a few pilots to estimate their own ambiguous user-RIS channel so as to obtain the entire cascaded channel. Simulation results validate that the proposed algorithm outperforms the existing algorithms in terms of both pilot overhead and estimation accuracy, and that its estimation performance improves as the number of users increases.

To combat the half-space coverage and enhance the flexibility of the reconfigurable intelligent surface (RIS) technology, a simultaneously transmitting and reflecting RIS (STAR-RIS) is applied in the radar-communication co-existence (RCC) system, where the signal through STAR-RIS is transmitted to opposite spaces, and STAR-RIS is utilized to handle the interference from the base station (BS) to the radar. A radar detection probability maximization problem by optimizing the transmit beamforming vector of the BS and the transmission-and reflection-coefficient matrices of the STAR-RIS is formulated, subject to the power constraint of the BS and the communication rate constraints of users. The problem is challenging to solve due to the highly coupled variables. We convert it into two sub-problems and propose an efficient alternating optimization (AO) algorithm to solve this non-convex problem. The simulation results validate the convergence of the proposed algorithm and the performance advantages of using STAR-RIS over conventional RIS.

Abstract This paper describes a pedestrian re-identification algorithm, which was developed by integrating semi-supervised learning and similarity-preserving generative adversarial networks (SPGAN). The pedestrian re-identification task aimed to rapidly capture the same target using different cameras. Importantly, this process can be applied in the field of security. Because real-life environments are complex, the number of detected identities is uncertain, and the cost of manual labeling is high; therefore, it is difficult to apply the re-identification model based on supervised learning in real-life scenarios. To use the existing labeled dataset and a large amount of unlabeled data in the application environment, this report proposes a semi-supervised pedestrian re-identification model, which combines a teacher–student model with SPGAN. SPGAN was used to reduce the difference between the target domain and the source domain by transferring the style of the labeled dataset from the source domain. Additionally, the dataset from the source domain was used after the style transfer to pre-train the model; this enabled the model to adapt more rapidly to the target domain. The teacher–student model and the transformer model were then employed to generate soft pseudo-labels and hard pseudo-labels (via iterative training) and to update the parameters through distillation learning. Thus, it retained the learned features while adapting to the target domain. Experimental results indicated that the maps of the applied method on the Market-to-Duke, Duke-to-Market, Market-to-MSMT, and Duke-to-MSMT domains were 70.2, 79.3, 30.2, and 33.4, respectively.

MOTIVATION: The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities. RESULTS: We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. AVAILABILITY AND IMPLEMENTATION: The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This article proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. First, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio-frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV’s trajectory, hovering time, and the RIS’s reflection coefficients. To solve this complex nonconvex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV’s trajectory and hovering time, in which a minorization–maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV’s trajectory, flight time, and the RIS’s reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving.

Resource allocation is conceived for cell-free (CF) massive multi-input multi-output (MIMO)-aided ultra-reliable and low latency communication (URLLC) systems. Specifically, to support multiple devices with limited pilot overhead, pilot reuse among the users is considered, where we formulate a joint pilot length and pilot allocation strategy for maximizing the number of devices admitted. Then, the pilot power and transmit power are jointly optimized while simultaneously satisfying the devices’ decoding error probability, latency, and data rate requirements. Firstly, we derive the lower bounds (LBs) of ergodic data rate under finite channel blocklength (FCBL). Then, we propose a novel pilot assignment algorithm for maximizing the number of devices admitted. Based on the pilot allocation pattern advocated, the weighted sum rate (WSR) is maximized by jointly optimizing the pilot power and payload power. To tackle the resultant NP-hard problem, the original optimization problem is first simplified by sophisticated mathematical transformations, and then approximations are found for transforming the original problems into a series of subproblems in geometric programming (GP) forms that can be readily solved. Simulation results demonstrate that the proposed pilot allocation strategy is capable of significantly increasing the number of admitted devices and the proposed power allocation achieves substantial WSR performance gain.

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss. However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network’s output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

Smart factories need to support the simultaneous communication of multiple industrial Internet-of-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multiple-input and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The log-function method and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is that increasing the number of devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively constant for centralized mMIMO.

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

In this paper, we develop a two-phase based uplink channel estimation strategy with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication system. Specifically, in Phase I, an OMP-based method is adopted to estimate the AoDs at the users. The remaining parameters including the common AoAs at the BS, the cascaded AoDs at the RIS, and the cascaded channel gains are estimated in Phase II. In particular, the estimation of cascaded AoDs and channel gains can be formulated as 1-sparse recovery problems by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for virtual single-antenna users. Finally, the theoretical number of pilots required for the proposed method are analyzed and the simulation results are presented to demonstrate the high channel estimation accuracy.

This paper analyzes and optimizes the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first propose a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results prove that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of $\mathcal{O}\left( {{{\log }_2}(MN)} \right)$, where M and N are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of $\mathcal{O}\left( {{{\log }_2}(MN)} \right)$. Furthermore, we propose a low-complexity majorization-minimization (MM)-based algorithm to optimize the sum user rate, where closed-form solutions are obtained in each iteration. Finally, simulation results validate all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.

This paper studies the worst-case robust beamforming design for a reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) network with imperfect channels. We aim to minimize the transmission power while satisfying the requirement of the worst-case quality of service (QoS). With the worst-case QoS constraints, unit-modulus constraints and imperfect channel state information (CSI), this problem is a non-convex optimization problem. To solve this problem, we propose a two-procedure algorithm by applying penalty function and semidefinite relaxation (SDR). Finally, simulation results illustrate that the RIS-aided NOMA system has better performance than the traditional NOMA system.

This paper provides a theoretical framework for understanding the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first introduce a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results demonstrate that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of$\mathcal {O}\left ({\log _{2}\left ({MN}\right)}\right)$, where$M$and$N$are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of$\mathcal {O}\left ({\log _{2}\left ({MN^{2}}\right)}\right)$; 3) either$M$or the transmit power can be reduced inversely proportional to$N$, while maintaining a given rate. Furthermore, we propose two low-complexity majorization-minimization (MM)-based algorithms to optimize the sum user rate and the minimum user rate, respectively, where closed-form solutions are obtained in each iteration. Finally, simulation results validate the accuracy of all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.

A new cloud-base height (CBH) inversion algorithm based on infrared hyperspectral radiation using a machine learning algorithm is proposed in this paper. We use the LBLRTM and DISORT model for forward research. The minimal-redundancy-maximal-relevance (mRMR) algorithm is used to extract the sensitive channels of CBH as the feature vectors. The CBHs measured by Vaisala CL31 ceilometer (VCEIL) are taken as the reference values. The artificial neural network (ANN) method with two hidden layers of 50 and 10 respective is applied to construct the mapping relationship between AERI radiation and CBH (ANN-CBH algorithm). The dataset is collected during the period from January 2012 to December 2017 at the ARM SGP site and NSA site. Among them, the data from 2012 to 2014 are used as the training set, while the data of 2015, 2016, 2017 of each site are respectively used as the testing set. Compared with the traditional physical algorithm, the ANN-CBH algorithm has higher accuracy. The correlation coefficients (CCs) between the inversion results of CBH from the ANN-CBH algorithm and the measurement results of the VCEIL are about 0.9 at SGP site and 0.85 at NSA site, while the CC of the CBH inversion results between CO2 slicing algorithm and VCEIL is only about 0.7 and 0.65, respectively. In addition, the experimental results indicate that the ANN-CBH algorithm is less affected by precipitable water vapor (PWV).

In this paper, we adopt a three-stage based uplink channel estimation protocol with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) communication system, in which both the base station (BS) and the RIS are equipped with a uniform planar array (UPA). Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated. To address the power leakage issue for the common angles-of-arrival (AoAs) estimation in this stage, we develop a low-complexity one-dimensional search method. In Stage II, a re-parameterized common BS-RIS channel is constructed with the estimated information from Stage I to estimate other users’ CSI. In Stage III, only the rapidly varying channel gains need to re-estimated. Furthermore, the proposed method can be extended to multi-antenna UPA-type users, by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for a virtual single-antenna user. An orthogonal matching pursuit (OMP)-based method is proposed to estimate the angles-of-departure (AoDs) at the users. Simulation results demonstrate that the proposed algorithm significantly achieves high channel estimation accuracy, which approaches the genie-aided upper bound in the high signal-to-noise ratio (SNR) regime.

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution due to its reduced cost, increased long-term reliability, and enhanced reliability. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is that the blocklength of channel codes is short, and the conventional Shannon’s Capacity is not applicable. In this paper, we consider the URLLC in a factory automation (FA) scenario. Due to densely deployed equipment in FA, wireless signal are easily blocked by the obstacles. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link, which can enhance the transmission reliability. In this paper, we focus on the performance analysis for IRS-aided URLLC-enabled communications in a FA scenario. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived under finite channel blocklength for seven cases: 1) Rayleigh fading channel; 2) With direct channel link; 3) Nakagami-m fading channel; 4) Imperfect phase alignment; 5) Multiple-IRS case; 6) Rician fading channel; 7) Correlated channels. Extensive numerical results are provided to verify the accuracy of our derived results.

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we are exploited to deal with the link outage issue caused by a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of the beamforming in the presence of random blockages, we formulate a stochastic optimization problem with the aim of minimizing the outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic majorization-minimization method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.

In this paper, we study the average packet error probability (APEP) and effective throughput (ET) of the control link in unmanned-aerial-vehicle (UAV) communications, where the ground central station (GCS) sends control signals to the UAV that requires ultra-reliable and low-latency communications (URLLC). To ensure the low latency, short packets are adopted for the control signal. As a result, the Shannon capacity theorem cannot be adopted here due to its assumption of infinite channel blocklength. We consider both free space (FS) and 3-Dimensional (3D) channel models by assuming that the locations of the UAV are randomly distributed within a restricted space. We first characterize the statistical characteristics of the signal-to-noise ratio (SNR) for both FS and 3D models. Then, the closed-form analytical expressions of APEP and ET are derived by using Gaussian-Chebyshev quadrature. Also, the lower bounds are derived to obtain more insights. Finally, we obtain the optimal value of packet length with the objective of maximizing the ET by applying one-dimensional search. Our analytical results are verified by the Monte-Carlo simulations.

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

In this paper, we investigate the robust transmission design for the intelligent reflecting surface (IRS) aided secure wireless communication systems, where a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers via IRS. The estimation error of the imperfect cascaded AP-IRS-user channels is considered in the robust beamforming. Specifically, a transmit power minimization problem is formulated subject to the outage rate probability of information leakage to Eves under the statistical cascaded CSI error model, and the beamformer at the transmitter, the covariance matrix of artificial noise (AN), and the IRS phase shifts are jointly optimized. To handle the resulting non-convex optimization problem, we first approximate the rate outage probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on the alternating optimization, penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power while ensures the system security.

In this paper, we study the resource allocation for a secure mission-critical IoT communication system with URLLC, where the security capacity formula under finite blocklength is adopted. In specific, we jointly optimize the power and channel bandwidth unit allocation to minimize the system power consumption subject to each device's security capacity requirement and total available channel bandwidth. We express the power for each device as a function of channel bandwidth unit, and equivalently transform the original problem into a channel bandwidth unit allocation problem. By relaxing the discrete variables into continuous ones, a sufficient condition when the transformed problem is a convex problem is provided. Efficient method is proposed to solve the problem. Simulation results confirm the performance advantage of our proposed algorithm over the benchmark method.

Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU's total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's transmit power and feasibility rate of the optimization problem.

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon's capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while the NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9) and low latency (say 1 ms) simultaneously. The achievable data rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transniitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstrate that the relay-assisted transmission significantly outperforms the OMA scheme.

The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users' rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

The weighted sum-rate maximization problem of ultra-dense cloud radio access networks is considered. The user-centric clustering is adopted for reducing the complexity. To reduce the training overhead, one only needs to estimate the intra-cluster channel-state information (CSI), while only the large-scale channel gains are available outside the cluster. We first derive the rate lower bound (LB) relying on Jensen's inequality. For the special case of non-overlapping clusters, the accurate data rate expression is derived in the closed form. The simulation results show the tightness of the LB for both the overlapped and non-overlapped cases. Then, we consider an alternative problem where the actual data rate is replaced by its LB, which constitutes a non-convex optimization problem. First, the globally optimal solution is obtained by applying the high-complexity outer polyblock approximation (OPA) algorithm. Then, we invoke the reduced-complexity modified weighted minimum mean square error (WMMSE) algorithm for mitigating the deleterious effects of the realistic imperfect CSI. For the subproblem solved by each WMMSE iteration, the beamforming vectors are derived in the closed form relying on the Lagrangian dual decomposition method. Finally, our simulation results show that the modified WMMSE algorithm's performance is comparable to that of the high-complexity OPA algorithm, which outperforms other benchmark algorithms.

The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit beamforming is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase synchronization is required. By exploiting the classic successive interference cancellation technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1-norm technique to approximate the l0-norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the coherent one in the low fronthaul capacity regime, which is the case in ultradense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed.

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBN ICR ) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine‐tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBN ICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency (EE) of a multiuser multiple-input multiple-output-aided distributed antenna system. The fronthaul link's power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints, some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the EE optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Second, a triple-layer iterative algorithm is proposed for solving the approximated EE optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened EE optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms are capable of achieving an order of magnitude higher EE than its conventional counterpart operating without considering link selection.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs), which are well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design, which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange-based algorithm in some special cases.

This paper studies the impact of hardware mismatch (11M) between the base station (BS) and the user equipment (UE) in the downlink (DL) of large-scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e., matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the upper bounds on achievable rates of MF and RZF with 11M are investigated, which are related to the statistics of the circuit gains of the mismatched hardware. Moreover, we present a study of 11M calibration, where we take zero-forcing (ZF) precoding as an example to compare two 11M calibration schemes, i.e., Pre-precoding Calibration (Pre-Cal) and Post-precoding Calibration (Post-Cal). The analysis shows that Pre-Cal outperforms Post-Cal schemes. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

In this paper, we consider the problem of maximizing the energy efficiency (EE) for multiple-input-multiple-output (MIMO) interference channels (ICs), subject to the per-link power constraint. To avoid extensive information exchange among all links, the optimization problem is formulated as a noncooperative game, where each link maximizes its own EE. We show that this game always admits a Nash equilibrium (NE) and the sufficient condition for the uniqueness of the NE is derived for the case of large enough maximum transmit power constraint. To reach the NE of this game, we develop a totally distributed EE algorithm, in which each link updates its own transmit covariance matrix in a completely distributed and asynchronous way. Some players may update their solutions more frequently than others or even use the outdated interference information. The sufficient conditions that guarantee the global convergence of the proposed algorithm to the NE of the game have been given as well. We also study the impact of the circuit power consumption on the sum EE performance of the proposed algorithm in the case when the links are separated sufficiently far away. Moreover, the tradeoff between the sum EE and the sum spectral efficiency (SE) is investigated with the proposed algorithm under two special cases: 1) low transmit power constraint regime; and 2) high transmit power constraint regime. Finally, extensive simulations are conducted to evaluate the impact of various system parameters on the system performance.

We consider the problem of maximizing the weighted sum energy efficiency (WS-EE) in ad hoc networks. To solve this problem in a distributed manner, one novel distributed adaptive-pricing algorithm is developed based on limited information exchange among the nodes. Specifically, each node updates its current interference information and broadcasts it to the other nodes. Having collected all this information, each node can adjust its transmit power accordingly with simple arithmetical operations. Then iterate these two steps. This algorithm is strictly proven to be convergent and can attain the KKT optimality conditions of the problem. Moreover, an alternative centralized algorithm based on gradient projection method is proposed to serve as the performance benchmark. Simulation results show that the proposed distributed algorithm converges rapidly. Furthermore, this distributed algorithm performs as well as the centralized one and significantly outperforms the existing algorithm in terms of the WS-EE.

We consider the problem of maximizing the energy efficiency (EE) for a MIMO interference channel (IC), with the power constraint on each link. To obtain totally distributed solutions, this problem is formulated as a noncooperative game. We show that this game always admits a Nash equilibra (NE). Importantly, the sufficient condition that one can check to guarantee the uniqueness of the NE is derived. To reach the NE of this game, we provide a totally distributed EE algorithm, in which each player employs the fractional programming to update his own solution. These updates can be performed in a completely distributed and asynchronous fashion. Sufficient conditions that guarantee the convergence of the algorithm have been given as well. Simulation results show that the proposed algorithm converges fast and significantly outperforms the existing algorithms in terms of the sum-EE or the sum-rate.

The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented.>