As a green multiple-input multiple-output (MIMO) structure, the heterogeneous hybrid analog-digital (H(2)AD ) MIMO architecture has been shown to own a great potential to replace the massive or extremely large-scale fully digital MIMO in the future wireless networks to address the three challenging problems faced by the latter: high energy consumption, high circuit cost, and high complexity. However, how to intelligently sense the number and direction of multiemitters via such a structure is still an open hard problem. To address this, we propose a two-stage sensing framework that jointly estimates the number and direction values of multiple targets. Specifically, three target number sensing methods are designed: an improved eigen-domain clustering (EDC) framework, an enhanced deep neural network (DNN) based on five key statistical features, and an improved 1-D convolutional neural network (1D-CNN) utilizing full eigenvalues. Subsequently, a low-complexity and high-accuracy direction of arrival (DOA) estimation is achieved via the introduced online microclustering (OMC-DOA) method. Furthermore, we derive the Cram & eacute;r-Rao lower bound (CRLB) for the H(2)AD under multiple-source conditions as a theoretical performance benchmark. Simulation results show that the developed three methods achieve 100% number of targets sensing at moderate-to-high signal-to-noise ratios (SNRs), while the improved 1D-CNN exhibits superior under extremely low SNR conditions. The introduced OMC-DOA outperforms existing clustering and fusion-based DOA methods in multisource environments.

This paper investigates the optimal local precoding design for cell-free massive MIMO systems, subject to per-user quality-of-service (QoS) constraints and per-access-point (AP) power limits. In this distributed setting, each AP determines its precoding strategy using only locally acquired instantaneous channel state information (CSI) alongside globally available statistical CSI. Since the precoding design based on the useand- then-forget (UatF) bound involves expectation terms, its optimization problem should be formulated as a functional optimization problem. Unlike function optimization problems whose solutions are numerical precoding values, the solution to a functional optimization problem is a precoding function. Consequently, each AP computes its precoding vectors in real-time by applying this derived optimal function to its locally acquired instantaneous CSI. By applying functional optimization theory and the Karush-Kuhn-Tucker (KKT) conditions, we rigorously prove that the optimal local precoder is structurally equivalent to a Local Minimum Mean Square Error (L-MMSE) precoder, with undetermined Lagrange multiplier parameters associated. We then derive an efficient iterative algorithm to compute these multipliers and establish its convergence to a unique fixed point that yields the optimal precoding solution, thereby providing a complete framework for optimal local downlink precoding. Simulation results demonstrate that the proposed method achieves substantial gains over state-of-the-art approaches, validating its effectiveness and robustness across various system configurations.

The convergence of ultra-reliable and low-latency communication (URLLC) with industrial networking has been envisioned as a leading solution for industrial internet of thing (IIoT) deployments, which increases flexibility without moderating requirements of latency and reliability. The industrial sites throughout the network demand hybrid flows composed of time-triggered (TT) traffic and event-triggered (ET) traffic, requiring stringent QoS metrics of low end-to-end (E2E) latency, minimal jitter, and low packet loss probability. However, ET traffic bears serious timeout risks, stemming from the resource preemption by TT traffic under the prioritized transmission mechanism. Motivated by the spatial and temporal isolation for alleviating overtime pressure, we propose the QoS-aware resource allocation as well as the routing and scheduling co-design for the E2E latency compression of hybrid traffic under constraints of timeliness, reliability, and determinism. To be specific, the timing analysis framework based on network calculus is established to compute the worst-case end-to-end latency (WCEL) of ET traffic. Following by that, the non-overlapping routing and as-soon-as-possible (ASAP) scheduling with radio resource adaptation is proposed to reduce the E2E latency, where the path planning selects disjointed path with minimal length from spatial domain, the time offset assignment arranges the earliest possible time offsets from temporal domain, and the resource adaptation allocates the required bandwidth, subchannels and thresholds of decoding error probabilities based on the convexity method. Simulation results validate the WCEL analysis and show the performance gain on E2E latency compression of the proposed co-design compared with other existing strategies.

The integration of reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) can extend wireless coverage and improve link reliability. However, the broadcast nature of wireless channels exposes the UAV-RIS system to severe information leakage risks in multi-eavesdropper environments, while the high energy consumption of the system poses an additional challenge to green communications. To balance secure transmission and green communication, this paper proposes a UAV-RIS-assisted multi-user multi-eavesdropper secure communication network and designs a virtual RIS partitioning mechanism. Specifically, the RIS is partitioned into multiple virtual subsegments, and by configuring the phase shifts of each sub-segment to align with the cascaded channel of a specific legitimate user or eavesdropper, signal enhancement and directional artificial noise (AN) jamming can be simultaneously achieved. On this basis, a system power consumption model is established, and joint optimization of UAV three-dimensional (3D) deployment, RIS partitioning, base station (BS) transmit precoding, AN covariance matrix, and RIS phase shifts is performed to maximize the secrecy energy efficiency (SEE). This problem is a mixed-integer non-convex fractional optimization problem. To solve it efficiently, this paper develops a double-layer iterative algorithm that combines the Dinkelbach transform and alternating optimization (AO). UAV deployment is solved via successive convex approximation (SCA), RIS partitioning is optimized using a discretized Capuchin search algorithm (CapSA), and semidefinite relaxation (SDR) is applied to the beamforming and phase shift subproblems. Numerical results demonstrate that the proposed scheme achieves significant advantages in improving the system SEE.

Cell-free massive multiple-input multiple-output (MIMO) systems exploit tight cooperation among access points to markedly improve signal quality and suppress inter-user interference, thereby outperforming conventional cellular architectures. However, achieving ideal full collaboration among access points requires enormous computational resources and backhaul capacity; therefore, retaining precoding for local computation on each AP is a more realistic option. In this paper, we derive the asymptotically optimal local precoding for cell-free massive MIMO by applying random matrix theory together with convex optimization techniques. Leveraging this theoretical foundation, an efficient iterative algorithm is proposed to compute the precoding coefficients; its optimality and convergence are rigorously established. Numerical simulations demonstrate that our precoding design achieves substantial transmit-power savings under given quality-of-service (QoS) constraints compared with existing methods.

In this paper, channel estimation (CE) for uplink hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub-block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub-blocks is derived, and the optimal number of sub-blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cram $\acute {e}$ r-Rao lower bound is derived to evaluate the proposed algorithm's performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy.

This paper investigates a novel covert integrated sensing and communication (ISAC) system, where an unmanned aerial vehicle (UAV) equipped with a reconfigurable holographic surface (RHS) serves multiple users in the downlink via rate-splitting multiple access (RSMA), in the presence of practical hardware impairments and imperfect channel state information (CSI) of a warden (Willie). Our objective is to maximize the weighted sum rate while satisfying constraints on sensing performance and communication covertness. Owing to the UAV’s trajectory optimization and the involved CSI uncertainties, the problem is highly nonconvex and challenging to solve directly. To address this, we develop an alternating optimization framework that decomposes the problem into tractable subproblems: the digital precoders and common rate allocation are optimized via successive convex approximation (SCA) and semidefinite relaxation (SDR). For enforcing covertness under Willie’s CSI uncertainty, we employ Kullback-Leibler (KL) divergence bounds derived from Pinsker’s inequality and leverage Bernstein-type inequality approximations. Simulation results demonstrate rapid convergence of the proposed algorithm and significant improvements in the weighted sum rate–sensing trade-off under covertness constraints. The proposed system outperforms baseline schemes without RSMA/RHS or with fixed trajectories, while exhibiting robustness to both CSI uncertainties and hardware impairments(HWIs).

In this paper, the channel estimation (CE) problem in multi-user hybrid-field communication systems enhanced by an active intelligent reflecting surface (IRS) is investigated. To effectively characterize the spatially non-stationary characteristics of large-scale active IRS under hybrid-field propagation, the uniform linear array of the active IRS is partitioned into multiple sub-blocks. Each user is assumed to be in the far-field region of each sub-block while remaining in the near-field region of the entire IRS, which enables dimensionality reduction in channel modeling and alleviates computational complexity. Moreover, under the total power constraint between the users and the active IRS, a closed-form solution for the optimal power allocation factor is rigorously derived. Furthermore, a novel multi-user pilot transmission strategy is designed, based on which efficient least squares (LS) estimators are developed for both the direct and cascaded links. To further enhance estimation performance, a deep learning based CE framework is proposed by integrating residual blocks into a U-Net architecture. The proposed network leverages the encoder-decoder pathway and skip connections for hierarchical feature extraction, while the residual modules strengthen representational capability and facilitate gradient propagation. Simulation results demonstrate that the proposed deep learning based estimator significantly outperforms conventional LS and minimum mean square error (MMSE) methods in terms of estimation accuracy, while reducing pilot overhead by seven-eighths.

Recently, as a green wireless technology, active reconfigurable intelligent surface (RIS) attracts numerous research activities due to its amplifying ability to combat the double-fading effect compared to passive one. How about its energy efficiency (EE) over passive one? Below, the EE of active RIS-aided wireless network in Rayleigh fading channels is analyzed. Using the law of large numbers, EE is derived as a function of five factors: power allocation factor, the number (N) of RIS elements, the total power, the noise variances at RIS and at user. To assess each factor's impact, the simple EE function for the concerning factor is given with others fixed. To evaluate the impact of N on EE, we establish an equation with the EE of active RIS equaling that of passive one, and three methods, bisection, Newton's method, and simulated annealing, are designed to find the roots of this equation. Simulation results show that as N tends to medium-scale or large-scale, the asymptotic performance formula is consistent with the exact EE expression well. As N varies from small-scale to large-scale, the active RIS intersects passive one at some point (N0 approximate to 210). When N < N0, active RIS performs better than passive one in terms of EE. Otherwise, there is a converse conclusion.

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a successive convex approximation based deployment optimization method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

This work presents a wideband RF self-interference (SI) canceler operating over 2-4GHz octave bandwidth. It employs a balun-assisted four-tap topology to achieve frequency-independent phase inversion implemented with low-cost commercial components. To mitigate the significant amplitude-phase coupling inherent in such nonideal hardware, a coupled device model is established to support a quadrature-aided hierarchical dynamic stochastic search (Q-HDSS) algorithm for solving the nonconvex optimization problem. Experimental results with a 2.45-GHz OFDM waveform demonstrate >40dB peak cancellation. Across the full 2-4GHz range, the system maintains >= 20 dB suppression, validating its suitability for in-band full-duplex (IBFD) transceivers.

Symbiotic radio (SR) is an emerging technology that exhibits high energy and spectral efficiency, where a cooperative receiver is designed to decode the primary and the secondary signals jointly. However, the ambiguity problem for joint detection arises due to the multiplication of the primary and the secondary signals, which leads to a degradation in detection performance. To address this problem, the secondary transmitter (STx) could adaptively adjust the modulation scheme based on the channel responses of the direct and the reflecting links. Multiple antennas enable to reconfigure these channel responses through active beamforming at the primary transmitter (PTx) and passive beamforming at the STx, leading to the coupling of the beamforming and the adaptive modulation. Considering this coupling, we propose a joint beamforming and adaptive modulation design scheme for SR systems with multiple antennas. To enhance both the performance of primary and secondary transmissions, we focus on the composite signal, which includes the primary and the secondary signals. Accordingly, we formulate an optimization problem as maximizing the minimum Euclidean distance of the composite signal. This problem involves jointly optimizing the active beamforming at the PTx, along with the adaptive modulation and passive beamforming at the STx, subject to the passive reflection constraint at the STx and the average power constraint at the PTx. As the formulated problem is non-convex, we decompose the original problem into two subproblems, which are then solved iteratively using semi-definite programming and difference-of-convex algorithms. To reduce the computational complexity, we further propose a suboptimal scheme. Moreover, we derive the theoretical symbol error rate for the proposed scheme, gaining insights into the role of active beamforming at the PTx. Finally, simulation results show the superiority of the proposed scheme and verify the accuracy of the theoretical analysis.

Due to the directive property of each antenna element, the received signal power can be severely attenuated when the emitter deviates from the array boresight, which will lead to a severe degradation in sensing performance along the corresponding direction. Although existing rotatable array sensing methods such as recursive rotation (RR-Root-MUSIC) can mitigate this issue by iteratively rotating and sensing, several mechanical rotations and repeated eigendecomposition operations are required to yield a high computational complexity and low time-efficiency. To address this problem, a pre-rotation initialization with recieve power as a rule is proposed to signifcantly reduce the computational complexity and improve the time-efficiency. Using this idea, a low-complexity enhanced direction-sensing framework with pre-rotation initialization and iterative greedy spatial-spectrum search (PRI-IGSS) is develped with three stages: (1) the normal vector of array is rotated to a set of candidates to find the opimal direction with the maximum sensing energy with the corresponding DOA value computed by the Root-MUSIC algorithm; (2) the array is mechanically rotated to the initial estimated direction and kept fixed; (3) an iterative greedy spatial-spectrum search or recieving beamforming method, moviated by reinforcement learning, is designed with a reduced search range and making a summation of all previous sampling variance matrices and the current one is adopted to provide an increasiong performance gain as the iteration process continues. To assess the performance of the proposed method, the corresponding CRLB is derived with a simplified rotation model. Simulation results demonstrate that the proposed PRI-IGSS method performs much better than RR-Root-MUSIC and achieves the CRLB in term of mean squared error due to the fact there is no sample accumulation for the latter.

As the foundation for interconnection in industrial systems, industrial networks require bounded low-latency, low-jitter, and high-reliability communication links. Due to the stringent quality-of-service (QoS) requirements, hyper-reliable and low-latency communications (HRLLC) is expected to serve as a key enabling technology for wireless industrial automation. Beyond the latency and reliability requirements emphasized by 5G ultra-reliable and low-latency communications (URLLC), HRLLC further requires low jitter to guarantee delay determinism. In this letter, leveraging the high accuracy of martingales in characterizing tail behavior, we develop an end-to-end latency and jitter analysis framework for strongly heterogeneous mesh networks. In this framework, the delay violation probability is investigated to characterize the trade-off between delay and reliability, and jitter is quantified as the delay second-order moment. Then, a scheduling method is proposed to minimize the jitter by optimizing the power allocation policy, bandwidth assignment, and delay components under QoS constraints. Simulation results validate our analysis and show that the proposed algorithm can effectively enhance delay determinism.

The convergence of hyper-reliable and low-latency communication (HRLLC) with industrial networking has been envisioned as the potential candidate for cyber-physical systems deployments, which increases the flexibility without moderating the stringent requirements of latency and reliability. Throughout the industrial network, event-triggered (ET) traffic bears serious performance degradation of timeliness, stemming from the resource preemption by time-critical time-triggered (TT) traffic under prioritized scheduling mechanisms. Furthermore, wireless links changes and finite blocklength regime in HRLLC result in transmission error, violation of latency requirement, and conservative resource allocation. Consequently, a cross-layer optimization framework is established to compress the total end-to-end (E2E) latency and bandwidth of ET flows under quality-of-service (QoS) constraints. The reliability is characterized with the decoding error probability with frequency diversity in finite blocklength regime, and the timeliness is characterized with the experienced E2E latency considering time offsets and routing of TT flows. Through spatial non-overlapping path planning that balances shorter length and fewer links contention, and physical optimal resource allocation that refines QoS-aware assignments of bandwidth, the number of subchannels, and decoding error probability threshold, the total latency compression and spectrum compaction can be achieved. Simulation results show the distinct performance gain of proposed cross-layer optimization framework compared with other methods.

Ultra-reliable and low-latency communications (URLLC) has great potential in wireless industrial automation, which increases flexibility without moderating the stringent quality-of-service (QoS). In URLLC-enabled Industrial Internet of Things (IIoT), mixed traffic is essential to support applications with differentiated latency requirements, where event-triggered (ET) traffic requires bounded end-to-end (E2E) latency, and time-triggered (TT) traffic demands deterministic latency. This article proposes a mixed traffic scheduling scheme with the preemption mode, in which TT traffic with a higher priority is allowed to preempt the transmission of ET traffic to ensure TT deterministic latency. However, this preemption may cause ET latency to exceed its requirements with constrained resources. To characterize the impact of TT preemption on the ET latency and the deterministic metrics of TT traffic, we derive the ET delay violation probability bound and the TT deterministic delay probability using the stochastic network calculus. To further achieve bandwidth-saving scheduling while ensuring the QoS requirements of mixed traffic, the credit-based shaper (CBS) configurations, radio resources and delay components are assigned under constraints of ultra-reliability, ET bounded latency and TT deterministic latency. Then, a three-step method is proposed to find the global optimal solution to minimize the total bandwidth. Simulation results validate our analysis and show the bandwidth-saving performance gain by jointly optimizing uplink and downlink resources with the proposed preemption-based scheduling scheme.

In this paper, the main factors affecting global energy efficiency (GEE) in active reconfigurable intelligent surface (RIS) assisted wireless communication network in Rayleigh fading channels is studied. Using the law of large numbers, asymptotic expressions for GEE with respect to the number N of RIS elements, the transmission power at base station (BS), the reflection power at RIS, the noise at RIS and the noise at user are derived. Theoretical analysis shows that when N tends towards a large-scale, the asymptotic performance of GEE is better. In addition, as transmission power at BS and the reflection power at RIS approach infinity, GEE gradually approaches 0. When the noise at RIS and the noise at user approach infinitesimal, GEE converges to a fixed value. The simulation results validate the correctness of the theoretical analysis. The GEE performance of active RIS assisted system has decreased due to the influence of noise. As N increases, the GEE performance of passive RIS assisted system gradually surpasses that of active RIS system.

Cell-free (CF) massive multiple-input multipleoutput (MIMO) system based on local operations is recognized as a viable next-generation network architecture with high performance. Leveraging large-dimensional random matrix theory, we have derived a deterministic equivalent performance expression for the performance of a CF MIMO system employing local regularized zero-forcing (L-RZF) precoding, and based on this performance expression, we have designed a high-performance regularization parameter optimization method, termed statistical channel state information aided L-RZF (SCSI-L-RZF). Under SCSI-L-RZF method, the CPU only needs to optimize the regularization parameters for each AP by utilizing statistical channel state information (CSI), while each AP only needs to design precoding by combining the regularization parameter and local CSI to achieve good performance gains. Simulation results demonstrate that our SCSI-L-RZF scheme outperforms the current mainstream maximum ratio transmission (MRT) precoding schemes, L-RZF and local team minimum mean square error (LT-MMSE), further narrowing the performance gap between local and global precoding.

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first proposed. To further enhance security without requiring additional storage for new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Building upon these three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the strengths of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed specifically for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the proposed AM-SUEO-DBLTKM-RFID protocol achieves both low computational complexity and low storage overhead, making it well-suited for deployment in resource-constrained, low-cost RFID tags.

In this paper, the channel estimation (CE) of intelligent reflecting surface-aided near-field (NF) multi-user communication is investigated. Initially, the least square (LS) estimator and minimum mean square error (MMSE) estimator for the estimated channel are designed, their mean square errors (MSEs) are derived, and the Cramér-Rao lower bound (CRLB) is derived to serve as a benchmark for performance evaluation. Subsequently, in view of the fact that the NF channel model is more sensitive to distance variations compared to the far-field model, this leads to pronounced discrepancies in the user channel characteristics in different regions. To effectively capture and utilize these diverse channel features, users are initially divided into distinct regions predicated on pivotal parameters, such as channel angle and distance. Correspondingly, a user region classifier based on convolutional neural networks is designed. Then, to fully harness the potential of deep residual networks (DRNs) in denoising, the aforementioned CE problem is reconceptualized as a denoising task, and a DRN-driven single region NF CE network, named SR-DRN-NFCE, is proposed. In addition, by integrating SR-DRN-NFCE networks corresponding to different regions and conducting joint training in a federated learning (FL) manner, a new network is obtained, named FL-DRN-NFCE. Simulation results demonstrate that the proposed FL-DRN-NFCE network outperforms LS, MMSE, and no residual connections in terms of MSE, and the proposed FL-DRN-NFCE method has higher CE accuracy than the SR-DRN-NFCE method.