Search Results (1,387)

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

A compact and low-profile S-slot antenna for millimeter-wave wireless communication applications is presented in this paper. The antenna employs an S-shaped slot etched within a ground plane and excited by a hook-shaped microstrip feeding line to radiate a linearly polarized wave with a bidirectional broadside radiation beam. The antenna geometrical parameters are optimized to cover the n257 and n261 5G bands of the 5G mobile communications. The proposed antenna is fabricated and measured. Simulated and measured results demonstrate good impedance matching, with a measured fractional bandwidth of 18.3% and a maximum realized gain of 4.8 dBi across the desired operating bandwidth for the S-slot antenna with extended ground plane necessary for the purpose of measurements. The performance remains largely unaffected when the ground plane is reduced, highlighting the antenna’s suitability for compact implementations. Consequently, the proposed antenna is well suited for indoor 5G small-cell deployments and future railway wireless communication systems. Moreover, it can serve as a unit element in MIMO arrays or larger antenna configurations. To further demonstrate scalability and system-level applicability, the antenna element is extended into a compact eight-element MIMO array providing dual linear polarization. The array exhibits low mutual coupling, an envelope correlation coefficient on the order of ${10}^{-3}$, and a diversity gain approaching 10 dB. These results demonstrate highly independent radiation characteristics and reliable MIMO performance in multipath environments. Full article

The use of millimeter-wave spectrum in fifth-generation (5G) systems is increasing the need for accurate prediction of received power and coverage in real deployment scenarios. In this context, ray tracing (RT) is a promising approach for site-specific analysis, although its reliability depends on how accurately different tools reproduce measurements in complex urban environments. This work presents a comparative assessment at 27 GHz of three RT tools: in-house Exact tool based on Vertical Plane Launching (VPL), Matlab 5G and open-source Sionna RT based on Shooting and Bouncing Rays (SBR). The comparison relies on a large outdoor walk-test campaign, including about 14,725 measurement points collected in a real urban area around a 27 GHz mMIMO base station, using real operator-provided antenna radiation patterns. Measured and simulated power levels are compared using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and a planning-oriented coverage-rate metric. The results show a reasonable agreement between simulations and measurements, with RMSE and MAE values around 10–12 dB, highlighting tool-specific behaviors related to boundary effects, interaction modeling, and high-power overestimation. This work confirms that RT is a flexible support for 5G preliminary network design, reducing the need for extensive drive tests. Full article

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks. Full article

A digital predistortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using Fast Fourier Transform (FFT) beamforming and so-called virtual-array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per PA is excessively costly for large arrays. This work shows that it is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity. Full article

Compact 5G millimeter-wave (mm-Wave) multiple-input multiple-output (MIMO) systems face a serious challenge as high isolation is required for high spectral efficiency. This paper presents a novel computational design framework for enhancing the isolation of a two-port ultra-wideband (UWB) MIMO antenna, specifically targeting the 5G n257 band (26.5–29.5 GHz). A pixelated metasurface is presented and optimized with the help of a binary-coded Grey Wolf Optimizer (B-GWO) algorithm through a MATLAB-Computer Simulation Technology (CST) co-simulation interface, which is used in contrast to some conventional decoupling structures. A Geometric Mirror Symmetry method is used to accelerate the optimization process, which halves the number of optimization variables and significantly reduces the computational load. Crucially, this symmetry is also a fundamental requirement to ensure that the reflection coefficients ($S_{11}$, $S_{22}$) of the antennas remain identical. The proposed design achieves isolation levels better than 20 dB across the entire target band, reaching a peak isolation of $32.58$ dB at $28.67$ GHz, while maintaining reflection coefficients ($S_{11}$, $S_{22}$) below $-10$ dB. The MIMO diversity performance is comprehensively validated with an Envelope Correlation Coefficient (ECC) $<0.005$, a Diversity Gain (DG) of $9.99$ dB, and a Total Active Reflection Coefficient (TARC) $<-10$ dB. Moreover, the suppression of surface waves enhances the realized gain to $4.51$ dBi, providing a $0.57$ dB improvement over the reference antenna. In addition, an equivalent passive RLC circuit model is constructed to observe the physical process of the pixelated surface, which shows the optimized structure as a band stop filter at the coupling frequency. The high correlation of the Equivalent Circuit Model and full-wave simulation outcomes confirms that the suggested design procedure is a strong verification alternative to physical fabrication. Full article

This research focuses on the trajectory tracking control of a Twin Rotor MIMO System (TRMS) with time-varying sinusoidal inputs. Initial design considerations include a backstepping controller integrated with a high-gain observer (HGO) to estimate unmeasured states. While the outcomes of the simulation show good accuracy of tracking, real-time implementation shows instability and performance degradation. This divergence is attributed to the static high gains of the observer that amplify measurement noise and inject inaccurate state estimates into the controller during actual deployment. To overcome this drawback without altering the core control structure, we propose a strategy of online gain tuning based on Interval Type-2 Takagi–Sugeno (TS) fuzzy logic. The proposed mechanism dynamically adjusts the observer gain based on estimation errors to balance the trade-off between convergence speed and noise sensitivity. Experimental evaluations on the physical TRMS confirm that the fuzzy-tuned observer eliminates instability in real-time. Quantitative analysis demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 65.6% in the Pitch axis and 92.3% in the Yaw axis compared to the fixed-gain counterpart. Full article

This paper presents a real-time experimental comparison of four control strategies—PID, Fractional-Order PID (FOPID), Fuzzy PID/PD, and Model-Free Control (MFC)—applied to trajectory tracking of a coupled 2-DOF Quanser Aero 2 helicopter. A linear MIMO model is identified to support controller design, and all approaches are evaluated under three operating conditions: coupled dynamics, static decoupling, and dynamic decoupling. Experimental performance is assessed using Integral Square Error, control effort, overshoot, and settling time metrics implemented on the QUARC real-time platform. The results show that interaction mitigation affects control performance. Static decoupling improves tracking accuracy, while dynamic decoupling reduces cross-coupling effects at the expense of increased noise sensitivity. Among the evaluated controllers, the Fuzzy PID/PD strategy achieves the best overall balance between tracking performance and control effort, whereas Model-Free Control provides smoother actuator behavior. The study offers practical experimental guidelines for selecting control strategies in coupled UAV systems. Full article

Cross-interface architectures are increasingly central to large-scale ocean observation systems, where underwater sensor nodes transmit data to spatially distributed buoys that relay information to terrestrial networks. In these deployments, the inherent broadcast nature of underwater acoustic (UWA) propagation enables a single node’s signals to be captured by multiple buoys. However, substantial and dynamic propagation delays lead to inherent reception asynchrony and severe multi-user interference. Conventional detection relies on large hydrophone arrays on single platforms and assumes strict synchronization, hindering scalability and elevating costs. This study proposes a distributed asynchronous reception framework for buoy-assisted UWA networks. Under a cloud software-defined acoustic (C-SDA) architecture, spatially separated buoys are treated as a virtual distributed multiple-input multiple-output (MIMO) receiver. We introduce a minimum-delay-based equivalent reconstruction to regularize the asynchronous structure, followed by blind channel identification and pilot-assisted synchronization for robust multi-user detection. By leveraging long-delay broadcast propagation as a source of spatial diversity, the framework facilitates scalable and cost-effective multi-user access. The results demonstrate that the architecture provides a practical paradigm for the underwater Internet of Things and long-term ocean observation. Full article

The evolution toward prospective sixth-generation (6G) wireless networks is expected to significantly increase user density, bandwidth demand, and architectural complexity, reinforcing the need for scalable multiple-input multiple-output (MIMO) deployments. In this context, two fundamentally different design strategies have emerged: scaling up centralized antenna arrays and scaling out distributed cooperative infrastructures. This paper presents a system-level comparative benchmark of scale-up and scale-out MIMO architectures under identical operating conditions of three representative downlink deployments: centralized Massive MIMO, centralized XL-Massive MIMO, and distributed Cell-Free MIMO. All architectures are assessed under identical urban channel conditions, transmit power, bandwidth, and traffic assumptions, considering sub-6 GHz (3.5 GHz) and millimeter-wave (28 GHz) frequency bands as proxies for 5G and prospective 6G operation. A unified Monte Carlo simulation framework is employed to jointly evaluate aggregate throughput, spectral efficiency, coverage performance, interference behavior, and energy efficiency over a wide range of user densities and service radii. The results highlight the distinct architectural trade-offs between centralized and distributed deployments: XL-Massive MIMO maximizes aggregate throughput and spatial reuse in dense hotspot scenarios, whereas Cell-Free MIMO provides superior coverage uniformity and improved energy efficiency in wide-area deployments. By isolating the impact of architectural scaling under consistent assumptions, the presented benchmark offers quantitative guidance for 6G network design and deployment planning. Full article

Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input multiple-output (MIMO) systems further exacerbates power consumption and hardware costs. To address these challenges, this paper investigates RIS-assisted millimeter-wave (mmWave) MIMO systems with low-resolution analog-to-digital converters (ADCs). Exploiting the inherent sparsity of mmWave channels and considering the distortion introduced by low-resolution quantization, we propose a compressive sensing (CS)-based channel estimation scheme. Furthermore, to mitigate the effects of angular leakage, we introduce an energy capture orthogonal matching pursuit (ECOMP) algorithm. Simulation results demonstrate that the proposed scheme not only improves channel estimation accuracy but also reduces pilot overhead and power consumption, while maintaining enhanced stability in high signal-to-noise ratio (SNR) regimes. Full article

The rapid growth of 5G necessitates wireless receivers capable of high-speed, low-latency communication under complex channel conditions. Traditional receivers struggle with the performance–complexity trade-off in massive MIMO systems, where linear detectors underperform and maximum likelihood (ML) detection becomes computationally prohibitive. Deep-learning-based model-driven approaches have demonstrated a favorable balance between detection performance and computational cost. However, despite their algorithmic promise, the transition of these learned detectors into practical, real-time systems is critically hampered by inefficient hardware mapping, resulting in suboptimal throughput, high resource overhead, and limited scalability. To bridge this gap, this paper presents LwAMP-Net, a dedicated FPGA accelerator for a lightweight learned AMP detector. We propose a modular and multi-mode hardware architecture for LwAMP-Net, featuring an outer-product-based dataflow that mitigates pipeline stalls and multi-mode processing elements that adapt to diverse computation patterns. These innovations jointly enhance computational parallelism and resource utilization on the FPGA. Implemented on a Xilinx XC7VX690T FPGA for a 128 × 8 MIMO system with 16QAM, the accelerator achieves a 49.2% higher normalized throughput per iteration, an 85.4% improvement in throughput per LUT slice, and a 12.7% improvement in throughput per DSP compared to the state-of-the-art methods. This work provides a complete architectural solution for deploying high-performance, hardware-efficient learned MIMO detectors in real-world systems. Full article

This study proposes a high-isolation four-port wideband MIMO antenna array designed for sub-6 GHz 5G, IoT, and radar applications. The array is fabricated on a polycarbonate substrate with overall dimensions of 500 × 500 mm² (ε_r = 2.8, h = 1 mm). Four orthogonally arranged modified circular patches with triangular ground planes and optimized inter-element spacing (D₁ = 90 mm) are employed in the antenna’s design to achieve an impedance bandwidth of 0.7–7.0 GHz (Fractional Bandwidth (FBW) > 163.63%) with |S_ii| < −10 dB across all ports. The measurement results indicate that the inter-port isolation is better than 15 dB (worst-case) across the 0.7–7 GHz band, exceeding 25 dB over 63.5% of the bandwidth (with a peak of approximately 50 dB); the envelope correlation coefficient (ECC) is ultra-low (<0.008); the total active reflection coefficient (TARC) is less than −10 dB for primary multi-port excitations; the mean effective gain (MEG) is balanced (≈−3 dB); and the group delay is consistent (~0.5 ns). With a maximum realized gain of 10 dBi, the antenna exhibits omnidirectional radiation patterns, showing a significant correlation between the simulation (CST Microwave Studio) and measurement results. The proposed antenna is particularly well-suited for use in high-throughput sub-6 GHz 5G base stations and wideband wireless systems, offering superior port isolation through multi-mode resonance without the need for metamaterials and outperforming existing four-port designs. Full article

This paper presents a performance evaluation of a long short-term memory (LSTM)-based precoder for cell-free (CF) massive multiple-input multiple-output (MIMO) systems in 6G networks operating under hardware impairments and imperfect channel state information (CSI). It also compares the proposed method with traditional Kalman, minimum mean square error (MMSE), and zero forcing (ZF) precoders. Simulations conducted at 2.4 GHz show that the LSTM-based scheme offers improved spectral efficiency (SE) and energy efficiency (EE) while remaining computationally feasible. Specifically, the LSTM precoder achieves an average per-user SE of 1.74 bps/Hz, representing gains of about 1.15% over Kalman, 3.45% over MMSE, 4.6% over ZF, and 5.75% over MRT. Under severe hardware impairments, it provides a 2.94% improvement over Kalman and a 5.88% improvement over MMSE. The total SE reaches 17.4 bps/Hz, increasing the overall system capacity by approximately 2.87% over Kalman, 4.02% over MMSE, 6.32% over ZF, and 8.05% over MRT when the number of users (K) is 10. The LSTM-based precoder also achieves the highest peak EE, indicating that its learning-driven adaptability yields higher SE for comparable power usage. Despite a slight increase in power consumption, its inference time remains shorter than both MMSE and ZF, offering a favorable balance between performance and computational complexity. Overall, the results demonstrate that a learning-driven, impairment-aware precoding approach provides significant advantages in terms of robustness and scalability for next-generation 6G CF massive MIMO networks, particularly in non-ideal hardware environments. Full article