Search Results (1,221)

To mitigate potential interference in a coexisting system, an ultra-wideband (UWB) antenna with triple-band-notched characteristics is proposed. Based on transmission line theory, three notched bands are achieved by utilizing the open- or short-circuited properties of microstrip line resonators and slot resonators. Each antenna element consists of a patch etched with three half-wavelength slots and a one-wavelength strip. Measurement results demonstrate that the antenna exhibits excellent rejection performance at the three designated frequency bands. Furthermore, the effects of array configuration and element deflection angle on mutual coupling are investigated using a 2 × 1 face-to-face multiple-in, multiple-out (MIMO) array. Finally, a two-element MIMO array with high isolation was fabricated and measured. Experimental results show that an isolation level better than 24.6 dB is maintained across the operating band. Full article

Echo separation has long been a challenging and prominent research focus for Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) systems. Digital beamforming (DBF) plays a critical role in achieving effective echo separation, but it often comes at the cost of high system complexity. This paper proposes a novel MIMO SAR scheme based on phase-coded waveforms applied to both inter-pulses and intra-pulses. By introducing phase coding in both dimensions and performing joint azimuth–elevation processing, the proposed method effectively suppresses interference arising during the echo separation process, thereby significantly improving separation performance. Additionally, the approach allows for a significantly simplified array configuration, reducing both hardware requirements and computational burden. The effectiveness and practicality of the proposed scheme are validated through numerical simulations and distributed scene experiments, highlighting its strong potential for application in MIMO SAR systems—particularly in cost-sensitive scenarios and systems with limited elevation channels. Full article

Stochastic geometry provides a powerful analytical framework for evaluating interference-limited cellular networks with randomly deployed base stations (BSs). While prior studies have examined limited channel state information at the transmitter (CSIT) and low-resolution analog-to-digital converters (ADCs) separately, their joint impact in multi-user multiple-input multiple-output (MIMO) systems remains largely unexplored. This paper investigates a downlink cellular network in which BSs are distributed according to a homogeneous Poisson point process (PPP), employing zero-forcing beamforming (ZFBF) with limited feedback, and receivers are equipped with one-bit ADCs. We derive a tractable approximation for the achievable spectral efficiency that explicitly accounts for both the quantization error from limited feedback and the receiver distortion caused by coarse ADCs. Based on this approximation, we determine the optimal feedback rate that maximizes the net spectral efficiency. Our analysis reveals that the optimal number of feedback bits scales logarithmically with the channel coherence time but its absolute value decreases due to coarse quantization. Simulation results validate the accuracy of the proposed approximation and confirm the predicted scaling behavior, demonstrating its effectiveness for interference-limited multi-user MIMO networks. Full article

In this paper, the extrapolation of a 2D multiple-input multiple-output (MIMO) array is proposed using the Burg algorithm to achieve higher angular resolution beyond that of the corresponding 2D MIMO virtual array. The main advantage of such an approach is that it allows us to dramatically decrease both the physical size and the number of antenna elements of the MIMO array. The performance and limitations of the Burg algorithm are examined through both simulation and experimentation at 77 GHz. The experimental methodology used to acquire 3D data of range, azimuth and elevation information with the 1D MIMO off-the-shelf radar is described. Using this method, the performance of the proposed array can be tested experimentally, especially at frequencies where it is desired to assess the antenna response prior to fabricating the antenna. Full article

This study addresses the challenge of cognitive waveform design for multiple-input–multiple-output (MIMO) radar systems operating in cluttered environments. It focuses on the key practical requirements for transmitting time-domain waveforms and proposes a novel approach. This method first determines the optimal frequency-domain waveform and then designs a time-domain waveform that closely approximates the frequency-domain solution. The primary objective is to enable MIMO radar systems to transmit orthogonal waveforms while accommodating various constraints. A frequency-domain waveform optimization model was initially developed using the principle of maximizing dual mutual information (DMI), and the energy spectral density (ESD) of the optimal waveform was derived using the water-filling method. Next, a time-domain waveform approximation model is constructed based on the minimum mean square error (MMSE) criterion, which incorporates constant modulus and peak-to-average power ratio (PAPR) constraints. To minimize the performance degradation of the waveform, an improved adaptive gradient descent genetic algorithm (GD-AGA) was proposed to synthesize multichannel orthogonal time-domain waveforms for MIMO radars. The simulation results demonstrate the effectiveness of the proposed model for enhancing the performance of MIMO radar. Compared with traditional genetic algorithms (GA) and two enhanced GA alternatives, the proposed algorithm achieves a lower ESD loss and better orthogonal performance. Full article

In massive multiple-input and multiple-output (mMIMO) systems, superimposed channel state information (CSI) feedback is developed to improve the occupation of uplink bandwidth resources. Nevertheless, the interference from this superimposed mode degrades the recovery performance of both downlink CSI and uplink data sequences. Although machine learning (ML)-based methods effectively mitigate superimposed interference by leveraging the multi-domain features of downlink CSI, the complex interactions among network model parameters cause a significant burden on system resources. To address these issues, inspired by sensing-assisted communication, we propose a novel superimposed CSI feedback method assisted by inactive sensing information that previously existed but was not utilized at the base station (BS). To the best of our knowledge, this is the first time that inactive sensing information is utilized to enhance superimposed CSI feedback. In this method, a new type of modal data, different from communication data, is developed to aid in interference suppression without requiring additional hardware at the BS. Specifically, the proposed method utilizes location, speed, and path information extracted from sensing devices to derive prior information. Then, based on the derived prior information, denoising processing is applied to both the delay and Doppler dimensions of downlink CSI in the delay—Doppler (DD) domain, significantly enhancing the recovery accuracy. Simulation results demonstrate the performance improvement of downlink CSI and uplink data sequences when compared to both classic and novel superimposed CSI feedback methods. Moreover, against parameter variations, simulation results also validate the robustness of the proposed method. Full article

The advent of sixth-generation (6G) wireless networks and cell-free massive multiple-input multiple-output (MIMO) architectures underscores the need for efficient resource allocation to support federated learning (FL) at the network edge. Existing approaches often treat communication, computation, and learning in isolation, overlooking dynamic heterogeneity and fairness, which leads to degraded performance in large-scale deployments. To address this gap, we propose a joint optimization framework that integrates communication–computation co-design, fairness-aware aggregation, and a hybrid strategy combining convex relaxation with deep reinforcement learning. Extensive experiments on benchmark vision datasets and real-world wireless traces demonstrate that the framework achieves up to 23% higher accuracy, 18% lower latency, and 21% energy savings compared with state-of-the-art baselines. These findings advance joint optimization in federated learning (FL) and demonstrate scalability for 6G applications. Full article

Carrier frequency offset (CFO) estimation is crucial for underwater acoustic (UWA) multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. By employing pilot symbols, a CFO estimation scheme utilizing least squares (LS)-based tentative channel estimation and equalization can achieve an improved CFO estimation performance. However, it suffers from performance degradation due to inaccurate tentative channel estimation in scenarios with relatively long channels or a relatively large number of transmitting transducers. To address this problem, we propose a sparse Bayesian learning (SBL)-based CFO estimation scheme, which employs the expectation-maximization SBL (EM-SBL) algorithm as the tentative channel estimator. In addition, to reduce computational complexity caused by matrix inversion, a refined scheme employing variational Bayesian inference (VBI) technology is proposed, which achieves comparable performance to the original scheme with lower complexity. Finally, numerical simulations demonstrate that our proposed schemes can achieve a remarkably low root mean square error (below ${10}^{-2}$) and outperform existing methods across diverse system configurations and simulated channels. Full article

A compact fork-shaped four-element multiple-input multiple-output (MIMO) antenna system with wide bandwidth for 5G millimeter-wave (mmWave) applications is presented. The antenna elements are arranged orthogonally to achieve a compact footprint of $20\times 26{\mathrm{mm}}^2$. To enhance the gain, a frequency selective surface (FSS) is placed above the MIMO system, providing an average gain improvement of 1.5 dB across the entire operating band and achieving a peak gain of 7.5 dB at 41 GHz. The proposed design operates in the Ka-band (22–46 GHz), making it well suited for 5G communications. The antenna exhibits an isolation greater than 20 dB and radiation efficiency exceeding 80% across the band. Moreover, key MIMO performance metrics, including diversity gain (DG ≈ 10) and envelope correlation coefficient (ECC < 0.05), meet the required standards. A prototype of the proposed system was fabricated and measured, with the experimental results showing good agreement with simulations. Full article

Expectation Propagation (EP) has emerged as a promising detection algorithm for large-scale multiple-input multiple-output (MIMO) systems owing to its excellent performance and practical complexity. However, transmit antenna correlation significantly degrades the performance of EP detection, especially when the number of transmit and receive antennas is equal and high-order modulation is adopted. Based on the fact that the eigenvector matrix of the channel transmit correlation matrix approaches asymptotically to a discrete Fourier transform (DFT) matrix, a DFT precoder is proposed to effectively eliminate transmit antenna correlation. Simulation results demonstrate that for high-order, high-dimensional massive MIMO systems with strong transmit antenna correlation, employing the proposed DFT precoding can significantly accelerate the convergence of the EP algorithm and reduce the detection error rate. Full article

This article presents a novel design method for realizing wideband operation and excellent isolation in fifth-generation (5G) multiple-input multiple-output (MIMO) systems. The proposed MIMO antenna employs a ground-plane slot in the shape of the Chinese character “工” and maintains an unbroken metal frame, thereby avoiding slot openings on the rim. The theory of characteristic modes (TCM) is applied to determine appropriate feeding structures and locations for two functional antenna modules. This design achieves wide bandwidth and high isolation without requiring additional decoupling structures, simplifying the overall system. Two prototype arrays, consisting of four and eight antenna elements, were implemented for 5G operation in the 3.4–5.0 GHz band. The measured results confirm isolation levels above 21.6 dB and 16.2 dB for the four- and eight-element arrays, respectively, with envelope correlation coefficients (ECCs) below 0.16. These results indicate that the proposed design is a promising solution for integration into 5G smartphones. Full article

Intelligent Reflective Surface (IRS)-aided Multiple-Input Multiple-Output (MIMO) systems have emerged as a promising solution to enhance spectral and energy efficiency in future wireless communications. However, accurate channel estimation remains a key challenge due to the passive nature and high dimensionality of IRS channels. This paper proposes a lightweight hybrid framework for cascaded channel estimation by combining a physics-based Bilinear Alternating Least Squares (BALS) algorithm with a deep neural network named ConvTrans-ResNet. The network integrates convolutional embeddings and Transformer modules within a residual learning architecture to exploit both local and global spatial features effectively while ensuring training stability. A series of ablation studies is conducted to optimize architectural components, resulting in a compact configuration with low parameter count and computational complexity. Extensive simulations demonstrate that the proposed method significantly outperforms state-of-the-art neural models such as HA02, ReEsNet, and InterpResNet across a wide range of SNR levels and IRS element sizes in terms of the Normalized Mean Squared Error (NMSE). Compared to existing solutions, our method achieves better estimation accuracy with improved efficiency, making it suitable for practical deployment in IRS-aided systems. Full article

Millimeter-wave multiple-input multiple-output synthetic aperture radar (MIMO-SAR) has been widely used in many scenarios such as geological exploration, post-disaster rescue, and security inspection. When faced with large complex scenes, the signal suffers from distortion problems due to amplitude-phase nonlinear aberrations, resulting in undesired artifacts. Many previous studies eliminate artifacts but result in missing target structures. In this paper, we propose to use chunked nonlinear normalized weights in conjunction with signal-to-noise ratio-based (SNR-based) multichannel fusion to address the above-mentioned problems. The chunked nonlinear normalized weights make use of the scene’s characteristics to separately perform the optimization of different regions of the scene. This approach significantly mitigates the effects of amplitude-phase distortion on signal quality, thereby facilitating the effective suppression of noise and artifacts. Applying SNR-based multichannel fusion solves the problem of missing target structures caused by the chunked weights. With the proposed techniques, we can effectively suppress artifacts and noise while maintaining the target structures to enhance the robustness of system. Based on practical experiments, the proposed techniques achieve the image entropy (IE) value, which reduces by approximately 1, and the image contrast (IC) value is increased by approximately 2~4. Furthermore, the computational time is only about 1.3 times that needed by the latest reported algorithm. Consequently, imaging resolution and system robustness are improved by implementing these techniques. Full article

The transfer-efficient power allocation problem for downlink simultaneous wireless information and power transfer (SWIPT) is investigated in massive multiple-input multiple-output (MIMO) systems in this paper. In the considered system, the base station (BS) equipped with a large number of antennas simultaneously transmits information and sends energy signals to multiple information and energy terminals equipped with a single antenna. The aim is to maximize transfer efficiency while meeting quality-of-service (QoS) requirements for all terminals. First, the closed-form expressions of achievable rates for each information terminal and the harvested energy for each energy terminal are obtained. Then, two optimization problems are formulated according to the obtained expressions, with the purpose of maximizing information transfer efficiency (ITE) and energy transfer efficiency (ETE). The maximizations of ITE and ETE are fractional programming problems and are difficult to solve directly. For this reason, the iterative optimization algorithm is proposed to solve the ITE maximization problem by transforming it into a subtractive form and then utilizing a successive convex approximation (SCA) method. Following a similar approach, another iterative optimization algorithm is proposed to solve the ETE maximization problem by transforming it into a subtractive form and then utilizing a linear programming method. Finally, numerical results demonstrate that the two iterative optimization algorithms can achieve good ITE and ETE, and we also reveal the trade-off between them in this work. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article