Search Results (303)

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

With the advancement of mobile communications, technologies based on high-element-count antenna systems—such as massive Multiple Input Multiple Output (massive MIMO)—are playing an increasingly important role in enhancing network capacity. However, they introduce new challenges in the measurement and evaluation of electromagnetic field (EMF) exposure. This study presents a detailed, laboratory-based methodology for assessing EMF exposure in cellular systems using Single Input Single Output (SISO) and MIMO technologies. To address the limitations of traditional exposure assessment techniques—particularly under the conditions introduced by 5G and active antenna systems—a shielded test environment with directional antennas was developed and applied across lowband and midband frequency ranges (700–2100 MHz). Downlink electromagnetic power density was measured under standardized modulation, coding, and bandwidth settings for both SISO and MIMO configurations. The results show that MIMO technology does not lead to a significant increase in EMF exposure compared to SISO, with average differences remaining below 1 dB. Moreover, in lower-frequency bands, massive MIMO systems can ensure the required user capacity at significantly lower transmission power, resulting in more than 15 dB reductions in EMF exposure. These findings confirm the potential of massive MIMO to enhance network performance while reducing the level of electromagnetic exposure. Full article

Precoding technology is one of the core technologies that significantly impacts the performance of massive Multiple-Input Multiple-Output (MIMO) Integrated Sensing and Communication (ISAC) systems. Traditional precoding methods, due to their inherent limitations, struggle to adapt to complex channel conditions. Although more advanced neural network-based precoding schemes can accommodate complex channel environments, they suffer from high computational complexity. To address these issues, this paper proposes a KAN-and-Attention based ISAC Precoding (KAIP) scheme for massive MIMO ISAC systems. KAIP extracts channel interference features through multi-layer attention mechanisms and leverages the nonlinear fitting capability of the Kolmogorov–Arnold Network (KAN) to generate precoding matrices, significantly enhancing system performance. Simulation results demonstrate that compared with conventional precoding schemes, the proposed KAIP scheme exhibits significant performance enhancements, including a 70% increase in sum rate (SR) and a 96% decrease in computing time (CT) compared with fully connected neural network (FCNN) based precoding, and a 4% improvement in received power (RP) over the precoding based on convolutional neural network (CNN). Full article

Integrated Sensing And Communication (ISAC) has been applied to the Internet of Things (IoT) network as a promising 6G technology due to its ability to enhance spectrum utilization and reduce resource consumption, making it ideal for high-precision sensing applications. However, while the introduction of millimeter Wave (mmWave) and massive Multiple-Input Multiple-Output (MIMO) technologies can enhance the performance of ISAC systems, they extend the near-field region, rendering traditional channel parameter estimation algorithms ineffective due to the spherical wavefront channel model. Aiming to address the challenge, we propose a tensor-based channel parameter estimation and localization algorithm for the near-field mmWave massive MIMO-Orthogonal Frequency Division Multiplexing (OFDM) ISAC systems. Firstly, the received signal at the User Terminal (UT) is constructed as a third-order tensor to retain the multi-dimensional features of the data. Then, the proposed tensor-based algorithm achieves the channel parameter estimation and target localization by exploiting the second-order Taylor expansion and intrinsic structure of tensor factor matrices. Furthermore, the Cramér–Rao Bounds (CRBs) of channel parameters and position are derived to establish the lower bound of errors. Simulation results show that the proposed tensor-based algorithm is superior compared to the existing algorithms in terms of channel parameter estimation and localization accuracy in ISAC systems for IoT network, achieving errors that approach the CRBs. Specifically, the proposed algorithm attains a 79.8% improvement in UT positioning accuracy compared to suboptimal methods at SNR = 5 dB. Full article

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

Cell-free massive multiple-input multiple-output (CF M-MIMO) is one of the most promising technologies for future wireless communication such as 5G and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. It has the ability to provide high spectral efficiency (SE) as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE) estimator. This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this paper proposed an efficient phase-aware element-wise minimum mean square error (PA-EW-MMSE) channel estimator with QR decomposition and a precoding matrix at the user side. The closed form uplink (UL) SE with the phase MMSE and proposed estimators are evaluated using MMSE combining. The energy efficiency and area throughput are also calculated from the SE. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation. Full article

Terahertz (THz) communication is regarded as a promising technology for future 6G networks because of its advances in providing a bandwidth that is orders of magnitude wider than current wireless networks. However, the large bandwidth and the large number of antennas in THz massive multiple-input multiple-output (MIMO) systems induce a pronounced beam split effect, leading to a serious array gain loss. To mitigate the beam split effect, this paper considers a delay-phase precoding (DPP) architecture in which a true-time-delay (TTD) network is introduced between radio-frequency (RF) chains and phase shifters (PSs) in the standard hybrid precoding architecture. Then, we propose a fast Riemannian conjugate gradient optimization-based alternating minimization (FRCG-AltMin) algorithm to jointly optimize the digital precoding, analog precoding, and delay matrix, aiming to maximize the spectral efficiency. Different from the existing method, which solves an approximated version of the analog precoding design problem, we adopt an FRCG method to deal with the original problem directly. Simulation results demonstrate that our proposed algorithm can improve the spectral efficiency, and achieve superior performance over the existing algorithm for wideband THz massive MIMO systems with angular spread. Full article

Swarm-based unmanned aerial vehicle (UAV) systems offer enhanced spatial coverage, collaborative intelligence, and mission scalability for various applications, including environmental monitoring and emergency response. However, their onboard processing is limited by stringent size, weight, and power constraints, posing challenges for real-time computation and autonomous control. This paper presents an integrated communication and computation framework that combines cloud–edge–end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) to enable scalable and efficient task offloading in UAV swarms. Furthermore, we implement a prototype system testbed with nine UAVs and validate the proposed framework through real-time object detection tasks. Results demonstrate over $30\%$ reduction in onboard computation and significant improvements in communication reliability, highlighting the framework’s potential for enabling intelligent, cooperative aerial systems. Full article

With the advancement of sixth-generation (6G) wireless communication technology, new demands have been placed on maritime communications. In maritime environments, factors such as evaporation ducts and sea waves significantly impact signal transmission. Moreover, in multi-user communication scenarios, interactions between different users introduce additional complexities. This paper proposes a novel channel model for maritime unmanned aerial vehicle (UAV) to multi-unmanned surface vehicle (USV) communications, which incorporates massive multiple-input–multiple-output (MIMO) antennas at both the transmitter (Tx) and receiver (Rx), while also accounting for the effects of evaporation ducts and sea waves on the channel. For the USV-single-user maritime model, the temporal auto-correlation function (ACF) and spatial cross-correlation function (CCF) are analyzed. For the UAV-to-multi-user channel model, key channel characteristics such as channel matrix collinearity (CMC) and channel capacity are examined. Finally, the accuracy and effectiveness of the proposed model are validated through a comparison between the measured and simulated data under a single-link environment. Meanwhile, a comparison between the CMC obtained from the proposed model and that derived from Ray-Tracing further verifies the model’s accuracy in multi-link environments. This model provides essential theoretical guidance for future 6G maritime communication systems. Full article

Massive multiple input–multiple output (M-MIMO) is a promising and pivotal technology in contemporary wireless communication systems that can effectively enhance link reliability and data throughput, especially in uplink scenarios. Even so, the receiving end requires more computational complexity to reconstitute the signal. This problem has emerged in fourth-generation (4G) MIMO system; with the dramatic increase in demand for devices and data in beyond-5G (B5G) systems, this issue will become yet more obvious. To take into account both complexity and signal-revested capability at the receiver, this study uses the matrix iteration method to avoid the staggering amount of operations produced by the inverse matrix. Then, we propose a highly efficient multi-user detector (MUD) named hybrid SOR-based Chebyshev acceleration (CHSOR) for the uplink of M-MIMO orthogonal frequency-division multiplexing (OFDM) and universal filtered multi-carrier (UFMC) waveforms, which can be promoted to B5G developments. The proposed CHSOR scheme includes two stages: the first consists of successive over-relaxation (SOR) and modified successive over-relaxation (MSOR), combining the advantages of low complexity of both and generating a better initial transmission symbol, iteration matrix, and parameters for the next stage; sequentially, the second stage adopts the low-cost iterative Chebyshev acceleration method for performance refinement to obtain a lower bit error rate (BER). Under constrained evaluation settings, Section (Simulation Results and Discussion) presents the results of simulations performed in MATLAB version R2022a. Results show that the proposed detector can achieve a 91.624% improvement in BER performance compared with Chebyshev successive over-relaxation (CSOR). This is very near to the performance of the minimum mean square error (MMSE) detector and is achieved in only a few iterations. In summary, our proposed CHSOR scheme demonstrates fast convergence compared to previous works and as such possesses excellent BER and complexity performance, making it a competitive solution for uplink M-MIMO B5G systems. Full article

Cell-free massive multiple-input multiple-output (mMIMO) technology utilizes collaborative signal processing to significantly improve system performance. In cell-free mMIMO systems, accurate channel state information (CSI) is a key element in improving the overall system performance. The existing statistical CSI acquisition methods for large-scale fading (LSF) processing schemes assume that each access points (APs) provides service to all user equipments (UEs) in the system. However, as the number of UEs or APs increases, the computational complexity of statistical CSI estimation tends to infinity, which is not scalable in large-scale networks. To address this limitation, this paper proposes a scalable statistical CSI estimation method under the user-centric cell-free mMIMO system, which blindly estimates the partial statistical CSI required for LSF schemes using uplink (UL) data signals. Additionally, the estimated partial statistical CSI can also be used for downlink (DL) LSF precoding (LSFP) or power control in fully distributed precoding. Simulation results show that under the LSFP scheme, the proposed method can achieve comparable spectral efficiency (SE) with the traditional CSI acquisition scheme while ensuring scalability. When applied to power control in fully distributed precoding, it significantly reduces the fronthaul link CSI overhead while maintaining a nearly similar SE performance compared to existing solutions. Full article

Reconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work introduces an RIS-assisted millimeter wave (mmWave) M-MIMO system to harvest the advantages of RIS and mmWave M-MIMO systems that are required for beyond fifth-generation (B5G) systems. The performance of the proposed system is evaluated under 3GPP TR 38.901 V16.1.0 5G channel models. Specifically, we considered indoor hotspot (InH)—indoor office and urban microcellular (UMi)—street canyon channel environments for 28 GHz and 73 GHz mmWave frequencies. Using the SimRIS channel simulator, the channel matrices were generated for the required number of realizations. Monte Carlo simulations were executed extensively to evaluate the proposed system’s average bit error rate (ABER) and sum rate performances, and it was observed that increasing the number of transmit antennas from 4 to 64 resulted in a better performance gain of ∼10 dB for both InH—indoor office and UMi—street canyon channel environments. The improvement of the number of RIS elements from 64 to 1024 resulted in ∼7 dB performance gain. It was also observed that ABER performance at 28 GHz was better compared to 73 GHz by at least ∼5 dB for the considered channels. The impact of finite resolution RIS on the considered 5G channel models was also evaluated. ABER performance degraded for 2-bit finite resolution RIS compared to ideal infinite resolution RIS by ∼6 dB. Full article

Cell-free massive multiple-input multiple-output (MIMO) networks eliminate cell boundaries and enhance uniform quality of service by enabling cooperative transmission among access points (APs). In conventional cellular networks, user equipment located at the cell edge experiences severe interference and unbalanced resource allocation. However, in cell-free massive MIMO networks, multiple access points cooperatively serve user equipment (UEs), effectively mitigating these issues. Beamforming and cooperative transmission among APs are essential in massive MIMO environments, making efficient power allocation a critical factor in determining overall network performance. In particular, considering power allocation from the central processing unit (CPU) to the APs enables optimal power utilization across the entire network. Traditional power allocation methods such as equal power allocation and max–min power allocation fail to fully exploit the cooperative characteristics of APs, leading to suboptimal network performance. To address this limitation, in this study we propose a convolutional neural network (CNN)-based power allocation model that optimizes both CPU-to-AP power allocation and AP-to-UE power distribution. The proposed model learns the optimal power allocation strategy by utilizing the channel state information, AP-UE distance, interference levels, and signal-to-interference-plus-noise ratio as input features. Simulation results demonstrate that the proposed CNN-based power allocation method significantly improves spectral efficiency compared to conventional power allocation techniques while also enhancing energy efficiency. This confirms that deep learning-based power allocation can effectively enhance network performance in cell-free massive MIMO environments. Full article