Search Results (164)

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

The covariance information at the transmitter side is often subject to mismatches due to various impairments. This paper considers a training design problem for multiple-input multiple-output (MIMO) systems when both channel and interference covariance matrices are imperfect at the transmitter side. We first derive the structure of the optimal training signal, minimizing the worst-case mean square error (MSE). With the training structure, the original problem becomes a simple power allocation problem. We propose a numerical optimal power allocation scheme and a closed-form suboptimal power allocation scheme. Simulation results show that the proposed schemes considerably outperform the conventional schemes in terms of the worst-case MSE and bit error rate (BER) performances, and the proposed closed-form training scheme has comparable performance to that of the optimal one. For example, the proposed schemes yield more than 2.5 dB signal-to-interference ratio (SIR) gains at a BER of ${10}^{-4}$. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

Higher-dimensional communications in optical fiber enables new possibilities, including increased transmission capacity and hyper-entangled state transfer. However, mode coupling between channels during transmission causes interference between channels and limits detection. In classical optical communications, MIMO (modes in modes out) is a means to deal with this issue; however, it is not possible to utilize this technology in quantum communications due to power limitations. Principal mode transmission is another means to deal with mode coupling and signal interference between channels. Conceptually, this can be used in quantum communications with some limitations. In this study, we numerically simulated this process using the time delay method and show how it can be implemented using two and four higher-dimensional quantum states, such as W or GHZ states. These numerical simulations are very illustrative of how the implementation proceeds. Full article

The unprecedented expansion of wireless networks has resulted in spectrum sharing between numerous connected devices, demanding advanced interference management and higher energy consumption, which exacerbates the carbon footprint. Near-field communication emerges as a promising solution to these challenges as it enables precise signal focusing which reduces power consumption by providing higher spatial multiplexing gains. This review explores how near-field (NF) multiple-input multiple-output (MIMO) beamforming can enhance energy efficiency by optimizing beamfocusing and minimizing unnecessary energy expenditure. We discuss the latest advancements in near-field beamforming, emphasizing energy-efficient strategies and sustainable practices. Recognizing which practical channel models are better suited for near-field communication, we delve into the integration of Electromagnetic Information Theory (EIT) as a joint model for realistic applications. We also discuss the channel models for near-field beamforming, incorporating EIT to provide a comprehensive overview of current methodologies. We further analyze the strengths and limitations of existing channel models and discuss the state-of-the-art models which address existing gaps. We also explore opportunities for the practical deployment of energy-efficient near-field beamforming systems. By summarizing future research directions, this review aims to advance the understanding and application of sustainable energy practices in near-field communication technologies. Full article

Continuous and robust trajectory tracking of unmanned aerial vehicles (UAVs) plays a crucial role in urban air transportation systems. Accordingly, this article presents an end-to-end spatiotemporal correlation integration (ESCI)-based UAV tracking framework by leveraging a high-resolution cascade multiple input multiple output (MIMO) radar. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this paper; the proposed method handles them jointly by integrating a continuous detection process into tracking. It not only eliminates the threshold decision-making process to avoid the loss of weak target information, but also significantly reduces the interference from other co-channel radars and strong clutters by exploring the spatiotemporal correlations within a sequence of radar frames, thereby improving the detectability of weak targets. In addition, to accommodate the time-varying number and extended size of radar reflections, with the ellipse spatial probability distribution model, the extended UAV with multiple scattering sources can be treated as an entity to track, and the complex measurement-to-object association procedure can be avoided. Finally, with Texas Instruments AWR2243 (TI AWR2243) we can utilize a cascade frequency-modulated continuous wave–multiple input multiple output (FMCW-MIMO) radar platform. The results show that the proposed method can obtain outstanding anti-interference performance for extended UAV tracking compared with state-of-the-art methods. Full article

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system. Full article

In this paper, the uplink spectral efficiency performance of a massive MIMO system based on full-duplex relay communication is investigated in Rician fading channels. The relay station is equipped with a large number of antennas, while multiple source and destination nodes are located at both ends of the transceiver. Each source and destination node is equipped with a single antenna. The relay station adopts Maximum Ratio Combining/Maximum Ratio Transmission (MRC/MRT) and Equal Gain Combining/Equal Gain Transmission (EGC/EGT) schemes to perform linear preprocessing on the received signals. Approximate expressions for uplink spectral efficiency under both MRC/MRT and EGC/EGT schemes are derived, and the effects of antenna number, signal-to-noise ratio (SNR), and loop interference on spectral efficiency are analyzed. In addition, the impact of full-duplex and half-duplex modes on system performance is compared, and a hybrid relay scheme is proposed to maximize the total spectral efficiency by dynamically switching between full-duplex and half-duplex modes based on varying levels of loop interference. Finally, a novel power allocation scheme is proposed to maximize energy efficiency under given total spectral efficiency and peak power constraints at both the relay and source nodes. The results show that the impact of loop interference can be eliminated by using a massive receive antenna array, leading to the disappearance of inter-pair interference and noise. Under these conditions, the spectral efficiency of the system can be improved up to $2N$ times, while the transmission power of the user and relay nodes can be reduced to $1/N_{\mathrm{rx}}$ and $1/N_{\mathrm{tx}}$, respectively. 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

In the advancement of wireless communication, multiple-input, multiple-output (MIMO) detection has emerged as a promising technique to meet the high throughput requirements of 6G networks. Traditionally, MIMO detection relies on conventional algorithms, such as zero forcing and minimum mean square error, to mitigate interference and enhance the desired signal. Mathematically, these algorithms operate as linear transformations or functions of received signals. To further enhance MIMO detection performance, researchers have explored the use of nonlinear transformations and functions by leveraging deep learning structures and models. In this paper, we propose a novel model that integrates the Viterbi algorithm with a graph neural network (GNN) to improve signal detection in MIMO systems. Our approach begins by detecting the received signal using the VA, whose output serves as the initial input for the GNN model. Within the GNN framework, the initial signal and the received signal are represented as nodes, while the MIMO channel structure defines the edges. Through an iterative message-passing mechanism, the GNN progressively refines the initial signal, enhancing its accuracy to better approximate the originally transmitted signal. Experimental results demonstrate that the proposed model outperforms conventional and existing approaches, leading to superior detection performance. Full article

Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) multicast communication in XL-MIMO cellular networks. The “many-to-many” sharing model permits one subcarrier to be shared among multiple D2D groups (DGs) and each DG to reuse multiple subcarriers. The objective is to maximize the total multicast data rate of DGs while meeting the data rate requirements of cellular users. This optimization problem is formulated as a 0–1 mixed-integer nonlinear programming problem, with the challenge lying in the fact that adjusting the subcarriers and the power of the user equipment alters the network’s carrier occupation and interference relationships, thereby increasing computational complexity. To address this challenge, a phased strategy is proposed. Initially, subcarrier allocation and coarse power allocation are conducted for cellular users. Subsequently, an adversarial multi-player multi-armed bandit framework is employed, treating DGs as players and subcarrier and power combinations as arms, to maximize the total multicast data rate. An improved Exp3 algorithm is utilized for selecting the optimal combination of arms. Finally, precise power allocation for cellular users is conducted based on the allocation results of the DGs. A comparative analysis of various simulations confirms the superiority of our algorithm over the established heuristic subcarrier assignment and proposed power allocation (HSAPP) and the channel allocation scheme using full information of device locations (CAFIL) approaches. Full article

The novel framework of an integrated aeronautical communication and radar system (IACRS) to realize spectrum sharing is investigated. A non-orthogonal multiple access (NOMA)-motivated multi-input–multi-output (MIMO) scheme is proposed for the dual-function system, which is able to detect multiple aircraft while simultaneously transmitting dedicated messages. Specifically, NOMA-inspired technology is utilized to enable dual-spectrum sharing. The superposition of communication and radar signals is facilitated in the power domain. Successive interference cancellation (SIC) is employed at the receiver to effectively mitigate inter-function interference. Subsequently, the regularity of the three-dimensional flight track and attitude is exploited to model the air-to-ground (A2G) MIMO channel. Based on this framework, a joint optimization problem is formulated to maximize the weighted achievable sum rate and the sensing signal–clutter–noise ratio (SCNR) while satisfying the rate requirements for message transmission and ensuring the radar detection threshold. An alternative optimization (AO) algorithm is proposed to solve the non-convex problem with highly coupled variables. The original problem is decoupled into two manageable subproblems: transmit beamforming of the ground base station combined with power allocation and receiver beamforming at the aircraft. The penalty-based approach and the successive rank-one constraint relaxation (SROCR) method are developed for iteratively handling the non-convex rank-one constraints in subproblems. Numerical simulations demonstrate that the proposed IACRS framework significantly outperforms benchmark schemes. Full article

In this paper, the detection-aided ordering schemes are proposed for linear minimum mean-squared-error (LMMSE) iterative soft interference cancellation (ISIC) in multiple-input multiple-output (MIMO) systems. Unlike the conventional LMMSE-ISIC ordering schemes that utilize the channel state information (CSI) only, the proposed ordering schemes utilize the receive signal vector and CSI for the ordering procedure. Then, for each candidate symbol, the sum of the likelihoods except the largest likelihood is calculated to estimate the reliability of the candidate symbol, where the likelihoods are calculated by the LMMSE or LMMSE-ISIC detection-aided ordering procedure. Thus, the proposed ordering schemes can provide a significantly more accurate ordering result than the conventional ordering schemes. As the detection-aided ordering schemes, non-iterative and iterative ordering schemes are proposed, and the constrained iterative ordering scheme is also proposed to resolve the high computational complexity of the original iterative ordering scheme. Numerical simulation results verify that the proposed detection-aided ordering schemes outperform the conventional ordering schemes in terms of convergence speed and error performance. Full article

Multi-input Multi-output Synthetic Aperture Radar (MIMO-SAR) systems significantly improve the performance of traditional SAR systems by providing more system freedom. However, in the working mode of the simultaneous transceiver, each receiving antenna will receive the scattered echoes of all transmitting antennas, resulting in the overlapping of echo data and serious related interference, which becomes the main obstacle to the further development and application of MIMO-SAR system. Therefore, achieving effective echo separation is the key technical challenge faced by the MIMO-SAR system. Space–time coding (STC) uses multiple dimensions, such as space, time, and frequency. Through the process of encoding and decoding in these dimensions, channel information can be obtained, and echo separation can be realized. STC is suitable for MIMO-SAR system on different platforms, such as airborne, and has wide applicability. When the traditional scheme uses STC as a coding scheme, it is generally limited by the two-dimensional sending and receiving matrix of Alamouti code. To solve this problem, a new STC scheme based on complex orthogonal matrix design is proposed in this paper. The scheme can form a multidimensional orthogonal STC matrix, recover the superposed signal by echo decoding, and improve the echo signal-to-noise ratio (SNR) of MIMO-SAR. In addition, the use of orthogonal frequency division multiplexing (OFDM) waveform can further reduce cross-correlation interference to achieve effective separation of MIMO-SAR echoes. The effectiveness of the waveform scheme is verified by numerical experiments. Full article

This work introduces a high-performance multi-input multi-output (MIMO) antenna design to operate at the 28 GHz band. The proposed four-port MIMO antenna, in which each port comprises a 1 × 8 series-fed array, achieves peak gains of 13 dBi along with bandwidths of 1 GHz. Enhanced antenna performance is achieved through the optimal spacing of antenna elements and a decoupling methodology comprising a well-designed metamaterial unit cell, leading to reduced interference between antenna arrays. The design shows significantly suppressed mutual coupling to be less than −40 dB, a diversity gain that is very close to 10 dB, an envelope correlation coefficient of 0.00012, and a channel capacity loss of 0.147 bit/s/Hz, at 28 GHz. The experimental assessments confirmed these developments, endorsing the suggested design as a robust contender for 5G mmWave communications. Full article