Search Results (56)

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

In this paper, we investigate a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access (NOMA) communication system where the STAR-RIS is mounted on an unmanned aerial vehicle (UAV) with adjustable altitude. Due to severe blockages in urban environments, direct links from the base station (BS) to users are assumed unavailable, and signal transmission is realized via the STAR-RIS. We formulate a joint optimization problem that maximizes the system sum rate by jointly optimizing the UAV’s altitude, BS beamforming vectors, and the STAR-RIS phase shifts, while considering Rician fading channels with altitude-dependent Rician factors. To tackle the maximum achievable rate problem, we adopt a block-wise optimization framework and employ semidefinite relaxation and gradient descent methods. Simulation results show that the proposed scheme achieves up to 22% improvement in achievable rate and significant reduction in bit error rate (BER) compared to benchmark schemes, demonstrating its effectiveness in integrating STAR-RIS and UAV in NOMA networks. 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

This paper proposes an adaptive pre-distortion method for mitigating LED nonlinear distortion in Visible Light Communication (VLC)-OFDM systems. The inherent nonlinear characteristics of LEDs disrupt the orthogonality among OFDM subcarriers, causing signal distortion and performance degradation. To overcome this issue while minimizing computational complexity at the transmitter, we introduce a feedback-based nonlinear parameter estimation approach using the Least Squares Method (LSM) and Median Based Method (MBM). These estimated parameters are then fed back to the transmitter, enabling efficient adaptive pre-distortion based on the inverse function of the estimated nonlinear characteristics. This approach reduces computational costs at the transmitter compared to conventional methods requiring high-performance processing. Additionally, we incorporate Frequency Symbol Spreading (FSS) to further enhance robustness against channel impairments such as Rician fading by equalizing the Signal-to-Noise Ratio (SNR) across subcarriers. Simulation results under various channel conditions, including AWGN, Rician fading, and realistic multi-LED lighting scenarios, demonstrate a significant improvement in Bit Error Rate (BER) performance, validating both the effectiveness and practical advantages of the proposed approach. Full article

Vehicle-to-vehicle (V2V) channels exhibit highly dynamic and non-stationary characteristics, posing significant challenges in designing reliable communication systems. The level crossing rate (LCR) and average fade duration (AFD) are two important second-order statistical properties that help characterize these rapid channel changes. This paper presents an in-depth analysis of the LCR and AFD for a two-dimensional non-isotropic scattering model of a V2V Rician flat fading channel. The study investigates the influence and impact of several key parameters on LCR and AFD, focusing on the impact of high vehicle traffic density (VTD). The results closely align with available empirical data and offer valuable insights into designing robust V2V communication systems that can adapt to the rapidly evolving channel conditions. Full article

The research and development of unmanned aerial vehicles (UAVs) is progressing rapidly, and they are expected to be used in a wide range of applications. In this paper, we evaluated the propagation characteristics of air-to-ground (A2G) communications used by UAVs. Specifically, we investigated the Rician K-factor, which is one of the indicators representing the impact on communication quality. We carried out radio wave propagation measurements for A2G communications at low altitudes in propagation environments with simple (S environment) and complex (C environment) structures within the measurement area and then performed a detailed evaluation of the effect of the distance from buildings, UAV altitude, and antenna installation on the Rician K-factor and propagation characteristics. The measurement and analytical results reveal that the Rician K-factor in an S environment was observed to be high due to the strong dominance of the direct wave. On the other hand, the Rician K-factor in a C environment decreased because of complex multiple reflected and diffracted waves caused by surrounding buildings. In addition, dummy fading signals generated from the useful path calculated with the ray-tracing method using a simple 3D analytical model showed a high degree of agreement with the experimental results. These outcomes provide key parameters for the optimal design of UAV-based A2G communication systems, contributing to the practical application of UAV operations. Full article

We propose a semi-supervised masked auxiliary classifier generative adversarial network (SM-ACGAN) that has good classification performance in situations where labeled training data are limited. To develop SM-ACGAN, we combine the strengths of SSGAN (semi-supervised GAN), ACGAN-SG (auxiliary classifier GAN based on spectral normalization and gradient penalty), and MaskedGAN. Additionally, we devise a novel masking technique that performs masking adaptively depending on the real/fake ratio of the input data and a novel regularization technique that stabilizes the generator training depending on the maximum ratio of the average power of the generated fake data to the average power of the noise latent variables. Finally, we devise a rule of selecting an appropriate quantity of unlabeled data and labeled fake data generated by the generator for effective data augmentation. Through simulations, we demonstrate that SM-ACGAN has lower root mean square error (RMSE) values and lower variance, demonstrating superior mobile speed measurement performance on Rician channels compared to ACGAN-SG, MaskedGAN, SSGAN, a CNN (convolutional neural network), and a DNN (deep neural network). Full article

In recent years, UAV-enabled wireless energy transfer (WET) has attracted significant attention for its ability to provide ground devices with efficient and stable power by flexibly navigating three-dimensional (3D) space and utilizing favorable line-of-sight (LoS) channels. At the same time, energy beamforming utilizing multiple antennas, in which energy beams are focused toward devices in desirable directions, has been highlighted as a key technology for substantially enhancing radio frequency (RF)-based WET efficiency. Despite its significant utility, energy beamforming has not been studied in the context of UAV-enabled WET system design. In this paper, we propose the joint design of UAV altitude and channel statistics based energy beamforming to minimize the overall charging time required for all energy-harvesting devices (EHDs) to meet their energy demands while reducing the additional resources and costs associated with channel estimation. Unlike previous works, in which only the LoS dominant channel without small-scale fading was considered, we adopt a more general air-to-ground (A2G) Rician fading channel, where the LoS probability as well as the Rician factor is dependent on the UAV altitude. To tackle this highly nonconvex and nonlinear design problem, we first examine the scenario of a single EHD, drawing insights by deriving an optimal energy beamforming solution in closed form. We then devise efficient methods for jointly designing altitude and energy beamforming in scenarios with multiple EHDs. Our numerical results demonstrate that the proposed joint design considerably reduces the overall charging time while significantly lowering the computational complexity compared to conventional methods. Full article

This paper proposes a drone-assisted NOMA communication system equipped with a reconfigurable intelligent surface (RIS). Given the Line-of-Sight nature of the Air-to-Ground link, a more realistic Rician fading environment is chosen for the study of system performance. The user’s outage performance and secrecy outage probability of the RIS-UAV-assisted NOMA downlink communication under the Rician channels are investigated. Jointly considering the Line-of-Sight and Non-Line-of-Sight links, the closed-form expressions of each user’s outage probability are derived by approximating the composite channels as Rician distributions to characterize the channel coefficients of the system’s links. Considering the physical layer security in the presence of the eavesdropper, the secrecy outage probability of two users is further studied. The relationship between the system outage performance and the Rician factor of the channel, the number of RIS elements, and other factors are analyzed. The results of this study show that compared with Rayleigh fading, the Rician fading is more practical with the actual Air-to-Ground links; the user’s outage probability and the secrecy outage probability are lower over the Rician channels. The number of RIS elements and the power allocation factor by the base station for the users are inversely proportional to the user’s outage probability, and RIS element number, path loss index, and distance factor also have a greater impact on the outage probability. Compared with OMA, NOMA has a certain enhancement to the system performance. Full article

The open nature of the wireless channel makes the drone communication vulnerable to adverse spoofing attacks, and the radio frequency fingerprint (RFF) identification is promising in effectively safeguarding the access security for drones. Since drones are constantly flying in the three dimensional aerial space, the unique RFF identification problem emerges in drone communication that the effective extraction and identification of RFF suffer from the time-varying channel effects and unavoidable jitterings due to the constant flight. To tackle this issue, we propose augmenting the training RFF dataset by regenerating the drone channel characteristics and compensate the fractional frequency offset. The proposed method estimates the Rician K value of the channel and curve-fits the statistical distribution, the Rician channels are regenerated using the sinusoidal superposition method. Then, a probabilistic switching channel is also set up to introduce the Rayleigh channel effects into the training dataset. The proposed method effectively addresses the unilateral channel effects in the training dataset and achieves the balanced channel effect distribution. Consequently, the pre-trained model can extract channel-robust RFF features in drone air-ground channels. In addition, by compensating the fractional frequency offset, the proposed method removes the unstable frequency components and retains the stable integer frequency offset. Then, the stable frequency offset features that are robust to environmental changes can be extracted. The proposed method achieves an average classification accuracy of 97% under spatial and temporal varying conditions. Full article

This paper investigates dual-hop Reconfigurable Intelligent Surface (RIS) wireless communication systems with malicious jamming, where the destination node faces jamming from a malicious jammer with a RIS-Equipped Unmanned Aerial Vehicle (UAV) relay. We model the channel gains for Tx-RIS and Jammer-RIS links with a Rician distribution, while the RIS-Rx link follows a Nakagami-m distribution, and the jamming status is modeled as a Bernoulli-distributed random variable. We derived and provided closed-form expressions for the probability density functions (PDFs) of the legitimate channel and jamming channel in RIS-Equipped UAV wireless communication systems. Additionally, a new closed-form expression for the PDF of the received signal-to-jamming ratio (SJR) is derived. Using the Gauss–Laguerre Approximation method, we calculate the Average Bit Error Rate (ABER) under Binary Phase Shift Keying (BPSK) and Quadrature Amplitude Modulation (QAM) schemes. We analyze the effects of jamming probability, the location of the legitimate RIS, and different channel conditions on ABER performance through theoretical analyses and simulation results. Our theoretical analyses and simulation results indicate that an increase in the probability of malicious jamming significantly raises the ABER. For example, under favorable channel conditions, the ABER for BPSK modulation was observed to be as low as ${10}^{-5}$, whereas under poor channel conditions, the ABER increased to ${10}^{-2}$. Additionally, by reducing the distance between the transmitter and the RIS, the ABER can be improved. The legitimate RIS performs better when closer to the transmitter. These findings highlight the critical impact of channel conditions and the deployment of the RIS on the overall system’s performance. Our results offer valuable insights into designing and evaluating the performance of RIS-Equipped UAV wireless communication systems in the presence of malicious jamming, aiding in the development of countermeasures to enhance system resilience and security. The derived expressions are validated through Monte Carlo simulations. Full article

As the Internet of Things (IoT) continues to expand, wireless communication is increasingly widespread across diverse industries and remote devices. This includes domains such as Operational Technology in the Smart Grid. Notably, there is a surge in resource-constrained devices leveraging wireless communication, especially with the advances of 5G/6G technology. Nevertheless, the transmission of wireless communications demands substantial power and computational resources, presenting a significant challenge to these devices and their operations. In this work, we propose the use of deep learning to improve the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) wireless receivers. By improving the BER performance of these receivers, devices can transmit with less power, thereby improving IoT devices’ battery life. The architecture presented in this paper utilizes a depthwise Convolutional Neural Network (CNN) for channel estimation and demodulation, whereas a Graph Neural Network (GNN) is utilized for Low-Density Parity Check (LDPC) decoding, tested against a proposed (1998, 1512) LDPC code. Our results show higher performance than traditional receivers in both isolated tests for the CNN and GNN, and a combined end-to-end test with lower computational complexity than other proposed deep learning models. For BER improvement, our proposed approach showed a 1 dB improvement for eliminating BER in QPSK models. Additionally, it improved 16-QAM Rician BER by five decades, 16-QAM LOS model BER by four decades, 64-QAM Rician BER by 2.5 decades, and 64-QAM LOS model BER by three decades. Full article

Recently, non-orthogonal multiple access (NOMA)-based space–air–ground integrated networks (SAGINs) have gained increasing attention due to their robust communication, broader coverage, and resource-saving advantages. However, it is imperative to consider physical layer security as a crucial performance metric in NOMA-based SAGINs. This study addresses this concern by constructing a NOMA-based free space optical (FSO)/radio frequency (RF) dual-hop SAGIN system with eavesdroppers on both links. The two new fading channel models were proposed, considering the FSO link’s fog absorption and the RF link’s stochastic distribution based on Málaga and shadowed Rician distributions. The closed-form expressions for the secrecy outage probability are derived for the SAGIN system. Monte Carlo simulations were conducted to validate the theoretical findings. The results revealed the influence of fog absorption and the stochastic geometry distribution on the SAGIN system. Full article

Active reconfigurable intelligent surface (ARIS) has sparked more attention due to its capability to overcome the impact of double fading. This paper introduces using an ARIS to aid non-orthogonal multiple access (NOMA) communications over cascade Rician fading channels, where the direct links between the base station and users are seriously blocked. By applying ARIS, it can amplify the superposed signals to overcome the double pass loss effect caused by passive RIS. Both ARIS and NOMA can have synergistic impacts on sixth-generation communication systems. New approximated and asymptotic expressions in terms of outage probability and ergodic data rate of the k-th user are deduced for ARIS-NOMA systems. Based on asymptotic analytical results, we further calculate the diversity order and high signal-to-noise ratio slope of the k-th user. Finally, the system throughput of ARIS-NOMA is discussed in the delay-constrained transmission mode. Monte Carlo numerical results are performed to verify that: (1) the outage behaviors of ARIS-NOMA are better than that of ARIS-assisted orthogonal multiple access (OMA); (2) as the impact of thermal noise caused by ARIS becomes larger, the communication performance from the base station to ARIS, then to users, becomes worse; (3) the ARIS-NOMA systems have the ability to provide the improved ergodic data rate relative to ARIS-OMA. Full article

This paper investigates the reconfigurable intelligent surface (RIS)-aided uplink multicell massive multiple-input multiple-output (mMIMO) communication system with transceiver hardware impairments (THWIs), and the practical and feasible two-timescale scheme is used to design the phase shifts of the RIS. We consider the Rician channel model and use maximal-ratio combining (MRC) technology to process the received signal at the base stations (BS). The expression of the uplink achievable rate is derived and analyzed. Moreover, the genetic algorithm (GA) is used to optimize the phase shifts of the RIS to maximize the data rate. Finally, the accuracy of the derived results is verified. The simulation results show that appropriately increasing the number of RIS reflecting elements can compensate for the performance loss caused by inter-cell interference and THWIs and reduce the demand for BS antennas, which can significantly reduce the hardware costs at the BS. Full article