International Journal of Communication Systems最新文献

Localization is a critical aspect of vehicular ad hoc networks (VANETs), essential for enhancing their safety and comfort applications. However, challenges arise in achieving accurate and timely localization particularly in dense and highly mobile vehicular networks, urban environments and indoor settings where Global Positioning System (GPS) signals are unavailable and unreliable. Several localization solutions have been proposed in the literature, but they could not achieve simultaneously the desired requirements especially when applied to vehicular networks. In this paper, we propose a novel hybrid solution for localizing nodes in VANETs with high accuracy and low latency. Our approach combines optimized localization techniques and hybridizes them with a robust MAC scheme designed for efficient broadcast of beacon messages that are used for the exchange of localization information. By dynamically adapting localization techniques based on road traffic conditions and localization regions while incorporating a time slot reuse policy and a dynamic beaconing rate adjustment in the MAC scheme, our solution aims to overcome the challenges of computational overhead, communication delays and reliability concerns. It is an economical solution that enables timely and precise localization even in the absence of GPS signals. Simulation results demonstrate the effectiveness of our approach in achieving accurate localization for dense and highly mobile VANETs.

In this paper, the problem of user pairing across subcarriers is investigated for the uplink non-orthogonal multiple-access (NOMA) systems, in which every subcarrier is assigned to exactly two users. Considering fixed transmit power for all users, we pair users such that sum-rate is maximized over all subcarriers. While different (near) optimum allocation schemes exist in the literature, they pertain to perfect channel state information (CSI), which is impractical. To overcome this limitation, we assume statistical CSI is available in the form of the first two moments of channel gains. Then, we maximize a lower bound on the sum rate over user pairings utilizing a block coordinate ascent (BCA) approach with guaranteed convergence to a suboptimal solution. Numerical results corroborate a satisfactory performance for our proposed algorithm versus the perfect CSI benchmark and a simple random assignment approach. For high signal to noise ratios (SNRs) and the more challenging frequency selective fading setup, the solution of our proposed algorithm comes close to those of the optimal pairing with perfect CSI. Furthermore, simulations revealed that if the proposed BCA is utilized assuming perfect CSI, it performs similar to the well-known global optimum in the frequency flat fading setup and outperforms the best existing near-optimal solution in the frequency selective fading scenario. These observations offer a testament to the strength of the proposed BCA algorithm in both perfect and imperfect CSI conditions.

Internet of Things (IoT) and cloud computing are becoming increasingly important in the solution of many industrial problems. Effective management of microgrid (MG) requires a strong and scalable information and communication technology (ICT) infrastructure. IoT devices with effective measurement and control capabilities have the potential to be very important in the MG environment. MG was run in both grid-connected and island mode. This paper proposes to improve the MG hierarchical control with IoT using CMPA-PINN techniques for islanding detection. The proposed hybrid method is the joint execution of both the Coronavirus Mask Protection Algorithm (CMPA) and physics-informed neural networks (PINNs). Hence, it is named as CMPA-PINN approach. The major goal of this proposed method is to reduce the deviation of voltage, frequency, and total harmonic distortion (THD). The proposed CMPA is used to optimize the traffic flow over a communication network, and the PINNs are used to predict the optimized traffic flow. By then, the MATLAB platform has adopted the proposed method, and the current process is used to compute its execution. The proposed technique outperforms all current systems, including maximum power point tracking (MPPT), multi-agent reinforcement learning (MARL), and deep reinforcement learning (DRL). The proposed approach shows the THD is 2%, which is lower than other existing systems.

Channel state information (CSI) plays a critical role for beamforming technology in frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems. In order to improve the beamforming accuracy, we present a novel codebook based CSI reconstruction algorithm to enhance CSI feedback performance, thereby improving communication quality. Based on the existing codebook feedback mechanism, initially, we leverage channel temporal correlation to smooth the precoding matrix using a sliding window approach, where both Type I and Type II codebooks are considered. Subsequently, a minimum mean square error (MMSE) based modification technique is introduced to modify the reconstruction result. The proposed CSI reconstruction method incorporates an adjustable weighting parameter within the sliding window, rendering it adaptable to channels with different user speeds, such as high-speed train communication channel. Moreover, our algorithm entirely operates at the base station (BS). Therefore, there is no additional feedback overhead compared with traditional codebook based CSI feedback methods. Numerical results present the proposed algorithm outperforms baselines for beamforming.

Mobile ad hoc networks (MANETs) integrated with the Internet of Things (IoT) form a decentralized communication framework crucial for 6G environments. However, ensuring secure and efficient routing in such networks remains a challenge due to their distributed nature and vulnerability to attacks. This paper introduces a heterogeneous local directed acyclic graph blockchain (HLDAG-BC) combined with recalling enhanced recurrent neural networks (RERNNs) for secure and efficient routing in MANET-IoT environments. The HLDAG-BC offers tamper-proof communication and identity-based conditional privacy-preserving authentication (ICPA) is a lightweight and secure node authentication scheme. Network nodes are grouped using the kernel neutrosophic c-means (KNCM) algorithm. The optimal cluster heads are chosen using the red piranha optimization (RPO) method. RERNN determines the shortest routing path to increase reliability and minimize latency. Furthermore, an HDLNN is used for intrusion detection to achieve robust network security. The HLDAG-BC-RERNN approach proposed shows that the packet delivery ratio improves by 31.35%, throughput by 34.56%, latency by 30.29%, and network lifetime by 28.67% compared to the existing approaches, as shown in the comprehensive evaluations. In conclusion, the proposed framework offers a scalable and secure solution for MANET-IoT networks, making it a viable approach for future 6G applications.

Recently, ambient backscatter-based communication is considered as an appealing approach, which enables energy-constrained nodes to transmit by backscattering ambient signals, and it has the potential of being integrated with RF-powered NOMA network. In this paper, we analyze the outage performance of a two-node system that works on ambient backscattered signal. One energy harvesting relay, working in time switching relaying (TSR) mode, which assists the transmission of NOMA signal of two users, acts as a backscatter device (BD) to transmit its information to a closely located IoT node by backscattering the signal received from two users. The IoT node transmits its information to the relay by backscattering the signal while the relay transmits downlink NOMA to two destination users. We evaluate the outage performance of backscattered signal at the relay and IoT node. System outage of the NOMA users and throughput of the backscattered transmission are also derived. The effect of backscattering factor on individual and system outage of NOMA users, outage of BD users, and backscattered throughput is indicated. Further, we show the impact of time switching factor on outage and throughput of backscattered network. Ergodic capacity of backscattered network is also evaluated.

The internet of things (IoT) is vulnerable to attacks due to its unique characteristics and numerous limitations, and it is highly prone to attack due to sensitive applications. Most of these attacks are aimed at routing and data transmission (which are the most vital pillars of IoT). So far, many research studies have been proposed to improve the security of routing and data transmission, and most of these methods have been developed based on trust models. Trust models are considered as a powerful and complementary tool for security systems that provide the ability to detect malicious nodes. However, most of these researches to advance their goals are only focused on examining the behavior of nodes during data transmission, and based on this, the trust value of nodes is calculated. This way of assessing trust is not enough due to the widespread attacks of malicious nodes. In this paper, a reliable routing method based on the optimization of IPv6 routing protocol for low power and lossy networks (RPL) routing protocol and trust models is introduced called TARRP (Trust Aware RPL Routing Protocol). TARRP is a two-step method in which the purpose of the first stage is to create a trusty and reliable topology. The second stage is to assess the trust and identify malicious nodes. In addition to implementing trust, TARRP also manages related recommendations and attacks. The results of simulations using cooja in different scenarios showed the superiority of TARRP in improving routing trust and data exchange compared to previous work.

Path loss modeling (PLM) for mmWave communications (mWC) is challenging due to the dynamic propagation environment, making it critical for effective 5G network planning and analysis. In this manuscript, hierarchical auto-associative polynomial convolutional neural network with gorilla troops optimization (GTO) is proposed for path loss modeling in mmWave 5G-IoT mobile communication systems (HA-PCNN-PLM-mWC-5G). The input images depicting buildings and roadways are obtained from Google Maps and enhanced local area multiscanning (E-LAMS) with distance dependencies are used to improve feature learning. Here, the improved bilateral texture filtering (IBTF) is applied to reduce noise in the input images and enhance image quality. Additionally, the spatial and spectral features are then extracted using the fast discrete curvelet transform with wrapping (FDCT-WRP), and these features are fed into the HA-PCNN model for path loss prediction. The model's performance is further improved through parameter optimization using GTO. Here, the implementation of the proposed model is done in Python tool and the performance metrics are analyzed. Thus, the proposed approach attains 6.2%, 2.27%, 4.08%, 11.88%, and 12.32% higher accuracy, 13.07%, 14.41%, 16.61%, 18.03%, and 9.08% lower mean squared error, and 27.55%, 24.05%, 23.48%, 20.05%, and 18.95% lower computation time than the existing approaches like AE-CNN-mmW-PLM-5G, ML-PLP-mWL-5G, CNN-mmW-PLM-FWA, ML-SRM-IoT, and PLP-EE-5G, respectively. Thus, the proposed model improves the accuracy of mmWave communication prediction in 5G-IoT systems, offering a more reliable solution for future 5G network implementations.

This paper presents a novel, wideband, and flexible coplanar waveguide (CPW) antenna design on a low-cost denim substrate. The antenna exhibits conformal properties and achieves robust dual-band performance. The antenna achieves a maximum gain of 6.18 dB at 6.5 GHz and a peak efficiency of 98% at 2.45 GHz in normal conditions. The fabricated prototype demonstrates excellent agreement between simulated and measured results. The simulated impedance bandwidth ranged from (2.20–3 GHz) for the first band and (4.41–7.43 GHz) for the second band. The measured results show that it offers −10 dB impedance bandwidths of 22% (2.2–2.75 GHz) for WBAN applications and 68% (3.63–7.43 GHz) for WLAN and WIFI-6E applications through strategically designed slots in the ground plane (symmetric U-slot and rectangular slot). Furthermore, the antenna's performance remains stable under various bending conditions and exhibits satisfactory behavior close to the human body, making it suitable for wearable applications. This cost-effective design presents a promising solution for integration into various consumer electronics. The proposed antenna design is a low-cost solution for various wearable consumer electronics applications. This antenna is a potential solution for enhanced workforce management, secure access control, and identification.

In the present work, we propose and investigate the performance of an outdoor, free-space optical (FSO) link employing a visible light communication (VLC) system under various weather and atmospheric turbulence conditions. The Kim and Carbonneau models have been applied for calculating fog and rain-induced attenuation, respectively, to predict the performance of the FSO link in specific regions. A bit error rate (BER) of 10⁻¹⁰ has been observed in case of clear, rain, and fog climate conditions at transmission ranges of 980, 950, and 930 m, respectively, under no turbulence conditions. A dataset comprising different performance parameters, including range, attenuation, and laser input power, was used as input features for various machine learning (ML) techniques. The prediction accuracy of artificial neural networks (ANN), random forest (RF), decision trees (DT), k-nearest neighbors (KNN), and gradient boosting regression (GBR) ML algorithms was assessed using the coefficient of determination (R²) and root mean square error (RMSE) as performance indices. The ANN model achieved the best R² value (0.9942), while RF provided the optimal RMSE (2.78). Effectiveness of ML models in accurate prediction of the system performance has been validated, and the resultant system may be employed for performance monitoring of impairments in optical networks.