International Journal of Communication Systems最新文献

In ad hoc millimeter wave (mmWave) wireless networks, nodes typically use directional antennas to cope with their high path loss problem. Directional neighbor discovery is a crucial technology in the first step of establishing the mmWave communication network. However, directional antennas introduce new challenges to the neighbor discovery: beam alignment and heterogeneous operating mode problems. Meanwhile, the continuous neighbor discovery process leads to significant energy consumption. To solve the above challenges, this paper introduces a directional neighbor discovery algorithm QDND with an adjustable duty cycle based on the Grid Quorum system. Firstly, we design a duty cycle adaptive control algorithm to avoid continuous neighbor discovery processes. Secondly, we propose a sector scanning algorithm to guarantee the beam alignment. Finally, we design an operating mode scheduling algorithm to enable two neighbors to work in different operating modes simultaneously. We conduct extensive simulations under different network scenarios to validate the performance of the QDND. The numerical analysis and simulation results show that QDND outperforms existing directional neighbor discovery algorithms in terms of ATTD and MTTD in different numbers of nodes, beamwidths, and duty cycles.

In this paper, a dual-hop cooperative nonorthogonal multiple access (NOMA) scheme is investigated in which two independent sources communicate simultaneously with their respective destinations over Rayleigh fading channel sharing a common time switching relay (TSR)-based energy harvesting decode and forward (DF) relay in an underlay cognitive radio network (CRN). A TSR protocol is used at the relay for harvesting energy from the signals of the two sources. After successful decoding the symbols of the sources using successive interference cancellation (SIC), the relay transmits a superposition coded (SC) composite signal to the destinations following downlink NOMA where SIC is performed at the destinations. Novel analytical expressions on outage probability of each user pair, system outage probability, and ergodic capacity of each user pair are derived. The transmit power allocation of each source satisfying an outage constraint of PU is obtained by solving a transcendental equation. A statistical distribution of the power harvested at the relay from two independent sources is also presented. Impact of tolerable interference threshold at the PU receiver, constraint on PU outage is indicated on outage and ergodic capacity of users. Monte Carlo simulation is performed using a testbed developed in MATLAB, and simulation results agree well with analytical results.

Although traditional BP neural networks have shown some improvement in ultrasonic indoor localization accuracy, it has a tendency to fall into the problem of local optimal solutions, which limits the localization accuracy. To address this issue, we propose the use of the artificial rabbit optimization (ARO) algorithm as an optimization strategy. The ARO algorithm dynamically adjusts and searches for weights and thresholds during the initialization and training of BP neural networks to find the global optimal solution. This approach efficiently explores the weight space and enhances the BP neural network's performance in ultrasonic localization tasks. Experiments have confirmed that the hybrid ARO-BP localization algorithm performs well in matching predicted trajectories with actual positions, especially in a 3D localization scenario constructed by six base stations. The algorithm produces excellent results in both line-of-sight (LOS) and non–line-of-sight (NLOS) environments, which are typical indoor settings. The ARO-BP neural network effectively reduces the average localization error and ensures high-precision localization under various transmission conditions and obstacle effects. In NLOS conditions, the positioning accuracy is improved by 16.05% with four tags and 10.92% with six tags, resulting in an average error reduction of 8.02 cm. The ARO-BP algorithm enhances positioning accuracy by 13.99% with four tags and 21.76% with six tags, resulting in an average error reduction of 12.01 cm. In conclusion, ARO-BP significantly improves the accuracy of ultrasonic localization in both LOS and NLOS indoor environments with reflections and diffractions. This advancement provides a new direction for the development of indoor positioning technology and is expected to lead to significant progress in practical applications within related fields.

An intuitive approach for investigating densely packed 12-port multi-input-multi-output (MIMO) antenna is investigated with improved isolation performance. Conventional dipole antenna elements are sectorially triangulated in a (3:3:3:3) manner that are sorted into cross-shaped profile corners asserting a premium footprint. This unique topology has been acknowledged to achieve effective impedance matching and simultaneously reduce mutual coupling in the proposed cross-coupled estate avoiding any additive decoupling network. The investigation shows that dipole arrays are coupled with a lumped L-C-L arrangement, whose dominant inductive resonance are stabilized by the center-fed capacitance. The arrangement has the inherent advantage of stable impedance bandwidth (6.5–7.5) GHz and effective decoupling between radiating elements with isolation magnitude ranges (24–30) dB. The simulated performance counterparts are meticulously experimented on a fabricated prototype to observe the potential scattering, isolation, and radiation parameters. The far-field envelope correlation coefficient between antenna elements shows minimal magnitude < 0.02, exhibiting its potential for diversity parameters. The radiation peak gain shows 7.5 dBi with stable omni-directive patterns in the principal operating planes with maximum radiative efficiency (60–70)%, finding its applicability for mid-band advanced (5G) applications.

With the rapid growth of the Internet of Things (IoT), securing interconnected devices is becoming increasingly critical. This paper introduces the LightShield intrusion detection system (IDS) to enhance intrusion detection in IoT environments using high-performance computing. LightShield features preprocessing of IoT data, ReliefF algorithm for feature selection, and a novel detection model based on LightGBM, a gradient boosting framework. The system leverages GPU acceleration for faster model validation, enabling real-time monitoring. By adapting to IoT characteristics, LightShield provides flexible, scalable defense against evolving cyber threats. Results show its potential to improve security in IoT ecosystems, offering valuable insights into anomaly-based intrusion detection and the future of secure IoT networks. The binary classification model displayed exceptional precision with a 99.82% accuracy in detecting potential attacks, and the multiclass classification model achieved a commendable 97.25% accuracy in classifying distinct attack types.

Feedback of channel state information (CSI) from users to base station (BS) is very vital for efficient user scheduling (US) in frequency division duplexing (FDD)–based massive multiple-input multiple-output (mMIMO) systems. The feedback overhead is one of the major bottlenecks for multi-user (MU) mMIMO systems as it increases with number of antennas and users. In the multicellular scenario, each and every BS schedules appropriate set of users based on the limited CSI feedback received from the users for achieving better throughput and fairness which are the prime objectives for such communication systems. To achieve the higher throughput, better fairness, and better scheduling gain with the least CSI, in this article, user grouping–based scheduling (UGS) strategy for multi-cell MU mMIMO FDD system is considered. The proposed UGS scheme achieves a better trade-off between the two prime objectives, that is, throughput and fairness. Moreover, hybrid precoding architecture is also used for this system as practically radio frequency (RF) chains are usually less in number to support less implementation complexity and budget. Our approach utilizes the angle of departure (AoD)–based adaptive codebook for controlling the amount of feedback. The variation of AoD is slow in nature as compared to path gains of the channel, which controls the feedback overhead. The proposed UGS using AoD codebook with limited channel feedback involves determining the beamspace path co-ordinates of each user from the AoD information fed back by users and then group the users into sets based on the AoD CSI. Specifically, UGS scheme is applied to reduce inter-user and inter-group interferences. By integrating an AoD adaptive subspace codebook with a hybrid precoding architecture and focusing on the dual objectives of maximizing throughput and fairness, we offer a novel solution that addresses the key challenges of inter-cell interference and limited CSI feedback. The numerical experiments show the effectiveness of the proposed UGS scheme for fifth generation and beyond wireless networks deployment.

Future communication networks face the predicament of scarce spectrum resources in order to accommodate the exponential proliferation of heterogeneous wireless devices. The significance of wireless signal recognition (WSR) is steadily growing, especially concerning spectrum monitoring, spectrum management, and secure communications, among other crucial domains. Numerous techniques for conducting WSR were presented in the literature; however, the majority of them demonstrate limited efficacy in low SNR scenarios. This work proposes a dimensionality reduction (DR)–based machine learning classifier (DR-MLC) that simultaneously performs modulation and signal classification in heterogenous waveform scenarios. Additionally, the model employs a projection technique (PT) for noise removal in input data. The proposed framework is the first of its kind in the wireless signal processing domain and its performance is extensively evaluated in low SNR conditions using DR techniques including principal component analysis (PCA), linear discriminant analysis (LDA), and kernel LDA (KLDA) in combination with various machine learning techniques such as support vector machine (SVM), $��K�$-nearest neighbor (KNN), and nearest mean (NM) methods. The publicly available RadComDynamic dataset is utilized for the experiment. The paper demonstrates the improvement in classification accuracy of the proposed model over the reference architectures taken, specifically in low SNR scenarios for both single and dual classification tasks.

The design and implementation of a six-port MIMO dielectric resonator (DR) antenna (DRA) featuring diverse radiation patterns for wireless applications has been rendered in this article. The antenna structure is organized as a coplanar waveguide (CPW), fed three cylindrical DRs installed on the upper and lower sides of the substrate to achieve diverse radiation patterns. The proclaimed MIMO antenna exhibits a measured working frequency spectrum from 5.16 to 6.04 GHz (fractional bandwidth = 15.74%) with an isolation exceeding −27 dB at the resonance frequency. The antenna is characterized by its high isolation due to the spatial positioning of DRs at each side on the modified ground plane of the FR-4 substrate and pattern diversity, which allows the antenna to maintain its MIMO performance parameters like envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and channel capacity loss (CCL) necessary for the MIMO antenna system within optimal limits. The closeness between simulated and experimental results makes the proclaimed antenna suitable for sub-6 GHz WLAN (5.15–5.925GHz), 5G (n46), and wireless fidelity (Wi-Fi) supported IoT applications.

Integrated sensing and communication (ISAC), wherein sensing and communication share the same frequency band and hardware, has emerged as a pivotal technology in future wireless systems. A thorough study is required for researchers because ISAC has attracted a lot of interest and attention from academia and industry. In this paper, we present a bibliometric review that seeks to map the current state of ISAC research by addressing three crucial aspects. The aspects are (1) locating key journals, and significant authors, and creating network maps within ISAC; (2) defining theme-based clusters based on keyword co-occurrence analysis; and (3) developing a strong research framework to direct future ISAC investigations. This study provides profound insights and a structured foundation for researchers, enhancing the understanding and development of ISAC technologies. By highlighting significant advancements and research trends, this review aims to foster further innovation and academic discourse in the field of ISAC.

The rapid development of technologies has attracted significant attention, with the social web and big data becoming key drivers of modern innovation. Although big data in the Social Internet of Things presents various energy-saving merits, problems such as network congestion and data communication reliability occur. In this article, a hybrid channel attention recurrent transformer-based adaptive marine predator algorithm is introduced to solve these problems. The main purpose of this approach is to improve the robustness and performance of SIoT systems. The hybrid channel attention recurrent transformer-based adaptive marine predator algorithm combines a hybrid recurrent neural network, a channel attention mechanism, and a transformer classifier. In this work, four datasets, including the water treatment plant, GPS trajectories, hepatitis dataset, and Twitter for sentiment analysis in Arabic are employed in validating the performance of a proposed model. The Savitzky–Golay filter is applied to reduce noise and eliminate unnecessary or irrelevant data. After data pre-processing, the hybrid channel attention recurrent transformer-based adaptive marine predator was introduced for classification, and this model is fine-tuned by the adaptive marine predator algorithm. In addition, the proposed model demonstrates strong scalability and applicability in real-world applications, making it an ideal solution for future Social Internet of Things systems.