International Journal of Communication Systems最新文献

The demand for maintenance-free, battery-less IoT devices, driven by sustainable Industry 4.0 practices, necessitates efficient RF energy harvesting solutions. Addressing the limitations of single-frequency harvesting, a novel dual-band, dual-polarized rectenna operating at 3.6 GHz and 5.8 GHz is proposed. The antenna resonates at 3.6 GHz with linearly polarized (LP) characteristics and 5.8 GHz with circularly polarized (CP) characteristics with impedance bandwidth (IBW) of 2.2% and 5.63%, respectively, achieving a gain of 4.85 dBi and 6.75 dBic, respectively. A rectenna consists of an antenna at the front end and an RF-DC rectifier at the backend. This approach maximizes power conversion efficiency by minimizing impedance mismatches and reflection losses and mitigates interference between the dual bands within the rectifier circuitry. The rectifier achieves peak efficiencies of 65.5% at 3.6 GHz with a 3 dBm input and 44% at 5.8 GHz with a −1 dBm input, both with 2KΩ load. Efficiency exceeds 30% at 3.6 GHz for an input range from −10 to 6.5 dBm and exceeds 20% at 5.8 GHz for an input range from −13 to 3 dBm. These design considerations enable reliable harvesting of ambient RF energy across a range of −20 to 20 dBm, enabling continuous IoT device operation. Our measurements confirm the design's effectiveness in converting low-power RF signals into usable energy, supporting sustainable and autonomous IoT deployments at 3.6 and 5.8 GHz.

This article presents the design and analysis of a dual-port Al₂O₃-based filtering antenna, commonly called a Filtenna. The feeding structure of the proposed Filtenna is engineered to generate two radiation nulls within the cylindrical ceramic. The initial single-port Filtenna design is subsequently modified into a dual-port configuration. A metasurface is introduced above the dual-port radiator to enhance performance, enabling the conversion of linear waves into circularly polarized waves. Both experimental and simulation results show a strong agreement, confirming that the developed radiator operates efficiently within the 2.43–2.65 GHz frequency range with a mutual coupling level less than −25 dB. The final design, incorporating the metasurface, achieves a realized gain of 7.5 dBi, while also exhibiting an axial ratio within the frequency range 2.45–2.55 GHz. Outside of the targeted frequency range, the antenna's gain drops significantly, falling below −15 dB on the lower side and −17 dB on the upper side of the band. This makes the aerial design appropriate for WiMAX application.

An ultrawideband double-ridge horn (DRH) antenna having a high gain and excellent impedance matching is presented. Two metal reflectors are perpendicular to the two ridges for improving the lower- and center-frequency gain, while two sets of metallic gird sidewalls are used to fix the two reflectors together for structural stability and further increase the lower frequency gain. Additionally, a fork-shaped dielectric plate, which is fixed between the ridges, is used to improve the higher frequency gain. To improve the impedance matching at 1–18 GHz, a microstrip line having a smoothly tapered ground plane is employed to feed the DRH antenna. Good impedance matching for VSWR < 1.85 and high gain ranging from 7.6 to 18.6 dBi are obtained for the designed DRH antenna.

Smart grid networks as the next generation of distributed grid systems demand diverse Quality of Service (QoS) requirements. In the case of time-critical services, going beyond the delay threshold leads to the malfunction of the entire network. Software-defined Networking (SDN) is supposed to improve the QoS of smart grids. But, issues such as traffic prioritization require traffic management policies, which are not provided in software-defined networks. Accordingly, in this paper, in order to prevent blackout and burnout in smart grid networks, a mechanism is introduced for QoS amendment in Software-defined-enabled Smart Grids (SDN-SG). Providing the QoS-aware dynamic controllers' load-balancing mechanism in SDN-SG is one primary innovation of this paper. Because it reduces the packet loss rate for time-critical services. The idea is equivalent to the dynamic QoS-aware controller placement in an sdn-enabled network, which is an NP-hard problem. Another novelty of this paper is ameliorating such an NP-hard complexity and diminishing the overhead of controller placement. It is done by multi-agent negotiations in holonic organization. Such an improvement in computational complexity contributes significantly to the QoS improvement. The superiority of the paper over similar relevant research is demonstrated by numerical analysis in terms of the degree of controllers' load-balancing, diminishing packet loss rate, improving delay, and reducing the controller placement overhead.

Due to the user's convenience, wireless technology is more frequently used in the current era. The extensively utilized wireless technologies are the wireless sensor networks (WSN). Although WSNs have numerous advantages over traditional communication technologies, nevertheless, they struggle with issues such as low battery life, control overheads, scalability, data aggregation, throughput, and so on. A huge amount of nodes scattered around a specific target region make up a WSN. The WSN models are widely used for surveillance, cutting-edge healthcare, and commercialized industrial automation. The WSN's energy efficiency needs to be improved because increased energy utilization limits the network's lifetime (LT). Therefore, an efficient dual CH selection mechanism in the WSN system by considering the challenges attained in the classical CH selection models has been developed. Hence, a novel and efficient dual CH routing mechanism is implemented and leads to improve the lifetime of WSNs. In the CH selection model, optimal paths are selected by the developed modified attack power-based hybridization of sailfish and whale optimization (MAP-HSWO). Further, the efficiency of the WSN is improved by focusing on objectives like delay, node degree, residual energy, throughput, intracluster and intercluster distance using MAP-HSWO to compute the effectualness of the network. At last, several experiments are executed in the proposed dual CH selection method over the classical techniques to compute the efficiency of the network.

Radio-frequency (RF) fingerprinting is an emerging technology for advanced device authentication. This work addresses the issue by improving both the algorithm and hardware to target real-time AI processing in communication systems. Here, efficient real-time communication system using canonical cortical graph neural network with high-level target navigation pigeon-inspired optimization (RCS-CCGNN-HTNPIO) is proposed. Initially, radio frequencies from multiple devices such as modem, TV, and mobile phone, these devices transmit RF signals, which are captured and processed via RF fingerprinting. Then, RF fingerprinting system is designed to distinguish between known signals and unknown signals. Afterwards, the canonical cortical graph neural network (CCGNN) for analyzing and identifying the specific RF fingerprint of each signal. The CCGNN evaluates the signals and categorizes them as known signal or unknown signal based on the RF fingerprint characteristics. Hence, the high-level target navigation pigeon-inspired optimization (HTNPIO) is used to optimize the CCGNN. The performance metrics provide a complete assessment of the system's ability to manage the demands of real-time processing in communication networks. The RCS-CCGNN-HTNPIO approach attains19.21%, 26.12%, and 30.15% higher accuracy compared with existing techniques likes deep learning based multidimensional radio-frequency fingerprinting enhancement approach for IoT device identification (DL-RFF-DI), embedding-assisted attentional deep learning for real-world RF fingerprinting of Bluetooth (ADL-RW-RFF), and multichannel attentive feature fusion for radio-frequency fingerprinting (MCA-RFF). The simulation results prove that the RCS-CCGNN-HTNPIO can provide a robust and adaptive solution for achieving high accuracy in next-generation radio access networks.

Design of a shorted microstrip line fed truncated corner square microstrip antenna is proposed for a polarization agile response on an electrically thinner substrate. In the circular polarized design, an axial ratio bandwidth of 1.8% with a reflection coefficient bandwidth of 79 MHz (9.9%) is achieved, whereas for the dual-polarized design, the configuration yields a reflection coefficient bandwidth of 81 MHz (10.1%) showing polarization agility along 45° over 53% of the bandwidth and along 135° over 47% of the bandwidth. Both the configuration yields a broadside radiation pattern with a gain of more than 5 dBi. To achieve an easy polarization switching, a reconfigurable design is presented. The design methodology is proposed for the circular polarized variation. Antenna redesigned using these guidelines shows similar circular polarized characteristics with an equivalent gain and bandwidth as that of the original configuration, thus satisfying the requirements of the E-GSM-900, T-GSM-900, and R-GSM-900 frequency bands.

Spectrum sensing (SS) is a significant processing of cognitive radio networks (CRNs) that enables cognitive users to detect the underutilized or unutilized primary users (PUs) and licensed users spectrum for effectual usage. The threshold value selection is a vital step in determining the state (appearance/non-appearance) of PU in the spectrum sensing, and it has a significant impact on the detection and false-alarm probability. When a targeted sensing parameter is achieved at low SNR, other sensing parameter considerably degrades. In this manuscript, adaptive sampling point with Q-learning–dependent sensing threshold for spectrum energy detection in cognitive radio networks (ASSTQL-STSED-CRNF) is proposed. Adaptive sampling point and Q-learning (ASSTQL) employs an adaptive threshold mechanism that adjusts the detection threshold based on the current noise conditions, thereby improving the accuracy of signal detection. The proposed approach utilizes a Q-learning approach to optimize the sampling points and sensing thresholds. The ASSTQL-STSED-CRNF technique significantly enhances spectral detection performance, especially in scenarios with unpredictable noise levels. The proposed method is simulated in MATLAB. The simulation outcomes demonstrate an increased probability of identification as the signal-to-noise ratio (SNR) rises. The proposed model attains lower power spectral density and high throughput when compared with existing models.

The development of numerous wireless sensor network (WSN) applications has sparked considerable interest in the use of these networks across various fields. These networks, which do not require infrastructure and are self-organizing, can be rapidly deployed in most locations to collect information about environmental phenomena and transmit it to relevant hubs for appropriate action in emergency situations. Sensor nodes (SNs) in WSNs function as both sensors and relay nodes in relation to one another. As energy in these networks is limited, the nodes are supplied with only a specific amount of power. Because these networks are often located in difficult and remote areas, node batteries cannot be recharged or replaced. As a result, energy conservation is one of the most pressing concerns in these networks. Consequently, this study proposes a novel optimization technique for clustering WSNs, combining the whale optimization method and the genetic algorithm. In this work, information is transferred between cluster heads (CHs) and the sink using a combination of whale optimization and evolutionary algorithms, focusing on reducing intracluster distances and energy consumption in cluster members (CMs), while achieving near-optimal routing. The implementation results demonstrate that the proposed technique outperforms previous methods in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, considering the evolutionary capabilities of both the whale optimization algorithm and the genetic algorithm.

Wireless sensor and actor networks (WSANs) are gaining substantial recognition because of their utility in inhospitable environments where humans have restricted accessibility. The extensive applications of WSANs in different domains require effectiveness, reliability, and some degree of robustness. These networks may experience frequent node failures due to their deployment in rough environments, such as energy depletion and onboard electronics failure of network nodes. These failures result in areas with no coverage, which may further deteriorate the standard of data accumulated. However, the network partitioning into disjoint fragments is the most severe repercussion that arises due to these failures. The network partitioning results in various negative impacts like obstruction in data exchange and restricted coordination among the nodes. Therefore, detecting partitioning in the network and restoring connectivity are important. This paper reviews the reported network partition detection and recovery techniques. The limitations of these recovery techniques are highlighted, along with the advantages of incorporating unmanned aerial vehicles (UAVs) in various ground wireless networks. UAVs are evolving to become a critical element of future wireless network technologies. This paper incorporates the analysis of UAV-assisted networks consisting of technical issues, challenges, and requirements related to the UAVs' employment in wireless networks. Thereafter, the reported UAV-assisted partition recovery techniques are discussed along with the application scenarios for incorporating UAVs in network partitioning detection and recovery problem. The paper also highlights the challenges for associating UAVs in the network recovery process.