International Journal of Communication Systems最新文献

The presented state of the art is a novel coplanar optically transparent antenna designed to operate in extended GSM, L-band, and UWB applications for vehicular communication. The presented radiator operates using a coplanar waveguide feed mechanism without a separate ground plane layer with a minimal conductive surface to enhance the transparency of the proposed antenna. The proposed antenna design has dimensions of 40 × 20 mm², and a transparent material of soda–lime glass is used as a substrate with a dielectric constant of 7. The radiating element over the substrate is the conductive film fluorine-doped tin oxide (FTO) with a sheet resistance of 7 Ω/sq. The operating frequency range of the designed antenna is from 0.86 to 14.45 GHz, which encloses the E-GSM (900 MHz), L-band (1–2 GHz), and UWB (3.1–10.6 GHz) frequencies. The peak gains at the operating frequencies are 1, 1.6, and 4.34 dBi, respectively. The designed antenna is fabricated and measured, and the performance metrics are plotted and compared. Since the transparent antenna is designed, the tendency of a substance to conduct light is measured as the optical transmittance, and it is calculated as 95% in the glass reference and 82% in the air reference.

The increasing demand for massaging networks that are stable and quick needs reevaluations of standard optical networking administration strategies. To improve the efficacy of optical networks by integrating machine learning (ML) approach for the best resource scheduling, this research presents an innovative dynamic block widow optimized random forest (DBWO-RF) strategy. To implement the DBWO-driven resource allocation method in accordance with the categorization and clustering findings, the RF method is incorporated with the software defined optical to achieve channel quality assessment after successfully clustering employs the RF approach to achieve channel quality assessment after successfully clustering traffic patterns using the fuzzy C-means (FCM) algorithm. To lessen the likelihood of blocking, the fragmentation-function-fit (FFF) algorithm was provided and the findings indicate that this approach possesses a reduced blocking risk. Using multiple approaches to modulation for various channel quality, the suggested resource allocation system leverages the DBWO approach to distribute the necessary resources based on various “traffic flow (TF)” clustering findings. The examination's outcomes demonstrate that, compared to other techniques under various given load levels, the present study has a reduced blocking risk, a sufficient complexity degree and greater effectiveness in the utilization of spectrum resources.

The cluster networking protocols are the roots that embed intelligent decision-making and enhance the lifespan of wireless sensor networks (WSNs). Wireless sensors with limited capabilities face several challenges due to the heterogeneous application environments. Especially, the mobility-incorporated sensors in most situations trouble the cluster network's robustness. Many cluster networking protocols have been presented in the past to enhance the network lifespan and data delivery ratio. However, they lack a dedicated and efficient mechanism for mobility assistance, an adequate cluster management process and cluster head selection criteria. To overcome these issues and for the uniform energy load distribution, we propose a mobility-compatible cache controlled cluster networking protocol (MC-CCCNP) in this paper. It is an energy-efficient cluster networking protocol that supports sensor movement. Network resource management and routing are controlled distributively by an optimal number of cache nodes. It defines a new strategy for cache node deployment based on neighbour density as well as a weight formula for cluster head selection and cluster formation based on the residual energy, the distance to the base station and the node velocity. It also includes techniques for detaching and reconnecting a mobile node to an appropriate cluster cache if it crosses the cluster boundary. We simulate and compare the performance of our protocol with the centralised energy-efficient clustering routing, energy-efficient mobility-based cluster head selection protocol and dual tier cluster-based routing protocols over different network configurations with varying mobility, scalability and heterogeneity. The MC-CCCNP showed remarkable improvements in energy utilisation uniformity and energy consumption. With the improved network lifespan, it also maintains a higher data throughput rate of 95% or more in almost all network configurations.

In wireless communication, impulsive noise often degrades channel quality, which poses challenges for equalizers. Although robust equalization methods offer some effectiveness, the occurrence of impulsive noise after training significantly impacts the symbol error rate (SER). To mitigate this issue, we propose a method that involves denoising the received signal using robust wavelet decomposition before equalization. This approach combines the discrete wavelet transform with median and morphological filters to reduce impulsive noise. A pre-impulsive noise detection mechanism triggers denoising only when impulsive noise is detected. We evaluate the SER performance of the proposed technique using simulations of a 16-QAM orthogonal frequency division multiplexing (OFDM) system with kernel interpolation-based frequency domain equalization (FDE) in a Rayleigh fading channel with impulsive noise. Results show that our approach achieves SER levels comparable to conventional methods in Gaussian noise scenarios, demonstrating its effectiveness in challenging wireless communication environments. The simulation results demonstrate that the proposed technique in non-Gaussian noise gives the SER on par with the FDE in a Gaussian noise environment.

5G communication technology is projected to provide extreme data rates that surpass user exposure, low power consumption, and greater short latency. A diverged multi-layer approach is implemented by cellular networks with macro-cells and various schemes of small cells to aid users with diverged quality of service (QoS) that affects more research by employing intervention management in 5G networks. Along with the escalating requirement for cellular services and adequate resources to furnish it and capable of handling the network traffic has become a resource distribution concern. The major concern is to facilitate the network jam having QoS. To overcome this concern, a potent investigation is developed for power and resource allocation, which is named as transit Archimedes optimization algorithm (TAOA). First, the non-orthogonal multiple access (NOMA) system module is created with the aid of power consumption and energy modules. Then, user clustering (UC) is performed to gather the NOMA users into single or multiple clusters utilizing deep embedded clustering (DEC) in accordance with user grouping parameters, like signal-to-interference and noise ratio (SINR), position, initial power, and channel gain. After that, sub-channel assignment and power allocation are done by the back propagation neural network (BPNN). Lastly, the presented module TAOA is performed to update the network parameters of BPNN, where TAOA is developed by the fusion of transit search (TS) optimization and Archimedes optimization algorithm (AOA). The analytic metrics utilized for finding the performance of the proposed TAOA-BPNN are achievable rate, energy efficiency, sum rate, and throughput. The experimental results demonstrate that the proposed method offers good performance with the achievable rate of 3.273 Mbits, energy efficiency of 0.00000000473 J, sum rate of 0.00000248 s, and throughput of 0.00000346 Mbps.

The frequent geographical changes of mobile nodes in Internet of Things (IoT) systems affect communication, activities, and behaviors. In such scenarios, it is crucial to establish a system model capable of evaluating quality of service (QoS) measures. However, the existing formal modeling techniques pose complexities in modeling mobility. To deal with these challenges, this study aims to propose a model that simplifies the process of modeling mobility within IoT systems. This paper presents a method for modeling mobility within IoT systems by leveraging a widely recognized extension of stochastic Petri nets known as stochastic reward nets (SRNs). The proposed method enhances the SRN model by incorporating the location concept, resulting in a novel extension called mobile SRN (MSRN). In this work, a case study utilizes the MSRN to evaluate the suggested features, examining various scenarios and investigating the impact of factors such as environmental conditions, sensor sampling rate, and the permissible distance of the node from the sink.

A new circular cavity-backed slot antenna, which produces unidirectional radiation patterns, has been developed for wireless communication systems. The antenna utilizes substrate-integrated waveguide (SIW) technology to create a low-profile cavity. The metal cladding on top of the cavity has an engraved circular ring slot for radiation, and a microstrip transition feeding mechanism is used to excite the perturbed circular slot antenna element located away from the centre of the cavity. Two rectangular cuts, including slots and a shorted via probe, improve radiation performance and bandwidth matching by nearly −40 dB over the working frequency range. An optimized spacing of 0.6 mm is established between the patch centre and the input microstrip transition feed, which produces a circularly polarized (CP) wave. The antenna uses CP SIW technology to operate within a wide impedance bandwidth of 12.18% (10.02–11.31 GHz) below −10 dB and an ARBW of 320 MHz (10.40–10.72 GHz). Both experimental and simulated results agree on s-parameters, and the antenna's gain, axial ratio, and radiation patterns are consistent. With 7.45 dB CP gain and planar integration, the antenna radiates unidirectional, making it suitable for lightweight wireless broadband applications.

The advanced technology in recent years that has achieved more attention among researchers and the social community is the wireless sensor network (WSN) that includes a number of nodes that are commonly distributed in remote zones. While deploying the WSN in huge areas, WSNs produce a massive amount of data. Thus, there is a significant need to process the data through efficient models. The data aggregation technique is the common solution widely employed to obstruct congestion on large-scale WSNs. However, the demanding part of the data aggregation scheme is to mitigate the network overhead without affecting the system efficiency. Most of the data transmitted by sensor nodes are repetitious and thus result in high power consumption. Therefore, sensor nodes should utilize an efficient data aggregation model for data transmission that minimizes duplicate data. In order to maintain such complications, this article proposes a hierarchical Taylor quantized kernel least mean square (HTQKLMS) filter for aggregating data in WSN. For this purpose, WSN is initially simulated, and then data aggregation is accomplished using developed HTQKLMS filter. Additionally, the HTQKLMS is derived by amalgamating the hierarchical fractional quantized kernel least mean square (HFQKLMS) filter with the Taylor series. Here, the data prediction mechanism is done by employing HFQKLMS model that is an integration of quantized kernel least mean square (QKLMS) and hierarchical fractional bidirectional least mean square (HFBLMS). Apart from this, data redundancy is achieved by broadcasting needed data utilizing data detected at the destination. Furthermore, HTQKLMS approach has delivered a minimum energy consumption of 0.0333 J and less prediction error of 0.0326.

Perfect detection of unused/underutilized frequency band of the primary user (PU) in the cognitive radio communication systems is an essential component for its efficient utilization by the secondary/cognitive user (CU) without any interference. In the cooperative spectrum sensing scenario, the presence or absence of PU is determined by analyzing the decisions of each CU engaged in the collaboration. In this paper, we have emphasized on the significance of spectrum sensing duration in the optimal threshold selection approach in the cooperative spectrum sensing scenarios for a range of the received PU signal signal-to-noise ratio (SNR) from low to high values. The sensing duration parameter is taken as t_s = 2.5 ms in the higher SNR environment, whereas in the lower SNR environment when the optimal threshold condition fails, t_s is kept dynamic to satisfy minimum target values of sensing performance parameters. We have computed the spectrum sensing performance parameters and throughput using the same approach and compared the results with those obtained from the constant false-alarm rate (CFAR), constant detection rate (CDR), and minimized error probability (MEP) threshold selection approaches. The spectrum sensing performance computed by the proposed approach is significantly improved as compared with the other reported approaches. At SNR = −24 dB, global probability of false alarm Q_f = 0.002 and global probability of detection Q_d = 0.999 are achieved. However, the throughput is relatively less for low SNR values.