International Journal of Communication Systems最新文献

The base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well-known NP-hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above-mentioned problem is modeled by covering a new 2-D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI-SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI-SLOB scheme over the other state-of-the-art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.

One of the advanced field in 5G cellular networks is the Massive Multiple-Input-Multiple-Output (MIMO), which creates a massive antenna array by offering numerous antennas at the destination. This grows as a hot research topic in the wireless sectors as it enhances the volume and spectrum usage of the channel. The spectral efficiency (SE) is maximized using the abundant antennas employed by MIMO using spatial multiplexing of consumers, which needs precise channel state information (CSI). The SE is affected by both pilot overhead and pilot contamination. To mitigate the contamination and to estimate the suitable channel for communication, an efficient strategy is introduced using the proposed Namib Beetle Aquila optimization (NBAO)_Deep Q network (DQN). Here, the optimal pilot location is identified by employing NBAO, which is an integration of Namib beetle optimization (NBO) and Aquila optimizer (AO). Moreover, DQN is introduced to determine the suitable channel and metrics, such as bit error rate (BER) and normalized mean square error (MSE) is used for evaluation. The normalized MSE channel estimation is utilized to mitigate the effects of pilot contamination. Additionally, designed NBAO + DQN have attained a value of 0.0006 and 0.0005 for BER and normalized MSE.

Deterministic communication plays a crucial role for real-time distributed embedded systems by guaranteeing a low end-to-end delay and minimal jitter. Conflict-aware iterated ILP-based scheduling (IIS) is an incremental approach to enhance the scalability of single-shot ILP computation to solve the computationally hard Qbv-compliant offline scheduling problem for IEEE 802.1 Time-Sensitive Networking (TSN). Taking into account the conflicting demands of the streams, IIS computes no-wait schedules by dividing the set of streams into disjoint partitions, each of which is incrementally scheduled by an ILP solver. In this work, defining the no-wait communication constraints for TSN scheduling, we propose a novel iterated scheduling approach, namely, O-IIS, with a backtracking mechanism and partial solution support extending the conventional IIS procedure. O-IIS relies on an ordering strategy to determine the order in which the constructed partitions will be processed by an ILP solver. We evaluate the performance of our approach in terms of schedulability success rate and schedule synthesis time using various network topologies and different partitioning schemes to construct the partitions. The evaluation results show that our approach provides significantly better scheduling performance compared to the existing work in the literature.

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.

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.