Showing papers in "Wireless Personal Communications in 2022"

An Efficient Real-Time Embedded Application Mapping for NoC Based Multiprocessor System on Chip

Abstract: The Network on Chip architecture’s performance metrics and inter-core communication are significantly impacted by the acceleration of the evolution of the components integrated on a single chip. Therefore, it is crucial to offer an effective mapping between the cores so that communication between them improves in order to solve such problems. Throughput and latency both have a higher impact on outperforming the network’s performance in NoC. In this research paper, an efficient mapping strategy implemented on the real-time embedded applications named ERTEAM is presented. In this algorithm, based on the minimum core average distance the mapping region is finalized, ensuring the overall mapping area reduced. The PE’s mapped according to the minimum communication energy in the selected mapping region. This research is evaluated on a set of embedded applications, which reveals a reduction in latency at 12.3% and 8.4%, the simulation time reduces at an average of 19% and 9.6%, the throughput increases at 14.5% and 7.8% and reduces the communication energy by 15.6% and 5.2% against Branch and Bound Based Mapping (BBPCR) and segmented brute-force mapping respectively. The proposed ERTEAM is simulated and tested on Xilinxs Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit using Xilinx Vivado 2020.2 software platform. The obtained hardware implementation results outperformed the delay and area metrics.

A Survey on Semantic Communications for Intelligent Wireless Networks

Abstract: Research on intelligent wireless network aims at the development of a human society which is ubiquitous and mobile, simultaneously providing solutions to the coverage, capacity, and computing issues. These networks will focus on provisioning intelligent use-cases through higher data-rates over the millimeter waves and the Tera-Hertz frequency. However, at such high frequencies, multiple non-desired phenomena such as, atmospheric absorption and blocking occur which create a bottleneck owing to resource scarcity. Hence, existing trend of exactly reproducing transmitted data at the receiver will result in a constant need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (relevance or context) of the received data. This article presents a detailed survey on the recent technological trends in regard to semantic communications for intelligent wireless networks. Initially, the article focuses on the semantic communications architecture including the model, and source and channel coding. Next, cross-layer interaction, and various goal-oriented communication applications are detailed. Further, overall semantic communications trends are presented following which, the key challenges and issues are detailed. Lastly, this survey article is an attempt to significantly contribute towards initiating future research in the area of semantic communications for the intelligent wireless networks.

Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel

Abstract: Cognitive radio is one of the most promising technologies due to the spectrum scarcity, especially in the microwave band. Spectrum sensing forms an essential functionality for CR systems. However, such detection performance is usually compromised by shadowing and fading channel conditions. Cooperative sensing is one of the crucial solutions to overcome degraded detection performance. A distributed architecture for the processing and fusion of sensing information is proposed to improve sensing performance and reduce reporting error. The decision fusion for cooperated users could be complex in dense network scenarios and reported sensing traffic may require significant bandwidth. The paper presents a new cooperative distributed PUs detection and proposes dynamic threshold based on controlled probability of false alarm to enhance sensing efficiency and reliability in a Rayleigh fading environments. A cooperative distributed PUs detection is realized by fusion node (FNs) that dynamically selected from the group members. The adaptive detection threshold is computed dynamically using the link quality indicators (LQIs) of the sensing channels. Moreover, the proposed scheme can significantly minimize the typically transmitted bits in the reporting channels. This work also deliberated the design parameters of the CR on the performance of fusion values. The simulation evaluation presents that the adapted threshold considerably improves the performance of the distributed cooperative sensing (DCS) process. The simulation results verified that the error was minimized remarkably. The ROC graph is notably enhanced for the sensing process in term of probability of detection and probability of false alarm. Finally, it was presented that with the proposed scheme the sensitivity requirements could be significantly enhanced up to 0.95.

Mutual Coupling Reduction in Microstrip Patch Antenna Arrays Using Simple Microstrip Resonator

Abstract: In this paper a novel ladder resonator is introduced to reduce mutual coupling effect in the patch antenna array structure. Applied patch antennas are operating at 2.45 GHz frequency, which specially used for MIMO (multiple input multiple output) systems. The edge-to-edge distance between two microstrip patch antennas is 0.05 λ. The proposed ladder resonator impressively blocks the surface current between two patch antennas at the operating frequency, which results in mutual effect reduction. The designed configuration has been analyzed, simulated and measured. Scattering parameters with and without of proposed resonator has been investigated. The result shows that, the proposed configuration increases isolation between two microstrip patch antennas about 44 dB.

Substrate Analysis on the Design of Wide-Band Antenna for Sub-6 GHz 5G Communication

Abstract: While copper is overwhelmingly used as the radiated part in microstrip antenna design studies, the choice of dielectric material offers a wide range of possibilities. At high frequencies, the effect of substrate permittivity on antenna performance is dramatically higher than low frequency microstrip antennas. For this purpose, in this study, a discussion on 4 different dielectric substrates to increase the overall efficiency of conventional sub-6 GHz 5G microstrip antenna is presented. A reference rectangular patch is modeled on FR4, Arlon AD300C, Rogers RO4003C and Mica substrates respectively. The radiating patch sizes are calculated and modeled for each dielectric substrate separately and then optimized for 5.65 GHz. Finally, gain and bandwidth analysis are performed with the help of CST Studio. Arlon AD300C, which is revealed to be the best in performance criteria analysis, is used for the proposed antenna fabrication and the simulated results are verified by bandwidth and gain measurements in a fully anechoic chamber. Finally, the advantages of the proposed antenna over some Sub-6 GHz 5G antennas with randomly selected substrates are confirmed by a comparative table.

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

Abstract: With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called δrsanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that δr sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art.