In remote surgery, industrial automation, and public safety services, which are mission-critical, ultra-reliable low-latency communications are necessary. However, conventional communication methods are unable to simultaneously satisfy the stringent requirements of strong security, high reliability, and ultra-low latency demanded by mission-critical applications. To overcome these limitations, this paper proposes a Federated Consensus Learning-based Latent Diffusion Model (FCLbLDM) for secure, reliable, and low-latency communications. Although the use of the Latent Diffusion Model optimizes system efficiency and strengthens data security by establishing an encrypted communication system and safeguarding sensitive data, it also distributes consensus and strengthens data integrity and reliability. This dual capacity has been used to significantly reduce communication delays with the proposed approach, achieving an ultra-low latency of 10.0144 ms and high reliability. Extensive experimentation in remote surgery, industrial automation, and public safety demonstrates how effectively work is carried out to ensure a safe, smooth environment. The FCLbLDM framework improves ultra-reliable, low-latency communication (URLLC) by enabling reliable, secure, and low-latency communication through the innovative combination of Federated Consensus Learning and LDMs, with optimization and encryption features.
Worldwide, wireless technologies have improved, and 5G networks are very useful for wireless communication. Besides, 5G is highly demanded because of its low latency and high speed for large data transfers. Moreover, 5G antennas provide high multiplexing diversity and gain, which enhances the capacity of the channel. However, traditional antennas have some limitations, such as high loss of propagation and low ability to handle compact sizes. In this study proposed a novel two port multiple input multiple output (MIMO) antenna design with a cross slotted square ring resonator metamaterial is proposed, which is arranged in a ladder structure that attempts to minimize coupling among wideband antenna elements. The dimensions of the proposed design is 60× 40x 1.6 mm3, and it is built on an FR4 epoxy substrate. The integration of a metamaterial structure effectively reduces mutual interaction between antenna elements. Signal integrity and isolation improve the overall performance of the antenna. The antenna is optimized for the 5G sub-6 GHz frequency range, which provides better wideband coverage with reduced interference. The proposed antenna performs well in both simulation and experimental results, demonstrating enhanced radiation attributes and lower coupling when compared to conventional MIMO antenna systems. Furthermore, the analysis of results shows the efficiency of the metamaterial structure, which is used to attain high performance in 5G communications. The proposed antenna achieves a reflection coefficient of − 23.84 dB at 2.3 GHz, − 19.53 dB at 3.0 GHz and − 12.717 dB at 6.6 GHz, demonstrating good impedance matching at the respective operating frequencies. In addition, excellent MIMO performance is achieved with an envelope correlation coefficient (ECC) of < 0.05, diversity gain (DG) close to 10 dB, and channel capacity loss (CCL) is less than 0.4 bit/sec/Hz.
This paper presents a new scheme for adding more antennas to user equipment (UE), without assigning an additional pilot sequence for each antenna. There are cases that require the addition of more antennas to the UE, such as overcoming the high free-space path loss in high-frequency bands (e.g., millimeter-wave and terahertz bands) and increasing the diversity order in low-SNR regimes. Although adding traditional antennas to the UE improves the spectral efficiency (SE) of the downlink (DL) and increases the diversity order (space diversity), it adversely affects the performance of the cellular system as it decreases its cell-user capacity (number of UEs that can be served in a cell) and degrades its channel state information (CSI) estimation quality. Therefore, the new scheme presented here for the DL scenario successfully addresses these adverse effects by enabling the addition of auxiliary antennas (defined here as pilot sequence-free antennas), rather than traditional ones, to the UE, thus obviating the need for the use of multiple pilot sequences (one per traditional antenna) that would otherwise be required. The immediate advantages of the proposed scheme in terms of maintaining the cell-user capacity, complexity reduction, and performance improvement are its main attractive features.
Due to the rapid development of wireless communication services and the growth of the Internet of Things, the requirement for spectrum resources is growing significantly. For effective utilization of the available spectrum and prevention of channel interference between users, sensing and estimating spectrum availability is fundamental for more efficient and dynamic spectrum resources utilisation. Additionally, due to shadowing, fading, and uncertainty in the receiver, the overall performance of spectrum sensing techniques is affected in such scenarios. By utilizing the spatial diversity of the nodes, the issues above can be overcome by following a cooperative spectrum sensing (CSS) approach. However, this approach increases the overhead and the time consumed for sensing the spectrum. Our work introduces CSS by clustering machine learning algorithms. Our research approach aims to address the problem of hidden terminals in cognitive radio networks by leveraging the spatial diversity of users. In addition, it seeks to reduce the amount of data transferred between users and the fusion center, which makes the final decision based on information from group representatives. Preliminary results indicate that it can lower energy consumption by more than 40
In networking, device participation has improved connectivity in the Internet of Things by adding heterogeneous devices. There are no standard IoT communication protocols due to limited resources. IoT device security is crucial and difficult to ensure. In order to detect attacks in SDN-based IOT settings, this study develops a revolutionary deep learning-based intrusion detection system. Initially, the input data were collected from the open source datasets, namely, the CICDS-001, UNSW-NB15, and NSL-KDD datasets. The input data are passed into the pre-processing stage to rescale the data properly by using min–max normalization and one hot encoding mechanism. The essential features from the pre-processed data are selected by the maximum data coefficient-based relief algorithm (MDC-ReF). Based on these features, the Logistic Chaotic Gazelle Optimization-based Deep Belief Long Short-term Memory Network has identified the attack and normal classes, which aids in the validation of incoming user requests. Here, the optimal parameters of the DB-LSTM are adjusted using the proposed LCGO algorithm. The performance of the LCGO-based DB-LSTM is evaluated using three different datasets: the New Version dataset of the KDD’99 dataset (NSL-KDD dataset), the UNSW-NB15 dataset and the Coburg Intrusion Detection Data Set (CIDDS-001) dataset. The proposed system attained 98
