Transactions on Emerging Telecommunications Technologies最新文献

The development of increasingly cutting-edge mobile apps like augmented reality, facial recognition, and natural language processing has been facilitated by the sharp rise in smartphone demand. The increased use of mobile devices like wireless sensors and wearable technology has led to a rapid increase in mobile applications. Due to the explosive growth of the Internet and distributed computing resources of edge devices in mobile edge computing (MEC), it is necessary to have a suitable controller to ensure effective utilization of distributed computing resources. However, the existing approaches can lead to more computation time, more consumption of energy, and a lack of security issues. To overcome these issues, this paper proposed a novel approach called Hybrid Random Forest-Extreme Gradient Boosting (HRF-XGBoost) to enhance the computation offloading and joint resource allocation predictions. In a wireless-powered multiuser MEC system, the starling murmuration optimization model is utilized to figure out the ideal task offloading options. XGBoost is combined with a random forest classifier to form an HRF-XGBoost architecture which is used to speed up the process while preserving the user's device's battery. An offloading method is created employing certain processes once the best computation offloading decision for Mobile Users (MUs) has been established. The experiment result shows that the method reduced system overhead and time complexity using the strategy of selecting fewer tasks alone by optimally eliminating other tasks. It optimizes the execution time even when the mobile user increases. The performance of the overall system can be greatly improved by our model compared to other existing techniques.

The smart grid network (SGN) is expected to leverage advances in the Internet of Things (IoT) to enable effective delivery and monitoring of energy. By integrating communication, computing, and information tools like smart sensors and meters to facilitate the process of monitoring, predictions, and management of power usage, the SGN can improve competence of power-grid architecture. However, the effective deployment of IoT-powered SGNs hinges on the deployment of strong security protocols. With the advent of quantum computers, classic cryptographic algorithms based on integer factorization and the Diffie-Hellman assumptions may not be suitable to secure the sensitive data of SGNs. Therefore, in this paper, a secure quantum-safe mutual authentication and key-exchange (MAKe) mechanism is proposed for SGNs, that make use of the hard assumptions of small integer solution and inhomogeneous small integer solution problems of lattice. The proposed protocol is intended to offer confidentiality, anonymity, and hashed-based mutual authentication with a key-exchange agreement. Similarly, this scheme allows creation and validation of the mutual trust among the smart-meters (SMs) and neighbourhood-area network gateway over an insecure wireless channel. A random oracle model is then used to perform the formal security analysis of the proposed approach. A thorough formal analysis demonstrates proposed algorithm's ability to withstand various known attacks. The performance analysis shows that the proposed approach outperforms other comparative schemes with respect to at least 22.07% of minimal energy utilization, 51.48% effective storage and communications costs, as well as 76.28% computational costs, and thus suitable for resource-constrained SGNs.

The cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi-objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks-based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances-based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi-Graph Representation Spatio-Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi-Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio-Temporal Attention (STAtt) module is then proposed to extract spatio-temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state-of-the-art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.

With the emerging challenges for the data rate requirements of 5G/6G applications and reusing the 4G infrastructure for 5G, it is necessary to understand the System-on-Chip (SoC) platform-based embedded co-design and implementation of the programmable and reconfigurable MIMO-OFDM system. For both uplink and downlink data transmissions, these applications require a larger data throughput as well as reduced bit error rates, latency, and increased spectral efficiency. This work describes the co-design and development of hardware and software for the MIMO-OFDM algorithms for 5G and 6G eNodeBs. An efficient design through computer architecture based on pipeline and parallelization using systolic and CORDIC has been applied for the IP development of the sub-components of the MIMO-OFDM systems. A Zynq platform with computing resources including PS, Mali GPU-400, and PL is utilized to increase the data rate for MIMO-OFDM system architecture co-design and implementation. The architecture approach used in this work enabled a data rate of 10–50 Gbps and beyond reaching Tbps based on the system's programmability and reconfigurability with an efficient SoC platform design. The design platform provides a programming feature such as MIMO-OFDM, OFDM, and MIMO without OFDM through software programming for the range of applications of the desired data rates. With 64-QAM modulation, the three channels' observed performance in the predicted multipath channel velocity of 15 km/h for pedestrians, vehicles, and AWGN is seen in simulation. To reach the application clock frequencies, the device's PLL (ZUI7EG) upscales and downscales clock frequencies from 750 to 1600 MHz using a configurable register. When the system is configured to operate as MIMO-OFDM or OFDM in order to get an execution throughput of 300 msec and a data throughput ranging from 71 Gbps to 1749 Gbps using 2 × 2/4 × 4 configurations. The device scalability depends on at present devices of advanced embedded reconfigurable architecture platform. Massive MIMO and multi-user MIMO will be used in the future to increase throughput and data rates. Additionally, future work will focus on creating a MIMO-OFDM hardware-software embedded architecture and testbed to enhance implementation and verification of the vehicle and pedestrian.

Accurate traffic forecasting is more necessary than ever for transportation departments, especially given its significant role in traffic planning, management, and control. However, most existing methods struggle to address complex spatial correlations on road networks, nonlinear temporal dynamics, and difficult long-term prediction. This article proposes a novel spatial temporal graph gated transformer (STGGT) to overcome these challenges. The suggested model differs from Google's transformer because it uses a hybrid architecture that integrates graph convolutional networks (GCNs), attention, and gated recurrent units (GRUs) instead of solely relying on attention. Specifically, STGGT uses GCNs to extract spatial dependencies, utilizes attention and GRUs to extract temporal dependencies, and handle long-term prediction. Experiments indicate that STGGT outperforms the state-of-the-art baseline models on two real-world traffic datasets of 9%–40%. The proposed model offers a promising solution for accurate traffic forecasting, simultaneously addressing the challenges of complex spatial correlations, nonlinear temporal dynamics, and long-term prediction.

6G network services demand significant computer resources. Network slicing offers a potential solution by enabling customized services on shared infrastructure. However, dynamic service needs in heterogeneous environments pose challenges to resource provisioning. 6G applications like extended reality and connected vehicles require service differentiation for optimal quality of experience (QoE). Granular resource allocation within slices is a complex issue. To address the complexity of QoE services in dynamic slicing, a deep reinforcement learning (DRL) approach called customized sub-slicing is proposed. This approach involves splitting access, transport, and core slices into sub-slices to handle service differentiation among 6G applications. The focus is on creating sub-slices and dynamically scaling slices for intelligent resource allocation and reallocation based on QoS requirements for each sub-slice. The problem is formulated as an integer linear programming (ILP) optimization problem with real-world constraints. To effectively allocate sub-slices and dynamically scale resources, the Advantage Actor-Critic (A2C)-based Network Sub-slice Allocation and Optimization (NS-AO) algorithm is proposed. Experimental results demonstrate that the proposed algorithm outperforms the state of the art in terms of training stability, learning time, sub-slice acceptance rate, and resilience to topology changes.

Recent advancements in distributed computing systems have shown promising prospects in enabling the effective usage of many next-generation applications. These applications include a wide range of fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among such solutions are the evolving vehicular fog computing (VFC) frameworks that make use of IEEE and 3GPP protocols and use advanced optimization algorithms. However, these approaches often rely on outdated protocols or computationally intensive mathematical techniques for solving or representing their optimization models. Additionally, some of these frameworks have not thoroughly considered the type of application during their evaluation and validation phases. In response to these challenges, we have developed the “predictive analytics and modules” (PAM) framework, which operates on a time and event-driven basis. It utilizes up-to-date 3GPP protocols to address the inherent unpredictability of VFC-enabled distributed computing systems required in smart healthcare systems. Through a combination of a greedy heuristic approach and a distributed offloading architecture, PAM efficiently optimizes decisions related to task offloading and computation allocation. This is achieved through specialized algorithms that provide support to computationally weaker devices, all within a time frame of under 100 ms. To assess the performance of PAM in comparison to three benchmark methodologies, the evaluation pathways that we employed are average response time, probability density function, pareto-analysis, algorithmic run time, and algorithmic complexity.

Cooperative spectrum sensing (CSS) has emerged as a promising strategy for identifying available spectrum resources by leveraging spatially distributed sensors in cognitive wireless sensor networks (CWSNs). Nevertheless, this open collaborative approach is susceptible to security threats posed by malicious sensors, specifically Byzantine attack, which can significantly undermine CSS accuracy. Moreover, in extensive CWSNs, the CSS process imposes substantial communication overhead on the reporting channel, thereby considerably diminishing cooperative efficiency. To tackle these challenges, this article introduces a refined CSS approach, termed weighted sequential detection (WSD). This method incorporates channel state information to validate the global decision made by the fusion center and assess the trust value of sensors. The trust value based weight is assigned to sensing samples, which are then integrated into a sequential detection framework within a defined time window. This sequential approach prioritizes samples based on descending trust values. Numerical simulation results reveal that the proposed WSD outperforms conventional fusion rules in terms of the error probability, sample size, achievable throughput, and latency, even under varying degrees of Byzantine attack. This innovation signifies a substantial advancement in enhancing the reliability and efficiency of CSS.

Multiple access techniques working on time-dispersive channels must employ spreading codes with good correlation properties to minimize multipath interference and multiple access interference. Popular spreading codes, such as the Walsh-Hadamard code (orthogonal) and maximal length sequence (m-sequence) (semi-orthogonal), suffer from intersymbol interference (ISI). ISI increases owing to the poor autocorrelation properties of the spreading codes, resulting in a deteriorated bit-error-rate (BER) performance. Recently, a cyclic interleaving transmission has been proposed to enhance multipath diversity in the presence of ISI. In this study, an identical code-data cyclic shift (ICDCS) transmission scheme is introduced to achieve improved multipath diversity in time-dispersive channels. The proposed ICDCS transmission is designed based on cyclically shifting identical codes and data. The code and data involved in the first set of spreading are cyclically shifted for the next set of spreading, and the two sets are blended to perform cyclic interleaving. The data are despread from each multipath component at the receiver using different codes derived from a single m-sequence through identical code cyclic shifting (ICCS). Consequently, multipath diversity increases, resulting in a significant reduction in the BER. ICDCS transmission effectively utilizes all multipath components and the codes derived from the ICCS to mitigate ISI. The simulation results confirm that the proposed ICDCS transmission significantly enhances the multipath diversity compared to the conventional cyclic interleaving transmission scheme.