Autonomous Vehicles in 5G and Beyond: A Survey

Unmanned air vehicle communication (UAV) systems have recently emerged as a quick, low-cost, and adaptable solution to numerous challenges in the next-generation wireless network. In particular, UAV systems have shown to be very useful in wireless communication applications with sudden traffic demands, network recovery, aerial relays, and edge computing. Meanwhile, non-orthogonal multiple access (NOMA) has been able to maximize the number of served users with the highest traffic capacity for future aerial systems in the literature. However, the study of joint optimization of UAV altitude, user pairing, and power allocation for the problem of capacity maximization requires further investigation. Thus, a capacity optimization problem for the NOMA aerial system is evaluated in this paper, considering the combination of convex and heuristic optimization techniques. The proposed algorithm is evaluated by using multiple heuristic techniques and deployment scenarios. The results prove the efficiency of the proposed NOMA scheme in comparison to the benchmark technique of orthogonal multiple access (OMA). Moreover, a comparative analysis of heuristic techniques for capacity optimization is also presented.

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Capacity Optimization of Next-Generation UAV Communication Involving Non-Orthogonal Multiple Access

In this paper, a trinotch band MIMO antenna is designed for upper and lower WLAN (5.1 GHz, 5.5 GHz) i.e., (802.11a/g/n/ac/ax) and satellite X-band (8.5 GHz). The placement of closely found notches at 5.1 GHz and 5.5 GHz is addressed by taking advantage from the placement of the same notch elements at appropriate distances from each other, to split single resonance into biresonances. Similarly, the third notch is produced at 8.5 GHz via a U-shaped slot added in the antenna. 2x1-MIMO antenna with notch bands is expected with better isolation by analysis of co-configurations and cross-configurations.

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MIMO Antenna with Notches for UWB System (MANUS)

Automatic modulation recognition (AMR) is used in various domains—from general-purpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.

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Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

The dynamical systems are comprised of two components that change over time: the state space and the observation models. This study examines parameter inference in dynamical systems from the perspective of Bayesian inference. Inference on unknown parameters in nonlinear and non-Gaussian dynamical systems is challenging because the posterior densities corresponding to the unknown parameters do not have traceable formulations. Such a system is represented by the Ricker model, which is a traditional discrete population model in ecology and epidemiology that is used in many fields. This study, which deals with parameter inference, also known as parameter learning, is the central objective of this study. A sequential embedded estimation technique is proposed to estimate the posterior density and obtain parameter inference. The resulting algorithm is called the Augmented Sequential Markov Chain Monte Carlo (ASMCMC) procedure. Experiments are performed via simulation to illustrate the performance of the ASMCMC algorithm for observations from the Ricker dynamical system. 

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Embedded Estimation Sequential Bayes Parameter Inference for the Ricker Dynamical System

Weighted utility aware computational overhead minimization of wireless power mobile edge cloud

Fifth generation (5G)-industrial Internet of things (IIoT) is the integration of IIoT and a private 5G network. The IIoT is a concept that involves incorporating smart objects, gadgets, and solutions into cutting-edge industrial operations to increase reliability, efficiency, and over-production costs. Furthermore, the integration of IIoT and 5G/beyond 5G (B5G) provides the potential for ubiquitous and instantaneous connectivity. The 5G architecture can handle the IIoT’s stringent ultra-low latency, realtime processing, high data rate, nearby storage, and reliability requirements. A new era of economic growth is predicted for IIoT-assisted 5G/B5G wireless networks. It should be noted that the majority of the work in IIoT is focused on the architecture, with reliability and throughput being largely ignored. This paper provides a comprehensive review of B5G assisted IIoT wireless networks, with a focus on enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services. Furthermore, it provides insights into various applications and key enabling technologies from the perspective of URLLC, eMBB, and their tradeoff.

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URLLC and eMBB in 5G Industrial IoT: A Survey

Fifth generation (5G) cloud-radio access network (C-RAN) aims at providing better performance and support to various applications with stringent data rate and latency requirements. In C-RAN, the processing units, known as the baseband units (BBUs), are segregated from the individual remote radio heads (RRHs) and moved to a convenient location to serve more than one RRH. This migration leads to an efficient resource allocation and cost-effective solution at the expense of a huge fronthaul traffic between the RRH and BBU hotel. The required fronthaul data rate largely depends on the employed functional split options. In this article, we propose a novel optimal BBU placement with a mixed functional split scheme to combat the fronthaul latency challenge while providing the network with greater flexibility and cost reduction. We introduce an integer linear programming (ILP)-based BBU placement problem with a mixed functional split selection approach to simultaneously minimize the number of BBU hotels and fibers, thereby reducing the network cost. Furthermore, a heuristic algorithm is proposed to solve the proposed model for large network scenarios. The obtained results show that an improvement of 25% and 50% is realized with the proposed scheme over the conventional fixed split option scheme for small and large network scenarios, respectively.

Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN Over WDM Access/Aggregation Network

High up-front capital expenditures impede the widespread deployment of passive optical networks (PONs). A multi-tenant solution, in which multiple network operators virtually share PON infrastructure and bandwidth resources, can result in significant cost savings. First, we investigate the viability of virtual PONs, in which virtual network operators share a portion of the upstream bandwidth in a frame. Each PON schedules its frame-level capacity using a dedicated dynamic bandwidth assignment algorithm, resulting in the generation of an independent bandwidth map (BMap). Second, we implement at the optical line terminal a merging engine that combines individual virtual BMaps into a single physical BMap, which is then transmitted to all optical network units. Our novel traffic merging algorithm is capable of consolidating traffic across all transmission containers. We apply this merging engine on top of the 10-Gbit-capable PON module and validate our approach using network simulator 3. The results show that our proposed intra-frame sharing technique, when integrated with our traffic merging algorithm, significantly outperforms the standard inter-frame sharing approach in terms of both latency and packet loss. Furthermore, we observed that best-effort traffic is affected most for all resource constraint scenarios of intra-frame sharing.

Merging engine implementation for intra-frame sharing in multi-tenant virtual passive optical networks

The semiconductor supply chain is vulnerable to multiple security attacks, such as hardware Trojan injection, intellectual property theft, and overproduction. The notion of zero-trust (ZT)– never trust, always verify– offers a promising opportunity for chip security by authenticating integrated circuits (ICs) when they are connected to critical computing systems. Before exchanging any data, the system establishes trust with the chip using industry security protocols. In this paper, we propose using the secure protocol and data model (SPDM) to establish chip-to-chip (C2C)- ZT communications. Furthermore, we present formal models for this solution and verify these models using state-of-the-art formal verification tools. The results show that the SPDM meets the requirements of the ZT architecture and can be used as a foundation for secure C2C interconnection.

https://doi.org/10.1109/access.2023.3285630

Ashfaq Ahmed

Papers

Weighted utility aware computational overhead minimization of wireless power mobile edge cloud

URLLC and eMBB in 5G Industrial IoT: A Survey

Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN Over WDM Access/Aggregation Network

Merging engine implementation for intra-frame sharing in multi-tenant virtual passive optical networks

Light-Weight Security Protocol and Data Model for Chip-to-Chip Zero-Trust