Integrated sensing and communication (ISAC) has received significant attention over the past five years as a key component of the 6G vision for next-generation communication systems. Starting with the fundamentals of ISAC, we present strategies focused on sensing-centric and communication-centric ISAC systems that satisfy both radar and communication objectives using the same physical hardware and waveform. The communication-centric ISAC focuses primarily on improving communication performance beyond 5G/6G cellular networks andWi-Fi standards, while also performing additional processing required for detection. On the other hand, the sensing-centric ISAC prioritizes sensing and detection capabilities while incorporating communication functions in the most efficient way possible. In this survey, we present a comprehensive review of both ISAC systems along with new approaches for enabling technologies and highlight unique challenges and existing solutions that are already available. We explore the roles of innovative functions in enabling ISAC, including joint waveform design, edge computing, vehicular sensing and communications, and their practical applications. We then discuss 3GPP standardization efforts related to ISAC as a future direction for the 6G vision. We also outline possible future research directions, with a specific emphasis on security, artificial intelligence, and machine learning.

Mobile edge computing was conceived to address the increasing computing demand generated by users at the communication network edge. It is expected to play a significant role in next-generation (5G, 6G, and beyond) communication systems as new applications such as augmented/extended reality, teleoperations, telemedicine, and gaming become prolific. As the networks become denser, more and more edge servers are expected to be deployed, and the question of task offloading becomes more complicated. In this study, we present a framework for task offloading in the presence of multiple edge servers that employ round-robin task scheduling. Most studies in the literature attempt to optimize the offloading process under the assumption that each user generates just a single task, or they generate one task every time slot in a discrete-time system where all the tasks are handled within a slot. Furthermore, first-come-first-served queueing models are typically used in studies where queueing is considered at all. The work presented is novel in that we assume continuous and stochastic task arrivals generated by multiple users and round-robin task scheduling at the edge servers. This setting is considerably more realistic with respect to the existing works, and we demonstrate through extensive simulations that round-robin task scheduling significantly reduces task delay. We also present a comparison of a number of server selection mechanisms.

Physical layer authentication is an important technique for cybersecurity, especially in military scenarios. Device classification using radio frequency fingerprinting, which is based on recognizing device-unique characteristics of the transient waveform observed at the beginning of a transmission from a radio device, is a promising method in this context. In this study, the effect of the ambient temperature on the performance of radio device classification based on RF fingerprinting is investigated. The waveforms of the transient regions of the transmissions are recorded as images, and ResNet50 and InceptionV3 networks for image classification are used to determine the radio devices. The radio devices used in the study belong to the same brand, model, and production date, making the problem more difficult than classifying radio devices of different brands or models. Our results show that high levels of accuracy can be attained using convolutional neural network models such as ResNet50 and InceptionV3 when the test data and the training data are collected at the same temperature, whereas performance suffers when the test data and the training data belong to different temperature values. We provide the performance figures of a blended training model that uses training data taken at various temperature values. A comparison of the two networks is also provided.