Secrecy Enhancement for UAV-enabled Integrated Sensing and Communication System

In this correspondence, we propose an unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where a full-duplex UAV equipped with uniform planar array (UPA) is adopted as a base station for the multiuser downlink communications, while sensing and jamming a passive ground eavesdropper. The goal of this work is to maximize the sum secrecy rate of ground users subject to the constraints of sensing accuracy and UAV's operational capability by jointly optimizing the transceiver beamforming and UAV's trajectory. To this end, we develop the algorithmic solution based on block coordinate descent (BCD) and semidefinite programming (SDP) relaxation techniques, whose performance is verified via simulations indicating its efficacy in improving communication security with the sufficient mission period.

Cell-Free MIMO Perceptive Mobile Networks: Cloud vs. Edge Processing

Perceptive mobile networks implement sensing and communication by reusing existing cellular infrastructure. Cell-free multiple-input multiple-output, thanks to the cooperation among distributed access points, supports the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, the distributed access points communicate over fronthaul links with a central processing unit acting as a cloud processor. This work explores four different types of PMN uplink solutions based on Cell-free multiple-input multiple-output, in which the sensing and decoding functionalities are carried out at either cloud or edge. Accordingly, we investigate and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation whereby the fronthaul quantization of signals received in the training and data phases are jointly designed to maximize the achievable rate under sensing requirements and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications.

Joint Mechanical and Electrical Adjustment of IRS-aided LEO Satellite MIMO Communications

In this letter, we propose a joint mechanical and electrical adjustment of intelligent reflecting surface (IRS) for the performance improvements of low-earth orbit (LEO) satellite multiple-input multiple-output (MIMO) communications. In particular, we construct a three-dimensional (3D) MIMO channel model for the mechanically-tilted IRS, and consider two types of scenarios with and without the direct path of LEO-ground user link due to the orbital flight. With the aim of maximizing the end-to-end performance, we jointly optimize tilting angle and phase shift of IRS along with the transceiver beamforming, whose performance superiority is verified via simulations.

Energy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement

With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust line-of-sight (LoS) connections with ground nodes owing to their free mobility and high altitude, especially for emergency and disaster response. This paper investigates a secure offloading system, where the UAV-mounted IRS assists the offloading procedures between ground users and an access point (AP) acting as an edge cloud. In this system, the users except the intended recipients in the offloading process are considered as potential eavesdroppers. The system aims to achieve the minimum total energy consumption of battery-limited ground user devices under constraints for secure offloading accomplishment and operability of UAV-mounted IRS, which is done by optimizing the transmit power of ground user devices, the trajectory and phase shift matrix of UAV-mounted IRS, and the offloading ratio between local execution and edge computing based on the successive convex approximation (SCA) algorithms. Numerical results show that the proposed algorithm can provide the considerable energy savings compared with local execution and partial optimizations.

Cache-assisted Mobile Edge Computing over Space-Air-Ground Integrated Networks for Extended Reality Applications

Extended reality-enabled Internet of Things (XRI) provides the new user experience and the sense of immersion by adding virtual elements to the real world through Internet of Things (IoT) devices and emerging 6G technologies. However, the computational-intensive XRI tasks are challenging for the energy-constrained small-size XRI devices to cope with, and moreover certain data requires centralized computing that needs to be shared among users. To this end, we propose a cache-assisted space-air-ground integrated network mobile edge computing (SAGIN-MEC) system for XRI applications, consisting of two types of edge servers mounted on an unmanned aerial vehicle (UAV) and low Earth orbit (LEO) equipped with cache and the multiple ground XRI devices. For system efficiency, the four different offloading procedures of the XRI data are considered according to the type of information, i.e., shared data and private data, as well as the offloading decision and the caching status. Specifically, the private data can be offloaded to either UAV or LEO, while the offloading decision of the shared data to the LEO can be determined by the caching status. With the aim of maximizing the energy efficiency of the overall system, we jointly optimize UAV trajectory, resource allocation and offloading decisions under latency constraints and UAV's operational limitations by using the alternating optimization (AO)-based method along with Dinkelbach algorithm and successive convex optimization (SCA). Via numerical results, the proposed algorithm is verified to have the superior performance compared to conventional partial optimizations or without cache.

Transceiver Design and Performance Analysis for LR-FHSS-based Direct-to-Satellite IoT

This paper presents a novel transceiver design aimed at enabling Direct-to-Satellite Internet of Things (DtS-IoT) systems based on long range-frequency hopping spread spectrum (LR-FHSS). Our focus lies in developing an accurate transmission method through the analysis of the frame structure and key parameters outlined in Long Range Wide-Area Network (LoRaWAN) [1]. To address the Doppler effect in DtS-IoT networks and simultaneously receive numerous frequency hopping signals, a robust signal detector for the receiver is proposed. We verify the performance of the proposed LR-FHSS transceiver design through simulations conducted in a realistic satellite channel environment, assessing metrics such as miss detection probability and packet error probability.

Energy-Efficient Vehicular Edge Computing with One-by-one Access Scheme

With the advent of ever-growing vehicular applications, vehicular edge computing (VEC) has been a promising solution to augment the computing capacity of future smart vehicles. The ultimate challenge to fulfill the quality of service (QoS) is increasingly prominent with constrained computing and communication resources of vehicles. In this paper, we propose an energy-efficient task offloading strategy for VEC system with one-by-one scheduling mechanism, where only one vehicle wakes up at a time to offload with a road side unit (RSU). The goal of system is to minimize the total energy consumption of vehicles by jointly optimizing user scheduling, offloading ratio and bit allocation within a given mission time. To this end, the non-convex and mixed-integer optimization problem is formulated and solved by adopting Lagrange dual problem, whose superior performances are verified via numerical results, as compared to other benchmark schemes.

Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) have been actively studied as moving cloudlets to provide application offloading opportunities and to enhance the security level of user equipments (UEs). In this correspondence, we propose a hybrid UAV-aided secure offloading system in which a UAV serves as a helper by switching the mode between jamming and relaying to maximize the secrecy sum-rate of UEs. This work aims to optimize (i) the trajectory of the helper UAV, (ii) the mode selection strategy and (iii) the UEs' offloading decisions under the constraints of offloading accomplishment and the UAV's operational limitations. The solution is provided via a deep deterministic policy gradient (DDPG)-based method, whose superior performance is verified via a numerical simulation and compared to those of traditional approaches.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Resonance Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the brain functional connectivity via dictionary learning and sparse coding (DLSC). In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation. We apply the proposed DLSC-based method to Human Connectome Project (HCP) motor tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar circuits in somatomotor networks show that the DLSC-based denoising framework can significantly improve the prominent connectivity patterns, in comparison to the temporal non-local means (tNLM)-based denoising method as well as the case without denoising, which is consistent and neuroscientifically meaningful within motor area. The promising results show that the proposed method can provide an important foundation for the high-resolution functional connectivity analysis, and provide a better approach for fMRI preprocessing.