Secrecy Energy Efficiency in Wireless Powered Heterogeneous Networks: A Distributed ADMM Approach

OAPA This paper investigates the physical layer security in heterogeneous networks (HetNets) supported by simultaneous wireless information and power transfer (SWIPT). We first consider a two-tier HetNet composed of a macrocell and several femtocells, where the macrocell base station (BS) serves multiple users in the presence of a malicious eavesdropper, while each femtocell BS serves a couple of Internet-of-things (IoT) users. With regard to the energy constraint of IoT users, SWIPT is performed at the femtocell BSs, and IoT users accomplish the reception of information and energy in a time-switching (TS) manner, where information secrecy is to be protected. To enhance the secrecy performance, we inject artificial noise (AN) into the transmit beam at both macrocell and femtocell BSs, and for the sake of achieving green communications, we formulate the problem of maximizing secrecy energy efficiency while considering the fairness in a cross-tier multi-cell coordinated beamforming (MCBF) design. To handle this resulting nonconvex max-min fractional program problem, we propose an iterative algorithm by applying successive convex approximation method. Then, we further develop a decentralized solution based on alternative direction multiplier method (ADMM), which reduces the overhead of information exchange among coordinated BSs and achieves good approximation performance. Finally, simulation results demonstrate the performance of the proposed AN-aided cross-tier MCBF design and verify the validity of distributed ADMM-based approach

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

Transmitter Optimization Techniques for Physical Layer Security

Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications. This thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing an unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Albeit many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance the capacity due to their easy implementation, pop up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed. However, because the UAVs mostly have limited energy storage, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed; conventional and machine learning (ML). Such classification helps understand the state of the art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of Communications Society (OJ-COMS

Holistic resource management in UAV-assisted wireless networks

Unmanned aerial vehicles (UAVs) are considered as a promising solution to assist terrestrial networks in future wireless networks (i.e., beyond fifth-generation (B5G) and sixth-generation (6G)). The convergence of various technologies requires future wireless networks to provide multiple functionalities, including communication, computing, control, and caching (4C), necessary for applications such as connected robotics and autonomous systems. The majority of existing works consider the developments in 4C individually, which limits the cooperation among 4C for potential gains. UAVs have been recently introduced to supplement mobile edge computing (MEC) in terrestrial networks to reduce network latency by providing mobile resources at the network edge in future wireless networks. However, compared to ground base stations (BSs), the limited resources at the network edge call for holistic management of the resources, which requires joint optimization. We provide a comprehensive review of holistic resource management in UAV-assisted wireless networks. Integrated resource management considers the challenges associated with aerial networks (such as three-dimensional (3D) placement of UAVs, trajectory planning, channel modelling, and backhaul connectivity) and terrestrial networks (such as limited bandwidth, power, and interference). We present architectures (source-UAV-destination and UAV-destination architecture) and 4C in UAV-assisted wireless networks. We then provide a detailed discussion on resource management by categorizing the optimization problems into individual or combinations of two (communication and computation) or three (communication, computation and control). Moreover, solution approaches and performance metrics are discussed and analyzed for different objectives and problem types. We formulate a mathematical framework for holistic resource management to minimize the linear combination of network latency and cost for user association while guaranteeing the offloading, computing, and caching constraints. Binary decision variables are used to allocate offloading and computing resources. Since the decision variables are binary and constraints are linear, the formulated problem is a binary linear programming problem. We propose a heuristic algorithm based on the interior point method by exploiting the optimization structure of the problem to get a sub-optimal solution with less complexity. Simulation results show the effectiveness of the proposed work when compared to the optimal results obtained using branch and bound. Finally, we discuss insight into the potential future research areas to address the challenges of holistic resource management in UAV-assisted wireless networks

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention