skip to main content


Title: Wireless link pairing toward secured 6G networks

Hybrid wireless networks are foreseen to play a major role in the visioning and planning of the sixth generation (6G) network. Most of the 6G applications are human-centric, and thus high security and privacy are key features. Recently, physical layer (PHY) security has become an emerging area of research. This work introduces a novel, to the best of our knowledge, PHY security approach called wireless link pairing (WiLP). In WiLP, signals received from both air interfaces in a hybrid radio frequency and optical network are required for successful signal reconstruction and processing at the receiver. The transmitted packets based on the IEEE 802.11 standards are redesigned, and improvements in performance are validated via simulations and experimental measurements using software-defined radio platforms. The obtained results demonstrate improvements in bit-error rate (BER) and the secrecy capacity for multiple modulation and coding schemes.

 
more » « less
NSF-PAR ID:
10171142
Author(s) / Creator(s):
;
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Optics Letters
Volume:
45
Issue:
14
ISSN:
0146-9592; OPLEDP
Page Range / eLocation ID:
Article No. 4005
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. The continuous increase in demanding for availability and ultra-reliability of low-latency and broadband wireless connections is instigating further research in the standardization of next-generation mobile systems. 6G networks, among other benefits, should offer global ubiquitous mobility thanks to the utilization of the Space segment as an intelligent yet autonomous ecosystem. In this framework, multi-layered networks will take charge of providing connectivity by implementing Cloud-Radio Access Network (C-RAN) functionalities on heterogeneous nodes distributed over aerial and orbital segments. Unmanned Aerial Vehicles (UAVs), High-Altitude Plat-forms (HAPs), and small satellites compose the Space ecosystem encompassing the 3D networks. Recently, a lot of interest has been raised about splitting operations to distribute baseband processing functionalities among such nodes to balance the computational load and reduce the power consumption. This work focuses on the hardware development of C-RAN physical (PHY-) layer operations to derive their computational and energy demand. More in detail, the 5G Downlink Shared Channel (DLSCH) and the Physical Downlink Shared Channel (PDSCH) are first simulated in MATLAB environment to evaluate the variation of computational load depending on the selected splitting options and number of antennas available at transmitter (TX) and receiver (RX) side. Then, the PHY-layer processing chain is software-implemented and the various splitting options are tested on low-cost processors, such as Raspberry Pi (RP) 3B+ and 4B. By overclocking the RPs, we compute the execution time and we derive the instruction count (IC) per program for each considered splitting option so to achieve the mega instructions per second (MIPS) for the expected processing time. Finally, by comparing the performance achieved by the employed RPs with that of Nvidia Jetson Nano (JN) processor used as benchmark, we shall discuss about size, weight, power and cost (SWaP-C)... 
    more » « less
  2. Massive MIMO is one of the key technologies in 5G wireless broadband, capable of delivering substantial improvements in capacity of next-generation wireless networks. However, due to its inherent complexity, its operation, reconfiguration, and enhancement present significant challenges and risks. In this paper we present RENEW, a fully programmable and observable massive MIMO network. We present the architectural design for full programmability at every layer of the wireless stack, from the radio hardware, including PHY and MAC layer configurations, all the way up to the network core functionality using network function virtualization. We also present mechanisms to enable observability at every layer of the stack. These include various indicators in the radio and core access network, hence enabling effective monitoring, troubleshooting, and performance evaluation of the network at large. 
    more » « less
  3. null (Ed.)
    Most prior works on deep learning-based wireless device classification using radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models, which were matured mainly for domains like vision and language. However, wireless RF data possesses unique characteristics that differentiate it from these other domains. For instance, RF data encompasses intermingled time and frequency features that are dictated by the underlying hardware and protocol configurations. In addition, wireless RF communication signals exhibit cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, and so on) that these signals inherently contain. In this article, we begin by explaining and showing the unsuitability as well as limitations of existing DNN feature design approaches currently proposed to be used for wireless device classification. We then present novel feature design approaches that exploit the distinct structures of RF communication signals and the spectrum emissions caused by transmitter hardware impairments to custom-make DNN models suitable for classifying wireless devices using RF signal data. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations. We end the article by presenting other feature design strategies that have great potential for providing further performance improvements of the DNN-based wireless device classification, and discuss the open research challenges related to these proposed strategies. 
    more » « less
  4. The open radio access network (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with artificial intelligence (AI) controllers. We circulated a survey among researchers, developers, and practitioners to gather their perspectives on O-RAN as a framework for 6G wireless research and development (R&D). The majority responded in favor of O-RAN and identified R&D of interest to them. Motivated by these responses, this paper identifies the limitations of the current O-RAN specifications and the technologies for overcoming them. We recognize end-to-end security, deterministic latency, physical layer real-time control, and testing of AI-based RAN control applications as the critical features to enable and discuss R&D opportunities for extending the architectural capabilities of O-RAN as a platform for 6G wireless. 
    more » « less
  5. Researchers are looking into solutions to support the enormous demand for wireless communication, which has been exponentially increasing along with the growth of technology. The sixth generation (6G) Network emerged as the leading solution for satisfying the requirements placed on the telecommunications system. 6G technology mainly depends on various machine learning and artificial intelligence techniques. The performance of these machine learning algorithms is high. Still, their security has been neglected for some reason, which leaves the door open to various vulnerabilities that attackers can exploit to compromise systems. Therefore, it is essential to evaluate the security of machine learning algorithms to prevent them from being spoofed by malicious hackers. Prior research has shown that the decision tree is one of the most popular algorithms used by 80% of researchers for classification problems. In this work, we collect the dataset from a laboratory testbed of over 100 Internet of things (IoT) devices. The devices include smart cameras, smart light bulbs, Alexa, and others. We evaluate classifiers using the original dataset during the experiment and record a 98% accuracy. We then use the label-flipping attack approach to poison our dataset and record the output. As a result, flipping 10%, 20%, 30%, 40%, and 50% of the poison data generated accuracies of 86%, 74%, 64%, 54%, and 50%, respectively. 
    more » « less