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Register a new userDeep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond
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The unprecedented requirements of the Internet of Things (IoT) have made fine-grained optimization of spectrum resources an urgent necessity. Thus, designing techniques able to extract knowledge from the spectrum in real time and select the optimal spectrum access strategy accordingly has become more important than ever. Moreover, 5G and beyond (5GB) networks will require complex management schemes to deal with problems such as adaptive beam management and rate selection. Although deep learning (DL) has been successful in modeling complex phenomena, commercially-available wireless devices are still very far from actually adopting learning-based techniques to optimize their spectrum usage. In this paper, we first discuss the need for real-time DL at the physical layer, and then summarize the current state of the art and existing limitations. We conclude the paper by discussing an agenda of research challenges and how DL can be applied to address crucial problems in 5GB networks.
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Due to its high mobility and flexible deployment, unmanned aerial vehicle (UAV) is drawing unprecedented interest in both military and civil applications to enable agile wireless communications and provide ubiquitous connectivity. Mainly operating in an open environment, UAV communications can benefit from dominant line-of-sight links; however, it on the other hand renders the UAVs more vulnerable to malicious eavesdropping or jamming attacks. Recently, physical layer security (PLS), which exploits the inherent randomness of the wireless channels for secure communications, has been introduced to UAV systems as an important complement to the conventional cryptography-based approaches. In this paper, a comprehensive survey on the current achievements of the UAV-aided wireless communications is conducted from the PLS perspective. We first introduce the basic concepts of UAV communications including the typical static/mobile deployment scenarios, the unique characteristics of air-to-ground channels, as well as various roles that a UAV may act when PLS is concerned. Then, we introduce the widely used secrecy performance metrics and start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems, and extend the discussion to a more general scenario where the UAVs mobility is further exploited. For both cases, respectively, we summarize the commonly adopted methodologies in the corresponding analysis and design, then describe important works in the literature in detail. Finally, potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS in 5G and beyond networks.
Deep learning can be used to classify waveform characteristics (e.g., modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has demonstrated that one of the most crucial challenges in wireless deep learning is to counteract the channel action, which may significantly alter the waveform features. The problem is further exacerbated by the fact that deep learning algorithms are hardly re-trainable in real time due to their sheer size. This paper proposes DeepFIR, a framework to counteract the channel action in wireless deep learning algorithms without retraining the underlying deep learning model. The key intuition is that through the application of a carefully-optimized digital finite input response filter (FIR) at the transmitters side, we can apply tiny modifications to the waveform to strengthen its features according to the current channel conditions. We mathematically formulate the Waveform Optimization Problem (WOP) as the problem of finding the optimum FIR to be used on a waveform to improve the classifiers accuracy. We also propose a data-driven methodology to train the FIRs directly with dataset inputs. We extensively evaluate DeepFIR on a experimental testbed of 20 software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices and a 24-class modulation dataset. Experimental results show that our approach (i) increases the accuracy of the radio fingerprinting models by about 35%, 50% and 58%; (ii) decreases an adversarys accuracy by about 54% when trying to imitate other devices fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset; (iv) increases by 2x the accuracy of the modulation dataset.
High transmission rate and secure communication have been identified as the key targets that need to be effectively addressed by fifth generation (5G) wireless systems. In this context, the concept of physical-layer security becomes attractive, as it can establish perfect security using only the characteristics of wireless medium. Nonetheless, to further increase the spectral efficiency, an emerging concept, termed physical-layer service integration (PHY-SI), has been recognized as an effective means. Its basic idea is to combine multiple coexisting services, i.e., multicast/broadcast service and confidential service, into one integral service for one-time transmission at the transmitter side. This article first provides a tutorial on typical PHY-SI models. Furthermore, we propose some state-of-the-art solutions to improve the overall performance of PHY-SI in certain important communication scenarios. In particular, we highlight the extension of several concepts borrowed from conventional single-service communications, such as artificial noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These techniques are shown to be effective in the design of reliable and robust PHY-SI schemes. Finally, several potential research directions are identified for future work.
Industrial automation has created a high demand for private 5G networks, the deployment of which calls for an efficient and reliable solution to ensure strict compliance with the regulatory emission limits. While traditional methods for measuring outdoor interference include collecting real-world data by walking or driving, the use of unmanned aerial vehicles (UAVs) offers an attractive alternative due to their flexible mobility and adaptive altitude. As UAVs perform measurements quickly and semiautomatically, they can potentially assist in near realtime adjustments of the network configuration and fine-tuning its parameters, such as antenna settings and transmit power, as well as help improve indoor connectivity while respecting outdoor emission constraints. This article offers a firsthand tutorial on using aerial 5G emission assessment for interference management in nonpublic networks (NPNs) by reviewing the key challenges of UAV-mounted radio-scanner measurements. Particularly, we (i) outline the challenges of practical assessment of the outdoor interference originating from a local indoor 5G network while discussing regulatory and other related constraints and (ii) address practical methods and tools while summarizing the recent results of our measurement campaign. The reported proof of concept confirms that UAV-based systems represent a promising tool for capturing outdoor interference from private 5G systems.
Unmanned aerial vehicle (UAV) wireless communications have experienced an upsurge of interest in both military and civilian applications, due to its high mobility, low cost, on-demand deployment, and inherent line-of-sight (LoS) air-to-ground channels. However, these benefits also make UAV wireless communication systems vulnerable to malicious eavesdropping attacks. In this article, we aim to examine the physical layer security issues in UAV systems. In particular, passive and active eavesdroppings are two primary attacks in UAV systems. We provide an overview on emerging techniques, such as trajectory design, resource allocation, and cooperative UAVs, to fight against both types of eavesdroppings in UAV wireless communication systems. Moreover, the applications of non-orthogonal multiple access, multiple-input and multiple-output, and millimeter wave in UAV systems are also proposed to improve the system spectral efficiency and to guarantee security simultaneously. Finally, we discuss some potential research directions and challenges in terms of physical layer security in UAV systems.
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