The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as 3D deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools, such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

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A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.

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Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

Since the early 1990s, a large number of chaos-based communication systems have been proposed exploiting the properties of chaotic waveforms. The motivation lies in the significant advantages provided by this class of non-linear signals. For this aim, many communication schemes and applications have been specially designed for chaos-based communication systems where energy, data rate, and synchronization awareness are considered in most designs. Recently, the major focus, however, has been given to the non-coherent chaos-based systems to benefit from the advantages of chaotic signals and non-coherent detection and to avoid the use of chaotic synchronization, which suffers from weak performance in the presence of additive noise. This paper presents a comprehensive survey of the entire wireless radio frequency chaos-based communication systems. First, it outlines the challenges of chaos implementations and synchronization methods, followed by comprehensive literature review and analysis of chaos-based coherent techniques and their applications. In the second part of the survey, we offer a taxonomy of the current literature by focusing on non-coherent detection methods. For each modulation class, this paper categorizes different transmission techniques by elaborating on its modulation, receiver type, data rate, complexity, energy efficiency, multiple access scheme, and performance. In addition, this survey reports on the analysis of tradeoff between different chaos-based communication systems. Finally, several concluding remarks are discussed.

Wireless Chaos-Based Communication Systems: A Comprehensive Survey

A new Multi-Carrier Differential Chaos Shift Keying (MC-DCSK) modulation is presented in this paper. The system endeavors to provide a good trade-off between robustness, energy efficiency and high data rate, while still being simple compared to conventional multi-carrier spread spectrum systems. This system can be seen as a parallel extension of the DCSK modulation where one chaotic reference sequence is transmitted over a predefined subcarrier frequency. Multiple modulated data streams are transmitted over the remaining subcarriers. This transmitter structure increases the spectral efficiency of the conventional DCSK system and uses less energy. The receiver design makes this system easy to implement where no radio frequency (RF) delay circuit is needed to demodulate received data. Various system design parameters are discussed throughout the paper, including the number of subcarriers, the spreading factor, and the transmitted energy. Once the design is explained, the bit error rate performance of the MC-DCSK system is computed and compared to the conventional DCSK system under multipath Rayleigh fading and an additive white Gaussian noise (AWGN) channels. Simulation results confirm the advantages of this new hybrid design.

Design and Analysis of a Multi-Carrier Differential Chaos Shift Keying Communication System

This paper provides a review of the principles of chaotic digital communications using correlator receivers. Modulation schemes using one and two chaotic basis functions, as well as coherent and noncoherent correlation receivers, are discussed. The performance of differential chaos shift keying (DCSK) in multipath channels is characterized. Results are presented for DCSK with multiuser capability and multiple bits per symbol.

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Chaotic communications with correlator receivers: theory and performance limits

FM-DCSK: A robust modulation scheme for chaotic communications

In binary Differential Chaos Shift Keying (DCSK), each information bit is mapped to the correlation between two pieces of a chaotic waveform. The receiver determines the correlation (which is proportional to the energy per bit) in order to demodulate the received signal. Since a chaotic signal is not periodic, the energy per bit is not constant and can only be estimated, even in the noise-free case. This estimation has a non-zero variance that limits the attainable data rate. This problem can be avoided if the energy per bit is kept constant. In this paper, the DCSK technique is combined with frequency modulation in order to achieve two properties: the excellent noise performance of DCSK is maintained; in addition, the energy per bit is kept constant in order not to limit the data rate. A low-pass equivalent model that significantly speeds up the simulation of an FM-DCSK system is also developed. Finally the noise performance of the proposed FM-DCSK system is given.

FM-DCSK: a novel method for chaotic communications

A survey of handover management in LTE-based multi-tier femtocell networks

Over the past five years many different modulation schemes have been proposed for chaotic communications. Of these, the DCSK and FM-DCSK techniques offer the best noise performance. However, even the noise performance of these schemes is a few dB worse than that of the conventional noncoherent FSK technique. The paper shows how the noise performance of DCSK and FM-DCSK systems can be improved considerably. Two solutions are considered: in the first version more than one information bit is sent using the same reference signal, while in the second version the received noisy sample functions are cleaned by averaging.

Zoltán Jakó

Papers

Chaotic communications with correlator receivers: theory and performance limits

FM-DCSK: A robust modulation scheme for chaotic communications

FM-DCSK: a novel method for chaotic communications

A survey of handover management in LTE-based multi-tier femtocell networks

Enhanced versions of DCSK and FM-DCSK data transmission systems