Future 5th generation (5G) networks are expected to enable three key services -enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project (3GPP) URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. The paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution (LTE) to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, the paper discusses the potential future research directions and challenges in achieving the URLLC requirements.

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Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. Multiple research groups have recently proposed to use spectrum cartography in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. In our novel approach, we exploit the sparsity of primary users in space to formulate the cartography process as a compressive sensing problem. Further, we present a novel algorithm for solving the cartography problem that builds on the well-known Orthogonal Matching Pursuit algorithm. We evaluate the performance of our approach by simulating a cognitive radio network where primary users are low power wireless microphones. Our simulation results show a significant improvement in reconstruction error, in comparison to two existing compressive sensing based methods.

Unmanned Aerial Vehicles (UAV) are gaining popularity in a range of areas and are already being used for a wide variety of purposes. While UAVs have many desirable features, limited battery lifetime is identified as a key restriction in UAV applications. Typical UAVs being electric devices, powered by on-board batteries, this constrain has limited their capabilities to a considerable extent. Thus planning UAV missions in an energy efficient manner is of utmost importance. To achieve this, for prediction of power consumption, it is necessary to have a reliable power consumption model. In this paper, we present a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities. The power consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

In this article, we aim to address the question of how to exploit the unlicensed spectrum to achieve ultra-reliable, low-latency communications (URLLC). Potential URLLC PHY mechanisms are reviewed and then compared via simulations to demonstrate their potential benefits to URLLC. Although a number of important PHY techniques help with URLLC, the PHY layer exhibits an intrinsic trade-off between latency and reliability, posed by limited and unstable wireless channels. We then explore MAC mechanisms and discuss multi-channel strategies for achieving low-latency LTE unlicensed-band access. We demonstrate, via simulations, that the periods without access to the unlicensed band can be substantially reduced by maintaining channel access processes on multiple unlicensed channels, choosing the channels intelligently, and implementing RTS/CTS.

Unmanned Aerial Vehicles (UAVs) are gaining popularity in many aspects of wireless communication systems. UAV-mounted mobile base stations (UAV-BSs) are an effective and costefficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. However, UAV-BSs have their limitations and complications, for instance, limited available energy. In addition, when several UAV-BSs are deployed to provide coverage to a specific area, the possibility of inter-UAV collisions and the interference to ground users increase. We propose Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) based methods to deploy UAV-BSs under energy constraints to provide efficient and fair coverage to the ground users, while minimising inter-UAV collisions and interference to ground users. The proposed methods outperform the baseline methods by an average increase of 38.94% in system fairness, 42.54% in individual user coverage, and 15.04% in total system coverage, in comparison with the baseline methods. INDEX TERMS Unmanned aerial vehicles (UAVs), wireless coverage, reinforcement learning.

Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. In our previous work we formulated spectrum cartography as a compressive sensing problem and we illustrated how cartography can be used in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. This paper investigates the performance of compressive sensing based approach to cartography in a fading environment where realtime channel estimation is not feasible. To accommodate for lack of channel information we take an iterative approach. We extend the well-known iteratively reweighted 1 minimisation approach by exploiting spatial correlation between two points in space. We evaluate the performance in an urban environment where Rayleigh fading is prominent. Our numerical results show a significant improvement in the probability of accurately making a spectrum sensing decision, in comparison to the well-known weighted approach and the traditional compressive sensing based method.

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