Cognitive UAV-Aided URLLC and mMTC Services: Analyzing Energy Efficiency and Latency

Ultra-reliable low-latency communications (URLLCs) and the adoption of unmanned aerial vehicles (UAVs) for network coverage improvement have emerged as key enabling communication paradigms for the successful deployment of mobile communication services envisioned in both the fifthgeneration (5G) and the sixth-generation (6G) networks. This paper provides a comprehensive survey about the current state-of-the-art (SoA) on UAV-enabled URLLC networks. The core idea is to highlight the main characteristics of this new network concept as well as its critical aspects. We first perform an overview of URLLC by illustrating the main features and related implementation chellenges. Subsequently, an indepth discussion on UAV-enabled networks is provided, with a special emphasis on how URLLCs and UAV communication can be classified as complementary paradigms. Finally, a comprehensive analysis and classification of the current research advancements on UAV-enabled URLLCs networks is carried out. This paper is concluded by pointing out some the open challenges and our visions related to future directions which should be undertaken in order to pave the way towards the practical implementation of this promising network architecture.INDEX TERMS Beyond fifth-generation (5G) , Ultra-reliable low-latency communications (URLLCs), unmanned aerial vehicles (UAVs) I. INTRODUCTION

Section: System Optimization Frameworkmentioning

confidence: 99%

Section: A Analytical Approachesmentioning

confidence: 99%

UAV-Enabled Ultra-Reliable Low-Latency Communications for 6G: A Comprehensive Survey

Masaracchia

Nguyen

et al. 2021

“…Despite its potential, there is only a few attempts on studying resource allocation of URLLC-enabled CRNs. By considering both massive machine-type communication (mMTC) and URLLC services for a cognitive unmanned aerial vehicle (UAV)-aided network, the authors in [20] first derived the analytical expressions of the throughput and then developed an efficient algorithm for EE maximization problem. Tackling the stringent requirement of latency and reliability in the URLLC and enhanced mobile broadband (eMBB) coexistence system was studied in [21], where the authors developed a dynamic spectrum allocation scheme to help alleviating the packet collisions of multiplexing URLLC and eMBB packets.…”

Section: A Related Workmentioning

confidence: 99%

Joint Design of Beamforming and Antenna Selection in Short Blocklength Regime for URLLC in Cognitive Radio Networks

Khanh

Nguyen

2021

This paper considers the short blocklength regime for cognitive radio networks (CRNs) to deliver ultra-reliable and low-latency communications (URLLCs) promised for beyond 5G networks. The secondary system consists of a secondary transmitter (ST) and multiple secondary users, which are allowed to access the same spectrum of licensed users (i.e., the primary system). Adopting linear beamforming at ST, we formulate the optimization problem of the energy-efficient maximization for the secondary system under the power constraint at ST and interference power constraints at primary receivers. In the short blocklength regime, the rate function is more complex and computationally intractable than the traditional Shannon rate function, which makes the formulated problem more difficult to solve. By leveraging techniques from the Dinkelbach method and the inner approximation method, we first devise newly approximated functions to convexify nonconvex constraints, and the iterative algorithm is then developed to obtain at least a locally optimal solution. To further enhance the energy efficiency of the secondary system, we consider a joint optimization of beamforming and antenna selection at ST, where binary variables are introduced to establish the operation modes of transmit antennas. To solve the mixed-integer nonconvex problem, we incorporate the penalty function into the objective function to dealing with the uncertainty of binary variables. Numerical results are provided to demonstrate the fast convergence and merits of the proposed algorithms, as well as to confirm the role of antenna selection in improving energy efficiency.INDEX TERMS Antenna selection, beamforming, broadcast channel, cognitive radio, Dinkelbach method, energy efficiency, inner approximation, mixed-integer programming.

“…This network satisfies URLLC requirements for UAV control and non-payload signal transmission. In [24], the performance of cognitive UAV-URLLCs based on NOMA and massive machine-type communication (mMTC) services has been analysed. Optimisation method is frequently used in UAV assisted URLLC system research to meet service quality rules [23], [25].…”

Section: Introductionmentioning

confidence: 99%

Aerial Reconfigurable Intelligent Surface-Enabled URLLC UAV Systems

Yin

Do‐Duy

et al. 2021

In this paper, we propose an aerial reconfigurable intelligent surface (RIS) system to support the stringent constraints of ultra-reliable low latency communication (URLLC). Specifically, unmanned aerial vehicles (UAVs) employed onboard RIS panels can act as repeaters to reflect the signal from macro base station (MBS) to all users in the networks. To overcome the dense networks' interference, we propose to use zero-forcing beamforming and time division multiplexing access (TDMA) scheme where each UAV can serve a number of users in its own cluster. We formulate a optimisation framework in terms of UAVs' deployment, power allocation at MBS, phase-shift of RIS, and blocklength of URLLC. Due to highly nonconvex and complex optimisation problem, we first consider to use a deep neural network (DNN) to solve the optimal UAVs' deployment. Then, the optimal resource allocation is proposed to provide the maximal reliability of the considered system with respect to the users' fairness. From the representative numerical results, our proposed scheme is shown to superior to other benchmarks which exhibits the positive impact of aerial RIS in supporting stringent demands of URLLC.INDEX TERMS Ultra-reliable low-latency communications (URLLC), Reconfigurable Intelligent Surface (RIS), Unmanned aerial vehicle (UAV).