Aerial Reconfigurable Intelligent Surface-Enabled URLLC UAV Systems

Abstract

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).

Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond

“…Due to densely distributed industrial equipment (such as metal machinery, unpredictable movement of things (robots and trucks), wooden structures, and thick pillars), wireless signals are easily blocked and reduce performance reliability. When the direct communication link is impeded, RIS is a viable choice to provide an alternate transmission link [ 21 , 25 , 221 , 222 ]. The communication link can be created using a RIS mounted on the factory ceiling or the wall, delivering mission-critical industrial communication with seamless, reliable connectivity.…”

Taming Aerial Communication with Flight-assisted Smart Surfaces in 6G Era

“…The trajectory optimization is efficiently solved via machine learning (ML) tools such as deep networks, while the passive beamforming optimization problem can be tackled via successive convex approximation (SCA) or semidefinite relaxation (SDR) [10]. In the multi-user scenario, one can alternatively schedule users with a time division multiple access (TDMA) scheme [11] or, in order to improve fairness and robustness, consider max-min approaches thereby assigning at each user a given figure of merit such as, e.g., the signal-to-noise ratio (SNR) or the bit error rate (BER), and aiming at maximizing the worst figure among the target users [12].…”

Outage performance analysis of RIS-assisted UAV wireless systems under disorientation and misalignment

“…As a result of these efforts two system models have been developed. In the first, the RIS is attached to the UAV [19]- [24], while in the second, the RIS is attached to a fixed position, such as a wall, and has a clear line of sight (LOS) to the RIS [25]- [33]. For both system models, great effort have been made to optimize and analyze their performance.…”

“…UAV placement, transmission block length, RIS meta-atoms (MAs) phase shifts (PSs), and power allocation in a UAV wireless system where the RIS was attached to the UAV. In [20], a problem of maximization the minimum signal-tonoise ratio (SNR) in a target area was formulated and solved for the same system model as in [19]. In [21], the performance of UAV with attached RIS wireless systems was quantified in terms of outage probability (OP), energy efficiency, and ergodic capacity, assuming that both the source (S)-UAV and UAV-destination channels experience Rician small-scale fading.…”

“…For both system models, great effort have been made to optimize and analyze their performance. In [19], the decoding error probability was minimized by optimizing the A.-A. A. Boulogeorgos and A. Alexiou are with the of Digital Systems, University of Piraeus Piraeus 18534 Greece (e-mails: al.boulogeorgos@ieee.org, alexiou@unipi.gr).…”