Extended Reality via Cooperative NOMA in Hybrid Cloud/Mobile-Edge Computing Networks

• Published in: IEEE internet of things journal Vol. 11; no. 7; p. 1
• Main Authors: Reifert, Robert-Jeron, Dahrouj, Hayssam, Sezgin, Aydin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Algorithms; central cloud; Cloud computing; cooperative NOMA; Criticality aspects; Delays; Devices; Edge computing; Extended reality; hybrid networks; Mathematical programming; Maximization; Mobile computing; mobile-edge computing; Network latency; NOMA; Nonorthogonal multiple access; Optimization; Performance evaluation; Power consumption; Power management; Resource management; uncrewed aerial vehicles; Unmanned aerial vehicles; User experience; X reality
• DOI: 10.1109/JIOT.2023.3336393
• ISSN: 2327-4662

Extended reality (XR) applications often perform resource-intensive tasks, which are computed remotely, a process that prioritizes the latency criticality aspect. To this end, this paper shows that through leveraging the power of the central cloud (CC), the close proximity of edge computers (ECs), and the flexibility of uncrewed aerial vehicles (UAVs), a UAV-aided hybrid cloud/mobile-edge computing architecture promises to handle the intricate requirements of future XR applications. In this context, this paper distinguishes between two types of XR devices, namely, strong and weak devices. The paper then introduces a cooperative non-orthogonal multiple access (Co-NOMA) scheme, pairing strong and weak devices, so as to aid the XR devices quality-of-user experience by intelligently selecting either the direct or the relay links toward the weak XR devices. A sum logarithmic-rate maximization problem is, thus, formulated so as to jointly determine the computation and communication resources, and link-selection strategy as a means to strike a trade-off between the system throughput and fairness. Subject to realistic network constraints, e.g., power consumption and delay, the optimization problem is then solved iteratively via discrete relaxations, successive-convex approximation, and fractional programming, an approach which can be implemented in a distributed fashion across the network. Simulation results validate the proposed algorithms performance in terms of log-rate maximization, delay-sensitivity, scalability, and runtime performance. The practical distributed Co-NOMA implementation is particularly shown to offer appreciable benefits over traditional multiple access and NOMA methods, highlighting its applicability in decentralized XR systems.