Design and Resource Allocation of NOMA-based Transmission Scheme for Industrial Collaborative AR

• Collaborative augmented reality (AR), which enables interaction and consistency in multi-user AR scenarios, is a promising technology for AR-guided remote monitoring, optimization, and troubleshooting of complex manufacturing processes. However, for uplink high data rate demands in collaborative-AR, the design of an efficient transmission and resource allocation scheme is demanding in resource-constrained wireless systems. To address this challenge, we propose a collaborative non-orthogonal multiple access (C-NOMA)-enabled transmission scheme by exploiting the fact that multi-user interaction often leads to common and individual views of the scenario (e.g., the region of interest). C-NOMA is designed as a two-step transmission scheme by treating these views separately and allowing users to offload the common views partially. Further, we define an optimization problem to jointly optimize the time and power allocation for AR users, with an objective of minimizing the maximum rate-distortion of the individual views for all users while guaranteeing a target distortion of their common view for its mutual significance. For its inherent non-linearity and non-convexity, we solve the defined problem using a primal-dual interior-point algorithm with a filter line search as well as by developing a successive convex approximation (SCA) method. The simulation results demonstrate that the optimized C-NOMA outperforms the non-collaborative baseline scheme by 23.94% and 77.28% in terms of energy consumption and achievable distortion on the common information, respectively. 

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Keyword

AR

interior-point filter line search

NOMA

rate-distortion

successive convex approximation (SCA)

Publication and Content Type

ref (subject category)

kon (subject category)