Optimizing Utility-Energy Efficiency for the Metaverse over Wireless Networks under Physical Layer Security

TL;DR

This paper develops an optimal algorithm for maximizing utility-energy efficiency in wireless Metaverse networks, considering application-specific utility, energy constraints, and physical-layer security, with proven global optimality and linear complexity.

Contribution

It introduces a novel global optimization algorithm for weighted sum-UEE in wireless networks, applicable to any concave utility function, with linear time complexity.

Findings

The proposed algorithm achieves global optimality for sum-of-ratios problems.

Simulation results show the algorithm outperforms existing methods.

The technique is applicable to other wireless network optimization problems.

Abstract

The Metaverse, an emerging digital space, is expected to offer various services mirroring the real world. Wireless communications for mobile Metaverse users should be tailored to meet the following user characteristics: 1) emphasizing application-specific perceptual utility instead of simply the transmission rate, 2) concerned with energy efficiency due to the limited device battery and energy intensiveness of some applications, and 3) caring about security as the applications may involve sensitive personal data. To this end, this paper incorporates application-specific utility, energy efficiency, and physical-layer security (PLS) into the studied optimization in a wireless network for the Metaverse. Specifically, after introducing utility-energy efficiency (UEE) to represent each Metaverse user's application-specific objective under PLS, we formulate an optimization to maximize the network's weighted sum-UEE by deciding users' transmission powers and communication bandwidths. The formulated problem belongs to the sum-of-ratios optimization, for which prior studies have demonstrated its difficulty. Nevertheless, our proposed algorithm 1) obtains the global optimum for the weighted sum-UEE optimization, via a transform to parametric convex optimization problems, 2) applies to any utility function which is concave, increasing, and twice differentiable, and 3) achieves a linear time complexity in the number of users (the optimal complexity in the order sense). Simulations confirm the superiority of our algorithm over other approaches. We explain that our technique for solving the sum-of-ratios optimization is applicable to other optimization problems in wireless networks and mobile computing.