A Network Resource Slicing Method for Consumer Electronics Based on Holographic Counterpart Real Time Monitoring

The rapid development of Internet of things leads to a significant increase in connected consumer electronic counts, resulting in growing demands for network resources, e.g., bandwidth and spectrum. Their traditional allocation methods struggle in dynamic environments, exhibiting slow decision-making, low efficiency, poor slice isolation, and limited fine-grained slicing capabilities. To solve these issues, this paper proposes a network resource slicing method with holographic counterpart real time monitoring. First, it establishes a three-tier cellular heterogeneous network architecture based on holographic counterparts suitable for consumer electronics. Next, it constructs a mapping relationship between holographic counterparts and resource slices, enabling fine-grained resource allocation within this framework. Then, it introduces a solving method by integrating long short-term memory networks for time-series data processing and long-term dependency capture, along with the Advantage Actor-Critic (A2C) algorithm for decision optimization to address dynamic and complex nature of network environments. Experiments result show that our method outperforms traditional ones in spectral and energy efficiency and system utility. Specifically, it achieves 8.2 bps/W in energy efficiency, which is 3.9% and 6.5% higher than A2C and deep Q-network respectively.