{'fraction_non_alphanumeric': 0.024824952259707194, 'fraction_numerical': 0.005092297899427116, 'mean_word_length': 4.977186311787072, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Optimal Resource Allocation for Superposition Coding-Based Hybrid Digital–Analog System

Hybrid digital–analog (HDA) video transmission is a new cross-layer design, which can be widely used in Internet of Things. The key problem of HDA video transmission is to find the optimal resource allocation between the digital and analog part. This paper presents a new general resource allocation algorithm for superposition coding-based HDA system. On one hand, in order to achieve successful decoding in digital part, the bitrate is controlled by the quantization parameter (QP), and the channel coding rate and modulation order are determined by the signal to interference noise power ratio, in which analog part is considered as the interference. On the other hand, the overall video quality is directly determined by the mean square error of analog part, which depends jointly on the data variance of the analog part, the power allocated to the analog part and the channel noise power. We propose a prediction model to describe how the data variance of the analog part changes with the QP in the digital part. Based on the proposed model, the power allocation of two parts can be quantitatively connected to form an optimization problem. We prove the convexity of the resource allocation problem and the gradient descent method is utilized in system implementation. With extensive simulations, the proposed algorithm is validated, achieving 1.4–5.3 dB gain over the conventional digital system, and 6.2–7.4 dB gain over pseudo-analog system in peak signal-to-noise ratio.