In order to decrease energy consumption caused by the dense deployment of pico base stations (PBSs) in heterogeneous cellular networks (HetNets), this paper first analyzes the energy efficiency (EE) of two-tier HetNets and then proposes a method to maximize the network EE by adjusting the PBS transmit power. The two-tier HetNets are modeled by the Poisson point process (PPP) and the Poisson hole process (PHP), and then the coverage probability of the macro base station (MBS) and the PBS in the two-tier HetNets is derived based on the mean interference to signal ratio (MISR). According to the user association probability, the coverage probability of the PPP-PHP HetNets is obtained. Then, the tractable expression of the average achievable rate is deduced on the basis of the relationship between the coverage probability and the average achievable rate. Finally, the expression of EE is derived and the EE optimization algorithm is proposed based on the PBS transmit power. The simulation results show that the PPP-PHP network is superior to the PPP-PPP network in terms of coverage probability and EE, and the network EE can be effectively improved by setting an appropriate PBS transmit power.

With the development of the marine economy and the increase in marine activities, deep saturation diving has gained significant attention. Helium speech communication is indispensable for saturation diving operations and is a critical technology for deep saturation diving, serving as the sole communication method to ensure the smooth execution of such operations. This study introduces deep learning into helium speech recognition and proposes a spectrogram-based dual-model helium speech recognition method. First, we extract the spectrogram features from the helium speech. Then, we combine a deep fully convolutional neural network with connectionist temporal classification (CTC) to form an acoustic model, in which the spectrogram features of helium speech are used as an input to convert speech signals into phonetic sequences. Finally, a maximum entropy hidden Markov model (MEMM) is employed as the language model to convert the phonetic sequences to word outputs, which is regarded as a dynamic programming problem. We use a Viterbi algorithm to find the optimal path to decode the phonetic sequences to word sequences. The simulation results show that the method can effectively recognize helium speech with a recognition rate of 97.89% for isolated words and 95.99% for continuous helium speech.

Helium speech, the language spoken by divers in the deep sea who breathe a high-pressure helium–oxygen mixture, is almost unintelligible. To accurately unscramble helium speech, a neural network based on deep learning is proposed. First, an isolated helium speech corpus and a continuous helium speech corpus in a normal atmosphere are constructed, and an algorithm to automatically generate label files is proposed. Then, a convolution neural network (CNN), connectionist temporal classification (CTC) and a transformer are combined into a speech recognition network. Finally, an optimization algorithm is proposed to improve the recognition of continuous helium speech, which combines depth-wise separable convolution (DSC), a gated linear unit (GLU) and a feedforward neural network (FNN). The experimental results show that the accuracy of the algorithm, upon combining the CNN, CTC and the transformer, is 91.38%, and the optimization algorithm improves the accuracy of continuous helium speech recognition by 9.26%.

Speech produced in helium–oxygen (heliox) environments in deep saturation diving exhibits pronounced spectral shifts and temporal distortions, which severely degrade automatic speech recognition (ASR) systems trained on normal-air corpora. Existing studies often adopt a restoration-then-recognition paradigm by training waveform mapping networks on paired heliox/air recordings. However, in realistic low-resource data collection, paired recordings are typically obtained by independent re-reading and are therefore not strictly time-aligned, which makes regression-style restoration more sensitive to pairing errors and increases the risk of front-end distortions. This paper proposes a robust recognition framework for heliox speech, termed PRL-DAS (Physics-informed Resampling and LoRA with Duration-Adaptive Speed). The framework consists of a physics-inspired linear resampling warm start (PhysSpeed), parameter-efficient Low-Rank Adaptation (LoRA), and duration-adaptive speed (DAS) inference enhancement. Specifically, we first apply physics-motivated linear resampling as a coarse warm start, and then perform mixed-domain LoRA fine-tuning of a Whisper foundation model to absorb residual non-linear differences. On a corpus of 1048 paired Chinese heliox utterances under leave-one-speaker-out (LOSO) evaluation, using Whisper-Medium as the base model, PhysSpeed followed by mixed-domain LoRA reduces the overall character error rate (CER) from 49.33% with PhysSpeed preprocessing only to 25.79%, while also improving performance on the normal domain. Furthermore, the full PRL-DAS framework applies Soft-DAS, a lightweight smooth schedule motivated by duration-dependent variation in the optimal resampling factor, and further reduces the overall CER to 24.37% without additional training cost.