Text-to-speech (TTS) conversion is a crucial technology for various applications, including accessibility, education, and entertainment. With the rapid growth of big data, TTS conversion systems face new challenges in terms of data size and diversity. In this paper, we propose to use the state-of-the-art language model ChatGPT to enhance TTS conversion for big data. We first introduce the background of TTS conversion and big data, and then review the existing TTS conversion systems and their limitations. Next, we describe the architecture and training of ChatGPT, and how it can be applied to TTS conversion. Finally, we evaluate the performance of the ChatGPT-based TTS conversion system on a large-scale real-world big data dataset, and compare it with the existing TTS systems. Our experimental results demonstrate that ChatGPT can significantly improve the quality and efficiency of TTS conversion for big data.

Non-orthogonal multiple access (NOMA) networks play an important role in defense communication scenarios. Deep learning (DL)-based solutions are being considered as viable ways to solve the issues in fifth-generation (5G) and beyond 5G (B5G) wireless networks, since they can provide a more realistic solution in the real-world wireless environment. In this work, we consider the deep Q-Network (DQN) algorithm-based user pairing downlink (D/L) NOMA network. We have applied the convex optimization (CO) technique and optimized the sum rate of all the wireless users. First, the near-far (N-F) pairing and near-near and far-far (N-N and F-F) pairing strategies are investigated for the multiple numbers of users, and a closed-form (CF) expression of the achievable rate is derived. After that, the optimal power allocation (OPA) factors are derived using the CO technique. Through simulations, it has been demonstrated that the DQN algorithms perform much better than the deep reinforcement learning (DRL) and conventional orthogonal frequency-division multiple access (OFDMA) schemes. The sum-rate performance significantly increases with OPA factors. The simulation results are in close agreement with the analytical results.