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This paper delves into the imperative need for coexistence and compatibility in the 470–694 MHz frequency band, as mandated by the World Radiocommunication Conference 2015 (WRC-15) and the WRC-23. It focuses on challenges in the coexistence of Digital Terrestrial Television Broadcasting (DTTB) and International Mobile Telecommunications-2020 (IMT-2020) services in downlink-only mode, particularly in adjacent-channel scenarios. Using Monte Carlo simulations, the study evaluates the probability of interference from IMT base stations with DTTB reception. The analysis thoroughly investigates the impact of the IMT transmitter's Adjacent Channel Leakage Ratio (ACLR) and the DTTB receiver's Adjacent-Channel Selectivity (ACS) on the probability of interference. The results demonstrate a significant degradation in the DTTB reception probability at the edge of coverage based on standard assumptions. To address these challenges, this paper provides recommendations for mitigating interference. These include defining enhanced ACLR regulations for IMT base stations, implementing antenna discriminations, providing specialized filters, and establishing national coordination procedures. The research provides valuable insights for informed decision making in spectrum management within the 470–694 MHz band, aiming to facilitate the coexistence of DTTB and IMT-2020 services, in line with international regulations and best practices. [ABSTRACT FROM AUTHOR]

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been applied widely in the field of communication. In this paper, a deep learning-based frequency modulation (FM) demodulation method is proposed for FM signal demodulation in complex electromagnetic environment. The received in-phase and quadrature (IQ) signals are fed into a designed model to recover the source signal. We perform experiments with random, voice and broadcasting signals under noise, multipath fading and single tone jamming scenarios. The results demonstrate that the proposed method outperforms the traditional method in terms of demodulation performance across all scenarios. Especially in the case of multipath fading and single tone jamming, our proposed method exhibits significant performance improvements over traditional demodulation. We further propose to use downsampling to reduce the computational complexity of DeepDeFM. The results indicate that by selecting a proper downsampling factor, it is possible to significantly reduce time complexity while maintaining high sound quality with minimal attenuation.

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network’s self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model’s performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.