The modeling of low earth orbit (LEO) satellite channel depends on its Doppler power spectrum. Due to satellite during transit, diversity and dynamic channel scene, the modeling of Doppler power spectrum has two serious problems: one is that the shape of the Doppler power spectrum will vary with the change of scenes and time, but the use of the existing traditional Doppler power spectrum models is difficult to accurately describe them. The other one is that the amount of measured data used for modeling is too large to handle, and data aliasing is easy to occur between different scenes, which will make it difficult to ensure the accuracy of model parameter fitting. In this paper, a two-side truncated asymmetric Doppler power spectrum model is proposed to universally describe the Doppler power spectrum during satellite transit. In addition, the atlas fingerprint method clustering is adopted to realize the classification of the measured data samples of Doppler power spectrum in multiple scenes, and the data with the strongest representation ability in each scene is selected to fit the model parameters. The simulation results show that the proposed model is in good agreement with the measured data. Therefore, parameter fitting using the proposed method can improve the accuracy of the model, so as to better describe the characteristics of LEO satellite channel fading in the frequency domain.

The discontinuous reception (DRX) mechanism has been adopted in a Long-Term Evolution (LTE) system as a core technology to prolong the user equipment's (UE's) battery lifetime. With the development of Mobile Internet, there is an increasingly urgent need to optimize the DRX performance to accommodate emerging applications. To describe the self-similarity exhibited by the applications, the truncated-Pareto distributed arrival traffic model is introduced into the LTE-DRX analytical framework. Following this premise, an online power-saving strategy (OPSS) has been designed on the basis of our previously proposed DRX analytical model. The OPSS, for the purpose of improving the UE's energy efficiency, is composed of an estimation phase and an optimization phase. In the first phase, several derived statistical estimators are deployed to capture the fluctuations of traffic conditions and DRX operations. It is shown that these estimators could estimate the statistics within just 1 s. In the second phase, the DRX configuration is optimized by considering the tradeoff between packet delay and power savings. Through simulations, we study the impact of various traffic conditions and the efficiency of OPSS. The results have demonstrated that the proposed OPSS could outperform the conventional LTE DRX mechanism in terms of both energy conservation and packet delay.

The 3rd Generation Partnership Project (3GPP) 5G Non-Terrestrial Networks (NTN) adopt Orthogonal Frequency Division Multiplexing (OFDM) to enable integrated space-ground networks via Low Earth Orbit (LEO) satellite global connectivity. However, the rapid movement of LEO satellites induces a significant non-uniform Doppler shift across subcarriers, resulting in the signal bandwidth changes that leads to sampling point offsets and severely impacting demodulation performance. Traditional Doppler compensation algorithms focus mainly on addressing the uniform Carrier Frequency Offset (CFO) caused by crystal oscillation. In LEO satellite broadband communication systems, the Sampling Frequency Offset (SFO), caused by the non-uniform Doppler frequency shift, can be tens of times greater than the CFO, leading to non-negligible phase rotation, inter-carrier interference (ICI), and inter-symbol interference (ISI). Consequently, a low-complexity algorithm is required to address the fast time-varying SFO — one of the core challenges in these systems. In this paper, we derive a closed-form expression for the signal distortion and propose a model-driven compensation method that leverages the predictability of satellite trajectories. The proposed method effectively removes phase rotation and mitigates ICI and ISI through fast Fourier transform (FFT) window adjustment and phase rotation compensation. The accuracy of the model is validated through extensive simulations and a real satellite communication trial. Results demonstrate that the proposed method supports higher-order modulations, leading to an average spectral efficiency improvement of approximately 50%. This pioneering research promises to ensure robust performance in dynamic LEO satellite environments.

Multi-beam satellite communication systems are promising architectures in the future. A packet is transmitted by multi-satellite and multi-beam cooperatively, which can provide efficient spectrum utilization, improve system throughput, and guarantee Quality of Services (QoS). In multi-beam satellite communication systems, multi-layer and multi-dimensional radio resources change dynamically, which leads to the discontinuity of optimal resources and the lack of mapping balance between packets and radio resources. To deal with these problems, we propose a cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm and an adaptive packet segmentation scheme based on the irregular gradient algorithm. The cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm simulates cross-layer and cross-dimension optimization for the high-dynamic and multi-dimensional radio resources by considering the channel state information (CSI) and QoS in the multi-beam satellite communication system. The optimal resources are taken as the weight of irregular gradient algorithm to segment packets and map packets to radio resources, which can realize the mapping balance between packets and radio resources and ensure the efficiency and reliability of communication. The simulations show that the new transmission scheme improves the normalized system throughput and user satisfaction index by 18.7% and 6.2%, respectively.

The fog radio access network (F-RAN) equipped with enhanced remote radio heads (eRRHs), which can pre-store some requested files in the edge cache and support mobile edge computing (MEC). To guarantee the quality-of-service (QoS) and energy efficiency of F-RAN, a proper content caching strategy is necessary to avoid coarse content storing locally in the cache or frequent fetching from a centralized baseband signal processing unit (BBU) pool via backhauls. In this paper we investigate the relationships among eRRH/terminal activities and content requesting in F-RANs, and propose an edge content caching strategy for eRRHs by mining out mobile network behavior information. Especially, to attain the inference for appropriate content caching, we establish a pre-mapping containing content preference information and geographical influence by an efficient non-uniformed accelerated matrix completion algorithm. The energy consumption analysis is given in order to discuss the energy saving properties of the proposed edge content caching strategy. Simulation results demonstrate our theoretical analysis on the inference validity of the pre-mapping construction method in static and dynamic cases, and show the energy efficiency achieved by the proposed edge content pre-caching strategy.