Satellite-Terrestrial Communication Systems Optimization

Satellite-terrestrial communication systems integrate space-based nodes with ground networks to deliver ubiquitous, high-capacity connectivity across heterogeneous environments. Optimisation in this domain seeks to allocate limited resources—spectrum, power and computational capacity—while ensuring quality of service, energy efficiency and fairness among users. Key challenges arise from distinct propagation characteristics, such as long delays, Doppler shifts in low Earth orbit links and variable shadowing in terrestrial segments. Advanced frameworks employ non-convex and fractional programming, game-theoretic models and machine-learning-driven heuristics to balance throughput, latency and energy consumption. Techniques such as non-orthogonal multiple access (NOMA), cognitive spectrum sharing and cooperative relaying—including unmanned aerial vehicle relays—have demonstrated significant gains in spectral efficiency and coverage. Practical applications encompass Internet of Things backhaul in remote regions, resilient disaster-relief networks and 6G backhaul integration. As systems evolve towards multi-layer constellations and dynamic spectrum environments, optimisation strategies increasingly leverage joint user pairing, relay selection and adaptive power control to meet the demands of global digital inclusion and seamless mobility.

Research from Nature Portfolio

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Research from all publishers

Recent work has explored energy-efficient resource allocation in satellite-assisted IoT networks, introducing an outer approximation algorithm to solve a concave fractional programming problem that jointly optimises energy consumption, throughput and fairness among devices. Simulations demonstrate robust performance under varying constellation sizes and quality-of-service requirements. Another comprehensive survey of hybrid satellite-terrestrial networks towards 6G has identified key architectures—ranging from geostationary and low Earth orbit constellations to aerial base stations—and compared design optimisation techniques for secure communication, spectrum sharing and cooperative relaying. The survey highlights open issues in standardisation, inter-system interoperability and hardware impairments. A further study on joint user pairing and power allocation in a non-orthogonal multiple access framework for GEO and LEO satellite networks proposes a matching-based pairing strategy combined with iterative convex decomposition to maximise system capacity under decoding thresholds. Numerical results validate the superiority of the proposed scheme over conventional assignments, with enhanced fairness and throughput across multi-layer satellite links.

Satellite-Terrestrial Communication Systems Optimization publication trend

The graph below shows the total number of articles in satellite-terrestrial communication systems optimization across all publications each year (not limited to Nature Index journals).

Technical terms

Non-orthogonal multiple access (NOMA): A multiple-access scheme allowing users to share time and frequency resources via superposition coding and successive interference cancellation to boost spectral efficiency.

Cognitive radio: A dynamic spectrum-access technique where transmitters sense their spectral environment and opportunistically utilise under-used frequency bands while protecting incumbent services.

Fractional programming: An optimisation paradigm for objective functions expressed as ratios, often used to trade off energy efficiency against data throughput.

Ergodic capacity: The long-term average achievable rate of a fading channel assuming ideal coding and interleaving over channel variations.

References

1. Satellite synergy: Navigating resource allocation and energy efficiency in IoT networks. Journal of Network and Computer Applications (2024).
2. Hybrid Satellite–Terrestrial Networks toward 6G: Key Technologies and Open Issues. Sensors (2022).
3. Joint User Pairing and Power Allocation for NOMA-Based GEO and LEO Satellite Network. IEEE Access (2021).