Abstract :
[en] In conventional satellite communication systems, onboard resource management follows pre-design approaches with limited flexibility. On the one hand, this can simplify the satellite payload design. On the other hand, such limited flexibility hardly fits the scenario of irregular traffic and dynamic demands in practice. As a consequence, the efficiency of resource utilization could be deteriorated, evidenced by mismatches between offered capacity and requested traffic in practical operations. To overcome this common issue, exploiting multi-dimension flexibilities and developing advanced resource management approaches are of importance for next-generation high-throughput satellites (HTS). Non-orthogonal multiple access (NOMA), as one of the promising new radio techniques for future mobile communication systems, has proved its advantages in terrestrial communication systems. Towards future satellite systems, NOMA has received considerable attention because it can enhance power-domain flexibility in resource management and achieve higher spectral efficiency than orthogonal multiple access (OMA). From ground to space, terrestrial-based NOMA schemes may not be directly applied due to distinctive features of satellite systems, e.g., channel characteristics and limited onboard capabilities, etc. To investigate the potential synergies of NOMA in satellite systems, we are motivated to enrich this line of studies in this dissertation. We aim at resolving the following questions: 1) How to optimize resource management in NOMA-enabled satellite systems and how much performance gain can NOMA bring compared to conventional schemes? 2) For complicated resource management, how to accelerate the decision-making procedure and achieve a good tradeoff between complexity reduction and performance improvement? 3) What are the mutual impacts among multiple domains of resource optimization, and how to boost the underlying synergies of NOMA and exploit flexibilities in other domains?
The main contributions of the dissertation are organized in the following four chapters: First, we design an optimization framework to enable efficient resource allocation in general NOMA-enabled multi-beam satellite systems. We investigate joint optimization of power allocation, decoding orders, and terminal-timeslot assignment to improve the max-min fairness of the offered-capacity-to-requested-traffic ratio (OCTR). To solve the mixed-integer non-convex programming (MINCP) problem, we develop an optimal fast-convergence algorithmic framework and a heuristic scheme, which outperform conventional OMA in matching capacity to demand.
Second, to accelerate the decision-making procedure in resource optimization, we attempt to solve optimization problems for satellite-NOMA from a machine-learning perspective and reveal the pros and cons of learning and optimization techniques. For complicated resource optimization problems in satellite-NOMA, we introduce deep neural networks (DNN) to accelerate decision making and design learning-assisted optimization schemes to jointly optimize power allocation and terminal-timeslot assignment. The proposed learning-optimization schemes achieve a good trade-off between complexity and performance.
Third, from a time-domain perspective, beam hopping (BH) is promising to mitigate the capacity-demand mismatches and inter-beam interference by selectively and sequentially illuminating suited beams over timeslots. Motivated by this, we investigate the synergy and mutual influence of NOMA and BH for satellite systems to jointly exploit power- and time-domain flexibilities. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the capacity-demand gap. The global optimal solution may not be achieved due to the NP-hardness of the problem. We develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the synthetic synergy of combining NOMA and BH, and their individual performance gains compared to the benchmarks.
Fourth, from the spatial domain, adaptive beam patterns can adjust the beam coverage to serve irregular traffic demand and alleviate co-channel interference, motivating us to investigate joint resource optimization for satellite systems with flexibilities in power and spatial domains. We formulate a joint optimization problem of power allocation, beam pattern selection, and terminal association, which is in the format of MINCP. To tackle the integer variables and non-convexity, we design an algorithmic framework and a low-complexity scheme based on the framework. Numerical results show the advantages of jointly optimizing NOMA and beam pattern selection compared to conventional schemes.
In the end, the dissertation is concluded with the main findings and insights on future works.
