Min Sheng

TL;DR: An optimization framework is put forward that considers all of these coupled factors to investigate the spectrum sharing problem in D2D networks and introduces an objective that combines the access rate and the network sum rate and then maximize it subject to users' quality-of-service requirements and resource allocation constraints.

TL;DR: This work proposes a state-action-reward-state-action (SARSA) based actor-critic reinforcement learning (SACRL) resource allocation strategy to achieve the optimal resource allocation and IoRT data scheduling with casual information at LEO satellites and simulations validate the convergence property and the effectiveness of the proposed SACRL algorithm.

TL;DR: This paper extends the traditional dynamic programming algorithms and proposes a finite-embedded-infinite two-level dynamic programming framework for optimal data scheduling under a stochastic data arrival SSN environment with joint consideration of contact selection, battery management, and buffer management while taking into account the impact of current decisions on the infinite future.

TL;DR: This letter analyzes the special structure of the problem and exploits it to obtain the optimal power splitting ratio and resource allocation strategy when one of them is fixed and results indicate that the algorithm achieves a better rate-energy tradeoff compared to other schemes.

TL;DR: An analytical framework for resource-limited small satellite networks is proposed to handle the challenges raised by the intertwined impacts of energy budgets, satellites' movements and delay requirements on data acquisition and delivery process, and an optimization problem aiming at maximizing the delay-constrained throughput is formulated.