Non-orthogonal multiple access (NOMA) is a promising radio access technique that enables massive connectivity and increased spectral efficiency. The deployment of aerial base stations (ABSs) as a relay is also an optimistic goal that fairly serves a large number of internet of things (IoT) devices. On one side, ABS-assisted communication leverages effective communication services for secondary IoT devices in smart cities. On the other hand, NOMA allows several IoT devices to concurrently acquire the same frequency-time resource. To this end, weighted sum-rate (WSR) is an essential goal because it allows numerous trade-offs between user fairness and sum-rate efficiency. Therefore, this work aims to investigate the WSR for an integrated aerial terrestrial network subject to cellular power and delay constraints in downlink NOMA. Herein, a theoretical insight-based low-complexity iterative solution is provided for optimal power and blocklength allocation to achieve maximum sum-rate. For this purpose, the mixed-integer non-linear problem is formulated and a low-complexity near-optimal solution is proposed. Numerical results show that the proposed scheme achieves a near-optimal solution and outperforms baseline techniques, i.e., the performance gain of 5.18% over the legacy OMA system for NOMA with two IoT devices per subcarrier.
The recent advances in low earth orbit (LEO) satellite-borne edge cloud (SEC) enable resource-limited users to access edge servers via a terrestrial station terminal (TST) for rapid task processing capability. However, the dynamic variation in the TST transmit power challenges the served users to develop optimal computing task processing decisions. In this paper, we propose an efficient pruning-split long short-term memory (LSTM) learning algorithm to address this challenge. First, we present an LSTM algorithm for TST transmit power prediction. The proposed algorithm is then pruned and split to decrease the computing workload and the communication resource consumption considering the limited computing resource of TSTs and served users' quality of service (QoS). Finally, an algorithm split layer selection method is introduced based on the real-time situation of the TST. The simulation results are shown to verify the effectiveness of the proposed pruning-split LSTM algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.