Many emerging applications require multi-hop wireless relaying, for which reliability requirements increase packet retransmissions, amplifying latency across hops. Existing works mostly focus on the simple two-hop, single-relay setting and ignore spatial diversity that enables a destination to receive independent noisy copies of data from multiple relays in parallel to improve error performance. In this paper, we consider a single-bit source message and construct learning-rate-optimal, delay-constrained multi-hop schemes by jointly designing time-varying, distributed relay mapping functions with destination decoding strategies. The learning-rate-optimal scheme, however, requires channel and relaying knowledge, which limits practical implementation. With the aim of practical implementation, we have also considered several low-complexity relay and destination strategies and analyzed their performances under different combinations. Numerical comparisons show that none of the alternatives universally dominate, and the system designer thus has to carefully opt for the best suitable schemes depending on the channel conditions. Finally, we show that carefully coordinating many low-quality relay channels in parallel can vastly outperform having only one high-quality relay channel, which demonstrates the spatial diversity gains for the first time in the learning over parallel-relay setting.

An uplink non-orthogonal multiple-access communication system is developed for heterogeneous non-terrestrial networks (NTNs) that employ both low-Earth orbit (LEO) satellite constellations and high-altitude platforms (HAPs) as relays with hybrid automatic repeat request capabilities. The system is designed as a random-access and interference-resistant network to support low-rate users. A common waveform and frequency band is used for all uplink transmissions (whether the transmitter is a user terminal or a HAP). Both theoretical and simulation-based performance analysis results are provided as a function of user transmit power and packet arrival rates for channels with interference. The use of HAP relays is shown to significantly improve throughput and reduce queue size relative to direct transmission to the LEO satellites, especially for the case of low-power users in high interference environments.

This paper studies the adaptive trajectory design of a rotary-wing UAV serving as a relay between ground nodes dispersed in a circular cell and a central base station. Assuming the ground nodes generate uplink data transmissions randomly according to a Poisson process, we seek to minimize the expected average communication delay to service the data transmission requests, subject to an average power constraint on the mobility of the UAV. The problem is cast as a semi-Markov decision process, and it is shown that the policy exhibits a two-scale structure, which can be efficiently optimized: in the outer decision, upon starting a communication phase, and given its current radius, the UAV selects a target end radius position so as to optimally balance a trade-off between average long-term communication delay and power consumption; in the inner decision, the UAV selects its trajectory between the start radius and the selected end radius, so as to greedily minimize the delay and energy consumption to serve the current request. Numerical evaluations show that, during waiting phases, the UAV circles at some optimal radius at the most energy efficient speed, until a new request is received. Lastly, the expected average communication delay and power consumption of the optimal policy is compared to that of static and mobile heuristic schemes, demonstrating a reduction in latency by over 50% and 20%, respectively.

This paper studies the trajectory optimization problem in a scenario where a single rotary-wing UAV acts as a relay of data payloads for downlink transmission requests generated randomly by two ground nodes (GNs) in a wireless network. The goal is to optimize the UAV trajectory in order to minimize the expected average communication delay to serve these random requests. It is shown that the problem can be cast as a semi-Markov decision process (SMDP), and the resulting minimization problem is solved via multi- chain policy iteration. The optimality of a two-scale optimization approach is proved: the optimal trajectory in the communication phase greedily minimizes the communication delay of the current request while moving between the current start position and a target end position (inner optimization); the end positions are selected to minimize the expected average long-term delay in the SMDP (outer optimization). Numerical simulations show that the expected average delay is minimized when the UAV moves towards the geometric center of the GNs during phases in which it is not actively servicing transmission requests, and demonstrate significant improvements over sensible heuristics. Finally, it is revealed that the optimal end positions of communication phases become increasingly independent of the data payload, for large data payload values.