In this paper, we investigate unmanned aerial vehicle (UAV) assisted communication systems that require quasi-balanced data rates in uplink (UL) and downlink (DL), as well as users’ heterogeneous traffic. To the best of our knowledge, this is the first work to explicitly investigate joint UL-DL optimization for UAV assisted systems under heterogeneous requirements. A hybrid-mode multiple access (HMMA) scheme is proposed toward heterogeneous traffic, where non-orthogonal multiple access (NOMA) targets high average data rate, while orthogonal multiple access (OMA) aims to meet users’ instantaneous rate demands by compensating for their rates. HMMA enables a higher degree of freedom in multiple access and achieves a superior minimum average rate among users than the UAV assisted NOMA or OMA schemes. Under HMMA, a joint UL-DL resource allocation algorithm is proposed with a closed-form optimal solution for UL/DL power allocation to achieve quasi-balanced average rates for UL and DL. Furthermore, considering the error propagation in successive interference cancellation (SIC) of NOMA, an enhanced-HMMA scheme is proposed, which demonstrates high robustness against SIC error and a higher minimum average rate than the HMMA scheme.

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

In this paper, we propose a cooperative time synchronization (CTS) scheme for cell-free massive multiple-input multiple-output (MIMO) systems, where synchronization packets are jointly broadcast by a set of coordinated access points (APs). Thanks to multiple timestamps available at each target node, the proposed CTS scheme enables a much higher accuracy in clock parameters estimation than the conventional non-cooperative synchronization approaches. In addition to the clock parameters between the target nodes and their associated APs, the clock deviations among APs are also jointly estimated, where the clock deviations can be fed back to their associated APs via acknowledgment for further clock adjustment. Furthermore, to mitigate the impact on synchronization performance under the packet loss scenario, a matrix completion-based CTS (MC-CTS) algorithm is proposed that complements the lost timestamp information. Simulation results demonstrate that the proposed CTS scheme presents a robust performance against the clock deviations among coordinated APs with packet overhead of approximately 50% less than that of the non-broadcast synchronization approaches, and the proposed MC-CTS algorithm effectively enhances the estimation performance of clock parameters, compared to just utilizing the CTS algorithm. Hence, the proposed algorithms are particularly suitable for time-sensitive cell-free massive MIMO systems.

We propose a novel low-complexity resource allocation scheme to provide a tradeoff between energy efficiency (EE) and spectral efficiency (SE) for multicarrier non-orthogonal multiple access (NOMA) systems with the constraint of user fairness. The proposed optimization approach provides high degree of freedom, as the tradeoff of EE and SE can be optimal by assigning their corresponding weights. Also, a closed-form expression is derived for subchannel power allocation with user fairness, requiring no high-complexity search. A suboptimal power allocation approach is proposed on each subchannel for NOMA user pairing. Numerical results show that the proposed NOMA system provides higher EE and SE than the state-of-art methods, while requiring low complexity.

We propose a hierarchical symbiotic transmission strategy with cooperative-nonorthogonal multiple access (C-NOMA) for multi-carrier cognitive radio networks, where the mutualistic and commensualistic transmission modes are employed in the primary network (PN) and secondary network (SN), by promoting some secondary users (SUs) as relays to assist communication of both PN and SN. The proposed strategy enables higher system throughput and supports much more user connectivity than the existing schemes with C-NOMA that focused on maintaining the performance of PN. Sub-carrier assignment and power allocation solutions are presented to further improve system throughput. Simulation results demonstrate the effectiveness of the proposed strategy.

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios.