Combining semi-grant-free (SGF) transmission with non-orthogonal multiple access transmission (NOMA) allows grant-free (GF) UEs to access the network through resource blocks that are solely allocated to grant-based (GB) UEs. A channel gain constraint for GF UEs is introduced to reduce the interference impact to GB UE, which causes an access fairness problems for GF UEs. Moreover, the power collision (PC) interference significantly degrades the interference cancellation performance. To address these issues, this paper proposes a layer SGF-NOMA random access (RA) procedure and a joint power back-off (PB) scheme. In the proposed RA procedure, GF UEs can freely share the GB UE’s resource blocks without channel gain constraints. To suppress the PC interference, the proposed PB scheme optimizes the power distribution in the NOMA group according to the variable interference experienced by the NOMA UEs. To maintain the signal-to-interference balance, more power is transferred to the UEs that suffer more interference. The closed-form expressions of the outage probability, successful access probability and sum data rate are derived via order statistic theory. Both the analytical and simulation results demonstrate that the layer SGF-NOMA procedure and joint PB scheme achieves significant performance gains over traditional schemes.

Carrier-sense multiple access with collision avoidance (CSMA/CA) is one of the fundamental medium access control (MAC) protocols for ad hoc networks. As a network’s size increases, its throughput degrades substantially because of packet collisions. To reduce the collision probability, combining non-orthogonal multiple access (NOMA) with CSMA/CA is a promising solution. However, existing NOMA-CSMA/CA protocols adopt distributed power selection and channel inversion power control, resulting in a high power collision probability and limiting the number of power levels. To address these issues, we propose a power back-off NOMA-based CSMA/CA (PBN-CSMA/CA) protocol for ad hoc networks. The proposed protocol achieves centralized power allocation, avoiding power collisions by employing the Zadoff-Chu (ZC) sequence and the power level allocation (PLA) frame. Additionally, power back-off (PB) control is used to set the transmission power, which expands the number of power levels and gives full play to the performance advantages of NOMA. To analyze the performance comprehensively, the closed-form expressions of the average outage probability, normalized saturation throughput, average packet delay and transmission energy consumption are theoretically analyzed. Both the analytical and simulation results demonstrate that the PBN-CSMA/CA protocol outperforms the existing NOMA-CSMA/CA and traditional CSMA/CA protocols, with significant throughput gains and delay reductions.

Carrier-sense multiple access with collision avoidance (CSMA/CA) is one of the fundamental medium access control (MAC) protocols for ad hoc networks. As a network increases in size, its throughput degrades substantially due to packet collision. To reduce the collision probability, a nonorthogonal multiple-access (NOMA)-based CSMA/CA (NCSMA) protocol is proposed. The proposed NCSMA protocol employs Zadoff-Chu (ZC) sequences and a NOMA power coefficient allocation (N-PCA) frame to select the NOMA node and schedule the power level, preventing packet collisions caused by random selection. Moreover, the normalized saturation throughput and the average packet delay of the proposed approach are theoretically analyzed, and closed-form expressions are derived. Simulation results show that the proposed protocol improves the normalized saturation throughput by 66.7% and reduces the average packet delay by 35.97% compared to those of the current best CSMA/CA protocol.

In the realm of flying ad-hoc network(FANETs), unmanned aerial vehicles (UAVs) frequently encounter challenges related to adaptability in complex jamming environments. To address this issue, our research introduces an innovative frequency hopping(FH) strategy that combines federated learning(FL) with double deep Q-Networks (DDQN). The UAVs adaptively select frequency hopping patterns by a locally tuned DDQN model, which they then share with a multi-access edge computing (MEC) server to facilitate global aggregation optimization. The study also introduces an difficulty-adaptive training mechanism to optimize the allocation of computational resources. Experimental validations of our method confirm its efficacy in significantly improving both the adaptability and overall performance of FANETs in challenging jamming conditions, demonstrating robust convergence and high accuracy.