Non-terrestrial network (NTN) enabled low-altitude economy (LAE) has emerged as a promising economic paradigm that leverages advanced air mobility (AAM) vehicles to revolutionize connectivity in the six-generation (6G) era. By deploying unmanned aerial vehicles (UAVs) as flying edge nodes, wireless caching can significantly alleviate network congestion and reduce latency, enabling the efficient handling of massive terrestrial user requests in LAE applications. However, the limited energy and storage capacity of UAVs pose significant challenges to provide persistent and diverse content delivery services. To address such limitations, this paper proposes a multi-UAV-enabled coded caching scheme for energy-efficient data delivery, in which both the communication coverage and cache hit are satisfied. Taking into account the dynamics of user mobility and user preferences, we design an energy minimization problem with the joint optimization of coding vectors, caching variables, user grouping, and updated UAV locations. We initially deploy UAVs using a constrained K-means clustering algorithm based on user locations, and evaluate the clustering effectiveness with the silhouette coefficient. Then, we solve this problem by proposing a multi-UAV enabled coded caching optimization (MUCCO) scheme, embedded with a novel projected distance-based user grouping method, semidefinite programming (SDP), and matching theory. The simulation results demonstrate that the proposed MUCCO scheme can achieve low energy consumption compared to other schemes, with scalable user density and file library size.

The ever-increasing requests of users in emergency communication scenarios lead to high data traffic and transmission delay, posing challenges for resource-limited space-air-ground integrated networks (SAGINs). To address this issue, this paper proposes a joint caching optimization and user selection (JCOUS) problem that leverages unmanned aerial vehicle (UAV) caching to maximize the residual energy of the satellite, considering the limited resources of UAVs. To address the complex time-coupling optimization problem with discrete variables, we propose a primal decomposition method to decouple the problem, and design an energy-efficient user selection algorithm with dynamic caching. Furthermore, to reduce computational complexity and cost, we consider a statistical scenario and maximize the statistical residual energy in the JCOUS problem. Simulation results verify that the proposed scheme can achieve a higher residual energy and fast optimization, thus realizing energy saving and quick decision making especially in large-scale computation-intensive SAGINs.

coauthors Rajiv Kumar has no contribution and was added incorrectly. e correct author list is shown above.

Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.

In fog-radio access networks (F-RANs), the loadings of backhaul is the bottleneck to fully explore the potential of cloud computing capability, which provide abundant computation resources to execute the computation tasks. In this paper, the performance of computation offloading F-RANs is studied to keep a balance between the tradeoff between the costs and the gains of different computation task processing modes. First, we focus on an opportunistic computation offloading strategy in F-RANs, and the computation offloading probability is analyzed based on a stochastic geometry-based model. Second, the computation offloading procedure in F-RANs can be modeled as a Jackson network of queueing system. A closed-form expression of average delay performance is derived, and the global optimal solution of the ratio of computation tasks handled by the cloud computing center is also provided to minimize the average processing delay. Finally, the simulation results are shown to verify the accuracy of analytical results and evaluate the performance gains of hybrid computation offloading in F-RANs.

This study proposes a semi‐blind receivers for two‐way multiple‐input multiple‐output (MIMO) relaying systems capable of jointly estimating the channels and symbols in a direct‐data approach. Resorting to a Khatri‐Rao coding scheme applied at both uplink and downlink transmission phases, the authors show that the signals received at the relay and user node are third‐order tensors satisfying the parallel factor (PARAFAC) and PARATUCK2 models, respectively. Combining these two tensor models, a semi‐blind receiver based on an integrated alternating least squares algorithm is proposed to estimate the channels and symbols transmitted by the user nodes without training sequences. The effectiveness of the proposed semi‐blind receiver is corroborated with numerical results, which show that the proposed semi‐blind receiver outperforms state‐of‐the‐art two‐stage training sequence estimator, while operating close to the tensor‐based channel estimator.