Mobile edge computing (MEC) is one of the key technologies for 5G networks and beyond, which brings computation resources in proximity to end users. While enabling computing capability at the network edge, it also introduces extra energy consumption to the network operation. Moreover, the non-uniform traffic distribution of mobile network makes the utilization of MEC platforms unbalanced. In this paper, we propose an online optimization strategy of MEC server (MECS) computation task offloading with sleep control scheme to minimize the long term energy consumption of the MECS network. First, the energy optimization problem under delay constraint is formulated which considers both the radio and computation resources. Then, a Lyapunov based approach is proposed to convert the long term optimization problem to a per-slot optimization problem which only requires information of current time slot. An online offloading algorithm is proposed to make decisions in each time slot. Finally, the system performance is evaluated and the impacts of several key parameters are analyzed. Simulation results demonstrate that our proposed strategy can significantly reduce the long term average energy consumption of the mobile edge network by 30% to 90% under different workload scenarios, while keeping a relatively low system delay compared with the schemes without MEC cooperation and sleep control.

To improve the energy efficiency for satellite downlink, a data offloading mechanism is studied for multi-cell satellite-terrestrial networks. Different services are first offloaded to the satellite-terrestrial terminal (STT) in each small cell and then forwarded to grounded users. By integrating the power allocation for satellite backhauling and the resource allocation for STT downlink, we aim to maximize the energy efficiency of data offloading in the multi-cell satellite-terrestrial networks. The problem is decomposed into two types of subproblems, where the downlink capacity in each cell, as well as the energy efficiency of satellite backhauling, are maximized. The optimal solution is approached by invoking a binary search aided algorithm, and simulation results demonstrate that the proposed algorithm effectively achieves the preferred energy-efficient allocation.

In this paper, we propose a denoise-and-forward network coding (DNF-NC) transmission scheme for its applications in two-way relay multiple-input and multiple-output (MIMO) systems. We first consider a scenario with a single pair of source nodes, and minimum mean square error (MMSE) receiver is applied at each node. The global optimal precoding design based on the mean square error (MSE) criterion can be achieved by solving two independent convex optimization problems. To achieve a better tradeoff between performance and complexity, an alternative power optimization approach is proposed using a channel diagonalization technique. Then, we proceed to considering a more challenging bidirectional communication scenario with multiple pairs of source nodes. With intrapair coordination at the sources, we modify DNF-NC by employing a signal alignment technique to combat interpair interference. The numerical results demonstrate that the proposed DNF-NC schemes can significantly improve bit error rate (BER) performance in both two scenarios, and such performance gains can be achieved with relatively low computational complexity.

In this paper a knowledge-and-data-driven graph neural network-based collaboration learning model is proposed for reliable aircraft recognition in a heterogeneous radar network. The aircraft recognizability analysis shows that: (1) the semantic feature of an aircraft is motion patterns driven by the kinetic characteristics, and (2) the grammatical features contained in the radar cross-section (RCS) signals present spatial-temporal-frequency diversity decided by both the electromagnetic radiation shape and motion pattern of the aircraft. Then a spatial-temporal-frequency graph attention convolutional network (STFGACN) is developed to distill semantic features from the RCS signals received by the heterogeneous radar network. Extensive experiment results verify that the STFGACN outperforms the baseline methods in terms of detection accuracy, and ablation experiments are carried out to further show that the expansion of the information dimension can gain considerable benefits to perform robustly in the low signal-to-noise ratio region.

Received signal strength (RSS) based source localization method is popular due to its simplicity and low cost. However, this method is highly dependent on the propagation model whose parameters are not easy to be captured in practice. Although source localization problem under uncertain propagation parameters has been wildly investigated, most existing works are devoted to the single source localization case. Nevertheless, multiple co-channel sources may transmit simultaneously in practice. In this paper, we estimate the locations of multiple co-channel sources from the superimposed RSS observations, while jointly inferring the parametric propagation model. Specifically, we first model the multiple sources localization (MSL) problem as being parameterized by the unknown source locations and propagation parameters, and then reformulate it as a joint parametric dictionary learning (PDL) and sparse signal recovery (SSR) problem, which is solved under the framework of sparse Bayesian learning with iterative parametric dictionary approximation. Moreover, a fast iterative update strategy is developed for the proposed method to reduce the complexity from $\mathcal{O}(MN^2)$ to $\mathcal{O}(MN)$ for high-dimensional large-scale problems, and the Cramèr-Rao lower bound (CRLB) is derived to analyze the theoretical estimation error bound. Finally, some important properties of the assumed MSL model and the proposed algorithms, as well as the future research directions are discussed. Comparing with the state-of-the-art sparsity-based MSL algorithms as well as CRLB, extensive simulations show the importance of jointly inferring the source locations and the propagation parameters, and highlight the effectiveness and superiority of the proposed methods.