A novel D2D–MEC method for enhanced computation capability in cellular networks

Device-to-device (D2D) communications and mobile edge computing (MEC) used to resolve traffic overload problems is a trend in the cellular network. By jointly considering the computation capability and the maximum delay, resource-constrained terminals offload parts of their computation-intensive tasks to one nearby device via a D2D connection or an edge server deployed at a base station via a cellular connection. In this paper, a novel method of cellular D2D–MEC system is proposed, which enables task offloading and resource allocation meanwhile improving the execution efficiency of each device with a low latency. We consider the partial offloading strategy and divide the task into local and remote computing, both of which can be executed in parallel through different computational modes. Instead of allocating system resources from a macroscopic view, we innovatively study both the task offloading strategy and the computing efficiency of each device from a microscopic perspective. By taking both task offloading policy and computation resource allocation into consideration, the optimization problem is formulated as that of maximized computing efficiency. As the formulated problem is a mixed-integer non-linear problem, we thus propose a two-phase heuristic algorithm by jointly considering helper selection and computation resources allocation. In the first phase, we obtain the suboptimal helper selection policy. In the second phase, the MEC computation resources allocation strategy is achieved. The proposed low complexity dichotomy algorithm (LCDA) is used to match the subtask-helper pair. The simulation results demonstrate the superiority of the proposed D2D-enhanced MEC system over some traditional D2D–MEC algorithms.

Downlink Resource Sharing and Caching Helper Selection Control Maximized Multicast Video Delivery Capacity in Dense D2D 5G Networks

In 5G ultra-dense networks, a large number of mobile users (MUs) request a huge amount of high data rate video traffic causing a peak congestion situation at the macro base station (MBS) and small-cell base stations. This situation certainly reduces the total video capacity delivered to the MUs. In this paper, we exploit the available spectrum and storage resources of the MUs as well as the wireless broadcast nature of device-to-device (D2D) communications to propose a joint downlink resource sharing and caching helper selection (DRS-CHS) control to maximize the multicast video delivery capacity in dense D2D 5G networks. We assume that the MUs are divided into different clusters in which they can communicate with each other by D2D communications. There are two types of MUs in each cluster including the requesting users (RUs) that request the video and the caching helpers (CHs) that have cached the video. In addition, there are some sharing users (SUs) that can share their downlink resources with the CHs and the RUs for D2D multicast communications. A DRS-CHS optimization problem is then formulated and solved for an optimal control process of how to select a CH in each cluster and how to assign an SU to share its downlink resource with the selected CH such that the total video delivery capacity multicasted from the CHs to the RUs in all clusters is maximized. Simulation results demonstrate the benefits of the proposed DRS-CHS control solution compared to other conventional benchmarks.

A Location Prediction-Based Helper Selection Scheme for Suspicious Eavesdroppers

This paper aims to improve security performance of data transmission with a mobile eavesdropper in a wireless network. The instantaneous channel state information (CSI) of the mobile eavesdropper is unknown to legitimate users during the communication process. Different from existing work, we intend to reduce power consumption of friendly jamming signals. Motivated by the goal, this work presents a location-based prediction scheme to predict where the eavesdropper will be later and to decide whether a friendly jamming measure should be selected against the eavesdropper. The legitimate users only take the measure when the prediction result shows that there will be a risk during data transmission. According to the proposed method, system power can be saved to a large degree. Particularly, we first derive the expression of the secrecy outage probability and set a secrecy performance target. After providing a Markov mobile model of an eavesdropper, we design a prediction scheme to predict its location, so as to decide whether to employ cooperative jamming or not, and then design a power allocation scheme and a fast suboptimal helper selection method to achieve targeted and efficient cooperative jamming. Finally, numerical simulation results demonstrate the effectiveness of the proposed schemes.