The concept of Mobile Edge Computing (MEC) has been recently highlighted as a key enabling technology for the deployment of sixth-generation (6G) wireless network services. On the other hand, the possibility of combining Unmanned Aerial Vehicles (UAV) with Reconfigurable Intelligent Surfaces (RIS) has also been recognized as a powerful communication paradigm able to provide improved propagation characteristics of wireless communication channels, as well as increased capacity and extended coverage. Then, the possibility of merging the characteristics of such a communication paradigm with the one provided through MEC represents a valid solution to fulfill the main requirements of 6G networks. In this paper, we consider the combination of computation offloading and resource allocation in an MEC-based system where the MEC server is hosted by a massive MIMO base station, which serves multiple macro-cells assisted by a UAV-equipped RIS. In this context, we focus on minimising the latency for executing tasks of all user equipment (UE) within the considered scenario. To tackle this problem, we formulate an optimisation problem that jointly optimises computation offloading from user equipment (UE) towards the MEC server, and communication resources in the underlying UAV-assisted and RIS-aided network. The extensive simulation results demonstrate how the proposed method outperforms in terms of providing reduced latency for the considered system when compared with other conventional schemes.

In this letter, we formulate a multi-tier caching and resource sharing (CRS) optimization problem for a high video streaming performance in 5G ultra-dense networks. Particularly, in user tier, the optimal sets of spectrum owning users (SUs) with available downlink resources, caching users (CUs) with cached video versions, and normal users (NUs) requesting the video versions are tripartite for video streaming over device-to-device (D2D) communications. In femto tier, the optimal caching strategy is found to indicate which video versions and where to cache them in the femtocell base station (FBSs). By cooperating with the video versions cached in macro tier, i.e., macro base station (MBS), the mobile users (MUs) can receive the video versions from D2D communications, FBSs, and MBS flexibly. The objective of the CRS solution is to maximize the video playback quality, while saving the caching storage of FBSs and satisfying a given throughput required by MUs and a given target signal to interference plus noise ratio of SUs.

Reconfigurable Intelligent Surfaces (RIS) have been highlighted by the research community as a key enabling technology for the enhancement of next-generation wireless network performance, including energy efficiency, spectral efficiency, and network throughput. This paper investigates how RIS-assisted communication can effectively maximize the downlink throughput of a cellular network. Specifically, the paper considers a communication scenario where a single base station serves multiple ground users with the aid of an RIS placed on a building facade. For such a communication scenario, we considered an optimization problem aimed at maximizing the overall downlink throughput by jointly optimizing power allocation at the base station and phase shift of RIS reflecting elements, subject to power consumption and quality-of-service constraints. To address its non-convex nature, the original optimization problem has been divided into two subproblems. The first one, for power control with fixed phase shift values, is a convex problem that can be easily solved. Subsequently, a phase shift searching procedure to solve the non-convex problem of RIS phase shift optimization has been adopted. The results from numerical simulations show that the proposed method outperforms other conventional methods proposed in the literature. In addition, computational complexity analysis has been conducted to prove the low complexity of the proposed method.

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