In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital and operational expenditure. Proactive network optimisation is widely acknowledged as one of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area.

In the fifth-generation (5G) mobile networks, the traffic is estimated to have a fast-changing and imbalance spatial-temporal distribution. It is challenging for a system-level optimisation to deal with while empirically maintaining quality of service. The 5G load balancing aims to address this problem by transferring the extra traffic from a high-load cell to its neighbouring idle cells. In recent literature, controller and machine learning algorithms are applied to assist the self-optimising and proactive schemes in drawing load balancing decisions. However, these algorithms lack the ability of forecasting upcoming high traffic demands, especially during popular events. This shortage leads to cold-start problems because of reacting to the changes in the heterogeneous dense deployment. Notably, the hotspots corresponding with skew load distribution will result in low convergence speed. To address these problems, this paper contributes to three aspects. Firstly, urban event detection is proposed to forecast the changes in cellular hotspots based on Twitter data for enabling context-awareness. Secondly, a proactive 5G load balancing strategy is simulated considering the prediction of the skewed-distributed hotspots in urban areas. Finally, we optimise this context-aware proactive load balancing strategy by forecasting the best activation time. This paper represents one of the first works to couple the real-world urban event detection with proactive load balancing.

In this article, a time-efficient mechanism is proposed to solve the joint unmanned aerial vehicle (UAV) base station deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput and reducing the time of online computations. In this work, two relevant subproblems are decoupled from the joint UDUA problem: the first one is denoted as the user association subproblem, for certain UAV base station (UAV-BS) positions, this subproblem is settled for finding the strategy which matches aerial and ground nodes optimally. The second subproblem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association subproblem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn–Munkres algorithm is used to solve the first subproblem as an equivalent bipartite matching problem. For the UAV-BS deployment subproblem, when the two user distributions own a high similarity, we theoretically prove that little performance decline will be introduced when the new user distribution’s optimal strategy is compared with choosing the optimal UAV-BS deployment strategy of stored user distributions. Based on our mathematical analyses, the similarity level between user distributions is well defined and becomes the key to solve the second subproblem. According to experimental findings, the proposed UDUA mechanism, when compared to benchmark approaches, can provide near-optimum system performance with regard to average downlink total transmission throughput and failure rate with significantly decreased computing time.

The computing power network (CPN) offers exceptional computational capabilities and reliable network services, with significant potential for future applications. To achieve ubiquitous coverage and efficient computational resource allocation, CPN can be seamlessly coordinated with low-cost autonomous aerial vehicle (AAV)-based mobile computing platforms. This article investigates an efficient low-altitude AAV-assisted computing power and resource allocation mechanism tailored for urban environments. The aim is to ensure seamless scheduling and efficient processing of computational tasks across various computing devices at different layers of the CPN, while minimizing AAV energy consumption and ensuring flight safety. First, this article proposes an Urban AAV-assisted CPN task-allocation and AAV trajectory-management decision-making problem. The AAV works until it safely lands, aiming to minimize overall task processing delay and AAV energy consumption while ensuring fairness in task allocation. Then, a novel AAV-protection-based multiagent deep deterministic policy gradient (UP-MADDPG) algorithm is introduced. It offers dynamic management of secure computing and communication flight paths when facing building blockages. Finally, we compared the proposed algorithm with baseline algorithms across various metrics. Experimental results demonstrate that the proposed algorithm achieves lower and more balanced task execution delay and AAV energy consumption while also improving fairness.

In this letter, we propose an accurate and efficient heterogeneous graph neural network-powered ray tracing (HGNN-RT) framework to predict path loss for urban scenarios. The proposed HGNN-RT effectively captures both global and local features, which include broad path attributes like total path length and line-of-sight (LoS) conditions, as well as specific interactions such as reflection angles and obstacles. Moreover, due to the fast information flow of the graph neural networks, the HGNN-RT achieves efficiency-improved path loss prediction in complex multi-reflection and multipath propagation channels. Experimental results demonstrate that the proposed approach achieves errors within 2 dB of RT benchmarks, while also being time-efficient and reliably generalizing to unseen environments for channel modeling.

This paper exploits an Unmanned Aerial Vehicle assisted Mobile Edge Computing (UAV-MEC) system to meet the computation demand of User Equipments (UEs). However, in conventional UAV-MEC architecture, a UAV is dedicated only to one single service, which will inevitably limit overall system performance. To address the above issue of the conventional UAVMEC architecture, we propose a hybrid UAV-MEC model. In this model, a UAV is treated as an agent to provide either relay or computing services. Moreover, we propose an MEC Multiple Agent UAV Trajectory and User Association Policy Gradient (MEC-MAUTUA-PG) algorithm to optimize offloading strategies of the UEs as well as the trajectory design of UAV. We show the superiority of the proposed algorithm in terms of delay and the safety of trajectory design of UAV in the environment.