Abstract
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.
Highlights
The 5G mobile network is the foundation of the future Cyber-Physical-Social Systems (CPSS) by supporting three highly heterogeneous services, enhanced mobile broadband, ultra-reliable and low latency communications, and massive machine type communications. 5G and beyond 5G services need to support an 600x to 2500x capacity increase [1], sub 1ms round-trip latency [1], and 10,000 or more low-rate devices per cell
The K-means is easy to implement with low complexity but requires a manual selection of the cluster number k, leading to a degree of arbitrary parameterisation based on user bias/intuition
Some variants of K-means can mitigate this problem by re-simulating a series of k values, but still meet the negative influence caused by noisy samples
Summary
The 5G mobile network is the foundation of the future Cyber-Physical-Social Systems (CPSS) by supporting three highly heterogeneous services, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (uRLLC), and massive machine type communications (mMTC). 5G and beyond 5G services need to support an 600x to 2500x capacity increase [1], sub 1ms round-trip latency [1], and 10,000 or more low-rate devices per cell. B. Ma et al.: Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning optimisation is the video context-aware scheduling, based on the user-side attention information, which is a promising implementation in CPSS [5] associated with the cloud-edge computing techniques. An example is proposed in [51] about optimising the network in a proactive and energy-efficient way They presented a framework with implementing a big-data-aware intelligent platform between the core network and Baseband Unit (BBU) pool for analysing user behaviour and network patterns to output control strategies. To provide applications of proactive optimisation by the cloud and edge computing in CPSS The remainder of this survey is organised as follows: Section II discusses related survey papers about online data, contexts, and network optimisation.
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