Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. In this paper, we study a multi-user multi-edge-node computation offloading problem. Since edge nodes' communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a non-cooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash equilibrium via a best response-based offloading mechanism. Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. Then, we propose a model-free reinforcement learning offloading mechanism which helps MUs learn their long-term offloading strategies to maximize their long-term utilities. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as local processing and random assignment, and achieves up to 87.87% average long-term payoffs compared to the perfect CSI case.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wire-less edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search. Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machine learning approaches in data training and processing have exhibited their limited computing capabilities to support the sixth-generation (6G) networks with highly dynamic applications and services. In this regard, the fast developing quantum computing with machine learning for 6G networks is investigated. Quantum machine learning algorithm can significantly enhance the processing efficiency and exponentially computational speed-up for effective quantum data representation and superposition framework, highly capable of guaranteeing high data storage and secured communications. We present the state-of-the-art in quantum computing and provide a comprehensive overview of its potential, via machine learning approaches. Furthermore, we introduce quantum-inspired machine learning applications for 6G networks in terms of resource allocation and network security, considering their enabling technologies and potential challenges. Finally, some dominating research issues and future research directions for the quantum-inspired machine learning in 6G networks are elaborated.

It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and connected people to connected intelligence. The main goal of 6G networks is to support massive connectivity for time-sensitive and computation-sensitive services in mission-critical applications. The creation of real-time optimization (RTO) enabled by the fast growing data analytic and machine learning will seize the opportunities for 6G wireless networks to support such immersive services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and tactile Internet. Recently, with the rapid development of quantum computers, quantum-inspired optimization and machine learning algorithms have been exploited as efficient solutions for future wireless networks. In this article, we provide a comprehensive view on the new concept of quantum-inspired RTO and its application to the optimal resource allocation for 6G wireless networks. Our main contributions are to introduce some of the initial research results and introduce the potentiality of quantum-inspired RTO on some 6G emerging technologies. Not only do we review the fundamental principles; we also explore the challenges and opportunities of this exciting research direction.

Proactive caching is considered as a cost-effective method to address the backhaul bottleneck problem in cellular network. In this work, we propose a novel popularity predicting–caching procedure that takes raw video data as input to determine an optimal cache placement policy, which deals with both published and unpublished videos. To anticipate the popularity of unpublished videos of which the statistical information is not available, we apply the content-based approach by extracting and condensing video features into a high-dimensional vector. Subsequently, we form G clusters of features representing the potential video categories (VCs) and map the feature vector into a G dimensional space, where each element indicates the percentage to which the video contains the features of the corresponding VC. Finally, we train a prediction model to foresee the popularity, where the set of published videos is used as training data. Lastly, the prediction with expert advice method is used to update the training set, and to gain insight into how the predictor output will deviate from the expert prediction, we address the concept of expected cumulative loss and derive the analytical expression for its upper bound. Extensive simulation results are shown to gain insight into our proposed system subject to different factors such as network size, cache capacity, and user’s preference profile. In summary, we show that applying intelligence-based content-aware proactive caching is an efficient approach to significantly improve the operation of cellular networks in the future.