The future of networking will be driven by the current emerging trends of combining the physical and virtual realities in cyberspace. Considering the ambient pandemic challenges, the role of virtual and augmented reality will definitely grow over time by transforming into the paradigm of the Metaverse of Things, where each person, thing or other entity will simultaneously exist within multiple synchronized realities. In this paper, we propose a novel framework for future metaverse applications composed of multiple synchronized data flows from multiple operators through multiple wearable devices and with different quality requirements. A new service quality model is proposed based on a customizable utility function for each individual data flow. The proposed approach is based on dynamic fine-grained data flow allocation and service selection using non-fungible tokens, which can be traded over the blockchain among users and operators in a decentralized mobile network environment.

The rapid increasing of the data volume in mobile networks forces operators to look into different options for capacity improvement. Thus, modern 5G networks became more complex in terms of deployment and management. Therefore, new approaches are needed to simplify network design and management by enabling self-organizing capabilities. In this paper, we propose a novel intelligent algorithm for performance optimization of the massive MIMO beamforming. The key novelty of the proposed algorithm is in the combination of three neural networks which cooperatively implement the deep adversarial reinforcement learning workflow. In the proposed system, one neural network is trained to generate realistic user mobility patterns, which are then used by second neural network to produce relevant antenna diagram. Meanwhile, third neural network estimates the efficiency of the generated antenna diagram returns corresponding reward to both networks. The advantage of the proposed approach is that it leans by itself and does not require large training datasets.

The concept of spectrum and infrastructure sharing appeared to solve the problem by allowing operators to cooperate between each other. However, the current architecture of mobile network, which is mostly based on the centralized management entities is not suitable for the spectrum and infrastructure sharing due to the complexity of billing procedure. Moreover, the operator oriented mobile network is slowing the overall technological development, because operators must balance between capital expenditures and profit margins. Such situation negatively affects users, because they are either experience lower quality of service or forced to pay higher price. In this paper, we propose a new intelligent network architecture, which leverage the blockchain technology to handle relationship between operators and users based on smart contracts. The new unlicensed spectrum sharing algorithm between operators using the virtual cryptocurrency has been developed based on game theory. Simulation results show that proposed algorithm achieves Nash equilibrium between operators within few seconds.

The densification of mobile network infrastructure has been widely used to increase the overall capacity and improve user experience. Additional tiers of small cells provide a tremendous increase in the spectrum reuse factor, which allows the allocation of more bandwidth per user equipment (UE). However, the effective utilization of this tremendous capacity is a challenging task due to numerous problems, including co-channel interference, nonuniform traffic demand within the coverage area, and energy efficiency. Existing solutions for these problems, such as stochastic geometry, cause excessive sensitivity to the pattern of the UE traffic demand. In this paper, we propose an intelligent solution for both coverage planning and performance optimization using unsupervised self-organizing map (SOM) learning. We use a combination of two different mobility patterns based on Bézier curves and Lévy flights for more natural UE mobility patterns compared with a conventional random point process. The proposed approach provides the advantage of adjusting the positions of the small cells based on an SOM, which maximizes the key performance indicators, such as average throughput, fairness, and coverage probability, in an unsupervised manner. Simulation results confirm that the proposed unsupervised SOM algorithm outperforms the conventional binomial point process for all simulated scenarios by up to 30% in average throughput and fairness and has an up to 6-dB greater signal-to-interference-plus-noise ratio perceived by the UEs.

Mobile edge computing (MEC) is currently one of the key technologies that can facilitate the evolution of the future digitized economy. MEC can provide ubiquitous computational capabilities through the multitier deployment of servers to ensure lower latencies and tighter integration with 5G, the Internet of Things, blockchains and artificial intelligence. In this paper, we propose a new approach to optimizing hardware resource allocation for edge nodes in a multitier MEC hierarchy. In addition to a centralized unit, we consider active antenna units and distributed units equipped with edge nodes of different computational capacities. A parametric Bayesian optimizer is implemented for hardware resource allocation to increase the overall computational capacity of a 5G-based MEC system. Simulation results show that for given budget constraints, the proposed solution outperforms pseudorandom resource allocation in terms of the proportion of computational tasks completed. The achievable gains are in the range of 20-40 %, depending on the task complexity and selected budget threshold.