Extreme Communication in 6G: Vision and Challenges for ‘in-X’ Subnetworks

The 6th Generation (6G) radio access technology is expected to support extreme communication requirements in terms of throughput, latency and reliability, which can only be achieved by providing capillary wireless coverage. In this paper, we present our vision for short-range low power 6G ‘in-X’ subnetworks, with the ‘X’ standing for the entity in which the cell in which the subnetwork is deployed, e.g., a production module, a robot, a vehicle, a house or even a human body. Such cells can support services that can be life-critical and that traditionally relied on wired systems. We discuss potential deployment options, as well as candidate air interface components and spectrum bands. Interference management is identified as a major challenge in dense deployments, which needs to handle also non-cellular types of interference like jamming attacks and impulsive noise. A qualitative example of interference-robust system design is also presented

On three use cases of multi-connectivity paradigm in emerging wireless networks

As envisioned by global network operators, the increasing trend of data traffic demand is expected to continue with exponential growth in the coming years. To cope with this rapid increase, significant efforts from the research community, industry and even regulators have been focused towards improving two main aspects of the wireless spectrum: (i) spectrum capacity and (ii) spectral efficiency. Concerning the spectrum capacity enhancement, the multi-connectivity paradigm has been seen to be fundamentally important to solve the capacity problem in the next generation networks. Multi-connectivity is a feature that allows wireless devices to establish and maintain multiple simultaneous connections across homogeneous or heterogeneous technologies. In this thesis, we focus on identifying the core issues in applying the multi-connectivity paradigm for different use cases and propose novel solutions to address them. Specifically, this thesis studies three use cases of the multi-connectivity paradigm. First, we study the uplink/downlink decoupling problem in 4G networks. More specifically, we focus on the user association problem in the decoupling context, which is considered challenging due to the conflicting objectives of different entities (e.g., mobile users and base stations) in the system. We use a combination of matching theory and stochastic geometry to reconcile competing objectives between users in the uplink/downlink directions and also from the perspective of base stations. Second, we tackle the spectrum aggregation problem for wireless backhauling links in unlicensed opportunistic shared spectrum bands, specifically, TV White Space (TVWS) spectrum. In relation to this, we present a DIY mobile network deployment model to accelerate the roll-out of high-end mobile services in rural and developing regions. As part of this model, we highlight the importance of low-cost and high-capacity backhaul infrastructure for which TVWS spectrum can be exploited. Building on that, we conduct a thorough analytical study to identify the characteristics of TVWS in rural areas. Our study sheds light on the nature of TVWS spectrum fragmentation for the backhauling use case, which in turn poses requirements for the design of spectrum aggregation systems for TVWS backhaul. Motivated by these findings, we design and implement WhiteHaul, a flexible platform for spectrum aggregation in TVWS. Three challenges have been tackled in this work. First, TVWS spectrum is fragmented in that the spectrum is available in non-contiguous manner. To fully utilize the available spectrum, multiple radios should be enabled to work simultaneously. However, all the radios have to share only a single antenna. The key challenge is to design a system architecture that is capable of achieving different aggregation configurations while avoiding the interference. Second, the heterogeneous nature of the available spectrum (i.e., in terms of bandwidth and link characteristics) requires a design of efficient traffic distribution algorithm that takes into account these factors. Third, TVWS is unlicensed opportunistic shared spectrum. Thus, the coordination mechanism between the two nodes of backhauling link is essential to enable seamless channel switching. Third, we study the integration of multiple radio access technologies (RATs) in the context of 4G/5G networks. More specifically, we study the potential gain of enabling the Multi-RAT integration at the Packet Data Convergence Protocol (PDCP) layer compared with doing it at the transport layer. In this work, we consider ultra-reliable low-latency communication (URLLC) as one of the motivating services. This work tackles the different challenges that arise from enabling the Multi-RAT integration at the PDCP layer, including, packet reordering and traffic scheduling

Contract-based scheduling of URLLC packets in incumbent Embb traffic

Recently, the coexistence of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services on the same licensed spectrum has gained a lot of attention from both academia and industry. However, the coexistence of these services is not trivial due to the diverse multiple access protocols, contrasting frame distributions in the existing network, and the distinct quality of service requirements posed by these services. Therefore, such coexistence drives towards a challenging resource scheduling problem. To address this problem, in this paper, we first investigate the possibilities of scheduling URLLC packets in incumbent eMBB traffic. In this regard, we formulate an optimization problem for coexistence by dynamically adopting a superposition or puncturing scheme. In particular, the aim is to provide spectrum access to the URLLC users while reducing the intervention on incumbent eMBB users. Next, we apply the one-to-one matching game to find stable URLLC-eMBB pairs that can coexist on the same spectrum. Then, we apply the contract theory framework to design contracts for URLLC users to adopt the superposition scheme. Simulation results reveal that the proposed contract-based scheduling scheme achieves up to 63% of the eMBB rate for the "No URLLC" case compared to the "Puncturing" scheme.Comment: Submitted to IEEE Acces

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility