Design of cellular, satellite, and integrated systems for 5G and beyond

5G AgiLe and fLexible integration of SaTellite And cellulaR (5G-ALLSTAR) is a Korea-Europe (KR-EU) collaborative project for developing multi-connectivity (MC) technologies that integrate cellular and satellite networks to provide seamless, reliable, and ubiquitous broadband communication services and improve service continuity for 5G and beyond. The main scope of this project entails the prototype development of a millimeter-wave 5G New Radio (NR)-based cellular system, an investigation of the feasibility of an NR-based satellite system and its integration with cellular systems, and a study of spectrum sharing and interference management techniques for MC. This article reviews recent research activities and presents preliminary results and a plan for the proof of concept (PoC) of three representative use cases (UCs) and one joint KR-EU UC. The feasibility of each UC and superiority of the developed technologies will be validated with key performance indicators using corresponding PoC platforms. The final achievements of the project are expected to eventually contribute to the technical evolution of 5G, which will pave the road for next-generation communications

Resourse Allocation in the Wireless Internet-of-Things

The Internet-of-Things (IoT) is widely regarded as a promising paradigm, marking a revolutionary shift in the way that technology interacts with the world. Despite the rising popularity and extensive integration of IoT across diverse domains, the development and deployment of these interconnected systems are met with considerable challenges. One notable challenge is the scarcity of spectrum resources, which poses a significant obstacle in accommodating the massive data transmission. In addition, small-sized mobile terminals are constrained by limited computation and energy resources. These limitations make the traditional standalone operation of devices increasingly unfeasible. In this thesis, we delve into comprehensive task offloading and resource allocation strategies to utilize of limited resources in wireless IoT. In the first part, we propose a full-dimensional task offloading scheme in the multi-layer computing network. On this basis, we formulate an mixed-integer nonlinear programming (MINLP) problem and develop an inverse reinforcement learning (IRL) based algorithm to solve this problem. Without sacrificing the global optimality, the algorithm can significantly accelerate the optimal branch-and-bound (B&B) algorithm. In the second part, we delve into strategies to optimize energy efficiency within the downlink cell-free massive MIMO systems. We develop a green energy scheme and formulate it as a non-convex MINLP problem. To solve this problem, we propose a novel optimization-embedded deep reinforcement learning (DRL) algorithm, which enjoys the benefits of directly inferring solutions for the formulated problem. In the last part, we develop a NOMA-based task offloading scheme in a multi-layer computing network. On this basis, we formulate the task offloading scheme as a non-convex mixed-integer optimization problem and propose a reincarnating DRL algorithm, where accumulated apriori information is incorporated for fast retraining

Integer-forcing architectures: cloud-radio access networks, time-variation and interference alignment

Next-generation wireless communication systems will need to contend with many active mobile devices, each of which will require a very high data rate. To cope with this growing demand, network deployments are becoming denser, leading to higher interference between active users. Conventional architectures aim to mitigate this interference through careful design of signaling and scheduling protocols. Unfortunately, these methods become less effective as the device density increases. One promising option is to enable cellular basestations (i.e., cell towers) to jointly process their received signals for decoding users’ data packets as well as to jointly encode their data packets to the users. This joint processing architecture is often enabled by a cloud radio access network that links the basestations to a central processing unit via dedicated connections. One of the main contributions of this thesis is a novel end-to-end communications architecture for cloud radio access networks as well as a detailed comparison to prior approaches, both via theoretical bounds and numerical simulations. Recent work has that the following high-level approach has numerous advantages: each basestation quantizes its observed signal and sends it to the central processing unit for decoding, which in turn generates signals for the basestations to transmit, and sends them quantized versions. This thesis follows an integer-forcing approach that uses the fact that, if codewords are drawn from a linear codebook, then their integer-linear combinations are themselves codewords. Overall, this architecture requires integer-forcing channel coding from the users to the central processing unit and back, which handles interference between the users’ codewords, as well as integer-forcing source coding from the basestations to the central processing unit and back, which handles correlations between the basestations’ analog signals. Prior work on integer-forcing has proposed and analyzed channel coding strategies as well as a source coding strategy for the basestations to the central processing unit, and this thesis proposes a source coding strategy for the other direction. Iterative algorithms are developed to optimize the parameters of the proposed architecture, which involve real-valued beamforming and equalization matrices and integer-valued coefficient matrices in a quadratic objective. Beyond the cloud radio setting, it is argued that the integer-forcing approach is a promising framework for interference alignment between multiple transmitter-receiver pairs. In this scenario, the goal is to align the interfering data streams so that, from the perspective of each receiver, there seems to be only a signal receiver. Integer-forcing interference alignment accomplishes this objective by having each receiver recover two linear combinations that can then be solved for the desired signal and the sum of the interference. Finally, this thesis investigates the impact of channel coherence on the integer-forcing strategy via numerical simulations

Enabling Technologies for 5G and Beyond: Bridging the Gap between Vision and Reality

It is common knowledge that the fifth generation (5G) of cellular networks will come with drastic transformation in the cellular systems capabilities and will redefine mobile services. 5G (and beyond) systems will be used for human interaction, in addition to person-to-machine and machine-to-machine communications, i.e., every-thing is connected to every-thing. These features will open a whole line of new business opportunities and contribute to the development of the society in many different ways, including developing and building smart cities, enhancing remote health care services, to name a few. However, such services come with an unprecedented growth of mobile traffic, which will lead to heavy challenges and requirements that have not been experienced before. Indeed, the new generations of cellular systems are required to support ultra-low latency services (less than one millisecond), and provide hundred times more data rate and connectivity, all compared to previous generations such as 4G. Moreover, they are expected to be highly secure due to the sensitivity of the transmitted information. Researchers from both academia and industry have been concerting significant efforts to develop new technologies that aim at enabling the new generation of cellular systems (5G and beyond) to realize their potential. Much emphasis has been put on finding new technologies that enhance the radio access network (RAN) capabilities as RAN is considered to be the bottleneck of cellular networks. Striking a balance between performance and cost has been at the center of the efforts that led to the newly developed technologies, which include non-orthogonal multiple access (NOMA), millimeter wave (mmWave) technology, self-organizing network (SON) and massive multiple-input multiple-output (MIMO). Moreover, physical layer security (PLS) has been praised for being a potential candidate for enforcing transmission security when combined with cryptography techniques. Although the main concepts of the aforementioned RAN key enabling technologies have been well defined, there are discrepancies between their intended (i.e., vision) performance and the achieved one. In fact, there is still much to do to bridge the gap between what has been promised by such technologies in terms of performance and what they might be able to achieve in real-life scenarios. This motivates us to identify the main reasons behind the aforementioned gaps and try to find ways to reduce such gaps. We first focus on NOMA where the main drawback of existing solutions is related to their poor performance in terms of spectral efficiency and connectivity. Another major drawback of existing NOMA solutions is that transmission rate per user decreases slightly with the number of users, which is a serious issue since future networks are expected to provide high connectivity. To this end, we develop NOMA solutions that could provide three times the achievable rate of existing solutions while maintaining a constant transmission rate per user regardless of the number of connected users. We then investigate the challenges facing mmWave transmissions. It has been demonstrated that such technology is highly sensitive to blockage, which limits its range of communication. To overcome this obstacle, we develop a beam-codebook based analog beam-steering scheme that achieves near maximum beamforming gain performance. The proposed technique has been tested and verified by real-life measurements performed at Bell Labs. Another line of research pursued in this thesis is investigating challenges pertaining to SON. It is known that radio access network self-planning is the most complex and sensitive task due to its impact on the cost of network deployment, etc., capital expenditure (CAPEX). To tackle this issue, we propose a comprehensive self-planning solution that provides all the planning parameters at once while guaranteeing that the system is optimally planned. The proposed scheme is compared to existing solutions and its superiority is demonstrated. We finally consider the communication secrecy problem and investigated the potential of employing PLS. Most of the existing PLS schemes are based on unrealistic assumptions, most notably is the assumption of having full knowledge about the whereabouts of the eavesdroppers. To solve this problem, we introduce a radically novel nonlinear precoding technique and a coding strategy that together allow to establish secure communication without any knowledge about the eavesdroppers. Moreover, we prove that it is possible to secure communications while achieving near transmitter-receiver channel capacity (the maximum theoretical rate)

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods

Energy-efficient LTE transmission techniques : introducing Green Radio from resource allocation perspective

Energy consumption has recently become a key issue from both environmental and economic considerations. A typical mobile phone network in the UK may consume approximately 40- 50 MW, contributing a significant proportion of the total energy consumed by the information technology industry. With the worldwide growth in the number of mobile subscribers, the associated carbon emissions and growing energy costs are becoming a significant operational expense, leading to the need for energy reduction. The Mobile VCE Green Radio Project has been launched, which targets to achieve 100x energy reduction of the current wireless networks by 2020. In this thesis, energy-efficient resource allocation strategies have been investigated taking the LTE system as an example. Firstly, theoretical analysis of energy-efficient design in cellular environments is provided according to the Shannon Theory. Based on a two-link scenario the performance of simultaneous transmission and orthogonal transmission for network power minimization under the specified rate constraints is investigated. It is found that simultaneous transmission consumes less power than orthogonal transmission close to the base station, but much more power in the cell-edge area. Also, simulation results suggest that the energy-efficient switching margins between these two schemes are dominated by the sum total of their required data rates. New definitions of power-utility and fairness metrics are further proposed, following by the design of weighted resource allocation approaches based on efficiency-fairness trade-offs. Apart from energy-efficient multiple access between different links, the energy used by individual base stations can also be reduced. For example, deploying sleep modes is an effective approach to reduce radio base station operational energy consumption. By periodically switching off the base station transmission, or using fewer transmit antennas, the energy consumption of base station hardware may decrease. By delivering less control signalling overhead, the radio frequency energy consumption can also be reduced. Simulation results suggest that up to 90% energy reduction can be obtained in low traffic conditions by employing time-domain optimization in each radio frame. The optimum on/off duty cycle is derived, enabling the energy consumption of the base station to scale with traffic loads. In the spatial-domain, an antenna selection criterion is proposed, indicating the most energy-efficient antenna configuration with the knowledge of users’ locations and quality of service requirements. Without time-domain sleep modes, using fewer transmit antennas could outperform full antenna transmission. However, with time-domain sleep modes, using all available antennas is generally the most energy-efficient choice