Radio Resource Management Scheme for URLLC and EMBB coexistence in a Cell-Less Radio Access network

We address the latency challenges in a high-density and high-load scenario for an ultra-reliable and low-latency communication (URLLC) network which may coexist with enhanced mobile broadband (eMBB) services in the evolving wireless communication networks. We propose a new radio resource management (RRM) scheme consisting of a combination of time domain (TD) and frequency domain (FD) schedulers specific for URLLC and eMBB users. We also develop a user ranking algorithm from a radio unit (RU) perspective, which is employed by the TD scheduler to increase the efficiency of scheduling in terms of resource consumption in large-scale networks. Therefore, the optimized and novel resource scheduling scheme reduces latency for the URLLC users (requesting a URLLC service) in an efficient resource utilization manner to support scenarios with high user density. At the same time, this RRM scheme, while minimizing the latency, it also overcomes another important challenge of eMBB users (requesting an eMBB service), namely the throughput of those who coexist in such highly loaded scenario with URLLC users. The effectiveness of our proposed scheme including time and frequency domain (TD and FD) schedulers is analyzed. Simulation results show that the proposed scheme improves the latency of URLLC users and throughput of the eMBB users compared to the baseline scheme. The proposed scheme has a 29% latency improvement for URLLC and 90% signal-to-interference-plus-noise ratio (SINR) improvement for eMBB users as compared with conventional scheduling policies.This work was supported by the European Union H2020 Research and Innovation Programme funded by the Marie Skłodowska-Curie ITN TeamUp5G Project under Grant 813391

Evolution Toward 5G Mobile Networks - A Survey on Enabling Technologies

In this paper, an extensive review has been carried out on the trends of existing as well as proposed potential enabling technologies that are expected to shape the fifth generation (5G) mobile wireless networks. Based on the classification of the trends, we develop a 5G network architectural evolution framework that comprises three evolutionary directions, namely, (1) radio access network node and performance enabler, (2) network control programming platform, and (3) backhaul network platform and synchronization. In (1), we discuss node classification including low power nodes in emerging machine-type communications, and network capacity enablers, e.g., millimeter wave communications and massive multiple-input multiple-output. In (2), both logically distributed cell/device-centric platforms, and logically centralized conventional/wireless software defined networking control programming approaches are discussed. In (3), backhaul networks and network synchronization are discussed. A comparative analysis for each direction as well as future evolutionary directions and challenges toward 5G networks are discussed. This survey will be helpful for further research exploitations and network operators for a smooth evolution of their existing networks toward 5G networks

Performance Analysis of Non-Orthogonal Multiple Access (NOMA) in C-RAN, H-CRAN and F-RAN for 5G Systems

The world of telecommunication is witnessing a swift transformation towards fifth generation (5G) cellular networks. The future networks present requisite needs in ubiquitous throughput, low latency, and high reliability. They are also envisioned to provide diversified services such as enhanced Mobile BroadBand (eMBB) and ultra-reliable low-latency communication (URLLC) as well as improved quality of user experience. More interestingly, a novel mobile network architecture allowing centralized processing and cloud computing has been proposed as one of the best candidates for fifth generation. It is denoted as Cloud Radio Access Network (CRAN) and Heterogeneous Cloud Radio Access Network (H-CRAN). Furthermore, the 5G architecture will be fog-like, namely fog radio access networks (F-RAN) enabling a functional split of network functionalities between cloud and edge nodes with caching and fog computing capabilities. Meanwhile non-orthogonal multiple access (NOMA) has been proposed as an promising multiple access (MA) technology for future radio access networks (RANs) to meet the heterogeneous demands for high throughput, low latency and massive connectivity. One of the main challenges of NOMA is that how well it is to be compatible with other emerging techniques for meeting the requirements of 5G. However, comprehensive performance analysis on NOMA and practical resource allocation designs in co-existence with other emerging networks have not been fully studied and investigated in the literature. This thesis focuses on potential performance enhancement brought by NOMA for the C-RAN, H-CRAN and F-RAN and is expected to address some of the aforementioned key challenges of 5G. The research work of this thesis can be divided into three parts. In the first part of our research, we focus on investigating the performance analysis of NOMA in a C-RAN. The problem of jointly optimizing user association, muting and power-bandwidth allocation is formulated for NOMA-enabled C-RANs. To solve the mixed integer programming problem, the joint problem is decomposed into two subproblems as 1) user association and muting 2) power-bandwidth allocation optimization. To deal with the first subproblem, we propose a centralized and heuristic algorithm to provide the optimal and suboptimal solutions to the remote radio head (RRH) muting problem for given bandwidth and transmit power, respectively. The second subproblem is then reformulated and we propose an optimal solution to bandwidth and power allocation subject to users data rate constraints. Moreover, for given user association and muting states, the optimal power allocation is derived in a closed-form. Simulation results show that the proposed NOMA-enabled C-RAN outperforms orthogonal multiple access (OMA)-based C-RANs in terms of total achievable rate, interference mitigation and can achieve significant fairness improvement. Our second work investigates the performance of NOMA in H-CRAN, where coordination of macro base station (MBS) and remote radio heads (RRHs) for H-CRAN with NOMA is introduced to improve network performance. We formulate the problem of jointly optimizing user association, coordinated scheduling and power allocation for NOMA-enabled H-CRANs. To efficiently solve this problem, we decompose the joint optimization problem into two subproblems as 1) user association and scheduling 2) power allocation optimization. Firstly the users are divided based on different interference they suffer. This interference-aware NOMA approach account for the inter-tier interference. Proportional fairness (PF) scheduling for NOMA is utilized to schedule users with a two-loop optimization method to enhance throughput and fairness. Based on the user scheduling scheme, optimal power allocation optimization is performed by the hierarchical decomposition approach. It is then followed by algorithm for joint scheduling and power allocation. Simulation results show that the proposed NOMA-enabled H-CRAN outperforms OMA-based H-CRANs in terms of total achievable rate and can achieve significant fairness improvement. In the third part of our research, we propose a NOMA-enabled fog-cloud structure in a novel density-aware F-RAN to tackle different aspects such as throughput and latency requirements of high and low user-density regions, in order to meet the heterogeneous requirements of eMBB and URLLC traffic. A framework of the multi-objective problem is formulated to cater the high throughput and low-latency requirements in a high and low user-density mode respectively. In the first problem, we study the joint caching placement and association strategy aiming at minimizing the average delay. To deal with the first problem, we apply McCormick envelopes and Lagrange partial relaxation method to transform it into three convex sub-problems, which is then solved by proposed distributed algorithm. The second problem is to jointly optimize transmission mode selection, subchannel assignment and power allocation to maximize the sum data rate of all fog user equipments (F-UEs) while satisfying fronthaul capacity and fog-computing access point (F-AP) power constraints. Moreover, for given transmission mode selection and subchannel assignment, the optimal power allocation is derived in a closed-form. Simulation results are provided for the proposed NOMA-enabled F-RAN framework and reveal that the ultra-low latency and high throughput can be achieved by properly utilizing the available resources

Mathematical Modelling and Analysis of Spatially Correlated Heterogeneous and Vehicular Networks - A Stochastic Geometry Approach

Heterogeneous Cellular Networks (HCNs) and vehicular communications are two key ingredients of future 5G communication networks, which aim at providing high data rates on the one former case and high reliability on the latter one. Nevertheless, in these two scenarios, interference is the main limiting factor, which makes achieving the required performance, i.e., data rate or reliability, a challenging task. Hence, in order to cope with such issue, concepts like uplink/downlink (UL/DL) decoupling, Interference-Aware (IA) strategies or cooperative communications with Cloud Radio Access Networks (CRANs) has been introduced for new releases of 4G and future 5G networks. Additionally, for the sake of increasing the data rates, new multiple access schemes like Non-Orthogonal Multiple Access (NOMA) has been proposed for 5G networks. All these techniques and concepts require accurate and tractable mathematical modelling for performance analysis. This analysis allows us to obtain theoretical insights about key performance indicators leading to a deep understanding about the considered techniques. Due to the random and irregular nature that exhibits HCNs, as well as vehicular networks, stochastic geometry has appeared recently as a promising tool for system-level modelling and analysis. Nevertheless, some features of HCNs and vehicular networks, like power control, scheduling or frequency planning, impose spatial correlations over the underlying point process that complicates significantly the mathematical analysis. In this thesis, it has been used stochastic geometry and point process theories to investigate the performance of these aforementioned techniques. Firstly, it is derived a mathematical framework for the analysis of an Interference-Aware Fractional Power Control (IAFPC) for interference mitigation in the UL of HCNs. The analysis reveals that IAFPC outperforms the classical FPC in terms of Spectral Efficiency (SE), average transmitted power, and mean and variance of the interference. Then, it is investigated the performance of a scheduling algorithm where the Mobile Terminals (MTs) may be turned off if they cause a level of interference greater than a given threshold. Secondly, a multi-user UL model to assess the coverage probability of different MTs in each cell is proposed. Then, the coverage probability of cellular systems under Hoyt fading (Nakagami-q) is studied. This fading model, allows us to consider more severe fading conditions than Rayleigh, which is normally the considered fading model for the sake of tractability. Thirdly, a novel NOMA-based scheme for CRANs is proposed, modelled and analyzed. In this scheme, two users are scheduled in the same resources according to NOMA; however the performance of cell-edge users is enhanced by means of coordinated beamforming. Finally, the performance of a decentralized Medium Access Control (MAC) algorithm for vehicular communications is investigated. With this strategy, the cellular network provides frequency and time synchronization for direct Vehicle to Vehicle (V2V) communication, which is based on its geographical information. The analysis demonstrates that there exists an operation regime where the performance is noise-limited. Then, the optimal transmit power that maximizes the Energy Efficiency (EE) of the system subject to a minimum capture probability constraint is derived