13 research outputs found
Robust radio resource allocation in MISO-SCMA assisted C-RAN in 5G networks
Abstract
In this paper, by considering multiple slices, a downlink transmission of a sparse code multiple access (SCMA) based cloud-radio access network (C-RAN) is investigated. In this setup, by assuming multiple-input and single-output (MISO) transmission mode, a novel robust radio resource allocation is proposed where considering uncertain channel state information, the worst case approach is applied. We consider a radio resource allocation problem with the objective to maximize the total sum rate of users subject to a minimum required rate of each slice and practical limitations of C-RAN and SCMA. To solve the proposed optimization problem in an efficient manner, an iterative method is deployed where beamforming and joint codebook allocation and user association subproblems are sequentially solved. By introducing auxiliary variables, the joint codebook allocation and user association subproblem is transformed into an integer linear programming, and to solve the beamforming optimization problem, minorization-maximization algorithm is applied. Via numerical results, the performance of the proposed algorithm is investigated versus different uncertainty level for different system parameters
Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations
In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this b
Resource allocation in an open RAN system using network slicing
Abstract
The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods
Energy efficiency through joint routing and function placement in different modes of SDN/NFV networks
Abstract
Network function virtualization (NFV) and software-defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalability, and deployment flexibility. However, migration to full SDN/NFV networks in order to serve these services is a time-consuming process and costly for mobile operators. This paper focuses on energy efficiency during the transition of mobile core networks (MCN) to full SDN/NFV networks and explores how energy efficiency can be addressed during such migration. We propose a general system model containing a combination of legacy nodes and links, in addition to newly introduced NFV and SDN nodes. We refer to this system model as partial SDN and hybrid NFV MCN, which can cover different modes of SDN and NFV implementations. Based on this framework, we formulate energy efficiency by considering joint routing and function placement in the network. Since this problem belongs to the class of non-linear integer programming problems, to solve it efficiently, we present a modified Viterbi algorithm (MVA) based on multi-stage graph modeling and a modified Dijkstra’s algorithm. We simulate this algorithm for a number of network scenarios with different fractions of NFV and SDN nodes and evaluate how much energy can be saved through such transition. Simulation results confirm the expected performance of the algorithm, which saves up to 70% energy compared to a network where all nodes are always on. Interestingly, the amount of energy saved by the proposed algorithm in the case of hybrid NFV and partial SDN networks can reach up to 60%–90% of the saved energy in full NFV/SDN networks