A RAN Resource Slicing Mechanism for Multiplexing of eMBB and URLLC Services in OFDMA based 5G Wireless Networks

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations

Joint Power and Resource Block Allocation for Mixed-Numerology-Based 5G Downlink Under Imperfect CSI

Fifth-generation (5G) of wireless networks are expected to accommodate different services with contrasting quality of service (QoS) requirements within a common physical infrastructure in an efficient way. In this article, we address the radio access network (RAN) slicing problem and focus on the three 5G primary services, namely, enhanced mobile broadband (eMBB), ultra-reliable and lowlatency communications (URLLC) and massive machine-type communications (mMTC). In particular, we formulate the joint allocation of power and resource blocks to the heterogeneous users in the downlink targeting the transmit power minimization and by considering mixed numerology-based frame structures. Most importantly, the proposed scheme does not only consider the heterogeneous QoS requirements of each service, but also the queue status of each user during the scheduling of resource blocks. In addition, imperfect Channel State Information (CSI) is considered by including an outage probabilistic constraint into the formulation. The resulting non-convex problem is converted to a more tractable problem by exploiting Big-M formulation, probabilistic to non-probabilistic transformation, binary relaxation and successive convex approximation (SCA). The proposed solution is evaluated for different mixed-numerology resource grids within the context of strict slice-isolation and slice-aware radio resource management schemes via extensive numerical simulations

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective

Scheduling in 5G networks : Developing a 5G cell capacity simulator.

La quinta generación de comunicaciones móviles (5G) se está convirtiendo en una realidad gracias a la nueva tecnología 3GPP (3rd Generation Partnership Project) diseñada para cumplir con una amplia gama de requerimientos. Por un lado, debe poder soportar altas velocidades y servicios de latencia ultra-baja, y por otro lado, debe poder conectar una gran cantidad de dispositivos con requerimientos laxos de ancho de banda y retardo. Esta diversidad de requerimientos de servicio exige un alto grado de flexibilidad en el diseño de la interfaz de radio. Dado que la tecnología LTE (Long Term Evolution) se diseñó originalmente teniendo en cuenta la evolución de los servicios de banda ancha móvil, no proporciona suficiente flexibilidad para multiplexar de manera óptima los diferentes tipos de servicios previstos por 5G. Esto se debe a que no existe una única configuración de interfaz de radio capaz de adaptarse a todos los diferentes requisitos de servicio. Como consecuencia, las redes 5G se están diseñando para admitir diferentes configuraciones de interfaz de radio y mecanismos para multiplexar estos diferentes servicios con diferentes configuraciones en el mismo espectro disponible. Este concepto se conoce como Network Slicing y es una característica clave de 5G que debe ser soportada extremo a extremo en la red (acceso, transporte y núcleo). De esta manera, las Redes de Acceso (RAN) 5G agregarán el problema de asignación de recursos para diferentes servicios al problema tradicional de asignación de recursos a distintos usuarios. En este contexto, como el estándar no describe cómo debe ser la asignación de recursos para usuarios y servicios (quedando libre a la implementación de los proveedores) se abre un amplio campo de investigación. Se han desarrollado diferentes herramientas de simulación con fines de investigación durante los últimos años. Sin embargo, como no muchas de estas son libres, fáciles de usar y particularmente ninguna de las disponibles soporta Network Slicing a nivel de Red de Acceso, este trabajo presenta un nuevo simulador como principal contribución. Py5cheSim es un simulador simple, flexible y de código abierto basado en Python y especialmente orientado a probar diferentes algoritmos de scheduling para diferentes tipos de servicios 5G mediante una implementación simple de la funcionalidad RAN Slicing. Su arquitectura permite desarrollar e integrar nuevos algoritmos para asignación de recursos de forma sencilla y directa. Además, el uso de Python proporciona suficiente versatilidad para incluso utilizar herramientas de Inteligencia Artificial para el desarrollo de nuevos algoritmos. Este trabajo presenta los principales conceptos de diseño de las redes de acceso 5G que se tomaron como base para desarrollar la herramienta de simulación. También describe decisiones de diseño e implementación, seguidas de las pruebas de validación ejecutadas y sus principales resultados. Se presentan además algunos ejemplos de casos de uso para mostrar el potencial de la herramienta desarrollada, proporcionando un análisis primario de los algoritmos tradicionales de asignación de recursos para los nuevos tipos de servicios previstos por la tecnología. Finalmente se concluye sobre la contribución de la herramienta desarrollada, los resultados de los ejemplos incluyendo posibles líneas de investigación junto con posibles mejoras para futuras versiones.The fifth generation of mobile communications (5G) is already becoming a reality by the new 3GPP (3rd Generation Partnership Project) technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose band width and delay requirements. Such diversity in terms of service requirements demands a high degree of flexibility in radio interface design. As LTE (Long Term Evolution) technology was originally designed with Mobile Broadband (MBB) services evolution in mind it does not provide enough flexibility to multiplex optimally the different types of services envisioned by 5G. This is because there is not a unique radio interface configuration able to fit all the different service requirements. As a consequence, 5G networks are being designed to support different radio interface configurations and mechanisms to multiplex these different services with different configurations in the same available spectrum. This concept is known as Network Slicing, and isa 5G key feature which needs to be supported end to end in the network (Radio Access, Transport and Core Network). In this way 5G Radio Access Networks (RAN) will add the resource allocation for different services problem to the user resource allocation traditional one. In this context, as both users and services scheduling is being left to vendor implementation by the standard, an extensive field of research is open. Different simulation tools have been developed for research purposes during the last years. However, as not so many of them are free, easy to use, and particularly none of the available ones supports Network Slicing at RAN level, this work presents a new simulator as its main contribution. Py5cheSim is a simple, flexible and open-source simulator based on Pythonand specially oriented to test different scheduling algorithms for 5G different types of services through a simple implementation of RAN Slicing feature. Its architecture allows to develop and integrate new scheduling algorithms in a easy and straight forward way. Furthermore, the use of Python provides enough versatility to even use Machine Learning tools for the development of new scheduling algorithms. The present work introduces the main 5G RAN design concepts which were taken as a baseline to develop the simulation tool. It also describes its design and implementation choices followed by the executed validation tests and its main results. Additionally this work presents a few use cases examples to show the developed tool’s potential providing a primary analysis of traditional scheduling algorithms for the new types of services envisioned by the technology. Finally it concludes about the developed tool contribution, the example results along with possible research lines and future versions improvements

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

5g new radio access and core network slicing for next-generation network services and management

In recent years, fifth-generation New Radio (5G NR) has attracted much attention owing to its potential in enhancing mobile access networks and enabling better support for heterogeneous services and applications. Network slicing has garnered substantial focus as it promises to offer a higher degree of isolation between subscribers with diverse quality-of-service requirements. Integrating 5G NR technologies, specifically the mmWave waveform and numerology schemes, with network slicing can unlock unparalleled performance so crucial to meeting the demands of high throughput and sub-millisecond latency constraints. While conceding that optimizing next-generation access network performance is extremely important, it needs to be acknowledged that doing so for the core network is equally as significant. This is majorly due to the numerous core network functions that execute control tasks to establish end-to-end user sessions and route access network traffic. Consequently, the core network has a significant impact on the quality-of-experience of the radio access network customers. Currently, the core network lacks true end-to-end slicing isolation and reliability, and thus there is a dire need to examine more stringent configurations that offer the required levels of slicing isolation for the envisioned networking landscape. Considering the factors mentioned above, a sequential approach is adopted starting with the radio access network and progressing to the core network. First, to maximize the downlink average spectral efficiency of an enhanced mobile broadband slice in a time division duplex radio access network while meeting the quality-of-service requirements, an optimization problem is formulated to determine the duplex ratio, numerology scheme, power, and bandwidth allocation. Subsequently, to minimize the uplink transmission power of an ultra-reliable low latency communications slice while satisfying the quality-of-service constraints, a second optimization problem is formulated to determine the above-mentioned parameters and allocations. Because 5G NR supports dual-band transmissions, it also facilitates the usage of different numerology schemes and duplex ratios across bands simultaneously. Both problems, being mixed-integer non-linear programming problems, are relaxed into their respective convex equivalents and subsequently solved. Next, shifting attention to aerial networks, a priority-based 5G NR unmanned aerial vehicle network (UAV) is considered where the enhanced mobile broadband and ultra-reliable low latency communications services are considered as best-effort and high-priority slices, correspondingly. Following the application of a band access policy, an optimization problem is formulated. The goal is to minimize the downlink quality-of-service gap for the best-effort service, while still meeting the quality-of-service constraints of the high-priority service. This involves the allocation of transmission power and assignment of resource blocks. Given that this problem is a mixed-integer nonlinear programming problem, a low-complexity algorithm, PREDICT, i.e., PRiority BasED Resource AllocatIon in Adaptive SliCed NeTwork, which considers the channel quality on each individual resource block over both bands, is designed to solve the problem with a more accurate accounting for high-frequency channel conditions. Transitioning to minimizing the operational latency of the core network, an integer linear programming problem is formulated to instantiate network function instances, assign them to core network servers, assign slices and users to network function instances, and allocate computational resources while maintaining virtual network function isolation and physical separation of the core network control and user planes. The actor-critic method is employed to solve this problem for three proposed core network operation configurations, each offering an added degree of reliability and isolation over the default configuration that is currently standardized by the 3GPP. Looking ahead to potential future research directions, optimizing carrier aggregation-based resource allocation across triple-band sliced access networks emerges as a promising avenue. Additionally, the integration of coordinated multi-point techniques with carrier aggregation in multi-UAV NR aerial networks is especially challenging. The introduction of added carrier frequencies and channel bandwidths, while enhancing flexibility and robustness, complicates band-slice assignments and user-UAV associations. Another layer of intriguing yet complex research involves optimizing handovers in high-mobility UAV networks, where both users and UAVs are mobile. UAV trajectory planning, which is already NP-hard even in static-user scenarios, becomes even more intricate to obtain optimal solutions in high-mobility user cases