Multi-Tenant Slicing for Spectrum Management on the Road to 5G

Vincenzi, Matteo; Antonopoulos, Angelos; Kartsakli, Elli; Vardakas, John S.; Alonso, Luis; Verikoukis, Christos

doi:10.1109/mwc.2017.1700138

Cited by 45 publications

(23 citation statements)

References 8 publications

(9 reference statements)

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“…There is also a dependency relation between the SINR(u,r) and (u), since the latter is based on the SINR to determine the serving SC. To simplify the procedure, the SINR(u,r) is first estimated using (u) based on the strongest received power, which is calculated in (3). Then, the obtained values are used to compute (u) based on the SINR and, lastly, the SINR(u,r).…”

Section: Performance Evaluation a Simulation Scenariomentioning

confidence: 99%

Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond

Muñoz¹,

Adamuz-Hinojosa²,

Navarro-Ortiz³

et al. 2020

IEEE Access

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The new fifth generation (5G) era has transformed previous mobile generations into fast, smart networks that will be more responsive and customizable. With network slicing, 5G networks can be dynamically adapted to the different needs of specific vertical industries. This capability has opened the opportunity to new business models whereby infrastructure owners can monetize their investment by leasing resources to third parties (i.e. tenants). In this respect, a challenging task for the owner of the radio access network infrastructure (i.e. the network provider) is the spectrum planning of multi-tenant scenarios. This paper proposes different alternatives of hiring capacity to the provider as well as a set of spectrum planning strategies, each giving a certain degree of flexibility to allocate resources per tenant. These strategies are evaluated in a 5G small cell multi-tenant network through snapshot-based simulations. The performance of the strategies is assessed in terms of scalability, spectrum isolation, utilization and efficiency. INDEX TERMS 5G networks, multi-tenancy, new radio, RAN slicing, small cells, spectrum planning.

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Section: Performance Evaluation a Simulation Scenariomentioning

confidence: 99%

Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond

Muñoz¹,

Adamuz-Hinojosa²,

Navarro-Ortiz³

et al. 2020

IEEE Access

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“…To this end, CRAN defines a suitable platform for resource allocation in 5G that may allow for both centralized and distributed control of a common pool of resources belonging to multiple operators [13] or multiple service providers [14]. Despite the recent CRAN advances, related work with respect to machine learning for resource allocation within the context of CRAN is still quite sparse.…”

Section: Related Workmentioning

confidence: 99%

Random forests for resource allocation in 5G cloud radio access networks based on position information

Imtiaz

Koudouridis

Ghauch

et al. 2018

J Wireless Com Network

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Next generation 5G cellular networks are envisioned to accommodate an unprecedented massive amount of Internet of things (IoT) and user devices while providing high aggregate multi-user sum rates and low latencies. To this end, cloud radio access networks (CRAN), which operate at short radio frames and coordinate dense sets of spatially distributed radio heads, have been proposed. However, coordination of spatially and temporally denser resources for larger sets of user population implies considerable resource allocation complexity and significant system signalling overhead when associated with channel state information (CSI)-based resource allocation (RA) schemes. In this paper, we propose a novel solution that utilizes random forests as supervised machine learning approach to determine the resource allocation in multi-antenna CRAN systems based primarily on the position information of user terminals. Our simulation studies show that the proposed learning based RA scheme performs comparably to a CSI-based scheme in terms of spectral efficiency and is a promising approach to master the complexity in future cellular networks. When taking the system overhead into account, the proposed learning-based RA scheme, which utilizes position information, outperforms legacy CSI-based scheme by up to 100%. The most important factor influencing the performance of the proposed learning-based RA scheme is antenna orientation randomness and position inaccuracies. While the proposed random forests scheme is robust against position inaccuracies and changes in the propagation scenario, we complement our scheme with three approaches that restore most of the original performance when facing random antenna orientations of the user terminal.

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“…The presented results validate the impact of varying these two criteria (i.e., controller connectivity and λ max reduction rate) on energy savings and network robustness. While the controller connectivity (denoted as C con ) is bounded by a number of neighbors between one and the controller degree, the allowed λ max reduction rate (denoted as δ) was normalized according to the following expression, where the term SNetCA' denotes the resilience-constrained version: δ = λ max (Original) − λ max (SNetCA') λ max (Original) (11) As is shown in Table 5 when C con = 1 and δ = 1, the resilience-constrained version of SNetCA behaves exactly as the resilience-agnostic one. However, less energy can be saved as more restrictive values of C con and δ are imposed, since each of these elements determines that a fewer number of links could be put into sleep mode by the pruning function of SNetCA.…”

Section: Resilience Concernsmentioning

confidence: 99%

Energy Efficiency and Network Performance: A Reality Check in SDN-Based 5G Systems

2017

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Abstract:The increasing power consumption and related environmental implications currently generated by large data networks have become a major concern over the last decade. Given the drastic traffic increase expected in 5G dense environments, the energy consumption problem becomes even more concerning and challenging. In this context, Software-Defined Networks (SDN), a key technology enabler for 5G systems, can be seen as an attractive solution. In these programmable networks, an energy-aware solution could be easily implemented leveraging the capabilities provided by control and data plane separation. This paper investigates the impact of energy-aware routing on network performance. To that end, we propose a novel energy-aware mechanism that reduces the number of active links in SDN with multiple controllers, considering in-band control traffic. The proposed strategy exploits knowledge of the network topology combined with traffic engineering techniques to reduce the overall power consumption. Therefore, two heuristic algorithms are designed: a static network configuration and a dynamic energy-aware routing. Significant values of switched-off links are reached in the simulations where real topologies and demands data are used. Moreover, the obtained results confirm that crucial network parameters such as control traffic delay, data path latency, link utilization and Ternary Content Addressable Memory (TCAM) occupation are affected by the performance-agnostic energy-aware model.

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Multi-Tenant Slicing for Spectrum Management on the Road to 5G

Cited by 45 publications

References 8 publications

Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond

Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond

Random forests for resource allocation in 5G cloud radio access networks based on position information

Energy Efficiency and Network Performance: A Reality Check in SDN-Based 5G Systems

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