Inter-Numerology Interference Analysis for 5G and Beyond

Kihero, Abuu B.; Solaija, Muhammad Sohaib J.; Yazar, Ahmet; Arslan, Hüseyin

doi:10.1109/glocomw.2018.8644394

Cited by 38 publications

(27 citation statements)

References 12 publications

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“…Consequently, RBs of similar numerologies produce less interference when they are allocated close to each other than RBs with very different numerologies. This result is also supported by authors in [22], where they show the signal-to-interference ratio (SIR) degradation as function of the numerology gap between different subbands.…”

Section: A Problem Formulationsupporting

confidence: 74%

Interference Minimization in 5G Physical-Layer Network Slicing

Zambianco

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2020

IEEE Trans. Commun.

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The interference resulting from densification of access points and the coexistence of different numerologies within the same spectrum severely hinders inter-slice isolation. We propose a slice allocation policy that enforces inter-slice isolation by minimizing the inter-slice interference suffered by each virtual operator. In detail, we design a binary quadratic non-convex optimization problem that minimizes i) the interslice interference generated by interfering base stations and ii) the inter-slice interference generated by the multiplexing of spectrum slices having different numerologies. We also provide a heuristic algorithm to render the solution scalable in practical scenarios. We assess the performance of both approaches by evaluating the signal-to-interference-plus-noise ratio (SINR) associated to each slice through simulations. Results reveal that the heuristic algorithm provides a solution comparable with the optimal one on different minimization scenarios. Moreover, a considerable SINR improvement is observed with respect to a base-line scheme that does not account for inter-slice interference.

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Section: A Problem Formulationsupporting

confidence: 74%

Interference Minimization in 5G Physical-Layer Network Slicing

Zambianco

Verticale

2020

IEEE Trans. Commun.

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“…Indeed, different numerologies (i.e., different subcarrier spacings) may coexist over a given frequency band, thus generating novel types of interference, known as inter-numerology interference (INI) [135]. In recent literature, several works analyzed the INI factors that impact the overall performance [136]. INI cancellation methods for 5G NR multi-numerology terrestrial systems were also investigated [137].…”

Section: Radio Resource Managementmentioning

confidence: 99%

Non-Terrestrial Networks in 5G & Beyond: A Survey

et al. 2020

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Fifth-generation (5G) telecommunication systems are expected to meet the world market demands of accessing and delivering services anywhere and anytime. The Non-Terrestrial Network (NTN) systems are able to satisfy the requests of anywhere and anytime connections by offering wide-area coverage and ensuring service availability, continuity, and scalability. In this work, we review the 3GPP NTN features and their potential for satisfying the user expectations in 5G & beyond networks. The state of the art, current 3GPP research activities, and open issues are summarized to highlight the importance of NTN over the wireless communication landscape. Future research directions are also identified to assess the role of NTN in 5G and beyond systems.

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“…However, the computation of the solution of the proposed problem is challenging due to the non-convexity of (5) whose optimization variable is integer. Furthermore, the complexity is further exacerbated by the highly non-linear equation defining the INI as shown in (2) and (3). These issues make the discussed problem formulation unpractical for real systems due to the stringent time requirements required to adapt the slice allocation to the wireless channel dynamic.…”

Section: A Optimal Resource Allocationmentioning

confidence: 99%

“…Differently from conventional orthogonal frequency division multiple access (OFDMA) schemes, such technology makes it possible to multiplex time-frequency resources, denoted as resource blocks (RB), having heterogeneous subcarrier spacing on the same physical layer. On one hand, this access scheme provides the flexibility to accommodate different radio requirements, on the other hand, the loss of orthogonality between RBs of different numerologies generates interference that hinders the transmission performance [2].…”

Section: Introductionmentioning

confidence: 99%

Spectrum Allocation for Network Slices with Inter-Numerology Interference using Deep Reinforcement Learning

Zambianco

Verticale

2020

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

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Network slicing and mixed-numerology schemes are essential technologies to efficiently accommodate different services in 5G radio access networks (RAN). To fully take advantage of these techniques, the design of spectrum slicing policies needs to account for the limited availability of the radio resources as well as the inter-numerology interference generated by slices employing different numerologies. In this context, we formulate a binary non-convex problem that maximizes the aggregate capacity of multiple network slices. The resulting spectrum allocation minimizes the inter-numerology interference under the frequent channel fluctuations characterizing the various users. To address the computational complexity of the designed objective function, we leverage deep reinforcement learning (DRL) to design a model-free solution computation. In detail, the trained centralized DRL agent exploits the channel fading statistic in order to provide a spectrum allocation that minimizes the internumerology interference. Results reveal that the proposed DRL scheme achieves performance that is comparable to the optimal one. It also outperforms a baseline scheme that statically allocate the radio resources.

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Inter-Numerology Interference Analysis for 5G and Beyond

Cited by 38 publications

References 12 publications

Interference Minimization in 5G Physical-Layer Network Slicing

Interference Minimization in 5G Physical-Layer Network Slicing

Non-Terrestrial Networks in 5G & Beyond: A Survey

Spectrum Allocation for Network Slices with Inter-Numerology Interference using Deep Reinforcement Learning

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