Quantification of Interference Constraint for Small Cells in Low SINR Regime With Steepest Ascent Method

The ubiquitous proliferation of small cells (SCs) is for boosting system capacity, coverage, and quality of services (QoS) for smart applications. However, interference from the adjacent SCs is inevitable due to their dense deployments — satisfying the low signal-to-interference-plus-noise ratio (SINR) regime. In this paper, we quantify the minimum interference constraint (IC) for SCs, which is critical for the multiplexing gain in the low SINR regime. The iterative steepest ascent (SA) method is used to quantify ICs for SCs. With the proof of convergence and the comparative analysis of the ideal and real cases, supported by the simulated results, we show that the expected quantization error of SCs is the best IC to be considered for SC systems.

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Publication Details

Journal
IEEE Access
Published
2024-12-31
DOI
https://doi.org/10.1109/access.2024.3524671
Citations
1
Primary Topic
Microfluidic and Bio-sensing Technologies
Type
article
Field-Weighted Citation Impact
0.18

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article

Quantification of Interference Constraint for Small Cells in Low SINR Regime With Steepest Ascent Method

Savitri Bevinakoppa, Sanjeeb Shrestha, Kashif Saleem, Xiaojing Huang et al.
1 citations
IEEE Access
Microfluidic and Bio-sensing Technologies
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article

Quantification of Interference Constraint for Small Cells in Low SINR Regime With Steepest Ascent Method

Savitri Bevinakoppa, Sanjeeb Shrestha, Kashif Saleem, Xiaojing Huang, Tony Jan
article en
1 citations

Abstract

The ubiquitous proliferation of small cells (SCs) is for boosting system capacity, coverage, and quality of services (QoS) for smart applications. However, interference from the adjacent SCs is inevitable due to their dense deployments — satisfying the low signal-to-interference-plus-noise ratio (SINR) regime. In this paper, we quantify the minimum interference constraint (IC) for SCs, which is critical for the multiplexing gain in the low SINR regime. The iterative steepest ascent (SA) method is used to quantify ICs for SCs. With the proof of convergence and the comparative analysis of the ideal and real cases, supported by the simulated results, we show that the expected quantization error of SCs is the best IC to be considered for SC systems.

IEEE AccessVol. 13
University of Technology Sydney (AU), King Saud University (SA), Torrens University Australia (AU)
King Saud University
Climate action
Openalex Percentile: Top 49%
Microfluidic and Bio-sensing Technologies
0.18
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