Grant-Free Access for mMTC: A Performance Analysis Based on Number of Preambles, Repetitions, and Retransmissions

Srinath, Harini Grama; Rana, Mrinal; Balasubramanya, Naveen Mysore

doi:10.1109/jiot.2022.3149013

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…This approximation is shown to be accurate enough to express the throughput in the analytical expression in the context of the mMTC in related works [14]. Third, our framework to derive the per-level offered load approximates the CDF of the channel in (2). Increasing M involves decreasing the analysis error.…”

Section: B Stochastic Geometry-based Throughput Analytical Modelmentioning

confidence: 80%

“…First, mMTC requires high throughput to accommodate such a massive number of users. Second, mMTC needs a grant-free (GF) access protocol (e.g., ALOHA) [2], i.e., a decentralized protocol without scheduling spectrum resources by the connected BS, to mitigate signaling overheads caused by such a massive number of users. Third, each user limits the maximum transmission power based on its battery and hardware limitations.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Geometry-based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA

Hirai,

Ueda,

Wakamiya

2024

Preprint

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This paper proposes an analytical framework for the throughput of the grant-free power-domain non-orthogonal multiple access (GF-NOMA) with user-specific constraints of selectable power levels and analyzes the achievable throughput. Our analytical framework uses stochastic geometry to reflect selectable power levels constrained by the maximum transmission power and channel of each user to an inhomogeneous offered load per level. This key idea enables our framework to analyze the throughput bounded by the geographical user distribution and derive a suitable selection strategy of power levels under the constraint. Our analytical results showed that our framework analyzed the throughput with only an analysis error of 0.1% compared with the Monte Carlo simulations, although the existing model overestimated 58% higher throughput. This paper also proposes a heuristic method based on our proposed analytical model to derive a suitable selection strategy of power levels. Our results highlight that the derived selection strategy on our analytical framework achieved 20% higher throughput than the baseline strategy, where each user randomly selects a power level under the power level constraint.

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Section: B Stochastic Geometry-based Throughput Analytical Modelmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%