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Pré-Publication, Document De Travail Année : 2023

Exploiting Device Heterogeneity in Grant-Free Random Access: A Data-Driven Approach

Résumé

Grant-free random access (GFRA) is now a popular protocol for large-scale wireless multiple access systems in order to reduce control signaling. Resource allocation in GFRA can be viewed as a form of frame slotted ALOHA, where a ubiquitous design assumption is device homogeneity. In particular, the probability that a device seeks to transmit data is common to all devices. Recently, there has been an interest in designing frame slotted ALOHA algorithms for networks with heterogeneous activity probabilities. These works have established that the throughput can be significantly improved over the standard uniform allocation. However, the algorithms for optimizing the probability a device accesses each slot require perfect knowledge of the active devices within each frame. In practice, this assumption is limiting as device identification algorithms in GFRA rarely provide activity estimates with zero errors. In this paper, we develop a new algorithm based on stochastic gradient descent for optimizing slot allocation probabilities in the presence of activity estimation errors. Our algorithm exploits importance weighted bias mitigation for stochastic gradient estimates, which is shown to provably converge to a stationary point of the throughput optimization problem. Numerical results show significant performance improvement both over standard frame slotted ALOHA and existing algorithms that do not account for activity estimation errors.
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hal-04130618 , version 1 (16-06-2023)

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  • HAL Id : hal-04130618 , version 1

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Alix Jeannerot, Malcolm Egan, Jean-Marie Gorce. Exploiting Device Heterogeneity in Grant-Free Random Access: A Data-Driven Approach. 2023. ⟨hal-04130618⟩
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