On the Modeling and Performance Assessment of Random Access with SIC

TL;DR

This paper reviews performance metrics for advanced random access schemes, highlights modeling inaccuracies affecting optimization, and demonstrates that E-SSA outperforms others in energy efficiency and throughput, emphasizing the need for accurate physical layer modeling.

Contribution

It provides a comprehensive analysis of RA modeling impacts, introduces a generic energy efficiency model, and compares advanced RA schemes with insights for improved design.

Findings

E-SSA achieves the best overall performance in throughput and energy efficiency.

Simplified reception models lead to inaccurate optimization results.

IRSA and CSA schemes require more accurate physical layer models for optimal performance.

Abstract

In this paper, we review the key figures of merit to assess the performance of advanced random access (RA) schemes exploiting physical layer coding, repetitions and collision resolution techniques. We then investigate RA modeling aspects and their impact on the figures of merit for the exemplary advanced RA schemes: Contention Resolution Diversity Slotted ALOHA (CRDSA), Irregular Repetition Slotted ALOHA (IRSA), Coded Slotted ALOHA (CSA) and Enhanced Spread-Spectrum ALOHA (E-SSA). We show that typical simplifications of the reception model when used to optimize RA schemes lead to inaccurate findings, both in terms of parameter optimization and figures of merit, such as the packet loss ratio (PLR) and throughput. We also derive a generic RA energy efficiency model able to compare the schemes in terms of the energy required to transmit a packet. The combination of achievable RA throughput at the target PLR and energy efficiency, for the same average user power investment per frame and occupied bandwidth, shows that E-SSA, which is an unslotted scheme, provides the best overall performance, while, in terms of the slotted schemes, CRDSA outperforms the more elaborated IRSA and CSA. This surprising results is due to the fact that the IRSA and CSA optimization has so far been performed using RA channel models that are not accurately reflecting the physical layer receiver behavior. We conclude by providing insights on how to include more accurate reception models in the IRSA and CSA design and optimization.