# On the Interplay between Deadline-Constrained Traffic and the Number of Allowed Retransmissions in Random Access Networks

## TL;DR

This paper explores how retransmissions and deadlines affect packet delivery in wireless networks using a random-access system.

## Contribution

The study introduces a novel analysis of the trade-off between retransmissions and packet deadlines in deadline-constrained traffic.

## Key findings

- The optimal transmission probability and number of retransmissions can be determined based on packet arrival rates and deadlines.
- Higher retransmission limits reduce drop rates but may increase delays in deadline-constrained traffic.
- Multi-packet reception capabilities improve throughput compared to the collision channel model.

## Abstract

In this paper, a network comprising wireless devices equipped with buffers transmitting deadline-constrained data packets over a slotted-ALOHA random-access channel is studied. Although communication protocols facilitating retransmissions increase reliability, a packet awaiting transmission from the queue experiences delays. Thus, packets with time constraints might be dropped before being successfully transmitted, while at the same time causing the queue size of the buffer to increase. To understand the trade-off between reliability and delays that might lead to packet drops due to deadline-constrained bursty traffic with retransmissions, the scenario of a wireless network utilizing a slotted-ALOHA random-access channel is investigated. The main focus is to reveal the trade-off between the number of retransmissions and the packet deadline as a function of the arrival rate. Towards this end, analysis of the system is performed by means of discrete-time Markov chains. Two scenarios are studied: (i) the collision channel model (in which a receiver can decode only when a single packet is transmitted), and (ii) the case for which receivers have multi-packet reception capabilities. A performance evaluation for a user with different transmit probabilities and number of retransmissions is conducted. We are able to determine numerically the optimal probability of transmissions and the number of retransmissions, given the packet arrival rate and the packet deadline. Furthermore, we highlight the impact of transmit probability and the number of retransmissions on the average drop rate and throughput.