# Study of Adaptive Activity-Aware Iterative Detection Techniques for Massive Machine-Type Communications

## TL;DR

This paper proposes adaptive activity-aware iterative detection techniques for massive machine-type communications, improving joint activity and data detection in sporadic uplink scenarios with low pilot symbol requirements.

## Contribution

It introduces an adaptive decision feedback detector and an $l_0$-norm regularized recursive least-squares algorithm tailored for mMTC, along with an LDPC-based iterative detection scheme.

## Key findings

- Enhanced detection performance over existing methods
- Effective activity detection with minimal pilot symbols
- Robustness in sporadic activity scenarios

## Abstract

This work studies the uplink of grant-free low data-rate massive machine-to-machine communications (mMTC) where devices are only active sporadically, which requires a joint activity and data detection at the receiver. We develop an adaptive decision feedback detector along with an $l_0$-norm regularized activity-aware recursive least-squares algorithm that only require pilot symbols. An iterative detection and decoding scheme based on low-density parity-check (LDPC) is also devised for signal detection in mMTC. Simulations show the performance of the proposed approaches against existing schemes.