Joint Channel Estimation and Device Activity Detection in Heterogeneous Networks
Leatile Marata, Onel Luis Alcaraz L\'opez, Eduardo Noboro Tominaga,, Hirley Alves

TL;DR
This paper addresses joint device activity detection and channel estimation in heterogeneous 5G networks with IoT devices, proposing AMP-based algorithms and new pilot strategies to improve performance amidst interference from eMBB devices.
Contribution
It introduces a novel AMP-based method for joint detection and estimation in heterogeneous networks, along with two pilot sequence strategies that enhance detection without harming eMBB services.
Findings
AMP algorithm effectively detects device activity and estimates channels.
Proposed pilot strategies improve detection performance.
System achieves reliable detection and estimation in interference scenarios.
Abstract
Internet of Things (IoT) has triggered a rapid increase in the number of connected devices and new use cases of wireless communications. To meet the new demands, the fifth generation (5G) of wireless communication systems features native machine type communication (MTC) services in addition to traditional human type communication (HTC) services. Some of the main challenges are the heterogeneous requirements and the sporadic traffic of massive MTC (mMTC), which makes the orthogonal allocation of resources infeasible. To overcome this problem, grant free non-orthogonal multiple access schemes have been proposed alongside with sparse signal recovery algorithms. While most of the related works have considered only homogeneous networks, we focus on a scenario where an enhanced mobile broadband (eMBB) device and multiple MTC devices share the same radio resources. We exploit the approximate…
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Taxonomy
TopicsWireless Communication Networks Research · Advanced MIMO Systems Optimization · Advanced Wireless Communication Techniques
Methodstravel james
