Multiantenna NOMA with Finite Blocklength: A Pragmatic Paradigm for Ultra-Dense Networking

TL;DR

This paper proposes a new NOMA system for IoT networks with many low-power sensors, using multiple antennas and FEC codes to enable efficient communication.

Contribution

A new NOMA design with FEC codes for ultra-dense IoT networks is proposed, showing near-capacity performance with reduced complexity.

Findings

Three FEC codes (CCs, polar codes, LDPCs) achieve near-capacity performance in finite-blocklength scenarios.

Convolutional codes offer comparable performance with lower complexity, suitable for IoT sensor networks.

Large dimensional analysis provides deterministic SINR expressions for massive connectivity scenarios.

Abstract

This paper addresses the design and performance analysis of nonorthogonal multiple access (NOMA) for ultra-dense networking of the Internet of Things (IoT) based on low-power sensors. The proposed NOMA schemes consist of an Nr-antenna access point and K single antenna sensors given K≫Nr. A power allocation technique and forward error correction (FEC) are combined to enable concurrent uplink transmission and the successful separation of all K sensors at the access point. In scenarios where K≫Nr, large dimensional analysis is employed to derive a deterministic expression for the received signal-to-interference-plus-noise ratio (SINR) within the finite blocklength regime. Three distinct Forward Error Correction (FEC) codes—convolutional codes (CCs), polar codes, and low-density parity-check codes (LDPCs)—are assessed. These evaluations indicate that all three codes achieve near-capacity performance while supporting massive connectivity in the finite-blocklength context. Notably, convolutional codes demonstrate comparable performance with reduced complexity, a desirable attribute for prolonging the life cycle of wireless sensor network-based IoT applications.

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