Sparse Vector Coding-based Multi-Carrier NOMA for In-Home Health Networks

Xuewan Zhang, Liuqing Yang, Zhiguo Ding, Jian Song, Yunkai Zhai, Di Zhang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In-home health networks greatly rely on the massive connected monitoring devices. Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can connect more monitoring devices and enhance the spectrum efficiency (SE) performance, which makes it an ideal solution to in-home health networks. However, conventional NOMA (C-NOMA) is mostly constrained to single-carrier scenario. The problem of multi-carrier NOMA lies in the inter-carrier interference (ICI) from neighboring carriers. In this article, we propose a sparse vector coding-based NOMA (SVC-NOMA) to suppress the ICI. We give closed-form expressions of capacity and symbol error rate (SER) performances for both C-NOMA and SVC-NOMA within the considered multi-carrier scenario. Simulation results demonstrate that compared to C-NOMA, SVC-NOMA has better capacity and SER performances. In addition, we find from our results that there is a trade-off between SVC-NOMA’s ICI suppression ability and the system capacity performance.

Original languageEnglish
JournalIEEE Journal on Selected Areas in Communications
DOIs
Publication statusAccepted/In press - 2020
Externally publishedYes

Keywords

  • Biomedical monitoring
  • capacity
  • Channel estimation
  • Encoding
  • In-home health network
  • Internet of Things
  • Monitoring
  • NOMA
  • non-orthogonal multiple access
  • sparse vector coding
  • Static VAr compensators
  • symbol error rate

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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