A Physical Layer Network Coding Design for 5G Network MIMO

Tong Peng, Yi Wang, Alister G. Burr, Mohammad Shikh-Bahaei

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents a physical layer network coding (PNC) approach for network MIMO (N-MIMO) systems to release the heavy burden of backhaul load. The proposed PNC approach is applied for uplink scenario in binary systems, and the design guideline serves multiple mobile terminals (MTs) and guarantees unambiguous recovery of the message from each MT. We present a novel PNC design criterion first based on binary matrix theories, followed by an adaptive optimal mapping selection algorithm based on the proposed design criterion. In order to reduce the real-time computational complexity, a two-stage search algorithm for the optimal binary PNC mapping matrix is developed. Numerical results show that the proposed scheme achieves lower outage probability with reduced backhaul load compared to practical CoMP schemes which quantize the estimated symbols from a log-likelihood ratio (LLR) based multiuser detector into binary bits at each access point(AP).

Original languageEnglish
Title of host publication2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647271
Publication statusPublished - 2018
Event2018 IEEE Global Communications Conference, GLOBECOM 2018 - Abu Dhabi, United Arab Emirates
Duration: 9 Dec 201813 Dec 2018

Publication series

Name2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings


Conference2018 IEEE Global Communications Conference, GLOBECOM 2018
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi

Bibliographical note

Funding Information:
This research is funded by EPSRC NetCoM project EP/K040006/1 and partially by EPSRC IoSIRE project EP/P022723/1.

Publisher Copyright:
© 2018 IEEE.


  • adaptive optimal mapping selection
  • backhaul load reduction
  • binary PNC
  • unambiguous detection

Cite this