SCIENCE CHINA Information Sciences, Volume 61, Issue 2: 022302(2018) https://doi.org/10.1007/s11432-016-9042-8

Fractional full duplex cellular network: a stochastic geometry approach

Wenping BI1,2,3, Limin XIAO1,2,3, Xin SU1,2,3, Shidong ZHOU1,2,3,*
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  • ReceivedNov 13, 2016
  • AcceptedFeb 28, 2017
  • PublishedJul 28, 2017


In-band full-duplex (FD) communication has been considered as a promising technology to enhance the spectral efficiency (SE) for the next generation of wireless system. However, the severe interference in FD cellular network may largely degrade the system performance, especially for cell edge users. In this paper, multi-cell cellular network consisting of BSs with FD capability and legacy half duplex (HD) users is studied. In order to relieve the received interference and enhance the system SE, a new resource allocation strategy named fractional FD (FFD) is proposed and its main idea and procedure are described as follows.Firstly, the frequency and time resources are partitioned into FD resource blocks (RBs) and HD RBs based on the network topology. Then all the users are classified into two groups named cell center users (CCUs) and cell edge users (CEUs) based on their channel conditions. At last, in each cell, each FD RB is allocated to a pair of CCUs with one in uplink and the other in downlink transmission direction, while only one CEU in the uplink (or downlink) transmission direction is scheduled over each HD RB. Tractable results of both the coverage probabilities and the ergodic rates of FFD, FD and HD systems are derived using stochastic geometry method. Numerical results show that, FFD can significantly improve the coverage probability of all users especially for CEUs compared with FD cellular system, and higher system SE is obtained compared with FD and HD cellular network. With proper design of the classification criterion and under the simulation settings of this paper, the SE of FFD system outperforms FD and HD system by $1.25$ and $1.38$ times, respectively.


This work was supported by National Basic Research Program of China (Grant No. 2012CB316002), National Natural Science Foundation of China (Grant No. 61631013), National High Technology Research and Development Program of China (863 Program) (Grant No. 2015AA01A706), National Natural Science Foundation of China (Grant No. 61321061), Tsinghua University Initiative Scientific Research Program (Grant No. 2015Z02-3), National S&T Major Project (Grant No. 2014ZX03001011), Key Project of International Science and Technology Innovation Cooperation Between the Government (Grant No. 2016YFE0122900), and Huawei Technologies.


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