Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications

The strict latency and reliability requirements of ultra-reliable low-latency communications (URLLC) use cases are among the main drivers in fifth generation (5G) network design. Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission. This prediction is exploited in the LA context to maximize the spectral efficiency while guaranteeing reliability at an arbitrary level. Numerical results are compared with state of the art interference prediction techniques for LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.

[1]  David Tse,et al.  Opportunistic beamforming using dumb antennas , 2002, IEEE Trans. Inf. Theory.

[2]  Jianping Pan,et al.  A Geometric Probability Model for Capacity Analysis and Interference Estimation in Wireless Mobile Cellular Systems , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.

[3]  Wanshi Chen,et al.  5G ultra-reliable and low-latency systems design , 2017, 2017 European Conference on Networks and Communications (EuCNC).

[4]  Petar Popovski,et al.  Towards Massive, Ultra-Reliable, and Low-Latency Wireless Communication with Short Packets , 2015 .

[5]  R. Bansal,et al.  Antenna theory; analysis and design , 1984, Proceedings of the IEEE.

[6]  Dirk P. Kroese,et al.  Kernel density estimation via diffusion , 2010, 1011.2602.

[7]  Emiliano Sisinni,et al.  Isochronous wireless communication system for industrial automation , 2016 .

[8]  Zexian Li,et al.  Link adaptation design for ultra-reliable communications , 2016, 2016 IEEE International Conference on Communications (ICC).

[9]  Mikko Valkama,et al.  Interference Analysis and Performance Evaluation of 5G Flexible-TDD Based Dense Small-Cell System , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[10]  Markus Rupp,et al.  A Circular Interference Model for Heterogeneous Cellular Networks , 2016, IEEE Transactions on Wireless Communications.

[11]  A. Sampath,et al.  On setting reverse link target SIR in a CDMA system , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[12]  Olav Tirkkonen,et al.  Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks , 2016, Inf..

[13]  Klaus I. Pedersen,et al.  Joint Link Adaptation and Scheduling for 5G Ultra-Reliable Low-Latency Communications , 2018, IEEE Access.

[14]  Martin Haenggi,et al.  On the Location-Dependent SIR Gain in Cellular Networks , 2019, IEEE Wireless Communications Letters.

[15]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[16]  Antti Toskala,et al.  LTE Advanced: 3GPP Solution for IMT-Advanced , 2012 .

[17]  H. Vincent Poor,et al.  Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale , 2018, Proceedings of the IEEE.