Analysing Wi-Fi/LTE Coexistence to Demonstrate the Value of Risk-Informed Interference Assessment Andra M. Voicu, Ljiljana Simić RWTH Aachen University, Germany J. Pierre de Vries Silicon Flatirons Centre, University of Colorado, USA Marina Petrova, Petri Mähönen RWTH Aachen University, Germany
Overview risk-informed interference assessment e.g. risk assessment for Wi-Fi/LTE coexistence results: risk assessment comprehensive, intuitive, quantitative e.g. Wi-Fi/LTE coexistence no regulatory intervention needed for technical coexistence better coexistence for Wi-Fi: sometimes with itself; sometimes with LTE in practice typically negligible interference risk
Introduction inter-technology spectrum sharing mutual interference interference assessment for spectrum regulators (operational bounds) engineers (performance optimization) complementary method to worst-case: risk assessment
Aim & Scope apply risk assessment to a real-life problem of inter-technology spectrum sharing e.g. Wi-Fi/LTE coexistence in 5 GHz band show benefit of risk-informed interference assessment policy perspective engineering perspective
LTE-in-unlicensed main variants LTE-U MAC: adaptive duty cycle Licensed Assisted Access (LAA) MAC: listen-before-talk (LBT) LTE-in-unlicensed and Wi-Fi operate in the 5 GHz band implicit policy question: is regulatory intervention required to ensure harmonious technical coexistence?
Risk-Informed Interference Assessment risk assessment uses likelihood-consequence for hazard scenarios risk assessment in spectrum management* (1) inventory of harmful interference hazard modes (2) define consequence metric (3) assess likelihood and consequence (4) aggregate findings for decision making *J. P. de Vries, Risk-informed interference assessment: A quantitative basis for spectrum allocation decisions, Telecommunications Policy, 2017
(1) inventory of harmful interference simulation model Wi-Fi incumbent APs coexisting with: LAA entrants LTE-U entrants Wi-Fi entrants
(1) inventory of harmful interference simulation model: Monte Carlo simulations indoor/indoor scenario outdoor/outdoor scenario (also w/o walls) incumbent APs: 10 entrant APs: 1 30 AP density: 600 12000 APs/km 2 incumbent APs: 10 entrant APs: 1 10 AP density: 7 150 APs/km 2
(1) inventory of harmful interference simulation model PARAMETERS NO. OF CHANNELS 1 4 (non-dfs) maximum (19 indoor, 11 outdoor) Channel selection scheme Interference type incumbents - random random entrants - co-channel random or sense co- & adjacent channel random or sense co- & adjacent channel
(2) define consequence metric two throughput metrics for the incumbents throughput degradation technical public policy question =,, Wi-Fi incumbent throughput for: standalone Wi-Fi incumbent Wi-Fi incumbent & Wi-Fi entrant throughput unfairness among incumbents engineering optimization insight Jain s fairness index: unfairness: 1
(2) define consequence metric throughput model of incumbent AP for downlink,
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model)
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U)
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate,
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate AP transmit power,
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate, propagation loss AP transmit power
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate AP transmit power co-channel interference, propagation loss
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate AP transmit power co-channel interference, adjacent channel interference propagation loss
(2) define consequence metric throughput model of incumbent AP for downlink, CSMA/CA protocol efficiency (Bianchi s model) degradation due to duty cycle frames (i.e. for coexistence with LTE-U) fraction of time when AP x transmits mapping user SINR to bit rate AP transmit power co-channel interference, adjacent channel interference propagation loss noise
(3) assess likelihood and consequence how do we read risk assessment charts? in general for our WiFi/LTE example (throughput degradation)
(3) assess likelihood and consequence & (4) aggregate findings throughput degradation: indoor/indoor, 1 channel, 1-30 entrants, standalone Wi-Fi throughput degradation increases with the AP density for regulator: LTE (LAA/LTE-U) is sometimes friend, sometimes foe to Wi-Fi for engineer: Wi-Fi (high risk, low degradation) vs. LTE (high risk, high degradation)
(3) assess likelihood and consequence & (4) aggregate findings throughput degradation: indoor/indoor, 1 channel, 1-30 entrants, Wi-Fi & Wi-Fi for regulator: similar likelihood of LTE being friend or foe to Wi-Fi
(3) assess likelihood and consequence & (4) aggregate findings unfairness among incumbents: indoor/indoor, 1 channel, 1-30 entrants for engineer: unfairness for incumbents higher with LTE than with Wi-Fi entrants
(3) assess likelihood and consequence & (4) aggregate findings throughput degradation: indoor/indoor, different no. of channels, 10 entrants throughput degradation decreases when no. channels increases for regulator & engineer: typical case: 19 channels and sense degrada on 0 for engineer: sense better than random for 4 channels
(3) assess likelihood and consequence & (4) aggregate findings throughput degradation: different scenarios, 1 channel, 10 entrants for regulator: LTE is consistently sometimes friend, sometimes foe to Wi-Fi
Conclusions risk-informed interference assessment: comprehensive, intuitive, and quantitative for regulator: LTE sometimes friend sometimes foe to Wi-Fi no interven on needed for technical coexistence for engineer: dense deployments: lower risk when Wi-Fi coexists with Wi-Fi sparse deployments: lower risk when Wi-Fi coexists with LTE in practice typically low risk for Wi-Fi coexisting with LTE-inunlicensed no interven on from regulator or engineer
Thank you!