Detecting LTE-U Duty Cycling Misbehavior for Fair Sharing with Wi-Fi in Shared Bands

TL;DR

This paper presents a method for spectrum managers to estimate LTE-U duty cycles using Wi-Fi observations, detect misbehavior exceeding duty cycle limits with high accuracy, and ensure fair coexistence in shared bands.

Contribution

It introduces a novel PHY-layer based scheme for detecting LTE-U duty cycle misbehavior without disrupting Wi-Fi operations, enhancing fairness in shared spectrum.

Findings

Estimates LTE-U duty cycle within +/- 1% accuracy

Detects duty cycle misbehavior with at least 95% probability

Maintains false alarm probability below 1%

Abstract

Coexistence of Wi-Fi and LTE Unlicensed (LTE-U) in shared or unlicensed bands has drawn growing attention from both academia and industry. An important consideration is fairness between Wi-Fi and duty cycled LTE-U, which is often defined in terms of channel access time, as adopted by the LTE-U Forum. Despite many studies on duty cycle adaptation design for fair sharing, one crucial fact has often been neglected: LTE-U systems unilaterally control LTE-U duty cycles; hence, as self- interested users, they have incentives to misbehave, e.g., transmitting with a larger duty cycle that exceeds a given limit, so as to gain a greater share in channel access time and throughput. In this paper, we propose a scheme that allows the spectrum manager managing the shared bands to estimate the duty cycle of a target LTE-U cell based on PHY layer observations from a nearby Wi-Fi AP, without interrupting normal Wi-Fi operations. We further propose a thresholding scheme to detect duty cycling misbehavior (i.e., determining if the duty cycle exceeds the assigned limit), and analyze its performance in terms of detection and false alarm probabilities. The proposed schemes are implemented in ns3 and evaluated with extensive simulations. Our results show that the proposed scheme provides an estimate within +/- 1% of the true duty cycle, and detects misbehavior with a duty cycle 2.8% higher than the limit with a detection probability of at least 95%, while keeping the false alarm probability less than or equal to 1%.