eOLLA: an enhanced outer loop link adaptation for cellular networks

Abstract

Link adaptation (LA) process is a core feature for the downlink of 3GPP long-term evolution (LTE) and LTE-advanced (LTE-A). Through a channel quality indicator (CQI), the receiver suggests to the base station (BS) an appropriate modulation and coding scheme (MCS) according to the current channel conditions. In order to overcome any non-ideality in this process, the outer loop link adaptation (OLLA) algorithm is used to adaptively modify the mapping from signal-to-noise ratio (SNR) to CQI. OLLA basically modifies the measured SNR by an offset, according to whether data packets are received correctly or not, in order to adjust the average block error rate (aBLER) to a target. Although the OLLA technique has been extensively used, there exists a lack of analysis in the literature about its dynamics and convergence conditions. In this paper, a deep analysis of this algorithm has been carried out in order to cover this gap. From this analysis, we propose a new approach to the OLLA, the enhanced OLLA (eOLLA), which is able to adaptively modify its step size as well as to update its offset according to the reception conditions even if no data packets have been received. Thus, for LTE-and LTE-A-realistic scenarios, simulation results show that the proposed eOLLA outperforms the traditional OLLA, achieving a performance gain of up to a 15 % in terms of throughput.

Contextual Multi-Armed Bandits for Link Adaptation in Cellular Networks

“…Several techniques have been proposed to improve the performance of OLLA. An enhanced OLLA scheme with dynamically adaptive step-sizes was proposed in [3] based on extensive theoretical analysis. This scheme was demonstrated to achieve up to 15% throughput gains compared to OLLA.…”

“…By appropriately setting the step size ratio, OLLA can be made to converge to a desired target error rate, TER [3]…”

“…Link adaptation refers to the problem of selecting the optimal transmission parameters in response to periodically observed, time-varying, wireless channel states. Given the widespread adoption of wireless communication, it should come as no surprise that link adaptation is an area of active research in cellular networks [3,9] as well as wireless local area networks [12].…”

Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning

“…Basically, OLLA modifies the SINR CQI-based estimation by an offset [4,5] which can be positive (making the MCS selection more conservative) or negative (when the CQI selection was too optimistic). This offset is updated based on transport blocks transmission success rate, so that the average block error rate is kept as close as possible to the predefined target [6].…”

Adaptive Modulation and Coding Based on Reinforcement Learning for 5G Networks

“…Conventional solutions to the AMC problem includes the fixed look-up table [3], also called inner loop link adaptation (ILLA), and the outer loop link adaptation (OLLA) technique, which further improves the look-up table by adapting the SNR thresholds. The OLLA technique was first proposed in [6], and was also addressed in [4], [7], [8].…”