Channel quality estimation and rate adaptation for cellular mobile radio

Abstract

We propose a technique to measure channel quality in terms of signal-to-interference plus noise ratio (SINR) for the transmission of signals over fading channels. The Euclidean distance (ED) metric, associated with the decoded information sequence or a suitable modification thereof, is used as a channel quality measure. Simulations show that the filtered or averaged metric is a reliable channel quality measure which remains consistent across different coded modulation schemes and at different mobile speeds. The average scaled ED metric can be mapped to the SINR per symbol. We propose the use of this SINR estimate for data rate adaptation, in addition to mobile assisted handoff (MAHO) and power control. We particularly focus on data rate adaptation and propose a set of coded modulation schemes which utilize the SINR estimate to adapt between modulations, thus improving data throughput. Simulation results show that the proposed metric works well across the entire range of Dopplers to provide near-optimal rate adaptation to average SINR. This method of adaptation averages out short-term variations due to Rayleigh fading and adapts to the long-term effects such as shadowing. At low Dopplers, the metric can track Rayleigh fading and match the rate to a short-term average of the SINR, thus further increasing throughput.

Multirate-aware Multicast Routing in MANETs

“…The rate selection procedure then uses the channel quality estimate to select an appropriate rate. A common technique of rate selection is to compare the value of the channel quality indicator against a list of threshold values representing boundaries between the data rates [4].…”

Automatic IEEE 802.11 rate control for streaming applications

“…Consequently, the SNR is linked to the packet delay and jitter, and the throughput, and holds the potential of providing rich feedback for automatic rate control [1]. Knowing the current SNR and the throughput-vs-SNR curves for each rate setting (e.g., Figure 1) solves the rate-selection problem instantly:…”

“…It concentrates on the way in which the noisy and drifting SNR (problem 1) can be used to determine the correct rate setting [1,13]. Holland et al [6] do address the issue of how to communicate back SNR values (problem 3), but their rate selection algorithm still relies on a straight SNR threshold technique.…”

Experimental assessment of the effects of cross-traffic on Wi-Fi video streaming

“…The Wi-Fi standard exploits a scarce, shared, and noisy spectrum, i.e. the unlicensed 2.4 GHz Industrial Scientific Medical (ISM) band, on which other devices may operate simultaneously [2][3][4][5][6].…”