Model-driven energy-aware rate adaptation

Abstract

Rate adaptation in WiFi networks has received significant attention recently. However, most existing work focuses on selecting the rate to maximize throughput. How to select a data rate to minimize energy consumption is an important yet under-explored topic. This problem is becoming increasingly important with the rapidly increasing popularity of MIMO deployment, because MIMO offers diverse rate choices (e.g., the number of antennas, the number of streams, modulation, and FEC coding) and selecting the appropriate rate has significant impact on power consumption.In this paper, we first use extensive measurement to develop a simple yet accurate energy model for 802.11n wireless cards. Then we use the models to drive the design of an energy-aware rate adaptation scheme. A major benefit of a model-based rate adaptation is that applying a model allows us to eliminate frequent probes in many existing rate adaptation schemes so that it can quickly converge to the appropriate data rate. We demonstrate the effectiveness of our approach using trace-driven simulation and real implementation in a wireless testbed.

Modeling WiFi Active Power/Energy Consumption in Smartphones

“…The authors claim that the packet size does not affect power consumption. However, [16], [24], [15] show a different result. In Section VI, we also show that the packet size largely affects the per-bit energy consumption.…”

“…A number of recent works have developed models of the power/energy consumption of different smartphone components [11], [12], [10], [13], [18], [19], [20], [21], [25], [24]. In the case of WiFi, one common feature of several of these models [11], [12], [13], [18], [19], regardless of their complexity, is the assumption of constant power consumption at each power state.…”

“…[24] builds a similar model of the energy consumption as a linear function of the packet transmission time (which can be converted to a linear model of power vs. throughput). The accuracy of the model in [20] is only evaluated for 4G and short HTTP transfers.…”

“…In other cases, the models are validated only at the location where the training dataset was collected, and under ideal conditions, without external interference [12], [13], [21], or with only one traffic type (typically small HTTP transfers [20], [21]). Finally, there is often no detailed information about the methodology used to collect the training dataset [20], [21], [24].…”

“…A few recent works have developed linear models of the active power consumption of a wireless interface as a function of the throughput [20], [24], [25] or have experimentally observed such a linear relationship [17]. However, all these works suffer from at least one of the limitations mentioned above, i.e., limited or no validation, lack of details about the data collection methodology, and testing only on older 802.11b/g smartphones.…”