# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 1 Introduction
The Onoe algorithm [8] is less sensitive to individual packet failure than the ARF algorithm by means of employing averaged credits as another condition to increase the rate. Fast-responsive link adaptation [9] decreases the rate in the same manner as ARF but tries to increase the rate more frequently if the transmission duration of the current rate is sufficiently long. The SampleRate algorithm [10] sends data at the rate that has the smallest predicted average packet transmission time. Loss-differentiating ARF [11] entails a new control frame, NAK [12], in IEEE 802.11, which indicates a channel error to the transmitter. Fail count increases only when a NAK is received whereas if no ACK, a collision is deemed to have occurred. Collision-aware rate adaptation [13] handles the inability of ARF to differentiate collisions from channel errors by adopting request-to-send probing and/or clear-channel-assessment detection. The robust rate adaptation algorithm [14] estimates the packet loss ratio during a given time window of observation and increases (decreases) the rate if this ratio is lower (higher) than a given lower (upper) threshold. In addition, to suppress collision losses, an adaptive request-to-send filter is employed. The drawback of the algorithm in [14] is that the algorithm may tend to be too aggressive for static stations and too conservative for moving stations. Stochastic automata rate adaptation [15] is based on stochastic learning automata that can estimate the probabilistic packet success rate. Sequential hypothesis testing-based rate control [16] is also a statistical-count-based link adaptation algorithm. Another category for link adaptation, as opposed to statistical-count-based link adaptation, is SNR-measurement-based or PHY-measurement-based link adaptation, an example of which is receiver-based AutoRate [17]. This algorithm allows the receiver to select the appropriate rate for the data packet during the request-to-send and clear-to-send packet exchange. Link adaptation methods based on a multitude of link quality metrics [18; 19; 20; 21; 22; 23; 24; 25] are other examples of PHY-measurement-based link adaptation. The effective goodput (which is the expected data payload length delivered divided by the expected transmission time spent on the frame delivery) is computed in [20] and the best set of PHY modes that maximizes this goodput is found. Exponential effective SNR of the orthogonal frequency division multiplexing (OFDM) system is proposed as a link quality metric in [21]. To estimate the packet error rate (PER), lookup tables of PER vs. SNR in the additive white Gaussian noise (AWGN) channel for all PHY modes are constructed first and then the average SNRs of all subcarriers are estimated. Subsequently, the effective SNR is calculated and the PER is found from the lookup table at this effective SNR. The motivation of [22] is that if the SNR distance from the AWGN PER curve is known (by means of a PER indicator), the PER of a specific frequency selective channel can be predicted accordingly. On the other hand, it is proved in [23] that the exponential effective SNR has a slightly better performance than the PER indicator in [22]. Ref. [24] calculates the PER, starting with the per-subcarrier post detection SNR from the channel matrix and then the bit error rate forms the effective SNR. It is proved in [25] that among the three link quality metrics, namely, the instantaneous SNR, the exponential effective SNR, and Shannon capacity, the exponential effective SNR usage shows the best performance. Yet another link quality metric exists, which provides better PER estimation accuracy than the exponential effective SNR, in case of fast link adaptation. This metric [26] is based on mutual information, which will be explained in Section 2. Some more variant algorithms exist as the PHY-measurement-based link adaptation [27; 28].