This paper proposes to exploit physical layer information towards improved rate selection in wireless networks. While existing schemes pick good transmission rates, this paper takes a step further towards computing the optimal bit rate. The main idea is to capture the channel behavior through symbol level dispersions, and “replay” these dispersions on different rate encodings of the same packet. The “replay ” action can be emulated at the receiver without requiring the transmitter to send the packet at every other rate. The maximum successful rate is likely to be the optimal rate of the received packet, and assuming that the channel remains coherent, the same rate can be prescribed for the next transmission. We design, implement, and evaluate this idea over a small testbed of USRP hardware and GNURadio software. Our proposal, called AccuRate, predicts a packet’s optimal rate 95 % of times when the packet is received correctly. When the packet is received in error, AccuRate computes its optimal rate with 93 % accuracy. In terms of throughput, we show that AccuRate improves over the state-of-the-art scheme SoftRate by around 10%, and is reasonably close to the optimal. 1

Abstract—We present the design and implementation of ADAM, the first adaptive beamforming based multicast system and experimental framework for indoor wireless environments. ADAM addresses the joint problem of adaptive beamformer design at the PHY layer and client scheduling at the MAC layer by proposing efficient algorithms that are amenable to practical implementation. ADAM is implemented on an FPGA platform and its performance is compared against that of omni-directional and switched beamforming based multicast. Our experimental results reveal that (i) switched multicast beamforming has limited gains in indoor multi-path environments, whose deficiencies can be effectively overcome by ADAM to yield an average gain of three-fold; (ii) the higher the dynamic range of the discrete transmission rates employed by the MAC hardware, the higher the gains in ADAM’s performance, yielding upto nine-fold improvement over omni with the 802.11 rate table; and (iii) finally, ADAM’s performance is susceptible to channel variations due to user mobility and infrequent channel information feedback. However, we show that training ADAM’s SNR-rate mapping to incorporate feedback rate and coherence time significantly increases its robustness to channel dynamics. I.