This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each received packet (even when the packet has no bit errors), to pick good bit rates. SoftRate’s novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2 × higher throughput than popular frame-level protocols such as SampleRate [4] and RRAA [24]. It also achieves 20 % more throughput than an SNR-based protocol trained on the operating environment, and up to 4 × higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single packet-time and its robustness to collision losses.

There has been burgeoning interest in wireless technologies that can use wider frequency spectrum. Technology advances, such as 802.11n and ultra-wideband (UWB), are pushing toward wider frequency bands. The analog-to-digital TV transition has made 100-250 MHz of digital whitespace bandwidth available for unlicensed access. Also, recent work on WiFi networks has advocated discarding the notion of channelization and allowing all nodes to access the wide 802.11 spectrum in order to improve load balancing. This shift towards wider bands presents an opportunity to exploit frequency diversity. Specifically, frequencies that are far from each other in the spectrum have significantly different SNRs, and good frequencies differ across sender-receiver pairs. This paper presents FARA, a combined frequency-aware rate adaptation and MAC protocol. FARA makes three departures from conventional wireless network design: First, it presents a scheme to robustly compute per-frequency SNRs using normal data transmissions. Second, instead of using one bit rate per link, it enables a sender to adapt the bitrate independently across frequencies based on these per-frequency SNRs. Third, in contrast to traditional frequency-oblivious MAC protocols, it introduces a MAC protocol that allocates to a sender-receiver pair the frequencies that work best for that pair. We have implemented FARA in FPGA on a wideband 802.11-compatible radio platform. Our experiments reveal that FARA provides a 3.1 × throughput improvement in comparison to frequency-oblivious systems that occupy the same spectrum.

RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (<2 dB for most links) similar to the near-ideal case of narrowband, frequency-flat channels. Unlike RSSI, this lets us predict the highest rate that will work for a link, trim transmit power, and more. We use trace-driven simulation to show that our rate prediction is as good as the best rate adaptation algorithms for 802.11a/g, even over dynamic channels, and extends this good performance to 802.11n.

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

We report a first-of-its-kind realization of directional transmission for smartphone-like mobile devices using multiple passive directional antennas, supported by only one RF chain. The key is a multi-antenna system (MiDAS) and its antenna selection methods that judiciously select the right antenna for transmission. It is grounded by two measurement-driven studies regarding 1) how smartphones rotate during wireless usage in the field and 2) how orientation and rotation impact the performance of directional antennas under various propagation environments. We implement MiDAS and its antenna selection methods using the WARP platform. We evaluate the implementation using a computerized motor to rotate the prototype according to traces collected from smartphone users in the field. Our evaluation shows that MiDAS achieves a median of 3dB increase in link gain. We demonstrate that rate adaptation and power control can be combined with MiDAS to further improve goodput and power saving. Real-time experiments with the prototype show that the link gain translates to 85 % goodput improvement for a low SNR scenario. The same gain translates to 51 % transmit power reduction for a high SNR scenario. Compared to other methods in realizing directional communication, MiDAS does not require any changes to the network infrastructure, and is therefore suitable for immediate or near-future deployment.

Abstract—Traffic querying, road sensing and mobile content delivery are emerging application domains for vehicular networks whose performance depends on the throughput these networks can sustain. Rate adaptation is one of the key mechanisms at the link layer that determine this performance. Rate adaptation in vehicular networks faces the following key challenges: (1) due to the rapid variations of the link quality caused by fading and mobility at vehicular speeds, the transmission rate must adapt fast in order to be effective, (2) during infrequent and bursty transmission, the rate adaptation scheme must be able to estimate the link quality with few or no packets transmitted in the estimation window, (3) the rate adaptation scheme must distinguish losses due to environment from those due to hiddenstation induced collision. Our extensive outdoor experiments show that the existing rate adaptation schemes for 802.11 wireless networks underutilize the link capacity in vehicular environments. In this paper, we design, implement and evaluate CARS, a novel Context-Aware Rate Selection algorithm that makes use of context information (e.g. vehicle speed and distance from neighbor) to systematically address the above challenges, while maximizing the link throughput. Our experimental evaluation in real outdoor vehicular environments with different mobility scenarios shows that CARS adapts to changing link conditions at high vehicular speeds faster than existing rate-adaptation algorithms. Our scheme achieves significantly higher throughput, up to 79%, in all the tested scenarios, and is robust to packet loss due to collisions, improving the throughput by up to 256% in the presence of hidden stations. I.

With the proliferation of mobile wireless devices such as smartphones and tablets that are used in a wide range of locations and movement conditions, it has become important for wireless protocols to adapt to different settings over short periods of time. Network protocols that perform well in static settings where channel conditions are relatively stable tend to perform poorly in mobile settings where channel conditions change rapidly, and vice versa. To adapt to the conditions under which communication is occurring, we propose the use of external sensor hints to augment network protocols. Commodity smartphones and tablet devices come equipped with a variety of sensors, including GPS, accelerometers, magnetic compasses, and gyroscopes, which can provide hints about the device’s mobility state and its operating environment. We present a wireless protocol architecture that integrates sensor hints in adaptation algorithms. We validate the idea and architecture by implementing and evaluating sensor-augmented wireless protocols for bit rate adaptation, access point association, neighbor maintenance in mobile mesh networks, and path selection in vehicular networks. 1

Despite many years of work in wireless mesh networks built using 802.11 radios, the performance and behavior of these networks in the wild is not well-understood. This lack of understanding is due in part to the lack of access to data from a wide range of these networks; most researchers have access to only one or two testbeds at any time. In recent years, however, 802.11 mesh networks networks have been deployed commercially and have real users who use the networks in a wide range of conditions. This paper analyzes data collected from 1407 access points in 110 different commercially deployed Meraki [28] wireless mesh networks, constituting perhaps the largest study of real-world 802.11 networks to date. After analyzing a 24-hour snapshot of data collected from these networks, we answer questions from a variety of active research topics, such as the accuracy of SNR-based bit rate adaptation, the impact of opportunistic routing, and the prevalence of hidden terminals. The size and diversity of our data set allows us to analyze claims previously only made in small-scale studies. In particular, we find that the SNR of a link is a good indicator of the optimal bit rate for that link, but that one could not make an SNR-to-bit rate look-up table that was accurate for an entire network. We also find that an ideal opportunistic routing protocol provides little to no benefit on most paths, and that “hidden triples”—network topologies that can lead to hidden terminals—are more common than suggested in previous work, and increase in proportion as the bit rate increases.

Commodity smartphones and tablet devices now come equipped with a variety of sensors, including accelerometers, multiple positioning sensors, magnetic compasses, and inertial sensors (gyros). In this paper, we posit that these sensors can be profitably used to improve the performance of wireless network protocols running on these mobile devices, and introduce the idea of using external sensor hints for this purpose. We focus on mobility hints, including the device’s state of motion, speed, direction of movement, and position. We outline how these hints can be used to: increase throughput by adapting bit rate selection to the state of movement; reduce the bandwidth required for estimating link delivery probabilities; improve the connectivity of routes in vehicular mesh networks using directionality hints; and enable access points to tailor the management of clients to their mobility.

Rate adaptation algorithms are critical to improving the throughput performance of WLANs. While previous studies have examined the performance of different algorithms in some detail using numerous measurements and simulations, there is a lack of a theory that would allow comparison across different channel conditions. This paper is a first step towards developing an abstract model that can allow us to (a) estimate or predict the performance of different algorithms and (b) allow us to make general statements about which algorithms would perform better, under what channel conditions. Our work is empirical in nature and uses three rate algorithms available in the Madwifi driver to examine the problem. We identify two key metrics, the speed of adaptation and the quality of adaptation, that taken together nicely encapsulate an algorithm’s performance. We then show how these metrics predict the throughput behavior of the three rate algorithms considered with an accuracy of over 70%. Furthermore, we show that these metrics can be used to make very general statements comparing the behavior of any pair of algorithms over a wide range of channels.