Apurv Bhartia

Harnessing frequency diversity in wi-fi networks

Wireless multicarrier communication systems transmit data by spreading it over multiple subcarriers and are widely used today owing to their robustness to multipath fading, high spectrum efficiency, and ease of implementation. In this paper, we use real measurements to show there is significant frequency diversity in Wi-Fi channels, and propose a series of techniques to explicitly harness such frequency diversity. In particular, we leverage the Channel State Information (CSI), which captures the SNR on each subcarrier to (i) map symbols to subcarriers according to their importance, (ii) effectively recover partially corrupted FEC groups and facilitate FEC decoding, and (iii) develop MAC-layer FEC to offer different degrees of protection to the symbols according to their error rates at the PHY layer. We further develop a rate adaptation approach that works together with these optimization schemes. Our trace-driven simulation and testbed experiments based on USRP clearly demonstrate the effectiveness of our approaches.

CRMA: collision-resistant multiple access

Efficiently sharing spectrum among multiple users is critical to wireless network performance. In this paper, we propose a novel spectrum sharing protocol called Collision-Resistant Multiple Access (CRMA) to achieve high efficiency. In CRMA, each transmitter views the OFDM physical layer as multiple orthogonal but sharable channels, and independently selects a few channels for transmission. The transmissions that share the same channel naturally add up in the air. The receiver extracts the received signals from all the channels and efficiently decodes the transmissions by solving a simple linear system. We implement our approach in the Qualnet simulator and show that it yields significant improvement over existing spectrum sharing schemes. We also demonstrate the feasibility of our approach using implementation and experiments on GNU Radios.

Large-scale Measurements of Wireless Network Behavior

Meraki is a cloud-based network management system which provides centralized configuration, monitoring, and network troubleshooting tools across hundreds of thousands of sites worldwide. As part of its architecture, the Meraki system has built a database of time-series measurements of wireless link, client, and application behavior for monitoring and debugging purposes. This paper studies an anonymized subset of measurements, containing data from approximately ten thousand radio access points, tens of thousands of links, and 5.6 million clients from one-week periods in January 2014 and January 2015 to provide a deeper understanding of real-world network behavior. This paper observes the following phenomena: wireless network usage continues to grow quickly, driven most by growth in the number of devices connecting to each network. Intermediate link delivery rates are common indoors across a wide range of deployment environments. Typical access points share spectrum with dozens of nearby networks, but the presence of a network on a channel does not predict channel utilization. Most access points see 2.4 GHz channel utilization of 20% or more, with the top decile seeing greater than 50%, and the majority of the channel use contains decodable 802.11 headers.

Model-driven energy-aware rate adaptation

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 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 converges to the appropriate data rate. We demonstrate the effectiveness of our approach using trace-driven simulation and real implementation in a wireless testbed.

Fast Resilient Jumbo frames in wireless LANs

With the phenomenal growth of wireless networks and applications, it is increasingly important to deliver content efficiently and reliably over wireless links. However, wireless performance is still far from satisfactory due to limited wireless spectrum, inherent lossy wireless medium, and imperfect packet scheduling. While significant research has been done to improve wireless performance, much of the existing work focuses on individual design space. We take a holistic approach to optimizing wireless performance and resilience. We propose Fast Resilient Jumbo frames (FRJ), which exploit the synergy between three important design spaces: (i) frame size selection, (ii) partial packet recovery, and (iii) rate adaptation. While these design spaces are seemingly unrelated, we show that there are strong interactions between them and effectively leveraging these techniques can provide increased robustness and performance benefits in wireless LANs. FRJ uses jumbo frames to boost network throughput under good channel conditions and uses partial packet recovery to efficiently recover packet losses under bad channel conditions. FRJ also utilizes partial recovery aware rate adaptation to maximize throughput under partial recovery. Using real implementation and testbed experiments, we show that FRJ out-performs existing approaches in a wide range of scenarios.

O3: optimized overlay-based opportunistic routing

Opportunistic routing achieves significant performance gain under lossy wireless links. In this paper, we develop a novel approach that exploits inter-flow network coding in opportunistic routing. A unique feature of our design is that it systematically optimizes end-to-end performance (e.g., total throughput). A key challenge to achieve this goal is a strong tension between opportunistic routing and inter-flow network coding: to achieve high reliability, opportunistic routing uses intra-flow coding to spread information across multiple nodes; this reduces the information reaching an individual node, which in turn reduces inter-flow coding opportunity. To address this challenge, we decouple opportunistic routing and inter-flow network coding by proposing a novel framework where an overlay network performs overlay routing and inter-flow coding without worrying about packet losses, while an underlay network uses optimized opportunistic routing and rate limiting to provide efficient and reliable overlay links for the overlay network to take advantage of. Based on this framework, we develop the first optimization algorithm to jointly optimize opportunistic routes, rate limits, inter-flow and intra-flow coding. We then develop a practical opportunistic routing protocol (O3) based on the optimization results. Using Qualnet simulation, we study the individual and aggregate benefits of opportunistic routing, inter-flow coding, and rate limits. Our results show that (i) rate limiting significantly improves the performance of all routing protocols, (ii) opportunistic routing is beneficial under high loss rates, whereas inter-flow coding is more effective under low loss rates, and (iii) O3 significantly out-performs state-of-the-art routing protocols by simultaneously leveraging optimized opportunistic routing, inter-flow coding, and rate limits.

Multi-point to multi-point MIMO in wireless LANs

Distributed multiple-input multiple-output (MIMO) promises a dramatic capacity increase. While significant theoretical work has been done on distributed MIMO at the physical layer, how to translate the physical layer innovation into tangible benefits to real networks remains open. In particular, realizing multi-point to multi-point MIMO involves the following challenges: (i) how to accurately synchronize multiple APs in phase and time in order to successfully deliver precoded signals to the clients, and (ii) how to develop a MAC protocol to effectively support multi-point to multi-point MIMO. In this paper, we develop a practical approach to address the above challenges. We implement multi-point to multi-point MIMO for both uplink and downlink to enable multiple APs to simultaneously communicate with multiple clients. We examine a number of important MAC design issues, such as how to access the medium, perform rate adaptation, support acknowledgments in unicast traffic, deal with losses/collisions, and schedule transmissions. We demonstrate its feasibility and effectiveness through a prototype implementation on USRP and SORA, two of the most well-known software defined radio platforms.

Smart Retransmission and Rate Adaptation in WiFi

Transmission failures are common in wireless networks due to dynamic channel conditions and unpredictable interference. To efficiently recover from failures, we proposea smart retransmission scheme where the receiver combines information received from multiple failed transmissions associated with the same frame. The smart retransmission has two distinguishing features: (i) it can simultaneously supportpartial retransmission and combines bits with low confidence, and (ii) it has the first combining-aware rate adaptation scheme, which selects the data rates for all transmissions associated withthe same frame to maximize overall throughput. We find thatcombining-aware rate adaptation is essential to harnessing thecombining gain. Using trace-driven simulation and USRP testbedexperiments, we demonstrate the feasibility and effectiveness ofour approach, and show it significantly out-performs the existingschemes, such as WiFi, partial packet recovery (PPR), andSOFT in terms of both throughput and energy.

IQ-Hopping: distributed oblivious channel selection for wireless networks

Interference in WiFi deployments is a growing problem due to the increasing popularity of WiFi. Therefore it is important that APs find the right channel to operate upon. Through a large scale measurement study involving over 10,000 WiFi APs we show that channel measurements and selection are most effective when performed frequently (every few minutes). This is because of the highly dynamic nature of WiFi traffic congestion. Our key contribution in this paper is a novel approach to distributed channel selection -- Ineffective time Quantum (IQ) Hopping, that is simple enough to be described in three lines and has provable optimality guarantees. IQ-Hopping does not require any explicit channel measurements and can react within a matter of several seconds to bad channel conditions, including microwave ovens, hidden interferers, or dynamically varying congestion. Through implementation and experiments on off-the-shelf WiFi routers (OpenWRT, MadWiFi), we demonstrate the effectiveness of IQ-Hopping.

Measurement-based, practical techniques to improve 802.11ac performance

Devices implementing newer wireless standards continue to displace older wireless technology. As 802.11ac access points (APs) are rapidly adopted in enterprise environments, new challenges arise. This paper first presents an overview of trends in enterprise wireless networks based on a large-scale measurement study, in which we collect data from an anonymous subset of millions of radio access points in hundreds of thousands of real-world deployments. Based on the observed data and our experience deploying wireless networks at scale, we then propose two techniques that we have implemented in Meraki APs to improve both overall network capacity and performance perceived by end users: (i) a dynamic channel assignment algorithm, TurboCA, that adjusts to frequent RF condition changes, and (ii) a novel approach, FastACK, that improves the end-to-end performance of TCP traversing high-throughput wireless links. Finally, we evaluate TurboCA with metrics taken from a variety of real-world networks and evaluate TCP performance of FastACK with extensive testbed experiments.