In this work we propose SERENADE: a large-scale, real-time, and flexible emulator for satellite networking. While existing tools rely on heavier container-based node emulation, SERENADE is written in Go, and models nodes using ultra-lightweight threads. This design enables SERENADE to realistically emulate up to several 100,000s of nodes (both satellite and ground nodes) on a single machine, a first of its kind. We ensure that scale does not come at the expense of responsiveness; SERENADE starts up in a few seconds and tracks satellite trajectories in real time (rather than being pre-computed), making it ultra-responsive to changes on-the-fly. Additionally, SERENADE is designed to interface easily with external applications, allowing for arbitrary and dynamic network inputs and measurements. These features make it the first satellite network emulator suitable to operate as a digital twin for satellite networking. Our evaluations highlight SERENADE’s ability to efficiently scale while maintaining a high degree of networking realism and remaining adaptable. We demonstrate SERENADE’s digital twin capabilities through several real-world case studies, including disaster relief and satellite constellation updates. We also use SERENADE to illustrate how many proposed frameworks for user-satellite association fail to effectively scale to large deployment scenarios and result in grossly under-utilized network resources.

Darknet traffic classification is challenging due to the use of different obfuscation techniques. In this paper, we rely on Explainable Artificial Intelligence (XAI) techniques such as LIME and SHAP to identify the most relevant features of Darknet traffic, and use combinations of these features to train deep learning models such as Convolutional Neural Networks and Long-Short Term Memory neural networks for classification. Our results on realistic datasets lead to a classification accuracy of 95% when pertinent features are used, compared to only 87% when all features considered; thus increasing the classification accuracy while reducing the cost.

Radio frequency (RF) techniques have shown promise for continuous contactless healthcare applications. However, real-world indoor environments pose challenges for existing systems, which may struggle to detect subtle physiological signals. This paper proposes Medusa, a novel wireless vital-sign sensing system designed for multi-view setups. It enables users to deploy distributed Multiple Input Multiple Output (MIMO) arrays into their daily living environments, facilitating vital-sign sensing in real-world settings. Unlike most existing single Commercial Off-The-Shelf (COTS) radar-based systems that operate under controlled settings Medusa's primary novelty lies in the design of a first-of-its-kind flexible multi-view vital sign sensing system that is view-agnostic, pose-agnostic, contactless, and can sense basic human vitals with good accuracy. Through our well-engineered hardware and software co-design, Medusa enables real-time processing of large distributed MIMO arrays, while balancing the tradeoff between Signal-to-Noise Ratio (SNR) and spatial diversity gain across each of its four distributed 4 × 4 sub-arrays for increased robustness. This is achieved using our novel unsupervised learning model which effectively recovers vital sign waveforms by decomposing the received signals. Extensive evaluations with 21 participants demonstrate Medusa's spatial diversity gain for real-world vital-sign monitoring, enabling free movement and orientation of subjects in both familiar and unfamiliar indoor environments.

The integration of Wake-Up Radios (WURs) into Wi-Fi presents a powerful paradigm for enabling multi-year battery life in power-constrained devices. However, existing approaches, such as the IEEE 802.11ba standard, rely on dedicated, spectrally-inefficient transmissions that consume valuable channel resources. This paper introduces AuraWake, a novel technique that creates a spectrally-efficient control plane by superimposing control signals directly onto ongoing Wi-Fi data packets, leveraging the primary link’s existing signal-to-noise ratio margin. We first present a detailed simulation model and a low-complexity architecture for the AuraWake receiver. Through comprehensive link-level simulation, we conduct a rigorous performance evaluation of its reliability and quantify its minimal, controllable impact on the primary data communication. We then demonstrate the utility of this superimposed control plane by applying it to solve critical, practical challenges in modern Wi-Fi. Our system-level analysis and simulation studies confirm that using AuraWake to mitigate clock drift for long-sleeping Target Wake Time (TWT) stations, enforce quiet periods to protect Restricted TWT (R-TWT) transmissions, and dynamically adapt TWT schedules significantly enhances the reliability and efficiency of these next-generation energy-saving features.

Mobile networks have increased spectral efficiency through advanced multiplexing strategies that are coordinated by base stations (BS) in licensed spectrum. However, external interference on clients leads to significant performance degradation during dynamic (unlicensed) spectrum access (DSA). We introduce the notion of network tomography for DSA, whereby clients are transformed into spectrum sensors, whose joint access statistics are measured and used to account for interfering sources. Albeit promising, performing such tomography naively incurs an impractical overhead that scales exponentially with the multiplexing order of the strategies deployed – which will only continue to grow with 5G/6G technologies.To this end, we propose a novel, scalable network tomography framework called NeTo-X that estimates joint client access statistics with just linear overhead, and forms a blue-print of the interference, thus enabling efficient DSA for future networks. NeTo-X’s design incorporates intelligent algorithms that leverage multi-channel diversity and the spatial locality of interference impact on clients to accurately estimate the desired interference statistics from just pair-wise measurements of its clients. The merits of its framework are showcased in the context of resource management and jammer localization applications, where its performance significantly outperforms baseline approaches and closely approximates optimal performance at a scalable overhead.

Radio environment maps provide a comprehensive spatial view of the wireless channel and are especially useful in on-demand UAV wireless networks where operators are not afforded the typical time spent planning base station deployments (e.g. emergency response). Equipped with an accurate radio environment map, a mobile UAV can quickly locate to an optimal location to serve users on the ground. Machine learning has recently been proposed as a tool to create radio environment maps from satellite images of the target environment. However, the highly dynamic nature that precipitates most on-demand aerial network deployments likely renders the satellite image data available for the environment to be inaccurate. In this paper we present, OREMAN, a hybrid offline-online system for aerial radio environment map creation which leverages a common sensing modality present on most UAVs: visual cameras. OREMAN combines a suite of off-line trained neural network models with an adaptive trajectory planning algorithm to iteratively predict/refine the radio environment map and estimate the most valuable trajectory locations. By using UAV vision, OREMAN arrives at a highly accurate map much faster and with fewer measurements than other approaches, and is very effective even in scenarios where no prior environmental knowledge is available.

With the proliferation of smart acoustic devices in everyday environments, low-power acoustic tags offer a promising choice for IoT applications. However, their highly limited operational range, throughput, and energy efficiency significantly restrict their viability for practical applications. In this work, we propose a first-of-its-kind distributed acoustic system called Disco. It consists of several low-power acoustic tags that can be flexibly composed on-demand to create an aperture array capable of distributed beamforming. The innovation of Disco lies in creating a ‘virtual’ distributed 2-speaker system that serves to wirelessly synchronize and enable distributed temporal beamforming at the tags independently. The low-power tags of Disco are prototyped with simple acoustic and analog-digital elements to create arrays comprising up to 8 tags. The beamforming performance of Disco scales with the number of tags in the array, delivering a multiple-fold increase in range, throughput, and energy efficiency. This advancement brings acoustic IoT applications closer to practical implementation.

WiFi-enabled Power Line Communications (PLC) range extenders can extend coverage in homes and enterprises. However, a dense deployment of a large number of PLC extenders in enterprise settings can cause an inefficient sharing of the PLC capacity, where many extenders contend for a share of access to the backhaul network comprising of the electrical wiring (power lines), thereby drastically impacting any gains from using these extenders on the wireless part of the network. In this paper, we address this issue by developing a framework, Priza, for clustering the WiFi-PLC extenders to intelligently form a DAS (distributed antenna system) to mitigate the inefficiency of sharing the PLC backhaul. By appropriately managing clustering and reuse, Priza improves the PLC backhaul sharing, while at the same time, harnessing the power pooling and diversity gains from DAS on the wireless part of the network, to boost user throughputs. We evaluate Priza via real testbed experiments and high-fidelity simulations and demonstrate that it can increase the aggregate throughput by up to 131.5% over the non-DAS reuse baseline, 74% over the best DAS baseline that constructs equally-sized DAS cells based on extender proximity, and 331.3% over a greedy DAS baseline that creates as large DAS cells as possible.

Future generation wireless systems are rapidly evolving to support positioning as a native feature using their communication infrastructure. Notwithstanding the dependence on a densely deployed infrastructure (for accurate positioning). We highlight the significant degradation due to the overhead of such an infrastructure-based positioning (IP) approach can bring communication performance warranting timely attention. We propose a fundamentally different paradigm of communication-aware collaborative positioning CO2P, whereby the burden of positioning is offloaded to client devices in an intelligent, communication-aware and collaborative (peer-peer) manner that reduces overhead and improves spatial reuse to preserve communication performance, without compromising on positioning accuracy. Through technically-sound algorithms CO2P addresses the underlying tradeoff between communication performance and positioning accuracy to deliver an efficient coexistence, providing a two-fold increase in both throughput and accuracy over conventional IP approaches. CO2P is also orchestrated as a practical, distributed, adoption-friendly solution that is realized using WiFi's positioning protocol.

With the recent popularity of active ultra-wideband (UWB) tracking beacons, the next era of low-power IoT aims to offer tracking as a core feature in numerous enterprise and industrial applications. However, generating high-bandwidth tracking signals on cost-effective, low-power tags in a large scale deployment, poses fundamental challenges in energy-cost-performance trade-offs, not afforded by existing solutions that fall short in one or more critical dimensions.

A large number of traffic collisions occur as a result of obstructed sight lines, such that even an advanced driver assistance system would be unable to prevent the crash. Recent work has proposed the use of around-the-corner radar systems to detect vehicles, pedestrians, and other road users in these occluded regions. Through comprehensive measurement, we show that these existing techniques cannot sense occluded moving objects in many important real-world scenarios. To solve this problem of limited coverage, we leverage multiple, curved reflectors to provide comprehensive coverage over the most important locations near an intersection. In scenarios where curved reflectors are insufficient, we evaluate the relative benefits of using additional flat planar surfaces. Using these techniques, we more than double the probability of detecting a vehicle near the intersection in three real urban locations, and enable NLoS radar sensing using an entirely new class of reflectors.

In this paper, we propose to use commercial off-the-shelf (COTS)monostaticRFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags) to monitor browsing activity of customers in front of display items in places such as retail stores. To this end, we proposeTagSee, a multi-person imaging system based on monostatic RFID imaging. TagSee is based on the insight that when customers are browsing the items on a shelf, they stand between the tags deployed along the boundaries of the shelf and the reader, which changes the multi-paths that the RFID signals travel along, and both the RSS and phase values of the RFID signals that the reader receives change. Based on these variations observed by the reader, TagSee constructs a coarse grained image of the customers. Afterwards, TagSee identifies the items that are being browsed by the customers by analyzing the constructed images. The key novelty of this paper is on achieving browsing behavior monitoring of multiple customers in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging. To achieve this, we first mathematically formulate the problem of imaging humans using monostatic RFID devices and derive an approximate analytical imaging model that correlates the variations caused by human obstructions in the RFID signals. Based on this model, we then develop a deep learning framework to robustly image customers with high accuracy. We implement TagSee scheme using a Impinj Speedway R420 reader and SMARTRAC DogBone RFID tags. TagSee can achieve a TPR of more than${\sim }90\%$and a FPR of less than${\sim }10\%$in multi-person scenarios using training data from just 3-4 users.

WiFi-capable PLC (Poiver Line Communications) plug-and-play extenders are becoming popular to improve WiFi range and coverage in homes and enterprises. As shown in prior work, unlike an Ethernet backhaul, the PLC backhaul may not support high data rates. In addition, clients (users) that are either far or partially occluded from the WiFi-PLC extender they associate with can experience fading and shadowing, which degrades the throughput on the wireless link. Thus, both the PLC and WiFi backhauls will influence a user’s end-to-end throughput. In this paper, we seek to exploit the presence of multiple PLC extenders that may be plugged in, by combining their transmissions in a distributed antenna system (DAS), to boost client throughputs in a home setting. Specifically, we design PLC-DAS to determine which PLC extenders are the best candidates for forming a joint DAS transmitter cluster to each client. PLC-DAS is designed based on a real measurement study and not only accounts for the WiFi link qualities from the extenders to the users, but also the PLC link qualities from each extender to a master router which is typically deployed in homes. PLC-DAS is flexible and can maximize the throughput under different fairness objectives. We evaluate PLC-DAS via extensive simulations and show that it can increase the aggregate throughput by up to 4.5x compared to blindly using all WiFi PLC extenders to form a DAS transmitter, while maintaining a fairness Jain’s index value of at least 0.97 with proportional and max-min fairness models.

A long-standing challenge in radios for wearables is to design ultra-low power, yet high performance receivers with good sensitivity and spectral efficiency while being compatible with WiFi. The vanilla envelope detector used in standard UHF RFID is the most popular receivers on backscatter tags since they are passive but suffer from poor sensitivity and cannot decode complex modulations, which makes them a poor choice for directly decoding data from WiFi packets.

We design and build SkyHaul, the first large-scale, self-organizing network of Unmanned Aerial Vehicles (UAVs) that are connected using a mm Wave wireless mesh backhaul. While the use of a mmWave backhaul paves the way for a new class of bandwidth-intensive, latency-sensitive cooperative applications (e.g. LTE coverage during disasters), the network of UAVs allows these applications to be executed at operating ranges that are far beyond the line-of-sight distances that limit individual UAVs today.

mmWave 5G networks promise to enable a new generation of networked applications requiring a combination of high throughput and ultra-low latency. However, in practice, mmWave performance scales poorly for large numbers of users due to the significant overhead required to manage the highly-directional beams. We find that we can substantially reduce or eliminate this overhead by using out-of-band infrared measurements of the surrounding environment generated by a LiDAR sensor. To accomplish this, we develop a ray-tracing system that is robust to noise and other artifacts from the infrared sensor, create a method to estimate the reflection strength from sensor data, and finally apply this information to the multiuser beam selection process. We demonstrate that this approach reduces beam-selection overhead by over 95% in indoor multi-user scenarios, reducing network latency by over 80% and increasing throughput by over 2× in mobile scenarios.

Power Line Communication (PLC) based WiFi extenders can improve WiFi coverage in homes and enterprises. Unlike in traditional WiFi networks which use an underlying high data rate Ethernet backhaul, a PLC backhaul may not support high data rates. Specifically, our measurements show that arbitrarily affiliating users to PLC-WiFi extenders or based on their WiFi channel qualities alone may lead to poor network performance due to the differences in PLC link capacities. Thus, in this paper we build a framework, WOLT, to solve the problem of assigning users to the appropriate PLC-WiFi extenders to increase the aggregate network throughput in an enterprise setting, where one may expect a relatively large number of power outlets. WOLT accounts for both the qualities of the two concatenated links viz., the PLC and WiFi links. It hinges on estimating the best capacity offered by the PLC links, and accounting for these while assigning users. It incorporates a polynomial-time algorithm that assigns only a subset of the users to maximize the aggregate throughput on the PLC links, and then assigns the remaining users such that the degradation in the aggregate throughput is minimized. WOLT is evaluated through simulations and real testbed experiments with commodity PLCWiFi extenders, and improves aggregate throughput by more than 2.5× compared to a greedy user association baseline.

The recent innovation of frequency-shifted (FS) backscatter allows for backscattering with commodity devices, which are inherently half-duplex. However, their reliance on oscillators for generating the frequency-shifting signal on the tag, forces them to incur the transient phase of the oscillator before steady-state operation. We show how the oscillator's transient phase can pose a fundamental limitation for battery-less tags, resulting in significantly low bandwidth efficiencies, thereby limiting their practical usage.

We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV's operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV's position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 - 0.95X of optimal, which is about 1.5 - 2X over other popular baseline schemes.

The advent of LTE into the unlicensed spectrum has necessitated the understanding of its operational efficiency when sharing spectrum with different radio access technologies. Our study reveals that LTE, owing to its inherent transmission characteristics, suffers significant performance degradation in the presence of interference caused by hidden terminals. This motivates the need for interference-awareness in LTE's channel access in unlicensed spectrum. To address this problem, we propose ELI. ELI's three-pronged solution equips the LTE base station with novel techniques to: (a) accurately detect and measure interference caused by hidden terminals, (b) collect interference statistics from clients across different channels with affordable overhead, and (c) leverage interference-awareness to improve its channel access performance. Our evaluations show that ELI can achieve 1.5-2x throughput gains over baseline schemes. Finally, ELI is LTE-LAA/MulteFire-standard compliant and can be deployed over the existing LTE-LAA implementation without any modifications.

The advances in unmanned aerial vehicle (UAV) technology have empowered mobile operators to deploy LTE base stations (BSs) on UAVs, and provide on-demand, adaptive connectivity to hotspot venues as well as emergency scenarios. However, today's evolved packet core (EPC) that orchestrates the LTE RAN faces fundamental limitations in catering to such a challenging, wireless and mobile UAV environment, particularly in the presence of multiple BSs (UAVs). In this work, we argue for and propose an alternate, radical edge EPC design, called SkyCore that pushes the EPC functionality to the extreme edge of the core network - collapses the EPC into a single, light-weight, self-contained entity that is co-located with each of the UAV BS. SkyCore incorporates elements that are designed to address the unique challenges facing such a distributed design in the UAV environment, namely the resource-constraints of UAV platforms, and the distributed management of pronounced UAV and UE mobility. We build and deploy a fully functional version of SkyCore on a two-UAV LTE network and showcase its (i) ability to interoperate with commercial LTE BSs as well as smartphones, (ii) support for both hotspot and standalone multi-UAV deployments, and (iii) superior control and data plane performance compared to other EPC variants in this environment.

The aim of this tutorial is to give the audience an overview of the landscape of the future generation of mobile networks, namely 5G. Contrary to popular view, 5G is not expected to be anchored on a single disruptive technology but rather supported by an amalgamation of multiple technologies. In essence, it is an evolution of several key technical advancements, whose synergy is expected to revolutionize the heterogeneity of use cases that can be "simultaneously" enabled by a single network. Such use cases range from throughput-focused mobile broadband (Gigabit peak rates) to latency/reliability-focused mission critical (e.g. augmented/virtual reality, autonomous driving) and density-focused massive connection (IoT) services. While physical layer advancements in the form of New Radio (NR) are an integral part of 5G, realizing the diverse use cases envisioned, will equally require innovation and flexible orchestration of its access, network and computing layers as well. This tutorial will provide an overview of some of these innovative ingredients that will constitute 5G, from the perspective of not just radio access network, but also core network and services/applications.

Deploying LTE networks in unlicensed spectrum requires us to move beyond coexistence mechanisms and understand the suitability of LTE's synchronous operation in a spectrum that is governed by asynchronous access principles. Our study reveals a fundamental conflict in LTE uplink access that arises between the scheduled nature of LTE's multi-user transmissions -- critical for leveraging the diversity (OFDMA) and multiplexing (multi-user MIMO) gains -- and the asynchronous nature of interference on the clients. The result is a significant loss in spectrum utilization and throughput that scales with the number of interfering terminals.

Evolved-Multimedia Broadcast Multicast Services (eMBMS) is a set of features in LTE networks to deliver bandwidth-intensive multimedia content on a point-to-multipoint basis to subscribers. The notion of a Single Frequency Network (SFN) in eMBMS allows base stations to synchronize and transmit signals in a coordinated fashion across the same frequency-time radio resources using a common modulation rate. While SFN boosts the channel quality of users via transmit diversity gain, the use of a common rate across base stations results in reduced utilization for those that can individually support much higher data rates for their users, even without the notion of an SFN. Excluding such base stations from the SFN helps them utilize their resources better by not being constrained by the common rate, but creates additional inter-cell interference from their independent transmissions. Striking a balance between SFN cooperation and resource utilization is crucial for efficiently delivering broadcast content as well as other unicast flows. We design BoLTE, which carefully addresses this tradeoff and evaluate it using a prototype implementation over an SFN testbed, realized over a cloud-based radio access network system, as well as large-scale NS3 simulations. We show that BoLTE improves overall system throughput by around 40%.

In this paper, we design and develop RIO, a novel battery-free touch sensing user interface (UI) primitive for future IoT and smart spaces. RIO enables UIs to be constructed using off-the-shelf RFID readers and tags, and provides a unique approach to designing smart IoT spaces. With RIO, any surface can be turned into a touch-aware surface by simply attaching RFID tags to them. RIO also supports custom-designed RFID tags, and thus allows specially customized UIs to be easily deployed into a real-world environment. RIO is built using the technique of impedance tracking: when a human finger touches the surface of an RFID tag, the impedance of the antenna changes. This change manifests as a change in the phase of the RFID backscattered signal, and is used by RIO to track fine-grained touch movement over both off-the shelf and custom built tags. We study this impedance behavior in-depth and show how RIO is a reliable UI primitive that is robust even within a multi-tag environment. We leverage this primitive to build a prototype of RIO that can continuously locate a finger during a swipe movement to within 3 mm of its actual position. We also show how custom-design RFID tags can be built and used with RIO, and provide two example applications that demonstrate its real-world use.

Growing demand for data and increasing number of devices are drastically changing the scale of operation in mobile networks. Future services and business models require efficient provisioning with enhanced traffic management. It is hard to meet these requirements on today's mobile networks that are deployed over specialized hardware. While operators are keen to adopt NFV (Network Function Virtualization) to virtualize their networks, virtualized mobile network deployments face a few technical barriers. To address these challenges, we design SCOPE that effectively applies concepts from SDN and distributed systems to realize NFV-based LTE core networks. Using centralized allocation, SCOPE effectively manages the resources across multiple telecom data-centers in a way to meet the traffic requirements. To enforce the computed al- locations, SCOPE includes flexible and efficient mechanisms to configure the data-plane. With full compliance to 3GPP- based protocols, SCOPE ensures faster and cost-effective deployments. The efficacy of SCOPE is shown using a prototype implementation and large-scale simulations.

The growing demand for data has driven the service providers (SPs) to provide differential treatment of traffic to generate additional revenue streams from content providers (CPs). While SPs currently only provide best-effort services to their CPs, it is plausible to envision a model in near future, where CPs are willing to sponsor quality of service for their content in exchange of sharing a portion of their profit with SPs. This quality sponsoring becomes invaluable especially when the available resources are scarce, such as in wireless networks, and can be accommodated in a non-neutral network. In this paper, we consider the problem of quality-sponsored data (QSD) in a non-neutral network. In our model, SPs allow CPs to sponsor a portion of their resources, and price it appropriately to maximize their payoff. The payoff of the SP depends on the monetary revenue and the satisfaction of end-users both for the non-sponsored and sponsored content, while CPs generate revenue through advertisement. Note that in this setting, end-users still pay for the data they use. We analyze the market dynamics and equilibria in two different frameworks, i.e., sequential and bargaining game frameworks, and provide strategies for: 1) SPs-to determine if and how to price resources and 2) CPs-to determine if and what quality to sponsor. The frameworks characterize different sets of equilibrium strategies and market outcomes depending on the parameters of the market.

The proliferation of smartphones and tablet devices is changing the landscape of user connectivity and data access from predominantly static users to a mix of static and mobile users. While significant advances have been made in wireless transmission strategies (e.g., beamforming and network MIMO) to meet the increased demand for capacity, such strategies primarily cater to static users. To cope with growing heterogeneity in data access, it is critical to identify and optimize strategies that can cater to users of various profiles to maximize system performance and more importantly, improve users' quality of experience. Toward this goal, we first show that users can be profiled into three distinct categories based on their data access (mobility) and channel coherence characteristics. Then, with real-world experiments, we show that the strategy that best serves users in these categories varies distinctly from one profile to another and belongs to the class of strategies that emphasize either multiplexing (e.g., network MIMO), diversity (e.g., distributed antenna systems) or reuse (e.g., conventional CSMA). Two key challenges remain in translating these inferences to a practical system, namely: 1) how to profile users and 2) how to combine strategies to communicate with users of different profiles simultaneously. In addressing these challenges, we present the design of TRINITY-a practical system that effectively caters to a heterogeneous set of users. We implement and evaluate a prototype of TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared with individual transmission strategies.

There is widespread agreement that future continuous interactive (CI) applications will require edge computing capabilities from mobile networks. There is also widespread expectation that the emerging 5G network architecture, with its constituent technology components, will be the context in which this will be realized. Indeed many of the components that will be part of such an environment have been studied in standalone manner. However, the question of whether an end-to-end combination of these components would satisfy application requirements, or indeed, how these components would be combined into service offerings by mobile network providers, have not been meaningfully addressed. Towards addressing these challenges, we propose ACACIA - a service abstraction framework that enables CI applications on edge clouds in mobile networks. Evaluation of our prototype implementation shows that our holistic approach provides a 70% end-to-end application level latency reduction when compared with existing cloud and mobile solutions.

The static capacity provisioning in traditional WiFi networks (WLANs) cannot cope with the high spatiotemporal traffic variations in high-density venues such as conference centers, stadiums etc. To guarantee reliable performance, venue owners are forced to over-provision their WLANs based on worst-case traffic demand estimations, increasing capital and operational expenses. We propose AmorFi, a radically new way of deploying WLANs to handle peak traffic demands with average-case provisioning. Our key idea is to decouple baseband processing from RF transmission (inspired by the cloud-RAN concept in cellular networks) and introduce software programmability to flexibly allocate WiFi capacity in real time based on varying traffic demands. We implement AmorFi using off-the-shelf WiFi APs over a RF-over-fiber cloud-RAN testbed. Our experiments and simulations demonstrate that the software-defined capacity allocation enabled with AmorFi delivers more than $2x$ throughput than traditional WLANs.

In this work, we explore the potential and impact of unlicensed LTE on WiFi in unlicensed spectrum. Our experiments demonstrate that the large asymmetry in the channel access methodologies employed by WiFi and LTE (carrier sensing/notification in WiFi, energy sensing alone in LTE-U), can result LTE-U completely blocking WiFi transmissions, and causing significant degradation to either technologies from collisions.

We propose Acacia- a service abstraction framework that enables continuous interactive (CI) applications on edge clouds in mobile networks. We will demonstrate the Acacia architecture and illustrate its feasibility by using an augmented reality application as an example use case.

Small cells form a critical component of next generation cellular networks, where spatial reuse is the key to higher spectral efficiencies. Interference management in the spatial domain through beamforming allows for increased reuse without having to sacrifice resources in the time or frequency domain. Existing beamforming techniques for spatial reuse, being coupled with client scheduling, face a key limitation in practical realization, especially with OFDMA small cells. In this context, we argue that for a practical spatial reuse system with beamforming, it is important to decouple beamforming from client scheduling. Further, we show that jointly addressing client association with beamforming is critical to maximizing the reuse potential of beamforming. Towards our goal, we propose ProBeam - a system for multi-cell beamforming and client association in OFDMA small cell networks. ProBeam incorporates two key components - a low complexity, highly accurate SINR estimation module that helps determine interference dependencies for beamforming between small cells; and an efficient, low complexity joint client association and beam selection algorithm for the small cells that accounts for scheduling at the small cells without being coupled with it. We have prototyped ProBeam on a WiMAX-based network of four small cells. Our evaluations reveal the accuracy of our SINR estimation module to be within 1 dB, and the reuse gains from joint client association and beamforming to be as high as 115 percent over baseline approaches.

Beamforming is a signal processing technique with numerous benefits in wireless communication. Unlike traditional omnidirectional communication, it focuses the energy of the transmitted and/or the received signal in a particular direction. Although beamforming has been extensively studied on conventional systems such as WiFi, little is known about its practical impact on performance in orthogonal frequency-domain multiple access (OFDMA) small-cell deployments. Since OFDMA schedules multiple clients (users) in the same frame in contrast to WiFi, designing intelligent scheduling mechanisms and at the same time leveraging beamforming is a challenging task. Unlike downlink, we show that the integration of beamforming with uplink scheduling projects an interesting tradeoff between beamforming gain on the one hand, and the power-pooling gain resulting from joint multiuser scheduling on the other hand. This, in turn, makes the uplink scheduling problem even hard to approximate. To address this, we propose algorithms that are simple to implement, yet provably efficient with a worst-case guarantee of 1/2. We implement our algorithms on a real WiMAX small-cell platform integrated with an eight-element phased-array beamforming antenna. Evaluations from both prototype implementation and trace-driven simulations show that the algorithms deliver throughput gains of over 40% compared to an omnidirectional scheme.

Cloud-based radio access networks (C-RAN) have been proposed as a cost-efficient way of deploying small cells. Unlike conventional RANs, a C-RAN decouples the baseband processing unit (BBU) from the remote radio head (RRH), allowing for centralized operation of BBUs and scalable deployment of light-weight RRHs as small cells. In this work, we argue that the intelligent configuration of the front-haul network between the BBUs and RRHs, is essential in delivering the performance and energy benefits to the RAN and the BBU pool, respectively. We propose FluidNet-a scalable, light-weight framework for realizing the full potential of C-RAN. FluidNet deploys a logically re-configurable front-haul to apply appropriate transmission strategies in different parts of the network and hence cater effectively to both heterogeneous user profiles and dynamic traffic load patterns. FluidNet's algorithms determine configurations that maximize the traffic demand satisfied on the RAN, while simultaneously optimizing the compute resource usage in the BBU pool. We prototype FluidNet on a 6 BBU, 6 RRH WiMAX C-RAN testbed. Prototype evaluations and large-scale simulations reveal that FluidNet's ability to re-configure its front-haul and tailor transmission strategies provides a 50% improvement in satisfying traffic demands, while reducing the compute resource usage in the BBU pool by 50% compared to baseline schemes.

Power line communications (PLC) offer an immediate means of providing high bandwidth connectivity in settings where there is no in-built network infrastructure. While there is recent work on understanding physical and MAC layer artifacts of PLC, its applicability and performance in multi-flow settings is not well understood. We first undertake an extensive measurement study that sheds light on the properties of PLC that significantly affect performance in multi-flow settings. Using the understanding gained, we design BOLT, a framework that adopts a learning-based approach to effectively manage and orchestrate flows in a PLC network. BOLT is flexible and is agnostic to standards; it can be used to implement scheduling algorithms that target different performance goals. We implement BOLT on three different testbeds using off-the-shelf PLC adapters and showcase its ability to effectively manage flows, delivering several folds throughput improvement over state-of-the-art solutions.

In addition to growth of data traffic, mobile networks are bracing for a significant rise in the control-plane signaling. While a complete re-design of the network to overcome inefficiencies may help alleviate the effects of signaling, our goal is to improve the design of the current platform to better manage the signaling. To meet our goal, we combine two key trends. Firstly, mobile operators are keen to transform their networks with the adoption of Network Function Virtualization (NFV) to ensure economies of scales. Secondly, growing popularity of cloud computing has led to advances in distributed systems. In bringing these trends together, we solve several challenges specific to the context of telecom networks. We present SCALE - A framework for effectively virtualizing the MME (Mobility Management Entity), a key control-plane element in LTE. SCALE is fully compatible with the 3GPP protocols, ensuring that it can be readily deployed in today's networks. SCALE enables (i) computational scaling with load and number of devices, and (ii) computational multiplexing across data centers, thereby reducing both, the latencies for control-plane processing, and the VM provisioning costs. Using an LTE prototype implementation and large-scale simulations, we show the efficacy of SCALE.

We investigate the open problem of characterizing the multiplexing gain offered by FD in a network of M cells (compared to the gain of two available on a single link). While self-interference cancellation is fundamental in realizing full duplex (FD) capability, the more challenging problem in a network-wide deployment of FD communication is a new form of uplink-downlink interference, namely UDI, caused by transmission of uplink clients on the downlink reception of other clients operating in the same frequency band during FD. We leverage spatial interference alignment (IA) as an effective approach to address UDI and characterize the scalability of the FD's multiplexing gain (in terms of M) by providing a closed-form expression. To the best of our knowledge, this is the first characterization of FD's multiplexing gain in a multi-cell network. We also provide an IA construction that can achieve the best scaling possible. Further, we extend our results to practical settings with limited number of clients and limited information sharing between access points.

Large-scale multiuser MIMO (MU-MIMO) systems have the potential for multi-fold scaling of network capacity. The research community has recognized this theoretical potential and developed architectures [1,2] with large numbers of RF chains. Unfortunately, building the hardware with a large number of RF chains is challenging in practice. CSI data transport and computational overhead of MU-MIMO beamforming can also become prohibitive under large network scale. Furthermore, it is difficult to physically append extra RF chains on existing communication equipments to support such large-scale MU-MIMO architectures.

Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.

To handle increased capacity demands, sophisticated MIMO-based transmission strategies, based on transmitter cooperation, have emerged. However, different types of users' channels (e.g., static vs mobile, stable vs dynamic channels) that make up today's enterprises, require different MIMO transmission strategies. With the wrong strategy, a user could even see a degradation in performance. Our overarching goal is to design and implement a framework, TRINITY, that can simultaneously cater to a heterogeneous mix of users, by intelligently combining a plurality of MIMO transmission strategies wherein the transmitters at different nodes can cooperate to deliver significant performance gains. Three key challenges that we address in building TRINITY are: (i) how to categorize users into channel profiles such that a single transmission strategy caters to the users of a profile, (ii) how to combine strategies to communicate with users of different profiles simultaneously, and (iii) what is the granularity of transmitter cooperation needed to balance efficiency with complexity. We implement and evaluate TRINITY on our WARP radio testbed. Our extensive experiments show that TRINITY's intelligent combining of transmission strategies improves the total network rate by 50%-150%, satisfies the QoS requirements of thrice as many users, and improves PSNR for video traffic by 10 dB compared to individual transmission strategies.

The growing demand for data has driven the Service Providers (SPs) to provide differential treatment of traffic to generate additional revenue streams from Content Providers (CPs). While SPs currently only provide best-effort services to their CPs, it is plausible to envision a model in near future, where CPs are willing to sponsor quality of service for their content in exchange of sharing a portion of their profit with SPs. In this paper, we introduce the problem of Quality-Sponsored Data (QSD) in cellular networks and study its implications on market entities in various scenarios. The direct coupling between the scarce wireless resources and the market decisions resulting from QSD is taken into account. In our model, SPs make a portion of their resources available for sponsorship by CPs, and price it appropriately to maximize their payoff, which depends on the monetary revenue and the satisfaction of end-users both for the non-sponsored and sponsored content, while CPs generate revenue through advertisement. We analyze the market dynamics and equilibria, and provide strategies for (i) SPs: to determine if and how to price resources, and (ii) CPs: to determine if and what quality to sponsor. We also discuss about the effects of different parameters of the model on market dynamics.

Every night, many smartphones are plugged into a power source for recharging the battery. Given the increasing computing capabilities of smartphones, these idle phones constitute a sizeable computing infrastructure. Therefore, for an enterprise which supplies its employees with smartphones, we argue that a computing infrastructure that leverages idle smartphones being charged overnight is an energy-efficient and cost-effective alternative to running certain tasks on traditional servers. While parallel execution models and schedulers exist for servers, smartphones face a unique set of technical challenges due to the heterogeneity in CPU clock speed, variability in network bandwidth, and lower availability than servers. In this paper, we address many of these challenges to develop CWC-a distributed computing infrastructure using smartphones. We implement and evaluate a prototype of CWC that employs a novel scheduling algorithm to minimize the makespan of a set of computing tasks. Our evaluations using a testbed of 18 Android phones show that CWC's scheduler yields a makespan that is 1.6× faster than other simpler approaches.

Malicious interference injection or jamming is one of the simplest ways to disrupt wireless communications. Prior approaches can alleviate jamming interference to a limited extent; they are especially vulnerable to a reactive jammer i.e., a jammer that injects noise upon sensing a legitimate transmission or wideband jamming. In this paper, we leverage the inherent features of OFDM (Orthogonal Frequency Division Multiplexing) to cope with such attacks. Specifically, via extensive experiments, we observe that the jamming signal experiences differing levels of fading across the composite sub-carriers in its transmission bandwidth. Thus, if the legitimate transmitter were to somehow exploit the relatively unaffected sub-carriers to transmit data to the receiver, it could achieve reasonable throughputs, even in the presence of the active jammer. We design and implement JIMS, a Jamming Interference Mitigation Scheme that exploits the above characteristic by overcoming key practical challenges. Via extensive testbed experiments and simulations we show that JIMS achieves a throughput restoration of up to 75 percent in the presence of an active jammer.

In today's OFDMA networks, the transmission power is typically fixed and the same for all the sub-carriers that compose a channel. The sub-carriers though, experience different degrees of fading and thus, the received power is different for different sub-carriers; while some frequencies experience deep fades, others are relatively unaffected. In this paper, we make a case for redistributing the power across the sub-carriers (subject to a fixed power budget constraint) to better cope with this frequency selectivity. Specifically, we design a joint power and rate adaptation scheme (called JPRA for short) wherein power redistribution is combined with sub-carrier level rate adaptation to yield significant throughput benefits. We further consider three variants of JPRA: (a) JPRA-Basic where, the power is redistributed across sub-carriers so as to support a maximum common rate across all the sub-carriers (b) JPRA-Intermediate where, the power is redistributed across sub-carriers so as to support a maximum common rate across a “subset” of sub-carriers such that the aggregate rate is maximized. (c) JPRA-Adaptive where, the goal is to redistribute power such that the transmission time of a packet is minimized. While the first two variants decrease transceiver complexity and are simpler, the third is geared towards achieving the maximum throughput possible. We implement all three variants of JPRA on our WARP radio testbed. Our extensive experiments demonstrate that JPRA can provide a 35 percent improvement in total network throughput in testbed experiments compared to FARA, a scheme where only sub-carrier level rate adaptation is used. We also perform simulations to demonstrate the efficacy of JPRA in larger scale networks.

Next generation wireless networks (i.e., WiMAX, LTE) provide higher bandwidth and spectrum efficiency leveraging smaller (femto) cells with orthogonal frequency division multiple access (OFDMA). The uncoordinated, dense deployments of femtocells however, pose several unique challenges relating to interference and resource management in OFDMA femtocell networks. Towards addressing these challenges, we propose RADION, a distributed resource management framework that effectively manages interference across femtocells. RADION's core building blocks enable femtocells to opportunistically determine the available resources in a completely distributed and efficient manner. Further, RADION's modular nature paves the way for different resource management solutions to be incorporated in the framework. We implement RADION on a real WiMAX femtocell testbed deployed in a typical indoor setting. Two distributed solutions are enabled through RADION and their performance is studied to highlight their quick self-organization into efficient resource allocations.

Next generation WLANs (802.11ac) are undergoing a major shift in their communication paradigm with the introduction of multi-user MIMO (MU-MIMO), transitioning from single-user to multi-user communications. We argue that the conventional AP deployment model of co-located antennas as well as their PHY and MAC mechanisms are not designed to realize the complete potential of MU-MIMO. We propose to leverage distributed antenna systems (DAS) to empower next generation 802.11ac networks. We highlight the multitude of benefits that DAS brings to MU-MIMO and 802.11ac in general. However, several challenges arise in the process of realizing these benefits in practice, where avoiding client modifications and making only minimal software modifications to APs is important to enable rapid adoption. Towards addressing these challenges, we present the design and implementation of MIDAS, the Multiple-Input Distributed Antenna System. MIDAS couples a DAS deployment of AP antennas with a suite of novel yet standards-compatible mechanisms at the PHY and MAC layers that best leverage the DAS deployment to maximize 802.11ac performance. Our WARP-based experimental evaluation demonstrates MIDAS's ability to significantly boost the performance of current 802.11ac design, demonstrating throughput gains over 802.11ac MU-MIMO for 100-200%, while remaining amenable to commercial adoption.

Device-to-device (D2D) communications is being pursued as an important feature in next generation cellular networks. D2D can improve resource utilization in two ways: Offloading cellular traffic to D2D, and Reuse of resources used by conventional cellular transmissions for D2D communication. In this paper, we show that in multi-cell environments that employ FFR (Fractional Frequency Reuse), the benefits from D2D toward reuse are limited. We then propose R2D2- a holistic approach to efficient offloading with D2D traffic. R2D2 leverages the flexible nature of D2D traffic (in using downlink/uplink resources) to cater effectively to the spatial and temporal asymmetry in traffic load both across and within cells. R2D2 incorporates a two time-scale solution: a coarse time-scale dynamic FFR scheme that leverages D2D traffic to determine the FFR patterns for downlink and uplink jointly among interfering sectors; and a fine time-scale scheduling solution that intelligently schedules cellular and D2D traffic jointly across DL (Downlink) and UL (Uplink) resources. We establish the hardness of the scheduling problem and present efficient and low complexity algorithms with approximation guarantees. Through extensive evaluations, we confirm that R2D2 delivers the offloading benefits of D2D, with its proposed algorithms performing very close to the optimal.

Mobile operators are leveraging WiFi to relieve the pressure posed on their networks by the surging bandwidth demand of applications. However, operators often lack intelligent mechanisms to control the way users access their WiFi networks. This lack of sophisticated control creates poor network utilization, which in turn degrades the quality of experience (QoE). To meet user traffic demands, it is evident that operators need solutions that optimally balance user traffic across cellular and WiFi networks. Motivated by the lack of practical solutions in this space, we design and implement ATOM - an end-to-end system for adaptive traffic offloading for WiFi-LTE deployments. ATOM has two novel components: (i) A network interface selection algorithm that maps user traffic across WiFi and LTE to optimize user QoE and (ii) an interface switching service that seamlessly re-directs ongoing user sessions in a cost-effective and standards-compatible manner. Our evaluations on a real LTE-WiFi testbed using YouTube traffic reveals that ATOM reduces video stalls by 3-4 times compared to naive solutions.

Enabling wireless full-duplex (from an AP) with multiple half-duplex (HD) clients is key to widespread adoption of full-duplex (FD) in commercial networks. However, enabling FD in such networks is fundamentally challenged by a new form of uplink-downlink interference (UDI), arising between HD clients operating simultaneously in the uplink and downlink directions. In this context, we first show that spatial interference alignment (IA) between clients is an effective and scalable technique to address UDI and hence enable FD in these networks, especially in the presence of MIMO.

With the growing demand for increased spectral efficiencies, there has been renewed interest in enabling full-duplex communications. However, existing approaches to enable full-duplex require a clean-slate approach to address the key challenge in full-duplex, namely self-interference suppression. This serves as a big deterrent to enabling full-duplex in existing cellular networks. Towards our vision of enabling full-duplex in legacy cellular, specifically LTE networks, with no modifications to existing hardware at BS and client as well as technology specific industry standards, we present the design of our experimental system FD-LTE, that incorporates a combination of passive SI cancellation schemes, with legacy LTE half-duplex BS and client devices. We build a prototype of FD-LTE, integrate it with LTE's evolved packet core and conduct over-the-air experiments to explore the feasibility and potential for full-duplex with legacy LTE networks. We report promising experimental results from FD-LTE, which currently applies to scenarios with limited ranges that is typical of small cells.

The classical definition of degrees of freedom (DoF) deals with the degrees of a communication channel or multiple communication channels in the limit of high SNR. This can be interpreted as the number of independent streams that can be sent in each communication channel in the high SNR regime. We introduce the concept of DoF per communication node where at a transmitting node the DoF is the number of independent dimensions that can be used for transmission and at each receiver node the DoF is the number of independent dimensions that can be used for receiving data signals. In general the communication channels or links in a network can be divided into two sets: the interfering channels and the intended channels; hence, the network may be considered as an overlay of two networks, respectively: the interfering network and data-intended network. In the classical form, DoF is defined for channels in the data-intended network. We illustrate a new interpretation of DoF that depends only on the interfering network and can be formalized in full generality based on degrees of freedom per node in the network. While the classical DoF has been studied generally in the context of interference and X-channels, the per node DoF concept generalizes the idea to other possible networks. Using this generalized notion of DoF, this paper provides new results on DoF for different networks and also makes a connection to the classical definition of DoF defined in interference and X-channels.

The demand for real-time video services coupled with the need for increased spectral efficiencies has brought a lot of attention to video multicasting in next generation OFDMA systems. Layered video provides an efficient solution to address the channel quality variation across users. However, the frequency selectivity across sub-channels introduced by OFDMA serves as a drawback for multicast scheduling. This significantly diminishes the benefits of video layering and can potentially degrade the system performance to worse than that without channel diversity. Layering the PHY with the help of multi-resolution modulation serves as an effective tool to help retain channel diversity and leverage the benefits of layered video. While layering the PHY provides benefits to multicast in general, we show that its benefits are much more significant in a system with channel diversity. The corresponding multicast scheduling problem now becomes all the more challenging with multi-resolution modulation. In addressing this problem, we provide efficient scheduling solutions both for systems with and without channel diversity. Our solutions have provable performance guarantees and are amenable to practical implementation. Further, evaluation in realistic scenarios reveals close to optimal performance of our proposed solutions as well as the large benefits obtained by layering the PHY in OFDMA systems with channel diversity.

With the next-generation cellular networks making a transition toward smaller cells, two-hop orthogonal frequency-division multiple access (OFDMA) relay networks have become a dominant, mandatory component in the 4G standards (WiMAX 802.16j, 3GPP LTE-Adv). While unicast flows have received reasonable attention in two-hop OFDMA relay networks, not much light has been shed on the design of efficient scheduling algorithms for multicast flows. Given the growing importance of multimedia broadcast and multicast services (MBMS) in 4G networks, the latter forms the focus of this paper. We show that while relay cooperation is critical for improving multicast performance, it must be carefully balanced with the ability to multiplex multicast sessions and hence maximize aggregate multicast flow. To this end, we highlight strategies that carefully group relays for cooperation to achieve this balance. We then solve the multicast scheduling problem under two OFDMA subchannelization models. We establish the NP-hardness of the scheduling problem even for the simpler model and provide efficient algorithms with approximation guarantees under both models. Evaluation of the proposed solutions reveals the efficiency of the scheduling algorithms as well as the significant benefits obtained from the multicasting strategy.

Malicious interference injection or jamming is one of the simplest ways to disrupt wireless communications. Prior approaches can alleviate jamming interference to a limited extent; they are especially vulnerable to a reactive jammer i.e., a jammer that injects noise upon sensing a legitimate transmission or wideband jamming. In this paper, we leverage the inherent features of OFDM (Orthogonal Frequency Division Multiplexing) to cope with such attacks. Specifically, via extensive experiments, we observe that the jamming signal experiences differing levels of fading across the composite sub-carriers in its transmission bandwidth. Thus, if the legitimate transmitter were to somehow exploit the relatively unaffected sub-carriers to transmit data to the receiver, it could achieve reasonable throughputs, even in the presence of the active jammer. We design and implement JIMS, a Jamming Interference Mitigation Scheme that exploits the above characteristic by overcoming key practical challenges. Via extensive testbed experiments and simulations we show that JIMS achieves a throughput restoration of up to 75% in the presence of an active jammer.

Carrier aggregation (CA) is an important feature of next generation cellular networks (LTE-advanced, LTE-A) that allows its users to aggregate upto 100 MHz of (dis-)contiguous spectral chunks to provide increased data rates. While the conventional approach of allowing LTE-A users to be configured on all component carriers, results in maximum diversity gain for scheduling, it also increases the users' power consumption and processing that scale with the number of component carriers. In light of the growing need to minimize energy consumption on mobile devices, we argue that it is possible to operate the LTE-A users on a small subset of component carriers to reduce their energy consumption, without any appreciable loss to the scheduling gain. A key step in realizing this goal however, is to address the joint problem of component carrier selection as well as scheduling and in turn forms the focus of this work. We highlight the hardness of the joint problem when the number of component carriers that can be activated for an LTE-A user is limited. Towards solving the problem, we consider various models that incorporate both contiguous and dis-contiguous CA as well as backlogged and finite user buffers and propose efficient, greedy algorithms with performance guarantees that are also simple to implement. Our evaluations based on LTE system parameters, reveal that our algorithms help realize 80-90% of the maximum scheduling gain with just half the component carriers and provide a 25% throughput gain over baseline load and signal power based carrier selection schemes.

Small cells have become an integral component in meeting the increased demand for cellular network capacity. Cloud radio access networks (C-RAN) have been proposed as an effective means to harness the capacity benefits of small cells at reduced capital and operational expenses. With the baseband units (BBUs) separated from the radio access units (RAUs) and moved to the cloud for centralized processing, the backhaul between BBUs and RAUs forms a key component of any C-RAN. In this work, we argue that a one-one mapping of BBUs to RAUs is highly sub-optimal, thereby calling for a functional decoupling of the BBU pool from the RAUs. Further, the backhaul architecture must be made re-configurable to allow the mapping between BBUs and RAUs to be flexible and changed dynamically so as to not just optimize RAN performance but also energy consumption in the BBU pool. Towards this end, we design and implement the first OFDMA-based C-RAN test-bed with a reconfigurable backhaul that allows 4 BBUs to connect flexibly with 4 RAUs using radio-over-fiber technology. We demonstrate the feasibility of our system over a 10 km separation between the BBU pool and RAUs. Further, real world experiments with commercial off-the-shelf WiMAX clients reveal the performance benefits of our reconfigurable backhaul in catering effectively to heterogeneous user (static and mobile clients) and traffic profiles, while also delivering energy benefits in the BBU pool.

Cloud-based radio access networks (C-RAN) have been proposed as a cost-efficient way of deploying small cells. Unlike conventional RANs, a C-RAN decouples the baseband processing unit (BBU) from the remote radio head (RRH), allowing for centralized operation of BBUs and scalable deployment of light-weight RRHs as small cells. In this work, we argue that the intelligent configuration of the front-haul network between the BBUs and RRHs, is essential in delivering the performance and energy benefits to the RAN and the BBU pool, respectively. We then propose FluidNet - a scalable, light-weight framework for realizing the full potential of C-RAN. FluidNet deploys a logically re-configurable front-haul to apply appropriate transmission strategies in different parts of the network and hence cater effectively to both heterogeneous user profiles and dynamic traffic load patterns. FluidNet's algorithms determine configurations that maximize the traffic demand satisfied on the RAN, while simultaneously optimizing the compute resource usage in the BBU pool. We prototype FluidNet on a 6 BBU, 6 RRH WiMAX C-RAN testbed. Prototype evaluations and large-scale simulations reveal that FluidNet's ability to re-configure its front-haul and tailor transmission strategies provides a 50% improvement in satisfying traffic demands, while reducing the compute resource usage in the BBU pool by 50% compared to baseline transmission schemes.

MIMO beamforming technology can scale wireless data rate proportionally with the number of antennas. However, the overhead induced by receivers' CSI (channel state information) feedback scales at a higher rate. In this paper, we address this fundamental tradeoff with Adaptive Feedback Compression (AFC). AFC quantizes or compresses CSI from 3 dimensions --- time, frequency and numerical values, and adapts the intensity of compression according to channel profile. This simple principle faces many practical challenges, e.g., a huge search space for adaption, estimation or prediction of the impact of compression on network throughput, and the coupling of different users in multi-user MIMO networks. AFC meets these challenges using a novel cross-layer adaptation metric, a metric extracted from 802.11 packet preambles, and uses it to guide the selection of compression intensity, so as to balance the tradeoff between overhead reduction and capacity loss (due to compression). We have implemented AFC on a software radio testbed. Our experiments show that AFC can outperform alternative approaches in a variety of radio environments.

Network MIMO (netMIMO) has potential for significantly enhancing the capacity of wireless networks with tight coordination of access points (APs) to serve multiple users concurrently. Existing schemes realize netMIMO by integrating distributed APs into one ``giant'' MIMO but do not scale well owing to their global synchronization requirement and overhead in sharing data between APs. To remedy this limitation, we propose a novel system, NEMOx, that realizes netMIMO downlink transmission for large-scale wireless networks. NEMOx organizes a network into practical-size clusters, each containing multiple distributed APs (dAPs) that opportunistically synchronize with each other for netMIMO downlink transmission. Inter-cluster interference is managed with a decentralized channel-access algorithm, which is designed to balance between the dAPs' cooperation gain and spatial reuse---a unique tradeoff in netMIMO. Within each cluster, NEMOx optimizes the power budgeting among dAPs and the set of users to serve, ensuring fairness and effective cancellation of cross-talk interference. We have implemented and evaluated a prototype of NEMOx in a software radio testbed, demonstrating its throughput scalability and multiple folds of performance gain over current wireless LAN architecture and alternative netMIMO schemes.

Small cells form a critical component of next generation cellular networks, where spatial reuse is the key to higher spectral efficiencies. Interference management in the spatial domain through beamforming allows for increased reuse without having to sacrifice resources in the time or frequency domain. Existing beamforming techniques for spatial reuse, being coupled with client scheduling, face a key limitation in practical realization, especially with OFDMA small cells. In this context, we argue that for a practical spatial reuse system with beamforming, it is important to decouple beamforming from client scheduling. Further, we show that jointly addressing client association with beamforming is critical to maximizing the reuse potential of beamforming.

To meet the capacity demands from ever-increasing mobile data usage, mobile network operators are moving toward smaller cell structures. These small cells, called femtocells, use sophisticated air interface technologies such as orthogonal frequency division multiple access (OFDMA). While femtocells are expected to provide numerous benefits such as energy efficiency and better throughput, the interference resulting from their dense deployments prevents such benefits from being harnessed in practice. Thus, there is an evident need for a resource management solution to mitigate the interference that occurs between collocated femtocells. In this paper, we design and implement one of the first resource management systems, FERMI, for OFDMA-based femtocell networks. As part of its design, FERMI: 1) provides resource isolation in the frequency domain (as opposed to time) to leverage power pooling across cells to improve capacity; 2) uses measurement-driven triggers to intelligently distinguish clients that require just link adaptation from those that require resource isolation; 3) incorporates mechanisms that enable the joint scheduling of both types of clients in the same frame; and 4) employs efficient, scalable algorithms to determine a fair resource allocation across the entire network with high utilization and low overhead. We implement FERMI on a prototype four-cell WiMAX femtocell testbed and show that it yields significant gains over conventional approaches.

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 a field programmable gate array (FPGA) platform, and its performance is compared against that of omnidirectional and switched beamforming based multicast. Our experimental results reveal that: 1) switched multicast beamforming has limited gains in indoor multipath environments, whose deficiencies can be effectively overcome by ADAM to yield an average gain of threefold; 2) the higher the dynamic range of the discrete transmission rates employed by the MAC hardware, the higher the gains in ADAM's performance, yielding up to ninefold improvement over omni with the 802.11 rate table; and 3) 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 signal-to-noise ratio (SNR)-rate mapping to incorporate feedback rate and coherence time significantly increases its robustness to channel dynamics.

Every night, a large number of idle smartphones are plugged into a power source for recharging the battery. Given the increasing computing capabilities of smartphones, these idle phones constitute a sizeable computing infrastructure. Therefore, for an enterprise which supplies its employees with smartphones, we argue that a computing infrastructure that leverages idle smartphones being charged overnight is an energy-efficient and cost-effective alternative to running tasks on traditional server infrastructure. While parallel execution and scheduling models exist for servers (e.g., MapReduce), smartphones present a unique set of technical challenges due to the heterogeneity in CPU clock speed, variability in network bandwidth, and lower availability compared to servers.

The proliferation of smartphones and tablet devices is changing the landscape of user connectivity and data access from predominantly static users to a mix of static and mobile users. While significant advances have been made in wireless transmission strategies (e.g., network MIMO) to meet the increased demand for capacity, such strategies primarily cater to static users. To cope with growing heterogeneity in data access, it is critical to identify and optimize strategies that can cater to users of various profiles to maximize system performance and more importantly, improve users' quality of experience.

Coordinated Multi-Point (CoMP) transmission is emerging as a concept that can substantially suppress interference, thus improving the capacity of multi-cell wireless networks. However, existing CoMP techniques either require sharing of data and channel state information (CSI) for all links in the network, or have limited capability of interference suppression. In this paper, we propose distributed interference alignment and cancellation (DIAC) to overcome these limitations. DIAC builds on a key intuition of interference locality — since each link is interfered with a limited number of neighboring links, it is sufficient to coordinate with those strong interferers and ignore others, in order to bound the overhead in CoMP. DIAC realizes the localized coordination by integrating interference cancellation and distributed interference alignment, and can be applied to both the uplink and downlink of multi-cell wireless networks. We validate DIAC using both model-driven and trace-based simulation where the traces are collected by implementing a MIMO-OFDM channel estimator on a software radio platform. Our experiments show that DIAC can substantially improve the degrees of freedom in multi-cell wireless networks.

The increase in mobile data usage is pushing broadband operators towards deploying smaller cells (femtocells) and sophisticated access technologies such as OFDMA. The expected high density of deployment and uncoordinated operations of femtocells however, make interference management both critical and extremely challenging. Femtocells have to use the same access technology as traditional macrocells. Given this, understanding the impact of the system design choices (originally tailored to well-planned macrocells) on interference management, forms an essential first step towards designing efficient solutions for next-generation femtocells. This in turn is the focus of our work. With extensive measurements from our WiMAX OFDMA femtocell testbed, we characterize the impact of various system design choices on interference. Based on the insights from our measurements, we discuss several implications on how to efficiently operate a femtocell network.

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.

The ability of relay networks to improve capacity and coverage has led to their adoption in next generation wireless broadband networks (WiMAX, LTE-advanced). Unlike conventional cellular networks, there is a multitude of design features that impacts the performance of these relay networks. This has consequently increased the need for efficient scheduling algorithms to optimize such design features. The focus of this work is to improve relay network throughput through adaptive resource usage in the form of two key design features - (i) adaptive frame segmentation, and (ii) spatial reuse - that are allowed by the relay standard. To this end, we design efficient scheduling algorithms that optimize these two features jointly. We provide both algorithms with performance guarantees as well as those with fast running times. Our study reveals that with the help of properly designed scheduling algorithms, adaptive resource usage can (i) boost the network throughput performance by up to 50%; and (ii) optimize network throughput effectively, providing an effect similar to dynamic relay placement.

Given that full duplex (FD) and MIMO both employ multiple antenna resources, an important question that arises is how to make the choice between MIMO and FD? We show that optimal performance requires a combination of both to be used. Hence, we present the design and implementation of MIDU, the first MIMO full duplex system for wireless networks. MIDU employs antenna cancellation with symmetric placement of transmit and receive antennas as its primary RF cancellation technique. We show that MIDU's design provides large amounts of self-interference cancellation with several key advantages: (i) It allows for two stages of additive antenna cancellation in tandem, to yield as high as 45 dB self-interference suppression; (ii) It can potentially eliminate the need for other forms of analog cancellation, thereby avoiding the need for variable attenuator and delays; (iii) It easily scales to MIMO systems, therefore enabling the coexistence of MIMO and full duplex. We implemented MIDU on the WARP FPGA platform, and evaluated its performance against half duplex (HD)-MIMO. Our results reveal that, with the same number of RF chains, MIDU can potentially double the throughput achieved by half duplex MIMO in a single link; and provide median gains of at least 20% even in single cell scenarios, where full duplex encounters inter-client interference. Based on key insights from our results, we also highlight how to efficiently enable scheduling for a MIDU node.

Beamforming is a signal processing technique with numerous benefits. Unlike with omni-directional communications, it focuses the energy of the transmitted and/or the received signal in a particular direction. Although beamforming has been extensively studied on conventional systems such as WiFi, little is known about its practical impact on OFDMA femtocell deployments. Since OFDMA schedules multiple clients (users) in the same frame (in contrast to WiFi), designing intelligent scheduling mechanisms and at the same time leveraging beamforming, is a challenging task.

In today's OFDMA networks, the transmission power is typically fixed and the same for all the sub-carriers that compose a channel. The sub-carriers though, experience different degrees of fading and thus, the received power is different for different sub-carriers; while some frequencies experience deep fades, others are relatively unaffected. In this paper, we make a case of redistributing the power across the sub-carriers (subject to a fixed power budget constraint) to better cope with this frequency selectivity. Specifically, we design a joint power and rate adaptation scheme (called JPRA for short) wherein power redistribution is combined with sub-carrier level rate adaptation to yield significant throughput benefits. We further consider two variants of JPRA: (a) JPRA-CR where, the power is redistributed across sub-carriers so as to support a maximum common rate (CR) across sub-carriers and (b) JPRA-MT where, the goal is to redistribute power such that the transmission time of a packet is minimized. While the first variant decreases transceiver complexity and is simpler, the second is geared towards achieving the maximum throughput possible. We implement both variants of JPRA on our WARP radio testbed. Our extensive experiments demonstrate that our scheme provides a 35% improvement in total network throughput in testbed experiments compared to FARA, a scheme where only sub-carrier level rate adaptation is used. We also perform simulations to demonstrate the efficacy of JPRA in larger scale networks.

Next generation wireless networks (i.e., WiMAX, LTE) provide higher bandwidth and spectrum efficiency leveraging smaller (femto) cells with orthogonal frequency division multiple access (OFDMA). The uncoordinated, dense deployments of femtocells however, pose several unique challenges relating to interference and resource management in these networks. Towards addressing these challenges, we propose RADION, a distributed resource management framework that effectively manages interference across femtocells. RADION's core building blocks enable femtocells to opportunistically find the available resources in a completely distributed and efficient manner. Further, RADION's modular nature paves the way for different resource management solutions to be incorporated in the framework. We implement RADION on a real WiMAX femtocell testbed deployed in a typical indoor setting. We extensively evaluate two solutions integrated with RADION, both via prototype implementation and simulations and quantify their performance in terms of quick and efficient self-organization.

Using beamforming antennas to improve wireless multicast transmissions has received considerable attention recently. A recent work proposes to partition all single-lobe beams into groups and to form composite multilobe beam patterns to transmit multicast traffic. Depending on how the power is split among the individual beams constituting a composite beam pattern, two power models are considered: 1) equal power split (EQP), and 2) asymmetric power split (ASP). This paper revisits the key challenge-beam partitioning in the beamforming-multicast problem-and makes significant progress in both algorithmic and analytic aspects of the problem. Under EQP, we propose a low-complexity optimal algorithm based on dynamic programming. Under ASP, we prove that it is NP-hard to have (3/2-ϵ)-approximation algorithm for any ϵ >; 0. For discrete rate functions under ASP, we develop an Asymptotic Polynomial-Time Approximation Scheme (APTAS), an asymptotic (3/2+ β)-approximation solution (where β ≥ 0 depends on the wireless technology), and an asymptotic 2-approximation solution to the problem by relating the problem to a generalized version of the bin-packing problem. In retrospect, we also obtain an asymptotic 2-approximation solution for the generalized bin-packing problem, which is of independent interest. For continuous rate functions under ASP, we develop sufficient conditions under which the optimal number of composite beams is 1,K, and arbitrary, respectively, whereKis the total number of single-lobe beams. Both experimental results and simulations based on real-world channel measurements corroborate our analytical results by showing significant improvement compared to state-of-the-art algorithms.

Recent works have considered the feasibility of full duplex (FD) wireless communications in practice. While the first FD system by Choi et.al. relied on a specific antenna cancellation technique to achieve a significant portion of self-interference cancellation, the various limitations of this technique prompted latter works to move away from antenna cancellation and rely on analog cancellation achieved through channel estimation. However, the latter systems in turn require the use of variable attenuator and delay elements that need to be automatically tuned to compensate for the self-interference channel. This not only adds complexity to the overall system but also makes the performance sensitive to wide-band channels. More importantly, none of the existing FD schemes can be readily scaled to MIMO systems.

Using beamforming antennas to improve wireless multicast transmissions has received considerable attention recently. The work in proposes to partition all single-lobe beams into groups and to form composite multi-lobe beam patterns to transmit multicast traffic. The key challenge left is, how to partition the beams into multiple groups in order to minimize the total multicast delay. This work makes significant progress in both algorithmic and analytic aspects of the problem. We prove that, under the asymmetric power split (ASP) model, it is NP-hard to have (3/2 - ε)-approximation algorithm for any ε >; 0. We then develop an APTAS, an asymptotic (3/2 + β)-approximation solution (where β ≥ 0 depends on the wireless technology), and an asymptotic 2-approximation solution to the problem by relating the problem to a generalized version of the bin-packing problem. Extensive trace-driven simulations based on real-world channel measurements corroborate our analytical results by showing significant improvement compared to state of the art algorithms.

The demand for increased spectral efficiencies is driving the next generation broadband access networks towards deploying smaller cells (femtocells) with sophisticated air interface technologies (Orthogonal Frequency Division Multiple Access or OFDMA). The projected dense deployment of femtocells however, makes interference and hence resource management both critical and extremely challenging. In this paper, we design and implement one of the first resource management systems, FERMI, for OFDMA-based femtocell networks. As part of its design, FERMI (i) provides resource isolation in the frequency domain (as opposed to time) to leverage power pooling across cells to improve capacity; (ii) uses measurement-driven triggers to intelligently distinguish clients that require just link adaptation from those that require resource isolation; (iii) incorporates mechanisms that enable the joint scheduling of both types of clients in the same frame; and (iv) employs efficient, scalable algorithms to determine a fair resource allocation across the entire network with high utilization and low overhead. We implement FERMI on a prototype four-cell WiMAX femtocell testbed and show that it yields significant gains over conventional approaches.

The demand for real-time video services coupled with the need for increased spectral efficiencies has brought a lot of attention to video multicasting in next generation OFDMA systems. Layered video provides an efficient solution to address the channel quality variation across users. However, the frequency selectivity across subchannels introduced by OFDMA serves as a drawback for multicast scheduling. This significantly diminishes the benefits of video layering and can potentially degrade the system performance to worse than that without channel diversity.

The proliferation of mobile applications and services involving group communications has increased the need for efficient wireless multicasting solutions in next generation broadband access networks. The availability of antenna arrays at the base stations makes adaptive beamforming especially attractive for multicasting; with beamforming, the received signal strength at the clients can be significantly improved. However, the problem of designing optimal transmit beamformers for multicast applications is challenging, mainly due to its non-convex nature. In this work, we design efficient transmit beamformers for wireless link layer multicasting where instantaneous channel state information is available at the transmitter. In addressing the non-convexity of the problem, we design non-iterative, near-optimal beamforming algorithms based on the optimality conditions derived from its Lagrangian formulation. We show that for real channels, e.g, where Pulse Amplitude Modulation (PAM) is used, the optimal solution can be obtained. To address the effectiveness of the proposed solution for the complex channel, we also derive an upper bound on the multicast rate based on the dual formulation of the problem. Evaluations reveal that our algorithms deliver a performance that is close to the upper bound, while significantly improving the multicast rate over state of the art solutions.

The use of smart antennas in multihop wireless networks has garnered significant attention over the last few years. Given the unique capabilities of smart antennas, and how they can improve performance in a typically constrained multihop wireless network (MWN) environment, the attention is with merit. However, not much light has been shed on MWNs that have nodes with varying antenna capabilities. While homogeneous MWNs with all nodes having the same antenna capabilities will have certain applications, we argue that MWNs with nodes having heterogeneous antenna capabilities are more likely to be the norm due to a variety of motivating factors. In the context of such heterogeneous smart antenna networks (HSANs), we investigate and motivate the need for a simple form of node cooperation called retransmit diversity. We show that while such a simple form of node cooperation cannot bring significant benefits to homogeneous omni-directional antenna networks, they can bring great gains (several folds improvement) to heterogeneous smart antenna networks. We then present several key properties pertaining to node cooperation in HSANs along with analytical reasoning. In the process, we identify a fundamental trade-off between exploiting smart antenna gain and cooperation gain, that undermines the ability of HSANs to leverage node cooperation to their maximum potential. To address this trade-off, we then present an adaptive cooperation mechanism and incorporate this mechanism through the design of a simple but efficient MAC protocol. The performance of the MAC protocol is evaluated through ns2 simulations along with a realistic physical layer channel model for smart antenna environments.

With the next generation cellular networks making a transition towards smaller cells, two-hop OFDMA relay networks have become a dominant, mandatory component in the 4G standards (WiMAX 802.16j, 3GPP LTE-Adv). While unicast flows have received reasonable attention in two-hop OFDMA relay networks, not much light has been shed on the design of efficient scheduling algorithms for multicast flows, thereby forming the focus of this work. We show that while relay cooperation is critical for improving multicast performance, it must be carefully balanced with the ability to multiplex multicast sessions and hence maximize aggregate multicast flow. To this end, we highlight strategies that carefully group relays for cooperation to achieve this balance. We then solve the multicast scheduling problem under two OFDMA sub-channelization models. We establish the NP-hardness of the scheduling problem even for the simpler model and provide efficient algorithms with approximation guarantees under both models. Evaluation of the proposed solutions reveals the efficiency of the scheduling algorithms as well as the significant benefits obtained from the multicasting strategy.

WLANs have become an important last-mile technology for providing internet access within homes and enterprises. In such indoor deployments, the wireless channel suffers from significant multipath scattering and fading that degrades performance. Beamforming is a smart antenna technology that adjusts the transmissions at the transmitter to reenforce the signals received through multiple paths at the receiver.

Several research works have argued that adaptive beamforming has the potential to realize the high spectral efficiency requirements of next-generation wireless standards, and is especially well-suited for multipath-rich environments such as indoors. Most works have been limited to theory; few works in literature address the practical benefits and realizability of adaptive beamforming. In this paper, we design and implement the first indoor WLAN beamforming system with multi-element array antennas and software radio platforms, that forms a testbed for exploration of practical benefits of beamforming, and evaluation of algorithms for efficient beamforming in diverse environments. In the process of building the system, we identify and address several challenges with practical beamforming that are often ignored in theoretical works. Most importantly, channel estimation for forming the best beam to a user is hindered by oscillator drifts on the transmitter and receiver side that introduce hard-to-isolate phase and frequency offsets from the estimated channel coefficients. We describe these issues and incorporate novel solutions in our system to address them without requiring hardware modifications. We use the system to demonstrate the realizable benefits of adaptive beamforming in a typical indoor office environment.

We consider the problem of scheduling users with backlogged and finite buffers on the multiple OFDM carriers (channels) over the two hops of the relay-enabled wireless network. Motivated by the recent 802.16j standard, we consider two sub-carrier grouping models (PUSC, AMC). While the extent of diversity gain varies depending on the model, spatial reuse is common to both the models and is crucial in delivering the promised throughput benefits of relays. Hence leveraging spatial reuse forms the prime focus of this work with diversity gains being leveraged when available. We establish the hardness of the problem under these two models and propose efficient approximation algorithms for the scheduling problem considered. Our solutions are simple to implement at the base station, while also providing worst case guarantees. The proposed solutions are evaluated to highlight their benefits in a variety of network conditions.

We consider the problem of efficient link-layer multicasting in wireless networks with switched beamforming antennas. The inherent tradeoff between multicasting and beamforming -- the broadcast advantage of low-gain omni-directional transmissions versus the high-gain but spatially restricted transmissions of beamforming antennas -- makes this problem especially challenging.

Femto cells are a cost-effective means of providing ubiquitous connectivity in future broadband wireless networks. While their primary purpose has been to improve coverage in current solutions, their decreased cell sizes in turn also provide improved cell capacity through increased spatial reuse. The demand for bandwidth-intensive IP services will soon necessitate the need to tap into this improved capacity. However, the lack of direct coordination between the macro and femto cells, and the completely distributed nature of femto cells make this an extremely challenging task. In this work, we address this challenge by providing efficient resource management solutions for OFDMA-based femto cells along with performance guarantees. In the process, we consider two models that tradeoff performance and overhead. We also propose a novel location-based resource management solution for leveraging maximal spatial reuse from femto cells. Our comprehensive evaluations indicate that in addition to providing improved coverage indoors, with carefully designed resource management solutions that leverage spatial reuse, femto cells have a great potential to increase the system performance by two folds.

We design, implement, and evaluate a vehicular communication system that improves uplink connectivity through multi-lobe beam pattern switching on a smart antenna. Directionality and base station-diversity are two well-known, independently developed mechanisms for improving the uplink connectivity of mobile clients. In this paper, we highlight that a system combining both mechanisms can achieve significant improvement in performance with multi-lobe beams that strike a tradeoff between directionality and diversity. This is in contrast to the mere steering of narrow beams used in conventional smart antenna systems. For tractability at vehicular speeds, our R2D2 system searches through a limited set of beam patterns with different numbers of lobes, and includes a two-stage algorithm that uses both runtime adaptation and cached candidate patterns. We design and evaluate several variants of run-time adaptation that tune the number and angle of lobes in the beam, and the bit rate. The design of these algorithms is guided by both analysis and real-world measurements with a smart antenna system mounted on a vehicle. These measurements with our prototype implementation show that R2D2 can achieve an uplink throughput increase of up to 154% over pure beamsteering and 45% over pure basestation diversity.

Relay-enabled wireless networks (eg. WIMAX 802.16j) coupled with orthogonal frequency division multiple access (OFDMA) as the air interface technology represent an emerging trend in future wireless infrastructure deployments. While the flexibility of OFDMA allows accommodation of heterogeneous (unicast and multicast) traffic, its combination with relays opens up a multitude of diversity and spatial reuse gains. However, leveraging these benefits to efficiently handle heterogeneous traffic calls for more sophisticated solutions, among which, user scheduling forms a key component. We ask the following specific question in this work: Can unicast and multicast traffic efficiently coexist in an OFDMA-based wireless relay network? We address the question by first identifying the key challenges that make coexistence difficult in the target environment. We show that scheduling of unicast and multicast traffic is tightly coupled even if they are isolated through orthogonal spectral allocations. We also show that the nature of gains that optimize the individual traffic are complementary and hence reveal a fundamental tradeoff in jointly optimizing the system for both unicast and multicast traffic in tandem. Using the insights gained, we propose an integrated scheduling strategy that strikes a good balance in delivering efficient performance to both the unicast and multicast flows. The proposed solution yields gains of over 60% over individual traffic specific strategies.

In this paper, we focus on securing communication over wireless data networks from malicious eavesdroppers, using smart antennas. While conventional cryptography based approaches focus on hiding the meaning of the information being communicated from the eavesdropper, we consider a complimentary class of strategies that limit knowledge of the existence of the information from the eavesdropper. We profile the performance achievable using simple beamforming strategies using a newly defined metric called exposure region. We then present three strategies within the context of an approach called virtual arrays of physical arrays to significantly improve the exposure region performance of a wireless LAN environment. Using simulations and analysis, we validate and evaluate the proposed strategies.

Relay-enabled wireless networks (eg. WIMAX 802.16j) represent an emerging trend for the incorporation of multi-hop networking solutions for last-mile broadband access in next generation wireless networks. The adoption of more sophisticated access technologies such as OFDM (orthogonal frequency division multiplexing) coupled with the relay-induced two-hop nature, provides two key benefits to these networks in the form of diversity and spatial reuse gains. However, leveraging these benefits calls for more sophisticated solutions, among which, user scheduling forms a key component.

The ability of smart antennas to improve performance in a typically constrained ad-hoc network environment, has helped them garner significant attention over the last few years. However, not much light has been shed on wireless ad-hoc networks that have nodes with varying antenna capabilities. While homogeneous ad-hoc networks with all nodes having the same antenna capabilities will have certain applications, we argue that ad-hoc networks with nodes having heterogeneous antenna capabilities are more likely to be the norm due to a variety of motivating factors. In the context of such heterogeneous smart antenna networks (HSANs), we investigate and motivate the need for a simple form of node cooperation called retransmit diversity. We show that while such a simple form of node cooperation cannot bring significant benefits to homogeneous omni-directional and smart antenna networks, they can bring several folds improvement to heterogeneous smart antenna networks. We then present several key properties pertaining to node cooperation in HSANs. In the process, we identify a fundamental trade-off between exploiting smart antenna gain and cooperation gain, that undermines the ability of HSANs to leverage node cooperation to their maximum potential. To address this tradeoff, we then present an adaptive cooperation mechanism and incorporate this mechanism through the design of a simple but efficient MAC protocol. The performance of the MAC protocol is evaluated throughns2 simulations.

Smart antennas include a broad range of antenna technologies ranging from the simple switched beam to the more sophisticated adaptive arrays and multiple input multiple output (MIMO) links. Their ability to exploit multiple degrees of freedom helps them operate in different strategies to achieve different objectives ranging from rate increase, range increase, transmission power reduction, and higher link reliability. While these antennas have been significantly researched at the PHY layer leading to results that are easily translatable to single-hop wireless networks, very little is understood about their use in multi-hop wireless ad-hoc networks. Specific unanswered questions in this context include: (i) what kind of performance improvements can the different technologies and their strategies provide? (ii) for each technology, which of the different possible strategies is the optimal strategy to employ for a given network condition? and (iii) for a given network setting, which of the different smart antenna technologies will deliver the best performance? In this paper, we systematically answer these questions by comprehensively evaluating the relative benefits of the different smart antenna technologies.

The low price of commodity wireless LAN cards and access points (APs) has resulted in the rich proliferation of high density WLANs in enterprise, academic environments, and public spaces. In such environments wireless clients have a variety of affiliation options that ultimately determine the quality of service they receive from the network. The state of the art mechanism behind such a decision typically relies on received signal strength, associating clients to that access point (AP) in their neighborhood that features the strongest signal. More intelligent algorithms have been further proposed in the literature. In this work we take a step back and look into the fundamental metrics that determine end user throughput in 802.11 wireless networks. We identify three such metrics pertaining to wireless channel quality, AP capacity in the presence of interference, and client contention. We modify the low level software functionality (firmware and microcode) of a commercial wireless adaptor to measure the necessary quantities. We then test, in a real testbed, the ability of each metric to capture end user throughput through a range of diverse network conditions. Our experimental results indicate that user affiliation decisions should be based on metrics that do not only reflect physical layer performance, or network occupancy, but also concretely capture MAC layer behavior. Based on the acquired insight, we propose a new metric that is shown to be highly accurate across all tested network scenarios.

Smart antennas have gained significant importance in multi-hop wireless networks in recent years, because of their sophisticated signal processing capabilities that hold the potential for increased data rates and reliability. In this work, we consider the problem of communication in multi-hop wireless networks with smart antennas (specifically digital adaptive arrays). These smart antennas provide degrees of freedom (DOFs) that can be used to suppress co-existing communication links, thereby increasing spatial reuse in the network. Thus, the communication problem comprises of not just determining a channel access mechanism to be used by the communication links, but also involves the determination of the communication pattern (usage of DOFs) to be used by each node during channel access. To the best of our knowledge, our work is the first step towards addressing this problem.We first consider the problem of determining the communication pattern to be used by the nodes and formulate it combinatorially with the goal of optimizing network performance through interference minimization. We present efficient centralized and distributed algorithms that are within a factor of ¾ and ½ of the optimum solution respectively. We then extend the distributed algorithm to incorporate TDMA-based scheduling in a purely localized manner. The distributed algorithms are then evaluated through simulations in ns2 and insights are drawn into the potential performance benefits of smart antennas in multi-hop wireless networks.

Abstract The use of smart antennas in wireless ad hoc networks has garnered attention over the last few years. Given the unique capabilities of smart antennas, and how they can improve performance in a typically constrained ad hoc network environment, the attention is with merit. However, not much light has been shed on wireless ad hoc networks that have nodes with varying antenna capabilities. While homogeneous ad hoc networks with all nodes having the same antenna capabilities will have certain applications, we argue that ad hoc networks with nodes having heterogeneous antenna capabilities are more likely to be the norm due to a variety of motivating factors. In this context, we answer two fundamental questions in this paper: (i)what is the theoretical capacity of an ad hoc network when its nodes have varying antenna capabilities? We consider nodes equipped with only omnidirectional antennas to those that are equipped with more sophisticated adaptive‐array and MIMO capable antennas. Perhaps more interestingly, we provide insights into what percentage of nodes in an ad hoc network have to be ‘smart’ to get a certain overall network capacity. (ii)What medium access control (MAC) and routing protocols should nodes use in such a heterogeneous network? We present MAC and routing protocols for ad hoc networks with heterogeneous antenna capabilities that allow nodes to interact with each othereffectivelydespite their heterogeneity, and in the process deliver considerably improved performance over simplistic strategies. We substantiate our theoretical results, and evaluate the proposed protocols using ns2‐based network simulations. Copyright © 2006 John Wiley & Sons, Ltd.

Smart antennas include a broad variety of antenna technologies ranging from the simple switched beams to the sophisticated digital adaptive arrays. While beam-forming antennas are good candidates for use in strong line of sight (LOS) environments, it is the multiple input multiple output (MIMO) technology that is best suited for multipath environments. In fact, the MIMO links exploit the multipath induced rich scattering to provide high spectral efficiencies. The focus of this work is to identify the various characteristics and tradeoffs of MIMO links that can be leveraged by routing layer protocols in rich multipath environments to improve their performance. To this end, we propose a routing protocol called MIR for ad-hoc networks with MIMO links, that leverages the various characteristics of MIMO links in its mechanisms to improve the network performance. We show the effectiveness of the proposed protocol by evaluating its performance through ns2 simulations for a variety of network conditions.

The communication model typically assumed in wireless ad-hoc networks is based on a traditional "pipelined relay" (PR} strategy. In PR, an end-to-end flow has multiple outstanding packets (or data units) along the path from the source to the destination. In this paper, we argue that due to several unique properties of wireless ad-hoc networks, PR can be fundamentally improved upon. We present a new non-pipelined relay (nPR) strategy, where end-to-end flows have exactly one outstanding packet (or data unit) along the end-to-end path. We show that nPR has the following properties: (i) under idealized network conditions, it provides performance improvement, in terms of end-to-end throughput capacity and network transport capacity over PR, and achieves proportional fairness; and (ii) under practical network conditions, it further increases the above performance improvements, both in terms of the throughput achieved, and in terms of the fairness between flows. Finally, we present a forwarding protocol that practically realizes nPR. Through analysis and ns2 based packet level simulations, we evaluate the performance of the proposed strategy, and that of the forwarding protocol.

Abstract — Correlation of data sent by different sensors in a wireless sensor network can be exploited during the data gathering process to improve energy efficiency. In this paper, we study the energy efficiency of correlation aware data aggregation trees under various sensor network conditions and the tradeoffs involved in using them. The following two related questions are specifically investigated in the study: (i) Is there any practical limit on the achievable improvement in energy efficiency in adopting a correlation aware aggregation structure as opposed to a correlation unaware structure? (ii) Is there a practical maximum useable delay bound that can deliver the maximum achievable improvement? In answering the above questions, we present comprehensive simulation results and draw inferences based on the results. We also conclude two rather surprising results that the energy improvement in using correlation aware aggregation is not significant under many network scenarios, and the maximum useable delay bound is not large compared with the delay along the maximum length shortest-path in the default shortest path tree. I.

Existing works have approached the problem of reliable transport in ad-hoc networks by proposing mechanisms to improve TCP's performance over such networks. In this paper we show through detailed arguments and simulations that several of the design elements in TCP are fundamentally inappropriate for the unique characteristics of ad-hoc networks. Given that ad-hoc networks are typically stand-alone, we approach the problem of reliable transport from the perspective that it is justifiable to develop an entirely new transport protocol that is not a variant of TCP. Toward this end, we present a new reliable transport layer protocol for ad-hoc networks called ATP (ad-hoc transport protocol). We show through ns2 based simulations that ATP outperforms both default TCP and TCP-ELFN.

We present a new medium access control (MAC) protocol for ad-hoc networks with multiple input multiple output (MIMO) links. Links that use multiple element arrays (MEAs) at both ends are referred to as MIMO links. MIMO links are known to provide extremely high spectral efficiencies in multipath channels by simultaneously transmitting multiple independent data streams in the same channel. MAC protocols have been proposed in related work for ad-hoc networks with other classes of smart antennas such as switched beam antennas. However, as we substantiate in the paper, the unique characteristics of MIMO links necessitate an entirely new MAC protocol. We identify several advantages of MIMO links, and discuss key optimization considerations that can help in realizing an effective MAC protocol for such an environment. We present a centralized algorithm that has the optimization considerations incorporated in its design. Finally, we present a distributed protocol that approximates the centralized algorithm, and compare its performance against that of baseline protocols that are variants of the CSMA/CA protocol.

Smart antennas represent a broad variety of antennas that differ in their performance and transceiver complexity. The superior capabilities of smart antennas, however, can be leveraged only through appropriately designed higher layer network protocols, including at the medium access control (MAC) layer. Although several related works have considered such tailored protocols, they do so in the context of specific antenna technologies. In this paper, we explore the possibility for a unified approach to medium access control in ad-hoc networks with smart antennas. We first present a unified representation of the PHY layer capabilities of the different types of smart antennas, and their relevance to MAC layer design. We then define a unified MAC problem formulation, and derive unified MAC algorithms from the formulation. Finally, using the algorithms developed, we investigate the relative performance trade-offs of the different technologies under varying network conditions.

we present a medium access control (MAC) protocol for ad hoc networks with multiple input multiple output (MIMO) links. MIMO links provide extremely high spectral efficiencies in multipath channels by simultaneously transmitting multiple independent data streams in the same channel. MAC protocols have been proposed in related work for ad hoc networks with other classes of smart antennas such as switched beam antennas. However, as we substantiate in the paper, the unique characteristics of MIMO links coupled with several key optimization considerations, necessitate an entirely new MAC protocol. We identify several advantages of MIMO links, and discuss key optimization considerations that can help in realizing an effective MAC protocol for such an environment. We present a centralized algorithm called stream-controlled medium access (SCMA) that has the key optimization considerations incorporated in its design. Finally, we present a distributed SCMA protocol that approximates the centralized algorithm and compare its performance against that of baseline protocols that are CSMA/CA variants.

Abstract In this paper, we study the performance of the transmission control protocol (TCP) over mobile ad‐hoc networks. We present a comprehensive set of simulation results and identify the key factors that impact TCP's performance over ad‐hoc networks. We use a variety of parameters including link failure detection latency, route computation latency, packet level route unavailability index, and flow level route unavailability index to capture the impact of mobility. We relate the impact of mobility on the different parameters to TCP's performance by studying the throughput, loss‐rate and retransmission timeout values at the TCP layer. We conclude from our results that existing approaches to improve TCP performance over mobile ad‐hoc networks have identified and hence focused only on a subset of the affecting factors. In the process, we identify a comprehensive set of factors influencing TCP performance. Finally, using the insights gained through the performance evaluations, we propose a framework calledAtraconsisting of three simple and easily implementable mechanisms at the MAC and routing layers to improve TCP's performance over ad‐hoc networks. We demonstrate thatAtraimproves on the throughput performance of a default protocol stack by 50%–100%. Copyright © 2004 John Wiley & Sons, Ltd.

Existing works have approached the problem of reliable transport in ad-hoc networks by proposing mechanisms to improve TCP's performance over such networks. In this paper we show through detailed arguments and simulations that several of the design elements in TCP are fundamentally inappropriate for the unique characteristics of ad-hoc networks. Given that ad hoc networks are typically stand-alone, we approach the problem of reliable transport from the perspective that it is justifiable to develop an entirely new transport protocol that is not a variant of TCP. Towards this end, we present a new reliable transport layer protocol for ad-hoc networks called ATP (ad-hoc transport protocol). We show through ns2 based simulations that ATP outperforms both default TCP and TCP-ELFN.