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.

Network slicing plays a crucial role in realizing 5G/6G advances, enabling diverse Service Level Agreement (SLA) requirements related to latency, throughput, and reliability. Since network slices are deployed end-to-end (E2E), across multiple domains including access, transport, and core networks, it is essential to efficiently decompose an E2E SLA into domain-level targets, so that each domain can provision adequate resources for the slice. However, decomposing SLAs is highly challenging due to the heterogeneity of domains, dynamic network conditions, and the fact that the SLA orchestrator is oblivious to the domain's resource optimization. In this work, we propose Odin, a Bayesian Optimization-based solution that leverages each domain's online feedback for provably-efficient SLA decomposition. Through theoretical analyses and rigorous evaluations, we demonstrate that Odin's E2E orchestrator can achieve up to 45% performance improvement in SLA satisfaction when compared with baseline solutions whilst reducing overall resource costs even in the presence of noisy feedback from the individual domains.

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.

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.

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.

The knowledge of link packet loss rates (PLRs) at different PHY layer configurations is vital for a number of wireless network optimization schemes. However, the very large number of PHY layer configurations offered by modern 802.11 n/ac networks has made probing-based PLR estimation at each available configuration extremely challenging. In this paper, we seek to answer the question “How to estimate the PLRs at each available PHY layer configuration with minimal overhead?” Our analysis of the PLR datasets collected from three 802.11 n/ac testbeds reveals that, for any given link, there are several configurations with similar PLR. However, capturing this similarity using well-known link quality indicators like RSSI, or PHY layer features such as MCS or number of MIMO streams is hard. Consequently, we explore the approach of clustering the available PHY layer configurations into a small number of clusters with similar PLR, independent of any other parameter, and only probe one representative configuration in each cluster. Using two real-world case studies - rate adaptation and multihop routing, we show that the proposed clustering-based PLR estimation helps network optimization schemes to reach optimal configurations faster leading to significant performance improvements.

We conduct an extensive experimental evaluation of a class of WiFi active power/energy consumption models for smartphones that are based on parameters readily available to the upper layers of the protocol stack. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. We study the properties of a previously proposed throughput-based model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios that have not been considered in previous studies. Our results show that the model works well in a number of scenarios, with both 802.11nand 802.11ac-equipped smartphones, and its accuracy can be largely improved with the knowledge of transport layer protocol and packet size. However, such knowledge makes the model more complex and results in largely reduced accuracy in high throughput settings or on hardware different from the one that was used for training. We further discuss a few practical issues related to the measurement and modeling methodology.

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.

This paper presents the first, to the best of our knowledge, detailed experimental study of 802.11n/ac throughput and power consumption in modern smartphones. We experiment with a variety of smartphones, supporting different subsets of 802.11n/ac features. We investigate the power consumption in various states of the wireless interface (sleep, idle, active), the impact of various features of 802.11n/ac (PHY bitrate, frame aggregation, channel bonding, MIMO) on both throughput and power consumption, and the tradeoffs between these two metrics. Some of our findings are significantly different from the findings of previous studies using 802.11n/ac wireless cards for laptop/desktop computers. We believe that these findings will help in understanding various performance and power consumption issues in today's smartphones and will guide the design of power optimization algorithms for the next generation of mobile devices.

We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.

In the recent past, link quality-based routing metrics, such as ETX and ETT, have been shown to significantly outperform the traditional hopcount metric in legacy 802.11a/b/g wireless mesh networks (WMNs). The new 802.11n standard introduces a number of enhancements at the MAC/PHY layers (MIMO technology, channel bonding, frame aggregation, and more aggressive modulation and coding schemes) marking the beginning of a new generation of 802.11 radios. In this paper, we conduct one of the first experimental studies of the performance of link quality-based routing metrics in a 21-node indoor 802.11n WMN. Our study reveals that the gains of link quality-based metrics over the hopcount metric in legacy 802.11 WMNs do not carry over in 802.11n MIMO WMNs. We analyze the causes of this behavior and make recommendations for the design of new routing metrics in 802.11n WMNs.

We conduct the first experimental study of the performance of link quality-based routing metrics in an 802.11n wireless mesh network (WMN). Link quality-based metrics have been shown to significantly outperform the traditional hopcount metric but they have only been evaluated over legacy 802.11a/b/g radios. The new 802.11n standard introduces a number of enhancements at the MAC and PHY layers (MIMO technology, channel bonding, frame aggregation, short guard interval, and more aggressive modulation and coding schemes) marking the beginning of a new generation of 802.11 radios. Our study in a 21-node indoor 802.11n WMN testbed reveals that the gains of link quality-based metrics over the hopcount metric in legacy 802.11 WMNs do not carry over in 802.11n MIMO WMNs. We analyze the causes of this behavior and make recommendations for the design of new routing metrics in 802.11n WMNs.