Delay-based protocols rely on end-to-end delay measurements to detect network congestion. However, in cellular networks, Radio Access Network (RAN) buffers introduce significant delays unrelated to congestion, fundamentally challenging these protocols’ assumptions. We identify two major types of RAN buffers - retransmission buffers and uplink scheduling buffers - that can introduce delays comparable to congestion-induced delays, severely degrading protocol performance. We present CellNinjia, a software-based system providing real-time visibility into RAN operations, and Gandalf, which leverages this visibility to systematically handle RAN-induced delays. Unlike existing approaches that treat these delays as random noise, Gandalf identifies specific RAN operations and compensates for their effects. Our evaluation in commercial 4G LTE and 5G networks shows that Gandalf enables substantial performance improvements - up to 7.49 × for Copa and 9.53 × for PCC Vivace - without modifying the protocols’ core algorithms, demonstrating that delay-based protocols can realize their full potential in cellular networks.

Current end-to-end congestion-control feedback is too slow to track rapid wireless dynamics in cellular networks. We identify Grant-to-Buffer Ratio (GBR)—the ratio of base-station uplink grants to mobile-reported demand—as a millisecond-scale RAN signal of uplink resource scarcity. Measurements across AT&T, Verizon, and T-Mobile LTE/5G FDD/TDD deployments show that GBR tracks base-station uplink load and reveals congestion earlier than end-to-end feedback. Because GBR is derived from the mandatory BSR-grant exchange, it requires no base-station changes and captures scheduler decisions at their native timescale. We then design GBR-CC, a dual-loop controller that updates the sender rate on each GBR sample, using GBR for fast adaptation and end-to-end delay trends as a conservative fallback. This design lets the sender react before queues inflate while still handling non-radio bottlenecks through the outer loop. GBR-CC runs on commodity mobile devices without extra hardware or external tools. Experiments on commercial cellular networks show that GBR-CC improves average throughput over GCC by 50%, while reducing median playout latency by 32–53% and freeze rate by 60%; compared with BBR, it improves average throughput by 5% and halves median RTT.

The real-time knowledge of mmWave device orientation offers dual benefits for wireless networks and Internet of Things (IoT) applications: it enhances communication and localization performance through link diagnosis, and it enables context inference with asset and wearable tracking. This paper presents MetaPol, a novel system architecture that augments commodity mmWave access points (APs) with a carefully designed ultra-low-cost anisotropic metasurface to non-invasively extract the orientation of client devices, without hindering data reception or modifying hardware. MetaPol leverages the polarization of transmitted electromagnetic (EM) waves as an accurate indicator of the orientation of linearly polarized antenna arrays, prevalent in commercial mmWave mobile and IoT devices. Yet, polarization sensing is rarely supported by commodity APs due to the need for two orthogonally polarized antenna arrays to capture the incident electric field. Instead, MetaPol creates virtual polarization channels to characterize impinging electric fields, through the conversions of wave polarization on the fly as it interacts with the metasurface. To design MetaPol, we model and exploit the properties of a unique anisotropic metamaterial based on C-shaped split-ring resonators. We discover that, when grouped in certain configurations, these meta-elements can convert the incident polarization in a deterministic way. We show that three polarization channels are sufficient for unambiguous orientation sensing, and we provide a corresponding three-shot non-coherent protocol that extracts user orientation by comparing the power received from distinct surface areas. Through extensive over-the-air experiments with more than 1000 measurements in the mmWave band, we demonstrate that MetaPol achieves a mean error of 2.6° across practical settings with negligible compromise to the underlying data communication link.

5G wireless networks leverage complex scheduling, retransmission, and adaptation mechanisms to maximize their efficiency. These mechanisms interact to produce significant fluctuations in uplink and downlink capacity and latency, markedly impacting the the performance of real-time communication and multimedia applications, such as video conferencing. These applications are particularly sensitive to such fluctuations, resulting in lag, stuttering, distorted audio, and low video quality. In this paper, we present a cross-layer view of 5G networks and their impact on and interaction with video-conferencing applications. We conduct novel, detailed measurements of both private CBRS and commercial carrier cellular network dynamics, capturing physical- and link-layer events and correlating them with their effects at the network and transport layers, and the video-conferencing application itself. Our two datasets comprise days of low-rate campus-wide Zoom telemetry data, and hours of high-rate, correlated WebRTC-network-5G telemetry data. Based on these data, we trace performance anomalies back to root causes, identifying 24 previously unknown causal event chains that degrade 5G video conferencing. Armed with this knowledge, we build Domino, a tool that automates this process and is user-extensible to future wireless networks and interactive applications.

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity is to enable large MIMO systems and thus improve wireless performance. The approach aims to leverage QA to expedite the computation required for theoretically optimal but computationally-demanding Maximum Likelihood detection to overcome the limitations of the currently deployed linear detectors. This paper presents X-ResQ, a QA-based MIMO detector system featuring flexible parallelism that is uniquely enabled by quantum Reverse Annealing (RA). Unlike prior designs, X-ResQ has many desirable parallel QA system properties and has effectively improved detection performance as more qubits are assigned. In our evaluations on a state-of-the-art quantum annealer, fully parallel X-ResQ achieves near-optimal throughput for 4 × 6 MIMO with 16-QAM using approx. 240 qubits achieving 2.5–5× gains compared against other classical and quantum detectors. We also implement and evaluate X-ResQ in the non-quantum digital setting for more comprehensive evaluations. This classical X-ResQ showcases the potential to realize ultra-large 1024 × 1024 MIMO, significantly outperforming other MIMO detectors, including the state-of-the-art RA detector classically implemented in the same way.

Whilesmalldrone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present${\sf Mighty}$, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video compression and transmission to a ground controller.${\sf Mighty}$innovates a high performance co-design among:(1)a ring oscillator-based, ultra-low power backscatter radio;(2)a spectrally-efficient, non-linear, low-power physical layer modulation and multi-chain radio architecture; and(3)a lightweight video compression codec-bypassing software design. Our co-design exploits synergies among these components, resulting in joint throughput and range performance that pushes the known envelope. We prototype${\sf Mighty}$on PCB board and conduct extensive field studies both indoors and outdoors. The power efficiency of${\sf Mighty}$is about 16.6 nJ/bit. A head-to-head comparison with aDJI Mini2drone's default video streaming system shows that${\sf Mighty}$achieves similar throughput at a drone-to-controller distance of up to 150 meters, with 34–55× improvement of power efficiency than WiFi-based video streaming solutions.

Tomorrow’s massive-scale Internet-of-Things (IoT) sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We proposeCross-Link Channel Prediction(CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP’s design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users. Moreover, we measure its performance with four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2x throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

Rapid delay variations in today's access networks impair the QoE of low-latency, interactive applications, such as video conferencing. To tackle this problem, we propose Athena, a framework that correlates high-resolution measurements from Layer 1 to Layer 7 to remove the fog from the window through which today's video-conferencing congestion-control algorithms see the network. This cross-layer view of the network empowers the networking community to revisit and re-evaluate their network designs and application scheduling and rate-adaptation algorithms in light of the complex, heterogeneous networks that are in use today, paving the way for network-aware applications and application-aware networks.

We present Wall-Street, a smart surface designed for vehicles to boost 5G mmWave connectivity for passengers. It improves mmWave connections in three ways: (1) it steers outdoor mmWave signals into the vehicle, ensuring all users have coverage; (2) it enables the vehicle to receive data from the current cell while measuring signals from potential handover cells, allowing smooth transitions without interrupting service; (3) during handovers, it combines/splits signals from/to both the current and new cells, creating a make-before-break connection. Our demonstration shows Wall-Street's versatile signal manipulation abilities. These include steering single beams, simultaneously reflecting and transmitting beams for neighboring cell measurements with concurrent communication, and combining or splitting beams for seamless cell transitions.

Physics-inspired computation and Ising machines have grown as a new alternative to conventional algorithms and have shown promising performance for several NP-Hard problems. However, the existing state-of-the-art focuses on empirical performance gains and either ignores the practical real-time constraints or makes strong assumptions about practical deployments. In this work, we utilize the SIMD capabilities of Intel Xeon CPU to implement an Ising solver for MIMO detection which meets the real-time processing and timing constraints of an LTE/5G system. We further evaluate the end-to-end performance of our proposed MIMO solver via trace-driven simulations with a hybrid MATLAB/NS3 simulator.

5G New Radio cellular networks are designed to provide high Quality of Service for application on wirelessly connected devices. However, changing conditions of the wireless last hop can degrade application performance, and the applications have no visibility into the 5G Radio Access Network (RAN). Most 5G network operators run closed networks, limiting the potential for co-design with the wider-area internet and user applications. This paper demonstrates NR-Scope, a passive, incrementally-deployable, and independently-deployable Standalone 5G network telemetry system that can passively measure fine-grained RAN capacity, latency, and retransmission information. Application servers can take advantage of the measurements to achieve better millisecond scale, application-level decisions on offered load and bit rate adaptation than end-to-end latency measurements or end-to-end packet losses currently permit. We demonstrate the performance of NR-Scope by decoding the downlink control information (DCI) for downlink and uplink traffic of a 5G Standalone base station in real-time.

Congestion-control algorithms for video-conferencing applications work well in wired networks but are fragile in cellular networks due to high delay variations and variable capacity in these networks. This paper investigates the causes of delay variations in cellular networks using a cross-layer approach. By measuring a WebRTC application over LTE at both the physical and network layers, we identify the effects of such delay inflation caused by physical-layer resource scheduling and link-layer retransmissions.

Reconfigurable antenna multiple-input multiple-output (MIMO) is a foundational technology for the continuing evolution of cellular systems, including upcoming 6G communication systems. In this paper, we address the problem of flexible/reconfigurable antenna configuration selection for point-to-point MIMO antenna systems by using physics-inspired heuristics. Firstly, we optimize the antenna configuration to maximize the signal-to-noise ratio (SNR) at the receiver by leveraging two basic heuristic solvers,i.e.,coherent Ising machines (CIMs), that mimic quantum mechanical dynamics, and quantum annealing (QA), where a real-world QA architecture is considered (D-Wave). A mathematical framework that converts the configuration selection problem into CIM- and QA- compatible unconstrained quadratic formulations is investigated. Numerical and experimental results show that the proposed designs outperform classical counterparts and achieve near-optimal performance (similar to exhaustive search with exponential complexity) while ensuring polynomial complexity. Moreover, we study the optimal antenna configuration that maximizes the end-to-end Shannon capacity. A simulated annealing (SA) heuristic which achieves near-optimal performance through appropriate parameterization is adopted. A modified version of the basic SA that exploits parallel tempering to avoid local maxima is also studied, which provides additional performance gains. Extended numerical studies show that the SA solutions outperform conventional heuristics (which are also developed for comparison purposes), while the employment of the SNR-based solutions is highly sub-optimal.

We present the Hybrid Polar Decoder (HyPD), a hybrid classical-quantum decoder design for Polar error correction codes, which are becoming widespread in today’s 5G and tomorrow’s 6G networks. HyPD employs CMOS processing for the Polar decoder’s binary tree traversal, and Quantum Annealing (QA) processing for the Quantum Polar Decoder (QPD)-a Maximum-Likelihood QA-based Polar decoder submodule. QPD’s design efficiently transforms a Polar decoder into a quadratic polynomial optimization form, then maps this polynomial on to the physical QA hardware via QPD-MAP, a customized problem mapping scheme tailored to QPD. We have experimentally evaluated HyPD on a state-of-the-art QA device with 5,627 qubits, for 5G-NR Polar codes with block length of 1,024 bits, in Rayleigh fading channels. Our results show that HyPD outperforms Successive Cancellation List decoders of list size eight by half an order of bit error rate magnitude, and achieves a 1,500-bytes frame delivery rate of 99.1%, at 1 dB signal-to-noise ratio. Further studies present QA compute time considerations. We also propose QPD-HW, a novel QA hardware topology tailored for the task of decoding Polar codes. QPD-HW is sparse, flexible to code rate and block length, and may be of potential interest to the designers of tomorrow’s 6G wireless networks.

Multiple-Input-Multiple-Output (MIMO) signal detection is central to nearly every state-of-the-art communication system, and enhancements in error performance and computational complexity of MIMO detection would significantly enhance data rate and latency experienced by the users. Theoretically, the optimal MIMO detector is the maximum-likelihood (ML) MIMO detector; however, due to its extremely high complexity, it is not feasible for large real-world communication systems. Over the past few years, algorithms based on physics-inspired Ising solvers, like Coherent Ising machines and Quantum Annealers, have shown significant performance improvements for the MIMO detection problem. However, the current state-of-the-art is limited to low-order modulations or systems with few users. In this paper, we propose an adaptive multi-stage Ising machine-based MIMO detector that extends the performance gains of physics-inspired computation to Large and Massive MIMO systems with a large number of users and very high modulation schemes (up to 256-QAM). We enhance our previously proposed delta Ising formulation and develop a heuristic that adaptively optimizes the performance and complexity of our proposed method. We perform extensive micro-benchmarking to optimize several free parameters of the system and evaluate our methods’ BER and spectral efficiency for Large and Massive MIMO systems (up to 32 users and 256-QAM modulation).

This paper presents Monolith, a high bitrate, low-power, metamaterials surface-based Orbital Angular Momentum (OAM) MIMO multiplexing design for rank deficient, free space wireless environments. Leveraging ambient signals as the source of power, Monolith backscatters these ambient signals by modulating them into several orthogonal beams, where each beam carries a unique OAM. We provide insights along the design aspects of a low-power and programmable metamaterials-based surface. Our results show that Monolith achieves an order of magnitude higher channel capacity than traditional spatial MIMO backscattering networks.

Exploiting (near-)optimal MIMO signal processing algorithms in the next generation (NextG) cellular systems holds great promise in achieving significant wireless performance gains in spectral efficiency and device connectivity, to name a few. However, it is extremely difficult to enable optimal processing methods in the systems, since the required computational amount increases exponentially with more users and higher data rates, while available processing time is strictly limited. In this regard, quantum signal processing has been recently identified as a promising potential enabler of the (near-)optimal algorithms in the systems, since quantum computing could dramatically speed up the computation via non-conventional effects based on quantum mechanics. Given existing quantum decoherence and noise on quantum hardware, parallel quantum optimization could accelerate the process even further at the expense of more qubit usage. In this paper, we discuss the parallelization of quantum MIMO processing and investigate a spin-level preprocessing method for relatively finer-grained decomposition that can support more flexible parallel quantum signal processing, compared to the recently reported symbol-level decomposition method. We evaluate the method on the state-of-the-art analog D-Wave Advantage quantum processor.

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

This article presents SoundSticker, a system for steganographic, in-band data communication over an acoustic channel. In contrast with recent works that hide bits in inaudible frequency bands, SoundSticker embeds hidden bits in the audible sounds, making them more reliably survive audio codecs and bandpass filtering, while achieving a higher data rate and remaining imperceptible to a listener. The key observation behind SoundSticker is that the human ear is less sensitive to the audio phase changes than the frequency and amplitude changes, which leaves us an opportunity to alter the phase of an audio clip to convey hidden information. We take advantage of this opportunity and build an OFDM-based physical layer. To make this PHY-layer design work for a variety of end devices with heterogeneous computation resources, SoundSticker addresses multiple technical challenges including perceivable waveform artifacts caused by the phase-based modulation, bit rate adaptation without channel sounding and real-time preamble detection. Our prototype on both smartphones and ESP32 platforms demonstrates SoundSticker’s superior performance against the state of the arts, while preserving excellent sound quality and remaining unaffected by common audio codecs like MP3 and AAC. Audio clips produced by SoundSticker can be found at https://soundsticker.github.io/ .

We present the Hybrid Polar Decoder (HyPD), a hybrid of classical CMOS and quantum annealing (QA) computational structures for decoding Polar error correction codes, which are becoming widespread in today's 5G and tomorrow's 6G networks. HyPD considers CMOS for the Polar code's binary tree traversal, and QA for executing a Quantum Polar Decoder (QPD)-a novel QA-based maximum likelihood submodule. Our QPD design efficiently transforms a Polar decoder into a quadratic polynomial optimization form amenable to the QA's optimization process. We experimentally evaluate HyPD on a state-of-the-art QA device with 5,627 qubits, for Polar codes of block length 1,024 bits, in Rayleigh fading channels. Our results show that HyPD outperforms successive cancellation list decoders of list size eight by half an order of bit error rate magnitude at 1 dB SNR. Further experimental studies address QA compute time at various code rates, and with increased QA qubit numbers.

Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that the quantum-inspired computing approach based on Coherent Ising Machines (CIMs) is a promising candidate for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the direct approach adopted in the existing literature on MIMO detection using Quantum Annealing. We evaluate our methods using a simplified, quantum-inspired model and show that our methods can achieve a near-optimal performance for several Large MIMO systems, like$16\times 16,20\times 20$, and$24\times 24$MIMO with BPSK modulation.

The last couple of years have seen an emergence of physics-inspired computing for maximum likelihood MIMO detection. These methods involve transforming the MIMO detection problem into an Ising minimization problem, which can then be solved on an Ising Machine. Recent works have shown promising projections for MIMO wireless detection using Quantum Annealing optimizers and Coherent Ising Machines. While these methods perform very well for BPSK and 4-QAM, they struggle to provide good BER for 16-QAM and higher modulations. In this paper, we explore an enhanced CIM model, and propose a novel Ising formulation, which together are shown to be the first Ising solver that provides significant gains in the BER performance of large and massive MIMO systems, like$16 \times 16$and$16 \times 32$, and sustain its performance gain even at 256-QAM modulation. We further perform a spectral efficiency analysis and show that, for a$16 \times 16$MIMO with Adaptive Modulation and Coding, our method can provide substantial throughput gains over MMSE, achieving$2\times$throughput for SNR$\leq 25$dB, and up to$1.5\times$throughput for SNR$\geq 30$dB.

The first low earth orbit satellite networks for internet service have recently been deployed and are growing in size, yet will face deployment challenges in many practical circumstances of interest. This paper explores how a dual-band, electronically tunable smart surface can enable dynamic beam alignment between the satellite and mobile users, make service possible in urban canyons, and improve service in rural areas. Our design is the first of its kind to target dual channels in the Ku radio frequency band with a novel dual Huygens resonator design that leverages radio reciprocity to allow our surface to simultaneously steer energy in the satellite uplink and downlink directions, and in both reflective and transmissive modes of operation. Our surface, Wall-E, is designed and evaluated in an electromagnetic simulator and demonstrates 94% transmission efficiency and a 85% reflection efficiency, with at most 6 dB power loss at steering angles over a 150 degree field of view for both transmission and reflection. With 75cm2 surface, our link budget calculations predict 4 dB and 24 dB improvement in the SNR of a link entering the window of a rural home in comparison to the free-space path and brick wall penetration, respectively.

With the continuous growth of the Internet of Things (IoT), the trend of increasing numbers of IoT devices will continue. To increase the network's capability to support a large number of active devices accessing a network concurrently, this work presents IoT-ResQ, a warm-started quantum annealing-based multi-device detector via quantum reverse annealing (RA). Unlike in typical quantum forward annealing (FA) protocol, IoT-ResQ's RA starts its search operation on a controllable candidate classical state, instead of a quantum superposition, and thus allows refined local quantum search around the initial state. This procedure can provide an opportunity of utilizing both conventional classical- and quantum-based detectors together in a hybrid synergy, to boost quantum optimization performance, mitigating the effect of quantum decoherence and noise on quantum hardware. In our evaluation, IoT-ResQ achieves nearly two to three orders of magnitude better BER and over 2X packet success rate with packet size of 32-byte compared to other quantum and conventional detectors at SNR 9 dB to support 48 active IoT devices with QPSK modulation (implying 48,000 deployed devices with 0.1% wake-up radio rate at a time), requiring ≈ 140 μs pure compute time for detection.

Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that new analog computing approaches, such as Coherent Ising Machines (CIMs), are promising candidates for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the naive approach and evolve it into a regularized, Ising-based tree search algorithm that achieves near-optimal performance. By means of numerical simulation using the Rayleigh fading channel model, we show that in principle, a MIMO detector based on a high-speed Ising machine (such as a CIM implementation optimized for latency) would allow a higher transmitter antennas (users)-to-receiver antennas ratio and thus increase the overall throughput of the cell by a factor of two or more for massive MIMO systems. Our methods create an opportunity to operate wireless systems using more aggressive modulation and coding schemes and hence achieve high spectral efficiency: for a$16\times 16$MIMO system, we estimate around$2.5\times $more throughput in the mid-SNR regime (≈12 dB) and$2\times $more throughput in the high-SNR regime (>20 dB) as compared to the industry standard, a Minimum-Mean Square Error (MMSE) linear decoder.

In a multi-user system with multiple antennas at the base station, precoding techniques in the downlink broadcast channel allow users to detect their respective data in a non-cooperative manner. Vector Perturbation Precoding (VPP) is a non-linear variant of transmit-side channel inversion that perturbs user data to achieve full diversity order. While promising, finding an optimal perturbation in VPP is known to be an NP-hard problem, demanding heavy computational support at the base station and limiting the feasibility of the approach to small MIMO systems. This work proposes a radically different processing architecture for the downlink VPP problem, one based on Quantum Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our design reduces VPP to a quadratic polynomial form amenable to QA, then refines the problem coefficients to mitigate the adverse effects of QA hardware noise. We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum Annealing device over a variety of design and machine parameter settings. With existing hardware, QAVP can achieve a BER of 10−4with 100µs compute time, for a 6 × 6 MIMO system using 64 QAM modulation at 32 dB SNR.

Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additional RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 × 160 and 80 × 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 × 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4--8 processing elements per subcarrier, which is approximately 1.4× throughput than linear detector-based Massive MIMO systems.

Small IoT devices deployed in challenging locations suffer from uneven 3D coverage in complex environments. This work optimizes indoor coverage with LAVA, a Large Array of Vanilla Amplifiers. LAVA is a standard-agnostic cooperative mesh of elements, i.e., RF devices each consisting of several switched input and output antennas connected to fixed-gain amplifiers. Each LAVA element is further equipped with rudimentary power sensing to detect nearby transmissions. The elements report power readings to the LAVA control plane, which then infers active link sessions without explicitly interacting with the endpoint transmitter or receiver. With simple on-off control of amplifiers and antenna switching, LAVA boosts passing signals via multi hop amplify-and-forward. LAVA explores a middle ground between smart surfaces and physical-layer relays. Multi-hopping over short inter-hop distances exerts more control over the end-to-end trajectory, supporting fine-grained coverage and spatial reuse. Ceiling testbed results show throughput improvements to individual Wi-Fi links by 50% on average and up to 100% at 15 dBm transmit power (193% on average, up to 8x at 0 dBm). ZigBee links see up to 17 dB power gain. For pairs of co-channel concurrent links, LAVA provides average per-link throughput improvements of 517% at 0 dBm and 80% at 15 dBm.

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.

With unprecedented increases in traffic load in today's wireless networks, design challenges shift from the wireless network itself to the computational support behind the wireless network. In this vein, there is new interest in quantum-compute approaches because of their potential to substantially speed up processing, and so improve network throughput. However, quantum hardware that actually exists today is much more susceptible to computational errors than silicon-based hardware, due to the physical phenomena of decoherence and noise. This paper explores the boundary between the two types of computation---classical-quantum hybrid processing for optimization problems in wireless systems---envisioning how wireless can simultaneously leverage the benefit of both approaches. We explore the feasibility of a hybrid system with a real hardware prototype using one of the most advanced experimentally available techniques today, reverse quantum annealing. Preliminary results on a low-latency, large MIMO system envisioned in the 5G New Radio roadmap are encouraging, showing approximately 2-10x better performance in terms of processing time than prior published results.

We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based decoder design for Low Density Parity Check (LDPC) error control codes, which have found many useful applications in Wi-Fi, satellite communications, mobile cellular systems, and data storage systems. QBP reduces the LDPC decoding to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. QBP's embedding design can support LDPC codes of block length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits. We evaluate performance on real quantum annealer hardware, performing sensitivity analyses on a variety of parameter settings. Our design achieves a bit error rate of 10--8 in 20 μs and a 1,500 byte frame error rate of 10--6 in 50 μs at SNR 9 dB over a Gaussian noise wireless channel. Further experiments measure performance over real-world wireless channels, requiring 30 μs to achieve a 1,500 byte 99.99% frame delivery rate at SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of 10--8 and a frame error rate of 10--6 at an SNR 2.5--3.5 dB lower. In terms of limitations, QBP currently cannot realize practical protocol-sized (e.g., Wi-Fi, WiMax) LDPC codes on current QA processors. Our further studies in this work present future cost, throughput, and QA hardware trend considerations.

Cellular networks are becoming ever more sophisticated and overcrowded, imposing the most delay, jitter, and throughput damage to end-to-end network flows in today's internet. We therefore argue for fine-grained mobile endpoint-based wireless measurements to inform a precise congestion control algorithm through a well-defined API to the mobile's cellular physical layer. Our proposed congestion control algorithm is based on Physical-Layer Bandwidth measurements taken at the Endpoint (PBE-CC), and captures the latest 5G New Radio innovations that increase wireless capacity, yet create abrupt rises and falls in available wireless capacity that the PBE-CC sender can react to precisely and rapidly. We implement a proof-of-concept prototype of the PBE measurement module on software-defined radios and the PBE sender and receiver in C. An extensive performance evaluation compares PBE-CC head to head against the cellular-aware and wireless-oblivious congestion control protocols proposed in the research community and in deployment, in mobile and static mobile scenarios, and over busy and idle networks. Results show 6.3% higher average throughput than BBR, while simultaneously reducing 95th percentile delay by 1.8x.

Low-power wide-area networks (LPWANs) are a compelling answer to the networking challenges faced by many Internet of Things devices. Their combination of low power, long range, and deployment ease has motivated a flurry of research, including exciting results on backscatter and interference cancellation that further lower power budgets and increase capacity. But despite the interest, we argue that unlicensed LPWAN technologies can only serve a narrow class of Internet of Things applications due to two principal challenges: capacity and coexistence. We propose a metric, bit flux, to describe networks and applications in terms of throughput over a coverage area. Using bit flux, we find that the combination of low bit rate and long range restricts the use case of LPWANs to sparse sensing applications. Furthermore, this lack of capacity leads networks to use as much available bandwidth as possible, and a lack of coexistence mechanisms causes poor performance in the presence of multiple, independently-administered networks. We discuss a variety of techniques and approaches that could be used to address these two challenges and enable LPWANs to achieve the promise of ubiquitous connectivity.

Wi-Fi localization and tracking face accuracy limitations dictated by antenna count (for angle-of-arrival methods) and frequency bandwidth (for time-of-arrival methods). This paper presents mD-Track, a device-free Wi-Fi tracking system capable of jointly fusing information from as many dimensions as possible to overcome the resolution limit of each individual dimension. Through a novel path separation algorithm, mD-Track can resolve multipath at a much finer-grained resolution, isolating signals reflected off targets of interest. mD-Track can localize human passively at a high accuracy with just a single Wi-Fi transceiver pair. mD-Track also introduces novel methods to greatly streamline its estimation algorithms, achieving real-time operation. We implement mD-Track on both WARP and cheap off-the-shelf commodity Wi-Fi hardware, and evaluate its performance in different indoor environments.

User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO centralized radio access network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 10 µs of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of 10-6 and a 1,500 byte frame error rate of 10-4.

This paper presents PLoRa, an ambient backscatter design that enables long-range wireless connectivity for batteryless IoT devices. PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway. PLoRa achieves this by a holistic RF front-end hardware and software design, including a low-power packet detection circuit, a blind chirp modulation algorithm and a low-power energy management circuit. To form a complete ambient LoRa backscatter network, we integrate a light-weight backscatter signal decoding algorithm with a MAC-layer protocol that work together to make coexistence of PLoRa tags and active LoRa nodes possible in the network. We prototype PLoRa on a four-layer printed circuit board, and test it in various outdoor and indoor environments. Our experimental results demonstrate that our prototype PCB PLoRa tag can backscatter an ambient LoRa transmission sent from a nearby LoRa node (20 cm away) to a gateway up to 1.1 km away, and deliver 284 bytes data every 24 minutes indoors, or every 17 minutes outdoors. We also simulate a 28-nm low-power FPGA based prototype whose digital baseband processor achieves 220 μW power consumption.

Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the “clean” subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.

In this paper, we investigate the aerial wireless channel, where a moving drone is deployed to stream content to a set of mobile clients on the ground over a small cell size. Experimental traces collected over more than twenty flights with multiple clients suggest that drone mobility in lateral or vertical path leads to time-selective and frequency-selective wireless channel for a low-altitude drone. The resulting aerial wireless channel can be predicted reasonably well when we model the channel based on the constructive and destructive interference patterns between the line-of-sight path and other propagation paths via nearby reflectors. We propose a novel channel prediction approach to predict the subcarrier SNRs for all clients as drone moves and a novel scheduling approach to select the subset of clients that maximize the network utility using the predicted SNRs. We have implemented the proposed approach on a commodity 802.11n chipset and evaluated in the field over twenty flights, each serving up to 17 live clients. Experiments demonstrate, for the first time, the feasibility of tracking and predicting the aerial Wi-Fi channel, resulting in up to a 56% increase in overall throughput as compared to the conventional 802.11n hotspot, while maintaining fairness across clients.

Smart spaces, such as smart homes and smart offices, are common Internet of Things (IoT) scenarios for building automation with networked sensors. In this paper, we suggest a different notion of smart spaces, where the radio environment is programmable to achieve desirable link quality within the space. We envision deploying low-cost devices embedded in the walls of a building to passively reflect or actively transmit radio signals. This is a significant departure from typical approaches to optimizing endpoint radios and individual links to improve performance. In contrast to previous work combating or leveraging per-link multipath fading, we actively reconfigure the multipath propagation. We sketch design and implementation directions for such a programmable radio environment, highlighting the computational and operational challenges our architecture faces. Preliminary experiments demonstrate the efficacy of using passive elements to change the wireless channel, shifting frequency "nulls" by nine Wi-Fi subcarriers, changing the 2 x 2 MIMO channel condition number by 1.5 dB, and attenuating or enhancing signal strength by up to 26 dB.

Various pioneering approaches have been proposed for Wi-Fi-based sensing, which usually employ learning-based techniques to seek appropriate statistical features, yet do not support precise tracking without prior training. Thus to advance passive sensing, the ability to track fine-grained human mobility information acts as a key enabler. In this paper, we propose Widar, a Wi-Fi-based tracking system that simultaneously estimates a human's moving velocity (both speed and direction) and location at a decimeter level. Instead of applying statistical learning techniques, Widar builds a theoretical model that geometrically quantifies the relationships between CSI dynamics and the user's location and velocity. On this basis, we propose novel techniques to identify frequency components related to human motion from noisy CSI readings and then derive a user's location in addition to velocity. We implement Widar on commercial Wi-Fi devices and validate its performance in real environments. Our results show that Widar achieves decimeter-level accuracy, with a median location error of 25 cm given initial positions and 38 cm without them and a median relative velocity error of 13%.

Increasing numbers of everyday objects in libraries, stores and warehouses are instrumented with passive RFID tags, resulting in a ripe opportunity for gesture-based interactions with people. By a simple act of picking up and gesturing with an RFID-tagged object, users can send their opinions and sentiments about that object to the cloud. Prior work in RFID-based gesture tracking relies on multiple bulky and expensive antennas and readers to function, which incurs unacceptable infrastructure costs for large-scale ubiquitous deployment (over an entire warehouse or mall, for example) thus hindering practical adoption. In this paper, we propose Pantomime, the first RFID-based gesture recognition system that uses just a single antenna per geographical area of coverage. Our key insight is to replace the conventional multiple antenna single tag tracking framework with an equivalent multiple tag single antenna system. Through a novel tag coordination protocol and a lightweight tracking algorithm, Pantomime enables accurate gesture tracking that works for objects tagged with just two RFID tags. We implement a real-time prototype of Pantomime with commercial off-the-shelf (COTS) RFID readers and antennas. Extensive evaluations and real-world case studies in a classroom and a retail store demonstrate that Pantomime achieves comparable gesture tracking accuracy (87%) to state-of-the-art multi-antenna schemes (88%) at a minimal deployment cost.

This paper presents the design and implementation of Wi-Fi Goes to Town, the first Wi-Fi based roadside hotspot network designed to operate at vehicular speeds with meter-sized picocells. Wi-Fi Goes to Town APs make delivery decisions to the vehicular clients they serve at millisecond-level granularities, exploiting path diversity in roadside networks. In order to accomplish this, we introduce new buffer management algorithms that allow participating APs to manage each others' queues, rapidly quenching each others' transmissions and flushing each others' queues. We furthermore integrate our fine-grained AP selection and queue management into 802.11's frame aggregation and block acknowledgement functions, making the system effective at modern 802.11 bit rates that need frame aggregation to maintain high spectral efficiency. We have implemented our system in an eight-AP network alongside a nearby road, and evaluate its performance with mobile clients moving at up to 35 mph. Depending on the clients' speed, Wi-Fi Goes to Town achieves a 2.4-4.7x TCP throughput improvement over a baseline fast handover protocol that captures the state of the art in Wi-Fi roaming, including the recent IEEE 802.11k and 802.11r standards.

Wireless sensing, tracking, and drawing technologies are enabling exciting new possibilities for human-machine interaction. They primarily rely on measurements of backscattered phase, amplitude, and Doppler signal distortions, and often require many measurements of these quantities---in time, or from multiple antennas. In this paper we present the design and implementation of PolarDraw, the first whiteboard in the air that sends differentially-polarized wireless signals to glean more precise tracking information from a tag. Leveraging information received from each polarization angle, our novel algorithms infer orientation and position of an RFID-tagged pen using just two antennas, when the user writes in the air or on a physical whiteboard. An experimental comparison in a cluttered indoor office environment compares two-antenna PolarDraw with recent state-of-the-art object tracking systems that use double the number of antennas, demonstrating comparable centimeter-level tracking accuracy and character recognition rates (88--94%), thus making a case for the use of polarization in many other tracking systems.

Due to frequency selective fading, modern wideband 802.11 transmissions have unevenly distributed bit BERs in a packet. In this paper, we propose to unequally protect packet bits according to their BERs. By doing so, we can best match the effective transmission rate of each bit to channel condition, and improve throughput. The major design challenge lies in deriving an accurate relationship between the frequency selective channel condition and the decoded packet bit BERs, all the way through the complex 802.11 PHY layer. Based on our study, we find that the decoding error of a packet bit corresponds to dense errors in the underlying codeword bits, and the BER can be truthfully approximated by the codeword bit error density. With above observation, we propose UnPKT, scheme that protects packet bits using different MAC-layer FEC redundancies based on bit-wise BER estimation to augment wide-band 802.11 transmissions. UnPKT is software-implementable and compatible with the existing 802.11 architecture. Extensive evaluations based on Atheros 9580 NICs and GNU-Radio platforms show the effectiveness of our design. UnPKT can achieve a significant goodput improvement over state-of-the-art approaches.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization.

Libraries, manufacturing lines, and offices of the future all stand to benefit from knowing the exact spatial order of RFID-tagged books, components, and folders, respectively. To this end, radio-based localization has demonstrated the potential for high accuracy. Key enabling ideas include motion-based synthetic aperture radar, multipath detection, and the use of different frequencies (channels). But indoors in real-world situations, current systems often fall short of the mark, mainly because of the prevalence and strength of multipath reflections of the radio signal off nearby objects. In this paper we describe the design and implementation of MobiTagbot, an autonomous wheeled robot reader that conducts a roving survey of the above such areas to achieve an exact spatial order of RFID-tagged objects in very close (1--6 cm) spacings. Our approach leverages a serendipitous correlation between the changes in multipath reflections that occur with motion and the effect of changing the carrier frequency (channel) of the RFID query. By carefully observing the relationship between channel and phase, MobiTagbot detects if multipath is likely prevalent at a given robot reader location. If so, MobiTagbot excludes phase readings from that reader location, and generates a final location estimate using phase readings from other locations as the robot reader moves in space. Experimentally, we demonstrate that cutting-edge localization algorithms including Tagoram are not accurate enough to exactly order items in very close proximity, but MobiTagbot is, achieving nearly 100% ordering accuracy for items at low (3--6 cm) spacings and 86% accuracy for items at very low (1--3 cm) spacings.

As 802.11 wireless networks proliferate, interference becomes increasingly severe, particularly in dense, urban environments. These networks are usually operated by different users (e.g., tenants in apartments). In this paper, we develop techniques for mitigating interference between such loosely cooperating 802.11 MIMO APs and clients, which do not share a high-speed wired backplane or central controller. We propose CoOperative Power Allocation (COPA), an approach to concurrent wireless medium access that combines fine-grained, per-subcarrier power allocation, nulling, and multi-stream transmission to claim capacity that status-quo approaches cannot. Jointly turning these knobs allows COPA to allocate subcarriers to senders partially, rather than all-or-nothing, and to embrace a measure of interference when doing so increases capacity.

In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.

This paper presents Recitation, the first software system that uses lightweight channel state information (CSI) to accurately predict error-prone bit positions in a packet so that applications atop the wireless physical layer may take the best action during subsequent transmissions. Our key insight is that although Wi-Fi wireless physical layer operations are complex, they are deterministic. This enables us to rehearse physical-layer operations on packet bits before they are transmitted. Based on this rehearsal, we calculate a hidden parameter in the decoding process, called error event probability (EVP). EVP captures fine-grained information about the receiver's convolutional or LDPC decoder, allowing Recitation to derive precise information about the likely fate of every bit in subsequent packets, without any wireless channel training. Recitation is the first system of its kind that is both software-implementable and compatible with the existing 802.11 architecture for both SISO and MIMO settings. We experiment with commodity Atheros 9580 Wi-Fi NICs to demonstrate Recitation's utility with three representative applications in static, mobile, and interference-dominated scenarios. We show that Recitation achieves 33.8% and 16% average throughput gains for bit-rate adaptation and partial packet recovery, respectively, and 6 dB PSNR quality improvement for unequal error protection-based video.

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.

Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed offline with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traffic scheduling algorithms further optimize Vigil's bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between five and 200 times greater than an approach that simply streams video over the wireless network. For a fixed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user's query.

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.

Signal processing on antenna arrays has received much recent attention in the mobile and wireless networking research communities, with array signal processing approaches addressing the problems of human movement detection, indoor mobile device localization, and wireless network security. However, there are two important challenges inherent in the design of these systems that must be overcome if they are to be of practical use on commodity hardware. First, phase differences between the radio oscillators behind each antenna can make readings unusable, and so must be corrected in order for most techniques to yield high-fidelity results. Second, while the number of antennas on commodity access points is usually limited, most array processing increases in fidelity with more antennas. These issues work in synergistic opposition to array processing: without phase offset correction, no phase-difference array processing is possible, and with fewer antennas, automatic correction of these phase offsets becomes even more challenging. We present Phaser, a system that solves these intertwined problems to make phased array signal processing truly practical on the many WiFi access points deployed in the real world. Our experimental results on three- and five-antenna 802.11-based hardware show that 802.11 NICs can be calibrated and synchronized to a 20° median phase error, enabling inexpensive deployment of numerous phase-difference based spectral analysis techniques previously only available on costly, special-purpose hardware.

This paper presents the design and implementation of Geosphere, a physical- and link-layer design for access point-based MIMO wireless networks that consistently improves network throughput. To send multiple streams of data in a MIMO system, prior designs rely on a technique called zero-forcing, a way of "nulling" the interference between data streams by mathematically inverting the wireless channel matrix. In general, zero-forcing is highly effective, significantly improving throughput. But in certain physical situations, the MIMO channel matrix can become "poorly conditioned," harming performance. With these situations in mind, Geosphere uses sphere decoding, a more computationally demanding technique that can achieve higher throughput in such channels. To overcome the sphere decoder's computational complexity when sending dense wireless constellations at a high rate, Geosphere introduces search and pruning techniques that incorporate novel geometric reasoning about the wireless constellation. These techniques reduce computational complexity of 256-QAM systems by almost one order of magnitude, bringing computational demands in line with current 16- and 64-QAM systems already realized in ASIC. Geosphere thus makes the sphere decoder practical for the first time in a 4 × 4 MIMO, 256-QAM system. Results from our WARP testbed show that Geosphere achieves throughput gains over multi-user MIMO of 2× in 4 × 4 systems and 47% in 2 × 2 MIMO systems.

Despite the important role that WiFi networks play in home and enterprise networks they are relatively weak from a security standpoint. With easily available directional antennas, attackers can be physically located off-site, yet compromise WiFi security protocols such as WEP, WPA, and even to some extent WPA2 through a range of exploits specific to those protocols, or simply by running dictionary and human-factors attacks on users' poorly-chosen passwords. This presents a security risk to the entire home or enterprise network. To mitigate this ongoing problem, we propose SecureArray, a system designed to operate alongside existing wireless security protocols, adding defense in depth against active attacks. SecureArray's novel signal processing techniques leverage multi-antenna access point (AP) to profile the directions at which a client's signals arrive, using this angle-of-arrival (AoA) information to construct highly sensitive signatures that with very high probability uniquely identify each client. Upon overhearing a suspicious transmission, the client and AP initiate an AoA signature-based challenge-response protocol to confirm and mitigate the threat. We also discuss how SecureArray can mitigate direct denial-of-service attacks on the latest 802.11 wireless security protocol. We have implemented SecureArray with an eight-antenna WARP hardware radio acting as the AP. Our experimental results show that in a busy office environment, SecureArray is orders of magnitude more accurate than current techniques, mitigating 100% of WiFi spoofing attack attempts while at the same time triggering false alarms on just 0.6% of legitimate traffic. Detection rate remains high when the attacker is located only five centimeters away from the legitimate client, for AP with fewer numbers of antennas and when client is mobile.

We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.

Rateless error correction codes hold great potential for increasing the capacity of practical wireless networks by obviating the need for transmitters to estimate the highest reliable rate of an unpredictable wireless channel and send information at that rate. But the accumulation and intensive processing of noisy bits works against rateless codes' adoption in mobile devices, where energy is at a premium due to limited battery capacity. In this work, we identify a new tradeoff between energy efficiency and wireless capacity that rateless codes can make in low signal-to-noise ratio or highly variable "grey zone" conditions. We propose Power-Aware Rateless Codes (PRC), a design that integrates with the medium access control portion of a rateless wireless system, giving the system a way of selectively sacrificing small amounts of wireless capacity for large savings in decoder computation effort, thus reducing radio power consumption in challenging radio environments.

Wireless networks play an important role in our everyday lives, at the workplace and at home. However, they are also relatively vulnerable: physically located off site, attackers can circumvent wireless security protocols such as WEP, WPA, and even to some extent WPA2, presenting a security risk to the entire network. To address this problem, we propose SecureAngle, a system designed to operate alongside existing wireless security protocols, adding defense in depth. SecureAngle leverages multi-antenna APs to profile the directions at which a client's signal arrives, using this angle-of-arrival (AoA) information to construct signatures that uniquely identify each client. We identify SecureAngle's role of providing a fine-grained location service in a multi-path indoor environment. With this location information, we investigate how an AP might create a "virtual fence" that drops frames received from clients physically located outside a building or office. With SecureAngle signatures, we also identify how an AP can prevent malicious parties from spoofing the link-layer address of legitimate clients. We discuss how SecureAngle might aid whitespace radios in yielding to incumbent transmitters, as well as its role in directional downlink transmissions with uplink AoA information.

Dense 802.11 wireless networks present a pressing capacity challenge: users in proximity contend for limited unlicensed spectrum. Directional antennas promise increased capacity by improving the signal-to-interference-plus-noise ratio (SINR) at the receiver, potentially allowing successful decoding of packets at higher bit-rates. Many uses of directional antennas to date have directed high gain between two peers, thus maximizing the strength of the sender's signal reaching the receiver. But in an interference-rich environment, as in dense 802.11 deployments, directional antennas only truly come into their own when they explicitly null interference from competing concurrent senders. In this paper, we present Cone of Silence (CoS), a technique that leverages software-steerable directional antennas to improve the capacity of indoor 802.11 wireless networks by adaptively nulling interference. Using in situ signal strength measurements that account for the complex propagation environment, CoS derives custom antenna radiation patterns that maximize the strength of the signal arriving at an access point from a sender while nulling inteference from one or more concurrent interferers. CoS leverages multiple antennas, but requires only a single commodity 802.11 radio, thus avoiding the significant processing requirements of decoding multiple concurrent packets. Experiments in an indoor 802.11 deployment demonstrate that CoS improves throughput under interference.

Wireless local area networks play an important role in our everyday lives, at the workplace and at home. However, wireless networks are also relatively vulnerable: physically located off-premises, attackers can circumvent wireless security protocols such as WEP, WPA, and even to some extent WPA2, presenting a security risk to the entire network. To address this problem, we propose SecureAngle, a system designed to operate alongside existing wireless security protocols, adding defense in depth. SecureAngle employs multiantenna APs to profile the directions at which a client's signal arrives, using this angle-of-arrival information to construct unique signatures that identify each client. With these signatures, we are currently investigating how a SecureAngle enabled AP can enable a "virtual fence" that drops frames injected into the network from a client physically located outside a building, and how a SecureAngle-enabled AP can prevent malicious parties from spoofing the link-layer address of legitimate clients.

This paper presents and evaluates two principles for wireless routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle algorithm to routing control traffic reduces route repair latency and sends fewer beacons.

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

Bit errors occur in wireless communication when interference or noise overcomes the coded and modulated transmission. Current wireless protocols may use forward error correction (FEC) to correct some small number of bit errors, but generally retransmit the whole packet if the FEC is insufficient. We observe that current wireless mesh network protocols retransmit a number of packets and that most of these retransmissions end up sending bits that have already been received multiple times, wasting network capacity. To overcome this inefficiency, we develop, implement, and evaluate a partial packet recovery (PPR) system.

WaveScope is a data management and continuous sensor data system that integrates relational database and signal processing operations into a single system. WaveScope is motivated by a large number of signal-oriented streaming sensor applications, such as: preventive maintenance of industrial equipment; detection of fractures and ruptures in various structures; in situ animal behavior studies using acoustic sensing; network traffic analysis; and medical applications such as anomaly detection in EKGs. These target applications use a variety of embedded sensors, each sampling at fine resolution and producing data at high rates ranging from hundreds to hundreds of thousands of samples per second. Though there has been some work on applications in the sensor network community that do this kind of signal processing (for example, shooter localization [5], industrial equipment monitoring [4], and urban infrastructure monitoring [2]), these applications are typically custombuilt and do not provide reusable high-level programming framework suitable for easily building new signal processing applications with similar functionality. This poster shows how WaveScope supports these types of application in a single, unified framework, providing both high run-time performance and easy application development.

Network congestion occurs when offered traffic load exceeds available capacity at any point in a network. In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays (as in wired networks), and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops.

Recent research in sensor networks, wireless location systems, and power-saving in ad hoc networks suggests that some applications' wireless traffic be modeled as an event-driven workload: a workload where many nodes send traffic at the time of an event, not all reports of the event are needed by higher level protocols and applications, and events occur infrequently relative to the time needed to deliver all required event reports. We identify several applications that motivate the event-driven workload and propose a protocol that is optimal for this workload. Our proposed protocol, named CSMA/p/sup */, is nonpersistent carrier sense multiple access (CSMA) with a carefully chosen nonuniform probability distribution p/sup */ that nodes use to randomly select contention slots. We show that CSMA/p/sup */ is optimal in the sense that p/sup */ is the unique probability distribution that minimizes collisions between contending stations. CSMA/p/sup */ has knowledge of N. We conclude with an exploration of how p/sup */ could be used to build a more practical medium access control protocol via a probability distribution with no knowledge of N that approximates p/sup */.

No abstract available.

This paper presents Span, a power saving technique for multi-hop ad hoc wireless networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network. Span builds on the observation that when a region of a shared-channel wireless network bag a sufficient density of nodes, only a small number of them need be on at any time to forward traffic for active connections.