Antenna array calibration is essential for mmWave networks to deliver multi-gigabit throughput, especially for emerging AI-driven applications like immersive XR. However, conventional calibration pipelines rely on laboratory-grade over-the-air (OTA) test cells and specialized instruments, which must be deployed per device and used repeatedly over its lifetime. These requirements significantly increase the cost and complexity of large-scale mmWave deployments. In this paper, we present 0cal, a novel technique that operates purely using everyday communication, without laboratory environments, external sensors, or hardware modifications. The core idea of 0cal is to decouple constant relative antenna gains from the variable Angle of Departure (AoD) encoded in routine signals. By leveraging the statistical diversity of in-field traffic, 0cal averages out AoD-dependent sinusoidal terms while preserving gain-dependent components. It further incorporates a codebook-based selection algorithm to synthesize an approximately uniform effective AoD subset from accumulated communication instances, ensuring robust performance even with skewed AoD distributions. We implement 0cal on an open-source mmWave platform; experimental results show that after approximately 700 instances, 0cal achieves SNR performance comparable to laboratory-based methods, demonstrating its practicality for large-scale deployments.

To maximize Signal-to-Noise Ratio (SNR), it is necessary to move beyond selecting beams from a codebook. While the state-of-the-art approaches can significantly improve SNR compared to codebook-based beam selection by exploiting the globally-optimal beam, they incur significant beam-training overhead, which limits the applicability to large-scale antenna arrays and the scalability for multiple users. In this paper, we propose FTP, a highly-scalable beam-training solution that can find the globally-optimal beam with minimal beam-training overhead. FTP works by estimating per-path direction along with its complex gain and synthesizes the globally-optimal beam from these parameters. Our design significantly reduces the search space for finding such path parameters, which enables FTP to scale to large-scale antenna arrays. We implemented and evaluated FTP on a mmWave experimental platform with 32 antenna elements. Our results demonstrate that FTP achieves optimal SNR performance comparable with the state-of-the-art while reducing the beam-training overhead by 3 orders of magnitude. Under simulated settings, we demonstrate that the gain of FTP can be even more significant for larger antenna arrays with up to 1024 elements.

Recent advancement in Frequency Domain Duplexing (FDD) enables wireless systems to use different frequency bands for uplink and downlink communication without explicit channel feedback information. The current state-of-the-art approaches either estimate the underlying variables in the uplink channel or use an artificial neural network architecture to estimate the downlink channel from the uplink channel. However, such techniques fail to perform accurately in multipath-rich environments and environments unseen during training. This paper presents HORCRUX, a physics-based machine learning system that can be generalized and scaled to any environment while predicting downlink channels with high accuracy and applies to single-antenna and MIMO systems. Our approach uses multiple neural networks, trained on the standard wireless channel model, firstly to divide the uplink channel into smaller sub-channels and secondly to generate coarse estimates for the variables for each of the underlying sub-channels. Finally, we use an efficient and fast optimization framework to get fine-tuned variable estimates to predict the downlink channel. We implement our system using software-defined radios. Our evaluations show that HORCRUX performs ~8 dB better than state of the art in downlink channel prediction accuracy in diverse wireless environments. 1

As the demand for high-speed and reliable wireless networks continues to increase, multi-user multiple-input multiple-output (MU-MIMO) technology has become a popular choice for wireless communication systems. However, this technology also brings new security challenges, one of which is the vulnerability during the channel sounding process. In this paper, we propose an active eavesdropping attack targeting MU-MIMO systems. The attack consists of two phases. First, the attacker sends a forged pilot packet to the victims. After that, the access point transmits streams intended for victims to the attacker, who operates in full-duplex mode and relays the streams to the victims. Compared to existing eavesdropping attacks targeting MU-MIMO systems, our proposed attack requires less prior knowledge and coordination from attackers and maximizes eavesdropping opportunities. We evaluate the proposed attack in various settings and prove its effectiveness with multiple victims and partial channel knowledge. Additionally, we explore the use of physical-layer features to detect our proposed attack.

Full-duplex devices can compromise the integrity of wireless channel measurements through signal relaying and several attacks have been proposed based on this vulnerability. Existing source authentication methods relying on previously-collected signatures face significant challenges in detecting these attacks because a relay attacker can gradually inject the channels so that the manipulated channels will fall within the tolerance range of the authentication methods and are mistaken as new signatures. In this paper, we propose RelayShield, a system for detecting malicious relays and recovering the legitimate transmitter-receiver channels from the manipulated channels. RelayShield requires only one channel measurement at the receiver. It analyzes signal path information resolved from input channels to detect relays and recover channels. RelayShield achieves over 95% detection accuracy with channels collected in two typical indoor environments. The recovered channels can support a wide range of applications, including secret generation protocols and sensing systems.

Recovering audio signals from a vibrating source by analyzing fluctuations in the received wireless signals has been shown possible. However, previous RF-based works are limited to recovering sounds from an active sound source. In this paper, we present mmMic and demonstrate the feasibility of utilizing millimeter-wave (mmWave) signals to recover intelligible speech from a passively vibrating object. Such advance has a direct security implication of RF-based acoustic eavesdropping. We implement mmMic on a commercial-off-the-shelf (COTS) mmWave radio and validate its feasibility through real-world experiments. The results show that the objective quality of the recovered speech by mmMic is comparable with state-of-the-art systems.

In this paper, we present RFDiaper, a commodity passive RFID based healthy diapering system, which can sense the diaper wetness (i.e., wet/dry) and identify pH value of urine absorbed by the diaper. To do so, we leverage the coupling effect between the urine absorbed by the diaper and RFID tag, thereby the phase and amplitude variation can indicate urine pH and diaper wetness. However, rich scattering and dynamic environment exhibit a great challenge for accurate diaper wetness sensing and urine pH identification. Therefore, we propose a twin-tag based dynamic environment mitigation approach for robust and healthy diapering. Specifically, by extracting the differential amplitude and phase from the co-located sensing tag and reference tag (i.e., twin-tag) attached on the diaper, the multipath effect and the other dynamic factors (e.g., diaper wearer's body, tag's orientation and temperature, etc.) can be mitigated. Then, we detect the diaper wetness and estimate the urine pH based on differential amplitude and phase. We have implemented RFDiaper's design and evaluated its effectiveness with the experiments using commercial off-the-shelf (COTS) RFID tags attached on the diaper worn by the doll and the human subjects. RFDiaper can achieve the median accuracy of around 96% for diaper wetness sensing and urine pH estimation error of around 0.23 in dynamic environment.

Age of information, as a metric measuring the data freshness, has drawn increasing attention due to its importance in many data update applications. Most existing studies have assumed that there is one single channel in the system. In this work, we are motivated by the plethora of multi-channel systems that are being developed, and investigate the following question: how can one exploit multi-channel resources to improve the age performance? We first derive a policy-independent lower bound of the expected long-term average age in a multi-channel system. The lower bound is jointly characterized by the external arrival process and the channel statistics. Since direct analysis of age in multi-channel systems is very difficult, we focus on the asymptotic regime, when the number of users and number of channels both go to infinity. In the many-channel asymptotic regime, we propose a class of Maximum Weighted Matching policies that converge to the lower bound near exponentially fast. In the many-user asymptotic regime, we design a class of Randomized Maximum Weighted Matching policies that achieve a constant competitive ratio compared to the lower bound. Finally, we use simulations to validate the aforementioned results.

A key performance metric in blockchains is the latency between when a transaction is broadcast and when it is confirmed (the so-called, confirmation latency). While improvements in consensus techniques can lead to lower confirmation latency, a fundamental lower bound on confirmation latency is the propagation latency of messages through the underlying peer-to-peer (p2p) network (in Bitcoin, the propagation latency is several tens of seconds). The de facto p2p protocol used by Bitcoin and other blockchains is based on random connectivity: each node connects to a random subset of nodes. The induced p2p network topology can be highly suboptimal since it neglects geographical distance, differences in bandwidth, hash-power and computational abilities across peers. We present Perigee, a decentralized algorithm that automatically learns an efficient p2p topology tuned to the aforementioned network heterogeneities, purely based on peers' interactions with their neighbors. Motivated by the literature on the multi-armed bandit problem, Perigee optimally balances the tradeoff between retaining connections to known well-connected neighbors, and exploring new connections to previously-unseen neighbors. Experimental evaluations show that Perigee reduces the latency to broadcast by 33%. Lastly Perigee is simple, computationally lightweight, adversary-resistant, and compatible with the selfish interests of peers, making it an attractive p2p protocol for blockchains.

FlexRadio, a recent breakthrough in wireless Multi-RF technology, has introduced a new way to unify MIMO and full-duplex into a single framework with a fully flexible design. FlexRadio allows a wireless node to use an arbitrary number of RF chains to support transmission and reception, which makes MIMO and full-duplex subset configurations of FlexRadio. This new architecture has greatly changed the feasibility constraint in wireless networks, which makes the design of high performance MAC layer algorithms even more challenging. First, the RF chain becomes a new resource that needs to be allocated across the network, and the optimal configuration depends not only on the network topology and flow demand, but also on the number of available RF chains at each node. Second, it is not clear how to jointly allocate links and RF chain resources based on the arrival rates and queueing dynamics. In this paper, we introduce a new virtual link model to characterize the feasibility constraint from the perspective of contending RF chain usage. Based on this novel model, a distributed CSMA-like framework is developed to fully leverage the flexibility of RF chain resource allocation.

Channel information plays an important role in modern wireless communication systems. Systems that use different frequency bands for uplink and downlink communication often need feedback between devices to exchange band specific channel information. The current state-of-the-art approach proposes a way to predict the channel in the downlink based on that of the observed uplink by identifying variables underlying the uplink channel. In this paper we present a solution that greatly reduces the complexity of this task, and is even applicable for single antenna devices. Our approach uses a neural network trained on a standard channel model to generate coarse estimates for the variables underlying the channel. We then use a simple and efficient single antenna optimization framework to get more accurate variable estimates, which can be used for downlink channel prediction. We implement our approach on software defined radios and compare it to the state-of-the-art through experiments and simulations. Results show that our approach reduces the time complexity by at least an order of magnitude (10x), while maintaining similar prediction quality.

With recent advances in in-band full duplex techniques, full duplex capable relays have been shown feasible in many recent works. Instead of generating data itself to transmit, a full duplex relay can forward the received signal while receiving it. It also has the opportunity to properly modify the signal before forwarding it, which has been explored to enable constructive and destructive full duplex relaying recently. With these designs, the signal through the full duplex relay could constructively or destructively add up with the signal from the direct link at the receiver side. As a result, the received signal power may be boosted or greatly reduced, compared with direct forwarding. In this paper, we do detailed analysis on whether such constructive and destructive full duplex relays are possible. We find that for OFDM signals, such relays can not be achieved if the additional latency of the relayed path is more than one sample time than the direct link. Our analysis and results challenge the assumptions on the relay latency requirements of previous works to realize such systems.

Each camera on digital devices is made of hundreds of thousands of sensors, with which it can separate and capture the light from spatial points in a fine-grain manner, enabling high-rate data transfer from the digital display to the camera, i.e. screen-camera communication. Compared with RF (Radio Frequency) technologies, visible light approaches are more convenient and secure.

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allows users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental test beds and simulations, where we compare the performance of EMIT with a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT and reveal significant gains over CSMA strategies in the case of IoT traffic.

In recent years there have been a few semi-supervised community detection approaches that use community membership information, or node metadata to improve their performance. However, communities have always been thought of as clique-like structures, while the idea of finding and leveraging other patterns in communities is relatively unexplored. Online social networks provide a corpus of real communities in large graphs which can be used to understand dataset specific community patterns. In this paper, we design a way to represent communities concisely in an easy to compute feature space. We design an efficient community detection algorithm that uses size and structural information of communities from a training set to find communities in the rest of the graph. We show that our approach achieves 10% higher F1 scores on average compared to several other methods on large real-world graph datasets, even when the training set is small.

A fundamental challenge in wireless multicast has been how to simultaneously achieve high-throughput and low-delay for reliably serving a large number of users. In this paper, we show how to harness substantial throughput and delay gains by exploiting multi-channel resources. We develop a new scheme called Multi-Channel Moving Window Codes (MC-MWC) for multi-channel multi-session wireless multicast. The salient features of MC-MWC are three-fold. (i) High throughput: we show that MC-MWC achieves order-optimal throughput in the many-user many-channel asymptotic regime. Moreover, the number of channels required by a conventional channel-allocation based scheme is shown to be doubly-exponentially larger than that required by MC-MWC. (ii) Low delay: using large deviations theory, we show that the delay of MC-MWC decreases linearly with the number of channels, while the delay reduction of conventional schemes is no more than a finite constant. (iii) Low feedback overhead: the feedback overhead of MC-MWC is a constant that is independent of both the number of receivers in each session and the number of sessions in the network. Finally, our trace-driven simulation and numerical results validate the analytical results and show that the implementation complexity of MC-MWC is low.

Reliable and timely delivery of periodic V2V (vehicle-to-vehicle) broadcast messages is essential for realizing the benefits of connected vehicles. Existing MAC protocols for ad hoc networks fall short of meeting these requirements. In this paper, we present, CoReCast, the first collision embracing protocol for vehicular networks. CoReCast provides high reliability and low delay by leveraging two unique opportunities: no strict constraint on energy consumption, and availability of GPS clocks to achieve near-perfect time and frequency synchronization.

A vast literature on secret sharing protocols now exists based on the folk theorem that the wireless channel between communicating parties Alice and Bob cannot be controlled or predicted by a third party in a fine-grain way. We find that the folk theorem unfortunately does not hold. In particular, we show how an adversary, using a customized full-duplex forwarder, can control the channel seen by Alice and Bob in fine granularity without leaving a trace, while predicting with high probability the secrets generated by any channel reciprocity based secret sharing protocol. An implementation of our proposed secret manipulator, called Channel Spoofer, on a software-defined radio platform empirically verifies Channel Spoofer's effectiveness in breaking several representative state-of-the-art secret sharing protocols. To the best of our knowledge, the proposed Channel Spoofer is the first practical attacker against all extant channel reciprocity based secret sharing protocols.

Full duplex techniques can potentially double the channel capacity and achieve lower delays by empowering two radios to simultaneously transmit in thesame frequency band.However, full duplex is only available between two adjacent nodes within the communication range. In this paper, we present BiPass to break this limitation.With the help of full duplex capable relays, weenable simultaneous bidirectional in-band cut-through transmissions between two far apart nodes, so they can do full duplex communications as if they were within each other's transmission range.To design such a system, we analyze interference patterns and propose a loop-back interference cancellation strategy. We identify the power amplification problem at relay nodes and develop an algorithm to solve it. We also develop a routing algorithm, an opportunistic forwarding scheme, and a real-time feedback strategy to leverage this system in ad-hoc networks.To evaluate the real world performance of BiPass, we build a prototype and conduct experiments using software defined radios. We show that BiPass can achieve 1.6x median throughput gain over state-of-the-art one-way cut-through systems, and 4.09x gain over the decode-and-forward scheme. Our simulations further reveal that even when the data traffic is not bidirectional, BiPass has 1.36x throughput gain and 47\% delay reduction overone-way cut-through systemsin large networks.

An essential step for achieving multiplexing gain in MIMO downlink systems is to collect accurate channel state information (CSI) from the users. Traditionally, CSIs have to be collected before any data can be transmitted. Such a sequential scheme incurs a large feedback overhead, which substantially limits the multiplexing gain especially in a network with a large number of users. In this paper, we propose a novel approach to mitigate the feedback overhead by leveraging the recently developed Full-duplex radios. Our approach is based on the key observation that using Full-duplex radios, when the base-station (BS) is collecting CSI of one user through the uplink channel, it can use the downlink channel to simultaneously transmit data to other (non-interfering) users for which CSIs are already known. By allowing concurrent channel probing and data transmission, our scheme can potentially achieve a higher throughput compared to traditional schemes using Half-duplex radios. The new flexibility introduced by our scheme, however, also leads to fundamental challenges in achieving throughout optimal scheduling. In this paper, we make an initial effort to this important problem by considering a simplified group interference model. We develop a throughput optimal scheduling policy with complexity O((N/I)I), where N is the number of users and I is the number of user groups. To further reduce the complexity, we propose a greedy policy with complexity O(N log N) that not only achieves at least 2/3 of the optimal throughput region, but also outperforms any feasible Half-duplex solutions. We derive the throughput gain offered by Full-duplex under different system parameters and show the advantage of our algorithms through numerical studies.

There has been a great interest in recognizing gestures using wireless communication signals. We are motivated in detecting extremely fine, subtle finger gestures with WiFi signals. We envision this technology to find applications in finger-gesture control, disabled-friendly devices, physical therapy etc. The requirements of mm-level sensitivity and user-friendly feature using existing WiFi signals pose great challenges. Here, we present Mudra, a fine-grained finger gesture recognition system which leverages WiFi signals to enable a near-human-to-machine interaction with finger motion.

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an Efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allow users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental testbeds, where we compare the performance of EMIT to a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT, and reveal significant gains over CSMA strategies in the case of IoT traffic.

There have been recent attempts to develop scheduling schemes for downlink transmission in a single cell of a multi-channel (e.g., OFDM-based) cellular network. These works have been quite promising in that they have developed low-complexity index scheduling policies that are delay-optimal (in a large deviation rate-function sense). However, these policies require that the channel is ON or OFF in each time-slot with a fixed probability (i.e., there is no memory in the system), while the reality is that due to channel fading and doppler shift, channels are often time-correlated in these cellular systems. Thus, an important open question is whether one can find simple index scheduling policies that are delay-optimal even when the channels are time-correlated. In this paper, we attempt to answer this question for time-correlated ON/OFF channels. In particular, we show that the class of oldest packets first (OPF) policies that give a higher priority to packets with a large delay is delay rate-function optimal under two conditions: 1) The channel is non-negatively correlated, and 2) The distribution of the OFF period is geometric. We use simulations to further elucidate the theoretical results.

To meet the growing demand for wireless data, it is time to move away from the age-old paradigm of prohibiting interfering nodes from transmissions. Instead, through proactive management of interference among multiple colliding packets, we can design high throughput wireless systems. This is well explored in the Information Theory community and there are also a few implementational efforts that have been recently reported. The existing solutions are nontrivial to use in real systems as they require either tight time/frequency synchronization or exchange of data between transmitters prior to the transmissions. These requirements are hard to meet in practice especially for uplink transmissions. This paper proposes BASIC, a lightweight multi-user uplink transmission strategy that does not require tight synchronization or exchange of samples among nodes, which makes it an attractive alternative compared to its counterparts. BASIC exploits receiver diversity by controlling the data rates of the clients. A novel greedy algorithm is proposed for data rate selection. We implement BASIC on a software-defined radio platform. Our experiments on a real testbed show that BASIC outperforms TDMA by 48% in terms of overall throughput. Our trace-driven simulations show up to 4.8x gain in throughput with similar flow fairness.

For a multicast group of n receivers, existing techniques either achieve high throughput at the cost of prohibitively large (e.g., O(n)) feedback overhead, or achieve low feedback overhead but without either optimal or near-optimal throughput guarantees. Simultaneously achieving good throughput guarantees and low feedback overhead has been an open problem and could be the key reason why wireless multicast has not been successfully deployed in practice. In this paper, we develop a novel anonymous-query based rate control, which approaches the optimal throughput with a constant feedback overhead independent of the number of receivers. In addition to our theoretical results, through implementation on a software-defined ratio platform, we show that the anonymous-query based algorithm achieves low-overhead and robustness in practice.

The time variance of a channel is exploited in interference alignment techniques. However, in the case of static channels, these interference alignment techniques do not hold, specifically for single-antenna systems. In this paper, we introduce simple data processing techniques that enable us to create the effect of a time varying channel from the underlying static channel. We call this channel the shadow channel. We exploit the time-varying nature of the relative channels introduced by the shadow channel for interference alignment. We demonstrate the throughput benefits of this interference alignment technique for different topologies. This technique can be thought of as operating over the two dimensions of the complex space of typical communication systems. In this paper, we present the feasibility of interference alignment techniques for single antenna nodes in static channel. We establish bound on the throughput gain due to this technique. Finally, we implement interference alignment over the shadow channel on the NI PXIe 1082 based software defined radio. We achieve throughput gains of upto 1.44X over TDMA systems using this interference alignment technique and upto 1.61X when this technique is coupled with interference cancellation.

This paper describes the design and implementation of AirExpress, a system that enables in-band wireless cut-through transmission. Unlike wired cut-through, wireless cut-through can reduce latency and improve throughput performance of the network at the same time. In AirExpress, all the forwarders along the cut-through path forward the signal they received immediately without decoding. The hierarchical structure of AirExpress enables its interference cancellation ability to handle all kinds of interference among the radios. Novel MAC and routing algorithms based on cut-through transmission are also proposed to support the realization of AirExpress in multi-hop mesh networks.

We implement a passive remote keystroke detection mechanism using only changes in the wireless channel. The detection algorithm does not require the user to wear any active devices nor does it require a change in the user's wireless transmission technique. The receiver system is implemented with five antennas. We cancel the signals received on multiple antennas. The key insight to realizing a fine-grained localization system is to exploit the extremely high sensitivity of cancellation performance (interference cancellation in full-duplex for example) to exact amplitude and phase matching. The receiver design introduces a delay mismatch between the received signal streams to guarantee imperfect cancellation across the transmission bandwidth except at one in-band frequency resulting in a trough in the cancellation spectrum. We detect keystrokes by forming an array of observed trough frequency across different antenna pairs.

A major challenge of wireless multicast is being able to support a large number of users while simultaneously maintaining low delay and low feedback overhead. In this paper, we develop a joint coding and feedback scheme named moving window network coding with anonymous feedback (MWNC-AF) that simultaneously achieves constant decoding delay and constant feedback overhead, irrespective of the number of receivers n, without sacrificing either throughput or reliability. We explicitly characterize the asymptotic decay rate of the tail probability of the decoding delay and prove that injecting a fixed amount of information bits into the MWNC-AF encoder buffer in each time slot (called “constant data injection process”) achieves the fastest decay rate, thus showing how to obtain delay optimality in a large deviation sense. We then investigate the average decoding delay of MWNC-AF and show that, when the traffic load approaches capacity, the average decoding delay under the constant injection process is at most one half of that under a Bernoulli injection process. We prove that the per-packet encoding and decoding complexities of MWNC-AF both scale as O(logn) and are thus insensitive to the increase of the number of receivers n. Our simulations further underscore the performance of our scheme through comparisons with existing schemes and show that the delay, encoding, and decoding complexities are low even for a large number of receivers, demonstrating the efficiency, scalability, and ease of implementability of MWNC-AF.

Recent breakthroughs in wireless communication show that by using new signal processing techniques, a wireless node is capable of transmitting and receiving simultaneously on the same frequency band by activating both of its RF chains, thus achieving full-duplex communication and potentially doubling the link throughput. However, with two sets of RF chains, one can build a half-duplex multi-input and multi-output (MIMO) system that achieves the same gain. While this gain is the same between a pair of nodes, the gains are unclear when multiple nodes are involved, as in a general network. The key reason is that MIMO and full-duplex have different interference patterns. A MIMO transmission blocks transmissions around its receiver and receptions around its transmitter. A full-duplex bidirectional transmission blocks any transmission around the two communicating nodes, but allows a reception on one RF chain. Thus, in a general network, the requirements for the two technologies could result in potentially different achievable throughput regions. This work investigates the achievable throughput performance of MIMO, full-duplex and their variants that allow simultaneous activation of two RF chains. It is the first work of its kind to precisely characterize the conditions under which these technologies outperform each other for a general network topology under a binary interference model. The analytical results in this paper are validated using software-defined radios.

Today's Enterprise Wireless LANs are comprised of densely deployed access points. This paper proposes BBN, an interference nulling scheme that leverages the high density of access points to enable multiple mobile devices to transmit simultaneously to multiple access points (APs), all within a single collision domain. BBN also leverages the capability of the APs to communicate with each other on the wired backbone to migrate most of the decoding complexity to the APs, while keeping the design at the mobile clients simple. Finally, we leverage the static nature of the access points to make BBN more practical in networks where the mobility of clients inhibit the use of traditional interference alignment schemes. We implement a prototype of BBN on USRP testbed showing its feasibility. The experiment results show that BBN provides a throughput gain of 1.48X over omniscient TDMA. Results from our trace-driven simulations show that BBN obtains a throughput of up to 5.6X over omniscient TDMA.

Global synchronization across time and frequency domains significantly benefits wireless communications. Multi-Cell (Network) MIMO, interference alignment solutions, opportunistic routing techniques in ad-hoc networks, OFDMA etc. all necessitate synchronization in either time or frequency domain or both. This paper presents sysname, a system that exploits the easily accessible and ubiquitous power line infrastructure to achieve synchronization in time and frequency domains across nodes distributed beyond a single-collision domain. sysname uses the power lines to transmit a reference frequency tone to which each node locks its frequency. sysname exploits the steady periodicity of delivered power signal itself to synchronize distributed nodes in time.

Large-scale Enterprise WLANs are amenable to centralized control and coordination through the wired backbone for improved performance. Distributed scheduling algorithms either fail to achieve high performance in real deployments due to their myopic view of interference characteristics, or take significant time to converge to a globally optimal solution. Thus, they are not reactive to current network conditions. Centralized packet scheduling algorithms do not suffer from the performance limitations of distributed approaches, but are non-trivial to implement. Tight time synchronization requirements make proposed centralized schemes difficult to use in practice. This paper proposes Relative Scheduling: a technique for triggering wireless transmissions through other wireless transmissions in a domino-like fashion, thus making tight time synchronization unnecessary. Through USRP experiments and trace-driven simulations, we show that our approach can achieve up to 1.96× the throughput of Distributed Coordination Function (DCF).

With recent advances in wireless systems, wireless in-band full duplex is proven possible. Prior work primarily allows a full duplex receiver to either send back a packet (bi-directional mode) or to forward another packet to its neighbor (secondary transmission). In our work, we look beyond a node pair and explore how a network can best utilize the full duplex capability. When a full duplex receiver does not have any packets to send back, concurrent transmissions (exposed transmissions) can be initiated. In a distributed channel access protocol, rapid signaling is crucial to identify the best mode for a given pair of transmitter and receiver, and to inform potential exposed terminals of transmission opportunities. In this paper, we present, RCTC, a fast and low overhead signaling mechanism based on Pseudo-random Noise (PN) sequences to enable multi-modal operation of wireless links in a distributed channel access setting to support concurrent transmissions in the neighborhood. Our prototype with USRPs shows up to 78% throughput gain. Extensive simulations over larger networks show a throughput gain of up to 131% for RCTC over the native full duplex scheme and up to 111% over a scheme that enables secondary transmission.

Metrics that aggregate the state of neighboring nodes are frequently used in wireless sensor networks. In this paper, we present two primitives that exploit simultaneous communications in 802.15.4 radios to enable a polling node to calculate with low power the number (or set) of its neighbors where some state predicate of interest holds. In both primitives, the poller assigns transmission powers and response lengths to its respective neighbors for their simultaneous response to each of its poll requests. The two primitives adopt complementary schemes for power assignment such that the Received Signal Strength Indicator (RSSI) of the respective signal from each neighbor is significantly different from that of all others in one primitive and nearly equivalent to that of the others in the other. The first primitive, LinearPoll, suits sparse networks and consumes energy that is linear in the size of its neighborhood, whereas the second primitive, LogPoll, suits dense networks and consumes constant energy. Compared to the state-of-the-art solutions that use multiple sub-carriers, our primitives are simpler and more compute-efficient while provide estimation with comparable quality. Compared to single-carrier solutions, our primitives achieve comparable quality at less than half the energy cost or richer information at comparable energy cost. They are also compatible with other radio physical layers. Based on our implementation for CC2420 radios on the TelosB platform, we evaluate the primitives in different wireless environments and neighborhood topologies to study their performance, the tradeoff between their estimation accuracy and energy cost, and methods for tuning their critical parameters, and we compare them with baseline counting protocols.

In this paper, we propose Symphony, a packet recovery architecture that encourages collisions among transmitters, and utilizes the unused capacity in the backbone to transmit recovered data packets and coordinate the efficient recovery of collided packets. Symphony improves the wireless throughput while incurring a low overhead on the typically under-utilized wired backbone. In Symphony, upon receiving the collided transmissions, the APs carefully suppress a subset of the transmissions. Realizing this idea in practice entails several challenges including identification of clients that have data to transmit and ensuring that the algorithm works despite imperfect time-synchronization and non-zero latency among APs. We present Symphony that addresses these challenges and show how it leverages Successive Interference Cancellation (SIC) to further increase the network throughput. Experiments performed on an USRP testbed shows that on an average, Symphony provides 43% and 187% higher throughput over Omniscient TDMA and IEEE 802.11, respectively. ns-3 based simulation results show that on an average, Symphony provides a throughput of up to 1.63x compared to omniscient TDMA and 5.6x compared to IEEE 802.11.

As its name suggests, "interference alignment" is a class of transmission schemes that aligns multiple sources of interference to minimize its impact, thus aiming to maximize rate in an interference network. To our knowledge, this paper presents the first real-time implementation of interference alignment. Other implementation in the literature are either done offline or assume a backchannel between participating nodes to perform alignment. On the other hand, this paper presents a blind interference alignment scheme, one that does not require channel state information at the transmitters or the knowledge of other transmitters data or the knowledge of data between receivers and functions in real-time.

In traditional wireless protocols, medium-access-control and physical-layer rate-allocation are performed separately. This paper makes the case for combining the two into a single cross-layer framework. It presents the design, implementation, and evaluation of queue-based channel-measurement and rate-allocation (Q-CMRA) that is based on this single cross-layer framework. Q-CMRA's distributed algorithms utilize both queue-state and channel-state to jointly control medium-access and rate-allocation. Such joint control is essential to improving spatial-reuse and total network throughput. In our experiments, Q-CMRA outperforms traditional CSMA-CA and doubles the total network throughput in some setups.

This paper presents a full duplex radio design using signal inversion and adaptive cancellation. Signal inversion uses a simple design based on a balanced/unbalanced (Balun) transformer. This new design, unlike prior work, supports wideband and high power systems. In theory, this new design has no limitation on bandwidth or power. In practice, we find that the signal inversion technique alone can cancel at least 45dB across a 40MHz bandwidth. Further, combining signal inversion cancellation with cancellation in the digital domain can reduce self-interference by up to 73dB for a 10MHz OFDM signal. This paper also presents a full duplex medium access control (MAC) design and evaluates it using a testbed of 5 prototype full duplex nodes. Full duplex reduces packet losses due to hidden terminals by up to 88%. Full duplex also mitigates unfair channel allocation in AP-based networks, increasing fairness from 0.85 to 0.98 while improving downlink throughput by 110% and uplink throughput by 15%. These experimental results show that a re- design of the wireless network stack to exploit full duplex capability can result in significant improvements in network performance.

This poster presents the design of single channel full-duplex wireless radios. The design uses a combination of RF and baseband techniques to achieve full-duplexing with minimal effect on link reliability. The poster shows two designs with different RF cancellation techniques, Antenna Cancellation and Signal Inversion Cancellation. It also discusses potential MAC and network gains with full-duplexing. It suggests ways in which a full-duplex system can solve some important problems with existing wireless systems including hidden terminals, loss of throughput due to congestion, and large end-to-end delays.

This paper discusses the design of a single channel full-duplex wireless transceiver. The design uses a combination of RF and baseband techniques to achieve full-duplexing with minimal effect on link reliability. Experiments on real nodes show the full-duplex prototype achieves median performance that is within 8% of an ideal full-duplexing system.

This paper explores metrics that capture to what degree packet reception on different links is correlated. Specifically, it explores metrics that shed light on when and why opportunistic routing and network coding protocols perform well (or badly). It presents a new metric, κ that, unlike existing widely used metrics, has no bias based on the packet reception ratios of links. This lack of bias makes κ a better predictor of performance of opportunistic routing and network coding protocols. Comparing Deluge and Rateless Deluge, Deluge's network coding counterpart, we find that κ can predict which of the two is best suited for a given environment. For example, irrespective of the packet reception ratios of the links, if the average κ of the link pairs is very high (close to 1.0), then using a protocol that does not code works better than using a network coding protocol. Measuring κ on several 802.15.4 and 802.11 testbeds, we find that it varies significantly across network topologies and link layers. κ can be a metric for quantifying what kind of a network is present and help decide which protocols to use for that network.

We present empirical measurements of the packet delivery performance of the latest sensor platforms: Micaz and Telos motes. In this article, we present observations that have implications to a set of common assumptions protocol designers make while designing sensornet protocols—specifically—the MAC and network layer protocols. We first distill these common assumptions in to a conceptual model and show how our observations support or dispute these assumptions. We also present case studies of protocols that do not make these assumptions. Understanding the implications of these observations to the conceptual model can improve future protocol designs.

Measuring 802.15.4 reception in three testbeds, we find that most intermediate links are bursty: they shift between poor and good delivery. We present a metric to measure this link burstiness and name it β. We find that link burstiness affects protocol performance and that β can predict the effects. We show that measuring β allows us to reason about how long a protocol should pause after encountering a packet failure to reduce its transmission cost. We find that using β as a guide to setting a single constant in a standard sensor network data collection protocol reduces its average transmission cost by 15%. In addition to data from 802.15.4 testbeds, we examine traces from 802.11b networks and find β has a broader relevance in the wireless domain.

Measuring low-level wireless network properties allows researchers to understand how protocols and applications perform in different environments. In this demo, we present SWAT - a software tool that automates gathering and analysis of network measurements. SWAT provides an interface for configuring experimental parameters in a network. It collects raw packet statistics such as the received signal strength and chip error, and provides modules for calculating and visualizing various metrics derived from these statistics.

This paper proposes a new sensornet protocol design goal: visibility. Visibility into behaviors at the network level will simplify debugging and ease the development process. We argue that increasing visibility is the responsibility of the network protocols themselves, and not solely the responsibility of existing debugging tools. We describe a quantitative visibility metric to evaluate and compare protocols, where visibility is defined as the energy cost of diagnosing the cause of a behavior in a protocol. The design and evaluation of Pull Collection Protocol, a novel multi-hop collection protocol, is an example of how to design for visibility without sacrificing throughput or node-level fairness. We also describe our optimizations for an existing protocol, Deluge, to increase its visibility and efficiency.

We present empirical measurements of the packet delivery performance of the Telos and MicaZ sensor platforms. At a high level, their behavior is similar to that of earlier platforms. They exhibit a reception "grey region," and temporal variations in packet loss. Looking more deeply, however, there are subtle differences, and looking deeper still, the patterns behind these complexities become clear. Environmental noise (802.11b) has high spatial correlation. Packet loss occurs when a receiver operating near its noise floor experiences a small decrease in received signal strength, rather than an increase in environmental noise. These variations cause the reception "grey region." Packet losses are highly correlated over short time periods, but are independent over longer periods. Based on these findings, current practices could be easily changed that would greatly improve efficiency and performance.