Mimicking an aperiodic nonlinear dynamic system is challenging as it is difficult to represent it using closed-form equations. A feedback-driven spike-based recurrent spiking neural network is a powerful computational model that can mimic such dynamical systems. We propose reservoirs-in-reservoir (R-i-R), a novel architecture to mimic the frequent pattern changes in space and time of an aperiodic nonlinear dynamic system. Here, a large reservoir is built by connecting multiple small reservoirs to a common output. These small reservoirs are individually specialized to mimic a portion of the input dynamic. The internal recurrent connections of each reservoir and its readout are trained using a recursive least squares (RLS)-based full first-order and reduced control error (full-FORCE) algorithm. To make the entire R-i-R architecture adaptable to the change in periodicity of an input, we implement a new cost function that incorporates a unique forgetting factor to control the fading and wind-up of the covariance matrix of each reservoir during training. We evaluate R-i-R using seven aperiodic nonlinear dynamic systems. We show that R-i-R with rate encoding reduces the error rate by an average 59% with 1.8X reduction in network size compared to state-of-the-art. To improve energy efficiency, we implement a time-to-first-spike encoding and show an average reduction of 39. 5% in the number of spikes.

This paper proposes a heterogeneous carrier-sense multiple access (CSMA) protocol named LoHEC as the first research attempt to improve energy fairness when applying CSMA to long-range wide area network (LoRaWAN). LoHEC is enabled by Channel Activity Detection (CAD), a recently introduced carrier-sensing technique to detect LoRaWAN signals even below the noise floor. The design of LoHEC is inspired by the fact that existing CAD-based CSMA proposals are in a homogeneous manner. In other words, they require LoRaWAN end devices to perform identical CAD regardless of the differences of their used network parameter – spreading factor (SF). This causes energy consumption imbalance among end devices since the consumed energy during CAD is significantly affected by SF. By considering the heterogeneity of LoRaWAN in terms of SF, LoHEC requires end devices to perform different numbers of CAD operations with different CAD intervals during channel access. Particularly, the number of needed CADs and CAD interval are determined based on the CAD energy consumption under different SFs. We conduct extensive experiments regarding LoHEC with a practical LoRaWAN testbed including 60 commercial off-the-shelf end devices. Experimental results show that in comparison with the existing solutions, LoHEC can achieve up to$0.85\times $improvement of the energy fairness on average.

Abstract Wireless communication devices must be protected from malicious threats, including active jamming attacks, due to the widespread use of wireless systems throughout our every‐day lives. Jamming mitigation techniques are predominately evaluated through simulation or with hardware for very specific jamming conditions. In this paper, an experimental software defined radio‐based RF jamming mitigation platform which performs online jammer classification and leverages reconfigurable beam‐steering antennas at the physical layer is introduced. A ray‐tracing emulation system is presented and validated to enable hardware‐in‐the‐loop jamming experiments of complex outdoor and mobile site‐specific scenarios. Random forests classifiers are trained based on over‐the‐air collected data and integrated into the platform. The mitigation system is evaluated for both over‐the‐air and ray‐tracing emulated environments. The experimental results highlight the benefit of using the jamming mitigation system in the presence of active jamming attacks.

Currently, wired respiratory rate sensors tether patients to a location and can potentially obscure their body from medical staff. In addition, current wired respiratory rate sensors are either inaccurate or invasive. Spurred by these deficiencies, we have developed the Bellyband, a less invasive smart garment sensor, which uses wireless, passive Radio Frequency Identification (RFID) to detect bio-signals. Though the Bellyband solves many physical problems, it creates a signal processing challenge, due to its noisy, quantized signal. Here, we present an algorithm by which to estimate respiratory rate from the Bellyband. The algorithm uses an adaptively parameterized Savitzky-Golay (SG) filter to smooth the signal. The adaptive parameterization enables the algorithm to be effective on a wide range of respiratory frequencies, even when the frequencies change sharply. Further, the algorithm is three times faster and three times more accurate than the current Bellyband respiratory rate detection algorithm and is able to run in real time. Using an off-the-shelf respiratory monitor and metronome-synchronized breathing, we gathered 25 sets of data and tested the algorithm against these trials. The algorithm's respiratory rate estimates diverged from ground truth by an average Root Mean Square Error (RMSE) of 4.1 breaths per minute (BPM) over all 25 trials. Further, preliminary results suggest that the algorithm could be made as or more accurate than widely used algorithms that detect the respiratory rate of non-ventilated patients using data from an Electrocardiogram (ECG) or Impedance Plethysmography (IP).

Abstract Low latency beamforming using phased antenna arrays is the key for practical deployment of envisioned millimetre wave (mmWave) Gbps mobile networks. This work aims towards reducing the overhead of the exhaustive sector‐level sweep phase of the analog beamforming adopted in the IEEE 802.11ad standard. This work is the first to propose the use of reconfigurable antenna single RF chain in the sub‐6 GHz new radio (NR) band to aid codebook‐based beam selection in the mmWave band of the NR. We exploit the congruence between the spatial propagation signatures of signals at both mmWave and sub‐6 GHz frequencies to reduce the beam search space. The simulation results show a significant reduction in mmWave beam search overhead up to on average and with an average gain loss of 3dB.

One of the major challenges faced by passive on-body wireless Internet of Things (IoT) sensors is the absorption of radiated power by tissues in the human body. We present a battery-less, wearable knitted Ultra High Frequency (UHF, 902-928 MHz) Radio Frequency Identification (RFID) compression sensor (Bellypatch) antenna and show its applicability as an on-body respiratory monitor. The antenna radiation efficiency is satisfactory in both free-space and on-body operations. We extract RF (Radio Frequency) sheet resistance values of three knitted silver-coated nylon fabric candidates at 913 MHz. The best type of fabric is selected based on the extracted RF sheet resistance. Simulated and measured performance of the antenna confirm suitability for on-body applications. The proposed Bellypatch antenna is used to measure the breathing activity of a programmable infant patient emulator mannequin (SimBaby) and a human subject. The antenna is highly sensitive to respiratory compression and relaxation. Fluctuations in the backscatter power level/Received Signal Strength Indicator (RSSI) in both cases range from 6 dB to 15 dB. The improved on-body read range of the proposed sensor antenna is 5.8 m, about 10 times higher than its predecessor wearable knitted strain sensing Bellyband antenna (0.6 m). The maximum simulated Specific Absorption Rate (SAR) on a human torso model is 0.25 W/kg, lower than the maximum allowable limit of 1.6 W/kg.

Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size.

Dynamic Spectrum Access (DSA) radios typically select their radio channels according to their data networking goals, a defined DSA spectrum operating policy, and the state of the RF spectrum. RF spectrum sensing can be used to collect information about the state of the RF spectrum and prioritize which channels should be assigned for DSA radio waveform transmission and reception. This paper describes a Greedy Channel Ranking Algorithm (GCRA) used to calculate and rank RF interference metrics for observed DSA radio channels. The channel rankings can then be used to select and/or avoid channels in order to attain a desired DSA radio performance level. Experimental measurements are collected using our custom software-defined radio (SDR) system to quantify the performance of using GCRA for a DSA radio application. Analysis of these results show that both pre and post-detection average interference power metrics are the most accurate metrics for selecting groups of radio channels to solve constrained channel assignment problems in occupied gray space spectrum.

Using a wearable electromyography (EMG) and an accelerometer sensor, classification of subject activity state (i.e., walking, sitting, standing, or ankle circles) enables detection of prolonged "negative" activity states in which the calf muscles do not facilitate blood flow return via the deep veins of the leg. By employing machine learning classification on a multi-sensor wearable device, we are able to classify human subject state between "positive" and "negative" activities, and among each activity state, with greater than 95% accuracy. Some negative activity states cannot be accurately discriminated due to their similar presentation from an accelerometer (i.e., standing vs. sitting); however, it is desirable to separate these states to better inform the risk of developing a Deep Vein Thrombosis (DVT). Augmentation with a wearable EMG sensor improves separability of these activities by 30%.

Traditional approaches to experimental characterization of wireless communication systems typically involves highly specialized and small-scale experiments to examine narrow aspects of each of these applications. We present the Drexel Grid SDR Testbed, a unified experimental framework to rapidly prototype and evaluate these diverse systems using: (i) field measurements to evaluate real time transceiver and channel-specific effects and (ii) network emulation to evaluate systems at a large scale with controllable and repeatable channels. We present the hardware and software architecture for our testbed, and describe how it is being used for research and education. Specifically, we show experimental network layer metrics in different application domains, and discuss future opportunities using this unique experimental capability.

OBJECTIVE: Utilizing passive radio frequency identification (RFID) tags embedded in knitted smart-garment devices, we wirelessly detect the respiratory state of a subject using an ensemble-based learning approach over an augmented Kalman-filtered time series of RF properties. METHODS: We propose a novel approach for noise modeling using a "reference tag," a second RFID tag worn on the body in a location not subject to perturbations due to respiratory motions that are detected via the primary RFID tag. The reference tag enables modeling of noise artifacts yielding significant improvement in detection accuracy. The noise is modeled using autoregressive moving average (ARMA) processes and filtered using state-augmented Kalman filters. The filtered measurements are passed through multiple classification algorithms (naive Bayes, logistic regression, decision trees) and a new similarity classifier that generates binary decisions based on current measurements and past decisions. RESULTS: Our findings demonstrate that state-augmented Kalman filters for noise modeling improves classification accuracy drastically by over 7.7% over the standard filter performance. Furthermore, the fusion framework used to combine local classifier decisions was able to predict the presence or absence of respiratory activity with over 86% accuracy. CONCLUSION: The work presented here strongly indicates the usefulness of processing passive RFID tag measurements for remote respiration activity monitoring. The proposed fusion framework is a robust and versatile scheme that once deployed can achieve high detection accuracy with minimal human intervention. SIGNIFICANCE: The proposed system can be useful in remote noninvasive breathing state monitoring and sleep apnea detection.

While the theoretical multiplexing gains of multiuser (MU) MIMO are substantial, the performance gains in practical systems such as IEEE 802.11ac and LTE/LTE-Advanced have been limited. One of the key limiting factors are spatially correlated user channels. In this paper, we evaluate the impact of pattern reconfigurable antennas (PRA) on downlink MU-MIMO transmission. PRAs are capable of dynamically adjusting their radiation pattern, providing an additional degree of freedom that can be leveraged to improve MU-MIMO performance by treating the array configuration and radiation characteristics as additional components in the joint optimization of adaptive system parameters. We conduct measurements in a statistically repeatable environment and demonstrate the gains provided by pattern diversity in decorrelating user channels. Our experimental results show significant gains in SINR and achievable sum rates of MU-MIMO linear precoders when appropriate directional beams are used to decorrelate the channel matrices and provide directional gain.

OFDM-based communication systems for Unmanned Aerial Vehicles (UAV) are designed predominately for high mobility scenarios with direct line of sight. The number of sub-channels used is selected to be small to mitigate the effects of inter-carrier interference (ICI) resulting from carrier frequency offset. We investigate interference effects in urban environments for quad-copter UAVs based on both ICI and inter-symbol interference (ISI), which results from multi-path scenarios. We propose that changing the number of sub-carriers used in the Air-to-Ground link improves bit error rate performance dependent upon the channel characteristics. In the presence of closely spaced buildings, a greater amount of multi-path will occur, motivating the use of more sub-carriers. A practical joint synchronizer is discussed to enable scalability of the proposed idea and characterize the source of ICI. Through the use of ray tracing models and realistic end-to-end simulations, we determine that UAV communications in urban environments benefit from adaptive sub-carrier counts to combat interference effects.

Beam-steering techniques using directional antennas are expected to play an important role in wireless network capacity expansion through ubiquitous small-cell deployment. However, integrating directional antennas into the existing wireless PHY and MAC stack of small cells has been challenging due to the added protocol overhead and lack of a robust antenna beam selection technique that can adapt well to environmental changes. This paper presents the design, implementation, and evaluation of LinkPursuit, a novel learning protocol for distributed antenna state selection in directional small-cell networks. LinkPursuit relies on reconfigurable antennas and a synchronous TimeDivision Multiple Access (TDMA) MAC to achieve simultaneous directional transmission and reception. Further, the system employs a practical antenna selection protocol based on the well known adaptive pursuit algorithm from the reinforcement learning literature. We implement a realtime prototype of LinkPursuit on the WARP platform and conduct extensive experiments to evaluate its performance. The empirical results show that appropriate use of directionality in LinkPursuit can result in higher network sum rates than omnidirectional transmission under various degrees of cross-link interference.

The widespread adoption of wireless devices in all aspects of the public's daily lives and the growing demand for professionals in the wireless communications field demonstrate the need for in-depth wireless engineering education. This increased demand triggered many academic institutions to start offering courses and programs in this area. As a part of our previous work, we provided a platform for students to keep up with the improvements in the wireless communications field and gain hands-on experience using Software Defined Radios (SDR). However, existing coursework and curricula in wireless networking is challenged by the fact that it is difficult to intuitively visualize how the nodes in the network are operating in a way that is linked to the everyday world. In this paper, we introduce visualizations of wireless lab exercises using an Augmented Reality (AR) mobile app. The AR app is designed and built for a wide selection of wireless communications courses, where we are able to visualize links between wireless nodes, antenna radiation patterns, and data throughput. In this paper, we experimentally assess and present the benefits of introducing AR-based visualization tools into wireless communications courses at both the undergraduate and graduate level in terms of student interest and change in skill level.

Blind interference alignment (IA) is a signaling scheme that suppresses interference in multi-user systems, without the knowledge of channel state information at the transmitter (CSIT). The key to performing IA without CSIT is the use of reconfigurable antennas (RA) that are capable of dynamically switching among a fixed number of radiation patterns to introduce artificial fluctuations in the channel. The radiation patterns used to realize blind IA have significant impacts on the overall performance of the system. Hence, an intelligent antenna pattern selection strategy is a crucial component of any practical RA-based blind IA implementation. In this work, we propose two reinforcement learning algorithms for selecting the optimal antenna configuration for blind IA. Furthermore, we evaluate the performance of these antenna mode selection techniques using over the air measurements on our software defined radio implementation of blind IA using a Reconfigurable Alford Loop Antenna that is capable of generating multiple radiation patterns. We quantify the performance of the algorithms in terms of received signal to interference and noise ratio (SINR) and show that our learning-based mode selection strategies are capable of choosing the highest performing mode 90% of the time and attain over 2 dB gain in SINR over other selection approaches.

Network-on-Chip (NoC) is a communication paradigm capable of facilitating a scalable interconnection infrastructure for multi core processors. Wireless NoCs have been introduced to improve the communication performance over long-distance processing nodes. Current on-chip antennas used in wireless NoCs communicate predominantly through surface waves, where the efficacy of the wireless nodes is partially determined by the radiation efficiency and transmission gain limited due to the conductivity loss of the silicon substrate. Recently, an on-chip propagation technique of radio waves was introduced, through the un-doped silicon layer as opposed to surface-waves prevalent in literature. The through-substrate propagation waves provide a unique solution to overcome the challenge of long-distance communication between processing nodes. In this work, overall improvements are shown compared to traditional wireless NoCs with the placement of antennas on undoped silicon (i.e. communicating through surface waves), simulated in NoC architectures across performance metrics of area, power consumption and latency.

With the increasing programmability and fast-paced dynamics of modern wireless systems, it has become more difficult to gain timely insights into wireless network operations. In this demonstration1 we present WiART, an augmented reality framework to help visualize and interact with wireless network activities in real time. WiART collects real-time radio and network statistics from participating network devices and depicts them on users' mobile devices in an intuitive way, leveraging the virtual information overlay of augmented reality. Specifically in our current implementation, WiART takes inputs from a cognitive radio link controlling beam-steerable reconfigurable antennas and annotates on a live mobile screen the active pre-measured radiation patterns. In the reverse flow, WiART enables users to select desired antenna radiation patterns directly in the mobile app and observe their effects on link performance in real time. These capabilities add an unprecedented level of instant visualization and interaction with wireless activities and provides valuable insights into the dynamics of a reconfigurable antenna-based cognitive radio network.

We have applied passive Radio Frequency Identification (RFID), typically used for inventory management, to implement a novel knit fabric strain gauge assembly using conductive thread. As the fabric antenna is stretched, the strength of the received signal varies, yielding potential for wearable, wireless, powerless smart-garment devices based on small and inexpensive passive RFID technology. Knit fabric sensors and other RFID biosensors can enable comfortable, continuous monitoring of biofeedback, but requires an integrated framework consisting of antenna modeling and fabrication, signal processing and machine learning on the noisy wireless signal, secure HIPAA- compliant data storage, visualization and human factors, and integration with existing medical devices and electronic health records (EHR) systems. We present a multidisciplinary, end-to-end framework to study, model, develop, and deploy RFID-based biosensors.

This paper addresses receiver (RX) signal processing in multiuser multiple-input multiple-output (MU-MIMO) systems. We focus on uplink orthogonal frequency-division multiple access (OFDMA)-based MU-MIMO communications under in-phase/quadrature (I/Q) imbalance in the associated radio frequency electronics. It is shown in the existing literature that transceiver I/Q imbalances cause cross-talk of mirror-subcarriers in OFDM systems. As opposed to typically reported single-user studies, we extend the studies to OFDMA-based MU-MIMO communications, with simultaneous user multiplexing in both frequency and spatial domains, and incorporate also external interference from multiple sources at RX input, for modeling challenging conditions in increasingly popular heterogeneous networks. In the signal processing developments, we exploit the augmented subcarrier processing, which processes each subcarrier jointly with its counterpart at the image subcarrier, and jointly across all RX antennas. Furthermore, we derive an optimal augmented linear RX in terms of minimizing the mean-squared error. The novel approach integrates the I/Q imbalance mitigation, external interference suppression, and data stream separation of multiple UEs into a single processing stage, thus avoiding separate transceiver calibration. Extensive analysis and numerical results show the signal-to-interference-plus-noise ratio (SINR) and symbol-error rate (SER) behavior of an arbitrary data stream after RX spatial processing as a function of different system and impairment parameters. Based on the results, the performance of the conventional per-subcarrier processing is heavily limited under transceiver I/Q imbalances, and is particularly sensitive to external interferers, whereas the proposed augmented subcarrier processing provides a high-performance signal processing solution being able to detect the signals of different users as well as suppress the external interference efficiently. Finally, we also extend the studies to massive MIMO framework, with very large antenna systems. It is shown that, despite the huge number of RX antennas, the conventional linear processing methods still suffer heavily from I/Q imbalances while the augmented approach does not have such limitations.

Reverberation chambers are closed reflective spaces that can emulate highly reverberant electromagnetic environments. The electromagnetic environment is primarily determined by the size of the cavity, effective conductivity, and leakage via apertures. In this effort, we investigate the performance of wireless OFDM communications in relation to the latter two by controlling the loading of a reverberation chamber and the effective aperture into a coupled cavity. A software defined radio measurement platform was used to assess the communication performance through a selection of link-level metrics including error vector magnitude, post processing signal-to-noise ratio, and throughput. The degradation of link quality is quantified for increasingly diffuse environments, as well as the improvement when leveraging a maximal ratio combining receiver diversity scheme. The link quality was found to improve in both the reverberation chamber and the coupled cavity for larger effective apertures. This result was analyzed using a time-dependent model for RF propagation in coupled cavities.

Cybersecurity is one of the fastest growing concerns in the world today. Recent global events show the need for more strict measures to protect the public from cyber attacks, which has triggered an increased demand for cybersecurity professionals. Many academic institutions began offering courses covering current cybersecurity concepts to satisfy this need. Although these courses educate students with proper skills, they lack the connection to current advancements in academic research. A more encompassing curriculum is needed in this rapidly growing field. For the most up-to-date education, we developed a course that takes a student-centric hands-on approach supported with Software Defined Radios to study current cybersecurity research projects in wireless networks. Our course consists of short lectures followed by lab sessions, where students implement security algorithms described by standards or by recent peer-reviewed research articles. Our results indicate that students appreciate the mixture of textbook and research topics being covered in the course and they feel more prepared for any future task in the cybersecurity field. While deviating from the textbook applies additional strain to educators, our paper shows that including current research practices in curriculum development efforts is a good investment towards a better educational outcome.

This paper describes a step by step approach in designing wireless physical layer modules starting from a software implementation in MATLAB to a hardware implementation using Xilinx SysGen and ModelSim. The described design flow promotes baseband physical layer research by providing high flexibility and speed to the process of module creation verification and deployment. The novelty introduced into our system lies within the flexible components created using this design flow, which enables on-the-fly modification of multiple parameters to suit various wireless protocols.

This paper develops an FPGA implementation of a trained coarse Carrier Frequency Offset estimation and correction scheme using MATLAB System Generator. The designed system is capable of supporting variable FFT sizes for Orthogonal Frequency Division Multiplexing signals and different pilot symbol structures making it compatible with a large number of wireless communication standards, unlike other work that is protocol specific. This design stands out from its more common implementations as it requires only one pilot symbol to be considered for synchronization by using a data-aided modified correlation scheme, allowing for an increase in throughput. The Bit Error Rate of the corrected signal received over an Additive White Gaussian Noise channel is compared to the case without correction. This scheme demonstrated increased performance throughput since only a single pilot symbol was used.

This paper describes a new software-defined radio (SDR) platform targeted for rapid prototyping of small-cell systems. The SDR hardware combines the signal processing power of Xilinx ML605 Virtex-6 FPGA board with the Nutaq Radio420X frequency-agile transceiver and reconfigurable antennas to form a highly versatile platform for spectrum sensing, spectrum access, and cooperative communications. We evaluate the platform with two example applications: an offline OFDM physical processing flow based on WARPLab, and a real-time online automatic gain control mechanism. The results show that our SDR platform can reliably handle both offline and online processing demands with the added benefit of frequency agility offered by a state-of-the-art radio transceiver.

Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the underlying DoA estimator is of uttermost importance. In this paper, we therefore propose a novel high performance DoA estimator for sectorized antennas that does not require cooperation between the transmitter and the localizing network. The proposed DoA estimator is broadly applicable with different sectorized antenna types and signal waveforms, and has low computational complexity. Using computer simulations, we show that our algorithm approaches the respective Cramer-Rao lower bound for DoA estimation variance if the signal-to-noise ratio (SNR) is moderate to large and also outperforms the existing estimators. Moreover, we also derive analytical error models for the underlying DoA estimation principle considering both free space as well as multipath propagation scenarios. Furthermore, we also address the fusion of the individual DoA estimates into a location estimate using the Stansfield algorithm and study the corresponding localization performance in detail. Finally, we show how to implement the localization in practical systems and demonstrate the achievable performance using indoor RF measurements obtained with practical sectorized antenna units.

In this paper, we propose a modified unitary multiple signal classification (MUSIC) algorithm for a two-port composite right/left handed (CRLH) leaky-wave antenna (LWA). The algorithm requires only real-valued operations to estimate direction of arrival (DoA) of the received signals, which will simplify future hardware implementation. The CRLH LWA consists of a cascade of metamaterial unit cells, periodically modulated using varactor diodes. By changing the voltages across series and shunt varactors, the antenna is able to uniformly steer its radiation pattern. The proposed modified unitary MUSIC algorithm uses both antennas' input ports to estimate the DoA. Existing MUSIC implementations requires eigenvalue decomposition in the complex-valued signal subspace, which results in highly complex hardware implementation. The computational complexity can be reduced using our proposed modified unitary MUSIC algorithm since it transforms the complex-valued covariance matrix of the received signals to a real-valued matrix using unitary transformations. The performance of the algorithm is experimentally demonstrated by using a CRLH LWA within an anechoic chamber facility and the estimated DoA results are in good agreement with the predicted angles.

Authentication schemes based on wireless physical layer channel information have gained significant attention in recent years. It has been shown in recent studies, that the channel based authentication can either cooperate with existing higher layer security protocols or provide some degree of security to networks without central authority such as sensor networks. We propose a Gaussian Mixture Model based semi-supervised learning technique to identify intruders in the network by building a probabilistic model of the wireless channel of the network users. We show that even without having a complete apriori knowledge of the statistics of intruders and users in the network, our technique can learn and update the model in an online fashion while maintaining high detection rate. We experimentally demonstrate our proposed technique leveraging pattern diversity and show using measured channels that miss detection rates as low as 0.1% for false alarm rate of 0.3% can be achieved.

We present a software library that aids in the design of mobile ad hoc networks (MANET). The OMAN design engine works by taking a specification of network requirements and objectives, and allocates resources which satisfy the input constraints and maximize the communication performance objective. The tool is used to explore networking design options and challenges, including: power control, adaptive modulation, flow control, scheduling, mobility, uncertainty in channel models, and cross-layer design. The unaddressed niche which OMAN seeks to fill is the general framework for optimization of any network resource, under arbitrary constraints, and with any selection of multiple objectives. While simulation is an important part of measuring the effectiveness of implemented optimization techniques, the novelty and focus of OMAN is on proposing novel network design algorithms, aggregating existing approaches, and providing a general framework for a network designer to test out new proposed resource allocation methods. In this paper, we present a high-level view of the OMAN architecture, review specific mathematical models used in the network representation, and show how OMAN is used to evaluate tradeoffs in MANET design. Specifically, we cover three case studies of optimization. The first case is robust power control under uncertain channel information for a single physical layer snapshot. The second case is scheduling with the availability of directional radiation patterns. The third case is optimizing topology through movement planning of relay nodes.

The acoustic through-metal channel is characterized by strong multipath components caused by the echoing of acoustic energy within the channel. Transmission at high data rates is therefore difficult to achieve with traditional single-carrier systems. This paper applies an adaptive bit-loading technique to the transmission of digital signals through metal barriers using ultrasonic signaling. The multi-carrier approach discussed here allows us to mitigate severe frequency selectivity of the through-metal communication link and improve spectral efficiency by exploiting the stationary nature of the channel. Experimental performance of bit loading is examined in an ultrasonic through-metal channel. Our results indicate that non-power-scaled rate adaptive bit loading significantly outperforms non-adaptive modulation. Adaptive bit loading was shown to adhere to a strict BER constraint while increasing data rates by roughly 240% from values of 5 Mbps to approximately 12 Mbps when compared to narrowband modulation techniques.

In this paper we investigate the performance of an antenna configuration selection algorithm for a pattern recon-figurable antenna in a 2 × 2 MIMO-OFDM system. Channel capacity measurements were performed over a single link in both Line-of-sight (LOS) and Non-LOS (NLOS) indoor environments. From these measurements, an adaptive configuration selection algorithm was developed and the scheme's performance gains relative to a non-reconfigurable antenna were quantified. Finally, the configuration selection algorithm was also paired with a modulation rate adaptive scheme in order to exploit the improved channel capacity available with reconfigurable antennas.

This paper describes the development of a new Software Defined Communications (SDC) testbed architecture. SDC aims to generalize the area of software defined radio to include propagation media not exclusively limited to radio frequencies (optical, ultrasonic, etc.). This SDC platform leverages existing and custom hardware in combination with reference software applications in order to provide a complete research and development platform. This platform can be used to implement current and future standards that make use of highly demanding communications techniques, including ultrawideband (UWB) radio and free-space optical communications. This paper describes the commercial and custom hardware that is being integrated into the platform, including the baseband hardware and the modular transceiver frontends. Furthermore, the paper describes the software development currently in progress with this platform, including the integration of available open source designs into the platform, and the development of custom IP for scalable OFDM PHY implementations in radio and optical communications. We seek to create a complete research platform for the commercial and academic wireless communities, capable of delivering the highest possible performance and flexibility while providing the necessary development tools and reference designs in order to minimize system learning curve and development cost.

A cross-layer scheme, namely ALOHA with Collision Resolution (ALOHA-CR), is proposed for high throughput wireless communications in a cellular scenario. Transmissions occur in a time-slotted ALOHA-type fashion but with an important difference: simultaneous transmissions of two users can be successful. The physical layer required to achieve this functionality is described and the statistical properties of the user delays are determined so that the probability of user separation is maximized. An implementation of ALOHA-CR on the Wireless Open Access Research Platform (WARP) testbed containing software defined radio nodes is discussed and experimental results are presented.

Multiple antenna communication systems are a solution to meet the demand for pervasive computing and ubiquitous wireless communications. As multiple antenna communication systems become more broadly deployed, integrating unobtrusive antennas into various form factors is becoming increasingly important. Antennas that are flexible and transparent can ease this design constraint. In this paper, we present a dipole conductive polymer antenna that is flexible and transparent. The focus of this paper is to show how well this antenna works in a communications network by evaluating channel capacity and packet error rate.

Multiple input multiple output (MIMO) communication systems in an ad-hoc network can provide high spectral efficiency. Several resource allocation methods have been presented and experimentally demonstrated to improve performance in a resource limited environment. Recently, a game theoretic method has been published with promising results. The goal of this paper is to present simulation and experimental results for this game theoretic technique.

This paper demonstrates a method that allows reconfigurable multi element antennas to select the antenna configuration at the receiver of a Multiple Input Multiple Output (MIMO) communication system. This antenna configuration selection scheme consists of using spatial correlation and average Signal to Noise Ratio (SNR) information to select the antenna radiation pattern at the receiver. We show that using this approach it is possible to achieve capacity gains in a multi element reconfigurable antenna system without modifying the data frame of a conventional MIMO system. We demonstrate our configuration selection algorithm through an analysis of reconfigurable circular patch antennas in realistic MIMO clustered channel models. Using this approach we also show that the functionality of the proposed selection scheme is connected to antenna parameters such as radiation efficiency, input impedance, the level of diversity between the radiation patterns of different antenna configurations, and the average system SNR. The capacity gain achievable with this configuration selection approach is calculated through numerical simulations using reconfigurable circular patch antennas at the receiver of a MIMO system that employs minimum mean square error receivers for channel estimation. The performance of the proposed method is compared to that of a MIMO system that estimates the channel transfer matrix for each antenna configuration in order to select the optimal receiver antenna radiation pattern. Channel capacity and Bit Error Rate (BER) results show the improvement offered by the proposed selection scheme relative to a conventional antenna selection technique for reconfigurable MIMO systems; in particular we show that the improvement increases with the number of configurations in the multi element reconfigurable antenna.

A rectangular patch antenna array for MIMO communications was simulated on a magnetic permeability enhanced metamaterial. The performance of this antenna array was studied relative to a similar array constructed on a regular substrate. The analysis was performed with respect to performance metrics such as degree of mutual coupling for different element spacing, achievable channel capacity, bandwidth and efficiency. The array built on the metamaterial substrate showed significant size reduction, less mutual coupling and significant channel capacity improvement compared to similar arrays on conventional substrates.

This paper simulates a typical indoor MIMO-UWB OFDM system using computational electromagnetics. The goal is to create a simulation using a real world environment and taking into account realistic antenna effects. The effects of the wireless channel are examined on a per subcarrier basis. Analysis of the simulation results shows that the channel response and subsequently, mutual information is highly dependent on subcarrier. Furthermore, our analysis shows that by taking advantage of channel knowledge, a great increase in mutual information can be obtained. This leads to the conclusion that adaptive modulation has the potential to offer significant increase in system capacity.

Previous research has shown that adaptive switching between multiple-input multiple-output (MIMO) transmission strategies like spatial multiplexing and beamforming increases link reliability and capacity gains, as compared to fixed transmission strategies. To get the full benefit of adaptive switching it is necessary to obtain accurate estimates of the SNR values when we switch between the transmission strategies. In this paper, it is shown that (relatively more) accurate switching point estimates can be obtained by taking into account real-life effects like mutual coupling and antenna efficiencies, for switching between statistical beamforming and spatial multiplexing. Using simulations, it is shown that accounting for these effects can make the switching point estimate more accurate by as much as 12 dB, compared to the case when the practical effects are not considered.

This paper considers interference characterization and management in wireless ad hoc networks using MIMO techniques. The power allocation in each link is built into a non-cooperative game where a utility function is identified and maximized. Due to poor channel conditions, some links have very low data transmission rates even though their transmit powers are high. Therefore, a mechanism for shutting down links is proposed in order to reduce cochannel interference and improve energy efficiency. The multiuser water-filling and the gradient projection methods are compared with the proposed game theoretic approach in terms of system capacity and energy efficiency. It is shown that using the proposed method with the link shut-down mechanism allows the MIMO ad hoc network to achieve the highest energy efficiency and the highest system capacity

Many researchers have done significant work to reduce fast fading in single channel wireless systems using prediction. We introduce a novel synthesis and prediction filter at the smart antenna base station to predict the vector channel in time-division duplex systems. We show the advantage of modeling each component of the vector channel with the same coefficients over modeling each channel separately. Experimental results from measurements taken in downtown Austin show that prediction of the vector channel is feasible, even as far as ten steps ahead. Ray tracing simulations of downtown Austin, assuming noiseless line-of-sight and nonline-of-sight channels, show that these predictions enhance downlink beamforming resulting in improvements in downlink received power in excess of 10 dB in nonline-of-sight scenarios compared to beamforming without predictions. Furthermore, this improvement allows an increase in the duplex interval by more than three times, assuming constant mobile velocity.

This paper investigates the benefit of mutual coupling compensation via a method of moments (MoM) approach in a uniform circular antenna array operating at 1.8 GHz. This mutual coupling compensation technique is applied to a direction of arrival (DOA) study of up to two cochannel mobile users. Field measurements and computer simulations are examined to explore the assumptions of the technique and verify its effect when using the Bartlett and MUSIC DOA algorithms. Computer simulations considering the application of the technique to down-link beamforming are also included. Experimental results show that the mutual coupling compensation technique improves up-link DOA algorithm performance primarily by reducing unwanted sidelobe levels. This reduction in sidelobe levels aids in down-link beamforming weight design. Specifically, simulation results show that use of the compensation technique allows DOA-based down-link beamforming algorithms to perform similarly to spatial signature-based algorithms. All field measurements were made using the smart antenna testbed at the University of Texas at Austin.