In this paper, we propose a two-stage weighted projection method (TS-WPM) for time-difference-of-arrival (TDOA)-based localization, providing provable improvements in positioning accuracy, particularly under high geometric dilution of precision (GDOP) and low signal-to-noise ratio (SNR) conditions. TS-WPM employs a two-stage iterative refinement approach that dynamically updates both range and position estimates, effectively mitigating residual errors while maintaining computational efficiency. Additionally, we extend TS-WPM to support cooperative localization by leveraging two-way time-of-arrival (TW-TOA) measurements, which enhances positioning accuracy in scenarios with limited anchor availability. To analyze TS-WPM, we derive its error covariance matrix and mean squared error (MSE), establishing conditions for its optimality and robustness. To facilitate rigorous evaluation, we develop a 3rd Generation Partnership Project (3GPP)-compliant analytical framework, incorporating 5G New Radio (NR) physical layer aspects as well as large-scale and small-scale fading. As part of this, we derive a generalized Cramér-Rao lower bound (CRLB) for multipath propagation and introduce a novel non-line-of-sight (NLOS) bias model that accounts for propagation conditions and SNR variations. Our evaluations demonstrate that TS-WPM achieves near-CRLB performance and consistently outperforms state-of-the-art weighted nonlinear least squares (WNLS) in high GDOP and low SNR scenarios. Moreover, cooperative localization with TS-WPM significantly enhances accuracy, especially when an insufficient number of anchors (such as 2) are visible. Finally, we analyze the computational complexity of TS-WPM, showing its balanced trade-off between accuracy and efficiency, making it a scalable solution for real-time localization in next-generation networks.

Most data-driven localization approaches rely on fingerprinting-based techniques, which require labor-intensive site surveys and periodic calibration to maintain accuracy. These efforts limit the scalability of fingerprinting-based methods. This paper presents a calibration-free approach for indoor localization using crowdsourced data without requiring location labels. The statistical information of the crowdsourced data is utilized to learn the signal propagation characteristics. We apply a cumulative distribution function (CDF) conversion to map signal strength measurements to distances from access points. This CDF conversion overcomes the limitations of the conventional logdistance path loss (LDPL) model and efficiently captures the effect of shadow fading and multipath. Based on the estimated distances, the target locations are determined using an improved trilateration algorithm. Our approach is fully unsupervised, requiring no location labels, and significantly reduces the need for site surveys. Experimental results demonstrate substantial improvements in localization performance compared to LDPL-based methods. Moreover, our localization accuracy using unlabeled data approaches that of the$k$-Nearest-Neighbor algorithm, which relies on labeled fingerprints.

In this paper, we derive the fundamental limits of low earth orbit (LEO) enabled localization by analyzing the available information in signals from multiple LEOs during different transmission time slots received on a multiple antennas and evaluate the utility of these signals for 9D localization (3D position, 3D orientation, and 3D velocity estimation). We start by deriving the Fisher Information Matrix (FIM) for the channel parameters that are present in the signals received from LEOs in the same or multiple constellations during multiple transmission time slots. To accomplish this, we define a system model that captures i) time offset between LEOs caused by having relatively cheap clocks, ii) frequency offset between LEOs, iii) the unknown Doppler rate caused by high mobility LEOs, and iv) multiple transmission time slots from a particular LEO. We transform the FIM for the channel parameters to the FIM for the location parameters and determine the required conditions for localization. To do this, we start with the 3D localization cases: i) 3D positioning with known velocity and orientation, ii) 3D orientation estimation with known position and velocity, and iii) 3D velocity estimation with known position and orientation. Subsequently, we derive the FIM for the full 9D localization case (3D position, 3D orientation, and 3D velocity estimation) in terms of the FIM for the 3D localization. Using these results, we determine the number of LEOs, the operating frequency, the number of transmission time slots, and the number of receive antennas that allow for different levels of location estimation. We then provide insights into the interaction between the number of LEOs, the operating frequency, the number of transmission time slots, and the number of receive antennas. One key result is that in the presence of time and frequency offsets and Doppler rate, it is possible to perform 9D localization (3D position, 3D velocity, and 3D orientation estimation) of a receiver by utilizing the signals from three LEO satellites observed during three transmission time slots received through multiple receive antennas.

Wireless positioning in Non-Line-of-Sight (NLoS) scenarios presents significant challenges due to multipath effects that lead to biased measurements and reduced positioning accuracy. This paper revisits electromagnetic field theory related to diffraction and in the context of wireless positioning and proposes a novel positioning technique that greatly improves accuracy in NLoS environments dominated by diffraction. The method is applied to a critical public safety use case: precisely locating at-risk individuals within buildings, with a particular focus on improving 3D positioning and z-axis accuracy. By leveraging the Geometrical Theory of Diffraction (GTD), the approach introduces an innovative NLoS path length model and a new NLOS positioning technique. Using Fisher information analysis, we establish the conditions required for 3D positioning and derive lower bounds on positioning performance for both 3D and z-axis estimates for the proposed NLOS positioning technique. Additionally, we propose an algorithmic implementation of the proposed NLoS positioning method using non-linear least squares estimation, which we term D-NLS. The positioning performance of our proposed NLOs positioning technique is validated using an extensive ray-tracing simulation. The numerical results highlight the superiority of our approach in outdoor-to-indoor environments, which directly estimates NLoS path lengths and delivers significant performance enhancements over existing methods for both 3D and z-axis positioning scenarios.

This work investigates the use of linear reinforce-ment learning to effectively jam an OFDM-modulated victim signal. Prior work has shown improved convergence with the use of linear bandits, a variant of reinforcement learning, to jam single-carrier digital phase-amplitude modulation schemes using time-domain (TD) jamming schemes. However, communication systems today typically employ orthogonal frequency division multiplexing (OFDM) to transmit data, particularly in 4G/5G networks. This work explores the use of linear Thompson Sampling (TS) to efficiently jam OFDM-modulated signals where the jammer may select from single-carrier and OFDM jamming schemes. We show that linear TS performs better than traditional reinforcement learning (UCB-1 algorithm) in terms of maximizing the victim symbol error rate (SER). We also draw novel insights by observing the action states to which the reinforcement learning algorithm converges to.

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.

The paper proposes a new architecture for Distributed MIMO (D-MIMO) in which the base station (BS) jointly transmits with wireless mobile nodes to serve users (UEs) within a cell for 6G communication systems. The novelty of the architecture lies in the wireless mobile nodes participating in joint D-MIMO transmission with the BS (referred to as D-MIMO nodes), which are themselves users on the network. The D-MIMO nodes establish wireless connections with the BS, are generally near the BS, and ideally benefit from higher SNR links and better connections with edge-located UEs. These D-MIMO nodes can be existing handset UEs, Unmanned Aerial Vehicles (UAVs), or Vehicular UEs. Since the D-MIMO nodes are users sharing the access channel, the proposed architecture operates in two phases. First, the BS communicates with the D-MIMO nodes to forward data for the joint transmission, and then the BS and D-MIMO nodes jointly serve the UEs through coherent D-MIMO operation. Capacity analysis of this architecture is studied based on realistic 3GPP channel models, and the paper demonstrates that despite the two-phase operation, the proposed architecture enhances the system’s capacity compared to the baseline where the BS communicates directly with the UEs.

In this paper, we rigorously derive the information in the signals received from low earth orbit (LEO) satellites, which are unsynchronized in time and frequency, and their utility for 3D position estimation. To enable this derivation, we define a system model that captures i) the time offset between LEOs caused by having cheap clocks, ii) the frequency offset between LEOs, and iii) multiple transmission time slots from a particular LEO. After this definition, we derive the Fisher information matrix (FIM) for the relevant channel parameters and transform the FIM for the channel parameters to the FIM for the 3D position. These derivations show the interactions between the number of LEOs, the operating frequency, the number of transmission time slots, and the number of receive antennas. Subsequently, these allow us to determine the minimal number of LEOs, the number of transmission time slots, and the number of receive antennas needed to determine the 3D position. One key result is that when the LEOs are unsynchronized in time and frequency and experience a high Doppler rate, the 3D position can be determined by observing a single LEO for four transmission time slots.

Large antenna arrays and reconfigurable intelligent surfaces (RIS) have been made available due to the use of higher frequency bands, and there is the possibility that these arrays can become disturbed. Hence, their orientation could change after deployment. Since low earth orbits (LEO) are being proposed to provide position, navigation, and timing services, and LEOs from different constellations could be unsynchronized in time and frequency and experience a high Doppler rate. We ask, "can unsynchronized LEOs provide 3D orientation for a ground receiver?" To answer this question, we introduce the Fisher information matrix (FIM) and use the FIM to quantify the available information needed for 3D orientation estimation utilizing signals received from LEOs during multiple transmission time slots across multiple receive antennas. We observe by analyzing the positive definitiveness of the FIM for the 3D orientation that irrespective of the presence or absence of both time and frequency offsets, the 3D orientation of the receiver can be estimated through the multiple TOA measurements received across the receive antennas from two LEO satellites during a single transmission time slot. We also observe by analyzing the positive definitiveness of the FIM for the 3D orientation that irrespective of the presence or absence of both time and frequency offsets, the 3D orientation of the receiver can be estimated through the multiple TOA measurements received across the receive antennas during two transmission time slots from a single LEO satellite.

This paper develops fundamental bounds for localization in orthogonal frequency division multiplexing (OFDM) systems aided by reconfigurable intelligent surfaces (RISs). Specifically, we start from the assumption that the position and orientation of a RIS can be viewed as prior information for RIS-aided localization in wireless systems and derive Bayesian bounds for the localization of a user equipment (UE). To do this, we first derive the Bayesian Fisher information matrix (FIM) for channel parameters to derive the Bayesian localization bounds. Then, to focus on the geometric channel parameters, we derive the equivalent Fisher information matrix (EFIM) and show that it has a definite structure. Subsequently, we show through the information loss associated with the EFIM that when the RIS reflection coefficients remain constant across all OFDM symbols, and there is no prior information about the nuisance parameters, the corresponding submatrix in the EFIM related to the RIS angle parameters is a zero matrix. As a result of the EFIM being a zero matrix, estimating the RIS-related angle channel parameters is not possible when the RIS reflection coefficients remain constant across all OFDM symbols. This observation is crucial for the estimation of the RIS-related angle parameters. It dictates that to estimate the RIS-related angle parameters, there must be more than one OFDM transmission with differing RIS reflection coefficients. Furthermore, due to this observation, we note that localization of a single antenna UE through the signals received from reflections from a single RIS to the UE is not feasible in the far-field when the RIS reflection coefficients remain constant across all OFDM symbols. We also show that the FIM for the RIS-related channel parameters can be decomposed into i) information provided by the receiver, ii) information provided by the transmitter, and iii) information provided by the RIS components. We then transform the Bayesian EFIM for geometric channel parameters to the Bayesian FIM for the UE position and orientation parameters and examine its specific structure under a particular class of RIS reflection coefficients.

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources. Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance.

This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance. We demonstrate through measurement-level simulations that effective waveform selection strategies can be quickly learned, even in cases where the radar must select from a large catalog of candidate waveforms. The radar learns to adaptively select a bandwidth for appropriate resolution and a slow-time unimodular code for interference mitigation in the scene of interest by optimizing an expected classification metric.

For emergency response scenarios like firefighting in urban environments, there is a need to both localize emergency responders inside the building and also support a high bandwidth communication link between the responders and a command-and-control center. The emergency networks for such scenarios can be established with the quick deployment of Unmanned Aerial Vehicles (UAVs). Further, the 3D mobility of UAVs can be leveraged to improve the quality of the wireless link by maneuvering them into advantageous locations. This has motivated recent propagation measurement campaigns to study low-altitude air-to-ground channels in both 5G-sub6 GHz and 5G-mmWave bands. In this paper, we develop a model for the link in a UAV-assisted emergency location and/or communication system. Specifically, given the importance of Line-of-Sight (LoS) links in localization as well as mmWave communication, we derive a closed-form expression for the LoS probability. This probability is parameterized by the UAV base station location, the size of the building, and the size of the window that offers the best propagation path. An expression for coverage probability is also derived. The LoS probability and coverage probabilities derived in this paper can be used to analyze the outdoor UAV-to-indoor propagation environment to determine optimal UAV positioning and the number of UAVs needed to achieve the desired performance of the emergency network.

This paper presents a rigorous examination of the estimation of the misorientation of a reconfigurable intelligent surface (RIS) based on the received signal when the user equipment (UE) is in the near or far fields of the RIS. The Bayesian analysis views the location of the RISs as a priori system-level information. With incorrect a priori information, the position and orientation offsets of the RISs become parameters that need to be estimated and fed back to the Base station (BS) for correction. Two key insights are obtained from our Bayesian analysis. First, the Bayesian equivalent Fisher information matrix (EFIM) for the channel parameters indicates that the RIS orientation offset cannot be estimated when there is an unknown phase offset in the received signal in the far-field propagation regime. Second, the corresponding EFIM for the channel parameters in the received signal observed in the near-field shows that this unknown phase offset does not hinder the estimation of the RIS orientation offset when the UE has more than one receive antenna.

Many modern wireless devices with accurate positioning needs have access to many vision sensors, such as a camera, radar, and Light Detection and Ranging (LiDAR). In numerous scenarios where wireless-based positioning is either inaccurate or unavailable, using information from vision sensors becomes highly desirable for determining the precise location of the wireless device. While localization utilizing vision information has been explored from different algorithmic perspectives, the underlying mathematical underpinnings of this problem space remain largely unexplored. Inspired by this, we develop a new analytical framework for vision-based localization in which error-free distance measurements in vision data are utilized to accurately determine the position of the target. Compared to wireless-based positioning, a notable differentiation of this approach is the inclusion of non-unique landmarks, such as lampposts, which may lack distinguishable features in the vision data. For instance, when the target is located close to a lamppost, it becomes challenging to precisely identify the specific lamppost (among several in the region) that is near the target. By assuming that the landmarks of various types follow a marked Poisson point process (PPP), we establish that three range measurements are sufficient for determining the correct combination of landmarks in a two-dimensional plane. When the number of measurements is less than three, there exists a potential for making errors in associating these range measurements with the corresponding landmark combination. We provide a mathematical characterization of this probability of error, which involves a novel joint distribution of key random variables.

This paper presents a rigorous Bayesian analysis of the information in the signal (consisting of both the line-of-sight (LOS) path and reflections from multiple reconfigurable intelligent surfaces (RISs)) that originate from a single base station (BS) and is received by a user equipment (UE). For a comprehensive Bayesian analysis, both near and far field regimes are considered. The Bayesian analysis views both the location of the RISs and previous information about the UE as a priori information for UE localization. With outdated a priori information, the position and orientation offsets of the RISs become parameters that need to be estimated and fed back to the BS for correction. We first show that when the RIS elements have a half wavelength spacing, this RIS orientation offset is a factor in the pathloss of the RIS paths. Subsequently, we show through the Bayesian equivalent Fisher information matrix (EFIM) for the channel parameters that the RIS orientation offset cannot be corrected when there is an unknown phase offset in the received signal in the far-field regime. However, the corresponding EFIM for the channel parameters in the received signal observed in the near-field shows that this unknown phase offset does not hinder the estimation of the RIS orientation offset when the UE has more than one receive antenna. Furthermore, we use the EFIM for the UE location parameters to present bounds for UE localization in the presence of RIS uncertainty. We rigorously show that regardless of size and propagation regime, the RISs are only helpful for localization when there is a priori information about the location of the RISs. Finally, through numerical analysis of the EFIM and its smallest eigenvalue, we demonstrate the loss in information when the far-field model is incorrectly applied to the signals received at a UE experiencing near-field propagation.

In this paper, we examine the use of a deep multi-layer perceptron architecture to classify received signals as one of seven common waveforms, single carrier (SC), single-carrier frequency division multiple access (SC-FDMA), orthogonal frequency division multiplexing (OFDM), linear frequency modulation (LFM), amplitude modulation (AM), frequency modulation (FM), and phase-coded pulse modulation used in communication and radar networks. Synchronization of the signals is not needed as we assume there is an unknown and uncompensated time and frequency offset. The classifier is open-set meaning it assumes unknown waveforms may appear. Isolation forest (IF) models acting as binary classifiers are used for each known signal class to perform detection of possible unknown signals. This is accomplished using the 32-length feature vector from a dense layer as input to the IF models. The classifier and IF models work together to monitor the spectrum and identify waveforms along with detecting unknown waveforms. Results showed the classifier had 100% classification rate above 0 dB with an accuracy of 83.2% and 94.7% at -10 dB and -5 dB, respectively, with signal impairments present. Results for the IF models showed an overall accuracy of 98% when detecting known and unknown signals with signal impairments present. IF models were able to reject all unknown signals while signals similar to known signals were able to pass through 2% of the time due to the contamination rate used during training. Overall, the entire system can classify correctly in an open-set mode with 98% accuracy at SNR greater than 0 dB.

Complementary to the fine-grained channel state information (CSI) and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes [i.e., beam SNR (bSNR)] during the millimeter-wave (mmWave) beam training phase were recently repurposed for Wi-Fi sensing applications, such as human activity recognition and indoor localization. This article proposes a multiband Wi-Fi sensing framework to fuse features from both CSI from 5-GHz bands and the mid-grained bSNR at 60 GHz with feature granularity matching (GM) that pairs feature maps from the CSI and bSNR at different granularity levels with learnable weights. To address the issue of limited labeled training data, we propose to pretrain an autoencoder-based multiband Wi-Fi fusion network in an unsupervised fashion. For specific sensing tasks, separate sensing heads can be attached to the pretrained fusion network with fine-tuning. The proposed framework is thoroughly validated for three sensing applications using in-house experimental data sets: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to a list of baseline methods demonstrates the effectiveness of GM. An ablation study is performed as a function of the amount of labeled data, the latent space dimension, and learning rates.

In this paper, we present a differential modulation and detection scheme for use in the uplink of a system with a large number of antennas at the base station, each equipped with low-resolution analog-to-digital converters (ADCs). We derive an expression for the maximum likelihood (ML) detector of a differentially encoded phase information symbol received by a base station operating in the low-resolution ADC regime. We also present an equal performing reduced complexity receiver for detecting the phase information. To increase the supported data rate, we also present a maximum likelihood expression to detect differential amplitude phase shift keying symbols with low-resolution ADCs. We note that the derived detectors are unable to detect the amplitude information. To overcome this limitation, we use the Bussgang Theorem and the Central Limit Theorem (CLT) to develop two detectors capable of detecting the amplitude information. We numerically show that while the first amplitude detector requires multiple quantization bits for acceptable performance, similar performance can be achieved using one-bit ADCs by grouping the receive antennas and employing variable quantization levels (VQL) across distinct antenna groups. We validate the performance of the proposed detectors through simulations and show a comparison with corresponding coherent detectors. Finally, we present a complexity analysis of the proposed low-resolution differential detectors.

The growing prominence of spectrum sharing technologies has spurred interest in spectrum monitoring technologies with the ability to identify unknown wireless signals. This paper presents a convolutional neural network (CNN) deep learning model to classify 4G LTE downlink, 4G LTE uplink, 5G NR downlink, 5G NR uplink, IEEE 802.11ax (WiFi 6), and Bluetooth Low Energy (BLE) 5.0 signals. The classifier operates on In-phase and Quadrature (I/Q) samples and does not require synchronization with the unknown signals. To improve the generalizability of the classifier, comprehensive signal datasets are generated to include a wide range of signal configurations found in the standards. These signals are impaired with additive white Gaussian noise (AWGN), Rayleigh or Ricean multipath fading channels, frequency offsets, and I/Q imbalances to make the signals more realistic. The exploration of time domain, frequency domain, and time-frequency domain features reveals high frequency resolution time-frequency domain features perform best. The proposed CNN model achieves a high classification accuracy in the presence of all of the aforementioned impairments, achieving over 94% accuracy for signal to noise ratios (SNR) greater than 0 dB.

A vector sensor is a type of sensor array with six collocated antennas that measures all electromagnetic field components of incident waves. The vector sensor has been shown to be advantageous in estimating both the angle of arrival and polarization of the incident sources. While direction-of-arrival (DOA) estimation with machine learning for linear arrays has been well studied, there has not been a similar solution for the vector sensor. In this paper, we propose a neural network that is capable of determining the number of the sources and estimating the DOA of each source based on the covariance matrix extracted from the received data. Additionally, a solution is proposed to associate the estimated DOAs to their sources, and a customized training loss function to address the field-of-view limit problem. Simulation results show that neural networks can achieve a reasonably accurate DOA estimation measured by the RMSE for up to five sources.

This paper describes a sequential, or online, learning scheme for adaptive radar transmissions that facilitate spectrum sharing with a non-cooperative cellular network. First, the interference channel between the radar and a spatially distant cellular network is modeled. Then, a linear Contextual Bandit (CB) learning framework is applied to drive the radar's behavior. The fundamental trade-off between exploration and exploitation is balanced by a proposed Thompson Sampling (TS) algorithm, a pseudo-Bayesian approach which selects waveform parameters based on the posterior probability that a specific waveform is optimal, given discounted channel information as context. It is shown that the contextual TS approach converges more rapidly to behavior that minimizes mutual interference and maximizes spectrum utilization than comparable online learning algorithms. Additionally, it is shown that the TS learning scheme results in a favorable SINR distribution compared to other online learning algorithms. Finally, the proposed TS algorithm is compared to a deep reinforcement learning model. Simulation results show that the TS algorithm maintains competitive performance with a more complex Deep Q-Network (DQN).

Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals. Additionally, recurrent neural networks typically process data on a sequential basis, enabling the potential for near real-time results. In this work, we investigate the novel usage of "just enough" decision making metrics for making decisions during inference based on a variable number of input received symbols. Since some signals are more complex than others, due to channel conditions, transmitter/receiver effects, etc., being able to dynamically utilize just enough of the received symbols to make a reliable decision allows for more efficient decision making in applications such as electronic warfare and dynamic spectrum sharing. To demonstrate the validity of this concept, four approaches to making "just enough" decisions are considered in this work and each are analyzed for their applicability to wireless communication machine learning applications.

Link stability is an essential factor that should be considered in the design procedure of Wireless Sensor Networks (WSN) since it plays an important role in choosing different design options. Link stability becomes even more important in the case of an ocean-surface WSN, where the links between the sensors may change constantly due to ocean wave movements. Despite the importance of ocean-surface WSNs and their advantages over the existing ocean monitoring methods, current research lacks a model that describes the stability of wireless links among the buoyant sensor nodes. The existing models mostly focus on static scenarios, where both transmitter and receiver are mounted on relatively static objects such as large boats, rigs, etc. However, in most cases of ocean-surface WSNs, sensors are often installed on floating objects to take advantage of energy-harvesting systems. To fill that void, in this paper we investigate ocean waves' effects on the Line-of-Sight (LoS) link between buoyant sensors in a homogeneous WSN. Specifically, we derive the blockage probability of LoS links between a buoyant transmitter and receiver pair due to the wave movements, and analyze how environmental effects such as wind speed affect it. The ensuing results from our simulations and theoretical analyses reveal very important and unexpected facts, which are crucial and must be considered in network design.

The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation. One of these problems is classifying the interference type added to a signal-of-interest, also known as interference classification. In this paper, we propose an interference-classification method using a deep neural network. We generate six distinct types of interfering signals then use both the power-spectral density (PSD) and the cyclic spectrum of the received signal as input features to the network. The computer experiments reveal that using the received signal PSD outperforms using its cyclic spectrum in terms of accuracy. In addition, the same experiments show that the feed-forward networks yield better accuracy than classic methods. The proposed classifier aids the subsequent stage in the receiver chain with choosing the appropriate mitigation algorithm and also can coexist with modulation-classification methods to further improve the classifier accuracy.

Recent advancements in radio frequency machine learning (RFML) have demonstrated the use of raw in-phase and quadrature (IQ) samples for multiple spectrum sensing tasks. Yet, deep learning techniques have been shown, in other applications, to be vulnerable to adversarial machine learning (ML) techniques, which seek to craft small perturbations that are added to the input to cause a misclassification. The current work differentiates the threats that adversarial ML poses to RFML systems based on where the attack is executed from: direct access to classifier input, synchronously transmitted over the air (OTA), or asynchronously transmitted from a separate device. Additionally, the current work develops a methodology for evaluating adversarial success in the context of wireless communications, where the primary metric of interest is bit error rate and not human perception, as is the case in image recognition. The methodology is demonstrated using the well known Fast Gradient Sign Method to evaluate the vulnerabilities of raw IQ based Automatic Modulation Classification and concludes RFML is vulnerable to adversarial examples, even in OTA attacks. However, RFML domain specific receiver effects, which would be encountered in an OTA attack, can present significant impairments to adversarial evasion.

Despite the advantages that ocean surface Wireless Sensor Networks (WSN) have over traditional ocean monitoring methods, ocean surface WSN research suffers from the lack of an accurate model that describes the stability of wireless links among sensor nodes. In this paper, we investigate ocean surface waves' effects on the Line-of-Sight (LoS) link between static sensors in a WSN. Specifically, we derive the blockage probability of LoS links between a transmitter and receiver pair due to wave movements, and analyze how environmental effects like wind speed affect it. Simulation results along with oceanographic measurements validate our analyses, making our model applicable to the design and planning of WSN in the ocean environment.

This papera1investigates deep reinforcement learning (DRL) based on a Recurrent Neural Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to as a Deep Recurrent Q-Network (DRQN). Specifically, we consider a scenario with multiple independent channels and multiple heterogeneous Primary Users (PUs). Two key challenges in our problem formulation are that we assume our DRQN node does not have any prior knowledge of the other nodes' behavior patterns and attempts to predict the future channel state based on previous observations. The goal of the DRQN is to learn a channel access strategy with a low collision rate but a high channel utilization rate. With proper definitions of the state, action and rewards, our extensive simulation results show that a DRQN-based approach can handle a variety of communication environments including dynamic environments. Further, our results show that the DRQN node is also able to cope with multi-rate and multi-agent scenarios. Importantly, we show the following benefits of using recurrent neural networks in DSA: (i) the ability to learn the optimal strategy in different environments under partial observations; (ii) robustness to imperfect observations and (iii) the ability to utilize multiple channels, and (iv) robustness in the presence of multiple agents.1A parton of this work was presented at MILCOM 2018 in [1].

In the past few decades, the localization literature has seen many models attempting to characterize the non-line-of-sight (NLOS) bias error commonly experienced in range measurements. These models have either been based on specific measurement data or chosen due to attractive features of a particular distribution, yet to date, none have been backed by rigorous analysis. Leveraging tools from stochastic geometry, this paper attempts to fill this void by providing the first analytical backing for an NLOS bias error model. Using a Boolean model to statistically characterize the random locations, orientations, and sizes of reflectors, and assuming first-order (i.e., single-bounce) reflections, the distance traversed by the first-arriving NLOS path is characterized. Under these assumptions, this analysis reveals that NLOS bias exhibits an exponential form and can in fact be well approximated by an exponential distribution - a result consistent with previous NLOS bias error models in the literature. This analytically derived distribution is then compared to a common exponential model from the literature, revealing this distribution to be a close match in some cases and a lower bound in others. Lastly, the assumptions under which these results were derived suggest this model is aptly suited to characterize NLOS bias in 5G millimeter wave systems as well.

Despite the advantages that ocean surface Wireless Sensor Networks (WSN) have over traditional ocean monitoring methods, ocean WSN research suffers from lack of an accurate model that describes the stability of wireless links among sensor nodes. In this paper, we are going to investigate ocean surface waves' effects on the Line-of-Sight (LoS) link between sensors in a homogeneous WSN. Specifically, we will derive the blockage probability of LoS links between a transmitter and receiver pair due to wave movements, and analyze how environmental effects like wind speed affect it. Simulation results along with oceanographic measurements validate our analyses, making our model applicable in design and planning of WSN in the ocean environment.

In localization, an outage occurs if the positioning mean squared error (MSE) exceeds a pre-defined threshold εth. For time-of-arrival-based localization, a key factor affecting the MSE is the relative positions of the anchors with respect to the target location. From a design point of view, characterizing the distribution of the MSE over an ensemble of anchor locations as seen from the perspective of a target is essential for providing probabilistic performance guarantees against outage. To solve this difficult problem, previous works have assumed all anchor-target links to have the same SNR (i.e., SNR homogeneity), which neglects the impact of link distance variation on the SNR and the positioning error; for instance, under an inverse-square law pathloss model, the outage probability can differ by orders of magnitude when compared with the homogeneous SNR assumption. In this paper, we derive an approximate expression for the MSE distribution under an inverse-square law pathloss model when the anchors are uniformly distributed around a target. Through simulations, we verify that our approximation can be used to estimate the number of anchors needed so that the outage probability is below 1%.

In this workshop, communications systems and wireless communications educators will experience and provide feedback on remote laboratory exercises and tutorials that employ an Internet-accessible, software-defined radio (SDR) based testbed. The tutorials introduce and demonstrate concepts relevant to spectrum sharing, cognitive radio, and other radio / wireless communications applications that involve spectrum agility. Students run, modify, and / or configure code for cognitive engines or adaptive controllers that make real-time modifications to radio waveform parameters such as operating frequency, transmitting power, signal bandwidth, modulation, and error correction. The controllers make adaptations to optimize over-the-air operation in challenging signal environments. The resulting radio link performance is measured using an experiment management framework and can be visualized using a web interface that displays performance metrics as well as three-dimensional and two-dimensional waterfall plots. In the process of working through the exercises, students learn to use the above tools, which also enable them to design and perform original experiments. This workshop will be valuable for anyone teaching or studying wireless communications or using interactive remote laboratories and tutorials to teach technical concepts.

Emerging communication network applications including fifth-generation (5G) cellular and the Internet-of-Things (IoT) will almost certainly require location information at as many network nodes as possible. Given the energy requirements and lack of indoor coverage of Global Positioning System (GPS), collaborative localization appears to be a powerful tool for such networks. In this paper, we survey the state of the art in collaborative localization with an eye toward 5G cellular and IoT applications. In particular, we discuss theoretical limits, algorithms, and practical challenges associated with collaborative localization based on range-based as well as range-angle-based techniques.

The advent of new wireless technologies in recent years has created a tremendous increase in the demand for wireless connectivity. The estimated number of mobile device connections exceeds eight billion globally, more than the population of the world, and the growth trend is rapidly increasing as the developing world penetration rate is still in its infancy. With the Internet-of-Things (IoT), it is expected that the total number of connections will reach over fifty billion in the next few years. In conjunction with wireless connectivity technology, the need for location awareness is paramount.

This two-part special issue covers relevant aspects of network localization and navigation: theoretical foundations, localization technologies, positioning algorithms, and network operation.

Localization performance in wireless networks has traditionally been benchmarked using the Cramér–Rao lower bound (CRLB), given afixedgeometry of anchor nodes and a target. However, by endowing the target and anchor locations with distributions, this paper recasts this traditional scalar benchmark as a random variable. The goal of this paper is to derive an analytical expression for the distribution of this nowrandomCRLB, in the context of Time-of-Arrival-based positioning. To derive this distribution, this paper first analyzes how the CRLB is affected by the order statistics of the anglesbetweenconsecutive participating anchors (i.e.,internodal angles). This analysis reveals an intimate connection between the second largest internodal angle and the CRLB, which leads to an accurate approximation of the CRLB. Using this approximation, a closed-form expression for the distribution of the CRLB,conditionedon the number of participating anchors, is obtained. Next, this conditioning is eliminated to derive an analytical expression for themarginalCRLB distribution. Since this marginal distribution accounts for all target and anchor positions, across all numbers of participating anchors, it therefore statistically characterizes localization error throughout anentirewireless network. This paper concludes with a comprehensive analysis of this new network-wide-CRLB paradigm.

Can we optimally attack networks (in terms of disrupting the ability of the nodes in the network from communicating) when the network topology is unknown? In this paper, we show that it is not always possible to do so when the network topology is unknown a priori. Specifically, we develop multi armed bandit-based techniques that enable the attacker to learn the best network attack strategies and also discuss the potential limitations that the attacker faces in such blind scenarios.

Although GPS is the most common method used for localization, it is sometimes insufficient due to its unavailability or unreliability. Therefore, signals of opportunity (SOP) arise as a promising alternative solution in such situations. In this paper, we examine a new technique for determining the location of devices based on novel (and naturally occurring) signals of opportunity rather than a fixed infrastructure. Precisely, our technique is based on tracking the sidereal motion of discrete astronomical radio sources (e.g., supernova remnants or radio galaxies). Unlike other types of SOP and GPS alternatives, our technique has the advantage of using permanent sources. In other words, since the sources are naturally occurring, they have the advantage of being permanent; they can't be disabled or shot down, and don't have any maintenance issues. These sources are also readily detectable at radio frequencies over a continuous range from HF through SHF, making jamming difficult. Given two antennas acting as an interferometer, the fringe rate (the rate at which the phase of the correlation of the discrete astronomical radio source being tracked for localization purposes changes) is estimated, and used for identifying the interferometer location in latitude and longitude. In this paper, building on our previous work, we perform a sensitivity analysis of our proposed technique. Specifically, we examine the impact of the baseline geometry, the estimation of the interferometer baseline, and the impact of the specific fringe rate value on the performance of the technique. Our results demonstrate the potential usefulness of the technique.

The accuracy of localization is highly degraded in indoor and harsh environments where source nodes either do not have connections with a sufficient number of anchor nodes due to strong attenuation or have very poor range estimates due to NLOS propagation. Cooperative localization is a technique in which the source nodes communicate not only with the anchor nodes, but also with each other. Hence, the source nodes can collect several additional measurements which significantly improve the localization performance. Although many studies have examined NLOS-degraded localization of a static node in noncooperative networks, and many others have examined the impact of cooperation for static localization, there is no work which considers cooperative tracking of mobile nodes. To address this open problem, in this work, we examine cooperative tracking, particularly in NLOS environments. More specifically, we develop a novel sensor tracking algorithm based on semidefinite programming (SDP) which has the ability to mitigate NLOS propagation. Our simulations show that the new SDP-based tracking algorithm outperforms the classic extended Kalman filter as well as the other recently proposed algorithms for noncooperative tracking in NLOS environments. We also show that the algorithm can be extended to cooperative networks, and that a substantial performance benefit is realized by cooperation.

In this paper, we study jamming attacks against wireless networks. Specifically, we consider a network of base stations (BSs) or access points (APs) and investigate the impact of a fixed number of jammers that are randomly deployed according to a Binomial point process. We investigate the network performance in terms of: 1) the outage probability and 2) the error probability of a victim receiver in the downlink of this wireless network. We derive analytical expressions for both these metrics and discuss in detail how the jammer network must adapt to the various wireless network parameters in order to effectively attack the victim receivers. For instance, we will show that with only 1 jammer per BS/AP: 1) the outage probability of the wireless network can be increased from 1% (as seen in the non-jamming case) to 80% and 2) when retransmissions are used, the jammers cause the effective network activity factor (and hence the interference among the BSs) to be doubled. Furthermore, we show that the behavior of the jammer network as a function of the BS/AP density is not obvious. In particular, a non-trivial behavior is seen, which indicates that the number of jammers required to attack the wireless network must scale with the BS density only until a certain value beyond which it decreases. In the context of error probability of the victim receiver, we study whether or not some recent results related to jamming in the point-to-point link scenario can be extended to the case of jamming against wireless networks. Numerical results are presented to validate all the theoretical inferences presented.

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. However, the MF approximation is not particularly effective for multi-signal detection. For this reason, we propose a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We also developed a low-complexity variation to the proposed construction in which Gaussian BP is applied to detection and expectation propagation links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection and, in general, detection in the presence of interference.

In this paper, we analyze jamming against wireless networks from an error probability perspective. Specifically, we investigate the impact of a fixed number of jammers against a network of base stations (BS) or access points (AP). We first derive analytical expressions for the error probability of a victim receiver in the downlink of this wireless network and later study whether or not some recent results related to jamming in the point-to-point link scenario can be extended to the case of jamming against wireless networks.

Detection of co-channel signals is important as wireless communication systems become increasingly dense. A particularly challenging case is single antenna reception in both the presence of inter-symbol interference and co-channel interference. Optimal joint maximum a posteriori probability (MAP) detection of the co-channel signals is prohibitively complex. Therefore, in this paper, we propose a factor graph-based iterative receiver which approximates joint MAP detection. In the receiver, noise is modeled with a Gaussian mixture distribution since studies have shown that the noise affecting wireless communication systems is often impulsive. Furthermore, the parameters of the factor graph model (channel and noise parameters) are iteratively estimated within the receiver. The proposed receiver is shown to the outperform state-of-the-art receiver algorithms while having a lower complexity in both Gaussian and impulsive noise. We also show significant gains from iterative parameter estimation, especially in non-Gaussian noise.

Distributed storage systems (DSSs) in the presence of an external wiretapper are considered. A DSS is parameterized by (n, k, d), in which the data are stored across n nodes (each with storage capacity α), and must be recoverable by accessing the contents stored on any k out of n nodes. If a node fails, any d ≥ k out of (n - 1) nodes help in the repair (regeneration) of the failed node (by sending dβ units of repair data, where β ≤ α), so that the data can still be recovered from the DSS. For such a (n, k, d)-DSS, security from the two types of wiretappers is investigated: 1) Type-I (node data) wiretapper, which can read the data stored on any ℓ <; k nodes and 2) Type-II (repair data) wiretapper, which can read the data that is used to repair a set of ℓ failed nodes. The focus of this paper is on the optimal tradeoff between the storage (α) and the repair bandwidth (dβ) in presence of a Type-I/Type-II wiretapper and the practically relevant constraint of exact repair in which a failed node must be replaced by its exact replica. In this paper, several new results and outer bounds for the storage-versus-exact-repair-bandwidth tradeoff(s) are obtained for the Type-I and Type-II security problems. Furthermore, new outer bounds are presented for the Type-II problem, which hold for general (n, k, d, ℓ) parameters. It is shown that these outer bounds strictly improve upon the existing cutset-based outer bounds. The key technical contribution of this paper is in developing novel information theoretic converse proofs for these problems. From our optimal characterization results, we show that in a Type-II setting, the only efficient point in the storage-versus-exact-repair-bandwidth tradeoff is the minimum bandwidth regenerating (MBR) point corresponding to α = dβ. This is in sharp contrast to the Type-I setting in which the optimal tradeoff allows a spectrum of operating points beyond the MBR point.

The use of state-of-the-art 3G cellular CDMA technologies in a utility owned AMI network results in a large amount of control traffic relative to data traffic, increases the average packet delay and hence are not an appropriate choice for smart grid distribution applications. Like the CDG, we consider a utility owned cellular like CDMA network for smart grid distribution applications and classify the distribution smart grid data as scheduled data and random data. Also, we propose SMAC protocol, which changes its mode of operation based on the type of the data being collected to reduce the data collection latency and control overhead when compared to 3G cellular CDMA2000 MAC. The reduction in the data collection latency and control overhead aids in increasing the number of smart meters served by a base station within the periodic data collection interval, which further reduces the number of base stations needed by a utility or reduces the bandwidth needed to collect data from all the smart meters. The reduction in the number of base stations and/or the reduction in the data transmission bandwidth reduces the CAPital EXpenditure CAPEX and OPerational EXpenditure OPEX of the AMI network. The proposed SMAC protocol is analyzed using markov chain, analytical expressions for average throughput and average packet delay are derived, and simulation results are also provided to verify the analysis.

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a multiarmed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that these algorithms converge to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sublinear, i.e., the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.

When dedicated positioning systems, such as GPS, are unavailable, a mobile device has no choice but to fall back on its cellular network for localization. Due to random variations in the channel conditions to its surrounding base stations (BS), the mobile device is likely to face a mix of both favorable and unfavorable geometries for localization. Analytical studies of localization performance (e.g., using the Cramér-Rao lower bound) usually require that one fix the BS geometry, and favorable geometries have always been the preferred choice in the literature. However, not only are the resulting analytical results constrained to the selected geometry, this practice is likely to lead to overly-optimistic expectations of typical localization performance. Ideally, localization performance should be studied across all possible geometric setups, thereby also removing any selection bias. This, however, is known to be hard and has been carried out only in simulation. In this paper, we develop a new tractable approach where we endow the BS locations with a distribution by modeling them as a Poisson point process (PPP), and use tools from stochastic geometry to obtain easy-to-use expressions for key performance metrics. In particular, we focus on the probability of detecting some minimum number of BSs, which is shown to be closely coupled with a network operator's ability to obtain satisfactory localization performance (e.g., meet FCC E911 requirements). This metric is indifferent to the localization technique (e.g., TOA, TDOA, AOA, or hybrids thereof), though different techniques will presumably lead to different BS hearability requirements. In order to mitigate excessive interference due to the presence of dominant interferers in the form of other BSs, we incorporate both BS coordination and frequency reuse in the proposed framework and quantify the resulting performance gains analytically.

In this paper, we mathematically characterize the improvement in device localizability achieved by allowing collaboration among devices. Depending on the detection sensitivity of the receivers in the devices, it is not unusual for a device to be localized to lack a sufficient number of detectable positioning signals from localized devices to determine its location without ambiguity (i.e., to be uniquely localizable). This occurrence is well-known to be a limiting factor in localization performance, especially in communications systems. In cellular positioning, e.g., cellular network designers call this the hearability problem. We study the conditions required for unique localizability and use tools from stochastic geometry to derive accurate analytic expressions for the probabilities of meeting these conditions in the noncollaborative and collaborative cases. We consider the scenario without shadowing, the scenario with shadowing and universal frequency reuse, and, finally, the shadowing scenario with random frequency reuse. The results from the latter scenario, which apply particularly to cellular networks, reveal that collaboration between two devices separated by only a short distance yields drastic improvements in both devices' abilities to uniquely determine their positions. The results from this analysis are very promising and motivate delving further into techniques which enhance cellular positioning with small-scale collaborative ranging observations among nearby devices.

The exponential back-off mechanism, proposed for reducing MAC- layer contention in the 802.11 standard, is sub-optimal in terms of the network throughput. This back-off mechanism and its improved variants are especially inefficient under unknown dynamics such as packet arrivals and user entry/exit. In this paper, we formulate the problem of optimizing this back-off mechanism as a Markov decision process, and propose online learning algorithms to learn the optimal back-off schemes under unknown dynamics. By exploiting the fact that some components of the system dynamics (such as protocol states) are known because the users follow the common 802.11 protocol, we propose a post-decision state (PDS)- based learning algorithm to speed up the learning process. Compared to traditional Q-learning algorithms, the advantages of the proposed online learning algorithm are that 1) it exploits partial information about the system so that less information needs to be learned in comparison to other learning algorithms, and 2) it removes the necessity for action exploration which usually impedes the learning process of conventional learning algorithms (such as Q-Learning). We prove the optimality of the proposed PDS-based learning algorithm and via numerical results demonstrate the improvement over existing protocols and Q-learning in terms of throughput and convergence speed. We first address this problem from a single-user perspective and later describe the challenges involved and present new insights into the multi-user learning scenarios, especially in cases where the MDP models of the users are coupled with each other.

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a novel multi-armed bandit framework where the jammer can choose various physical layer parameters such as signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs i.e., the case when the victim does not change its communication technique despite the presence of interference. We prove that our learning algorithm converges to the optimal jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sub-linear, i.e. the learning is fast, which is important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against adaptive transmitter-receiver pairs.

It is well-known that non-line-of-sight (NLOS) propagation degrades the accuracy of localization in indoor and harsh environments. In such environments, source nodes either do not have connections with a sufficient number of anchor nodes due to strong attenuation or have very poor range estimates due to NLOS propagation. Cooperative localization, in which a source node communicates with both anchor nodes and other source nodes, can benefit such scenarios by providing additional measurements that can significantly improve the localization performance. While most of the earlier works have studied static node localization in noncooperative networks, cooperative tracking of mobile nodes in NLOS environments is still an open problem. To address this open problem, in this work we develop an technique for cooperative tracking applicable to NLOS environments. More specifically, we develop a novel node tracking algorithm based on semidefinite programming (SDP) which has the ability to mitigate NLOS propagation. Our simulations show that the proposed SDP estimator outperforms the classic extended Kalman filter for cooperative networks in NLOS environments.

Next generation wireless communications systems are pushing the limits of both energy efficiency and spectral efficiency. This presents a challenge at the receiver when it comes to accomplishing tasks such as synchronization, channel estimation, and equalization and has motivated the development of code-aided iterative receiver algorithms in the technical literature. In this paper, we focus on the task of frame synchronization. While previous work has predominately assumed an additive white Gaussian noise channel, we develop code-aided frame synchronization algorithms for multipath channels. An iterative receiver is presented which integrates frame synchronization with iterative channel estimation, equalization, demodulation, and decoding. The receiver design includes a novel frame pre-processing stage to reduce the complexity of the proposed receiver. The complexity and performance of the proposed receiver is compared with that of a receiver based on conventional synchronization. The results demonstrate that the proposed receiver is capable of achieving a gain of up to 3 dB while increasing complexity by only 20%.

Jamming attacks can significantly impact the performance of wireless communication systems, and can lead to significant overhead in terms of re-transmissions and increased power consumption. This paper considers the problem of optimal jamming over an additive white Gaussian noise channel. We derive the optimal jamming signal for various digital amplitude-phase-modulated constellations and show that it is not always optimal to match the jammer's signal to the victim signal in order to maximize the error probability at the victim receiver. Connections between the optimum jammer obtained in this analysis and the well-known pulsed jammer, popularly analyzed in the context of spread spectrum communication systems, are illustrated. The gains obtained by the jammer when it knows the victim's modulation scheme and uses the optimal jamming signals obtained in this paper as opposed to conventional additive white Gaussian noise jamming are evaluated in terms of the additional signal power needed by the victim receiver to achieve the same error rates under these two jamming strategies. We then extend these findings to obtain the optimal jamming signal distribution: 1) when the victim uses an orthogonal frequency-division multiplexing (OFDM)-modulated signal and 2) when there is multiple jammer attacking a single victim transmitter-receiver pair. Numerical results are presented in all the above cases to validate the theoretical inferences presented.

Jamming attacks can significantly impact the performance of wireless communication systems, and can lead to significant overhead in terms of re-transmissions and increased power consumption. This paper considers the problem of optimal jamming over an additive white Gaussian noise channel. We derive the optimal jamming signal for various digital amplitude-phase modulated constellations. We show that it is not always optimal to match the jammer's signal to the victim signal in order to maximize the error probability at the victim receiver. Connections between the optimum jammer obtained in this analysis and the well-known pulsed jammer, popularly analyzed in the context of spread spectrum communication systems are illustrated. Further, we evaluate the value of the knowledge of the victim's modulation schemes by comparing the performance of the optimal jamming signals with conventional additive white Gaussian noise jamming. Numerical results are presented in order to validate the theoretical inferences presented.

In this paper, we investigate the benefit of inter-node collaboration in multidimensional location estimation. In particular, for networks with reference nodes at known locations and source nodes whose locations are unknown and to be estimated, we establish the value of collaboration for source node position estimation by presenting proof of a decreasing Cramér-Rao lower bound as additional source nodes (meeting some minimum connectivity requirements) are introduced into the collaborative position estimation problem. Prior work has shown this for one-dimensional location estimation; however, the previous proof as presented is not easily extendable to multidimensional location estimation. Following the completion of the proof, the minimum connectivity conditions for two-dimensional positioning using time-of-arrival and received-signal-strength ranging information are discussed. Lastly, the theoretical result is verified with numerical results through simulation.

In this paper, joint sensor synchronization and localization using time-of-arrival measurements is studied. In wireless sensor networks, the accuracy of the clock synchronization among nodes has a great impact on the performance of the localization using time-based ranging methods. The clocks of the anchor nodes are typically synchronized with each other, while those of the source nodes must be synchronized with the anchor nodes. Each source node has its own clock characterized by clock offset and clock skew. Synchronization is the process of determining these clock parameters for the source node, while localization is the process of estimating its location. Generally, the estimation problem is broken down into two subproblems, where the synchronization is first performed and then the source node is localized. However, in this paper, a joint synchronization and localization framework is considered and examined, as it is expected to provide better accuracy, especially in dynamic networks. The system model for joint synchronization and localization is first introduced. The maximum likelihood (ML) estimator is then derived which is shown to be highly nonlinear and nonconvex. The ML estimator does not have a closed-form solution and must be solved by computationally complex and iterative algorithms. A novel linear estimator is derived which has a closed-form solution with significantly lower complexity. The performance of the proposed linear estimator is evaluated through computer simulations. Results show that the proposed linear estimator outperforms the previously considered estimators, especially in low signal-to-noise ratios.

Distributed storage systems (DSS) in the presence of a passive eavesdropper are considered in this paper. A typical DSS is characterized by 3 parameters (n, k, d) where, a file is stored in a distributed manner across n nodes and can be recovered entirely from any k out of n nodes. Whenever a node fails, d ∈ [k, n) nodes help in repairing the failed node. The focus of this work is on the exact repair capabilities of a DSS, where a failed node is replaced with an identical node. Securing this DSS from passive eavesdropping attacks is studied in this paper. The eavesdropper is capable of wiretapping the repair process of a subset of nodes in the storage system. The main contribution of this paper is the optimal characterization of the secure storage-vs-exact-repair-bandwidth tradeoff region which prior to this work was unknown. We focus on the simplest nontrivial instances of this problem, namely (n, k, d) = (3, 2, 2) and (4, 3, 3), and present novel information-theoretic converse proofs that validate these optimal tradeoff regions.

In this paper, asynchronous sensor localization using time-of-arrival (TOA) measurements is studied. Accurate TOA-based localization requires perfect timing synchronization between the source and anchor nodes. In asynchronous networks, the anchor nodes are assumed to be synchronized, while the clock of the source node needs be synchronized with those of the anchor nodes. Although synchronization and localization are typically performed separately, in this work a joint synchronization and localization framework is considered, as it is expected to provide significant improvement over two-step approaches. The clock parameters (clock offset and skew) of the source node are estimated jointly with its location. The corresponding Cramér-Rao lower bound (CRLB) and the maximum likelihood (ML) estimator of the system model are derived. The ML estimator is highly nonlinear and nonconvex which must be solved with computationally complex algorithms. Alternatively, a novel semidefinite programming (SDP) estimator is introduced by relaxing the original ML minimization problem into a convex problem. Computer simulations show that the proposed SDP estimator outperforms other previously proposed estimators.

Intelligent Transportation Systems (ITS) have emerged to utilize different technologies to enhance the performance and quality of transportation networks. Many applications of ITS need to have a highly accurate location information from the vehicles in a network. The Global Positioning System (GPS) is the most common and accessible technique for vehicle localization. However, conventional localization techniques which mostly rely on GPS technology are not able to provide reliable positioning accuracy in all situations. This paper presents an integrated localization algorithm that exploits all possible data from different resources including GPS, radio-frequency identification, vehicle-to-vehicle and vehicle-to-infrastructure communications, and dead reckoning. A localization algorithm is also introduced which only utilizes those resources that are most useful when several resources are available. A close-to-real-world scenario has been developed to evaluate the performance of the proposed algorithms under different situations. Simulation results show that using the proposed algorithms the vehicles can improve localization accuracy significantly in situations when GPS is weak.

In this paper, cooperative positioning for Long Term Evolution (LTE) cellular systems using Radio Frequency Pattern Matching (RFPM) is studied. Having the locations of users in a cellular system supports many applications such as location-based services and E911. Although Global Positioning System (GPS) can be found in every smartphone, its poor performance in indoor and dense environments encourages the development of cellular network-based solutions. Cellular localization has emerged in which the user locations can be determined by measurements obtained within the network without the aid of any external sources (e.g., GPS). Several positioning techniques have been considered in Release 9 of the 3rd Generation Partnership Project (3GPP) document such as Observed Time Difference Of Arrival (OTDOA) and Enhanced Cell ID (E-CID). In OTDOA, the User Equipment (UE) measures the time difference of signals from multiple eNodeBs and uses a trilateration algorithm to find its location. However, OTDOA performance can be highly degraded in rich multipath and non-line-of-sight environments particularly when there is strong interference. Hence, many companies and researchers are also evaluating other techniques such as RFPM in which the user location is estimated by comparing UE measurements with a series of reference data. RFPM is independent of line-of-sight conditions and has good performance in dense urban and indoor environments. In the current LTE networks, the UE can only communicate with eNodeBs. However, there are scenarios in which the UE is not able to communicate with a sufficient number of eNodeBs and cannot find its location without ambiguity. In this paper, a cooperative localization technique for LTE systems is proposed in which leveraging the Device-to-Device (D2D) communications protocol the UE communicates not only with eNodeBs but also with other UEs. It will be shown through computer simulations that the proposed positioning algorithm can significantly improve the positioning performance in LTE networks.

The concept of an iterative receiver has gained attention as a means of performing reliable synchronization, especially at the low signal-to-noise ratios enabled by error correction codes. In this paper, we consider joint detection of the information bits and estimation of the channel gain, carrier phase, symbol timing, frame timing, and noise power. Our particular focus is on the frame timing where we evaluate the complexity of the iterative receiver by characterizing the frame offset distribution. A method for dynamically choosing the set of frame offsets processed by the iterative receiver is presented. The receiver utilizes the expectation-maximization algorithm to perform estimation and the sum-product algorithm to perform soft demodulation and decoding. Numerical results are presented to characterize the frame offset distribution and to demonstrate the receiver's performance.

Jamming attacks can significantly impact the performance of wireless communication systems. In addition to reducing the capacity, such attacks may lead to insurmountable overhead in terms of retransmissions and increased power consumption. In this paper, we consider the multiple-input single-output (MISO) broadcast channel (BC) in the presence of a jamming attack in which a subset of the receivers can be jammed at any given time. Further, countermeasures for mitigating the effects of such jamming attacks are presented. The effectiveness of these antijamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT). The main contribution of this paper is the characterization of the DoF region of the two user MISO BC under various assumptions on the availability of CSIT and JSIT. Partial extensions to the multiuser broadcast channels are also presented.

Jamming attacks can significantly impact the performance of wireless communication systems, and lead to insurmountable overhead in terms of re-transmissions and increased power consumption. In this paper, we consider the multi-user multiple-input single-output (MISO) broadcast channel (BC) in the presence of jamming attacks in which a subset of the users can be selectively jammed at any given time. We present countermeasures for mitigating the effects of such jamming attacks. The effectiveness of these anti-jamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT).

Wireless sensor localization using received signal strength (RSS) measurements is investigated in this paper. Most studies for RSS localization assume that the shadowing components are uncorrelated. However in this paper, we assume that the shadowing is spatially correlated. Under this condition, it can be shown that the localization accuracy can be improved if the correlation among links is taken into consideration. Avoiding the maximum likelihood (ML) convergence problem, we derive a novel semidefinite programming (SDP) approach by converting the corresponding noncovex ML estimator into a convex one. The performance of the proposed SDP estimator is compared with the ML estimator and previously considered estimators. Computer simulations show that the proposed SDP estimator outperforms the previously considered estimators in both uncorrelated and correlated shadowing environments.

In this paper, asynchronous wireless source localization using time-of-arrival (TOA) measurements is studied. In TOA localization, the travel time of the signal between the source node and anchor nodes is measured and used to estimate range. In synchronous networks, the anchor nodes know when the source node starts transmission. In asynchronous networks, however, the source transmit time is unknown and TOA measurements have a positive bias due to the synchronization error which could lead to a large localization error. One way to tackle this problem is to use time-difference-of-arrival (TDOA) measurements which do not depend on the source transmission time. However, in this work, applying an alternative approach, we estimate the source transmit time as a nuisance parameter jointly with the source location. The optimal maximum likelihood (ML) estimator is derived. To avoid the ML convergence problem, a novel semidefinite programming (SDP) technique is proposed by converting the noncovex ML problem into a convex one. Computer simulations showing superior performance of the proposed SDP estimator are conducted.

In this paper, a comparative study of network-coded bi-directional amplify-and-forward (BD-AF) relaying is presented. In bi-directional relay networks, communication is performed over two phases: the broadcasting phase, and the cooperation phase. In the broadcasting phase, both source nodes broadcast their signals simultaneously to the N relay nodes, while in the cooperation phase, transmission is based on one of two modes: (1) time-division (TD), or (2) multiple-access (MA). In the TD-BD-AF scheme, each relay node is allocated a time-slot to transmit its processed signal, while in the MA-BD-AF scheme, all the N relay nodes simultaneously transmit network-coded signals to both source nodes, in a single time-slot. Moreover, a suboptimal relay selection (i.e. SRS-BD-AF) that approximately maximizes the sum-of-rates is proposed. Optimal and suboptimal sum-of-rates maximizing power allocations are studied under the TD-BD-AF and MA-BD-AF schemes, respectively, where it is shown that the MA-BD-AF scheme reduces to the SRS-BD-AF scheme. Symbol error rate performance analysis is provided, where it is shown that both the TD-BD-AF and SRS-BD-AF schemes achieve full diversity. Imperfect timing synchronization is analyzed and it is demonstrated that the SRS-BD-AF outperforms the other schemes in terms of the achievable rate. Simulation results are provided to complement the theoretical analysis.

The geolocation of RF signals has many civilian and military applications. One technique for geolocation involves using the cross ambiguity function (CAF) to calculate the time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) of the emitted signal using two receivers. Until recently most RF signals fell under the category of Single-Input-Single-Output (SISO). Now however, Multiple-Input-Multiple-Output (MIMO) technologies such as Wi-Fi's IEEE 802.11n are becoming more ubiquitous. The inherent use of multiple antennas in MIMO technologies has an effect on the geolocation of these signals. This paper therefore explores the effect of spatially multiplexed MIMO signals on the cross ambiguity function in regards to the calculation of TDOA and FDOA, and the resulting effect on geolocation.

The selection of the appropriate communication technology for different smart grid applications has drawn a great attention in the recent past. In this paper, we propose a Hybrid Spread Spectrum (HSS) based Advanced smart Metering Infrastructure (AMI) that reduces the overhead and latency in data transfer when compared to the use of 3G/4G technologies for smart meter data collection. We present a preliminary PHY and MAC layer design of a HSS based AMI network and evaluate their performance using matlab and NS2 simulations.

In this paper, a performance analysis of network-coded bidirectional amplify-and-forward (BD-AF) multi-relay networks is presented. In such networks, communication is performed over two phases: the broadcasting phase, and the cooperation phase. In the broadcasting phase, both source nodes broadcast their signals simultaneously to the intermediate JV relay nodes. The cooperation phase is based on one of two schemes: (1) multiple-access (MA) and (2) single relay selection (RS) transmission. In the MA-BD-AF scheme, all N relay nodes simultaneously transmit linearly coded signals to both source nodes. A simple suboptimal relay selection scheme (i.e. SRS-BD-AF) is proposed such that the relay that maximizes the sum-of-rates is selected to transmit its network coded signal to both source nodes. Finally, a suboptimal sum-of-rates maximizing relay power allocation under the MA-BD-AF scheme is formulated and shown to reduce to the SRS-BD-AF scheme. Also, a symbol error rate performance analysis is provided, where it is shown that the SRS-BD-AF scheme achieves full diversity. Simulation results are provided to complement the theoretical analysis.

Training is the task of guiding a cognitive radio engine through the process of learning a desired system's behavior and capabilities. The training speed and expected performance during this task are of paramount importance to the system's operation, especially when the system is facing new conditions. In this paper, we provide a thorough examination of cognitive engine training, and we analytically estimate the number of trials needed to conclusively find the best-performing communication method in a list of methods sorted by their possible throughput. We show that, even if only a fraction of the methods meet the minimum packet success rate requirement, near maximal performance can be reached quickly. Furthermore, we propose the Robust Training Algorithm (RoTA) for applications in which stable performance during training is of utmost importance. We show that the RoTA can facilitate training while maintaining a minimum performance level, albeit at the expense of training speed. Finally, we test four key training techniques (ε-greedy; Boltzmann exploration; the Gittins index strategy; and the RoTA) and we identify and explain the three main factors that affect performance during training: the domain knowledge of the problem, the number of methods with acceptable performance, and the exploration rate.

In this work, we propose a set-theoretic approach to collaborative position location for wireless networks. The proposed method borrows the concept from the parallel projection method (PPM), originally developed for signal recovery with inconsistent convex feasibility sets, modifies and extends the technique to an iterative and distributed numerical algorithm to estimate node locations, based on incomplete and noisy internode distance estimates. We demonstrate that in the case of noncollaborative position location, the proposed method is analytically equivalent to the parallel implementation of Kaczmarz Algorithm that is guaranteed to converge to a local minimizer and thus a stationary point. For collaborative position location, the proposed iterative PPM is computationally much more efficient than existing methods such as SDP and MDS-MAP, while achieving comparable or better localization accuracy and robustness to non-line-of-sight (NLOS) bias. Finally, our proposed method can be implemented in a parallel and distributed fashion, and is scalable for large network deployment.

This letter presents an empirical analysis of the impact of spreading bandwidth on fading performance of direct-sequence spread spectrum signals. Over 20000 measurements were recorded in an indoor office environment at chip rates of 25 MHz, 100 MHz, 225 MHz, 400 MHz, and 500 MHz. A continuous wave signal was also used to characterize the fading of narrowband signals. Three particular effects are examined as chip rate (i.e., bandwidth) increases: energy splintering due to temporal dispersion, reduction in the fading of the total received energy due to reduced multipath interaction and the fading reduction per resolvable component. The effects are characterized for line-of-sight and non-line-of-sight environments and are used to determine the BER performance of a BPSK system when operating in the presence of all three effects. The results are also compared with the theoretical relationship between signal energy variation and chip rate. The analysis demonstrates that the use of a chip rate greater than 225 MHz (or bandwidth greater than approximately 200 MHz) provides only minor improvements in fading performance in these environments.

The problem of maximizing the sum-rate of a MIMO interference channel is investigated. Each receiver node is assumed to perform single user detection by treating interference from other users as Gaussian noise. It is assumed that all the users share a single frequency band and no pre-coding is performed over time. The sum-rate maximization in such a setup is a longstanding open problem due to its non-linear non-convex nature. The solution, to date, has only been approximated using the local optimization algorithms. In this paper, we couple the branch and bound strategy with the reformulation and linearization technique (BB/RLT) to develop a global optimization algorithm that finds a provably optimal solution. This problem is essentially an optimal power control problem over spatial channels and should not be confused with some recent developments such as Interference Alignment (IA) that typically require pre-coding over temporal, spectral or spatial dimensions. As a comparison with the state of the art, we compare the sum-rate achievable in the current system with the one predicted by IA and draw some interesting conclusions. It should be noted however, that even though the sum-rate achievable by IA can be predicted by assuming N/2 degrees of freedom in an N-user interference channel, the feasibility of IA over a limited number of signaling dimensions is an open problem.

In this work, we propose a set-theoretic approach to collaborative position location for wireless networks. The proposed method borrows the concept from the parallel projection method (PPM), originally developed for signal recovery with inconsistent convex feasibility sets, revises and extends the technique to an iterative and distributed numerical framework to estimate node locations, based on incomplete and noisy inter-node distance estimates. We demonstrate that the proposed iterative PPM is computationally much more efficient than existing methods, while achieving comparable and often better localization accuracy and robustness to non-line-of-sight (NLOS) bias. Our proposed method can be implemented in a parallel and distributed fashion, and is scalable for large network deployment.

While significant research effort has been dedicated to wireless localization over the past decades, most aspects of location security have been overlooked. In particular, adversaries can take advantage of security vulnerabilities of current location systems to launch location spoofing attacks, thus disguising their position in the network. In this paper, we address issues associated with location spoofing attack detection by examining relative location error rather than its absolute value. Specifically, we propose novel statistical and pattern matching techniques called relative error detection (RED) and topological residual fingerprint matching (TRFM) for detecting both signal strength and beamforming attacks. Also, the concept of geometric filtering is developed to considerably improve location reliability by exploiting the geometry of nodes.

Indoor positioning has become a hot research topic due to a plethora of interesting applications ranging from emergency responder tracking to location-based services. In this work we focus on the problem of network localization also sometimes called collaborative localization where a network of nodes is to be localized using both connections to anchors (when they exist) and connections between unlocalized nodes [1]. Although several algorithms have been investigated in the literature (e.g., [2]), most have assumed line-of-sight (LOS) propagation which is uncommon in most indoor environments. Although the impact of non-LOS (NLOS) propagation has been considered in the literature (e.g., [3], [4]), this has typically been limited to traditional single-node localization such as location estimation in cellular systems. Specifically, in this work we propose a technique for collaborative position location designed to effectively handle NLOS propagation and demonstrate the performance improvement possible over existing methods.

In this paper, we propose and investigate a low-cost and low-complexity wireless ambulatory human locomotion tracking system that provides a high ranging accuracy (intersensor distance) suitable for the assessment of clinical gait analysis using wearable ultra wideband (UWB) transceivers. The system design and transceiver performance are presented in additive-white-gaussian noise and realistic channels, using industry accepted channel models for body area networks. The proposed system is theoretically capable of providing a ranging accuracy of 0.11 cm error at distances equivalent to interarker distances, at an 18 dB SNR in realistic on-body UWB channels. Based on real measurements, it provides the target ranging accuracy at an SNR = 20 dB. The achievable accuracy is ten times better than the accuracy reported in the literature for the intermarker-distance measurement. This makes it suitable for use in clinical gait analysis, and for the characterization and assessment of unstable mobility diseases, such as Parkinson's disease.

In this paper, we investigate the optimal performance of collaborative position location. In particular, we develop a branch-and-bound (BB) solution search strategy, coupled with the reformulation linearization technique (RLT), to solve the maximum likelihood estimation (MLE) problem for collaborative position location, which is in general a nonlinear and nonconvex optimization problem. Compared with existing work which has only approximately solved the MLE problem, our approach is guaranteed to produce the (1 - ¿)-optimal solution to the MLE for arbitrarily small ¿. With a guaranteed optimal solution to the MLE, we show that for some node geometries in noncollaborative position location, which can be viewed as a special case of collaborative position location, the Cramer-Rao lower bound (CRLB) for an unbiased estimator is no longer a meaningful performance benchmark. We demonstrate that the timeof- arrival (TOA) based MLE is in general a biased estimator and it sometimes has a mean square error (MSE) smaller than the CRLB, and thus can serve as a more practical performance benchmark. Finally, we compare the MLE with some existing position location schemes and demonstrate that it also serves as a good performance benchmark for collaborative position location.

In this paper, we present a Cognitive Engine (CE) design for link adaptation and apply it to a system which can adapt its use of multiple antennas in addition to modulation and coding. Our design moves forward the state of the art in several ways while having a simple structure. Specifically, the CE only needs to observe the number of successes and failures associated with each set of channel conditions and communication method. From these two numbers, the CE can derive all of its functionality. First, it can estimate confidence intervals of the packet success rate (PSR) using the Beta distribution. A low computational approximation to the CDF of the Beta distribution is also presented. Second, the designed CE balances the tradeoff between learning and short-term performance (exploration {vs.} exploitation) by applying the Gittins index. Third, the CE learns the radio abilities independently of the operation objectives. Thus, if an objective changes, information regarding the radio's abilities is not lost. Finally, prior knowledge such as capacity, BER curves, and basic communication principles are used to both initialize the CE's knowledge and maximize the learning rate across different channel conditions. The proposed CE is demonstrated to have the ability to learn in a dynamic scenario and quickly approach maximal performance.

In this paper, we develop a general mathematical framework to incorporate dynamic spectrum access in a multiuser MIMO network. This framework is particularly helpful in computing the maximum achievable system capacity of a resulting multiple-band multiuser MIMO network. The mathematical formulation to maximize the system capacity is shown to be quite similar to that of a well studied single-band multiuser MIMO network. It is further shown that the capacity maximization problem is equivalent to finding the optimal eigenvalues of the input symbol covariance matrices of the users in each frequency band. Due to the dependence of the eigenvalues on the physical characteristics of the system, such as orientation of the antennas and the channel conditions, it is difficult to achieve their optimal values in general. Because of this difficulty in achieving the optimal capacity, we also consider the suboptimal MIMO techniques (specifically beamforming) and study their capacity performance in a multiple-band multiuser MIMO system.

In this paper, we propose a new localization technique using differential received signal strength (DRSS) which does not require signal source cooperation for location estimation. Specifically, we introduce a DRSS-based localization framework as well as its geometric interpretation for both local and global positioning to facilitate understanding of the approach. Then, a least-squares (LS) optimization framework is formulated for DRSS-based location estimation (DRLE) which makes full use of the DRSS measurements available. Our study shows that the localization performance of DRLE and its RSS-based counterpart (RLE) is substantially affected by spatially correlated shadow fading under which their localization behaviors are found to be different. Finally, we argue that DRLE has practical advantages over other positioning techniques, and show that its location accuracy is comparable or even superior to RLE.

This paper presents novel single-source localization techniques for a sensor network that is capable of obtaining both direction and range estimates by considering an optimization approach. Specifically, linear programming and non-linear programming techniques are developed and compared to a classic linear least squares technique. It is shown that the proposed techniques achieve superior results to the least squares approach in most cases.

In this paper we study low-power impulse radio ultra-wideband (IR-UWB) correlation receivers with suboptimal templates, as a promising candidate for a highly accurate wearable human locomotion tracking system. Such a system is theoretically capable of providing a ranging accuracy of 1mm in practical multipath fading channels at a SNR of 18dB. This ranging accuracy is ten times better than the ranging accuracy provided by currently available systems. Furthermore, we study the theoretical BER and the improved Ziv-Zakai lower-bound on ranging accuracy in AWGN and dense multipath fading channels. We show that low-power is traded for a minimal performance loss for both BER and TOA accuracy.

In this paper, we define the problem of balancing exploration vs. exploitation in a cognitive engine controlled multi-antenna communication system in terms of the classical multi-armed bandit framework. We then employ the e-greedy strategy and Gittins' indices methods for addressing the problem in a system with no prior information. Results show that the Gittins' indices assuming a normal reward process had the best overall performance compared to the Gittins' indices with a Bernoulli reward process and the e-greedy strategy. The latter was found to be more consistent albeit inefficient for most of the cases except in the case of both a low number of trials and a low SNR in which it was found to have better performance than the other methods. Nevertheless, the Gittins' indices method should be generally preferred as it is more consistent than the e-greedy strategy across different scenarios.

Impulse ultra wideband (UWB) communications promises a number of potential benefits for use in wireless sensor networks-particularly in forest environments where it can provide robust operation along with the ability to combine communications with precision position location. Additionally, MIMO communications for UWB provides the ability to take advantage of the rich multipath diversity inherent to UWB communications in order to provide enhanced robustness to fading as well as improved coverage in non line-of-sight scenarios. In this paper, we present a detailed analysis of the spatial correlation of UWB signals in a forest environment. Propagation measurements were performed using a 620 picosecond duration UWB pulse in four different forest environments: light brush, light, medium, and dense forest at distances of up to 50 meters. The mean spatial correlation was observed to vary based on forest environment, antenna type, antenna array orientation, and transmitter-receiver separation distance. These measurement results should aid in the development of MIMO algorithms for UWB outdoor communications.

This paper proposes a practical alternative to the traditional link budget approach for impulse-based UWB systems (I-UWB), which is based on the Friis formula. The proposed link budget approach overcomes the narrowband limitations of the traditional link budget by proposing the use of an antenna-pulse coupling gain. This gain is specific for an antenna tx-rx pair and the I-UWB pulse used and can be easily determined by taking a frequency or a time domain measurement. The paper introduces the approach and then verifies its accuracy by presenting the results of several measurements at distances ranging from 1 to 20 m.

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.

Direct extensions of distributed greedy interference avoidance (IA) techniques developed for centralized networks to networks with multiple distributed receivers (as in ad hoc networks) are not guaranteed to converge. Motivated by this fact, we develop a waveform adaptation (WA) algorithm framework for IA based on potential game theory. The potential game model ensures the convergence of the designed algorithms in distributed networks and leads to desirable network solutions. Properties of the game model are then exploited to design distributed implementations of the algorithm that involve limited feedback in the network. Finally, variations of IA algorithms including IA with respect to legacy systems and IA with combined transmit-power and WA adaptations are investigated.

Various iterative algorithms for interference avoidance (IA) in networks with co-located receivers, suitable for distributed implementation, have been proposed in the literature. In this paper, the IA problem is cast in a game-theoretic framework and is formulated as a potential game. This formulation accommodates previously proposed algorithms and, in addition, gives us a framework that enables the design of new distributed and convergent algorithms for IA including algorithms with nonidentical utility functions for the users. Two new convergence results for potential games are then derived. The first result establishes the convergence of a class of potential games to the global solution while following best response iterations and when noise is added. The second result establishes the convergence of potential games to the Nash equilibria of the game while following random better response iterations. The first result combined with the potential game formulation allows us to show that for a large class of network scenarios, arbitrarily small noise assures the convergence of best response IA algorithms, including the eigeniterations, to an arbitrarily small neighborhood of the globally optimal signature sequence set. The second result enables the design of reduced feedback mechanisms for IA that converge to desirable solutions.

A spatio-temporal model for indoor non-line-of-sight (NLOS) impulse-based ultra-wideband channels, comprising clusters of multipath components with different times and angles of arrival, is proposed. This model is a spatial extension of the fixed-cluster temporal channel model, based on clustering patterns observed both in the times and angles of arrival of multipath components. It is demonstrated that the proposed extension accurately models the spatial correlation as well as the temporal properties of measured ultra-wideband signals in an indoor NLOS environment.

Ultra-wideband (UWB) technology is being proposed for several short range wireless applications. Some of the applications, such as position location devices for rescue personnel, may require the antenna height to be close to the ground. In scenarios where the antenna height is very low (from 0 to 30 cm), propagation characteristics and channel modeling are needed to provide important insights for the application design. This paper presents time-domain measurements and channel characterization of the indoor UWB channel at three different antenna heights (7.5 cm, 26.5 cm, and 108.5 cm). The transmitting and receiving antennas are placed at heights referred to as near ground, middle ground, and above ground. The effects of the antenna height on the channel characteristics are analyzed. Both large and small-scale characteristics are evaluated and the results for different antenna heights are compared. The results show that large scale characteristics exhibit monotonic behavior with respect to antenna height while small scale characteristics do not follow this behavior. The possible explanations for the obtained results are also discussed.

Over the past decade, research and development in wireless sensor networks has investigated novel and exciting applications for a number of different scenarios. Impulse ultra wideband (UWB) communications promises a number of benefits for use in wireless sensor networks-particularly in forest environments where it can provide robust operation along with the ability to combine communications with precision position location. In this paper, we present measurement results and empirical models for impulse ultra wideband (UWB) propagation in a forest environment. Path loss measurements were performed using a 620 picosecond duration UWB pulse, and over 14000 measurements were recorded in 93 different locations in four different forest environments: light brush, light, medium, and dense forest. Transmitters and receivers were separated by distances ranging from 4 to 50 meters. Path loss exponents were found to range from 2.2-4.3, depending on the antenna and forest density; results which are in agreement with the few existing broadband forest propagation studies presented in the literature. Small-scale fading analysis indicated that UWB signals experience Rician fading, with K-factors in the range of 8-24 dB. These measurements and models should aid in the development of future UWB outdoor sensor networks.

In ad hoc position-location networks, location information is obtained through the sequential estimation of node locations. An unlocalized node can estimate its location based on range and location estimates of nearby localized (";anchor";) nodes, and subsequently provide range and location information to other unlocalized nodes in its vicinity. In such distributed location-estimation scenarios, the accuracy of node location estimates is degraded due to the propagation of localization errors, particularly in NLOS propagation environments. This paper discusses the use of a novel method of mitigating the propagation of localization error using the linear programming framework proposed by the authors in [1]. This method utilizes multi-hop distance estimates to create additional constraints on the feasible region for a node's location, thereby limiting the propagation of error even in NLOS propagation environments.

In this paper we present a technique for improving the location performance based on the Global Positioning System (GPS) for networks of nodes in harsh environments and demonstrate its efficacy via a combination of simulations and measurements in forests. The technique relies on the use of Ultra-wideband (UWB) signals to measure time-of-flight (TOF) and consequently the range between nodes to improve localization in harsh forest environments. Specifically, we create a system of range equations based on network connectivity and solve this system of non-linear equations using a Least-Squares Non-Linear optimization technique using any available GPS information as the initial estimate. Based on our simulations and measurements, the improved technique results in a localization accuracy in forests that is on par with clear-field reference GPS measurements.

In this paper, we propose a collaborative localization scheme which utilizes, in addition to the range estimates to nodes with known locations (anchors), the range estimates between un- localized nodes to estimate their positions. The proposed collaborative method is incorporated into the linear programming (LP) framework proposed in Venkatesh, S. and Buehrer, R. M., (2006) and is implemented in a distributed fashion. We also adopt a constrained nonlinear optimization method to deal with the degenerate cases in the LP approach, i.e., when an unlocalized node has less than three line-of-sight (LOS) range estimates to localized nodes. Simulation results suggest that this collaborative quasi-LP framework can improve the localization accuracy as well as increase the percentage of nodes that are able to be localized, as compared to the standard sequential least-squares (LS) estimator.

The biggest challenge faced by dynamic spectrum sharing (SS) systems is the design of SS schemes that do not adversely impact existing legacy systems in the absence of perfect knowledge. We address this issue by developing a framework to evaluate the interference profile at a legacy receiver under different system scenarios which include the hidden node and imperfect-sensing problems. By analyzing the interference distributions and by comparing the two basic approaches to SS - interference-avoidance-based overlay and interference averaging-based underlay - we identify desirable characteristics for SS radio systems.We then leverage this knowledge to motivate the use of a hybrid SS approach that combines the benefits of the two basic approaches and substantially reduces the impact to the legacy system. The advantage provided by this approach is shown to increase with an increase in the bandwidth available to the SS system. In addition, the approach is more robust to imperfect information. The inclusion of log-normal shadowing is shown to further accentuate these performance trends.

It is well-known that multiuser detection techniques such as successive interference cancellation (SIC) can obtain significant performance improvement over traditional matched filter receivers in code division multiple access (CDMA) systems. Due to its simplicity and usefulness, the SIC structure has received significant attention for several years. In this letter, we make three contributions to the understanding of this receiver structure. First, we derive a closed form expression for received power profile of linear SIC with arbitrary spreading gain and SINR requirements. Second, using this expression we examine the performance of SIC under various ordering criteria based on spreading gain, path loss, and SINR/BER requirement. Third, we show that using a very simple and well-known sorting algorithm, we can obtain a cancellation order and set of power control levels which are very near the optimum in terms of minimizing total transmit power.

This paper presents a joint power control and waveform adaptation algorithm for networks with non-colocated receivers, amenable to a distributed implementation. The proposed algorithm allows users to meet their target signal to interference plus noise ratio (SINK) requirements while reducing the transmit power-levels in the network. The performance of the algorithm is investigated via theoretical-analysis and simulations. It is shown that the joint algorithm results in better solutions than a pure power-control or a pure waveform adaptation algorithm.

In this paper, we propose an ultra wide band (UWB)-based full-body motion capture system for gait analysis. The system is designed to reduce the implementation complexity and cost of current gait analysis setups, which require expensive locomotion laboratories and equipment. The solution exploits recent developments in wearable computing (i.e., electronic textiles) and relies on estimating the relative distance between body-worn sensors through time-of-flight measurements. While such a system may find application in several areas, substantial research is still required to prove its feasibility and accuracy. From this perspective, the present contribution addresses two key aspects to enable its development: i) the interaction between UWB electromagnetic pulses and the human body is analyzed and, by contrast to most results in the literature, the body- induced pulse distortion is characterized in terms of an equivalent channel impulse response, and ii) by explicitly taking into account that body-induced distortion, the accuracy of various time-of- flight-based ranging algorithms is investigated.

In an underground mine, wireless systems based on ultra wideband (UWB) signals have the potential to improve the response time to mine emergencies by providing fast, reliable communications as well as precision position location. Much of the UWB research to date has focused on the in-building and (to a lesser degree) outdoor propagation environments. As a result, little information is available on the nature of UWB propagation in an underground mining environment. This paper presents preliminary path loss and power delay profile measurement results for a typical room-and-pillar underground mine located in southwest Virginia. Transmitters and receivers were separated by distances ranging from 20 m to over 300 m in both line-of- sight and non-line-of-sight configurations. These results indicate that for long corridor environments, similar to indoor hallways, a waveguide effect results in better than free space propagation. Additionally, UWB signals were found to experience less fading over a local area than CW signals. UWB signals also provide the opportunity to aid in position location by achieving very accurate time-of-arrival measurements, or via RF fingerprinting techniques. We show that initial attempts to determine position based on such an approach were also very promising. The results presented here are insufficient in number to make some definitive statements about UWB propagation in underground mines but are only a first step towards characterizing that propagation. Additionally, the initial results are similar to the trends seen in indoor environments thus providing optimism that UWB may be a viable physical layer for wireless systems in underground mines.

In this paper a UWB-based sensor system for cargo transfer is designed, simulated, implemented and tested. This system was designed to address the problem of safely transferring cargo crates between ships on the open seas. UWB antennas are placed on the four corners of the cargo crate, providing the information needed by a ranging/positioning algorithm that estimates the orientation and distance of the ship's deck from the crate. This information is fed to a non-linear control system which controls the landing crane. We focus on the sensor system in this paper. Furthermore, the system is successfully tested in a 1/24 scale prototype demonstration.

A framework to construct convergent interference avoidance (IA) algorithms in networks with multiple distributed receivers (as in ad hoc networks) based on potential game theory is developed in this paper. This is motivated by the fact that direct extensions of distributed greedy IA techniques for centralized networks to these de-centralized networks do not always lead to convergence. Some channel conditions that lead to non-convergence are also identified in the paper. A waveform adaptation algorithm for IA, designed on the basis of the framework, is then proposed. It is shown that this algorithm leads to a reduction of the interference in the network and also incorporates fairness in the allocation of resources.

In this paper, a narrowband interference (NBI) mitigation scheme for ultra-wide bandwidth (UWB) signals using multiple receive antennas is examined. The low spatial fading of UWB signals relative to NBI signals is exploited to provide "interference selection diversity". Whereas classical selection diversity is designed to maximize the desired received signal power, the aim of interference selection diversity is to minimize the effective NBI power. Expressions for the probability of error of the selection diversity scheme are derived for both Rayleigh and Ricean NBI fading scenarios. The improvement in received signal-to-interference ratio is also mathematically investigated. It is shown that doubling the number of antennas results in a 3-dB performance improvement for the Rayleigh fading case. Less substantial gains are observed under Ricean fading.

In this paper, we discuss the use of power control in Ultra-Wideband (UWB) Position-Location Networks (PoLoNets)1, where the goal is to periodically estimate the locations of mobile nodes that move through a network of location-aware reference nodes. The reference nodes provide Time-of-Arrival (TOA) based range estimates that are used to estimate the locations of mobile nodes at regular intervals. We examine the accuracy of location-estimation through the Cramer-Rao lower bound (CRLB) and show that the localization accuracy fluctuates or "fades" as a mobile node moves through the network of reference nodes. The use of power control is shown to improve the robustness of location-estimates and provide higher localization accuracy. Two power control approaches are presented and compared with the empirically-obtained optimal solutions.

In this paper, we propose a linear programming approach to the problem of non-line-of-sight (NLOS) error mitigation in sensor networks. The locations of sensor nodes can be estimated using range or distance estimates from locationaware “anchor ” nodes. In the absence of line-of-sight (LOS) between the sensor and anchor nodes, e.g., in indoor networks, the NLOS range estimates can be severely biased. If these biased range estimates are directly incorporated into practical location estimators such as the Least-Squares (LS) estimator without the mitigation of these bias errors, this can potentially lead to degradation in the accuracy of sensor location estimates. On the other hand, discarding the biased range estimates may not be a viable option, since the number of range estimates available may be limited. We present a novel NLOS bias mitigation scheme, based on linear programming, that (i) allows us to incorporate NLOS range information into sensor location-estimation, but (ii) does not allow NLOS bias errors to degrade sensor localization accuracy.

In this paper, we study concatenated coding for MIMO-OFDM systems. The proposed concatenated system achieves full spatial and frequency diversity at much lower complexity in terms of number of states without any bandwidth expansion. In general, coding for MIMO-OFDM systems is known as space frequency time (SFT) coding. This paper focuses on applying a powerful class of trellis codes known as IQ-TCM. We illustrate the benefits of IQ-TCM in concatenated SFT codes. The results of this study show the performance improvements of IQ-TCM over conventional TCM at the same number of states and spectral efficiency. The reason for that is the larger effective length of IQ-TCM. Also, the simulation results emphasize the importance of an appropriate interleaver design since there is a great loss in performance and diversity if the block interleaver is not designed carefully.

The propagation of near-ground indoor ultra-wideband channel is an important issue with significant impacts on the future direction and scope of ultra wideband (UWB) technology and its applications. Near-ground applications are effective where the use of scatter nodes/devices is used in a conventional application, such as rescue personnel. The goal of this article is to present an assessment and characterization of near-ground UWB channels and the potential for near-ground communication systems. Time-domain measurements for UWB indoor propagation channels were taken with transmit and receive antennas placed directly on the ground in order to characterize statistical properties of the channel's impulse response. Both large- and small-scale models were evaluated and compared to existing results for above-ground measurements. The model parameters that were extracted from measurements prove to be in accordance with expected physical trends from previous work. These preliminary results could be advantageous in the design of future near-ground indoor UWB systems capable of fusing propagation and communication functionalities

We examine the capacity of cooperative MIMO networks, specifically power allocation methods at the relay. Our principle finding is that power allocation using SVD and waterfilling at the source and the relay cannot achieve maximum capacity in cooperative MIMO networks. We find the optimal power allocation at the relay for the two antenna case when waterfilling is used for the direct path. A modified waterfilling power allocation method at the relay is used to increase the overall cooperative capacity when the relay is close to the source. The proposed power allocation method achieves high capacity gain in cooperative MIMO networks using a simple method when the relay has no channel information concerning the direct path. As the distance between the source and the relay increases, the outage probability at the relay plays an important role in cooperative networks. To improve the outage probability at the relay, we use a waterfilling method for the channel between the source and the relay. Finally, we compare the performance of five power allocation techniques for the multiple antenna case in terms of outage probability

In this paper, we analyze the problem of medium access control (MAC) layer design for impulse-based ultra-wideband (UWB) position-location networks (PoLoNets). We focus our attention on PoLoNets in which stationary reference nodes are deployed in an ad hoc manner and determine their locations based on a small number of fixed anchors with known locations. Location and range information is propagated through the network of reference nodes in order to periodically estimate the locations of mobile nodes. The principal objective of our design is to minimize the localization error and convergence time of location estimates in the presence of node mobility and multipath. The properties of bounds on node localization accuracy in the presence range and location-estimation errors are derived. These results serve as a connection between the problem of multiple-access design and the accuracy of location estimates. A spread-spectrum based MAC protocol is developed that is shown to outperform the traditional carrier-sense multiple-access (CSMA) protocol in terms of accuracy and the convergence time of location-estimates

In this paper, we build on known bounds on localization in sensor networks and provide new insights that can be used in multiple-access design from a localization perspective. Specifically, we look at the Cramer-Rao lower bound (CRLB)for the estimation of a sensor's location given unbiased Gaussian range estimates from a set of location-aware "anchor" nodes. A novel characterization of the accuracy of sensor location-estimates is derived, which provides new insights into the design of multiple-access schemes from the perspective of sensor localization accuracy. These insights are validated through the investigation of the performance of a spread-spectrum based multiple-access scheme in an ultra-wideband sensor network

Traditional synchronization techniques applied to impulse-radio ultra-wideband (UWB) result in prohibitively long acquisition times, due to the extremely large search space. Additionally, in dense multipath environments, there exist a larger number of cells within the uncertainty region that can lead to acquisition lock. Locking to an arbitrary multipath component may result in unacceptable performance for many applications (range error in positioning systems for example). In this paper, we present a modified framework for the analysis of UWB acquisition which accommodates multiple lock cells. The framework divides the acquisition process into two distinct phases. The two phases are termed "coarse" and "fine" acquisition. The coarse acquisition phase is a fast implementation of traditional serial search which takes advantage of the large number of cells which can terminate the search process. Fine acquisition exploits statistics derived from the first phase and the clustered nature of multipath arrivals to determine the earliest arriving path, even when it is severely attenuated. We show that the first phase provides a substantial improvement in mean acquisition time compared with traditional serial search and that the second phase provides robust estimation of the first arriving path in ranging applications.

This letter presents two results for multiuser wireless systems employing dirty-paper coding strategies along with greedy scheduling over the broadcast multiple-input multiple-output channel. Specifically, an efficient and suboptimal downlink scheduler is proposed to approximate the maximum sum rate using equal power allocation, and it is shown to approach the maximum sum rate of optimal power allocation for a large number of users under optimal scheduling. The second result demonstrates that the average maximum sum rate can be tightly upper bounded when spatial multiplexing is maximized.

In this paper we examine large and small scale channel modeling for ultra-wideband signals. Specifically, we examine the impact of UWB signals on traditional channel modeling techniques. This examination is based on measurements taken in an indoor office environment as part of the DARPA NETEX Program. Our findings suggest that while traditional channel modeling approaches are appropriate, care must be taken in both applying the results and interpreting them. In particular, the wide bandwidths do not invalidate the traditional exponential decay versus distance approach to path loss, although it is important to separate antenna effects from path effects. Additionally, the traditional small scale approach which uses the time-invariant linear filter to represent the channel impulse response can still be applied to UWB signals. However, one must use the model with caution as "paths" from the model may no longer represent physical transmission paths but rather may simply represent frequency distortion of individual pulses. We also examine the path loss exponents obtained from measurements and compare them to previous measurement campaigns. Finally, small scale statistics such as delay spread and the energy per "path" are also examined and compared to previous results

We propose and analyze a novel single channel direction finding (DF) algorithm based on phase-locked loops (PLL). The algorithm is evaluated via Matlab simulation to determine the accuracy of the estimated angle-of-arrival (AOA) of a target signal in an AWGN channel. The proposed algorithm is shown to provide exceptional accuracy. Additionally, we present results from a hardware implementation using a software radio platform and an eight-element circular array; they validate the simulation results.

This paper investigates peak-to-average power ratio (PAR) reduction with constrained transmit power for spatially precoded downlink transmission, based on orthogonal frequency-division multiplexing (OFDM). We generalize the concept of multiuser Tomlinson-Harashima (T-H) precoding and propose a nonlinear spatial precoder, termed a complex sphere precoder (CSP), which has the same throughput and error performance as the T-H precoder, but achieves both a low PAR and low average transmit power. Our study shows that a sequential CSP over individual tones achieves a significant PAR reduction while requiring a moderate transmit power increase. The suboptimal per-tone lattice search constitutes a good compromise between performance and complexity.

This paper describes a one-hop self-organized clustering (SOC) algorithm developed specifically for ultra-wideband (UWB) networks. Our research is motivated by the strict regulation of the transmit power of UWB devices, as imposed by U.S. Federal Communications Commission (FCC). To comply with FCC power regulations and to maximize network capacity, we propose a heuristic clustering algorithm to form clusters that have the minimum total power emission subject to constraints on radio range and multiple access capacity. Simulation results show that the proposed algorithm performs better than a random selection algorithm in terms of power emission under all circumstances. Furthermore, the performance of the SOC algorithm can approach that of the k-means algorithm when an appropriate upper limit on the number of nodes in a cluster is imposed.

This paper considers packet transmission over a multiple-input-and-multiple-output (MIMO) Gaussian broadcast channel (GBC). Specifically, we compare and contrast two transmission strategies built on dirty paper coding (DPC) and the Bell labs layered space-time architecture (BLAST), respectively, under channel-aware scheduling, and demonstrate the advantage of a distributed MIMO architecture at a system performance level. For the DPC-based strategy, we also present an efficient suboptimal scheduling algorithm using equal transmit power allocation across users with a maximum order of spatial multiplexing which achieves near optimal scheduling performance for a sufficient number of users.

In this letter, we propose modifications to existing transmit-diversity techniques to provide transmit diversity in code-division multiple-access cellular systems which must support both second-generation and third-generation mobiles on a common carrier. The proposed method, which we call symmetric-sweep transmit diversity, combines phase-sweep transmit diversity (PSTD) and space-time spreading (STS) in a way that allows second-generation and non-STS-capable mobiles to obtain the full advantage of PSTD, while third-generation mobiles obtain the full advantage of STS.

We consider the use of multiple antennas at the transmitter and/or the receiver to provide open-loop spatial diversity in a multiuser wireless data network. With channel quality information (CQI) available to the transmitter, and by always scheduling the transmission to the active user having the best channel conditions at the time of scheduling, another form of diversity, termed multiuser diversity, is obtained in a data system. This paper provides an analysis of the interaction between these two forms of diversity. From a network point of view, we prove that the asymptotic sum rate, in the limit of a large number of active homogeneous users and subject to the same average total transmit power, is inversely related to the number of transmit antennas for independent and identically distributed (i.i.d.) flat Rayleigh fading channels. In the case of i.i.d. flat Rician fading, the asymptotic sum rate also depends inversely on the number of transmit antennas, but directly on the number of receive antennas. Numerically, we show that the total diversity gain is also constrained by finite CQI quantization and channel fading statistics.

Third-generation code-division multiple access (CDMA) cellular systems incorporate a downlink transmission technique called transmit diversity (TD). This paper provides a comprehensive investigation of the performance and practical implementation issues of open-loop transmit diversity schemes for the IS-2000 third-generation cellular CDMA standard. Discussed in detail are orthogonal transmit diversity (OTD) and space-time spreading (STS) diversity schemes. STS is a TD technique that is motivated by space-time coding principles originally described for narrowband systems. OTD is a TD technique that obtains diversity not at the symbol level, but in the decoding process, and has performance that is in general lower bounded by STS, which obtains diversity combining prior to decoding. Thus, STS always outperforms OTD, with the improvement particularly significant in the presence of weak convolutional codes. Probability of error analysis is performed for STS under the assumptions of imperfect channel estimates, correlation between antennas, and unequal pilot power allocations. Extensions to STS are provided for the multicarrier version of the standard and four transmit antennas. Simulation studies are performed to detail the performance of both open-loop TD schemes with convolutional coding and closed-loop power control consistent with the 3GPP2/IS-2000 standard. Many of these results were generated in the course of the IS-2000 standardization procedure. Performance is studied in radio environments which experience flat Rayleigh fading, frequency selective Rayleigh fading, spatially selective fading, as well as Ricean fading with various K factors. Some additional results are presented for cases where mobile receivers have two receive antennas. Implementation issues are also considered including the impact of antenna delay differences on performance, transmitter and receiver architectures, and computational complexity.

In this letter, we present a comparison between the bit error rate (BER) performance of a uniform circular array (UCA) and a uniform linear array (ULA) assuming quadrature phase-shift keying (QPSK) and maximal-ratio-combining (MRC) in a mobile radio communication environment. The results are based on analysis, assuming a flat Rayleigh fading channel with omni-directional antennnas and considering the azimuthal plane only. The analytical BER is derived as a function of the spatial fading correlation for both types of antenna arrays. Results show that for similar aperture sizes the UCA outperforms the ULA when considering all angles-of-arrival. However, there is considerable variability over central angle-of-arrival (AOA) for low-to-moderate angle spreads. For angles-of-arrival concentrated near the broadside of the linear array, the ULA typically performs as well as or better than the UCA. A truncated Gaussian AOA (AOA) distribution is assumed to model spatial correlation and the numerical results focus on four element arrays.

In this paper, we present model-based techniques for joint detection of two near equal power co-channel MSK signals. It is shown that an MSK signal is piecewise linear in the parameters of interest and therefore can be represented in the standard linear form. The problem of joint detection is then reduced to one of parameter estimation and this allows us to develop receiver architectures based on the minimum variance and minimum mean square error criterion. Simulation results have shown that model-based techniques provide a performance gain of about 12 dB for equal power signals and about 8-10 dB when the signals are near equal power (SIR/spl sim/1dB) over the conventional correlation receiver.

Multiuser detection (MUD) performance can be significantly better than the conventional matched filter receiver in CDMA systems. Further, since optimal MUD is exponentially complex, research has mainly focused on suboptimal approaches such as successive interference cancellation (SIC). SIC requires a geometric distribution of received powers to achieve equal performance for all received signals. We propose a power control scheme for SIC to achieve this profile (or the profile corresponding to any desired and achievable set of error rates) based on frame-error rate (FER) or bit-error rate (BER). Specifically, we derive the relationship between received power and BER for linear SIC and show that for unlimited mobile powers, a deterministic distributed BER-based outer-loop power control drives the received powers to the optimal power profile. The convergence of the deterministic BER-based algorithm is examined in the absence of inner loop power control errors. The simulated performance of a stochastic version of this algorithm is examined using instantaneous FER measurements. The stochastic algorithm is shown to provide unbiased estimates of the true power updates and converge to the optimal power vector provided the mobiles have unlimited power. We consider power limits for specific mobiles and find that individual mobile limits do not affect the performance of other signals. We examine the impact of system loading, multiple FER targets, error correction, and inner loop power control error on the performance of the algorithm.

In this paper, we present the comparative spatial fading correlation function of a uniform linear array (ULA) for various angle-of-arrival (AOA) distributions in a mobile radio environment. The spatial fading correlation for ULA is a function of the angle spread and distance between elements. Angle spread is a measure of the energy distribution in AOA and is defined to be the variance in this work. Three AOA distributions are considered in this work: uniform, truncated Gaussian, and Laplacian. Results show that the three AOA distributions give similar spatial fading correlation for the same angle spread suggesting that the variance of the distribution is more important than the actual distribution. Computer simulations are carried out to verify the analytical results.

With the advent of new wireless mobile Internet technologies, there has been a significant increase in demand for capacity on the forward link of cellular systems. While significant enhancements to the system have improved the performance of the system, there continues to be a greater need for even more capacity on the forward link. Further capacity can only be derived by exploiting the spatial distribution of users. By intelligently steering energy related to a mobile only in the direction of a mobile, capacity can be increased. Use of intelligent antenna techniques coupled with transmit diversity techniques offer the most robust performance gains across a variety of environments. This paper discusses an antenna array architecture which exploits a combination of diversity and coherent antenna arrays to achieve peak performance in a cdma2000 environment. Performance results are given for this antenna architecture under a number of fading conditions.

In this contribution, we calculate the received power distribution required to obtain equal bit-error rate (BER) performance for all links in a code-division multiple-access system employing a linear successive interference cancellation (SIC) receiver at the base station for an additive white Gaussian noise channel. We show that the variance of the decision statistic of the linear SIC receiver can be formulated in a nonrecursive manner that allows calculation of the power profile necessary to obtain equal signal-to-noise-plus-interference ratios for all received signals when cancellation order is determined based on average power. When equal BER performance is required, this formulation allows capacity limits to be determined for a required signal-to-interference-plus-noise ratio (SINR) or an SINR limitation to be calculated for a given capacity. We also show that the power profiles required are significantly larger than those obtained when perfect cancellation is assumed, highlighting the inadequacy of such an assumption.

The implementation of advanced DS-CDMA receivers based on multiuser detection principles is becoming a reality thanks to the combination of an improved understanding of the theoretical basis of multiuser detection and advances in digital, mixed-signal, and RF technologies. Due to their lower complexity, subtractive interference cancellation approaches are attractive for the practical implementation of multiuser detection. In a parallel interference cancellation receiver, it is practical to use the soft outputs of a matched filter bank for amplitude estimation. A bias arises in the decision statistics, however, due to imperfect estimation and interference cancellation. In this paper, the source of the bias is explicitly recognized, and a partial interference cancellation scheme that mitigates the negative effects of biased estimation and significantly improves system performance is proposed. A practical real-time algorithm that significantly reduces the implementation complexity of this scheme without sacrificing performance is then derived. To facilitate a software radio implementation, the signal processing complexity of the approach is characterized. The real-time processing algorithm is tested via implementation in software on a floating-point general-purpose DSP. The prototype includes a flexible software-based architecture which performs IF sampling and uses digital downconversion prior to baseband processing. The hardware test setup is described, and the results are presented and compared with simulation and analytical results. The experimental results confirm the simulation and analytical results which show large performance gains over the conventional matched filter.

We comment on partial parallel interference cancellation as discussed in the paper by Divsalar et al. (see ibid. vol.46, p.258-68, 1998). The aforementioned work showed that by multiplying the symbol estimates by a factor less than unity in the early stages of cancellation, the performance of parallel cancellation can be improved relative to full ("brute force") cancellation. In this paper we analyze the improvement of parallel cancellation when using partial cancellation, and provide additional insight into the gains. Specifically, we show that the decision statistic is biased when linear (soft) estimates of the symbol or channel are used for cancellation. Partial cancellation improves the performance in this case by reducing the decision statistic bias.

This paper presents a baseband real-time DSP implementation of a multiuser receiver built upon an improved strategy for parallel interference cancellation. Significant performance gains are obtained by using a partial parallel interference cancellation scheme that mitigates the negative effects of biased estimation and imperfect MAI cancellation that are intrinsic to the complete interference cancellation approach. An attractive feature of the implementation is that it has a computational complexity that is linear in the number of users. Experimental test results confirm substantial improvements over the conventional receiver.

We compare throughput bounds for a frequency-hopped multiple-access (FHMA) system employing variable rate as well as fixed rate coding (FRC). Nonfading as well as Rayleigh-fading channels are explored. The throughput bounds for the variable rate coding (VRC) schemes are based on the assumption that the number of active users in the system, m, is known at any time while the bounds for the FRC scheme assumes that the fixed code rate is optimized for the user population mean, /spl lambda/, where the population is assumed to be Poisson distributed. We present bounds for theoretical "perfect" codes which achieve the capacity as well as bounds for Reed-Solomon (RS) codes of practical block lengths. Finally, we show the dependence of a VRC scheme upon the accuracy of the estimates of m and present a general comparison of the gains of using VRC versus a realistic FRC scheme.

In this paper we derive the asymptotic multiuser efficiency (AME) of M-stage interference cancellation receivers and show that as the number of stages increases to infinity, the receiver is equivalent to the well known decorrelator. This fact holds provided certain bounds on the total cross-correlation between users are met. Thus, in such cases the M-stage receiver has an asymptotic multiuser efficiency which is greater than zero as M/spl rarr//spl infin/ for all user energies, i.e. it is near-far resistant. Additionally, while not strictly near-far resistant in the case of M

Parallel multistage interference cancellation is a promising approach for practical implementation of multiuser detection. However, as this paper shows, direct implementation of multistage parallel interference cancellation (i.e. complete cancellation of the estimated interference) results in biased estimates and consequently reduced performance. A simple technique that mitigates the effect of the bias and provides significant performance gains over the direct implementation is presented.

We consider the use of multistage parallel interference cancellation at the base station of a code-division multiple-access (CDMA) wireless system. Previous work in this area has demonstrated the potential for significant improvements in capacity and near-far resistance. However, most previous work has assumed perfect synchronization with the signals of interest. Practical systems will experience phase jitter and timing errors. We undertake an analysis of the effects of phase and timing errors, obtaining a closed form result for bit-error rate (BER) performance after an arbitrary number of stages of cancellation in an additive white Gaussian noise (AWGN) channel. This result is shown to agree well with simulations. Simulation results are also presented for the important case of frequency selective Rayleigh fading. The results from both analysis and simulations demonstrate that interference cancellation is fairly robust to phase and timing errors.

We consider a simple model for adaptive multistage interference cancellation within a CDMA system, and seek to develop an accurate analytical expression for the performance of this system. Previous work on interference cancellation has relied heavily on simulation techniques or a simple Gaussian approximation (GA). The standard GA ran lead to bit-error rate (BER) results which are optimistic for the conventional receiver, and this also occurs when the approximation is applied to the interference cancellation problem. Additionally, this approximation does not allow the second order effects of the multiple access interference (MAI) to be included in the performance estimates. Several improvements on the standard GA have been suggested which result in accurate performance results for a standard CDMA receiver. This paper presents an analytical expression for the probability of bit error for an adaptive multistage interference canceller, using an improved Gaussian approximation (IGA) for MAI. The BER at any stage of interference cancellation can be recursively computed from the signal-to-noise ratio (SNR), the statistics of the random powers of users, and the processing gain of the CDMA system. The performance of the resulting EER expression is compared with simulation results. Since the second order effects of MAI can be included, the analytical framework presented here can also be used to evaluate the performance of multistage interference cancellation in arbitrary fading environments, and we present results for the performance of interference cancellation in lognormal fading environments.