State estimation is essential in systems where the true state must be inferred from noisy measurements. The Kalman filter remains a core tool used for state estimation across various fields, including aerospace, navigation, and signal processing. Traditional estimation methods typically rely on fixed sampling strategies, which can limit performance in dynamic or resource-constrained environments. In this letter, we introduce a new adaptive sampling framework for the Kalman filter that optimizes the measurement sampling matrix to minimize mean square error under a power constraint. In the proposed framework, sampling decisions respond both to the evolving state and to shifts in the estimation objective. We propose two sampling methods that enable dynamic, resource-efficient sampling while enhancing estimation accuracy. The first method sequentially designs sampling matrices and applies to general systems. The second method jointly optimizes the sampling matrices over an entire block; this method further improves estimation performance and could be used for a more specialized problem. We also numerically show that the proposed methods outperform other heuristic sampling techniques.

Integrated sensing and communications (ISAC) is a key enabler of new applications, such as precision agriculture, extended reality (XR), and digital twins, for 6G wireless systems. However, the implementation of ISAC technology is very challenging due to practical constraints such as high complexity. In this paper, we introduce a novel ISAC waveform design strategy, calledthe spatial-division ISAC (SD-ISAC) waveform, which simplifies the ISAC waveform design problem by decoupling it into separate communication and radar waveform design tasks. Specifically, the proposed strategy leverages the null-space of the communication channel to superimpose sensing signals onto communication signals without interference. This approach offers multiple benefits, including reduced complexity and the reuse of existing communication and radar waveforms. We then address the problem of optimizing the spatial and temporal properties of the proposed waveform. We develop a low-complexity beampattern matching algorithm, leveraging a majorization-minimization (MM) technique. Furthermore, we develop a range sidelobe suppression algorithm based on manifold optimization. We provide comprehensive discussions on the practical advantages and potential challenges of the proposed method, including null-space feedback. We evaluate the performance of the proposed waveform design algorithm through extensive simulations. Simulation results show that the proposed method can provide similar or even superior performance to existing ISAC algorithms while reducing computation time significantly.

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal’s polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target signal-to-interference-plus-noise ratio (SINR) leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for mean square error (MSE) minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks, by optimizing the placement of unmanned aerial vehicle (UAV) relays, relay beamforming, and receive combining. The design is separated into two problems: beamforming-aware UAV placement optimization and transceiver design for minimum SINR maximization. A significant challenge in beamforming-aware UAV placement optimization is the lack of instantaneous channel state information (CSI) prior to deploying UAV relays, making it difficult to derive the beamforming SINR in non-orthogonal multi-user transmission. To address this issue, an approximation of the expected beamforming SINR is derived using the narrow beam property of a massive MIMO base station. Based on this, a UAV placement algorithm is proposed to provide UAV positions that improve the minimum expected beamforming SINR among users, using a difference-of-convex framework. Subsequently, after deploying the UAV relays to the optimized positions, and with estimated CSI available, a joint relay beamforming and receive combining (JRBC) algorithm is proposed to optimize the transceiver to improve the minimum beamforming SINR among users, using a block-coordinate descent approach. Numerical results show that the UAV placement algorithm combined with the JRBC algorithm provides a 4.6 dB SINR improvement over state-of-the-art schemes.

Dual-function radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to exploit distortion due to multi-user and radar transmission. We propose a majorization-minimization (MM)-based solution to the formulated problem. To accelerate convergence, we propose an improved majorizing function that leverages a novel diagonal matrix structure. We then evaluate the proposed algorithm via comprehensive simulations.

Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challenges, this paper introduces a novel simulation-enhanced data augmentation method for machine learning (ML) pathloss prediction. Our method integrates synthetic data generated from a cellular coverage simulator and independently collected real-world datasets. These datasets were collected through an extensive measurement campaign in different environments, including farms, hilly ter-rains, and residential areas. This comprehensive data collection provides vital ground truth for model training. A set of channel features was engineered, including geographical attributes derived from LiDAR datasets. These features were then used to train our prediction model, incorporating the highly efficient and robust gradient boosting ML algorithm, CatBoost. The integration of synthetic data, as demonstrated in our study, significantly improves the generalizability of the model in different environments, achieving a remarkable improvement of approximately 12 dB in terms of mean absolute error for the best-case scenario. Moreover, our analysis reveals that even a small fraction of measurements added to the simulation training set, with proper data balance, can significantly enhance the model's performance.

At the Indiana Department of Transportation, work orders are pivotal for payroll accounting, equipment utilization, and resource allocation in winter road maintenance operations. However, efficient management is hindered by the manual generation of work orders for a vast fleet of over 1000 snow plows and the associated personnel. Existing telematics research often focuses on small-scale short-term scenarios, overlooking the extended analysis required for large fleet management. Challenges in data acquisition and the unique nature of winter road maintenance further complicate the situation. This paper addresses these issues and underscores the urgent need for work order automation in winter road maintenance. It introduces work order verification and generation algorithms for operation information extraction from GPS tracks, with improved details and accuracy compared to current manual processes. The paper also demonstrates two open-source proof-of-concept programs-a Matlab implementation for algorithm development and a user-friendly web app-with real-life state-scale examples, highlighting the tangible benefits of the proposed solution.

On-demand deployment of mobile communication infrastructure has emerged as a promising solution for extending cellular coverage in rural areas. However, most current research focuses on theoretically optimizing the trajectory of unmanned aerial vehicle (UAV) relays/base stations in simplified geographic scenarios. These network-side attempts have failed to provide low-cost, practical solutions to remove today’s digital gap. This paper proposes a large-scale simulation methodology based on real-life, high-precision geographic data. With its help, we provide a user-centric approach to coverage extension using follow-me relays. We focus on the rural case exemplified by Indiana, U.S., and present quantitative coverage analyses via channel simulation. Our results show that one UAV relay added to follow the user of interest at 10m high can effectively bring over 20% more area into coverage from the user’s point of view. With the relay added at 50m, the most challenging channel the network has to deal with to guarantee 90% area-wise coverage will experience a 20dB path loss reduction. These site-specific analyses can also be applied to future wireless network planning problems, including network-side equipment deployment simulation and optimization for millimeter-wave and terahertz communications.

This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1°, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU-R M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28 GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential models for the spatial/angular autocorrelation coefficient under distance and alignment effects.

6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existing challenges of mmWave communication and adds new challenges associated with the wider bandwidths, more antennas, and harsher propagations. Notably, the frequency- and spatial-wideband (dual-wideband) effects are significant at sub-THz. This paper presents a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels. A set of frequency-dependent array response matrices are constructed, enabling channel recovery from multiple observations across subcarriers via multiple measurement vectors (MMV). Using the temporal correlation, MMV least squares (LS) is designed to estimate the channel based on the previous beam support, and MMV compressed sensing (CS) is applied to the residual signal. We refer to this as the MMV-LS-CS framework. Two-stage (TS) and MMV FISTA-based (M-FISTA) algorithms are proposed for the MMV-LS-CS framework. Leveraging the spreading loss structure, a channel refinement algorithm is proposed to estimate the path coefficients and time delays of the dominant paths. To reduce the computational complexity and enhance the beam resolution, a sequential search method using hierarchical codebooks is developed. Numerical results demonstrate the improved channel estimation accuracy of MMV-LS-CS over state-of-the-art techniques.

This paper introduces AGAPECert, an Auditable, Generalized, Automated, Privacy-Enabling, Certification framework capable of performing auditable computation on private data and reporting real-time aggregate certification status without disclosing underlying private data. AGAPECert utilizes a novel mix of trusted execution environments, blockchain technologies, and a real-time graph-based API standard to provide automated, oblivious, and auditable certification. Our technique allows a privacy-conscious data owner to run pre-approvedOblivious Smart Contractcode in their own environment on their own private data to produce Private Automated Certifications. These certifications are verifiable, purely functional transformations of the available data, enabling a third party to trust that the private data must have the necessary properties to produce the resulting certification. Recently, a multitude of solutions for certification and traceability in supply chains have been proposed. These often suffer from significant privacy issues because they tend to take a ”shared, replicated database” approach: every node in the network has access to a copy of all relevant data and contract code to guarantee the integrity and reach consensus, even in the presence of malicious nodes. In these contexts of certifications that require global coordination, AGAPECert can include a blockchain to guarantee ordering of events, while keeping a core privacy model where private data is not shared outside of the data owner's own platform. AGAPECert contributes an open-source certification framework that can be adopted in any regulated environment to keep sensitive data private while enabling a trusted automated workflow.

In this work, a compressed sensing-aided wideband MIMO-OFDM channel training framework is proposed to reduce the training overhead in slowly-varying channels with frequency- and spatial-wideband (dual-wideband) effects. To combat the beam squint effect, a set of frequency-dependent array response matrices are constructed, enabling the recovery of the sparse beamspace channel from multiple observations across OFDM subcarriers, via multiple measurement vectors (MMV). A channel training algorithm (MMV-LS-CS) is proposed to estimate slowly-varying multipath channel parameters: MMV least squares (MMV-LS) is first used to estimate the channel on the previous beam index support, followed by MMV compressed sensing (MMV-CS) on the residual to estimate the time-varying multipath components. Finally, a channel refining algorithm is proposed to estimate the gains and time delays of the dominant channel paths jointly on pilot subcarriers. Numerical results show that MMV-LS-CS achieves more accurate and robust channel estimation than the state-of-the-art approach on slowly-varying dual-wideband MIMO-OFDM: given a moderate SNR of 20 dB, our algorithm attains$\text{NMSE}=0.15$, as opposed to the state-of-the-art which attains$\text{NMSE}=0.43$in the same configuration. Besides, MMV-LS-CS necessitates$\text{SNR} =14\ \text{dB}$to achieve the spectral efficiency of 6 bit/s/Hz/stream, while the state-of-the-art scheme needs$\text{SNR}=17\ \text{dB}$to attain the same spectral efficiency.

The high cost required by traditional measurement campaigns often limits the amount of data that can be obtained, to the detriment of data-intensive modeling techniques such as machine learning. This work addresses the limitations from the measurement system and environment by changing the traditional channel modeling approach. Simulation was used as an auxiliary means to obtain data, showing the broader applicability of a site-specific model. More specifically, we explore the possibility of augmenting channel measurements with simulation predictions to acquire comprehensive sets of mm-wave channel information for improved modeling. Path loss measurements from a 28-GHz campaign in a coniferous forest were utilized in conjunction with semi-empirical statistical ray tracing simulations to evaluate the performance of measurement-based channel models beyond the specific measurement region from which they were developed. The root-mean-square deviations between model predictions and simulation results are 11.3 dB for an ITU woodland model and 6.8 dB for a site-specific model we published in a previous manuscript. Furthermore, the site-specific model was demonstrated to agree with simulation predictions at distances and locations we were unable to measure. These results show a broad applicability of our site-specific model as well as a mechanism to derive accurate models from a combination of measurement and simulation data.

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on-the-field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users' positions, given received power measurements on a small subset of positions. Based on these predictions and on positional side information, a small subset of beams is recommended to reduce the training overhead of beam-alignment. Numerical results evaluated with the Quadriga channel simulator demonstrate that the proposed algorithm achieves correct alignment with high probability using small training overhead: given power measurement on only 20% of the possible positions when using a discrete coverage area, our algorithm attains a probability of correct alignment of 80%, with only 2% of trained beams, as opposed to a state-of-the-art scheme which achieves 50% correct alignment in the same configuration. To the best of our knowledge, this is the first work to consider the beam recommendation problem based on measurements collected on a small subset of positions.

With the rapid popularity of unmanned aerial vehicles (UAVs, also known as drones), UAV data relay has demonstrated potential extending wireless communication coverage, especially for rural areas. The flexibility of this approach has attracted research attention from a variety of areas, including Internet of Things, intelligent transportation systems, and digital agriculture. However, most current research effort focuses on modeling and theoretically optimizing data relay systems via UAV trajectories in simplified geographic environments, while taking advantage of UAVs for practical wireless communication networks requires large-scale quantitative performance analysis results based on real-life environment information. In this paper, we propose algorithms for generating large-scale blockage and path loss maps via terrain-based channel modeling for cellular communication systems with fixed-height relay drones. Our analyses reveal the coverage ratios for Tippecanoe County and the Wabash Heartland Innovation Network region in Indiana, with relay drones simulated at different heights. A coverage ratio gain over 40% can be achieved at a drone height of 100 m, compared to a typical pedestrian height of 1.5 m. These site-specific analyses are important in locating poorly covered spots and quantifying the coverage improvement from UAV data relay.

This paper presents millimeter wave propagation measurements at 28 GHz for a typical suburban environment using a 400-megachip-per-second custom- designed broadband sliding correlator channel sounder and highly directional 22-dBi (15° half-power beamwidth) horn antennas. With a 23-dBm transmitter installed at a height of 27m to emulate a microcell deployment, the receiver obtained more than 5000 power delay profiles over distances from 80m to 1000m at 50 individual sites and on two pedestrian paths. The resulting basic transmission losses were compared with predictions of the over-rooftop model in recommendation ITU-R P.1411-9. Our analysis reveals that the traditional channel modeling approach may be insufficient to deal with the varying site-specific propagations of millimeter waves in suburban environments. For line-of-sight measurements, the path loss exponents obtained for the close-in (CI) free space reference distance model and the alpha-beta-gamma (ABG) model are 2.00 and 2.81, respectively, which are close to the recommended site-general value of 2.29. The root mean square errors (RMSEs) for these two reference models are 9.93dB and 9.70dB, respectively, which are slightly lower than that for the ITU site-general model (10.34dB). For non-line-of-sight measurements, both reference models, with the resulting path loss exponents of 2.50 for the CI model and 1.12 for the ABG model, outperformed the site-specific ITU model by around 14dB RMSE.

Recently, video has been applied in different industrial applications including autonomous driving vehicles. However, to develop autonomous farming vehicles, the video analysis must be targeted for specific farming activities. So an important first step is to classify the videos into their specific farming activity. In this paper, we propose a video classification framework that includes two branches that process videos differently based on their motions. A gradient-based method is proposed for separating videos into two subsets which are then processed by different feature sampling strategies. The result shows that two motion-based feature sampling strategies provide more efficient features; thus better classification performances are achieved. We also discuss how the feature sampling strategy influences the classification accuracy and the computational efficiency. In addition to farming videos, this proposed system can also be applied to classify videos captured from various camera movements, such as hand-held or first-person cameras.

Wireless relay networking has been proposed as a solution for extending coverage in the past few decades. The relay network facilitates communication between users who are unable to reliably share information due to severe pathloss or blockage. In this paper, we utilize spatial diversity of distributed multiple-input multiple-output systems for the relay network. Hence, the relay network consists of many separate relay nodes. Due to limited computational power, we assume each relay node receives the sum of the transmissions from all users and then performs one-bit quantization. The quantization bits from the relay network are broadcast back to the users through a downlink channel that is modeled as a low-rate binary symmetric channel. Based on the noisy quantization bits from the relay network and its own prior transmission, each user detects the transmitted symbols from other users. We first derive the maximum likelihood detector for the described system. Then we develop a sub-optimal detector called the orthogonal subset maximum likelihood (OSML) detector, which exploits only a subset of relay nodes for detection, to reduce the computational complexity. By using combinatorial geometry, we derive the minimum number of required relay nodes for the OSML detector to operate. The derived results are verified through numerical simulations.

This paper introduces a system model for simultaneous information and power transfer (SWIPT) over inductively coupled circuits from the standard communication-theoretic perspective. It is shown that the rate-energy (R-E) regions, which characterize the performance of SWIPT, for inductively coupled circuits can be obtained by circuit analysis. To evaluate the performance, the R-E regions for SISO and SIMO circuit models are calculated by numerical analysis.

Physical layer network coding has attracted extensive theoretical interest, although relatively little research has been done in support of deployment to wireless networks where internode synchronization is difficult to achieve. In particular, wireless networks constructed with inexpensive and commercially available software defined radio technology, or more generally, radio front-end samplers connected to internet-based remote processors (i.e., the Internet of Things network) may exhibit large time, frequency, and phase offsets that are difficult to control. In this paper, we define an asynchronous discrete-time model that accounts for these impairments as part of the information transfer between network users and a relay. Derived from this model are maximum likelihood algorithms for relay parameter estimation and a symbol decoder inspired from asynchronous multi-user detection. Additionally, null space-based frequency offset estimation that reduces computational complexity is proposed. Simulation results and the design and performance of a two-user system implemented with the Universal Software Radio Peripheral (USRP) platform and GNU radio are included to demonstrate the proof of concept. Our results indicate that the physical layer network coding technique can be successfully deployed and yields significant benefits even in the presence of impairments found in practical settings.

Recently, there has been considerable interest in new tiered network cellular architectures, which would likely use many more cell sites than found today. Two major challenges will be i) providing backhaul to all of these cells and ii) finding efficient techniques to leverage higher frequency bands for mobile access and backhaul. This paper proposes the use of outdoor millimeter wave communications for backhaul networking between cells and mobile access within a cell. To overcome the outdoor impairments found in millimeter wave propagation, this paper studies beamforming using large arrays. However, such systems will require narrow beams, increasing sensitivity to movement caused by pole sway and other environmental concerns. To overcome this, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a corresponding performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. The results show significant performance improvement with reduced search time.

Transportation agencies have invested in extensive infrastructure for vehicle detection and speed estimation. Although knowledge of vehicle speeds helps characterize traffic flow, vehicle accelerations can lead to better characterization. Vehicle accelerations are important in designing signal timings with respect to yellow intervals and green extensions for dilemma zone protection. Vehicle acceleration models are also used in studies of vehicle emissions. This paper develops an algorithm that uses existing inductive loops and magnetometers in speed trap configurations to measure acceleration. The algorithm chosen is the maximum-likelihood estimator, given an additive white Gaussian noise model for noise. The algorithm is found to have an error of about 0.02 g.

In this letter, an optimum frame synchronizer is proposed for coded modulations in channels with uncertainties. Coded modulations include various frame synchronization scenarios, e.g., convolutionally coded transmissions and nonlinear modulations with memory. Frame synchronization is proposed as a maximum a posteriori probability estimation implemented using trellis path search for Markov chain decoding. In addition, time-varying uncertainties such as frequency offset and phase noise are jointly estimated via per-survivor processing as frame synchronization proceeds. The proposed frame synchronizer exploits the coding gain of coded modulations to achieve better performance than conventional frame synchronizers. We show that the resulting frame synchronizer consists of a correlation term and two data correction terms. Numerical results show that the proposed frame synchronizer is robust to uncertainties at the receiver and it exhibits improved performance.

A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.

Link travel times are crucial for advanced traveler information systems and traffic management applications. However, current systems for estimating them still have shortcomings that need to be addressed. In this paper, we propose a novel framework for vehicle reidentification via signature matching using signal processing techniques and a travel time estimation algorithm that is robust to potential (and often inevitable) vehicle misidentifications. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by embedded roadway sensors. Statistical and multirate signal processing methods are used to develop data-postprocessing algorithms that are critical to the subsequent signature-matching problem, which is formulated using optimal techniques from communication theory. A probabilistic modeling of the generated matching assignments and an unsupervised data-clustering technique are then used to devise a travel time estimation algorithm. The proposed method is tested under a real traffic scenario, and accurate link travel time measures are reported.

Recent widespread use of multimedia-enabled devices has prompted the industry to look at systems which can provide a distributed, synchronized content consumption experience. Motorola has partnered with Purdue University's eStadium project to create the Rich Immersive Sports Experience (RISE) project. RISE is an attempt to design and develop such a system geared toward use in suites in Purdue's Ross-Ade football stadium. The core of the system is Motorola's Media Bundle technology that represents content and timing information for the TV screens and smart phones used in the system. This paper discusses the design process for the RISE system, how Media Bundles are used, and our solutions to issues of synchronization, speed, reliability, and scalability.

It is difficult for users in multiuser multiple-input multiple-output (MU-MIMO) systems to obtain co-channel interference (CCI) statistics without user cooperation. We propose a technique through which each user can effectively obtain the statistics of the interference that it experiences in a precoded MU-MIMO system. This allows true maximum-likelihood detection to be performed in place of minimum-distance detection. Also, we propose a low-complexity perturbation codebook decoder that attempts to mitigate the effects of both CCI and AWGN. The effectiveness of this decoder in reaching near-optimal performance is shown through Monte Carlo simulations.

It has become clear that the original mobile Web model of people using their portable devices to surf normal Web- pages must be refocused on the creation of applications that are tailored for the mobile experience. The eStadium project is our attempt at creating such an application. It provides live "infotainment" such as real-time statistics, instant replay videos, and venue information to Purdue football fans via a public mobile Web application and an on- demand video delivery system. In this paper, we discuss the design, implementation, and operation of the eStadium system and the lessons learned from five years of serving real sports fans.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is a spectrally efficient modulation scheme which combines the advantages of having multiple antennas at both transmitter and receiver and the ease of equalization afforded by OFDM. When channel state information (CSI) is available at the transmitter a simple technique called linear preceding can be used to improve the error rate performance in both beamforming and spatial multiplexing systems. However, in frequency division duplex (FDD) systems there is typically a lack of channel reciprocity and precoder matrices have to be designed at the receiver and sent back through a limited feedback channel with the help of codebooks. By constraining the precoders to lie on the Grassmann manifold optimal codebook design for the single carrier MIMO channel has been well studied. If these codebooks are used in a MIMO-OFDM system and precoder information for all subcarriers is sent back, the amount of feedback can get prohibitively large. In this paper we use simple geometrical ideas on the Grassmann manifold to further reduce this feedback requirement. The performance of these algorithms are shown to provide improvement over existing schemes.

In this paper, we present a unified approach for optimum frame synchronization where the observed symbols are modeled as the output of a Markov chain corrupted by additive white Gaussian noise (AWGN). This model encompasses many different frame synchronization situations e.g., convolutionally coded transmissions and nonlinear modulations with memory, such as continuous-phase modulation. The proposed frame synchronizer is implemented with trellis path search used for Markov chain decoding and provides better performance than conventional synchronizers because it is able to exploit coding gain. The proposed synchronizer includes many previously proposed synchronizers either as special cases or as suboptimal approximations. The numerical results show the performance gain of the proposed frame synchronizer for convolutional codes with linear modulation, transmission over known intersymbol-interference channels, and nonlinear continuous-phase modulation signals. The proposed frame synchronizer is derived for the case of continuous transmission but may also be applied to certain packet transmission scenarios

In this paper, we provide a comprehensive study on the performance of the quadrature amplitude modulation (QAM) in the presence of nonlinear amplifiers. The study includes both in-band and out-of-band distortion by incorporating nonlinearity induced adjacent channel interference (ACI). Two objective functions proposed in the literature are studied: 1) total degradation and 2) total bit energy-to-noise power density ratio for a target bit error rate. We also consider both fixed and adaptive DC bias controlled amplifiers. The two objective functions result in identical designs in the case of fixed bias amplifiers but not in the case of adaptive bias controlled amplifiers. In the later case the second objective function is more useful.

Recently, a modified Cramer-Rao bound has been proposed for the problem of estimating non-random parameters in the presence of random nuisance parameters. In its scalar or vector form, the modified Cramer-Rao bound (MCRB) is simple to evaluate and shown to be tight in some cases. Unfortunately, the MCRB is proven to be at most as tight as conventional Cramer-Rao bound (CRB). Another bound, the Miller-Chang bound (MCB), has also been evaluated in the literature. It applies to a more restricted class of estimators, namely, those are uniformly unbiased with respect to the random nuisance parameters. The MCB was shown to be much tighter than the CRB in several cases. In this article, we present a new bound, the modified Bhattacharyya bound (MBB). Like the MCRB, the MBB is easy to calculate. Taking the MCRB as a special case, the MBB is proven to be at least as tight as the MCRB. A case study on timing estimation shows that the MBB is not only tighter than the MCRB, but tighter than the CRB in low SNR range for the problem studied. It is also observed that significant improvement can be achieved by calculating bigger J matrix. Another bound, the modified Bhattacharyya bound 2 (MBB2) is also presented. As an extension of the MCB, the MBB2 is found to be at least as tight as the MCB.

Advanced receiver based techniques to alleviate the effects of nonlinear distortion in the bandlimited coded nonlinear satellite channel are considered. We propose a reduced complexity combined maximum likelihood equalization and decoding method using a nonlinear Volterra channel model in the trellis coded system. Our scheme includes conventional nonlinear compensation based on centroid estimation and decoding metric modification as a special case. Both analysis and simulation results show significant performance gain compared with separate Volterra equalization and decoding and also centroid estimation based decoding. Several methods of performance analysis in the nonlinear channel are discussed and compared.

Future CDMA systems may be required to operate with a low processing gain in order to accommodate high rate users. The resulting increase in channel dispersion will have a detrimental impact on code-timing acquisition. Two maximum-likelihood code-timing acquisition algorithms are proposed for multipath channels: a multiuser estimator and a single-user estimator. Multipath diversity is exploited in the estimators via maximum ratio combining to increase the signal-to-interference ratio. In addition, correlations between signals from different paths are explicitly incorporated in the test statistics of both estimators. Both help to improve the estimation accuracy and decrease mean acquisition time. Extensive simulations indicate that the estimators are robust to near-far power ratio and channel dispersion. Moreover, the gain achieved by incorporating channel dispersion in estimator design is significant.

Future CDMA systems will sometimes be required to operate with low processing gain in order to accommodate high rate users. The resulting increase in channel dispersion can have a detrimental impact on code-timing acquisition. Two maximum likelihood code-timing acquisition algorithms are proposed for multipath channels: a multiuser estimator and a single-user estimator. Multipath diversity is exploited in the estimators via maximum ratio combining to increase the signal-to-interference ratio. In addition, correlations between signals from different paths are explicitly incorporated in the test statistics of both estimators. The Cramer-Rao bounds are found and the performances of the estimators are examined via simulations. The results indicate that the estimators are robust to near-far power ratio and channel dispersion. Moreover, the gain achieved by incorporating channel dispersion in estimator design is significant.

We present an analytical method for computing the autocorrelation and conditional mean of the output of a bandlimited nonlinear amplifier. The method, which is applicable to modulations having /spl pi//2 symmetry, includes the effects of additive noise at the input to the nonlinear amplifier in addition to pulse shaping and the resulting intersymbol interference present in the nonlinear amplifier output. The autocorrelation and conditional mean calculations given in this paper form the basis of an analytical design method which may be used in several satellite communication problems including the design of amplifier output filtering, the joint optimization of pulse shaping and receiver filters, and the design of optimal data predistortion. The method of this paper provides an attractive alternative to Monte Carlo based designs in terms of both accuracy and computational complexity.

This paper examines some of the complexity issues arising in the real time implementation of the symbol timing subsystem of a narrowband wireless modem. The modem prototype has been designed for 4 kHz channels in the 220-222 MHz land mobile band. In an effort to achieve high bandwidth efficiency, the modem architecture employs transmitter diversity, pilot symbol assisted modulation, and trellis coded modulation. An experimental, non-real time system has been implemented and extensively field tested demonstrating bandwidth efficiencies in excess of 3 bits per second per Hz. The work on this project is focused on the real-time implementation of the baseband receiver functions using the Texas Instruments C54/spl times/ fixed point digital signal processor. Here we report on some of the performance/complexity tradeoffs present in the design of a DSP implementation of the digital filter and square symbol timing recovery algorithm.

In applications requiring soft-decision metrics, e.g., systems with interleaved coded modulation, symbol-by-symbol detection is preferred to sequence detection. This paper develops the optimum soft-output algorithm (OSA) for the demodulation of continuous phase modulated (CPM) signals. Since this optimum detector is computationally complex, a class of suboptimum symbol-by-symbol detectors, called the reduced state soft-output algorithm (RS-SOA) is developed. By varying certain parameters, the RS-SOA offers an effective tradeoff between performance and complexity, as measured by arithmetic operation count and the number of integrators required. Additionally, some simple complexity reduction schemes can be used in conjunction with the OSA and the RS-SOA. The properties of the OSA and the RS-SOA, and the various complexity reduction schemes are explored in an extensive simulation study.

The stability of variable stepsize LMS (VSLMS) algorithms with uncorrelated stationary Gaussian data is studied. It is found that when the stepsize is determined by the past data, the boundedness of the step size by the usual stability condition of fixed stepsize LMS is sufficient for the stability of VSLMS. When the stepsize is also related to the current data, the above constraint is no longer sufficient. Instead, both the upper bound and the lower bound of the stepsize must be within a smaller region. An exact expression of the stability region is developed for a single tap filter. The results are verified by computer simulations.

In many identification and tracking problems, an accurate estimate of the measurement noise variance is available. A partially adaptive LMS-type algorithm is developed which can exploit this information while maintaining the simplicity and robustness of LMS. This noise constrained LMS (NCLMS) algorithm is a type of variable step-size LMS algorithm, which is derived by adding constraints to the mean-square error optimization. The convergence and steady-state performance are analyzed. Both the theoretical results and simulations show that NCLMS can dramatically outperform LMS, RLS and other variable step-size LMS algorithms in a sufficiently noisy environment.

Pilot-symbol-assisted modulation (PSAM) is a method to reduce the effects of fading in mobile communications by periodically inserting known symbols in the data stream. The receiver uses these pilot symbols to derive its amplitude and phase reference. One aspect of this procedure which has not received much attention in the literature is the method used by the receiver to locate the pilot symbols. This paper uses optimum frame synchronization techniques to develop two synchronizers for PSAM systems; one is based on a standard maximum likelihood (ML) estimation formulation, and the other is a sequential testing algorithm. Both methods use a simple quadratic correlation filter with an energy correction factor. Simulation results and a theoretical analysis are presented.

Accurate and wide-area estimates of vehicle velocity and traffic spatial and temporal densities will be essential components of future algorithms for freeway and arterial street control, for incident prediction and detection, and for optimization in route selection. Algorithms like these figure prominently in the current research and development of intelligent vehicle-highway systems (IVHS). This paper presents an approach to a class of vehicle monitoring problems which is based upon a video backbone sensor and multiple target tracking (MTT). The method allows the integration of measurements made from other sensors like inductive loops, microwave radars, and laser range profilers.

A novel implementation of Root-MUSIC is introduced based on the observation that the MUSIC null spectrum created with a beamspace noise eigenvector is bandpass exhibiting nulls at the locations of in-band sources. Thus, after telescoping to element space and modulating to broadside, the telescoped noise eigenvector may be decimated yielding a small order polynominal with the separation between signal roots increased by the decimation factor. Depending on the out-of-band response of the beamformers, the telescoped noise eigenvector may be passed through a lowpass filter prior to decimation to preserve in-band source nulls. A low cost algorithm is realized by invoking linearity and performing the modulation, filtering, and decimation operations a priori on the beamformers yielding a simple transformation to be applied to the beamspace noise eigenvectors.>

Recently, a theory of Hankel operators associated with two-dimensional (2D) linear time-invariant systems has been developed. It is applicable to the class of noncausal systems with rational transfer functions. An algorithm for model order estimation and parameter identification has been developed based upon the singular value decomposition of a generalized Hankel Matrix. Applications are found in system identification and autoregressive moving average (ARMA) spectrum estimation. The characterization of the 2D Hankel operator constructed from sinusoidal data is discussed. In this case, the rank behavior is shown to be markedly different from that which holds for rational data. This fact, different from the case in 1D, suggests a method to separate sinusoids in 2D from colored noise obeying a rational difference equation.>

The authors classify and compare many of the linear prediction models that have appeared recently in the literature. The models considered are classified as quarter-plane causal, half-plane causal, three-quadrant causal, and noncausal. Comparisons are made of the corresponding notions of innovations, regularity, and singularity, and the possibility of Wold-like decompositions for each is discussed. The present work is motivated by the use of these linear prediction schemes as models for rational, multidimensional spectrum estimation.>

Methods are presented for the identification of noncausal, rational, multidimensional systems from covariance data in connection with the development of noncausal models in multidimensional power spectrum estimation. It is shown how a recently proposed notion of state for noncausal systems and the resulting rank properties can be used for model estimation. The general class of noncausal systems studied encompasses the quarter-plane causal, all-pole, separable, and factorizable models previously considered for 2-D spectrum estimation.>