Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as empirical models and ray tracing, often fall short, either due to insufficient details or because of challenges for real-time applications. Inspired by the newly proposed 3D Gaussian Splatting method in the computer vision domain, which outperforms other methods in reconstructing optical radiance fields, we propose RF-3DGS, a novel approach that enables precise site-specific reconstruction of radio radiance fields from sparse samples. RF-3DGS offers high efficiency, requiring only a few minutes for training and achieving fast inference for any arbitrary receiver pose within milliseconds. Furthermore, RF-3DGS can provide fine-grained Spatial Channel State Information (Spatial-CSI) of these paths, including the channel gain, the delay, the angle of arrival (AoA), and the angle of departure (AoD). Our experiments, calibrated through real-world measurements, demonstrate that RF-3DGS not only significantly improves reconstruction quality, training efficiency, and rendering speed compared to state-of-the-art methods, but also holds great potential for supporting wireless communication and advanced applications such as Integrated Sensing and Communication (ISAC). Code and dataset are available athttps://github.com/SunLab-UGA/RF-3DGS

The quasi-optical propagation of millimeter-wave (mmWave) signals enables high-accuracy localization algorithms that employ geometric approaches or machine learning models. However, most algorithms require information on the indoor environment, may entail the collection of large training datasets, or bear an infeasible computational burden for commercial off-the-shelf (COTS) devices. In this work, we propose to use tiny neural networks (NNs) to learn the relationship between angle difference-of-arrival (ADoA) measurements and locations of a receiver in an indoor environment. To relieve training data collection efforts, we resort to an algorithm-supervised approach by bootstrapping the training of our neural network through location estimates obtained from a state-of-the-art localization algorithm. We evaluate our scheme via mmWave measurements from indoor 60-GHz double-directional channel sounding. We process the measurements to yield dominant multipath components, use the corresponding angles to compute ADoA values, and finally obtain location fixes. Results show that the tiny NN achieves sub-meter errors in 74% of the cases, thus performing as good as or even better than the state-of-the-art algorithm, with significantly lower computational complexity.

Sensing biometric markers such as respiration rate (RR) and heart rate (HR) in non-medical contexts using the high resolution of Millimeter-Wave (mmWave) Wi-Fi networks has recently gathered considerable attention. A significant challenge in deploying a Wi-Fi system capable of performing sensing tasks is to minimize the overhead on the communication tasks associated with acquiring sensing information, both in terms radio resources and memory usage. In this paper, we explore the potential of IEEE 802.11bf passive sensing as a means to mitigate overhead, while effectively estimating both RR and HR. We showcase the potential to develop a low overhead Wi-Fi system that precisely captures vital signs, even in demanding situations, such as rapidly increasing RR interfering with HR, by integrating microdoppler processing with super-resolution eigenvector noise subspace analysis. The results shows that the proposed methodology enables RR and HR estimation without any radio-resource overhead and requiring very limited memory usage.

Switched phased array systems operating at high sample rates generate large amounts of data during measurements of radio channels, but many scenarios contain only few multipath components. Compressed Sensing suggests in these cases Nyquist-rate samples are wasteful in terms of data size. It has been observed that structural parameters, i.e., time of flight and angle of arrival, of the propagation paths are temporally consistent, as they vary slowly in time.Hence, we propose a local temporally consistent signal model that includes delay and angle of arrival, and also their time-derivatives, coherently connecting multiple radio channel snapshots. This allows to use a cyclic compression scheme consisting of a few compression matrices that extract mutually incoherent information from adjacent snapshots. Last, we present an algorithm to extract specular multipath components from these compressive measurements.We verify our findings on simulated data and real measurements. On simulated data we observe a good agreement of the estimates with the available ground-truth and show that the proposed cyclic compression scheme improves estimation accuracy. On measured data, we compare the estimates from data obtained at Nyquist rate to data compressed to 17% of the original size and find good agreement as well.

Sensing with communication waveforms has drawn growing interest thanks to the ubiquitous availability of wireless networks. However, the required sensing resources may not always be available in a communication system. In addition, the communication system may have limited bandwidth, beamwidth, and transmit power, which could limit the sensing accuracy. To investigate such challenges, in this article, we study the feasibility of using the sector-level sweeping (SLS) procedure of IEEE 802.11ad to provide opportunistic indoor radar sensing service, which is vital to smart Internet of Things (IoT) applications. In particular, we design a framework to estimate the target’s spatial position with respect to delay and angle by employing the multiple signal classification (MUSIC) super-resolution algorithms. We conduct an extensive performance evaluation to understand the tradeoffs between sensing accuracy and required sensing resources in terms of system configurations (e.g., antenna array size and the overlapping of neighboring beams) and the impact of signal-to-noise ratio (SNR). Furthermore, based on the human multipath reflections captured from a real-world measurement campaign, we reconstruct the sensing channel, investigate the feasibility of monitoring the gesture behavior in a smart home environment, and discuss some findings and insights.

A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped with an omni-directional transmitter and a receiver with a uniform planar array (UPA) are considered. An accurate mathematical model is developed for the spatial frequency response of the sounder that incorporates the non-ideal cross-polar beampatterns for the UPA elements. Due to the limited Field-of-View (FoV) of each element, the model is extended to accommodate multi-FoV measurements in distinct azimuth directions. A beamspace representation of the spatial frequency response is leveraged to develop three progressively complex algorithms aimed at solving the single-snapshot maximum likelihood estimation problem: greedy matching pursuit (CLEAN), space-alternative generalized expectation-maximization (SAGE), and RiMAX. The first two are based on purely specular MPCs whereas RiMAX also accommodates diffuse MPCs. Two approaches for performance evaluation are proposed, one with knowledge of ground truth parameters, and one based on reconstruction mean-squared error. The three algorithms are compared through a demanding channel model with hundreds of MPCs and through real measurements. The results demonstrate that CLEAN gives quite reasonable estimates which are improved by SAGE and RiMAX. Lessons learned and directions for future research are discussed.

Efficient design of integrated sensing and communication systems can minimize signaling overhead by reducing the size and/or rate of feedback in reporting channel state information (CSI). To minimize the signaling overhead when performing sensing operations at the transmitter, this paper proposes a procedure to reduce the feedback rate. We consider a threshold-based sensing measurement and reporting procedure, such that the CSI is transmitted only if the channel variation exceeds a threshold. However, quantifying the channel variation, determining the threshold, and recovering sensing information with a lower feedback rate are still open problems. In this paper, we first quantify the channel variation by considering several metrics including the Euclidean distance, time-reversal resonating strength, and frequency-reversal resonating strength. We then design an algorithm to adaptively select a threshold, minimizing the feedback rate, while guaranteeing sufficient sensing accuracy by reconstructing high-quality signatures of human movement. To improve sensing accuracy with irregular channel measurements, we further propose two reconstruction schemes, which can be easily employed at the transmitter in case there is no feedback available from the receiver. Finally, the sensing performance of our scheme is extensively evaluated through real and synthetic channel measurements, considering channel estimation and synchronization errors. Our results show that the amount of feedback can be reduced by 50% while maintaining good sensing performance in terms of range and velocity estimations. Moreover, in contrast to other schemes, we show that the Euclidean distance metric is better able to capture various human movements with high channel variation values.

Virtual reality (VR) coupled with 360° video has been used in a variety of areas, including gaming, remote learning, and healthcare, among others. The 360° video on which VR applications are based today is mostly low resolution and, in order to improve the user experience, bandwidth requirements must increase significantly. Spatial multiplexing (SM) at millimeter wave (mmWave) is an enabling technology introduced in IEEE 802.11ay to support high throughput applications. However, since IEEE 802.11ay commercial off-the-shelf devices are not yet available and the cost for implementation of mmWave testbeds is prohibitive, the expected SM performance in a real application is still unknown. In this paper, we design a mmWave multi-user (MU)-multiple-input multiple-output (MIMO) link-level high fidelity simulation platform, based on IEEE 802.11ay, which is shared as an open source code package. Our simulation platform consists of a measurement-based mmWave channel model and a digital transceiver. To support VR applications, we design the analog-digital hybrid precoders and combiners, enabling SM for MU-MIMO transmissions. We provide an extensive evaluation of the IEEE 802.11ay PHY in terms of throughput and error rates. Our platform reveals that in a living room environment, two users can support up to four streams achieving more than 20Gbit/sec data-rate per user, enabling the transmission of uncompressed 4K videos.

The design of integrated sensing and communication (ISAC) systems has drawn recent attention for its capacity to solve a number of challenges. Indeed, ISAC can enable numerous benefits, such as the sharing of spectrum resources, hardware, and software, and improving the interoperability of sensing and communication. In this article, we seek to provide a thorough investigation of ISAC. We begin by reviewing the paradigms of sensing-centric design, communication-centric design, and co-design of sensing and communication. We then explore the enabling techniques that are viable for ISAC (i.e., transmit waveform design, environment modeling, sensing source, signal processing, and data processing). We also present some emergent smart-world applications that could benefit from ISAC. Furthermore, we describe some prominent tools used to collect sensing data and publicly available sensing data sets for research and development, as well as some standardization efforts. Finally, we highlight some challenges and new areas of research in ISAC, providing a helpful reference for ISAC researchers and practitioners, as well as the broader research and industry communities.

Millimeter-wave channel measurements for a meeting room, lecture room and open plan office floor were used to analyse and model the spatial consistency of channel clusters. Particularly, how the angles of arrival and delays of extracted multi-path components in each cluster varied with small changes in receiver location. We extended the KPowerMeans algorithm, for classifying captured multi-path components into clusters from the delay/angular domains, to include the location domain to allow the spatial consistency of the clusters between locations to be analysed. The observed spatial consistency in measurements was then used to validate whether the 3GPP spatial consistency procedure (3GPP SC-I) reflects real world spatial consistency. The 3GPP spatial consistency procedure for ensuring spatial consistency of cluster parameters during mobile user simulations was then applied for each environment, allowing for comparison between the ‘predicted’ cluster parameters and the measured cluster parameters. The 3GPP model/procedure showed a good fit for all environments but highlighted the impact of the environment on the amount of accuracy of the procedure.

5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications. Accurate performance evaluation is fundamental for the design of reliable mmWave networks, with accuracy rooted in the fidelity of the channel models. At mmWaves, the model must account for the spatial characteristics of propagation since networks will employ highly directional antennas to counter the much greater pathloss. In this regard, Quasi-Deterministic (QD) models are highly accurate channel models, which characterize the propagation in terms of clusters of multipath components, given by a reflected ray and multiple diffuse components of any given Computer Aided Design (CAD) scenario. This paper introduces a detailed mathematical formulation for QD models at mmWaves, that can be used as a reference for their implementation and development. Moreover, it compares channel instances obtained with an open source National Institute of Standards and Technology (NIST) QD model implementation against real measurements at 60 GHz, substantiating the accuracy of the model. Results show that, when comparing the proposed model and deterministic rays alone with a measurement campaign, the Kolmogorov-Smirnov (KS) test of the QD model improves by up to 0.537.

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

In this paper, we analyze the physical layer of IEEE 802.11ay, the new standard for next-generation Wi-Fi operating in the unlicensed 60-GHz band, expected for release in 2019. Realistic physical-layer analysis is based on a multipath fading channel model reduced from mobile measurements, however most millimeter-wave channel sounders to date can only characterize static environments because sweeping the angular space of a channel typically takes hours due to the slow mechanical rotation of single directional antennas. Our state-of-the-art 60-GHz channel sounder, rather, employs arrays of electronically switched antennas so that a full channel sweep can be taken in fractions of a millisecond. This enabled us to conduct an extensive channel measurement campaign in an indoor environment with pedestrian motion. The fading model reduced from the measurements was us\ed to feed our 802.11ay transceiver implementation, an amendment to its 802.11ad predecessor incorporating 8x8 single-user MIMO. The analysis resulted in bit-error-rate curves for various transceiver parameters, namely phased-array-antenna dimension, number of RF chains, and modulation and coding scheme.

We describe an activity of the 5G mmWave Channel Sounder Alliance to verify the hardware performance of channel sounders operating at mmWave frequencies. Such verification procedures are critical when attempting to compare data from sounders having different architectures in various environments. Two different methods are described and illustrated with simple measurement examples.

To date, we have designed and assembled millimeter-wave channel sounders at 60 GHz and 83 GHz. They can estimate the angle-of-departure and angle-of-arrival of channel multipath components as well as their delay and Doppler frequency shift. In addition, due to the fast acquisition time and because the receiver is mounted on a mobile robot, the systems can collect measurements for hundreds of different transmitter-receiver configurations in just minutes. It follows that channel-model reduction, including the multipath- component clustering process, must be reliable, consistent, and unsupervised. In this paper, we describe a simple clustering process tailored to the properties of millimeter-wave channels that fully exploits the multi-dimensionality of the extracted multipath components and requires only a few tunable parameters. Through extensive experimentation, we have verified that the process is robust and delivers consistent results across five different environments and across both frequency bands investigated. Illustrative examples are provided.

In this paper, the control channel for secondary users in opportunistic spectrum access networks is considered. Given a licensed bandwidth for primary users, we compare the performance of three control-channel schemes: dedicated, underlay, and overlay. The key performance metric for comparison is the primary data rate, i.e., how it is affected by interference from secondary control messaging. The comparison is conducted via a configurable testbed implementation of a primary and a secondary network, each composed from two USRP radios connected through an RF channel emulator. The MATLAB/Simulink software toolbox is used to develop the baseband models for the radio transceivers. Trade-offs between the three schemes, as well as insight on the practical implementation of NC-OFDM in opportunistic spectrum access systems, are provided.

In wireless channel propagation modeling, the multipath arrivals of a transmitted signal appear in clusters at the receiver. Because the notion of clusters tends to be intuitive rather than well-defined, cluster identification has traditionally been carried out through human visual inspection. Besides time-consuming for large-scale measurement campaigns, this approach is subjective and will vary from person to person, leading to arbitrary selection of clusters. To address these concerns, automatic clustering algorithms have emerged in the past decade. Most, however, are laden with settings which are very sensitive to different radio-frequency environments, again leading to arbitrary selection. In this paper, we propose a novel clustering algorithm based on the kurtosis metric which, in related work, has been used precisely for its channel independence. We compare ours to two recent algorithms through a standard validation method on simulated channel impulse responses from five different environments. The proposed algorithm delivers better results and, because it has no channel-specific settings, is inherently robust to varying channel conditions.

Mandated by the 2006 United States Miner Act, reliable two-way communications in mines has drawn the interest of network engineers in recent years. Critical to the design of these systems is an accurate channel propagation model. Given the elementary geometry seen in most tunnels, models that approximate them as a rectangular waveguide have been developed. These models are extremely accurate in vehicular tunnels because - since the tunnel is typically cast from concrete - the cross section is uniform throughout and the surface roughness is negligible. Mines, however, do not conform to these two conditions. In this paper, we extend the waveguide model to tunnels with varying cross section and measurable surface roughness. The effectiveness of the proposed model is validated through in-house field measurements collected in a vehicular tunnel and in a coal mine. We show that while the original model performs well in the former, it falters in the latter. The extended model, however, predicts reliably in the mine as well.

We report on models developed for peer-to-peer (ground-based) wireless channels for an urban environment in the 700 MHz and 4.9 GHz bands, both allocated for public safety and “emergency responder” applications. Results are based upon measurements taken in downtown Denver, CO for link distances up to approximately 100 m. Heretofore, measurement-based models for an urban environment in these bands and for low antenna heights have not been developed. Our measurements employed a vector network analyzer, from which log-distance models for path loss and dispersive channel models have been extracted. Our dispersive channel models employ a statistical algorithm for the number of multipath components, previously used only in indoor settings. The channel models should be useful for public safety communication system designers.

Emerging technologies for mesh networks can provide users with last-mile service to an access point by forwarding data through wireless relays instead of through expensive wireline infrastructure. While an extensive amount of literature on the subject has been amassed in the last decade, existing papers model network traffic flow solely as a function of routing topology, neglecting contention at the Media Access Control layer; as a result, the inbound flow to a relay station is independent of the transmission success rate from forwarding stations. This leads to overestimation of traffic flow, especially at network operation approaching full capacity, and in turn makes for inaccuracies in predicting throughput and delay. In our model, the inbound flow depends on the transmission success rate as well. Other novel contributions are the incorporation of a half-duplex contention model we developed in previous work, which captures both uplink and downlink traffic, and a generic framework to represent any mesh routing topology (minimum-hop, minimum-airtime, etc.).

Conversion from analog television in the United States combined with the appeal for broadband public safety communications is generating a lot of interest in the so-called 700 MHz band between 698-806 MHz. To our knowledge, no channel propagation model for this band exists to date. In this work, we derive such a model reduced from a measurement campaign realized through a stepped frequency system. The campaign includes 89 transmitter-receiver configurations in seven different environments relevant to residential, commercial and public safety communications, ranging from subterranean mine tunnels to an oil refinery, from mid-size to high-rise buildings. The stochastic impulse response model is complete with 17 parameters which enables reconstruction for use in simulation engines, amongst others.

Statistical analysis of Ultra-Wideband (UWB) signals is undertaken to examine the existence of short term fading and shadowing. Based on the data collected from multiple locations and 4 different sites using a single transmitter and a 96-element receiving antenna in the 2-8 GHz range, hypothesis testing was performed to explore the statistical fit to composite probability density functions such the double Rayleigh, double Nakagami and the K distribution as well as the Nakagami and lognormal. The tests clearly showed that excellent match existed with the K distribution, with the Nakagami distribution being the next best. These results point to the existence of shadowing besides the short fading reported by other researchers. They can also assist in the development of fading and shadowing mitigation techniques.

Accurate signal measurement in the spatial-temporal domain is critical, amongst other applications, to the commercial success of UWB-MIMO communication systems and UWB standalone location systems. Antenna arrays can measure the spatial dimension of a signal through beamforming; to this end we chose to implement the uniform circular array in our spatial-temporal channel sounder due to its constant beam pattern around the azimuth angle. In wideband systems such as ours, it is important that the beam pattern also be constant across the band of operation to enable linear methods for multipath extraction or sidelobe suppression. Frequency-invariant beamforming can achieve this but at the expense of greater noise. Most literature treating the practical implementation of frequency-invariant beamforming concentrates on mutual coupling and physical-array imperfection. Rather in this paper we propose a novel filtering technique specific to the characteristic noise in frequency-invariant beamforming. It employs the eigendecomposition followed by least-squares minimization and proves robust even at low signal-to-noise ratios as substantiated through simulations as well as measurements using our sounder.

Despite the potential for high-speed communications, stringent regulatory mandates on Ultra-Wideband (UWB) emission have limited its commercial success. By combining resolvable UWB multipath from different directions, Multiple-Input Multiple-Output (MIMO) systems can drastically improve link robustness or range. In fact, a plethora of algorithms and coding schemes already exist for UWB-MIMO systems, however these papers use simplistic channel models in simulation and testing. While the temporal characteristics of the UWB channel have been well documented, surprisingly there currently exists but a handful of spatial-temporal models to our knowledge, and only two for bandwidths in excess of 500 MHz. This paper proposes a comprehensive spatial-temporal model for the frequency spectrum 2-8 GHz, featuring many novel parameters. In order to extract the parameters, we conduct an extensive measurement campaign using a vector network analyzer coupled to a virtual circular antenna array. The campaign includes 160 experiments up to a non line-of-sight range of 35 meters in four buildings with construction material varying from sheetrock to steel.

High-resolution imaging through walls and other materials using microwave signals serves amongst other applications in the rapid detection of human maneuvering, rescue missions in collapsed buildings, and target feature extraction. While narrowband Doppler radar in the millimeter-wave or infrared spectrum can provide good resolution through clothing and packaging, penetration through denser sheetrock, plaster, and brick requires operation below 10 GHz; this band however yields poor resolution. As an alternative, Ultra-Wideband radar operating in this band boosts the bandwidth which translates into fine range resolution; still it requires an aperture length of several meters for comparable cross-range resolution. The associated cost and portability in realizing such an aperture through antenna arrays or fixed-length scanners have limited their lengths to the order of 1 meter in prototype systems to date. In this work, we propose a novel aperture taking form as a variable-length scanner or mobile robot. The wide dynamic range of our system coupled with its unrestricted aperture length allows us to generate high-resolution images up to a range of 8 meters or more.

Fine time resolution enables Ultra-Wideband (UWB) ranging systems to reliably extract the first multipath arrival corresponding to the range between a transmitter and receiver, even when attenuated in strength compared to later arrivals. Bearing systems alone lack any notion of time and in general select the arrival coinciding with the strongest path, which is rarely the first one in non line-of-sight conditions. Complementing UWB ranging systems with bearing capabilities allows indexing the arrivals as a function of both time and angle to isolate the first, providing precision range and angle. In order to gauge the limits of the joint UWB system, we carry out close to 20000 measurements up to 45 m in non line-of-sight conditions in four separate buildings with dominant wall material varying from sheet rock to steel. In addition, we report performance for varying bandwidth and center frequency of the system.

While the frequency-dependence of the wireless channel may be negligible for narrow to wideband signals, it has been shown that modeling this dependence for bandwidths in excess of 2 GHz improves channel reconstruction up to 40%. Yet to our knowledge, only Molisch et al. have done so for the ultra-wideband channel. Their benchmark frequency model however represents the average dependence over the collection of multi-path arrivals in the channel rather than that of individual arrivals. Building on the geometric theory of diffraction, we propose a novel frequency model forindividualarrivals according to the propagation events on their paths between the transmitter and receiver. We extract the model parameters from an extensive measurement campaign of 3000 channel frequency sweeps in three separate buildings combined with raytracing simulations, and show that ours fits the gathered data more closely than the benchmark model.

The falling price and reduced size of sensors for monitoring spatially-sensitive environmental properties such as temperature, light, sound, and vibration have motivated research in location algorithms in recent years. To our knowledge, the algorithm which achieves the best performance refines erroneous measurements through an optimization program whose quadratic constraints force the sensors to be consistent with the geometry of the physical world. Since the program is non-convex, the authors relax the constraints to render it convex for which efficient solution methods exist. We propose solving a similar optimization program however by applying convex geometrical constraints directly, necessitating no relaxation of the constraints and in turn ensuring a solution still compliant with the physical world. We show through extensive experimentation that ours outperforms the competing algorithm across all network parameters. In addition, this paper formulates a distributed version of our algorithm which achieves the same globally optimal objective function as the centralized version, and reports the messaging overhead for its convergence.

Ultra wideband technology shows promise for precision ranging due to its fine time resolution to resolve multipath fading and the presence of lower frequencies in the baseband to penetrate walls. While a concerted effort has been conducted in the extensive modeling of the indoor UWB channel in recent years, to our knowledge only two papers have reported ranging performance, but for limited range and fixed bandwidth and center frequency. In principle boosting power can guarantee connectivity between transmitter and receiver, but not precision due to the distorting effects of walls and other objects in the direct path. In order to gauge the limits of UWB ranging, we carry out 5000 measurements up to an unprecedented 45 m in non line-of- sight conditions in four separate buildings with dominant wall material varying from sheet rock to steel. In addition, we report performance for varying bandwidth and center frequency of the system.

Interest in dense sensor networks due to falling price and reduced size has motivated research in sensor location in recent years. To our knowledge, the algorithm which achieves the best performance in sensor location solves an optimization program by relaxing the quadratic geometrical constraints of the network to render the program convex. In recent work we proposed solving the same program, however by applying convex geometrical constraints directly, necessitating no relaxation of the constraints and in turn ensuring a tighter solution. This paper proposes a distributed version of our algorithm which achieves the same globally optimal objective function as the decentralized version. We conduct extensive experimentation to substantiate the robustness of our algorithm even in the presence of high levels of noise, and report the messaging overhead for convergence.

To our knowledge, the indoor location system which currently achieves the best performance using inexpensive off-the-shelf equipment locates a mobile within 1.5 meters with probability 77% in hallways. Even while maintaining this accuracy, the system often reports logical errors such as the mobile in the wrong cubicle of an office or even on the wrong side of a wall when broadening the domain of application to within rooms. We propose an extension of the work using the same Markov localization framework, however incorporating system dynamics (necessitating no post-processing of the output) and motion constraints which implicitly encode the physical properties of the survey area. Our system retains the advantages of its predecessor of low cost, wireless LAN connectivity and security, and large-scale deployment, however extending the survey area from simple hallways to the whole office environment, while maintaining the same precision without logical errors.

Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of "parts" such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function that exhibits a high degree of correlation with the tracking error.

A distributed kinetic spanning tree algorithm is proposed for routing in wireless mobile ad hoc networks. Assuming a piecewise linear motion model for the nodes, the sequence of shortest-path spanning trees is determined, valid until the time of the next node trajectory change. By computing the sequence of trees using one execution of the distributed routing algorithm, in contrast to computing the tree for a single time instant, the number of routing messages is substantially reduced. Moreover, the total power required to route through the trees as a function of time is also lower.

Tracking an object in a sequence of images can fail due to partial occlusion or clutter Robustness can be increased by tracking a set of "parts", provided that a suitable set can be identified. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function highly correlated with the tracking error.

We propose a novel two-layer neural network to answer a point query in R(n) which is partitioned into polyhedral regions; such a task solves among others nearest neighbor clustering. As in previous approaches to the problem, our design is based on the use of Voronoi diagrams. However, our approach results in substantial reduction of the number of neurons, completely eliminating the second layer, at the price of requiring only two additional clock steps. In addition, the design process is also simplified while retaining the main advantage of the approach, namely its ability to furnish precise values for the number of neurons and the connection weights necessitating neither trial and error type iterations nor ad hoc parameters.

We propose a novel two-layer neural network to answer a point query in R/sup n/ which is partitioned into polyhedral regions. Such a task solves amongst others nearest neighbor clustering. As in previous approaches to the problem, our design is based on the use of Voronoi diagrams. However, our approach results in substantial reduction of the the number of neurons, completely eliminating the middle layer at the price of requiring only one additional clock step. In addition, the design process is also simplified while retaining the main advantage of the approach, namely its ability to furnish precise values for the number of neurons and the connection weights requiring neither trial and error type iterations nor ad-hoc parameters.