Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a prominent example, Principal Component Analysis (PCA) projects data points onto the eigenvectors of their covariance matrix, capturing the directions of maximum variance. This mapping, however, falls short in two directions: it fails to capture information in low-variance directions, relevant when, e.g., the data contains high-variance noise; and it provides unstable results in low-sample regimes, especially when covariance eigenvalues are close. CoVariance Neural Networks (VNNs), i.e., graph neural networks using the covariance matrix as a graph, show improved stability to estimation errors and learn more expressive functions in the covariance spectrum than PCA, but require training and operate in a labeled setup. To get the benefits of both worlds, we propose Covariance Scattering Transforms (CSTs), deep untrained networks that sequentially apply filters localized in the covariance spectrum to the input data and produce expressive hierarchical representations via nonlinearities. We define the filters as covariance wavelets that capture specific and detailed covariance spectral patterns. We improve CSTs' computational and memory efficiency via a pruning mechanism, and we prove that their error due to finite-sample covariance estimations is less sensitive to close covariance eigenvalues compared to PCA, improving their stability. Our experiments on age prediction from cortical thickness measurements on 4 datasets collecting patients with neurodegenerative diseases show that CSTs produce stable representations in low-data settings, as VNNs but without any training, and lead to comparable or better predictions w.r.t. more complex learning models.

In this paper, we consider transmit beamforming in distributed integrated sensing and communication (ISAC) systems with large arrays. We propose generalized sparse coordinated multipoint transmission (GS-CoMP), a novel coordination strategy that serves a carefully selected subset of UEs while simultaneously sensing targets. Our approach designs sparse transmit precoders to ensure desired communication and sensing signal-to-interference-plus-noise ratios (SINRs), balancing backhaul rate and transmit power requirements. We focus on both unstructured and line-of-sight (LoS) scenarios. For the setting with a moderate number of antennas and unstructured channels, we present a convex semidefinite programming (SDP)-based iterative solver. For large array systems with LoS channels, we introduce a low-complexity method based on power allocation and spatial window design. Numerical simulations show that GS-CoMP significantly reduces transmit power compared to limited cooperation schemes while requiring a much lower backhaul rate than fully cooperative DISAC systems. Additionally, the proposed low-complexity methods achieve performance comparable to full SDP-based precoding with several orders of magnitude reduction in runtime.

Federated constrained learning allows us to learn a global model with some specific structure to enhance performance. Most existing federated learning techniques assume data is homogeneously or independently and identically distributed (iid) across clients. However, this iid assumption rarely holds in practice, and the averaging operation on the server’s side leads to degradation in performance at the client level. In this paper, we propose FedFW-Prox, a novel and computationally efficient federated Frank-Wolfe algorithm that learns personalized models for each client, which guarantees better performance even with heterogeneous data distributions across clients. Instead of modifying the objective function, we introduce a proximal regularization term at the update step at each client, simplifying the update process. This regularization term ensures that local models remain close to the global model, promoting convergence to the global optimal point rather than the local optima. The proposed method guarantees a sublinear convergence of $\mathcal{O}(1/\sqrt k )$ for smooth convex objective functions, where k denotes the iteration number. We empirically evaluate the effectiveness of the proposed approach on various machine-learning tasks.

Decentralized learning has emerged as a popular method due to its excellent scalability and parallel implementation of stochastic gradient methods. However, the main challenges in decentralized learning include the communication overhead and privacy concerns associated with sharing gradients with neighbors. In this work, we design a novel deep quantizer to address these simultaneously, ensuring differential privacy and communication efficiency. Specifically, we learn a deep quantizer such that the induced quantization noise can be modeled as additive Gaussian noise. We also propose an encoding-decoding scheme that ensures unbiasedness and bounded variance for the quantized output. For smooth objective functions, we bound the function values in terms of the norm of the gradient, the second-order gradient moment, and the induced quantization noise. We demonstrate superior performance of decentralized stochastic gradient with the proposed deep quantizer for a least square regression problem. Additionally, we analyze the performance of the proposed approach for various quantization levels, privacy budgets, batch sizes, and numbers of nodes in the network.

In this paper, we propose a non-parametric task-agnostic representation learning method for cell complexes (CCs). Specifically, we propose a scattering transform for CCs that extends geometric scattering to CCs. In addition, we introduce scattering transforms for product cell complexes (PCCs), which are a particular type of CCs that can be factored as the Cartesian product of two smaller simplicial complexes (SCs). We utilize the intrinsic product structure of PCCs to develop separable filters across the two constituent factors of PCCs. The proposed scattering transform for PCCs is computationally efficient, as it can be expressed in terms of scattering on the individual SCs. The effectiveness of the proposed model is demonstrated through experiments on various tasks, such as trajectory classification, missing edge feature estimation, and edge feature prediction, on both synthetic and real-world PCCs.

In this work, we focus on recovering signals over simplicial complexes from subsampled observations. In particular, we subsample a simplicial signal of a certain order and focus on recovering multi-order bandlimited simplicial signals of one order higher and one order lower, wherein the observations are collected using a neighborhood aggregation sampling mechanism. To do so, we assume that the simplicial signal admits the Hodge decomposition that relates simplicial signals of different orders. Next, we propose a simple least squares estimator for recovery. We also provide theoretical conditions on the number of aggregations and size of the sampling set required for faithful reconstruction as a function of the bandwidth of simplicial signals to be recovered. Numerical experiments are provided to show the effectiveness of the proposed method.

In this paper, we propose DP-FedFW, a novel Frank-Wolfe based federated learning algorithm with local (ϵ,δ)-differential privacy (DP) guarantees in a constrained learning setting. In DP-FedFW, we perturb local models to ensure privacy while communicating with the server, and each client performs several Frank-Wolfe steps to arrive at a local model. The proposed method guarantees (ϵ,δ)-DP for each client and has a sublinear convergence of $\mathcal{O}$(1/k) for smooth convex objective functions, where k is the number of communication rounds and an asymptotic convergence for smooth non-convex objective functions. The theoretical analysis shows that given an (ϵ,δ)-DP requirement, the proposed algorithm’s performance improves with the number of clients and the batch size. We empirically validate the efficacy of the proposed method on several constrained machine learning tasks.

We consider recovery of signals over product cell structures from subsampled observations. In particular, we consider product cell complexes, which can be factorized as the Cartesian product of two simplicial complexes. We focus on recovering edge and node signals from subsampled signals on the factor simplicial complexes. To do so, we first express bandlimited edge signals on the product cell complex as a direct sum of the Kronecker product of bandlimited edge and node signals on the factor simplicial complexes. Then, we propose a simple least squares solution for estimating the edge signal on the product complex. Next, we leverage the Helmholtz-Hodge decomposition on product spaces and propose a simple least squares estimator to recover node signals on the product cell complex. We evaluate the proposed method on both synthetic and real data.

Simplicial neural networks (SNNs) are deep models for higher-order graph representation learning. SNNs learn low-dimensional embeddings of simplices in a simplicial complex by aggregating features of their respective upper, lower, boundary, and coboundary adjacent simplices. The aggregation in SNNs is carried out during training. Since the number of simplices of various orders in a simplicial complex is significantly large, the memory and training-time requirement in SNNs is enormous. In this work, we propose a scalable simplicial-aware neural network (SaNN) model with a constant run-time and memory requirements independent of the size of the simplicial complex and the density of interactions in it. SaNN is based on pre-aggregated simplicial-aware features as inputs to a neural network, so it has a strong simplicial-structural inductive bias. We provide theoretical conditions under which SaNN is provably more powerful than the Weisfeiler-Lehman (WL) graph isomorphism test and as powerful as the simplicial Weisfeiler-Lehman (SWL) test. We also show that SaNN is permutation and orientation equivariant and satisfies simplicial-awareness of the highest order in a simplicial complex. We demonstrate via numerical experiments that despite being computationally economical, the proposed model achieves state-of-the-art performance in predicting trajectories, simplicial closures, and classifying graphs.

Simplicial neural network models are becoming popular for processing and analyzing higher-order graph data, but they suffer from high training complexity and dependence on task-specific labels. To address these challenges, we propose simplicial scattering networks (SSNs), a parameter-free model inspired by scattering transforms designed to extract task-agnostic features from simplicial complex data without labels in a principled manner. Specifically, we propose a simplicial scattering transform based on random walk matrices for various adjacencies underlying a simplicial complex. We then use the simplicial scattering transform to construct a deep filter bank network that captures high-frequency information at multiple scales. The proposed simplicial scattering transform possesses properties such as permutation invariance, robustness to perturbations, and expressivity. We theoretically prove that including higher-order information improves the robustness of SSNs to perturbations. Empirical evaluations demonstrate that SSNs outperform existing simplicial or graph neural models in many tasks like node classification, simplicial closure, graph classification, trajectory prediction, and simplex prediction while being computationally efficient.

In this paper, we consider reconstruction of smooth, aka bandlimited signals, on a product graph from a subset of unlabeled observations. That is, we do not know from which nodes the observations are gathered. Traditional graph signal reconstruction methods assume that the node indices or labels of the observed graph signals might be perfectly known. However, in practice, the node indices of observations are unavailable due to data gathering constraints. If the node and observation correspondences are ignored, the reconstruction performance naturally deteriorates. To address this limitation, we propose PGSR-Perm that jointly estimates the graph signals along with the underlying correspondences. We also derive sufficient conditions on the number of unlabeled observations required for faithful recovery. Finally, we demonstrate the efficacy of PGSR-Perm on synthetic and real-world datasets.

We consider transmit beamforming and reflection pattern design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems to jointly precode communication symbols and radar waveforms. We treat two settings of multiple users and targets. In the first, we use a single RIS to enhance the communication performance of the ISAC system and design beams with good cross-correlation properties to match a desired beampattern while guaranteeing a desired signal-to-interference-plus-noise ratio (SINR) for each user. In the second setting, we use two dedicated RISs to aid the ISAC system, wherein the beams are designed to maximize the worst-case target illumination power while guaranteeing a desired SINR for each user. We propose solvers based on alternating optimization as the design problems in both cases are non-convex optimization problems. Through numerical simulations, we demonstrate the advantages of RIS-assisted ISAC systems. In particular, we show that the proposed single-RIS assisted ISAC system improves the minimum user SINR while suffering from a moderate loss in radar target illumination power. On the other hand, the dual-RIS assisted ISAC system improves both minimum user SINR as well as worst-case target illumination power at the targets, especially when the users and targets are not directly visible to the ISAC transmitter.

In this paper, we present a novel reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with 1-bit quantization at the ISAC base station. An RIS is introduced in the ISAC system to mitigate the effects of coarse quantization and to enable the co-existence between sensing and communication functionalities. Specifically, we design a transmit precoder to obtain 1-bit sensing waveforms having a desired radiation pattern. The RIS phase shifts are then designed to modulate the 1-bit sensing waveform to transmit M-ary phase shift keying symbols to users. Through numerical simulations, we show that the proposed method offers significantly improved symbol error probabilities when compared to MIMO communication systems having quantized linear precoders, while still offering comparable sensing performance as that of unquantized sensing systems.

In this paper, we introduce $\texttt{TopoSRL}$, a novel self-supervised learning (SSL) method for simplicial complexes to effectively capture higher-order interactions and preserve topology in the learned representations. $\texttt{TopoSRL}$ addresses the limitations of existing graph-based SSL methods that typically concentrate on pairwise relationships, neglecting long-range dependencies crucial to capture topological information. We propose a new simplicial augmentation technique that generates two views of the simplicial complex that enriches the representations while being efficient. Next, we propose a new simplicial contrastive loss function that contrasts the generated simplices to preserve local and global information present in the simplicial complexes. Extensive experimental results demonstrate the superior performance of $\texttt{TopoSRL}$ compared to state-of-the-art graph SSL techniques and supervised simplicial neural models across various datasets corroborating the efficacy of $\texttt{TopoSRL}$ in processing simplicial complex data in a self-supervised setting.

Radio maps find numerous applications in wireless communications and mobile robotics tasks, including resource allocation, interference coordination, and mission planning. Although numerous existing techniques construct radio maps from spatially distributed measurements, the locations of such measurements are predetermined beforehand. In contrast, this paper proposes spectrum surveying, where a mobile robot such as an unmanned aerial vehicle (UAV) collects measurements at a set of locations that are actively selected to obtain high-quality map estimates in a short surveying time. This is performed in two steps. First, two novel algorithms, a model-based online Bayesian estimator and a data-driven deep learning algorithm, are devised for updating a map estimate and an uncertainty metric that indicates the informativeness of measurements at each possible location. These algorithms offer complementary benefits and feature constant complexity per measurement. Second, the uncertainty metric is used to plan the trajectory of the UAV to gather measurements at the most informative locations. To overcome the combinatorial complexity of this problem, a dynamic programming approach is proposed to obtain lists of waypoints through areas of large uncertainty in linear time. Numerical experiments conducted on a realistic dataset confirm that the proposed scheme constructs accurate radio maps quickly.

In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to model the dependence of node attributes on core scores of the nodes of a graph through a latent graph structure. Using the proposed model, we jointly infer a sparse graph and nodal core scores that induce dense (sparse) connections in core (respectively, peripheral) parts of the network. Numerical experiments on a variety of real-world data indicate that the proposed method learns a core-periphery structured graph from node attributes alone, while simultaneously learning core score assignments that agree well with existing works that estimate core scores using graph as input and ignoring commonly available node attributes.

We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-based geometric constraints while being scalable for processing large scale datasets with multiple views. It is based on an autoencoder architecture with graph convolutional neural network layers. We experiment with our approach on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state-of-the-art multiview representation learning techniques.

This paper focuses on channel estimation for mmWave MIMO systems with 1-bit spatial sigma-delta analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). The channel estimation performance with 1-bit spatial sigma-delta modulators (i.e., ADCs or DACs) depends on the quantization noise modeling. Therefore, we present a new method for modeling the quantization noise by leveraging the deterministic input-output relation of the 1-bit spatial sigma-delta modulator. Using this new noise model, we propose a channel estimation algorithm for a narrowband single-user mmWave line-of-sight MIMO system by determining the unknown angles and path attenuation that characterize the flat-fading channel. Through simulations, we demonstrate that the performance of the developed method is comparable to the traditional unquantized system and significantly better than the conventional 1-bit quantized system.

In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning problem as the factor graph Laplacian matrix estimation problem. To estimate the factor graph Laplacian matrices, we assume that the data is smooth with respect to the underlying product graph. When the training data is noise free or complete, learning factor graphs can be formulated as a convex optimization problem, which has an explicit solution based on the water-filling algorithm. The developed framework is illustrated using numerical experiments on synthetic data as well as real data related to air quality monitoring in India.

We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a distribution of signals that are smooth or bandlimited on a known graph. However, instead of observing these signals, we observe a signed version of them and only at a subset of the nodes on the graph. Our goal is to estimate the true underlying graph signals from our observations. To achieve this, we propose a generative adversarial network (GAN) where the key is to incorporate graph-aware losses in the associated minimax optimization problem. We illustrate the benefits of the proposed method via numerical experiments on hand-written digits from the MNIST dataset.

In this paper, the focus is on the gain and phase calibration of sparse sensor arrays to localize more sources than the number of physical sensors. The proposed technique is a blind calibration method as it does not require any calibrator sources. Joint estimation of the gain errors, phase errors, and source directions is a complicated non-convex optimization problem, which is transformed into a convex optimization problem by exploiting the underlying algebraic structure. It is shown that the developed solver is suitable for analog as well as one-bit measurements. Numerical experiments based on sparse rulers are provided to illustrate the developed theory.

An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.

In this paper the focus is on sampling and reconstruction of signals supported on nodes of arbitrary graphs or arbitrary signals that may be represented using graphs, where we extend concepts from generalized sampling theory to the graph setting. To recover such signals from a given set of samples, we develop algorithms that incorporate prior knowledge on the original signal when available such as smoothness or subspace priors related to the underlying graph. For reconstructing arbitrary signals, we constrain the reconstruction to the graph, and provide a consistent reconstruction method, in which both the reconstructed signal and the input yield exactly the same measurements. Given a set of graph frequency domain samples, the sampling and interpolation operations may be efficiently implemented using linear shift-invariant graph filters.

In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the covariance matrix of the kernel regressors to establish metrics for the subset selection problem. Despite the NP-hard nature of the problem, we introduce efficient algorithms for finding approximate solutions for the proposed metrics. Finally, numerical experiments demonstrate the applicability of the proposed strategies.

In this paper, we present a calibration algorithm for acoustic vector sensors arranged in a uniform linear array configuration. To do so, we do not use a calibrator source, instead we leverage the Toeplitz blocks present in the data covariance matrix. We develop linear estimators for estimating sensor gains and phases. Further, we discuss the differences of the presented blind calibration approach for acoustic vector sensor arrays in comparison with the approach for acoustic pressure sensor arrays. In order to validate the proposed blind calibration algorithm, simulation results for direction-of-arrival (DOA) estimation with an uncalibrated and calibrated uniform linear array based on minimum variance distortion less response and multiple signal classification algorithms are presented. The calibration performance is analyzed using the Cramér-Rao lower bound of the DOA estimates.

Factor analysis decomposition, i.e., decomposition of a covariance matrix as a sum of a low-rank positive semidefinite matrix and a diagonal matrix is an important problem in a variety of areas, such as signal processing, machine learning, system identification, and statistical inference. In this letter, the focus is on computing the factor analysis decomposition from a set of quadratic (or symmetric rank-one) measurements of a covariance matrix. Commonly used minimum trace factor analysis heuristic can be adapted to solve this problem when all the measurements are available. However, the resulting convex program is not suitable for processing large-scale or streaming data. Therefore, this letter presents a low-complexity iterative algorithm, which recovers the unknowns through a series of rank-one updates. The iterative algorithm performs better than the convex program when only a finite number of data snapshots are available.

In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response (MVDR) beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power (or signal-to-noise ratio). Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.

In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we parameterize the graph topology using an edge sampling function. That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which provides an explicit handle to control the sparsity level of the graph. We solve the sparse graph learning problem given some training data in both the noiseless and noisy settings. Given the true smooth data, the posed sparse graph learning problem can be solved optimally and is based on simple rank ordering. Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.

We study the sensor selection problem for field estimation, where a best subset of sensors is activated to monitor a spatially correlated random field. Different from most commonly used centralized selection algorithms, we propose a decentralized architecture where sensor selection can be carried out in a distributed way and by the sensors themselves. A decentralized approach is essential since each sensor has access only to the information (e.g., correlation) in its neighborhood. To make distributed optimization possible, we decompose the global cost function into local cost functions that require only the information in local neighborhoods of sensors. We then employ the alternating direction method of multipliers (ADMM) to solve the proposed sensor selection problem. In our algorithm, each sensor solves small-scale optimization problems, and communicates directly only with its immediate neighbors. Numerical results are provided to show the effectiveness of our approach.

In this paper we focus on the relative position and orientation estimation between rigid bodies in an anchorless scenario. Several sensor units are installed on the rigid platforms, and the sensor placement on the rigid bodies is known beforehand (i.e., relative locations of the sensors on the rigid body are known). However, the absolute position of the rigid bodies is not known. We show that the relative localization of rigid bodies amounts to the estimation of a rotation matrix and the relative distance between the centroids of the rigid bodies. We measure all the unknown pairwise distances between the sensors, which we use in a constrained least squares estimator. Furthermore, we also allow missing links between the sensors. The simulations support the developed theory.

An offline sampling design problem for Gaussian detection is considered in this paper. The sensing operation ismodeled by a selection vector, whose sparsity order is determined by the prescribed global error probability. Since the numerical optimization of the error probability is difficult, equivalent simpler costs, viz., the Kullback-Liebler distance and Bhattacharyya distance are optimized. The sensing problem is formulated and solved sub-optimally using convex optimization techniques. It is shown that the sensing problem can be solved optimally for conditionally independent Gaussian observations. Further, we show that for non-identical sensor observations, the number of sensors required to achieve a certain detection performance decreases as the sensors become more correlated.

Existing solutions to the sensor placement problem are based on sensor selection, in which the best subset of available sampling locations is chosen such that a desired estimation accuracy is achieved. However, the achievable estimation accuracy of sensor placement via sensor selection is limited to the initial set of sampling locations, which are typically obtained by gridding the continuous sampling domain. To circumvent this issue, we propose a framework of continuous sensor placement. A continuous variable is augmented to the grid-based model, which allows for off-the-grid sensor placement. The proposed offline design problem can be solved using readily available convex optimization solvers.

This paper presents the channel measurements performed within a closed metal cabinet at 60 GHz covering the frequency range 57-62 GHz. Two different volumes of an empty metal cupboard are considered to emulate the environment of interest (an industrial machine). Furthermore, we have considered a number of scenarios such as line of sight, non line of sight, and placing absorbers. A statistical channel model is provided to aid short-range wireless link design within such a reflective and confined environment. Based on the measurements, the large- and small-scale parameters are extracted and fitted using the standard log-normal and Saleh-Valenzuela models, respectively. The obtained results are characterized by a very small path loss exponent, a single cluster phenomenon, and a significantly large root-mean-square (RMS) delay spread. The results show that covering a wall with absorber material dramatically reduces the RMS delay spread. Finally, the proposed channel model is validated by comparing the measured channel with a simulated channel, where the simulated channel is generated from the extracted parameters.

Sensor selection is an important design task in sensor networks. We consider the problem of adaptive sensor selection for applications in which the observations follow a non-linear model, e.g., target/bearing tracking. In adaptive sensor selection, based on the dynamical state model and the state estimate from the previous time step, the most informative sensors are selected to acquire the measurements for the next time step. This is done via the design of a sparse selection vector. Additionally, we model the evolution of the selection vector over time to ensure a smooth transition between the selected sensors of subsequent time steps. The original non-convex optimization problem is relaxed to a semi-definite programming problem that can be solved efficiently in polynomial time.

Rigid body localization refers to a problem of estimating the position of a rigid body along with its orientation using anchors. We consider a setup in which a few sensors are mounted on a rigid body. The absolute position of the rigid body is not known, but, the relative position of the sensors or the topology of the sensors on the rigid body is known. We express the absolute position of the sensors as an affine function of the Stiefel manifold and propose a simple least-squares (LS) estimator as well as a constrained total least-squares (CTLS) estimator to jointly estimate the orientation and the position of the rigid body. To account for the perturbations of the sensors, we also propose a constrained total least-squares (CTLS) estimator. Analytical closed-form solutions for the proposed estimators are provided. Simulations are used to corroborate and analyze the performance of the proposed estimators.

In this paper, we jointly solve the problem of transmit antenna selection and zero-forcing (ZF) precoding in a multiple input multiple output (MIMO) system. A new problem formulation is proposed which enables efficient semi-definite programming (SDP) to solve the originally non-convex problem of antenna selection. This has been accomplished by imposing the Group Lasso sparsity promoting term in the precoding design criterium as a convex relaxation of the ℓ0-norm operation. For the selected set of antennas, we then minimize the overall transmit power, subject to a constraint on the maximum achievable throughput. Simulation results reveal the power saving advantage of the proposed algorithm compared to a randomly selected subset of antennas.

A fully asynchronous network with one sensor andManchors (nodes with known locations) is considered in this letter. We propose a novel asymmetrical time-stamping and passive listening (ATPL) protocol for joint clock synchronization and ranging. The ATPL protocol exploits broadcast to not only reduce the number of active transmissions between the nodes, but also to obtain more information. This is used in a simple estimator based on least-squares (LS) to jointly estimate all the unknown clock-skews, clock-offsets, and pairwise distances of the sensor to each anchor. The Cramér-Rao lower bound (CRLB) is derived for the considered problem. The proposed estimator is shown to be asymptotically efficient, meets the CRLB, and also performs better than the available clock synchronization algorithms.

In this paper, we analyze the performance of an IEEE 802.15.4 radio link in the 2360-2400 MHz band to support the ongoing Medical Body Area Network (MBAN) standardization activities in IEEE 802.15. There has been a lot of interest recently in opening the 2360-2400 MHz band for secondary allocations to promote MBAN innovations by providing a spectrum with less interference. In this work, we characterize the primary services in this band, focusing on Electronic News Gathering/Outside Broadcasting (ENG/OB) and Aeronautical Mobile Telemetry (AMT) systems. We study the performance in terms of the Packet Error Rate (PER) of an 802.15.4 MBAN radio link implemented on a Universal Software Radio Peripheral 2 (USRP2), in the presence of interference from these systems. A cognitive radio approach is proposed by implementing a spectrum sensing engine based on energy detection on USRP2. Our measurement results show an improvement in the performance of the radio along with primary user protection. In addition, an analytical expression for the packet error rate of the MBAN radio link with spectrum sensing is provided for a given Primary User (PU) activity, which matches well with the measured performance results.

Optimization of hard fusion spectrum sensing using the k-out-of-N rule is considered. Two different setups are used to derive the optimal k. A throughput optimization setup is defined by minimizing the probability of false alarm subject to a probability of detection constraint representing the interference of a cognitive radio with the primary user, and an interference management setup is considered by maximizing the probability of detection subject to a false alarm rate constraint. It is shown that the underlying problems can be simplified to equality constrained optimization problems and an algorithm to solve them is presented. We show the throughput optimization and interference management setups are dual. The simulation results show the majority rule is optimal or near optimal for the desirable range of false alarm and detection rates for a cognitive radio network. Furthermore, an energy efficient setup is considered where the number of cognitive radios is to be minimized for the AND and the OR rule and a certain probability of detection and false alarm constraint. The simulation results show that the OR rule outperforms the AND rule in terms of energy efficiency.