pwc-archive/papers-with-abstracts huggingface dataset:
MIMO-OFDM Joint Radar-Communications: Is ICI Friend or Foe?
Authors: Musa Furkan Keskin, Henk Wymeersch, Visa Koivunen
Date: 2021-03-29 00:00:00

Inter-carrier interference (ICI) poses a significant challenge for OFDM joint radar-communications (JRC) systems in high-mobility scenarios. In this paper, we propose a novel ICI-aware sensing algorithm for MIMO-OFDM JRC systems to detect the presence of multiple targets and estimate their delay-Doppler-angle parameters. First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. To account for the presence of multiple targets at a given angle, we devise an iterative interference cancellation based orthogonal matching pursuit (OMP) procedure, where at each iteration the generalized likelihood ratio test (GLRT) detector is employed to form decision statistics, providing as by-products the maximum likelihood estimates (MLEs) of radar channels and CFOs. In the final step, target detection is performed in delay-Doppler domain using target-specific, ICI-decontaminated channel estimates over time and frequency, where CFO estimates are utilized to resolve Doppler ambiguities, thereby turning ICI from foe to friend. The proposed algorithm can further exploit the ICI effect to introduce an additional dimension (namely, CFO) for target resolvability, which enables resolving targets located at the same delay-Doppler-angle cell. Simulation results illustrate the ICI exploitation capability of the proposed approach and showcase its superior detection and estimation performance in high-mobility scenarios over conventional methods.
Modified Vector Quantization for Small-Cell Access Point Placement with Inter-Cell Interference
Authors: Govind R. Gopal, Elina Nayebi, Gabriel Porto Villardi, Bhaskar D. Rao
Date: 2020-11-05 00:00:00
Tasks: Fairness, Quantization

In this paper, we explore the small-cell uplink access point (AP) placement problem in the context of throughput-optimality and provide solutions while taking into consideration inter-cell interference. First, we briefly review the vector quantization (VQ) approach and related single user throughput-optimal formulations for AP placement. Then, we investigate the small-cell case with multiple users and expose the limitations of mean squared error based VQ for solving this problem. While the Lloyd algorithm from the VQ approach is found not to strictly solve the small-cell case, based on the tractability and quality of resulting AP placement, we deem it suitable as a simple and appropriate framework to solve more complicated problems. Accordingly, to minimize ICI and consequently enhance achievable throughput, we design two Lloyd-type algorithms, namely, the Interference Lloyd algorithm and the Inter-AP Lloyd algorithm, both of which incorporate ICI in their distortion functions. Simulation results show that both of the proposed algorithms provide superior 95\%-likely rate over the traditional Lloyd algorithm and the Inter-AP Lloyd algorithm yields a significant increase of up to 36.34\% in achievable rate over the Lloyd algorithm.
Smoothing-Averse Control: Covertness and Privacy from Smoothers
Authors: Timothy L. Molloy, Girish N. Nair
Date: 2021-03-23 00:00:00
Tasks: State Space Models

In this paper we investigate the problem of controlling a partially observed stochastic dynamical system such that its state is difficult to infer using a (fixed-interval) Bayesian smoother. This problem arises naturally in applications in which it is desirable to keep the entire state trajectory of a system concealed. We pose our smoothing-averse control problem as the problem of maximising the (joint) entropy of smoother state estimates (i.e., the joint conditional entropy of the state trajectory given the history of measurements and controls). We show that the entropy of Bayesian smoother estimates for general nonlinear state-space models can be expressed as the sum of entropies of marginal state estimates given by Bayesian filters. This novel additive form allows us to reformulate the smoothing-averse control problem as a fully observed stochastic optimal control problem in terms of the usual concept of the information (or belief) state, and solve the resulting problem via dynamic programming. We illustrate the applicability of smoothing-averse control to privacy in cloud-based control and covert robotic navigation.
Mathematical Theory of Computational Resolution Limit in Multi-dimensions
Authors: Ping Liu, Hai Zhang
Date: 2021-03-22 00:00:00
Tasks: Super-Resolution

Resolving a linear combination of point sources from their band-limited Fourier data is a fundamental problem in imaging and signal processing. With the incomplete Fourier data and the inevitable noise in the measurement, there is a fundamental limit on the separation distance between point sources that can be resolved. This is the so-called resolution limit problem. Characterization of this resolution limit is still a long-standing puzzle despite the prevalent use of the classic Rayleigh limit. It is well-known that Rayleigh limit is heuristic and its drawbacks become prominent when dealing with data that is subjected to delicate processing, as is what modern computational imaging methods do. Therefore, more precise characterization of the resolution limit becomes increasingly necessary with the development of data processing methods. For this purpose, we developed a theory of "computational resolution limit" for both number detection and support recovery in one dimension in [arXiv:2003.02917[cs.IT], arXiv:1912.05430[eess.IV]]. In this paper, we extend the one-dimensional theory to multi-dimensions. More precisely, we define and quantitatively characterize the "computational resolution limit" for the number detection and support recovery problems in a general k-dimensional space. Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems. Our main results are derived using a subspace projection strategy. Finally, to verify the theory, we proposed deterministic subspace projection based algorithms for the number detection and support recovery problems in dimension two and three. The numerical results confirm the phase transition phenomenon predicted by the theory.
Thomson's Multitaper Method Revisited
Authors: Santhosh Karnik, Justin Romberg, Mark A. Davenport
Date: 2021-03-22 00:00:00

Thomson's multitaper method estimates the power spectrum of a signal from $N$ equally spaced samples by averaging $K$ tapered periodograms. Discrete prolate spheroidal sequences (DPSS) are used as tapers since they provide excellent protection against spectral leakage. Thomson's multitaper method is widely used in applications, but most of the existing theory is qualitative or asymptotic. Furthermore, many practitioners use a DPSS bandwidth $W$ and number of tapers that are smaller than what the theory suggests is optimal because the computational requirements increase with the number of tapers. We revisit Thomson's multitaper method from a linear algebra perspective involving subspace projections. This provides additional insight and helps us establish nonasymptotic bounds on some statistical properties of the multitaper spectral estimate, which are similar to existing asymptotic results. We show using $K=2NW-O(\log(NW))$ tapers instead of the traditional $2NW-O(1)$ tapers better protects against spectral leakage, especially when the power spectrum has a high dynamic range. Our perspective also allows us to derive an $\epsilon$-approximation to the multitaper spectral estimate which can be evaluated on a grid of frequencies using $O(\log(NW)\log\tfrac{1}{\epsilon})$ FFTs instead of $K=O(NW)$ FFTs. This is useful in problems where many samples are taken, and thus, using many tapers is desirable.
Thresholding Greedy Pursuit for Sparse Recovery Problems
Authors: Hai Le, Alexei Novikov
Date: 2021-03-17 00:00:00

We study here sparse recovery problems in the presence of additive noise. We analyze a thresholding version of the CoSaMP algorithm, named Thresholding Greedy Pursuit (TGP). We demonstrate that an appropriate choice of thresholding parameter, even without the knowledge of sparsity level of the signal and strength of the noise, can result in exact recovery with no false discoveries as the dimension of the data increases to infinity.
Data Discovery Using Lossless Compression-Based Sparse Representation
Authors: Elyas Sabeti, Peter X. K. Song, Alfred O. Hero III
Date: 2021-03-15 00:00:00
Tasks: Data Compression, Denoising, Dimensionality Reduction, Image Denoising

Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision. While overcomplete dictionaries are required for sparse representation of multidimensional data, orthogonal bases represent one-dimensional data well. In this paper, we propose a data-driven sparse representation using orthonormal bases under the lossless compression constraint. We show that imposing such constraint under the Minimum Description Length (MDL) principle leads to a unique and optimal sparse representation for one-dimensional data, which results in discriminative features useful for data discovery.
Reconfigurable Intelligent Surface -Assisted Ambient Backscatter Communications -- Experimental Assessment
Authors: Romain Fara, Dinh-Thuy Phan-Huy, Philippe Ratajczak, Abdelwaheb Ourir, Marco Di Renzo, Julien de Rosny
Date: 2021-03-15 00:00:00
Tasks: TAG

Sixth generation (6G) mobile networks may include new passive technologies, such as ambient backscatter communication or the use of reconfigurable intelligent surfaces, to avoid the emission of waves and the corresponding energy consumption. On the one hand, a reconfigurable intelligent surface improves the network performance by adding electronically controlled reflected paths in the radio propagation channel. On the other hand, in an ambient backscatter system, a device, named tag, communicates towards a reader by backscattering the waves of an ambient source (such as a TV tower). However, the tag's backscattered signal is weak and strongly interfered by the direct signal from the ambient source. In this paper, we propose a new reconfigurable intelligent surface assisted ambient backscatter system. The proposed surface allows to control the reflection of an incident wave coming from the exact source location towards the tag and reader locations (creating hot spots at their locations), thanks to passive reflected beams from a predefined codebook. A common phase-shift can also be applied to the beam. Thanks to these features, we demonstrate experimentally that the performance of ambient backscatter communications can be significantly improved.
Fast Antenna and Beam Switching Method for mmWave Handsets with Hand Blockage
Authors: Wan-Ting Shih, Chao-Kai Wen, Shang-Ho, Tsai, Shi Jin
Date: 2021-03-15 00:00:00

Many operators have been bullish on the role of millimeter-wave (mmWave) communications in fifth-generation (5G) mobile broadband because of its capability of delivering extreme data speeds and capacity. However, mmWave comes with challenges related to significantly high path loss and susceptibility to blockage. Particularly, when mmWave communication is applied to a mobile terminal device, communication can be frequently broken because of rampant hand blockage. Although a number of mobile phone companies have suggested configuring multiple sets of antenna modules at different locations on a mobile phone to circumvent this problem, identifying an optimal antenna module and a beam pair by simultaneously opening multiple sets of antenna modules causes the problem of excessive power consumption and device costs. In this study, a fast antenna and beam switching method termed Fast-ABS is proposed. In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules. As such, unmasked antenna modules and their corresponding beam pairs can be rapidly selected for switching to avoid the problem of poor quality or disconnection of communications caused by hand blockage. Thorough analysis and extensive simulations, which include the derivation of relevant Cram\'{e}r-Rao lower bounds, show that the performance of Fast-ABS is close to that of an oracle solution that can instantaneously identify the best beam of other antenna modules even in complex multipath scenarios. Furthermore, Fast-ABS is implemented on a software defined radio and integrated into a 5G New Radio physical layer. Over-the-air experiments reveal that Fast-ABS can achieve efficient and seamless connectivity despite hand blockage.
Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications
Authors: Chengcheng Xu, Bruno Clerckx, Shiwa Chen, Yijie Mao, Jianyun Zhang
Date: 2021-03-14 00:00:00

Dual-Functional Radar-Communication (DFRC) system is an essential and promising technique for beyond 5G. In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apart from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference. RSMA relies on multi-antenna Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers, and the split and encoding of messages into common and private streams. We design the message split and the precoders of the radar sequence and communication streams to jointly maximize the Weighted Sum Rate (WSR) and minimize the radar beampattern approximation Mean Square Error (MSE) subject to the per antenna power constraint. An iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed to solve the problem. Numerical results first show that RSMA-assisted DFRC achieves a better tradeoff between WSR and beampattern approximation than Space-Division Multiple Access (SDMA)-assisted DFRC with or without radar sequence, and other simpler radar-communication strategies using orthogonal resources. We also show that the RSMA-assisted DFRC frameworks with and without radar sequence achieve the same tradeoff performance. This is because that the common stream is better exploited in the proposed framework. The common stream of RSMA fulfils the triple function of managing interference among communication users, managing interference between communication and radar, and beampattern approximation. Therefore, by enabling RSMA in DFRC, the system performance is enhanced while the system architecture is simplified since there is no need to use additional radar sequence and SIC. We conclude that RSMA is a more powerful multiple access for DFRC.
Blind Diagnosis for Millimeter-wave Large-scale Antenna Systems
Authors: Rui Sun, Weidong Wang, Li Chen, Guo Wei, Wenyi Zhang
Date: 2021-01-25 00:00:00
Tasks: Diagnostic

Millimeter-wave (mmWave) communication systems rely on large-scale antenna arrays to combat large path-loss at mmWave band. Due to hardware characteristics and deployment environments, mmWave large-scale antenna systems are vulnerable to antenna element blockages and failures, which necessitate diagnostic techniques to locate faulty antenna elements for calibration purposes. Current diagnostic techniques require full or partial knowledge of channel state information (CSI), which can be challenging to acquire in the presence of antenna failures. In this letter, we propose a blind diagnostic technique to identify faulty antenna elements in mmWave large-scale antenna systems, which does not require any CSI knowledge. By jointly exploiting the sparsity of mmWave channel and failure pattern, we first formulate the diagnosis problem as a joint sparse recovery problem. Then, the atomic norm is introduced to induce the sparsity of mmWave channel over continuous Fourier dictionary. An efficient algorithm based on alternating direction method of multipliers (ADMM) is proposed to solve the formulated problem. Finally, the performance of the proposed technique is evaluated through numerical simulations.
Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks
Authors: X. Gao, Y. Liu, X. Liu, L. Song
Date: 2021-03-09 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).
Synchronization and Control for Multi-Weighted and Directed Complex Networks
Authors: Xiwei Liu
Date: 2021-03-28 00:00:00

The study of complex networks with multi-weights has been a hot topic recently. For a network with a single weight, previous studies have shown that they can promote synchronization. But for complex networks with multi-weights, there are no rigorous analysis to show that synchronization can be reached faster. In this paper, the complex network is allowed to be directed, which will make the synchronization analysis difficult for multiple couplings. In virtue of the normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices, we prove that if the Chebyshev distance between NLEVec is less than some value, which is defined as the allowable deviation bound, then the synchronization and control will be realized with sufficiently large coupling strengths, i.e., all coupling matrices do accelerate synchronization. Moreover, adaptive rules are also designed for the coupling strength.
Data-Driven Structured Policy Iteration for Homogeneous Distributed Systems
Authors: Siavash Alemzadeh, Shahriar Talebi, Mehran Mesbahi
Date: 2021-03-22 00:00:00

Control of networked systems, comprised of interacting agents, is often achieved through modeling the underlying interactions. Constructing accurate models of such interactions--in the meantime--can become prohibitive in applications. Data-driven control methods avoid such complications by directly synthesizing a controller from the observed data. In this paper, we propose an algorithm referred to as Data-driven Structured Policy Iteration (D2SPI), for synthesizing an efficient feedback mechanism that respects the sparsity pattern induced by the underlying interaction network. In particular, our algorithm uses temporary "auxiliary" communication links in order to enable the required information exchange on a (smaller) sub-network during the "learning phase" -- links that will be removed subsequently for the final distributed feedback synthesis. We then proceed to show that the learned policy results in a stabilizing structured policy for the entire network. Our analysis is then followed by showing the stability and convergence of the proposed distributed policies throughout the learning phase, exploiting a construct referred to as the "Patterned monoid.'' The performance of D2SPI is then demonstrated using representative simulation scenarios.
A Temporal Logic-Based Hierarchical Network Connectivity Controller
Authors: Hans Riess, Yiannis Kantaros, George Pappas, Robert Ghrist
Date: 2020-09-01 00:00:00
Tasks: Integrated sensing and communication

In this paper, we consider networks of static sensors with integrated sensing and communication capabilities. The goal of the sensors is to propagate their collected information to every other agent in the network and possibly a human operator. Such a task requires constant communication among all agents which may result in collisions and congestion in wireless communication. To mitigate this issue, we impose locally non-interfering connectivity constraints that must be respected by every agent. We show that these constraints along with the requirement of propagating information in the network can be captured by a Linear Temporal Logic (LTL) framework. Existing temporal logic control synthesis algorithms can be used to design correct-by-construction communication schedules that satisfy the considered LTL formula. Nevertheless, such approaches are centralized and scale poorly with the size of the network. We propose a hierarchical LTL-based algorithm that designs communication schedules that determine which agents should communicate while maximizing network usage. We show that the proposed algorithm is complete and demonstrate its efficiency and scalability through analysis and numerical experiments.
Graph-Based Multiobject Tracking with Embedded Particle Flow
Authors: Wenyu Zhang, Florian Meyer
Date: 2021-03-16 00:00:00
Tasks: Object, object-detection, Object Detection

Seamless situational awareness provided by modern radar systems relies on effective methods for multiobject tracking (MOT). This paper presents a graph-based Bayesian method for nonlinear and high-dimensional MOT problems that embeds particle flow. To perform operations on the graph effectively, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance with a relatively small number of particles even if object states are high dimensional and sensor measurements are very informative. Simulation results demonstrate reduced computational complexity and memory requirements as well as favorable detection and estimation accuracy in a challenging 3-D MOT scenario.
Morning commute in congested urban rail transit system: A macroscopic model for equilibrium distribution of passenger arrivals
Authors: Jiahua Zhang, Kentaro Wada, Takashi Oguchi
Date: 2021-02-26 00:00:00

This paper proposes a macroscopic model to describe the equilibrium distribution of passenger arrivals for the morning commute problem in a congested urban rail transit system. We use a macroscopic train operation sub-model developed by Seo et al (2017a,b) to express the interaction between the dynamics of passengers and trains in a simplified manner while maintaining their essential physical relations. The equilibrium conditions of the proposed model are derived and a solution method is provided. The characteristics of the equilibrium are then examined through analytical discussion and numerical examples. As an application of the proposed model, we analyze a simple time-dependent timetable optimization problem with equilibrium constraints and reveal that a "capacity increasing paradox" exists such that a higher dispatch frequency can increase the equilibrium cost. Furthermore, insights into the design of the timetable are obtained and the timetable influence on passengers' equilibrium travel costs are evaluated.