Stefan L. Sumsky, Taylor Somma, S. Santaniello, Mark Schomer
{"title":"Modularity-Based Detection of Ripples in Scalp EEG","authors":"Stefan L. Sumsky, Taylor Somma, S. Santaniello, Mark Schomer","doi":"10.1109/IEEECONF44664.2019.9048848","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048848","url":null,"abstract":"Ripples (80–250Hz) are promising markers of epileptogenic activity, but the diagnostic value of ripples in scalp EEG remains debated. In this study, we propose an unsupervised, cluster-based method to detect candidate ripples in scalp EEG and sort ripples according to their morphology and information content in the time-frequency domain. We also correlate the presence of ripples to the presence of interictal spikes, which are clinically recognized markers of epileptogenic activity. Our method combines feature-based agglomerative clustering and correlation-based community detection and was tested on scalp EEG from 3 children with epilepsy (age: 10±1 [mean ± SD], 2 male, 1 female). For each patient, one epoch of EEG during wakefulness and one epoch during sleep (stage N2–N3) were considered (wakefulness: 12.57±3.39 min; sleep: 14.68±0.49 min, mean ± SD). The proposed method showed high specificity in detecting ripples while rejecting artifacts and resulted in a minimal set of ripple templates that are consistent across patients and sleep condition. Also, the rate of ripples was higher in EEG channels that presented spikes (0.38±0.07 versus 0.24±0.07 ripples/min [mean ± SD]). Altogether, results indicate that morphology and spectral content of scalp ripples may be patient-independent and specific to the epileptogenic activity, which suggest scalp ripples as viable markers for noninvasive epilepsy diagnosis.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"35 1","pages":"250-253"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82632936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jarkko Kaleva, Nitin Jonathan Myers, Antti Tölli, R. Heath, Upamanyu Madhow
{"title":"Short Range 3D MIMO mmWave Channel Reconstruction via Geometry-aided AoA Estimation","authors":"Jarkko Kaleva, Nitin Jonathan Myers, Antti Tölli, R. Heath, Upamanyu Madhow","doi":"10.1109/IEEECONF44664.2019.9048890","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048890","url":null,"abstract":"In some millimeter wave (mmWave) applications, such as wearables, the distance between the transceivers is relatively short. Further, the channel has significant angular spread in both azimuth and elevation domains even in line-of-sight (LoS). Under such conditions, hybrid mmWave architectures with multiple analog uniform planar arrays (UPAs) potentially allow spatial multiplexing even in LoS provided that the high rank structure of the channel is captured. The conventional far-field channel estimation methods are not generally suitable for these scenarios and perform poorly. We consider parametrized spatial channel estimation, where the known antenna array geometry is exploited to recover the angle-of-arrivals (AoAs) of the 3D multiple-input multiple-output (MIMO) channel. The channel is then reconstructed using these AoA estimates and the known geometry. We show that conventional maximum a posteriori (MAP) estimation of the channel parameters suffers from high computational complexity and may not be not applicable for low powered devices. To this end, we propose a lower complexity message passing algorithm for short range channel estimation. We show, by numerical examples, that the proposed technique achieves good performance with fewer pilot resources when compared to compressed sensing or antenna specific pilot based channel estimation.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"66 1","pages":"427-431"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85775358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Near-Optimal Quantization for LoS MIMO with QPSK Modulation","authors":"Ahmet Dundar Sezer, Upamanyu Madhow","doi":"10.1109/IEEECONF44664.2019.9048696","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048696","url":null,"abstract":"As signaling bandwidths increase, analog-to-digital conversion becomes a fundamental bottleneck for modern all-digital baseband signal processing architectures. Motivated by emerging millimeter (mm) wave communication systems, we investigate the impact of severe quantization for 2 × 2 and 4×4 line-of-sight (LoS) multi-input and multi-output (MIMO) systems employing QPSK. Unlike prior work on MIMO with low-precision quantization, channel state information is utilized only at the receiver (i.e., transmit precoding is not employed). Rather than designing an optimal quantizer, we focus on quantizers with regular structure, and ask whether high-SNR performance approaches that of an unquantized system. First, we prove for a 2×2 MIMO system that phase-only quantization (attractive because it does not require automatic gain control) is unable to achieve this, but that 2-level amplitude and 8-level phase quantization can achieve the maximum data rate of 4 bits per channel use as SNR gets large. We then show that quantizer design based on conventional minimum mean squared quantization error (MMSQE) criterion performs worse than a quantizer based on equal-probability regions. We show that I/Q quantization with 16 regions per antenna using the equal probability criterion achieves the unquantized benchmark at high SNR, which is a maximum data rate of 8 bits per channel use. We illustrate our investigations via numerical examples.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"32 1","pages":"1015-1020"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88961796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Simultaneous Track and Search Multiple-Channel Multiple-User Receiver (MCMUR) for Joint Radar-Communications Systems","authors":"A. Chiriyath, C. Richmond, D. Bliss","doi":"10.1109/IEEECONF44664.2019.9049054","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9049054","url":null,"abstract":"A multiple-channel multiple-user receiver (MCMUR) exploits both spatial and temporal degrees of freedom to separate radar and communications signals coexisting jointly in the same frequency band, and to cancel interference waveforms. In this paper, we present the simultaneous track and search MCMUR, a novel mode of operation for the multi-antenna joint receiver. We discuss this novel operation of the receiver and present theoretical bounds on the detection and estimation performance of the simultaneous track and search MCMUR. We present a theoretically assessed receiver operating characteristic (ROC) analysis of the MCMUR herein to better understand the performance gains of the MCMUR at false alarm rates that are more informative of an actual radar systems' performance. We also measure the detection and estimation performance of the joint system through the detection and estimation information rates respectively.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"8 1","pages":"544-549"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89849142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Ngo, M. Guillaud, Alexis Decurninge, Sheng Yang, Subrata Sarkar, P. Schniter
{"title":"Non-Coherent Multi-User Detection Based on Expectation Propagation","authors":"K. Ngo, M. Guillaud, Alexis Decurninge, Sheng Yang, Subrata Sarkar, P. Schniter","doi":"10.1109/IEEECONF44664.2019.9049073","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9049073","url":null,"abstract":"In this paper, we propose a novel soft-output multi-user detector for non-coherent multiple access with Grassmannian signaling under Rayleigh block fading. Our detector is based on expectation propagation (EP) approximate inference and has polynomial complexity in the number of users. A simplified version of this scheme coincides with a scheme based on soft minimum-mean-square-error (MMSE) estimation and successive interference cancellation (SIC). Both schemes, especially EP, produce accurate approximates of the true posterior. They outperform a baseline decoder based on projecting the received signal onto the subspace orthogonal to the interference in terms of both hard-detected symbol error rate and coded bit error rate.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"30 1","pages":"2092-2096"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86588238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hieu Tran-Dinh, T. Vu, S. Chatzinotas, B. Ottersten
{"title":"Energy-efficient Trajectory Design for UAV-enabled Wireless Communications with Latency Constraints","authors":"Hieu Tran-Dinh, T. Vu, S. Chatzinotas, B. Ottersten","doi":"10.1109/IEEECONF44664.2019.9048942","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048942","url":null,"abstract":"This paper studies a new energy-efficient unmanned aerial vehicle (UAV)-enabled wireless communications, where the UAV acts as a flying base station (BS) to serve the ground users (GUs) within some predetermined latency limitations, e.g., requested timeout (RT). Our goal is to design the UAV trajectory to minimize the total energy consumption while satisfying the RT requirement from every GU, which is accomplished via two consecutive subproblems: traveling time minimization and energy minimization problems. Firstly, we propose two exhaustive search and heuristic algorithms based on the traveling salesman problem with time window (TSPTW) in order to minimize the UAV’s traveling time without violating the GUs’ RT requirements. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm achieves a trade-off between the performance and complexity. Secondly, we minimize the total energy consumption, for a given trajectory, via a joint optimization of the UAV’s velocity along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. In particular, it is shown that the proposed solutions outperform the reference in terms of both energy consumption and outage performance.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"5 1","pages":"347-352"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86428176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Correcting Deletions in Probabilistic Non-Binary Segmented Burst Deletion Channels","authors":"Chen Yi, J. Kliewer","doi":"10.1109/IEEECONF44664.2019.9048839","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048839","url":null,"abstract":"Consider a burst deletion channel where in a block of L consecutive non-binary symbols at most a single burst deletion of length b symbols exists. Existing schemes for this scenario leverage non-binary de Bruijn sequences to perfectly locate deletions. In contrast, we propose to solely use binary marker patterns in combination with a new soft-decision decoder. In this scheme, deletions are soft located by assigning a posteriori probabilities for the location of every burst deletion event of length of at most b, and replaced by erasures, then the resulting errors are further corrected by an outer channel code. Such a scheme has an advantage over the non-binary scheme as it in general increases the rate compared to an approach based on non-binary de Bruijn sequences, with only a minor loss in performance of locating a deletion error. Also, the proposed scheme provides a better error correction performance than existing schemes for the same code rate.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"33 1","pages":"1349-1353"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82551532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. S. Bedi, Alec Koppel, Brian M. Sadler, V. Elvira
{"title":"Compressed Streaming Importance Sampling for Efficient Representations of Localization Distributions","authors":"A. S. Bedi, Alec Koppel, Brian M. Sadler, V. Elvira","doi":"10.1109/IEEECONF44664.2019.9048744","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048744","url":null,"abstract":"Importance sampling (IS) is the standard Monte Carlo tool to compute integrals involving random variables such as their mean or higher-order moments. This procedure permits localizing a source signal corrupted by observation noise whose distribution is arbitrary, in contrast to typical Gaussian assumptions. We note that IS is asymptotically consistent as the number of Monte Carlo samples, and hence Dirac deltas (particles) that parameterize the density estimate, go to infinity. Unfortunately, allowing the number of particles in the density approximation to go to infinity is computationally intractable. Here we present a methodology for only keeping a finite representative subset of particles and their augmented importance weights that is nearly statistically consistent. To do so, we approximate importance sampling in two ways: we (1) replace the deltas by kernels, yielding kernel density estimates; (2) and sequentially project the kernel density estimates onto nearby lower-dimensional subspaces. Theoretically, the asymptotic bias of this scheme is characterized by a compression parameter and the kernel bandwidth, which yields a tunable trade-off between statistical consistency and memory. We then evaluate the validity of the proposed approach for a localization problem in wireless systems, and observed that the proposed algorithm and yields a favorable trade-off between memory and accuracy.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"10 1","pages":"477-481"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81255639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Local Geometry of Orthogonal Dictionary Learning using L1 Minimization","authors":"Qiuwei Li, Zhihui Zhu, M. Wakin, Gongguo Tang","doi":"10.1109/IEEECONF44664.2019.9049030","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9049030","url":null,"abstract":"Feature learning that extracts concise and general- izable representations for data is one of the central problems in machine learning and signal processing. Sparse dictionary learning, also known as sparse coding, distinguishes from other feature learning techniques in sparsity exploitation, allowing the formulation of nonconvex optimizations that simultaneously uncover a structured dictionary and sparse representations. Despite the popularity of dictionary learning in applications, the landscapes of these optimizations that enable effective learning largely remain a mystery. This work characterizes the local optimization geometry for a simplified version of sparse coding where the L1 norm of the sparse coefficient matrix is minimized subject to orthogonal dictionary constraints. In particular, we show that the ground-truth dictionary and coefficient matrix are locally identifiable under the assumption that the coefficient matrix is sufficiently sparse and the number of training data columns is sufficiently large.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"110 1","pages":"1217-1221"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78377812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Machine Learning Solution for Beam Tracking in mmWave Systems","authors":"Daoud Burghal, Naveed A. Abbasi, A. Molisch","doi":"10.1109/IEEECONF44664.2019.9048770","DOIUrl":"https://doi.org/10.1109/IEEECONF44664.2019.9048770","url":null,"abstract":"Utilizing millimeter-wave (mmWave) frequencies for wireless communication in mobile systems is challenging since it requires continuous tracking of the beam direction. Recently, beam tracking techniques based on channel sparsity and/or Kalman filter-based techniques were proposed where the solutions use assumptions regarding the environment and device mobility that may not hold in practical scenarios. In this paper, we explore a machine learning-based approach to track the angle of arrival (AoA) for specific paths in realistic scenarios. In particular, we use a recurrent neural network (R-NN) structure with a modified cost function to track the AoA. We propose methods to train the network in sequential data, and study the performance of our proposed solution in comparison to an extended Kalman filter based solution in a realistic mmWave scenario based on stochastic channel model from the QuaDRiGa framework. Results show that our proposed solution outperforms an extended Kalman filter-based method by reducing the AoA outage probability, and thus reducing the need for frequent beam search.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"19 1","pages":"173-177"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79448240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}