IEEE Journal of Selected Topics in Signal Processing

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will li... View full abstract»

End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that ... View full abstract»

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc ... View full abstract»

Massive multiple-input multiple-output (MIMO) wireless communications refers to the idea equipping cellular base stations (BSs) with a very large number of antennas, and has been shown to potentially allow for orders of magnitude improvement in spectral and energy efficiency using relatively simple (linear) processing. In this paper, we present a comprehensive overview of state-of-the-art research... View full abstract»

We conduct an in depth study on the performance of deep learning based radio signal classification for radio communications signals. We consider a rigorous baseline method using higher order moments and strong boosted gradient tree classification, and compare performance between the two approaches across a range of configurations and channel impairments. We consider the effects of carrier frequenc... View full abstract»

Large-scale (or massive) multiple-input multiple-out put (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose-to the best of our knowledge-the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)... View full abstract»

The potential of using of millimeter wave (mmWave) frequency for future wireless cellular communication systems has motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods which require one radio frequency (RF) chain per antenna element is not viable for large-scale antenna arrays due to the high co... View full abstract»

Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ) error term combined with a sparseness-inducing regularization term. Basis pursuit, the least absolute shrinkage and selection operato... View full abstract»

Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation - convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. This architecture concatenates a trained CNN with a standard BP decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and... View full abstract»

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterat... View full abstract»

Millimeter wave (mmWave) communications has been regarded as a key enabling technology for 5G networks, as it offers orders of magnitude greater spectrum than current cellular bands. In contrast to conventional multiple-input-multiple-output (MIMO) systems, precoding in mmWave MIMO cannot be performed entirely at baseband using digital precoders, as only a limited number of signal mixers and analo... View full abstract»

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher d... View full abstract»

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human-computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using audi... View full abstract»

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account S... View full abstract»

With the increasing presence of cognitive radio networks as a means to address limited spectral resources, improved wireless security has become a necessity. In particular, the potential of a node to impersonate a licensed user demonstrates the need for techniques to authenticate a radio's true identity. In this paper, we use deep learning to detect physical-layer attributes for the identification... View full abstract»

In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not... View full abstract»

Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the sup... View full abstract»

Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the mon... View full abstract»

We propose an integrated end-to-end automatic speech recognition (ASR) paradigm by joint learning of the front-end speech signal processing and back-end acoustic modeling. We believe that “only good signal processing can lead to top ASR performance” in challenging acoustic environments. This notion leads to a unified deep neural network (DNN) framework for distant speech processing that can achiev... View full abstract»

One of the challenges in the design of large-scale multiuser MIMO-OFDM systems is developing low-complexity detection algorithms. To achieve this goal, we leverage message passing algorithms over the factor graph that represents the multiuser MIMO-OFDM systems and approximate the original discrete messages with continuous Gaussian messages through the use of the minimum Kullback-Leibler (KL) diver... View full abstract»

L1-norm principal-component analysis (L1-PCA) is known to attain sturdy resistance against faulty points (outliers) among the processed data. However, computing the L1-PCA of large datasets, with high number of measurements and/or dimensions, may be computationally impractical; in such cases, incremental solutions could be preferred. At the same time, in many applications it is desired to track th... View full abstract»

The science and art of Spatial Audio is concerned with the capture, production, transmission, and reproduction of an immersive sound experience. Recently, a new generation of spatial audio technology has been introduced that employs elevated and lowered loudspeakers and thus surpasses previous `surround sound' technology without such speakers in terms of listener immersion and potential for spatia... View full abstract»

This paper considers the detection of the presence/absence of signals in uncertain low SNR environments. Small modeling uncertainties are unavoidable in any practical system and so robustness to them is a fundamental performance metric. The impact of these modeling uncertainties can be quantified by the position of the "SNR wall" below which a detector will fail to be robust, no matter how long it... View full abstract»

In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization framework and the successive convex approximation framework propo... View full abstract»

With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the s... View full abstract»