In-band full-duplex (IBFD) operation has emerged as an attractive solution for increasing the throughput of wireless communication systems and networks. With IBFD, a terminal is allowed to transmit receive simultaneously in same frequency band. This tutorial paper reviews main concepts IBFD wireless. One biggest practical impediments presence self-interference, i.e., interference that modem's transmitter causes its own receiver. surveys wide range self-interference mitigation techniques....

We propose novel cooperative transmission protocols for delay-limited coherent fading channels consisting of N (half-duplex and single-antenna) partners one cell site. In our work, we differentiate between the relay, broadcast (down-link), multiple-access (CMA) (up-link) channels. The proposed are evaluated using Zheng-Tse diversity-multiplexing tradeoff. For relay channel, investigate two classes cooperation schemes; namely, amplify forward (AF) decode (DF) protocols. first class, establish...

Orthogonal frequency division multiplexing (OFDM) systems may experience significant inter-carrier interference (ICI) when used in time- and frequency-selective, or doubly selective, channels. In such cases, the classical symbol estimation schemes, e.g., minimum mean-squared error (MMSE) zero-forcing (ZF) estimation, require matrix inversion that is prohibitively complex for large lengths. An analysis of ICI generation mechanism leads us to propose a novel two-stage equalizer whose...

When recovering a sparse signal from noisy compressive linear measurements, the distribution of signal's non-zero coefficients can have profound effect on recovery mean-squared error (MSE). If this was priori known, then one could use computationally efficient approximate message passing (AMP) techniques for nearly minimum MSE (MMSE) recovery. In practice, however, is unknown, motivating robust algorithms like LASSO-which minimax optimal-at cost significantly larger non-least-favorable...

Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep sparse linear inverse problem, where one seeks recover a signal from few noisy measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in same way approximate message passing (AMP) algorithms them iterations: through Onsager correction. First, "learned AMP" network significantly improves...

We develop a broadband channel estimation algorithm for millimeter wave (mmWave) multiple input output (MIMO) systems with few-bit analog-to-digital converters (ADCs). Our methodology exploits the joint sparsity of mmWave MIMO in angle and delay domains. formulate problem as noisy quantized compressed-sensing solve it using efficient approximate message passing (AMP) algorithms. In particular, we model angle-delay coefficients Bernoulli-Gaussian-mixture distribution unknown parameters use...

In this paper, we consider the problem of full-duplex bidirectional communication between a pair modems, each with multiple transmit and receive antennas. The principal difficulty in implementing such system is that, due to close proximity modem's antennas its antennas, outgoing signal can exceed dynamic range input circuitry, making it difficult-if not impossible-to recover desired incoming signal. To address these challenges, systems that use pilot-aided channel estimates perform...

In this paper we consider the problem of full-duplex multiple-input multiple-output (MIMO) relaying between multi-antenna source and destination nodes. The principal difficulty in implementing such a system is that, due to limited attenuation relay's transmit receive antenna arrays, outgoing signal may overwhelm its limited-dynamic-range input circuitry, making it difficult-if not impossible-to recover desired incoming signal. While explicitly modeling transmitter/receiver dynamic-range...

The standard linear regression (SLR) problem is to recover a vector x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> from noisy observations y = Ax + w. approximate message passing (AMP) algorithm proposed by Donoho, Maleki, and Montanari computationally efficient iterative approach SLR that has remarkable property: for large i.i.d. sub-Gaussian matrices A, its per-iteration behavior rigorously characterized scalar state-evolution whose...

We develop channel estimation agorithms for millimeter wave (mmWave) multiple input output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO is sparse due to propagation characteristics, problem formulated as a compressed sensing problem. propose modified EM algorithm that exploits sparsity and has better performance than conventional algorithm. also present second solution using generalized approximate message passing (GAMP) solve this optimization The...

In phase retrieval, the goal is to recover a signal x ∈ C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> from magnitudes of linear measurements Ax xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> . While recent theory has established that M ≈ 4N intensity are necessary and sufficient generic x, there great interest in reducing number through exploitation sparse \mbi which known as compressive retrieval. this work, we detail novel,...

We extend the generalized approximate message passing (G-AMP) approach, originally proposed for high-dimensional generalized-linear regression in context of compressive sensing, to generalized-bilinear case, which enables its application matrix completion, robust PCA, dictionary learning, and related matrix-factorization problems. In first part paper, we derive our Bilinear G-AMP (BiG-AMP) algorithm as an approximation sum-product belief propagation limit, where central-limit theorem...

We propose a novel algorithm for compressive imaging that exploits both the sparsity and persistence across scales found in 2D wavelet transform coefficients of natural images. Like other recent works, we model structure using hidden Markov tree (HMT) but, unlike ours is based on loopy belief propagation (LBP). For LBP, adopt recently proposed "turbo" message passing schedule alternates between exploitation HMT compressive-measurement structure. latter, leverage Donoho, Maleki, Montanari's...

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, data acquisition process for MRI inherently slow, which motivates undersampling and thus drives need accurate, efficient reconstruction methods from undersampled datasets. In this article, we describe "plug-and-play" (PnP) algorithms image recovery. We first...

We propose strategies for mmWave communications that exploit the inherent sparsity of channels in angle and delay domains. In particular, we use aperture shaping to ensure a sparse virtual-domain MIMO channel representation; fast FFT-based modulation demodulation schemes expose virtual-channel coefficients; pilot design facilitates LASSO-based sparse-channel estimation; spectrally efficient precoding decoding, via Lanczos algorithm waterfilling over both frequency angle. Numerical...

Regularization by Denoising (RED), as recently proposed Romano, Elad, and Milanfar, is powerful image-recovery framework that aims to minimize an explicit regularization objective constructed from a plug-in image-denoising function. Experimental evidence suggests the RED algorithms are state-of-the-art. We claim, however, does not explain algorithms. In particular, we show many of expressions in paper Romano et al. hold only when denoiser has symmetric Jacobian, demonstrate such symmetry...

In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored a Bayesian perspective. While there has been handful previously proposed CS algorithms in literature, ability to perform inference on high-dimensional problems computationally efficient manner remains elusive. response, we propose probabilistic signal model that captures both amplitude and support correlation...

A low-complexity recursive procedure is presented for minimum mean squared error (MMSE) estimation in linear regression models. Gaussian mixture chosen as the prior on unknown parameter vector. The algorithm returns both an approximate MMSE estimate of vector and a set high posterior probability mixing parameters. Emphasis given to case sparse Numerical simulations demonstrate performance illustrate distinctions between MAP model selection. parameters not only provides basis selection, but...

The standard linear regression (SLR) problem is to recover a vector x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> from noisy observations y = Ax + w. approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari computationally efficient iterative approach SLR that has remarkable property: for large i.i.d. sub-Gaussian matrices A, its periteration behavior rigorously characterized scalar...

In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, which collection of sparse signal vectors that share common support are recovered from undersampled noisy measurements. The algorithm, AMP-MMV, capable exploiting temporal correlations amplitudes non-zero coefficients, and provides soft estimates as well underlying support. Central to approach an extension recently developed techniques...

This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, support signal coefficients is parameterized by hidden binary pattern, and a structured probabilistic prior (e.g., Markov random chain/field/tree) assumed on pattern. Exact inference discussed an approximate scheme, based loopy belief propagation (BP), proposed. The proposed scheme iterates between exploitation observation-structure pattern-structure, closely related to...

The generalized approximate message passing (GAMP) algorithm is an efficient method of MAP or approximate-MMSE estimation $x$ observed from a noisy version the transform coefficients $z = Ax$. In fact, for large zero-mean i.i.d sub-Gaussian $A$, GAMP characterized by state evolution whose fixed points, when unique, are optimal. For generic however, may diverge. this paper, we propose adaptive damping and mean-removal strategies that aim to prevent divergence. Numerical results demonstrate...

The approximate message passing (AMP) algorithm originally proposed by Donoho, Maleki, and Montanari yields a computationally attractive solution to the usual ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularized least-squares problem faced in compressed sensing, whose is known be robust signal distribution. When drawn i.i.d from marginal distribution that not least-favorable, better performance can attained using Bayesian...

Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. are now expected deal with ever more complex models, requiring sophisticated computational inference techniques. This has driven the development of statistical based stochastic simulation optimization. Stochastic optimization algorithms computationally intensive tools for performing in models that analytically intractable beyond scope deterministic methods. They have been...