Transmit Precoding for Interference Exploitation in the Underlay Cognitive Radio Z-channel

This paper introduces novel transmit beamforming approaches for the cognitive radio (CR) Z-channel. The proposed transmission schemes exploit non-causal information about the interference at the SBS to re-design the CR beamforming optimization problem. This is done with the objective to improve the quality of service (QoS) of secondary users by taking advantage of constructive interference in the secondary link. The beamformers are designed to minimize the worst secondary user's symbol error probability (SEP) under constraints on the instantaneous total transmit power, and the power of the instantaneous interference in the primary link. The problem is formulated as a bivariate probabilistic constrained programming (BPCP) problem. We show that the BPCP problem can be transformed for practical SEPs into a convex optimization problem that can be solved, e.g., by the barrier method. A computationally efficient tight approximate approach is also developed to compute the near-optimal solutions. Simulation results and analysis show that the average computational complexity per downlink frame of the proposed approximate problem is comparable to that of the conventional CR downlink beamforming problem. In addition, both the proposed methods offer significant performance improvements as compared to the conventional CR downlink beamforming, while guaranteeing the QoS of primary users on an instantaneous basis, in contrast to the average QoS guarantees of conventional beamformers

Robust Hybrid Precoding for Interference Exploitation in Massive Mimo Systems

In this paper, we consider a multiuser massive MIMO system with hybrid analog-digital precoding architecture. The phase shifters in the hybrid precoding architecture are assumed to be imperfect, where the true values of both phase and magnitude of the phase shifters are different from their nominal values. For a given analog precoding matrix, we develop an iterative algorithm to compute robust digital precoders based on the interference exploitation approach to eliminate any potential symbol errors due to the phase shifter impairments. Numerical experiments demonstrate the performance of the proposed algorithm and show its advantage over a conventional robust precoding technique

Interference Exploitation-Based Hybrid Precoding With Robustness Against Channel Errors

The extremely high cost associated with massive multiple-input multiple-output (MIMO) systems when it is employed with fully digital precoding can be reduced by applying hybrid precoding at an expense of increased transmit power. In such a hybrid precoding system, the transmit power required to achieve a certain quality-of-service (QoS) can be significantly reduced by employing the constructive interference (CI) precoding technique. However, as illustrated in the paper, the symbol error rate (SER) performance of CI-based precoding is very sensitive to channel errors. To address this challenge we propose a hybrid precoding approach with robustness against channel quantization error and channel estimation error. Simulation results demonstrate the superior energy efficiency of the proposed robust hybrid precoding when compared to that of a conventional non-robust precoding scheme in achieving a required QoS target

Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

Extended Successive Convex Approximation for Phase Retrieval with Dictionary Learning

Phase retrieval aims at reconstructing unknown signals from magnitude measurements of linear mixtures. In this paper, we consider the phase retrieval with dictionary learning problem, which includes an additional prior information that the measured signal admits a sparse representation over an unknown dictionary. The task is to jointly estimate the dictionary and the sparse representation from magnitude-only measurements. To this end, we study two complementary formulations and develop efficient parallel algorithms by extending the successive convex approximation framework using a smooth majorization. The first algorithm is termed compact-SCAphase and is preferable in the case of less diverse mixture models. It employs a compact formulation that avoids the use of auxiliary variables. The proposed algorithm is highly scalable and has reduced parameter tuning cost. The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models. It also renders simple incorporation of additional side constraints. The performance of both methods is evaluated when applied to blind sparse channel estimation from subband magnitude measurements in a multi-antenna random access network. Simulation results demonstrate the efficiency of the proposed techniques compared to state-of-the-art methods.Comment: This work has been submitted to the IEEE Transactions on Signal Processing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Interference Exploitation-Based Hybrid Precoding With Robustness Against Phase Errors

Hybrid analog-digital precoding significantly reduces the hardware costs in massive multiple-input multiple-output (MIMO) transceivers when compared with fully digital precoding at the expense of increased transmit power. In order to mitigate the above-mentioned shortfall, we use the concept of constructive interference-based precoding, which has been shown to offer significant transmit power savings when compared with the conventional interference suppression-based precoding in fully digital multiuser MIMO systems. Moreover, in order to circumvent the potential quality-of-service degradation at the users due to the hardware impairments in the transmitters, we judiciously incorporate robustness against such vulnerabilities in the precoder design. Since the undertaken constructive interference-based robust hybrid precoding problem is nonconvex with infinite constraints and thus difficult to solve optimally, we decompose the problem into two subtasks, namely, analog precoding and digital precoding. In this paper, we propose an algorithm to compute the optimal constructive interference-based robust digital precoders. Furthermore, we devise a scheme to facilitate the implementation of the proposed algorithm in a low-complexity and distributed manner. We also discuss the block-level analog precoding techniques. The simulation results demonstrate the superiority of the proposed algorithm and its implementation scheme over the state-of-the-art methods

Feline calicivirus virulent systemic disease: Clinical epidemiology, analysis of viral isolates and in vitro efficacy of novel antivirals in australian outbreaks

Feline calicivirus (FCV) causes upper respiratory tract disease (URTD) and sporadic outbreaks of virulent systemic disease (FCV-VSD). The basis for the increased pathogenicity of FCVVSD viruses is incompletely understood, and antivirals for FCV-VSD have yet to be developed. We investigated the clinicoepidemiology and viral features of three FCV-VSD outbreaks in Australia and evaluated the in vitro efficacy of nitazoxanide (NTZ), 2′-C-methylcytidine (2CMC) and NITD008 against FCV-VSD viruses. Overall mortality among 23 cases of FCV-VSD was 39%. Metagenomic sequencing identified five genetically distinct FCV lineages within the three outbreaks, all seemingly evolving in situ in Australia. Notably, no mutations that clearly distinguished FCVURTD from FCV-VSD phenotypes were identified. One FCV-URTD strain likely originated from a recombination event. Analysis of seven amino-acid residues from the hypervariable E region of the capsid in the cultured viruses did not support the contention that properties of these residues can reliably differentiate between the two pathotypes. On plaque reduction assays, dose–response inhibition of FCV-VSD was obtained with all antivirals at low micromolar concentrations; NTZ EC50, 0.4–0.6 µM, TI = 21; 2CMC EC50, 2.7–5.3 µM, TI > 18; NITD-008, 0.5 to 0.9 µM, TI > 111. Investigation of these antivirals for the treatment of FCV-VSD is warranted

Targeted online liquid chromatography electron capture dissociation mass spectrometry for the localization of sites of in vivo phosphorylation in human Sprouty2

We demonstrate a strategy employing collision-induced dissociation for phosphopeptide discovery, followed by targeted electron capture dissociation (ECD) for site localization. The high mass accuracy and low background noise of the ECD mass spectra allow facile sequencing of coeluting isobaric phosphopeptides, with up to two isobaric phosphopeptides sequenced from a single mass spectrum. In contrast to the previously described neutral loss of dependent ECD method, targeted ECD allows analysis of both phosphotyrosine peptides and lower abundance phosphopeptides. The approach was applied to phosphorylation analysis of human Sprouty2, a regulator of receptor tyrosine kinase signaling. Fifteen sites of phosphorylation were identified, 11 of which are novel