MIMO-OFDM channel estimation in the presence of carrier frequency offset

A multiple-input multiple-output (MIMO) wireless communication system with orthogonal frequency division multiplexing (OFDM) is expected to be a promising scheme. However, the estimation of the carrier frequency offset (CFO) and the channel parameters is a great challenging task. In this paper, a maximum-likelihood- (ML-) based algorithm is proposed to jointly estimate the frequency-selective channels and the CFO in MIMO-OFDM by using a block-type pilot. The proposed algorithm is capable of dealing with the CFO range nearly ±1/2 useful OFDM signal bandwidth. Furthermore, the cases with timing error and unknown channel order are discussed. The Cramér-Rao bound (CRB) for the problem is developed to evaluate the performance of the algorithm. Computer simulations show that the proposed algorithm can exploit the gain from multiantenna to improve effectively the estimation performance and achieve the CRB in high signal-to-noise ratio (SNR). © 2005 Hindawi Publishing Corporation

Suppression approach to main-beam deceptive jamming in FDA-MIMO radar using nonhomogeneous sample detection

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this work, we address the issue of hyper-parameter auto-tuning using neural network (NN)-based learning. Inspired by the empirical auto-tuner, we design and learn a NN-based auto-tuner, and show that considerable improvement in convergence rate and recovery performance can be achieved

A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space-Time Adaptive Processing

We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments

Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to "model" the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods

Discoidin Receptor 2 Controls Bone Formation and Marrow Adipogenesis

Cell–extracellular matrix (ECM) interactions play major roles in controlling progenitor cell fate and differentiation. The receptor tyrosine kinase, discoidin domain receptor 2 (DDR2), is an important mediator of interactions between cells and fibrillar collagens. DDR2 signals through both ERK1/2 and p38 MAP kinase, which stimulate osteoblast differentiation and bone formation. Here we show that DDR2 is critical for skeletal development and differentiation of marrow progenitor cells to osteoblasts while suppressing marrow adipogenesis. Smallie mice (Ddr2slie/slie), which contain a nonfunctional Ddr2 allele, have multiple skeletal defects. A progressive decrease in tibial trabecular bone volume/total volume (BV/TV) was observed when wild‐type (WT), Ddr2wt/slie, and Ddr2slie/slie mice were compared. These changes were associated with reduced trabecular number (Tb.N) and trabecular thickness (Tb.Th) and increased trabecular spacing (Tb.Sp) in both males and females, but reduced cortical thickness only in Ddr2slie/slie females. Bone changes were attributed to decreased bone formation rather than increased osteoclast activity. Significantly, marrow fat and adipocyte‐specific mRNA expression were significantly elevated in Ddr2slie/slie animals. Additional skeletal defects include widened calvarial sutures and reduced vertebral trabecular bone. To examine the role of DDR2 signaling in cell differentiation, bone marrow stromal cells (BMSCs) were grown under osteogenic and adipogenic conditions. Ddr2slie/slie cells exhibited defective osteoblast differentiation and accelerated adipogenesis. Changes in differentiation were related to activity of runt‐related transcription factor 2 (RUNX2) and PPARγ, transcription factors that are both controlled by MAPK‐dependent phosphorylation. Specifically, the defective osteoblast differentiation in calvarial cells from Ddr2slie/slie mice was associated with reduced ERK/MAP kinase and RUNX2‐S319 phosphorylation and could be rescued with a constitutively active phosphomimetic RUNX2 mutant. Also, DDR2 was shown to increase RUNX2‐S319 phosphorylation and transcriptional activity while also increasing PPARγ‐S112 phosphorylation, but reducing its activity. DDR2 is, therefore, important for maintenance of osteoblast activity and suppression of marrow adipogenesis in vivo and these actions are related to changes in MAPK‐dependent RUNX2 and PPARγ phosphorylation. © 2016 American Society for Bone and Mineral Research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135235/1/jbmr2893_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135235/2/jbmr2893.pd

Prophylactic abdominal drainage following appendectomy for complicated appendicitis: A meta-analysis

BackgroundTo date, the value of prophylactic abdominal drainage (AD) following appendectomy in patients with complicated appendicitis (CA), including adults and children, has yet to be determined. This paper presents a meta-analysis of the effects of prophylactic AD on postoperative complications in patients with CA, with the goal of exploring the safety and effectiveness of prophylactic AD.MethodsPubMed, Science Direct, Web of Science, Cochrane Library, and Embase databases were searched for relevant articles published before August 1, 2022. The primary outcomes were the complication rates [overall incidence of postoperative complications, incidence of intra-abdominal abscess (IAA), wound infection (WI), and postoperative ileus (PI), and the secondary outcome was the perioperative outcome]. The meta-analysis was performed with STATA V. 16.0A.ResultsA total of 2,627 articles were retrieved and 15 high-quality articles were eventually included after screening, resulting in a total of 5,123 patients, of whom 1,796 received AD and 3,327 did not. The results of this meta-analysis showed that compared with patients in the non-drainage group, patients in the drainage group had longer postoperative length of hospitalization (LOH) (SMD = 0.68, 95% CI: 0.01–1.35, P = 0.046), higher overall incidence of postoperative complications (OR = 0.50, 95% CI: 0.19–0.81, P = 0.01), higher incidence of WI (OR = 0.30, 95% CI: 0.08–0.51, P = 0.01) and PI (OR = 1.05, 95% CI: 0.57–1.54, P = 0.01), the differences were statistically significant. However, there was no significant difference in the incidence of IAA (OR = 0.10, 95% CI: −0.10 to 0.31, P = 0.31) between the two groups. The results of subgroup meta-analysis showed that in the adult subgroup, the overall incidence of postoperative complications in the drainage group was higher than that in the non-drainage group (OR = 0.67, 95% CI: 0.37–0.96, P = 0.01). However, there were no significant differences in IAA (OR = 0.18, 95% CI: −0.28 to 0.64, P = 0.45) and WI (OR = 0.13, 95% CI: (−0.40 to 0.66, P = 0.63) and PI (OR = 2.71, 95% CI: −0.29 to 5.71, P = 0.08). In the children subgroup, there were no significant differences in the incidence of IAA (OR = 0.51, 95% CI: −0.06 to 1.09, P = 0.08) between the two groups. The overall incidence of postoperative complications (OR = 0.46, 95% CI: 0.02–0.90, P = 0.04), incidences of WI (OR = 0.43, 95% CI: 0.14–0.71, P = 0.01) and PI (OR = 0.75, 95% CI: 0.10–1.39, P = 0.02) were significantly higher than those in the non-drainage group.ConclusionThis meta-analysis concluded that prophylactic AD did not benefit from appendectomy, but increased the incidence of related complications, especially in children with CA. Thus, there is insufficient evidence to support the routine use of prophylactic AD following appendectomy