Search

The dramatic increment of Internet of Things (IoT) devices has become a challenging problem in wireless communications. The IoT devices have the characteristic of sporadic transmission, which causes severe signaling overhead and latency problems for the grant-based multiple access systems. To solve this problem, grant-free non-orthogonal multiple access (GF-NOMA), with user equipments directly performing uplink transmission to the base station, has emerged as a promising solution. Without pre-allocating the resources, the active user detection (AUD) is required in GF-NOMA, and the massive number of devices operated under limited frequency resources makes the AUD problem challenging. Inspired by the interference cancellation framework, we propose a novel model-based deep learning (DL) architecture, called active user detection-interference cancellation neural network (AUD-ICNN), to address the AUD problem under the frequency selective fading channel, without needing the channel state information (CSI) and the user sparsity information. Specifically, the proposed AUD-ICNN exploits the signal structure to estimate the activation probability and then cancel the interference according to the estimated activation probability. Simulation results show that the proposed AUD-ICNN outperforms the conventional compressive sensing (CS) algorithms and significantly reduces the error rate compared to the state-of-the-art DL architectures. Meanwhile, the complexity is reduced by 6.9 and 13.9 times compared to the state-of-the-art DL and CS methods, respectively. Furthermore, unlike the existing DL-based architectures, the proposed AUD-ICNN uses a single neural network to deal with different user sparsity. Finally, a transfer learning method is proposed to extend the proposed AUD-ICNN to a robust sparsity estimation neural network.

The chr12q24.13 locus encoding OAS1–OAS3 antiviral proteins has been associated with coronavirus disease 2019 (COVID-19) susceptibility. Here, we report genetic, functional and clinical insights into this locus in relation to COVID-19 severity. In our analysis of patients of European (n= 2,249) and African (n= 835) ancestries with hospitalized versus nonhospitalized COVID-19, the risk of hospitalized disease was associated with a common OAS1haplotype, which was also associated with reduced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clearance in a clinical trial with pegIFN-λ1. Bioinformatic analyses and in vitro studies reveal the functional contribution of two associated OAS1exonic variants comprising the risk haplotype. Derived human-specific alleles rs10774671-A and rs1131454-A decrease OAS1 protein abundance through allele-specific regulation of splicing and nonsense-mediated decay (NMD). We conclude that decreased OAS1expression due to a common haplotype contributes to COVID-19 severity. Our results provide insight into molecular mechanisms through which early treatment with interferons could accelerate SARS-CoV-2 clearance and mitigate against severe COVID-19.

With the proliferation of heterogeneous wireless networks, classical information-theoretical methods can no longer provide accurate mathematical models to analyze the complicated network performance for the regulation and optimization of networks. The emerging landscape of big data and machine learning has provided a new paradigm for the design and optimization of intelligent wireless networks. In this article, a general architecture for data-cognition-empowered intelligent wireless networks is proposed. Characteristics of wireless data, the frequently used utilities for data cognition, and data cognition methods are discussed to depict the building blocks and challenges in the exploration of the architecture for data-cognition-empowered intelligent wireless networks.

In heterogeneous networks, interference is one of the critical issues. In this letter, we consider a two-tier downlink network composed of macrocell base stations (MBSs) and femtocell base stations (FBSs), where the locations of MBSs and FBSs are modeled by two independent Poisson point processes (PPPs). FBSs are assumed to be cognitive and become inactive when within a certain range of active MBSs. Due to the inactivation regions, the location distribution of active FBSs no longer follows PPP, complicating the calculation of coverage probability. To circumvent this, we propose an approximation to the coverage probability for both the open access and closed access policies.

In this letter, we propose a low-complexity recursive decoding algorithm for rateless RaptorQ codes, the next generation error correction codes adopted by several standards such as DVB-H. Conventionally, the decoding of RaptorQ codes requires inverting a huge receive code generator matrix (RCGM). Instead of such costly matrix inversion, we propose to calculate the inverse of the transmit code generator matrix (TCGM) beforehand. Then, based on this pre-calculated inverse, the Sherman-Morrison formula is applied to recursively compute the inverse of the RCGM at run time. Most computations are thus shifted offline. Moreover, this recursive decoding distributes the computations among different time slots, thereby significantly shortening the decoding latency and improving the hardware utilization. Numerical simulations demonstrate that the computational complexity of the proposed recursive decoding is as low as 6.5% of that required by the conventional method using direct matrix inversion.

Among different sensors used in machine-to-machine (M2M) networks, video sensors can provide the richest information. However, much higher bandwidth and power consumption limit the feasibility of wide deployment. To integrate visual data acquisition ability into M2M networks, the design of low-power distributed video sensors is the key. This paper presents the power analysis of distributed video sensors. The power profile of a distributed video sensor node is first shown from measurement results, followed by detailed discussion of video coding engine selection, where the state-of-the-art H.264/AVC coding engine is compared with distributed video coding engine. Moreover, the role of video content analysis is addressed. This paper can provide a fundamental basis for future distributed video sensor design.

Diffusion-based molecular communications emerges due to the need for communication and networking among nanomachines, and molecular biological signaling networks. Inspired by the special molecular channel characteristics, we reveal the communication theoretical analogs with and differences from well-known wireless communications, particularly channel coding, intersymbol interference, multiple-input multipleoutput, and new design concepts in this article.

The recently-proposed channel-adaptive multiple-input multiple-output (CA-MIMO) detection significantly reduces the average complexity of MIMO detection. Aiming at the just-acceptable error rate (JAER), the detection methods are switched according to the channel condition. In this letter, we formulate the switching of detection methods as a multiple-choice knapsack problem (MCKP). Then, based on the linear relaxation MCKP, the lower and upper bounds of the potential complexity saving of CA-MIMO are derived, where the former comes from our proposed switching strategy. The simulations show that the lower and upper bounds are tightly matched, demonstrating our two contributions: the evaluation of the optimal performance of the CA-MIMO detection without solving the NP-hard MCKP, and the design of an efficient and practical switching strategy.

Triple-negative breast cancer (TNBC) remains difficult to treat and urgently needs new therapeutic options. Nintedanib, a multikinase inhibitor, has exhibited efficacy in early clinical trials for HER2-negative breast cancer. In this study, we examined a new molecular mechanism of nintedanib in TNBC. The results demonstrated that nintedanib enhanced TNBC cell apoptosis, which was accompanied by a reduction of p-STAT3 and its downstream proteins. STAT3 overexpression suppressed nintedanib-mediated apoptosis and further increased the activity of purified SHP-1 protein. Moreover, treatment with either a specific inhibitor of SHP-1 or SHP-1-targeted siRNA reduced the apoptotic effects of nintedanib, which validates the role of SHP-1 in nintedanib-mediated apoptosis. Furthermore, nintedanib-induced apoptosis was attenuated in TNBC cells expressing SHP-1 mutants with constantly open conformations, suggesting that the autoinhibitory mechanism of SHP-1 attenuated the effects of nintedanib. Importantly, nintedanib significantly inhibited tumor growth via the SHP-1/p-STAT3 pathway. Clinically, SHP-1 levels were downregulated, whereas p-STAT3 was upregulated in tumor tissues, and SHP-1 transcripts were associated with improved disease-free survival in TNBC patients. Our findings revealed that nintedanib induces TNBC apoptosis by acting as a SHP-1 agonist, suggesting that targeting STAT3 by enhancing SHP-1 expression could be a viable therapeutic strategy against TNBC.