Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this ...paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF-CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.
In recent years, unmanned aerial vehicles (UAVs) have unlocked numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve ...the Quality-of-Service in these applications, the 3rd Generation Partnership Project has considered the use of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of the sensory data, the concept of age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a lower AoI than a greedy algorithm, policy gradient algorithm, and overlay U2D scheme.
Distributed storage that leverages cellular device-to-device (D2D) underlay has attracted rising research interest due to its potential to offload cellular traffic, improve spectral efficiency and ...energy efficiency, and reduce transmission delay. This paper investigates the overall transmission cost minimization problem based on a content encoding strategy to download a new content item or repair a lost content item in D2D-based distributed storage systems while guaranteeing users' quality of service. In addition to the optimization of the coding parameters, the cost minimization problem also considers the distribution of content items, the selection of content helpers for each content requester, and the spectrum reuse for establishing D2D links in between. Formulating a hypergraph-based three-dimensional matching problem among content helpers, requesters, and cellular user resources, we present a local search based algorithm with low complexity for optimization. Numerical results demonstrate the performance and the effectiveness of our proposed approach.
-1To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning ...technique by collaboratively training a shared learning model on edge devices. The number of resource blocks when using traditional orthogonal transmission strategies for FL linearly scales with the number of participating devices, which conflicts with the scarcity of communication resources. To tackle this issue, over-the-air computation (AirComp) has emerged recently which leverages the inherent superposition property of wireless channels to perform one-shot model aggregation. However, the aggregation accuracy in AirComp suffers from the unfavorable wireless propagation environment. In this paper, we consider the use of intelligent reflecting surfaces (IRSs) to mitigate this problem and improve FL performance with AirComp. Specifically, a novel performance-oriented long-term design scheme that integrated design multiple communication rounds to minimize the optimality gap of the loss function is proposed. We first analyze the convergence behavior of the FL procedure with the absence of channel fading and noise. Based on the obtained optimality gap which characterizes the impact of channel fading and noise in different communication rounds on the ultimate performance of FL, we propose both online and offline schemes to tackle the resulting design problem. Simulation results demonstrate that such a long-term design strategy can achieve higher test accuracy than the conventional isolated design approach in FL. Both the theoretical analysis and numerical results exhibit a "later-is-better" principle, which demonstrates the later rounds in the FL procedure are more sensitive to aggregation error, and hence more resources are required over time.
This paper investigates a novel intelligent reflecting surface (IRS)-based symbiotic radio (SR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary ...transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. Based on the IRS's symbol period, we distinguish two scenarios, namely, commensal SR (CSR) and parasitic SR (PSR), where two different techniques for decoding the IRS signals at the IR are employed. We formulate bit error rate (BER) minimization problems for both scenarios by jointly optimizing the active beamformer at the base station and the phase shifts at the IRS, subject to a minimum primary rate requirement. Specifically, for the CSR scenario, a penalty-based algorithm is proposed to obtain a high-quality solution, where semi-closed-form solutions for the active beamformer and the IRS phase shifts are derived based on Lagrange duality and Majorization-Minimization methods, respectively. For the PSR scenario, we apply a bisection search-based method, successive convex approximation, and difference of convex programming to develop a computationally efficient algorithm, which converges to a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithms and show that the proposed SR techniques are able to achieve a lower BER than benchmark schemes.
Proton pump inhibitors (PPIs) are used for the long-term treatment of gastroesophageal disorders and the non-prescription medicines for acid reflux. However, there is growing concerns about PPI ...misuse, overuse and abuse. This study aimed to develop an animal model to examine the effects of long-term use of PPI in vivo. Twenty one Wistar rats were given omeprazole orally or intravenously for 30 days, and caerulein as a positive control. After euthanization, the serum and stool were collected to perform MS-based quantitative analysis of metabolites. We carried out 16S-based profiling of fecal microbiota, assessed the expression of bile acid metabolism regulators and examined the immunopathological characteristics of bile ducts. After long-term PPI exposure, the fecal microbial profile was altered and showed similarity to those observed in high-fat diet studies. The concentrations of several metabolites were also changed in various specimens. Surprisingly, morphological changes were observed in the bile duct, including ductal epithelial proliferation, micropapillary growth of biliary epithelium, focal bile duct stricture formation and bile duct obstruction. These are characteristics of precancerous lesions of bile duct. FXR and RXRα expressions were significantly reduced, which were similar to that observed in cholangiocarcinoma in TCGA and Oncomine databases. We established a novel animal model to examine the effects of long-term use of omeprazole. The gut microbes and metabolic change are consequences of long-term PPI exposure. And the results showed the environment in vivo tends to a high-fat diet. More importantly, we observed biliary epithelial hyperplasia, which is an indicator of a high-fat diet.
While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with ...intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.
Here we demonstrate association of variants in the mitochondrial asparaginyl-tRNA synthetase NARS2 with human hearing loss and Leigh syndrome. A homozygous missense mutation (c.637G>T; p.Val213Phe) ...is the underlying cause of nonsyndromic hearing loss (DFNB94) and compound heterozygous mutations (c.969T>A; p.Tyr323* + c.1142A>G; p.Asn381Ser) result in mitochondrial respiratory chain deficiency and Leigh syndrome, which is a neurodegenerative disease characterized by symmetric, bilateral lesions in the basal ganglia, thalamus, and brain stem. The severity of the genetic lesions and their effects on NARS2 protein structure cosegregate with the phenotype. A hypothetical truncated NARS2 protein, secondary to the Leigh syndrome mutation p.Tyr323* is not detectable and p.Asn381Ser further decreases NARS2 protein levels in patient fibroblasts. p.Asn381Ser also disrupts dimerization of NARS2, while the hearing loss p.Val213Phe variant has no effect on NARS2 oligomerization. Additionally we demonstrate decreased steady-state levels of mt-tRNAAsn in fibroblasts from the Leigh syndrome patients. In these cells we show that a decrease in oxygen consumption rates (OCR) and electron transport chain (ETC) activity can be rescued by overexpression of wild type NARS2. However, overexpression of the hearing loss associated p.Val213Phe mutant protein in these fibroblasts cannot complement the OCR and ETC defects. Our findings establish lesions in NARS2 as a new cause for nonsyndromic hearing loss and Leigh syndrome.
Introducing cooperative coded caching into small cell networks is a promising approach to reducing traffic loads. By encoding content via maximum distance separable (MDS) codes, coded fragments can ...be collectively cached at small-cell base stations (SBSs) to enhance caching efficiency. However, content popularity is usually time-varying and unknown in practice. As a result, cached content is anticipated to be intelligently updated by taking into account limited caching storage and interactive impacts among SBSs. In response to these challenges, we propose a multi-agent deep reinforcement learning (DRL) framework to intelligently update cached content in dynamic environments. With the goal of minimizing long-term expected fronthaul traffic loads, we first model dynamic coded caching as a cooperative multi-agent Markov decision process. Owing to the use of MDS coding, the resulting decision-making falls into a class of constrained reinforcement learning problems with continuous decision variables. To deal with this difficulty, we custom-build a novel DRL algorithm by embedding homotopy optimization into a deep deterministic policy gradient formalism. Next, to empower the caching framework with an effective trade-off between complexity and performance, we propose centralized, and partially and fully decentralized caching controls by applying the derived DRL approach. Simulation results demonstrate the superior performance of the proposed multi-agent framework.
This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for ...their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namely fully dynamic IRS beamforming (FDBF) , partially dynamic IRS beamforming (PDBF) , and static IRS beamforming (SBF) , to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice.
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