The pilot spoofing attack is one kind of active eavesdropping conducted by a malicious user during the channel estimation phase of the legitimate transmission. In this attack, an intelligent adversary spoofs the transmitter on the estimation of channel state information (CSI) by sending the identical pilot signal as the legitimate receiver, in order to obtain a larger information rate in the data transmission phase. The pilot spoofing attack could also drastically weaken the strength of the received signal at the legitimate receiver if the adversary utilizes large enough power. Motivated by the serious problems the pilot spoofing attack could cause, we propose an efficient detector, named energy ratio detector (ERD), by exploring the asymmetry of received signal power levels at the transmitter and the legitimate receiver when there exists a pilot spoofing attack. Our analysis shows that by setting the ratio of received signal power levels at the transmitter and the legitimate receiver as the test statistic, the detecting threshold is derived without using the knowledge of the CSI of the legitimate channel as well as the illegitimate channel. Furthermore, we study the performance of the proposed ERD in various special cases in order to obtain useful insights. Numerical results are presented to further demonstrate the performance of our proposed ERD.Index Terms-Physical layer security, pilot spoofing attack, energy ratio detector, active eavesdropping.

Non-orthogonal multiple access (NoMA) as an efficient way of radio resource sharing can root back to the network information theory. For generations of wireless communication systems design, orthogonal multiple access (OMA) schemes in time, frequency, or code domain have been the main choices due to the limited processing capability in the transceiver hardware, as well as the modest traffic demands in both latency and connectivity. However, for the next generation radio systems, given its vision to connect everything and the much evolved hardware capability, NoMA has been identified as a promising technology to help achieve all the targets in system capacity, user connectivity, and service latency. This article will provide a systematic overview of the state-of-the-art design of the NoMA transmission based on a unified transceiver design framework, the related standardization progress, and some promising use cases in future cellular networks, based on which the interested researchers can get a quick start in this area.

Exploiting earth-abundant electrocatalysts with comparable high performance and stability to the benchmarking noble metal-based catalysts for oxygen evolution reaction (OER) is of fundamental importance for promising sustainable energy conversion and storage technologies. Herein, we report an in situ grown zinc doped cobalt–iron layered double hydroxide (ZnFeCo LDH) with a unique needle-like nanostructure and partial amorphous phase for highly efficient OER catalysts. Benefitting from the nanoneedle arrays structure, partial amorphous phase, tunable zinc doping, and surface trivalent cobalt ions, partly amorphous Zn doped FeCo LDH 1D nanoneedle arrays (PA-ZnFeCo LDH) exhibited superior electrocatalytic OER activity, with a small Tafel slope of 58.73 mV per decade, an exceptional overpotential of 221, 276, and 294 mV to drive 10, 100, and 300 mA cm–2, respectively, and long-term electrochemical stability of 100 000 s. This work offers insights into the rational design and synthesis of unique 1D non-noble metal hydroxide with partial amorphous phase as highly efficient OER electrocatalysis.

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Li-O 2 batteries with ultrahigh theoretical energy densities usually suffer from lowpractical discharge capacities and inferior cycling stability owingt ot he cathode passivation caused by insulating dischargeproducts and by-products.Here, at rifunctional ether-based redox mediator,2 ,5-di-tert-butyl-1,4-dimethoxybenzene (DBDMB), is introduced into the electrolyte to capture reactive O 2 À and alleviate the rigorous oxidative environment of Li-O 2 batteries.Thanks to the strong solvation effect of DBDMB towards Li + and O 2 À ,i tn ot only reduces the formation of by-products (a high Li 2 O 2 yield of 96.6 %), but also promotes the solution growth of large-sized Li 2 O 2 particles,a voiding the passivation of cathode as well as enabling al arge dischargec apacity.M oreover,D BDMB makes the oxidization of Li 2 O 2 and the decomposition of main by-products (Li 2 CO 3 and LiOH) proceed in ah ighly effective manner,p rolonging the stability of Li-O 2 batteries (243 cycles at 1000 mAh g À1 and 1000 mA g À1).

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The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i.e., app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. To efficiently classify nodes (e.g., apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.