In elastic cloud service systems, it is a challenge to evaluate and match the fluctuating resource demand of workloads. Existing studies typically monitor workload characteristics and build models that map these characteristics to actual demand. However, workload characteristics are multidimensional, and the impact of each dimension on resource demand differs, so it requires differentiated treatment when building models. This paper proposes a Correlation-Based Universal Resource Matching (CBURM) strategy to realize a Heterogeneous and Elastic Cloud Service System (HECSS). CBURM consists of a Correlation-based resource Demand Evaluation (CDE) method and a Universal Resource Measurement (URM) scheme. Specifically, CDE discriminates the relevance of each dimension in workload characteristics, based on the correlations between workload characteristics and the demand. Then, it generates resource demand decisions dimension by dimension, from the most relevant to the least relevant dimensions. After that, it generates a complete decision tree model to evaluate subsequent workload demand for heterogeneous resources. Finally, URM optimizes the resource allocation to achieve a low-overhead resource matching. Experimental results show that, URM reduces the total comprehensive operation cost by 82%+, compared to a normal resource allocation scheme. Additionally, CDE outperforms two state-of-the-art methods (LTP and 2SP), with its performance closer to the ideal baseline. Specifically, CDE achieves a 40.275% overall resource saving rate, which is 38.62% higher than LTP and 8.46% higher than 2SP. Besides, CDE achieves a 92.43% average service quality satisfaction ratio, higher than the 82.9% and 88.83% achieved respectively by LTP and 2SP.

In this paper, we propose a novel scheme for wireless power transfer (WPT) in the radiative near-field (Fresnel) region, which based on a dynamically reconfigurable holographic metasurface aperture capable of focusing power to multiple spots simultaneously. The layout of the metamaterial elements and their tuning states is determined using holographic design principles, in which the interference pattern of reference mode and the desired radiated field pattern leads to the required phase distribution over the surface of the aperture. In contrast to the existing research studies, the proposed scheme is not only designed to achieve free multi-focuses, but also with low- dimensionality, high transmission efficiency, real- time continuous reconfigurability and so on. The transfer efficiency is also analyzed. The accuracy of the analysis is confirmed using numerical simulation.

In this paper, we address physical layer security for amplify-and-forward (AF) multi-antenna relay systems in the presence of multiple eavesdroppers. A robust joint design of cooperative beamforming (CB) and artificial noise (AN) is proposed with imperfect channel state information (CSI) of both the destination and the eavesdroppers. We aim to maximize the worst-case secrecy rate subject to the sum power and the per-antenna power constraints at the relay. Such joint design problem is non-convex. By utilizing the semidefinite relaxation (SDR) technique, S-procedure and the successive convex approximation (SCA) algorithm, the original non-convex optimization problem is recast into a series of semidefinite programs (SDPs) which can be efficiently solved using interior-methods. Simulation results are presented to verify the effectiveness of the proposed design.

This paper considers a MIMO communication system that includes a transmitter, a legitimate receiver and an eavesdropper. In such a scenario, the eavesdropper has the knowledge of the pilot and utilizes pilot contamination to reduce the secrecy rate. Firstly, we analyze the effects of malicious pilot contamination on channel estimation. The more the pilot contamination power is, the more the eavesdropping performance enhances, by deducing the secrecy rate and its asymptotic expression in the data transmission phase. Besides, the optimal power allocation scheme for a full-duplex eavesdropper to simultaneously transmit pilot contamination and noise jamming is further studied. Simulation results show that the average secrecy rate with pilot contamination is 0.899bits/s/Hz less than that with noise jamming when the jamming power is 10 mW.

In this work, a robust artificial noise (AN) aided transmit method is proposed for a multicast multiple-input single-output (MISO) system in the presence of multiple single-antenna eavesdroppers. With the imperfect covariance-based channel state information (CSI), we aim to minimize the transmit power at the transmitter under the multicast secrecy rate constraint and the maximum transmit power constraint. The resulting optimization problem involves the joint optimization of the beamforming vector and AN covariance matrix. After using the semidefinite relaxation (SDR) technique, we derive the exact reformulation of the original optimization through the Lagrange duality. Then this non-convex problem is transformed into a one-variable optimization problem, which can be handled by solving a series of semidefinite programs (SDPs). Simulation results demonstrate the effectiveness of the proposed robust transmit method.

In the wireless Ad-hoc networks, the Relays which transmit message are selfish, they need to consume their energy and compute resources to transmit message. For the problems of the message transmission interrupted by Relay selfishness and the decrease of secrecy rates, we have proposed a Relay-selection method to ensure the security of the Ad-hoc network using game theory incentive mechanism. The method introduces virtual currency, which encourages and regulates the cooperation between different nodes, and proposes the incentives mechanism of twice quotation competition based on virtual currency measurement. According to the secrecy rate and quotation, source nodes will select one Relay which makes the maximum secrecy rate utility. Simulations show that the proposed method improves the average secrecy rate per user up by 20%~32% compared with the conventional Relay selection method.

The unstable characteristic of ill-conditioned system is used to guarantee wireless security in this paper. The use of ill-conditioned theory to protect wireless transmissions is proposed as a way to secure wireless networks at physical layer. Firstly, the problem of randomized transmission is converted to that of solving ill-conditioned equation to guarantee wireless transmissions with inherent low-probability-of interception (LPI). Secondly, a conditional ill-conditioned optimization function is constructed. The function of legitimate node is stable because of its matched channel. But the function of eavesdropper is randomized because that she has not the legitimate conditions, which relate to the wireless medium contains domain-specific information. In this context, we propose a security scheme for wireless security in time-variant channels. In this scheme, the variable part of channels is converted to additional conditions, which can be solved by optimizing parameters. In contrast to conventional physical-layer security techniques, the scheme exploits the redundancy of ill-conditioned problem's solutions instead of array redundancy for deliberate signal randomization, which effectively randomizes the eavesdropper's received signals but not the authorized receiver's signals. Extensive simulations and analysis results show that our proposed scheme is useful for securing wireless transmissions.