10,884 research outputs found
Rank-Two Beamforming and Power Allocation in Multicasting Relay Networks
In this paper, we propose a novel single-group multicasting relay beamforming
scheme. We assume a source that transmits common messages via multiple
amplify-and-forward relays to multiple destinations. To increase the number of
degrees of freedom in the beamforming design, the relays process two received
signals jointly and transmit the Alamouti space-time block code over two
different beams. Furthermore, in contrast to the existing relay multicasting
scheme of the literature, we take into account the direct links from the source
to the destinations. We aim to maximize the lowest received quality-of-service
by choosing the proper relay weights and the ideal distribution of the power
resources in the network. To solve the corresponding optimization problem, we
propose an iterative algorithm which solves sequences of convex approximations
of the original non-convex optimization problem. Simulation results demonstrate
significant performance improvements of the proposed methods as compared with
the existing relay multicasting scheme of the literature and an algorithm based
on the popular semidefinite relaxation technique
Noise-Enhanced Information Systems
Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many physical, biological, and engineered systems. Indeed stochastic facilitation (SF) has been found critical for certain biological information functions such as detection of weak, subthreshold stimuli or suprathreshold signals through both experimental verification and analytical model simulations. In this paper, we present a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems. System performance is evaluated not only in terms of signal-to-noise ratio but also in terms of other more relevant metrics such as probability of error for signal detection or mean square error for parameter estimation. As an important new instance of SF, we also discuss the constructive effect of noise in associative memory recall. Potential enhancement of image processing systems via the addition of noise is discussed with important applications in biomedical image enhancement, image denoising, and classification
Software Grand Exposure: SGX Cache Attacks Are Practical
Side-channel information leakage is a known limitation of SGX. Researchers
have demonstrated that secret-dependent information can be extracted from
enclave execution through page-fault access patterns. Consequently, various
recent research efforts are actively seeking countermeasures to SGX
side-channel attacks. It is widely assumed that SGX may be vulnerable to other
side channels, such as cache access pattern monitoring, as well. However, prior
to our work, the practicality and the extent of such information leakage was
not studied.
In this paper we demonstrate that cache-based attacks are indeed a serious
threat to the confidentiality of SGX-protected programs. Our goal was to design
an attack that is hard to mitigate using known defenses, and therefore we mount
our attack without interrupting enclave execution. This approach has major
technical challenges, since the existing cache monitoring techniques experience
significant noise if the victim process is not interrupted. We designed and
implemented novel attack techniques to reduce this noise by leveraging the
capabilities of the privileged adversary. Our attacks are able to recover
confidential information from SGX enclaves, which we illustrate in two example
cases: extraction of an entire RSA-2048 key during RSA decryption, and
detection of specific human genome sequences during genomic indexing. We show
that our attacks are more effective than previous cache attacks and harder to
mitigate than previous SGX side-channel attacks
Noise Enhanced M-ary Composite Hypothesis-Testing in the Presence of Partial Prior Information
Cataloged from PDF version of article.In this correspondence, noise enhanced detection is studied for M-ary composite hypothesis-testing problems in the presence of partial prior information. Optimal additive noise is obtained according to two criteria, which assume a uniform distribution (Criterion 1) or the least-favorable distribution (Criterion 2) for the unknown priors. The statistical characterization of the optimal noise is obtained for each criterion. Specifically, it is shown that the optimal noise can be represented by a constant signal level or by a randomization of a finite number of signal levels according to Criterion 1 and Criterion 2, respectively. In addition, the cases of unknown parameter distributions under some composite hypotheses are considered, and upper bounds on the risks are obtained. Finally, a detection example is provided in order to investigate the theoretical results. © 2010 IEEE
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