866 research outputs found
Robust Beamforming for Secrecy Rate in Cooperative Cognitive Radio Multicast Communications
In this paper, we propose a cooperative approach to improve the security of
both primary and secondary systems in cognitive radio multicast communications.
During their access to the frequency spectrum licensed to the primary users,
the secondary unlicensed users assist the primary system in fortifying security
by sending a jamming noise to the eavesdroppers, while simultaneously protect
themselves from eavesdropping. The main objective of this work is to maximize
the secrecy rate of the secondary system, while adhering to all individual
primary users' secrecy rate constraints. In the case of passive eavesdroppers
and imperfect channel state information knowledge at the transceivers, the
utility function of interest is nonconcave and involved constraints are
nonconvex, and thus, the optimal solutions are troublesome. To address this
problem, we propose an iterative algorithm to arrive at a local optimum of the
considered problem. The proposed iterative algorithm is guaranteed to achieve a
Karush-Kuhn-Tucker solution.Comment: 6 pages, 4 figures, IEEE ICC 201
Joint Fractional Time Allocation and Beamforming for Downlink Multiuser MISO Systems
It is well-known that the traditional transmit beamforming at a base station
(BS) to manage interference in serving multiple users is effective only when
the number of users is less than the number of transmit antennas at the BS.
Non-orthogonal multiple access (NOMA) can improve the throughput of users with
poorer channel conditions by compromising their own privacy because other users
with better channel conditions can decode the information of users in poorer
channel state. NOMA still prefers that the number of users is less than the
number of antennas at the BS transmitter. This paper resolves such issues by
allocating separate fractional time slots for serving the users with similar
channel conditions. This enables the BS to serve more users within the time
unit while the privacy of each user is preserved. The fractional times and
beamforming vectors are jointly optimized to maximize the system's throughput.
An efficient path-following algorithm, which invokes a simple convex quadratic
program at each iteration, is proposed for the solution of this challenging
optimization problem. Numerical results confirm its versatility.Comment: IEEE Communications Letters (To Appear
Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
Directly modeling the inherent hierarchy and shared structures of human behaviors, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model\u27s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modeling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.<br /
A Deep Learning Approach to Network Intrusion Detection
Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments
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