368 research outputs found
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
A look into the information your smartphone leaks
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Some smartphone applications (apps) pose a risk to users’ personal information. Events of apps leaking information stored in smartphones illustrate the danger that they present. In this paper, we investigate the amount of personal information leaked during the installation and use of apps when accessing the Internet. We have opted for the implementation of a Man-in-the-Middle proxy to intercept the network traffic generated by 20 popular free apps installed on different smartphones of distinctive vendors. This work describes the technical considerations and requirements for the deployment of the monitoring WiFi network employed during the conducted experiments. The presented results show that numerous mobile and personal unique identifiers, along with personal information are leaked by several of the evaluated apps, commonly during the installation process
Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections
Random partial update sum-squared autocorrelation minimization algorithm for channel shortening (RPUSAM).
Partial updating is an effective method for
reducing computational complexity in adaptive filter implementations.
In this work, a novel random partial update
sum-squared auto-correlation minimization (RPUSAM)
algorithm is proposed. This algorithm has low computational
complexity whilst achieving improved convergence
performance, in terms of achievable bit rate, over a
partial update sum-squared auto-correlation minimization
(PUSAM) algorithm with a deterministic coefficient update
strategy. The performance advantage of the RPUSAM
algorithm is shown on eight different carrier serving area
test loops (CSA) channels and comparisons are made with
the original SAM and the PUSAM algorithms
Closed-loop extended orthogonal space time block coding for four relay nodes under imperfect synchronization
In future collaborative wireless communication systems with high
data rate, interference cancellation is likely to be required in cooperative
networks at the symbol level to mitigate synchronization
errors. In this paper, we therefore examine closed-loop extended
orthogonal space time block coding (CL EO-STBC) for four relay
nodes and apply parallel interference cancellation (PIC) detection
scheme to mitigate the impact of imperfect synchronization. Simulation
results illustrate that the closed-loop EO-STBC scheme under
imperfect synchronization can achieve good performance with
simple linear processing and outperform previous methods. Moreover,
a PIC scheme is shown to be very effective in mitigating impact
of imperfect synchronization with low structural and computational
complexity
Adaptive partial update channel shortening in impulsive noise environments
Partial updating is an effective method for reducing computational complexity in adaptive filter implementations. In this paper adaptive partial update channel shortening algorithms in impulsive noise environments are proposed. These algorithms are based on updating a portion of the coefficients at each time sample instead of the entire set of coefficients. These algorithms have low computational complexity whilst retaining essentially identical performance to the sum-absolute autocorrelation minimization (SAAM) algorithm due to Nawaz and chambers. Simulation studies show the ability of the deterministic partial update SAAM (DPUSAAM) algorithm and the Random Partial Update SAAM (RPUSAAM)algorithm to achieve channel shortening and hence an acceptable level of bitrate within a multicarrier system
Heuristic pattern correction scheme using adaptively trained generalized regression neural networks
In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studie
A modified underdetermined blind source separation algorithm using competitive learning
The problem of underdetermined blind source separation
is addressed. An advanced classification method
based upon competitive learning is proposed for automatically
determining the number of active sources
over the observation. Its introduction in underdetermined
blind source separation successfully overcomes
the drawback of an existing method, in which the goal
of separating more sources than the number of available
mixtures is achieved by exploiting the sparsity of
the non-stationary sources in the time-frequency domain.
Simulation studies are presented to support the
proposed approach
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