Digital signal processing algorithms and structures for adaptive line enhancing

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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

Full interference cancellation for an asymptotically full rate asynchronous cooperative four relay network

We propose the use of simple full interference cancellation (FIC) and orthogonal frequency-division multiplexing (OFDM) within a two-hop asynchronous cooperative four relay network. This approach can achieve the full available diversity and asymptotically full rate. The four relay nodes are arranged as two groups of two relay nodes with offset transmission scheduling. Therefore, the source can serially transmit data to the destination and the overall rate can approach one when the number of samples is large. However, the four-path relay scheme may suffer from inter-relay interference which is caused by the simultaneous transmission of the source and another group of relays. The FIC approach is therefore used to remove inter-relay interference; OFDM with cyclic prefix (CP) and time-reversion is applied at the source and relays respectively, in order to combat timing errors. Uncoded and coded bit error rate simulations confirm the utility of the scheme