Simultaneous channel accesses from mass machine-type communications (MTC) devices may congest the random-access channels (RACHs) of LTE-A networks. Currently, 3GPP selects extended access barring (EAB) mechanism as the baseline solution to relieve the congestion of RACHs by barring low-priority devices. Different settings of EAB parameters may re-shape the arrival process of MTC traffic and thus, lead to unpredictable performance. This paper presents an analytical model to investigate the performance of the EAB algorithm on the RACHs in LTE-A networks. Computer simulations were conducted to verify the accuracy of the analysis. The optimal values of paging cycle and repetition period of system information block type 14 (SIB14) can then be obtained from the analytical model subject to a target quality-of-service (QoS) constraint.

Recommending music based on a user’s music preference is a way to improve user listening experience. Finding the correlation between the user data (e.g., location, time of the day, music listening history, emotion, etc.) and the music is a challenging task. In this paper, we propose an emotion-aware personalized music recommendation system (EPMRS) to extract the correlation between the user data and the music. To achieve this correlation, we combine the outputs of two approaches: the deep convolutional neural networks (DCNN) approach and the weighted feature extraction (WFE) approach. The DCNN approach is used to extract the latent features from music data (e.g., audio signals and corresponding metadata) for classification. In the WFE approach, we generate the implicit user rating for music to extract the correlation between the user data and the music data. In the WFE approach, we use the term-frequency and inverse document frequency (TF-IDF) approach to generate the implicit user ratings for the music. Later, the EPMRS recommends songs to the user based on calculated implicit user rating for the music. We use the million songs dataset (MSD) to train the EPMRS. For performance comparison, we take the content similarity music recommendation system (CSMRS) as well as the personalized music recommendation system based on electroencephalography feedback (PMRSE) as the baseline systems. Experimental results show that the EPMRS produces better accuracy of music recommendations than the CSMRS and the PMRSE. Moreover, we build the Android and iOS APPs to get realistic data of user experience on the EPMRS. The collected feedback from anonymous users also show that the EPMRS sufficiently reflect their preference on music.

The network simulator 2 (ns-2) is a popular and powerful simulation tool for the simulation of packet-switched networks, which provides substantial support for simulation of TCP, routing, and MAC protocols over wired and wireless networks, such as wireless LANs, mobile ad hoc networks (MANETs), and satellite communications, etc, and is widely used in both academia and industry. Although many protocol modules have been implemented in the ns-2, the IEEE 802.16 broadband wireless access networks (BWANs) or WiMAX module has not been contributed yet. Thus, in this paper, we present our detailed design and implementation of the WiMAX module based on the IEEE 802.16 standard with the point-to-multipoint (PMP) mode for the ns-2. The implemented module comprises fundamental functions of the service-specific convergence sublayer (CS), the MAC common part sublayer (CPS), and the PHY layer. A simple call admission control (CAC) mechanism and the scheduler are also included in this module.

The integration of SDN into legacy networks causes both operational and deployment issues. In this context, this paper proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in Hybrid SDN-based Fog Computing IoT Systems (IHSF). The proposed IHSF approach has three solutions: (i) a novel algorithm to deploy SDN switches between legacy switches to improve network observability. (ii) A K-Nearest Neighbor Regression algorithm to predict in real-time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance. (iii) A Reliable and Time-sensitive Deep Deterministic Policy Gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio.

In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user. The log file contains the history of all users who use the PMRS. The PMRS extracts the user's history from the log file and recommends music under each genre. We use the million song dataset (MSD) to evaluate the PMRS. To show the working of the PMRS, we developed a mobile application (an Android version). We used the confidence score metrics for different music genre to check the performance of the PMRS.

Group paging is one of the solutions proposed to deal with the radio access network (RAN) overload problem resulted from bursty machine-type communications (MTC) traffic in long-term evolution-advanced (LTE-A) networks. In group paging, the base station normally reserves a fixed amount of random access opportunities (RAOs) for the grouped users to perform random access (RA) during a paging access interval. However, the number of contending users is quickly decreased and thus, static allocation of RAOs is not efficient. This paper presents a dynamic resource allocation (DRA) scheme which dynamically adjusts the reserved RAOs for group paging based on the estimated number of contending users in each RA slot. Simulation results demonstrate that, compared with the traditional static allocation scheme, the proposed DRA scheme can improve the utilization of RAOs by 9% under a target access success probability constraint of 90%.