Ultra-dense networks represent the trend for future wireless 5G networks, which can provide high transmission rates in dense urban environments. However, a massive number of small cells are required to be deployed in such networks, and this requirement increases interference and number of handovers (HOs) in heterogeneous networks (HetNets). In such scenario, mobility management becomes an important issue to guarantee seamless communication while the user moves among cells. In this paper, we propose an auto-tuning optimization (ATO) algorithm that utilizes user speed and received signal reference power to adapt HO margin and time to trigger. The proposed algorithm aims to reduce the number of frequent HOs and HO failure (HOF) ratio. The performance of the proposed algorithm is evaluated through simulation with a two-tier model that consists of 4G and 5G networks. Simulation results show that the average rates of ping-pong HOs and HOF are significantly reduced by the proposed algorithm compared with other algorithms from the literature. In addition, the ATO algorithm achieves a low call drop rate and reduces HO delay and interruption time during user mobility in HetNets.

Connectivity in recent wireless communication is accessible anywhere because of the large footprints of both cellular and WiFi networks. Yet, the broadcasting in wireless technology increases the susceptibility of signals to contemporary challenges such as interference, fading, and distortion. In addition, the energy of signals is lost because of Doppler effects and scattering caused by the obstacles in the channel. The use of smaller base stations, exploitation of higher frequencies and millimetre waves, and the expansion towards massive multiple‐input multiple‐output makes beamforming one of the most important key technologies in fifth generation (5G) systems. Besides the energy efficiency enhancements because of the narrower beamwidths, it reduces the broadcasting probability by adaptively steering the signals towards the targeted receivers while reducing the reception for nearby receiver, that is less interference. It also increases the energy efficiency of the signals because of the narrow beamwidth. In this work, the 5G technology challenges, two of the most known channel models, applications is briefly received. The fundamentals of beamforming technology are also described and provide the necessary information to understand the technology.

The Forward scattering radars (FSRs) are special types of Bistatic radars in which detected targets should exist in the narrow baseline to obtain their tracking at an angle of 180 degree. This gives the radar several features such as target classification which makes FSR more privileged in comparison to traditional radar systems. Existing research works concerning the ground target detection and classification have utilized neural network for the identification processes and compared it to other statistical models in terms of signal complexity. However, these works considered limited number of scenarios and thereby, the results are insufficient to create an automatic classification system. This study investigates and analyses the classification of ground targets in FSR using Machine-learning (ML) techniques, and proposes a hybrid model for ground target classification. The analysis in this paper represent a foundation for a potential use of pre-processing and signal processing techniques, statistical analysis, and ML in radar applications. The obtained results show that the k-nearest neighbor classifier (KNN) achieves the best performance in all examined scenarios. Additionally, combining multiple pre-processing techniques enhances the accuracy of classification by approximately 30.2% and increases the overall accuracy to more than 99%.

Software Defined Network (SDN) is one of the modern networking technologies that provide network flexibility and simplifies network management. Virtual SDN (vSDN) enhances the flexibility of sharing physical networking resources by multiple slices representing multiple tenants or services where each tenant has control over their services or applications over the Virtual Network (VN). Network virtualization gives service providers more flexibility to offer new and innovative services with extra efficiency and reliability. Running multiple virtual networks over a given infrastructure creates challenges for efficient resource allocation mechanisms to avoid congestion and resource starvation and to maintain Service Level Agreement (SLA), where resource management in vSDN is carried out by hypervisors. Few studies have addressed dynamic resource allocation in the vSDN domain. Therefore, to efficiently utilize the resources of the virtualized networking infrastructure, network hypervisors must be proactive with self-reconfiguration capabilities to assign the physical resources and be highly adaptable and react to changing vSDN future demands. Thus, dynamic learning-based hypervisors are to improve hypervisor operations. Based on that, this study aims to enhance the vSDN technology to provide an enhanced proactive dynamic slice resource allocation mechanism, to improve traffic delivery and resource utilization. This can be fulfilled by proposing an enhanced intelligent forecasting model for vSDN slice resource utilization based on improved statistical and Machine Learning (ML) techniques. The proposed model will react dynamically to the concept drifts and then be utilized to develop a resource allocation mechanism for vSDN slice resource allocation. The improved dynamic forecasting resource allocation mechanism is verified through available real network traces datasets from various sources. The DLVisor with its Dynamic Learning Framework (DLF) can reduce overutilization and, consequently, resource starvation by 100% compared to the related benchmark.

It is ascertained that the current communication systems will not be able to support the future network demands due to the increasing traffic, limitation of frequency resources, and high level of interference. Recently, beamforming techniques have been introduced to reduce the interference by redirecting the transmission towards the desired users only. While such beamforming enables better interference mitigation and improved network performance, the concerns on its effectiveness in dense deployment environments are arising. In this paper, the prospect of interference avoidance in location aware small cell environments using time division multiple access (TDMA) and beamforming is studied. The interference is reduced by identifying the aggressor small cell and transmit the beams towards the desired users at different times. Simulation results show that the proposed scheme is able to enhance the signal to interference plus noise ratio (SINR) by approximately 18 dB, enhance the user throughput by about 10 Mbps in comparison to small cell on/off control scheme with a discovery signal (SCon/off-DS), and improve the fairness index to about 95% in comparison to the baseline scheme. It is believed that the presented results promote the proposed scheme as an efficient interference management paradigm for the fifth generation (5G) communication systems.

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