Channel estimation techniques for OFDM systems based on a pilot arrangement are investigated. Channel estimation based on a comb type pilot arrangement is studied through different algorithms for both estimating the channel at pilot frequencies and interpolating the channel. Channel estimation at pilot frequencies is based on LS and LMS methods while channel interpolation is done using linear interpolation, second order interpolation, low-pass interpolation, spline cubic interpolation, and time domain interpolation. Time-domain interpolation is obtained by passing to the time domain by means of IDFT (inverse discrete Fourier transform), zero padding and going back to the frequency domain by DFT (discrete Fourier transform). In addition, channel estimation based on a block type pilot arrangement is performed by sending pilots in every sub-channel and using this estimation for a specific number of following symbols. We have also implemented a decision feedback equalizer for all sub-channels followed by periodic block-type pilots. We have compared the performances of all schemes by measuring bit error rates with 16QAM, QPSK, DQPSK and BPSK as modulation schemes, and multipath Rayleigh fading and AR based fading channels as channel models.

Advances in low-power and low-cost sensor networks have led to solutions mature enough for use in a broad range of applications varying from health monitoring to building surveillance. The development of those applications has been stimulated by the finalization of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer for sensor networks. One of the MAC schemes proposed is slotted carrier sense multiple access with collision avoidance (CSMA/CA), and this paper analyzes whether this scheme meets the design constraints of those low-power and low-cost sensor networks. The paper provides a detailed analytical evaluation of its performance in a star topology network, for uplink and acknowledged uplink traffic. Both saturated and unsaturated periodic traffic scenarios are considered. The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF only in the use of a per user Markov model to capture the state of each user at each moment in time. The key assumptions to enable this important simplification and the coupling of the per user Markov models are however different, as a result of the very different designs of the 802.15.4 and 802.11 carrier sensing mechanisms. The performance predicted by the analytical model is very close to that obtained by simulation. Throughput and energy consumption analysis is then performed by using the model for a range of scenarios. Some design guidelines are derived to set the 802.15.4 parameters as function of the network requirements.

Ensuring a seamless connection during the mobility of various User Equipment (UE) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operation, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs‘ mobility as well as future research directions on mobility management in 5G networks and beyond

With the rapid increase in the number of mobile users, wireless access technologies are evolving to provide mobile users with high data rates and support new applications that include both human and machine-type communications. Heterogeneous networks (HetNets), created by the joint installation of macro cells and a large number of densely deployed small cells, are considered an important solution to deal with the increasing network capacity demands and provide high coverage to wireless users in future fifth generation (5G) wireless networks. Due to the increasing complexity of network topology in 5G HetNets with the integration of many different base station types, in 5G architecture mobility management has many challenges. Intense deployment of small cells, along with many advantages it provides, brings important mobility management problems such as frequent handover (HO), HO failure, HO delays, ping-pong HO and high energy consumption which will result in lower user experience and heavy signal loads. In this paper, we provide a comprehensive study on the mobility management in 5G HetNet in terms of radio resource control, the initial access and registration procedure of the user equipment (UE) to the network, the paging procedure that provides the location of the UE within the network, connected mode mobility management schemes and beam level mobility and beam management. Besides, this paper addresses the challenges and suggest possible solutions for the 5G mobility management.

The channel estimation techniques for OFDM sys- tems based on pilot arrangement are investigated. The channel es- timation based on comb type pilot arrangement is studied through different algorithms for both estimating channel at pilot frequen- cies and interpolating the channel. The estimation of channel at pilot frequencies is based on LS and LMS while the channel inter- polation is done using linear interpolation, second order interpo- lation, low-pass interpolation, spline cubic interpolation, and time domain interpolation. Furthermore, the channel estimation based on block type pilot arrangement is performed by sending pilots at every sub-channel and using this estimation for a specific number of following symbols. We have also implemented decision feedback equalizer for all sub-channels followed by periodic block-type pi- lots. We have compared the performances of all schemes by mea- suring bit error rate with 16QAM, QPSK and DQPSK as mod- ulation schemes, and multi-path Rayleigh fading and AR based fading channels as channel models.

The massive growth of mobile users and the essential need for high communication service quality necessitate the deployment of ultra-dense heterogeneous networks (HetNets) consisting of macro, micro, pico and femto cells. Each cell type provides different cell coverage and distinct system capacity in HetNets. This leads to the pressing need to balance loads between cells, especially with the random distribution of users in numerous mobility directions. This paper provides a survey on the intelligent load balancing models that have been developed in HetNets, including those based on the machine learning (ML) technology. The survey provides a guideline and a roadmap for developing cost-effective, flexible and intelligent load balancing models in future HetNets. An overview of the generic problem of load balancing is also presented. The concept of load balancing is first introduced, and its purpose, functionality and evaluation criteria are then explained. Besides, a basic load balancing model and its operational procedure are described. A comprehensive literature review is then conducted, including techniques and solutions of addressing the load balancing problem. The key performance indicators (KPIs) used in the evaluation of load balancing models in HetNets are presented, along with the concurrent optimisation of coverage (CCO) and mobility robustness optimisation (MRO) relationship of load balancing. A comprehensive literature review of ML-driven load balancing solutions is specifically accomplished to show the historical development of load balancing models. Finally, the current challenges in implementing these models are explained as well as the future operational aspects of load balancing.