Ultra-dense Networks Research Articles

A Review of Miscellaneous Spectrum Sensing Algorithms in 5G Ultra-dense Networks

The continual development of advanced networks within the Fifth Generation (5G) of wireless systems, and beyond, has seen the rise of multiple important research directions. These include cognitive radio (CR) and ultra-dense networks (UDNs), which are the focus of this article. The CR systems rely on an accurate assessment of the radio environment, which is provided by the spectrum sensing functionality. A review of such algorithms that are characterized by the detection of miscellaneous features of the received signal, together with their performance comparison, is presented. In addition, the application of a simple and adequate solution is assessed through its probability of detection, for a relevant UDN system model under the critical density limitation for the access point (AP) deployment

A Distributed Caching Approach for Minimizing Average Transmission Delay in Ultra-Dense Networks

Caching at the network edge is a promising approach to alleviate the backhaul congestion pressure and reduce transmission delay in ultra-dense networks (UDNs). However, the existing content caching methods do not fully explore and exploit potential caching and computation capabilities. In this paper, we propose a distributed caching approach as well as the corresponding content addressing method. Furthermore, a problem of caching optimization with the target to minimize the average transmission delay is investigated. We transform this nondifferentiable nonlinear programming problem into a linear programming problem. Then we utilize an Alternating Direction Method of Multipliers (ADMM) algorithm to solve this problem. Simulation results reveal that the proposed algorithm can converge to the global optimum and our proposed distributed caching approach reduce the average transmission delay significantly. In popular caching strategy, the average transmission delay in our proposed method decreases over 30% and 50% when compared with caching maximum distance separable (MDS) packets and caching entire files, respectively.

Performance Analysis of UL-DL Decoupled Access in Two-Tier Cellular Heterogeneous Networks

With the advent of the 5th generation mobile communication technology (5G) era, cellular networks are evolving towards highly heterogeneous and ultra-dense network structures, posing challenges for achieving high communication rates. Existing coupled access methods can no longer meet user demands. As a promising solution to this issue, decoupled uplink-downlink access has gained significant attention. This paper investigates the performance of decoupled uplink-downlink access in a two-tier cellular network model. We obtain the analytical expressions of association probabilities, distance distribution, and spectral efficiency of base stations (BSs) when femto-cell BSs are selected for uplink access and macro-cell BSs are selected for downlink access by a user device. Our findings provide insights into the selection of access patterns and decoupled methods for better efficiency and quality of service. Furthermore, our model and approach can guide the design and deployment of next-generation networks to enhance performance.

Dynamic packet duplication for reliable low latency communication under mobility in 5G NR-DC networks

The fifth generation (5G) networks and beyond are key to meeting the exponentially increasing demands of next generation services for high throughput and reliable low latency communication under various mobility scenarios. These promising features have critical gaps to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Millimeter wave (mmWave) communications is a key enabler for a significant increase in the performance of these networks. However, due to the extremely limited transmission range of mmWave frequencies, 5G network deployments are designed to have several small cells operating in the mmWave frequency range using Ultra-dense networking (UDN) techniques to provide continuous coverage. But, such deployments not only face challenges in terms of a higher number of handovers, higher latency, lower reliability, and higher interference levels but also in terms of site acquisition, logistics, unbalanced load distributions, and power requirements. Multi-connectivity can improve the performance of UDNs and provide better deployment strategies as it provides multiple simultaneous links between the User Equipment (UE) and base stations. In such systems, packet duplication can be used to meet the stringent reliability and latency requirements of modern cellular networks as data packets are duplicated and transmitted concurrently over two or more independent links. The downside to packet duplication, however, is the increased usage of resources like spectrum, power, etc. In this work, we explore and analyze different techniques that could aid in reducing radio resource utilization without sacrificing the improvements in reliability and latency observed through packet duplication. To perform this study, we develop a novel 5G deployment with new radio dual connectivity (NR-DC) and packet duplication to improve reliability. We then analyze possible enhancements in the system to improve radio resource utilization when packet duplication is implemented. In this article, we propose and evaluate this novel 5G network deployment with multi-connectivity using Simu5G network simulator for enabling future 5G systems. The proposed 5G architecture is shown to meet the requirements of next generation applications. Our simulations show that the proposed techniques improve the throughput by up to 165.72%, the latency by up to 91.22%, and the packet loss decreases to near zero compared to a single link system.

Energy‐efficient data transmissions in heterogeneous ultra‐dense networks using a hybrid metaheuristic optimization

SummaryThe heterogeneous ultra‐dense network (UDN) is a complex network environment where the small cells (SCs) are densely populated to acquire data transmission. The UDN is mainly adopted to deal with the explosive growth of mobile data and its consequential energy consumption issues. The UDN consists of mobile users, restricting the SCs from offering seamless services as the movement may disrupt the transmissions. To provide an effective solution, this paper introduces an energy‐efficient framework that enables effective data transmissions irrespective of the users' mobility. The proposed model considers the clustered SC deployment where the four‐tiered architecture is adopted. The architecture includes a macro base station (BS), microcells, picocells, and femtocells. The SCs are responsible for transferring the data received from the mobile users to the macro BS. The proposed model introduces the hybrid algorithm called the firefly oriented multiverse optimization (FF‐MVO) algorithm to identify the most optimal path for data transmission. This algorithm works iteratively to identify the optimal path to reach the macro BS for each transmission from the user. The proposed model is simulated in the network simulator 3 (NS3) platform, and the results are evaluated with the existing models. The outcomes proved that the proposed algorithm is more optimal than the other models in finding the optimal path to result in energy‐efficient transmissions. The proposed method achieved an average energy consumption of 0.24 J, an average energy efficiency of 10.965 bits/s/J and an average network throughput of 33.907 Gbps.

Energy and spectrum efficient cell switch-off with channel and power allocation in ultra-dense networks: A deep reinforcement learning approach

The high density of small cells in the Ultra-Dense Network (UDN) has increased the capacity and the coverage of Fifth Generation (5G) cellular networks. However, with increasing the number of Small Base Stations (SBSs), energy consumption rises sharply. One suggested method to reduce energy consumption is to manage the SBS On/Off switching. Moreover, due to spectrum constraints, the Power Control and Resource Allocation (PCRA) are other significant issues in UDN, which affect the Energy Efficiency (EE) and the Spectrum Efficiency (SE). Recent works in UDN have not presented the optimal SBSs On/Off switching and PCRA technique simultaneously to maximize the EE and the SE while ensuring Quality of Service (QoS) requirements of User Equipments (UE). In this paper, a distributed method based on a multi-agent Deep Q-Network (DQN) is proposed to deal with the mentioned challenges simultaneously. Therefore, each SBS can learn a policy for managing On/Off switching and downlink PCRA using two DQNs. The proposed method seeks to optimize the EE and the SE as well as guarantee the minimum required data rate of UEs. Simulation results show that the proposed method improves the EE and the SE compared to previous solutions. Furthermore, unlike previous distributed approaches that use the UEs as learning agents, the proposed method uses the SBSs as agents. Thus, the signaling overhead and computational complexity of the UEs decrease.

Beamforming design based on particle swarm algorithm in IRS-assisted communication system

5G and the next generation of wireless technologies are designed to meet the needs of vertical industrial applications like Smart Cities, Industry 4.0, and Smart Grid. These requirements necessitate increased network capacity in order to provide a ubiquitous wireless connection for a large number of devices. Many technologies, such as mm-wave (MMW) communications, massive MIMO systems, and ultra-dense networks (UDN), have been developed in recent years to achieve these goals. However, these technologies have high implementation costs or consume a lot of energy. Intelligent Reflective Surface (IRS) is a low-cost, energy-efficient meta-surface made up of several low-cost passive components. When controlled by the controller, the IRS components can reflect an occurrence signal with a predetermined phase shift for beamforming. This not only improves system performance, but it also addresses the millimeter wave penetration performance issue (MMW). In this paper, a mathematical model of a wireless communication system with IRS in the line-of-sight (LoS) case is developed, as well as a channel model between the base station, the smart reflecting surface, and the user, and a millimeter-wave large-scale fading model. It is investigated the beamforming matrix optimization problem on the base station surface and the intelligent reflecting surface.

Energy-Efficient User Association in mmWave/THz Ultra-Dense Network via Multi-Agent Deep Reinforcement Learning

As a key enabler for 5G and 6G wireless communications, millimeter-wave (mmWave) and terahertz (THz) ultra-dense network (UDN) has received a great deal of attention recently. The mmWave/THz UDN can greatly improve the capacity of cellular network by exploiting the reduced path loss of densified network and the wide range of frequency spectrum. In order to maximize the capacity gain of UDN, the associations between small base stations (SBSs) and users should be designed deliberately. However, since computational complexity and signaling overhead increase exponentially with the numbers of SBSs and users, it is very difficult to apply the conventional centralized approaches in the UDN. In this paper, we propose a decentralized user association technique based on multi-agent actor-critic (AC) to maximize the energy efficiency of UDN. In our technique, actor network determines the user association of the SBS using the local observation and critic network informs the energy-efficient user association decision to the actor network. In doing so, the DRL agent in each SBS can find out the user association decision maximizing the network energy efficiency. From the extensive simulations, we demonstrate that the proposed scheme achieves more than 50 % average energy efficiency gain over the conventional user association techniques.

Energy Efficient Clustering and Resource Allocation Strategy for Ultra-Dense Networks: A Machine Learning Framework

The ultra-dense network structure of 5G with dense femto-cells deployment, is identified as a prospective way out for the problem of growing demand for cellular services. However, optimum resource allocation among dense and random deployed femto-cell base stations (FBSs) is a challenging task since there are severe interferences. To overcome these interferences and to obtain efficient resource allocation, a two-stage cluster-based resource allocation scheme has been proposed. In the first step, an efficient dynamic clustering algorithm based on unsupervised learning is proposed which group the FBSs in an optimal number of clusters while balancing the traffic load in each cluster. Afterward, we focus on the green resource allocation problem by using a cooperative methodology. A cloud-based multi-agent reinforcement learning algorithm has been proposed in a decentralized way. The cloud server provides fast-flexible access, large storage space, and turns down operational expenditure. Further, the reinforcement model exploits a brief representation of Q-value to resolve the problem of large stateaction space, diminish the computational complexity and hasten the convergence. The proposed scheme is evaluated in real time experimental setup. The experimental results and the numerical results verify that the proposed scheme significantly improves energy efficiency with a QoS guarantee.

Machine Learning for Time-of-Arrival Estimation With 5G Signals in Indoor Positioning

Location-based service in the indoor environment is playing a crucial role in different application scenarios. The introduction of technologies such as ultra-dense network and massive multiple-input multiple-output enables fifth-generation (5G) cellular signals, as a new generation of cellular network signals, to show unique advantages in indoor positioning. This paper describes 5G reference signal structures that can be used for navigation. A high-precision time-of-arrival estimation method based on 5G downlink signal is proposed that can be realized by edge computing. A software-defined receiver (SDR) based on machine learning to extract navigation observations from 5G signals is then developed. In simulation, the error sources of SDR in additive white gaussian noise channel and multipath channel were analyzed, and the possible ranging accuracy achieved by 5G signals in the developed SDR was evaluated. In field experiments, commercial 5G signals deployed by operators were collected, and the performance of SDR in practical applications was evaluated. The feasibility in practical applications of the proposed SDR is demonstrated, and high pseudorange measurement accuracy can be achieved.

A Novel Framework of Federated and Distributed Machine Learning for Resource Provisioning in 5G and Beyond Using Mobile-Edge SCBS

The future needs of the telecommunication system lie in deploying a heterogeneous ultra-dense network with varied topographical use cases. However, this increase in ultra-denseness in 5g and beyond poses several challenges in resource allocation, requiring an accurate learning-based prediction. This paper proposes a novel framework using Federated Learning (FL) and Distributed Machine Learning (DML) for Mobile Edge based resource provisioning to User Equipment (UEs). This work formulates the correlation-based novel procedures between UEs in applying Federated and Distributed Machine Learning through Kolmogorov tests for predicting SNR. The correlations of the distribution obtained through the Kolmogorov test check the extent of Independent and Identically Distributed (IID) -ness between modelled data and evaluate the global model for resource provisioning accuracy. Further, correlation-based DML is also employed to balance the computational load of a mobile edge, which acts as a small cell base station and a computational node. In this approach, we account for correlation-based resource predictive model training to balance the uniform computational load by data distribution methods among the neighbouring mobile Edge SCBS nodes for computation. Together with both DML and FL, we create a novel Framework for resource prediction with minimal time for achieving high accuracy without over-fitting.

Heterogeneous Ultradense Networks With Traffic Hotspots: A Unified Handover Analysis

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.

Algorithm for Topology Search Using Dilution of Precision Criterion in Ultra-Dense Network Positioning Service Area

User equipment (UE) location estimation in emerging 5G/B5G/6G Ultra-Dense Networks (UDNs) is a breakthrough technology in future wireless info-communication ecosystems. Apart from communication aspects, network infrastructure densification promises significant improvement in UE positioning accuracy. Unlike networks of previous generations, an increased number of gNodeBs (gNBs) per unit area and/or volume in UDNs allows to perform measurements for UE positioning only with those base stations whose topologies are most suitable from the geometric point of view. Quantitative measurements of gNB topology suitability include horizontal (HDOP), vertical (VDOP), and position (PDOP) dilution of the precision (DOP) criteria on the plane, in height, and in space, respectively. In the current work, we formalize a set of methods for gNB topology search using time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and combined TOA–AOA and TDOA-AOA measurements. The background of the topology search using DOP criteria is a significantly increased number of gNBs per unit volume in UDNs. Based on a simulation, we propose a novel approach for a topology search in a positioning service area, resulting in a PDOP less than one for the Gazprom Arena with only five gNBs. The contribution of the current research includes algorithm and software for an iterative search of all possible gNB and UE locations in space, minimizing UE geometric DOP. The practical application of the algorithm is the gNB topology substantiation for the given positioning scenarios in 5G/B5G/6G UDNs.

Low complexity joint spectrum resource and power allocation for ultra dense networks

In this paper, we propose a low complexity spectrum resource allocation scheme cross the access points (APs) for the ultra dense networks (UDNs), in which all the APs are divided into several AP groups (APGs) and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells. Furthermore, we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall throughput. The problem is formulated as a mixed-integer nonconvex optimization (MINCP) problem which is difficult to solve in general. The joint optimization problem is decomposed into two subproblems in terms of the spectrum allocation and power allocation respectively. For the spectrum allocation, we model it as a auction problem and a combinatorial auction approach is proposed to tackle it. In addition, the DC programming method is adopted to optimize the power allocation subproblem. To decrease the signaling and computational overhead, we propose a distributed algorithm based on the Lagrangian dual method. Simulation results illustrate that the proposed algorithm can effectively improve the system throughput.

Joint Clustering and Resource Allocation Optimization in Ultra-Dense Networks with Multiple Drones as Small Cells Using Game Theory.

In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE.

Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL-Based NORA Scheme

Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design problem, or only resolve it in the single-cell scenario. However, this problem in multi-cell scenario is more complex and difficult to solve due to the presence of inter-cell interference. Towards this end, we propose a Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications (HTC) and MTC for 5G and beyond UDN, where the power level pool design problem in multi-cell scenario is our focus. In order to deal with the complexity caused by multiple optimization objectives and inter-cell interference, we present a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem, where each small base station acts as an agent to learn a suitable gap between adjacent power levels. Extensive numerical comparisons demonstrate the superior performances of our proposed scheme in multiple perspectives.

Preface

With great, successful, and prestigious history of ICETAC, the 2022 3rd International Conference on Electrical Technology and Automatic Control (ICETAC 2022) not only sets a solid foundation but also has the potential to be even greater than ever before. The conference took place in Macau, China during December 2nd-4th, 2022 in the form of virtual conference.ICETAC 2022 provided an international cooperation and exchange platform for experts and scholars in related fields, and promoted the scientific and academic information interchange among all the participants and the industrialization cooperation of academic achievements in Electrical Technology and Automatic Control. It is not only an exceptional academic conference, but also a great platform to discover new perspective, and inspire new thinking.During the conference, we had about 100 participations both as speakers and participants, including researchers, industrial experts, professionals, etc. To make this conference on schedule, we had a three-part plenary including a keynote speech delivered by three resources persons from different parts of China and other countries. All the keynote speakers have delivered expertise lectures by imparting the latest findings from their respective research domains. Among them, Assoc. Prof. Lisu Yu from Nanchang University, China addressed a keynote speech on A Novel Multiple Access Scheme for Visible Light Communication Networks in 6G. He provided an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity and proposed to apply SCMA to a massively distributed access system whose architecture is based on fiber-based visible light communication, ultra-dense networks, and NOMA. Under this framework, he considered the interactions between optical fronthauls and wireless access links, and he also outlined a number of promising directions associated with SCMA. It was a wonderful opportunity for all the participants to interact with the experts and specialists to get their advice or suggestions.The proceedings of ICETAC 2022 contain quantities of excellent papers grouped by topics. All submissions were performed double blind review to check their quality of content, level of innovation, significance, originality and legibility, covering but not limited to the following topics: Electric Vehicle Technology, Intelligent Control System, Automation and Instrumentation, Motors and Electrical Appliances, Fault Detection, etc.We would like to express our gratitude to all the participants for their fruitful work and excellent contribution to ICETAC 2022. Especially, we appreciate the enormous work of the editorial board and reviewers in the preparation of this volume. Also, our special thanks and appreciation go to the Journal of Physics: Conference Series. Hopefully, all participants and other interested readers can benefit scientifically from the proceedings and find it rewarding in the process.The Committee of ICETAC 2022List of Committee member is available in this Pdf.

XAuth: Secure and Privacy-Preserving Cross-Domain Handover Authentication for 5G HetNets

5G networks are highly heterogeneous, with ultradense base stations (BSs), due to the low penetration of millimeter waves and the availability of different access technologies. However, the continuous heterogeneity and densification of 5G networks pose great challenges to network security, especially for user mobility support. In the process of user handover between BSs or between different network domains, user access authentication and security session establishment are far riskier compared to 4G networks. On one hand, the overhead of handover authentication increases significantly as handovers become more frequent in an ultradense network. On the other hand, the differentiation of security schemes in heterogeneous networks poses a big challenge to handover authentication. Successfully designing a secure, privacy-preserving, and efficient handover authentication protocol for heterogeneous and ultra-dense 5G networks would substantially expand the prospects of future 5G network applications. Although numerous solutions (e.g., challengeresponse-based, public key cryptography-based, physical layer information-based, and blockchain-based solutions) have been proposed to solve the cross-domain handover authentication problem, most of them surfer from security and privacy vulnerabilities and unreasonable performance overhead. In this paper, we propose XAuth, a secure and privacy-preserving authentication protocol for both intra-domain and inter-domain handover in 5G Heterogeneous Networks (HetNets) based on blockchain. The proposed protocol can achieve mutual authentication, key agreement between User Equipment (UE) and target network, and is characterized by forward secrecy, backward secrecy, user anonymity, and conditional privacy preservation. Formal security analysis and comprehensive performance evaluation demonstrate the security and effectiveness of the proposed protocol.

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

SummaryThe ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s.

A mobility aware handover scheme for 4G LTE system

Reduction in cell size in an ultra-dense network faces the challenge of increase in frequency of handovers and signaling overhead (mobility management) as well as inter-cell interference. Such frequent handovers leads to increased packet loss and blocking rate if the handover latency is very high, which have adverse effect on connectivity and performance. However, most existing schemes have the common characteristics of increasing spectral and energy efficiency due to increase in the number of micro cell on a network, thus decreases the general network performance. In this study, an improved scheme was developed for fixed mobility management pattern (FMP). A comparison module was established and an identifier algorithm that uses the user equipment international mobile equipment identity (IMEI) as a decisive component of its cell selection camping process was designed. Network simulator (NS-3) was used to study the impact of macro cell and inter site distance (ISD) on user throughput and battery life usage using mobility pattern of 3km/h, with A3 event based measurement for ISD 50, 150, 300, 500, and 1000 respectively. The performance evaluation metrics used are; reference signal received power (RSRP), flow monitor and handover time. The FMP results were compared with micro cell performance in an ultra-dense network. The RSRP result from FMP was higher than that of micro cell at ISD 50 and 150 respectively (-71.36, -73.97 and 83.77, 85.13). The handover time for FMP was higher than that of micro cell except for ISD 50 which was 50s and 51s respectively. The flow monitor of FMP showed a packet loss ratio of 0.0001 compare to 0.21 of micro cell. The battery life usage shows that FMP used 5943±8.7mw compare to micro cell which used 1680.2±15.7 mw. These results showed that FMP is efficient in managing the frequency of handover and battery life consumption for better connectivity and performance.