Heterogeneous Network Scenario Research Articles

Ant colony optimization-based solution to optimize load balancing and throughput for 5G and beyond heterogeneous networks

The escalating demand for data in wireless communication systems has posed significant challenges in recent years. This trend is predicted to continue, with explosive data usage and evolving quality of service demands from mobile users. The rapid increase in traffic demand, combined with the intricate nature of heterogeneous network (HetNet) scenarios, has significantly heightened the challenges confronting mobile network operators. These challenges encompass service quality, load distribution, coverage, and the overall user experience. Conventional approaches that prioritize maximum received power in the cell association mechanism tend to sustain network imbalances within the HetNets, making it difficult to cater for the diverse traffic requirements of mobile users. In this study, instead of focusing solely on enhancing individual user downlink rates, we maximize the number of users whose downlink needs are satisfied by integrating a cell range extension (CRE) technique with an ant colony optimization algorithm. Our proposed method considers both the workload of base stations and the signal to interference-plus-noise ratio of user devices to formulate an objective function aimed at calculating specific CRE bias values for individual small base stations. A comparative analysis of the proposed approach with existing techniques demonstrates its effectiveness. Simulation results underscore the success of our proposed strategy in meeting users’ throughput needs while reducing network imbalances and call drop rates.

Energy efficient and coverage aware grey wolf optimizer based clustering process for Software-defined wireless sensor networks

Clustering has been adopted as a critical technique for reducing energy consumption in wireless sensor networks (WSNs). Furthermore, the selected group of Cluster Heads (CHs) should enhance the network’s coverage while minimizing any impact on its energy efficiency. Although distributed CHs election processes have improved the network’s lifespan, they have struggled to provide adequate coverage. Moreover, they usually use impractical assumptions like unlimited transmission range and location cognition. On the other hand, centralized meta-heuristic-based CHs selection algorithms have shown remarkable improvement in clustering problems using centralized decisions. However, software-defined wireless sensor networks (SDWSN) have gained a prevalent use in recent years. SDWSN consists of stripping control from nodes and centralizing it in an external controller. This paper introduces an energy-efficient and coverage-aware Grey wolf optimizer (GWO) based clustering process for SDWSN. Based on a novel fitness function, our proposed algorithm aims to maximize coverage, decrease blind spots, and preserve energy in the network. Our algorithm is widely evaluated on various homogeneous and heterogeneous network scenarios, with varied network sizes and base station (BS) locations, versus prominent clustering algorithms. The obtained results testify that our proposed algorithm shows better performance in energy efficiency and CHs distribution. Our algorithm improved coverage up to a mean of 31.42%, 27.72%, 18.69%, 21.12%, 13.81%, 4.14%, 3.8% and 8.31% compared to CREEP, DEEC, I-LEACH, HEESR, BRE-LEACH, PSO-ECHS, EPSO-CEO, and GWO-C, respectively, in all simulation cases.

A Novel Opportunistic Access Algorithm Based on GCN Network in Internet of Mobile Things

The Internet of Things (IoT) will be widely used in all areas of life and transportation as the 5th Generation (5G) communication technology matures and becomes commercially available. Especially in the field of railway transportation, the IoT technology can alleviate the challenge caused by insufficient wireless spectrum resources and improve the railway communication performance. However, the existing IoT is made up of a large heterogeneous network. In such a super-dense heterogeneous network scenario, how to allocate the most appropriate access point (AP) according to the needs of users has become a problem demanding prompt solution, which also brings additional challenges for Intelligent Transportation System (ITS) to develop green and efficient network communication technology. Therefore, focusing on the selection and access of heterogeneous networks in the Railway IoT, this paper studies the spatial characteristics of the intelligent spectrum situation of the internet of mobile things in the railway scenario, and establishes the opportunistic access situation of Railway IoT based on the Graph Convolutional Neural (GCN) network. Furthermore, we utilize the GCN network to mine the spatial correlation between different APs, and propose a railway communication AP decision algorithm based on GCN network combined with the traditional heterogeneous network multi-attribute decision algorithm. Our experimental results prove that the proposed algorithm can effectively reduce transmission delay and improve the throughput of the communication system.

IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

Performance of dense wireless sensor networks is often degraded due to communication interference and time synchronization issues. Existing machine learning & deep learning models that propose bioinspired & pre-emptive packet-analysis solutions for these tasks either have high complexity, or high deployment costs. Moreover, these models cannot be scaled for heterogeneous node & traffic types, which limits their applicability when applied to real-time scenarios. To overcome these issues, this text proposes design of an interference-aware routing model with time synchronization capabilities for dense wireless sensor network deployments. The network initially collects temporal clock states & packet delivery performance of different nodes on heterogeneous traffic scenarios. These traffic patterns are converted into frequency, entropy, Gabor, and Wavelet components. The converted components are used to train an ensemble set of Naïve Bayes (NB), k Nearest Neighbour (kNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. These classifiers assist in identification of optimal clock deviations and set of routing paths. These routing paths are further fine-tuned via use of a Bacterial Foraging Optimization (BFO) Model, which assists in identification of interference-aware paths. The BFO Model uses a temporal fitness function that fuses throughput, communication delay, energy levels, and packet delivery performance for different set of contextual communications. Due to which, the model is able to showcase lower end-to-end delay, higher throughput, lower energy consumption, and higher packet delivery performance when compared with existing routing methods under high density nodes & heterogeneous network scenarios. The model showcases 99% PDR, 18.3% lower delay, 19.5% higher energy efficiency and 10.4% lower delay levels when compared with existing methods.

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

Design of a Hybrid Bio-inspired Model for Improving Addressing Capabilities of IPv6 Networks

Addressing in IPv6 networks is a complex task, that involves multiple decision criterion that include base addressing, subnet addressing, location & energy aware addressing, and temporal performance aware addressing constraints. To incorporate these constraints a wide variety of models are proposed by researchers, and most them utilize location-aware addressing and do not consider multimodal parameters. Schemes that consider these parameters are either highly complex, or cannot be scaled for heterogeneous network scenarios. To overcome these limitations, this text proposes design of a novel hybrid bioinspired model that assists in improving addressing capabilities of IPv6 networks. The proposed model bee colony optimization (BCO), genetic algorithm (GA), and particle swarm optimization (PSO) in order to improve addressing quality for different network types. Initially, GA is used to stochastically assign location-specific addresses to a simulated network, which is incrementally tuned by PSO via a cognitive & social learning process. The fine-tuned addresses are further optimized via integration of BCO model, which assists in integrating energy awareness. Final addresses are initially simulated with exhaustive communication test, and then deployed to real-time networks for optimized operations. Due to which, the assigned addresses are observed to be delay & energy efficient, thereby assisting in deploying them for real-time use cases. The proposed addressing model was tested under different scaled networks, and an energy efficiency of 8.3%, with delay reduction of 6.5% was achieved when compared with various state-of-the-art methods, which assists in deploying the model for multiple scaled network scenarios.

GAN for Load Estimation and Traffic-Aware Network Selection for 5G Terminals

In the face of the user-centric access network architecture adopted by the fifth-generation (5G) mobile communication network terminals, the communication capability of terminals faces significant challenges. In this case, the combination of 5G and artificial intelligence (AI) has become a significant trend to meet the various communication needs of terminal devices. Toward the problem that the data analysis and decision making for cell load estimation are primarily accomplished on the access side of the network, terminals can only passively access the network, but cannot predict the load estimation on the access side of the network in advance and cannot make network selection decisions in real time. In this article, we propose a cell load estimation algorithm based on a generative adversarial network (GAN) for 5G mobile communication networks, which considers estimating the cell load according to the wireless information measured by the terminals. The algorithm effectively estimates the cell load at the terminal side, which reflects the intelligence of the terminals and solves the problems of low data transmission rate, high signaling cost, and time delay in the existing techniques for load estimation schemes at the network side, assisting users in making a real-time decision. The performance is evaluated through the system-level simulation, and the results indicate that the proposed model improves load estimation accuracy, simultaneously improving the network throughput and reducing the packet queuing delay, suitable for different heterogeneous network scenarios.

Optimized Fault Diagnosis Algorithm under GAN and CNN Hybrid Model

The network fault diagnosis algorithm is one of the most important algorithms to solve network transmission problems. In traditional network fault diagnosis, it mainly analyzes and troubleshoots the fault manually by comparing the alarm information of the network performance index with the expert experience database. Diagnosis methods based on manpower analysis will take up much material resources and manpower and increase maintenance costs. Therefore, there is an urgent need for a more efficient and intelligent fault diagnosis technology. In addition, as the current network is becoming more and more complex, and based on this consideration, a semi-supervised fault diagnosis algorithm is studied in this paper. It is a combination of the GAN and CNN models. Meanwhile, the method of combining relief and mutual information is applied to reducing the dimensionality of network feature parameters, and the optimal feature combination is selected. The fluctuations in the convergence of the generated confrontation network model are stabilized. Moreover, the simulation software is used to build the heterogeneous wireless network scenario studied. Meanwhile, an improved fault diagnosis model is constructed to verify that in the case of both GAN and CNN models, the accuracy of fault diagnosis algorithm can reach 98.6%, which is significantly higher than other comparative analysis methods. It has contributed to ensuring the user’s service experience and reducing the cost of network maintenance and operation.

Resource Efficient Deployment and Data Aggregation in Pervasive IoT Applications (Smart Agriculture)

Aims: Internet of Things (IoT) is the evolution of the Internet designed to sense, collect, analyze and distribute the data via IoT devices that form its core component. An important aspect of pervasive IoT applications is its resource-constrained devices. Most of the real-time Edge-IoT applications generate a huge amount of data, which add to the resource consumption in these devices. To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Objectives: To save resources in such applications, efficient node deployment and data aggregation techniques can be used. This paper presents the design and modeling of node deployment and data aggregation in Edge-IoT applications along with the homogeneous and heterogeneous network scenarios for smart agriculture. Methods: For heterogeneous scenarios, we propose a clustering approach, Superior Aggregator Resource Efficient Clustering (SAREC), to address the resource constraints in pervasive Edge-IoT applications. The comparison of homogeneous and heterogeneous networks is based on LEACH and SAREC protocols, respectively. Results: The results show that SAREC is 25% more efficient in energy utilization and network lifetime than LEACH. The results also show that SAREC is more efficient in terms of storage and processing time as compared to LEACH. Conclusion: Node deployment is an important aspect in determining the architecture, which plays an important role in resource management in pervasive applications of IoT. The IoT nodes are distributed in a selected geographical location and the topology of the network is pre-decided to form an Edge-IoT network. In such an environment, the nodes are deployed to sense, aggregate and analyze the data. This paper presents a pervasive Edge-IoT network along with the mathematical modeling consisting of deployment and aggregation models. An Edge-IoT network for smart agriculture has been deployed and analysis of resource utilization has been performed in homogeneous and heterogeneous scenarios of the network. The resource limitations in pervasive IoT network motivated us to develop a SAREC approach for such Edge-IoT applications that optimizes the use of resources. The comparison of the proposed SAREC protocol is made with respect to LEACH protocol on the basis of energy, network lifetime, number of alive nodes, storage and processing time. The results show that SAREC protocol is 25% more efficient in energy utilization and network lifetime than LEACH. It is also evident from the results that the SAREC is more efficient in terms of storage and processing time as compared to LEACH.

No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

NB-IoT is the standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can achieve IoT requirements nevertheless, it suffers a low rate and capacity. On the other hand, Unmanned aerial vehicles (UAV) and Non-Orthogonal Multiple Access (NOMA) are promising technology used to enhance the throughput, capacity, and coverage of wireless communication networks. In this paper, we propose a heterogeneous network scenario where a UAV small Base Station (UBS) is used to assist the LTE Macro Base Station (MBS) with the help of the Non-Orthogonal Multiple Access technique to solve the NB-IoT throughput and capacity issues. Matching game based no-regret learning algorithm is proposed to optimize the NB-IoT device association and using NOMA pairing at each base station to provide the maximum system total rate and capacity. Simulation results show that our proposed scheme increases the total rate of the system by 60% and the system capacity by at least 80%, compared to NOMA without UAV and the total rate and capacity of the system by 200% and 85% respectively, with OMA scheme.

CMG2Vec: A composite meta-graph based heterogeneous information network embedding approach

Heterogeneous information network embedding has been intensively studied in the past years. However, existing methods require users to manually assign meta-paths or meta-graphs in advance. Meanwhile, most of previous approaches only consider a single type of meta-path or meta-graph which is usually sparse and biased, and thus the node representations learned may be incomprehensive and inaccurate. To tackle these limitations, we proposed an extensible semantic description structure, called Composite Meta-Graph(CMG). By virtue of such a structure, users do not need to worry about selection of an appropriate meta-path or meta-graph. Rich semantic relations and rich structural contexts between nodes of different types and of different distances can be elaborated accurately according to CMG. Moreover, a CMG based heterogeneous information embedding framework, namely CMG2Vec, is also proposed. By expanding the auto-encoder into a heterogeneous network scenario, CMG2Vec can embed proximities between nodes of multiple orders learned from CMG into latent representations after a series of encoding–decoding non-linear mapping. During the fusing process, an attention mechanism is adopted to automatically learn weights of these latent vectors, which enables each final node representation to focus on proximity of the most informative order. Experimental results on three large-scale datasets demonstrate that our method outperforms existing state-of-the-art homogeneous and heterogeneous network embedding approaches in three network mining tasks in terms of node classification, node clustering, and node similarity search.

Additive Logarithmic Weighting for Balancing Video Delivery Over Heterogeneous Networks

The demand of media delivery services has increased with the popularity of social media and with the evolution of the user's devices (i.e., smartphones, laptops, and tablets) pushing towards new contents distribution models. The coexistence of go-live and on-demand media content requires a combined broadcast/unicast delivery model with the efficient management of the wireless access as a key issue. A twofold target needs to be reached: optimizing the load balance among coexisting networks and offering adequate quality of service (QoS) to users. To achieve this target for mobile video service delivery over heterogeneous networks (HetNet) scenarios, this paper proposes a solution based on an additive logarithmic weighting (ALOW) algorithm combining received signal power, network load, packet delay, user's equipment, and user's credit budget. ALOW is optimized by means of a cooperative game theory (GATH) approach. The proposed solution, named ALOWGATH (i.e., ALOW + GATH), has been tested on realistic HetNet scenarios and compared to the state of the art of the network selection and balancing algorithms. Results show an improved performance in terms of throughput, satisfaction index and overall video quality delivered, with reduced computational complexity.

Particle Swarm-Based Cell Range Expansion for Heterogeneous Mobile Networks

Traffic demand in wireless communication systems has emerged as a key issue over recent decades. An ever-increasing trend is projected for the next few years, with explosive data traffic expected to materialize and mobile users imposing new quality of service requirements. This growing traffic demand, combined with increasingly complex heterogeneous network (HetNet) scenarios, has presented ever more challenges for mobile network operators in terms of service, coverage, load balancing, and quality of service. Considering the traditional association mechanism based on maximum power received, HetNets tend to remain unbalanced, making it challenging to satisfy mobile users' traffic requirements. In this paper, instead of trying to maximize the achievable downlink rate per user, we couple the cell range expansion (CRE) technique with a particle swarm optimization (PSO) algorithm to maximize the number of users whose downlink requirements are met. The proposed scheme considers both the loads of base stations and the signal-to-interference-plus-noise ratio (SINR) of user equipment to model an objective function that seeks to compute specific CRE bias values per small base station. The proposed scheme is also compared with some classical PSO implementations. Numerical results validate the performance of the proposed schemes, which effectively fulfill users' data traffic requirements by reducing network imbalance.

Heterogeneous Energy and Traffic Aware Sleep-Awake Cluster-Based Routing Protocol for Wireless Sensor Network

Sensor nodes heterogeneity if not properly utilized could lead to uneven energy consumption and load imbalanced across the network, which degrades the performance of the network. Routing algorithms should try to achieve energy-efficiency and load-balancing among the heterogeneous nodes to prolong network lifetime. One of the solutions is by using duty-cycling in cluster-based routing such as in Sleep-awake Energy Efficient Distributed (SEED) clustering algorithm to minimize redundant transmission to achieve energy efficiency. However, this scheme suffers from idle listening problem, which lead to energy wastage across the network. Moreover, SEED cannot cope with an environment with sensor nodes with heterogeneous traffic rate. To cope with energy and traffic heterogeneity issues among sensor nodes, a traffic and energy aware routing protocol (TEAR) is proposed. TEAR avoids selecting node with low energy and high traffic rate for cluster head role to achieve load balancing. However, TEAR does not avoid redundant transmission from the sensor nodes that are in close distances. In this paper, we proposed a hybrid method called energy and traffic aware sleep-awake (ETASA) mechanism to improve energy efficiency and enhanced load balancing in heterogeneous wireless sensor network scenario. Unlike prior methods, in ETASA, the paired nodes alternate into sleep and awake mode based on node’s energy and traffic rate. Moreover, we revised the conventional TDMA scheduling in SEED by allocating one slot for group of pairs in a cluster. This is done to address idle listening problem to minimize energy consumption. The proposed method improves the cluster head selection technique that selects high energy, low traffic and nodes with high number of pairs to improve balanced energy consumption. The proposed approach is evaluated and compared against the state-of-the-art baseline protocols. The result shows that the proposed ETASA has 16% and 15% lifetime improvements against TEAR and SEED.

Uplink Low Power Based Radio Resource Management in Wireless Heterogeneous Networks

Due to the rapid rise of energy consumption and limited battery power of wireless user equipment (UE), low power communication becomes very important. Heterogeneous network deployment can significantly reduce regional power consumption. For heterogeneous network scenarios, this paper proposes an uplink low-power resource allocation scheme considering battery power limitation and user QoS requirements. Based on the assumption that each user has access to at most one base station (BS), the scheme is modeled as a mixed integer nonlinear programming (MINLP) problem. Since each user has access to one BS at most, the transmitting power matrix from the UE to the BSs (UE-to-BS matrix) is a sparse matrix. The MINLP problem can be transformed into an optimization problem searching sparse solution. In this paper, the reweighted L1 norm is introduced as a penalty function to remove the integer constraint and to regulate the sparsity of the UE-to-BS transmitting power matrix. In order to obtain the optimal resource allocation scheme, a reweighted L1 norm penalty based radio resource management (RRM) algorithm is proposed. Simulation results show that the reweighted L1 norm penalty based RRM algorithm has good convergence performance and the results are very close to optimal solutions. The proposed resource management scheme can prolong the working time of low-energy users and guarantee the QoS requirements of UE in the system.

A Three-Stage Fuzzy-Logic-Based Handover Necessity Estimation and Target Network Selection Scheme for Next Generation Heterogeneous Networks

The network performance is an imperative factor for the customers to select a mobile network operator (MNO). The customers demand seamless mobility and services with minimal packet loss and ultra-low latency from the subscribed MNO. Device-to-Device (D2D) communication is one of the key enabling solutions of fifth generation (5G), which has the potential to enhance throughput, latency, packet loss rate (PLR) performances of the network. 5G is expected to support high mobility and smaller range heterogeneous cells. This leads to frequent handovers. The unessential handovers may cause wastage of network resources. The improper network selection may prompt extreme quality degradation. In this work, a three-stage fuzzy-logic-based handover necessity estimation and target selection scheme is proposed for general heterogeneous networks. The simulation results prove that PLR, number of handovers executed and throughput performances of the proposed scheme are superior than the conventional and fuzzy-based multi-attribute decision-making (MADM) schemes. Even though this scheme is demonstrated for D2D application, it can be extended for any heterogeneous network scenarios.

A reliable, secure, and energy efficient smart grid node allocation algorithm for heterogeneous network scenarios

SummaryThe legacy power grid was becoming inefficient in the last decades, mostly due to a changed behavior in energy consumption and generation. To this aim, a novel approach, called smart grid (SG), has been introduced, enabling a smarter management of the electrical power grid, mostly based on a more efficient monitoring and management of the energy consumption and generation by exploiting the presence of sensing and actuator nodes. To properly work, the SG nodes need to interact among themselves and with the centralized monitoring system; therefore, an integrated SG communication network (SGCN) should be considered in the design. In this paper, wireless communication technologies have been considered for the SGCN due to their advantages with respect to wired connections, by focusing on heterogeneous networks (HetNets). However, because of radio resource shortage, an optimization process for their deployment should be considered. We resort in the following to a cost functions approach for optimizing the allocation of the SG node types to different Radio Access Technologies by considering both communication and energy aspects. The numerical results highlight that, thanks to the proposed allocation approach, it is possible to implement an efficient HetNet for supporting different SG node types by trading off between communication requirements and energy consumption.

Improving network resources allocation in smart cities video surveillance

Smart cities is an emerging concept that allows the usage of technology to improve the quality and efficiency of services delivered to the population of a given city, considering a vast application domain. Some applications, like smart surveillance using Internet of Things sensors demand the transmission of high amounts of traffic with strict quality of service, what may not be available at all times. One solution to deal with this traffic overload is to implement the concept of resources sharing. Considering this context, in this article, a heterogeneous network scenario is considered to implement a multi-purpose real time video surveillance application which can be applied to both smart cities and military surveillance such as borderline security. The proposed solution considers three crucial aspects: the QoS requirements of the real time video application; the cost-benefit of the spectrum allocation; and the time constraints involved in a vertical handover operation to avoid interference. The main contribution of the article are: (I) inclusion of a multilevel resources broker in the Smart Cities architecture, (II) a QoS-enabled solution for video surveillance to provide borderline security, and (III) a low overhead control mechanism to avoid interfering with the legitimate network traffic. Results obtained via simulations show that after the proposed solution is implemented delay and jitter are kept below their specific thresholds and therefore the QoS is guaranteed considering the coexistence of up to 500 IoT sensors. Nearly 70% of the traffic is accommodated either in IEEE 802.11 or IEEE 802.22 networks, which offer relatively low cost resources. These results are also compared with two state of the art related works and show that the proposed solution performs better than the related work in the analyzed scenarios. Finally, with respect to the time constraints, the implementation of the proposed solution leads to fast vertical handover, attaining times up to 46% lower than the maximum allowed handover duration.

Dynamic scheduling for wireless multicast in massive MIMO HetNet

Consider a massive multiple input and multiple output (MIMO) heterogeneous network (HetNet) scenario where the macro base station is coupled with low-power nodes (LPNs) to provide wireless multicast transmission to user equipments (UEs) within a single macro cell. The Markov decision process (MDP) model is adopted to model the dynamic scheduling problem introduced by the mobility of UEs. With asymptotic conclusion for the massive MIMO multicast transmission, we propose a MDP-based off-line scheduling problem with determined system parameters and derive a linear programming (LP) model to solve the problem. As for the system with undetermined parameters, we adopt Q-learning algorithm to solve the optimal policy. To derive the low-complexity algorithm, the state aggregation method is utilized to build an series of sub-problems to estimate the original complex MDP problem. Finally we use the MDP Toolbox in MATLAB to run simulations to test the proposed algorithm. The performance of the proposed algorithm is shown and analyzed in details.

On the Coexistence of LTE-LAA in the Unlicensed Band: Modeling and Performance Analysis.

Long term evolution (LTE) technology leveraging the unlicensed band is anticipated to provide a solution for the challenges stemming from the rapid growth of mobile wireless services, the scarcity of available licensed spectrum, and the expected significant increase in mobile data traffic. Ensuring fair operation in terms of spectrum sharing with current unlicensed spectrum incumbents is a key concern relative to the success and viability of Unlicensed LTE (U-LTE). This paper addresses the problem of modeling and evaluating the coexistence of LTE license-assisted-access in the unlicensed band. The paper presents a novel analytical model using Markov chain to accurately model the LAA listen-before-talk scheme, as specified in the final technical specification 36.213 of 3GPP release 13 and 14. Furthermore, model validation is demonstrated through numerical and simulation results comparison. Model performance evaluation is examined and contrasted with IEEE 802.11 distributed coordination function. Finally, a comprehensive coexistence performance analysis is conducted for both homogeneous and heterogeneous network scenarios and coexistence results are presented and discussed herein.