Long Term Evolution-Advanced Research Articles

Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems

In order to enable low-latency computation-intensive applications for mobile user equipments (UEs), computation offloading becomes critical necessary. We tackle the computation offloading problem in a mixed fog and cloud computing system, which is composed of an long term evolution-advanced (LTE-A) small-cell based fog node, a powerful cloud center, and a group of UEs. The optimization problem is formulated into a mixed-integer non-linear programming problem, and through a joint optimization of offloading decision making, computation resource allocation, resource block (RB) assignment, and power distribution, the maximum delay among all the UEs is minimized. Due to its mixed combinatory, we propose a low-complexity iterative suboptimal algorithm called BTFA based joint computation offloading and resource allocation algorithm (FAJORA) to solve it. In FAJORA, first, offloading decisions are obtained via binary tailored fireworks algorithm; then computation resources are allocated by bisection algorithm. Limited by the uplink LTE-A constraints, we allocate feasible RB patterns instead of RBs, and then distribute transmit power among the RBs of each pattern, where Lagrangian dual decomposition is adopted. Since one UE may be allocated with multiple feasible patterns, we propose a novel heuristic algorithm for each UE to extract the optimal pattern from its allocated patterns. Simulation results verify the convergence of the proposed iterative algorithms, and exhibit significant performance gains could be obtained compared with other algorithms.

Orchestrating Resource Management in LTE-Unlicensed Systems With Backhaul Link Constraints

Long term evolution (LTE)-unlicensed, an extension of LTE Advanced to unlicensed spectrum, can provide high performance and seamless user experience. To reap the full benefits of the LTE-unlicensed deployment, efficient resource allocation and interference management are critical to ensuring a harmonious coexistence between LTE-unlicensed and WiFi systems. In this paper, we study a resource orchestration scheme for an LTE-unlicensed network where small cells share the same unlicensed spectrum with a WiFi system. An optimization problem for channel and power allocations is formulated to maximize the overall network utility, which is an NP-hard problem. The problem is constrained on meeting the desired data rate demands of the served small-cell users, the capacity-limited backhaul links, and the maximum tolerable interference at the WiFi access point. To solve this challenging problem, a distributed solution based on Lagrangian relaxation is proposed to assist the LTE-unlicensed network in making decisions on channel allocation and transmit power. Furthermore, low-complexity solutions are devised upon applying the one-to-one matching game theory. The simulation results with practical parameter settings show that the proposed algorithms converge to the suboptimal solution after a small number of iterations in the considered examples.

Robust matching algorithms for carrier aggregated heterogeneous networks

In order to address the increasing demands for high data rates in wireless communications, carrier aggregation (CA) is introduced in Long Term Evolution-Advanced (LTE-A) Release-10 for HetNets, a platform for the implementation of features with new functionalities. One of the major challenges in CA HetNets is to find an appropriate resource allocation algorithm for various nodes with different data rate requirements. Stable matching (SM) algorithm is one of the most popular allocation algorithm in the last decade. In this study, an important variation of the many-to-one SM (MSM) algorithm is proposed as partial feedback SM (PFM) algorithm, to achieve fair and stable subcarrier allocation by considering rate requirements in a HetNet. The stability concerns for CA HetNets are investigated for the proposed variation of the MSM algorithm in order to determine the rate satisfaction of both user equipments and the entire HetNet. The rate and fairness performances of the proposed algorithm are also compared with the optimal rate algorithm (ORA), which achieves the maximum rate, and with the proportional fair (PF) algorithm, which is widely used for fair resource allocation problems. Computer simulations show that the proposed variations of SM algorithms are very robust even with partial channel state information (CSI) in terms of rate and fairness.

A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks

Coordinated multi-point (CoMP) transmission is one of the key features for long term evolution advanced (LTE-A) and a promising concept for interference mitigation in 5th generation (5G) and beyond future densely deployed wireless networks. Due to the cost of coordination among many transmission points (TP), radio access network (RAN) needs to be clustered into smaller groups of TPs for coordination. In this paper, we develop a novel, load-aware clustering model by employing a merge/split concept from coalitional game theory. A load-aware utility function is introduced to maximize both spectral efficiency (SE) and load balancing (LB) objectives. We show that proposed load-aware clustering model dynamically adapts into the network load conditions providing high SE in low-load conditions and results in better load distribution with significantly less unsatisfied users in over-load conditions while keeping SE at comparable levels when compared to a greedy clustering model. Simulation results show that the proposed solution can reduce the number of unsatisfied users due to over-load conditions by 68.5% when compared to the greedy clustering algorithm. Furthermore, we analyze the stability of the proposed solution and prove that it converges to a stable partition in both homogeneous network (HN) and random network (RN) with and without hotspot scenarios. In addition, we show the convergence of our algorithm into the unique clustering solution with the best payoff possible when such a solution exists.

A Novel Fractional Frequency Reuse Scheme for Interference Management in LTE-A HetNets

The Heterogeneous Network (HetNet) has emerged as one of the most promising developments toward achieving the target of the Long Term Evolution-Advanced (LTE-A) systems. However, Co-Channel Interference (CCI) is one of the critical challenges of HetNet, that degrades the overall performance of a system. Therefore, an appropriate Radio Resource Management (RRM) mechanism is required to deploy and expand the HetNets properly. In this paper, a new RRM strategy called Fractional Frequency Reuse with Three Sectors and Three Layers (FFR-3SL) technique is proposed. The FFR-3SL efficiently utilizes the radio resources and alleviates the effects of CCI in LTE-A HetNets and thereby improving the system performance. In order to implement the proposed strategy, the entire macrocell coverage area is segmented into three sectors and three layers, while the total bandwidth is divided into seven subbands. Subsequently, the subbands are accurately distributed among femtocells and macrocells by employing the proposed algorithm. As a result, the co-tier and cross-tier interferences are managed on a prior basis. The Monte Carlo simulation is performed to evaluate and compare the performance of the proposed method with the existing methods. The simulation results show that the proposed method achieves higher throughput and better capacity in LTE-A HetNet system. Furthermore, the efficiency of the system is improved with regard to user satisfaction in terms of signal to interference and noise ratio (SINR) values.

Patient-Centric Cellular Networks Optimization Using Big Data Analytics

Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body-connected medical IoT sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.

A compendious study of device-to-device communication underlaying cellular networks

Device-to-device (D2D) communication is recognised as an emerging technology used in the cellular networks with many advantages such as peer-to-peer (P2P) services, higher spectral efficiency, enhanced throughput and higher data transmission capacity for the fourth generation (4G) and beyond cellular networks. To attain the rising demands of the cellular users, there is always a need to enhance the network transmission capacity of cellular networks. In Long-Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, the study of D2D communications is being interested in academia, industry person and standardisation bodies. In near future, there will be billions of connected user equipment. The next generation networks (NGNs) that to be heterogeneous in nature and demanding higher data rates, lesser delays, enhanced system transmission capacity and better throughput with large coverage. As accessible licensed spectrum assets are limited and for the survival of mobile network operators, it is required to facilitate users as per their rising demands. This survey paper explores the detailed literature of D2D communication for cellular network including their classification based on spectrum used, comparison with other short-distance wireless communication technologies, mobile data traffic growth prediction, advantage and disadvantages, applications and the open issues with work done so far.

Multiplying Channel Capacity: Aggregation of Fragmented Spectral Resources

The explosive growth of mobile Internet-based applications and ecosystems has been driving insatiable demands for data transmission speed. From third generation to fourth generation, the typical data rate of cellular networks has been significantly enhanced from a megabits-per-second to almost a gigabits-per-second level [1]. According to Shannon?s communication theory, an increase of frequency bandwidth plays the key role in expanding channel capacity. However, the highly scattered allocation of long-term evolution (LTE) bands does not support contiguous expansion of bandwidth and acts as the major barrier to further channel capacity enhancement. Therefore, the LTE Advanced standard proposed the concept of carrier aggregation (CA) for the first time, as illustrated in Figure 1(a) [2]. This enables concurrent operation of multiple spectral resources such that the channel bandwidth can be effectively multiplied without increasing the signal-to-noise ratio across the entire channel [3].

Appropriate Resource Allocation for Effective Mobile Communication in 4G LTE- Advanced

Objective: Neighboring cells become progressively worse especially for cell edge users due to Inter Cell Interference (ICI) in Orthogonal Frequency Division Multiple Access (OFDMA). Frequency Reuse Factor is to mitigate ICI by proper allocation of resources to users in network layout. Methods/Statistical Analysis: This study presents a self-organized resource allocation scheme for automatically selecting the reuse factor based on number of cell edge users and cell center user. Findings: This study explains about the new scheduling metric based on transmitted energy per bit and this provides the resources to users which are having low ration of transmitted energy per bit to avoid the wastage of resources. Applications/Improvements: Due to consideration of different reuse factor and also scheduling metric, this algorithm provides better performance for edge users in terms of Energy cell efficiency, Signal to Noise Interference Cell Ratio (SINR) and Fairness Index. Keywords: Center Users, Edge Users, 4G LTE Advanced, Round Robin (RR), Maximum Carrier to Interference Ratio (MCI)

Self-Organisation Network (SON) Dengan Mekanisme Load Balancing

Load balancing is one of the mechanism used in the self-organization network (SON) to balance the traffic at the overloaded base station with the adjacent low-loaded base station. Load balancing is done by adjusting the handover parameters (metric) to obtain the optimal traffic balance. In this work, the adjusted parameters are the capacity of the cell. Cell capacity is strongly influenced by the bandwidth, modulation type, and the bit rate used by the user. The performance of load balancing was tested by a simulation and network test-bed measurement. The testing results on the Long-Term Evolution Advanced network showed the greater the bandwidth the greater the capacity of the cell. Moreover, the larger type of modulation, the cell capacity will also be greater. On the other hand, the greater bit rate used by the user, then the cell capacity will decrease. The calculation analysis of cell capacity is taken as the basic operation for load balancing procedure. A load balancing process algorithm is introduced to describe the mentioned procedure. The algorithm also considers the ping-pong effect that might occur due to the delay on the handover process.

PSEH: A provably secure and efficient handover AKA protocol in LTE/LTE-A network

In order to accomplish the increasing security demands of handover-based applications such as mobile multimedia services and transportation system, various handover Authentication and Key Agreement (AKA) protocols were proposed by the researchers in Long Term Evolution-Advanced (LTE-A) network. However, these AKA protocols fail to preserve the privacy of communication entities and suffer from numerous attacks. In addition, these protocols incur high network overhead which doesn’t suit for the resource-constrained mobile devices in LTE/LTE-A network. Also, the protocols can’t establish the key forward/backward secrecy (KFS/KBS) and overcome the key escrow problem. To avoid the above-mentioned security issues, we propose the provably secure and efficient handover (PSEH)-AKA protocol based on double-trapdoor chameleon hash function. The PSEH-AKA protocol is proven secure under random oracle model to obtain the mutual authentication, session key secrecy, integrity and resistance from malicious attacks. The proposed handover protocol accommodates all the security demands such as KFS/KBS, privacy-preservation and establishes the secure session key between the communication entities. Furthermore, the formal verification using Automated Validation of Internet Security Protocols and Applications (AVISPA) tool shows the correctness and informal analysis shows the security properties of the protocol. The performance evaluation of the PSEH-AKA protocol is carried out with existing handover AKA protocols in terms of storage, communication, and computation overhead. The evaluation results show that the protocol achieves substantial efficiency and secure against the identified attacks.

Efficient management of interference and power by jointly configuring ABS and DRX in LTE-A HetNets

Long term evolution-advanced (LTE-A) is a mobile communication standard widely used for broadband wireless access. It allows large macrocells and small picocells coexisting in a heterogeneous network (HetNet) to increase throughput and facilitate deployment. To handle signal interference between cells, LTE-A enhances channel quality of picocells by suspending the macrocell’s transmission in an almost blank subframe (ABS). Moreover, LTE-A adopts the discontinuous reception (DRX) method to let a user equipment (UE) adaptively disable its transceiver to save energy. However, existing studies view ABS and DRX as independent methods and address cell interference and energy saving separately. The paper points out that they have much in common, and develops a joint interference and power management (JIPM) mechanism to improve LTE-A performance by integrating ABS and DRX. Based on channel conditions and traffic demands of UEs, JIPM computes the amount of resources given to each UE and adjusts the parameters of ABS and DRX accordingly. Simulation results show that JIPM achieves higher energy efficiency and lower packet dropping of real-time flows, as compared with other schemes.

Throughput Analysis of 3GPP Licensed-Assisted Access Using Multiple Carriers

Due to the urgent demands for wider bandwidth to support continuously blooming wireless applications, deploying Long-Term Evolution Advanced (LTE-A) networks to unlicensed spectrum has been regarded as one of the most crucial remedies to tackle the shortage of available bandwidth on licensed spectrum. This craving consequently drives the development of 3GPP Licensed-Assisted Access (LAA) to sustain both downlink and uplink transmissions of LTE-A (known as LAA networks) on 5 GHz unlicensed spectrum. With vast bandwidth to accommodate a considerable number of bands, simultaneously utilizing multiple bands (carriers) on unlicensed spectrum is of practical interests to enhance data rates, and has been supported in downlink transmissions of LAA. However, to maintain fairness among coexisting users in sharing unlicensed radio resources, transmissions on unlicensed spectrum are restricted by regional regulations such as listen-before-talk (LBT). Particular procedures are also framed by LAA to impose barriers of channel access if an eNB wishes to utilize more than one carriers. Due to the lack of an analytical model to characterize the performance of exploiting multiple carriers, it is a critical challenge for an eNB to determine the optimum number of utilized carriers, which has been the primary concern obstructing the practice of multi-carrier access. In this paper, we consequently provide an analytical model capturing the performance in terms of the number of successful channel accesses (throughput) for an eNB utilizing multiple carriers, to serve the major demand of enhancing data rates. Taking co-channel interference from WiFi and other eNBs into account, the provided model facilitates an eNB to estimate the throughout under given WiFi activity, number of deployed eNBs, and number of utilized carriers. The provided performance analysis thus paves essential foundations for further performance optimization in practical LAA deployment.

CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

Machine-to-machine (M2M) devices with their expected exponential booming in the near future, will be one of the significant factors to influence all mobile networks. Inevitably, the expected huge number of M2M devices causes saturation problems, and leads to remarkable impacts on both M2M and human-to-human (H2H) traffics, services, and applications. The research free-space lack requires creating an appropriate model which describes the functionality of long-term evolution-advanced (LTE-A) and long-term evolution for machine (LTE-M), through mathematical frameworks to evaluate relevant performance metrics. In this study, we bridge this gap by proposing a continuous-time Markov chain (CTMC) model as a stochastic process tool to characterise the H2H/M2M coexistence based on analytical equations. Afterwards, the authors validate the proposed model through extensive Monte Carlo simulations. Eventually, it becomes approachable to characterise the impact of H2H/M2M coexistence in one LTE-A/LTE-M radio resource allocation in dense areas and under disaster scenarios. The simulation results show that using a prioritise LTE-A system for both M2M and H2H traffics is convenient in dense area scenarios, while in emergency cases, it is more appropriate to use a non-prioritise traffic strategy to keep H2H and M2M traffics working properly at the same time.

Tac-U: A traffic balancing scheme over licensed and unlicensed bands for Tactile Internet

The low-frequency licensed spectrum resources have become scarce due to the various applications such as haptic communications supported by the next generation (5G) networks. Therefore, we have to improve the utilization of wireless resources and provide an effective radio resource management solution for the application of Tactile Internet. On the other hand, the Long Term Evolution Advanced (LTE-A) networks will become an integral part of the 5G ecosystem. Therefore, exploring the potential of LTE-A networks for haptic communications will become an important step toward realizing the Tactile Internet. In this paper, we investigate the utilization of LTE in unlicensed bands (LTE-U) technology for resource-constrained tactile networks, which enables a portion of the haptic communications to be used in unlicensed bands. Moreover, we propose Tac-U, which is an LTE-U technology-based traffic balancing scheme for Tactile Internet. We consider the real-time channel interference and traffic conditions of licensed and unlicensed bands to model a mathematical Tactile Internet, in which the LTE-U coexists with Wi-Fi and LTE, and formulate the system utility to evaluate the performance of the proposed traffic balancing scheme. Simulation results show that the proposed scheme can quickly calculate the optimal parameters, and significantly increase the sum utility of the system.

File distribution via proximity group communications in LTE-advanced public safety networks

A primary technological challenge after a disaster is rapid deployment of temporary infrastructure to provide communications for disaster management workers. 3GPP Release 12 defines proximity service (ProSe) to allow direct communications within users for Long Term Evolution-Advanced (LTE-A) public safety. To support group communications, each group members have to receive data sent from the group application server (GAS) maintained in ProSe. This paper discusses the use of vehicle-mounted mobile base station to provide group communications over a two-tier heterogeneous architecture with the aid of GAS and ProSe links. In this method, each vehicle-mounted mobile base station (BS) works in a cell breathing manner to expand or shrink the cell coverage periodically. This way can ensure GAS having the up-to-date proximity knowledge of all users while the part of high-cost group data traffic due to the users located far from the BS can be offloaded to low-cost ProSe links instead. We formulate the minimum cost problem of the ProSe-based File Distribution (PFD) in such architecture. We prove the minimum PFD problem is NP-complete and then proposes three sources selection algorithms for PFD, namely Greedy algorithm, Defer algorithm, and Recursive algorithm. Extensive simulation study is conducted to evaluate performance of the proposed algorithms.

Energy-efficient power allocation for massive M2M communication over LTE-A cellular uplink

In order to enable massive machine-to-machine (M2M) communication in Long Term Evolution-Advanced (LTE-A) cellular network, a machine-type communication gateway (MTCG) is introduced to solve the issue of control signaling overheads. In this paper, a power allocation algorithm based on the method of Lagrange multipliers is proposed for massive M2M communication over LTE-A cellular uplink. The proposed algorithm can decrease the signaling overheads by enabling the machine-type communication device (MTCD) to access the base station (BS) via a MTCG that collects the data from a group of M2M devices and forward it to the BS. The core objective of the proposed algorithm is to maximize the total energy efficiency of a group of M2M devices, while satisfying the time delay of the M2M devices, by coordinating the power transmission of the MTCDs and the MTCG. The simulation results demonstrate that the proposed algorithm outperforms the heuristic algorithm and has relatively close performance to the optimal design.

Supervised learning framework for covert channel detection in LTE‐A

Covert channels transmit secret information by using the existing resources which were not designed for communication. As a major approach to information leakage, covert channels are rapidly gaining popularity with the exponentially growth of cloud and network resources. Long Term Evolution Advance (LTE-A) has dominated the mobile telecommunication networks, which brings an elevation of the risk of covert channels. In this study, the authors propose a supervised learning scheme based on support vector machine (SVM) for the covert channel detection in LTE-A. Based on the fact that the covert channel using the header fields of LTE-A protocol would change the regularity, goodness of fit or correlation of the data traffic, they present behaviour characteristics statistics index (CSI) in the LTE-A protocol to evaluate the changes. According to CSI, they extract the classification feature vectors from the data traffic stream, based on which an SVM classifier used for classifying the channel as covert or overt is trained for testing on the channel under investigation. Experiment results show that the authors' proposed detection scheme is high-efficiency in terms of detection accuracy, sensitivity and specificity, which has great potential to serve as a new idea for the detection of covert channel in LTE-A.

Analysis of energy consumption and latency in advanced wireless networks through DRX mechanism

Ever-increasing need for high data rate and wide penetration of subscriber base are the need for the constant evolution of transmission of mobile traffic data. The global mobile-manufacturing industry is growing rapidly. Energy consumption by wireless communication systems is a vital concern in the present world, not merely to reduce energy consumption but also to mitigate environmental degradation due to effluence caused by extreme energy consumption. While Long Term Evolution-Advanced (LTE-A) method is a standardized fourth-generation portable communication tool for the compelling demands for rapid wireless communication, the extreme energy consumption during the high-speed processing signal on LTE-A user equipment (UE) is a key concern to be addressed for its effective implementation. The discontinuous reception (DRX) based on two contradicting parameters, namely energy consumption and latency, can be optimized for the efficient utilization of UE with optimal energy consumption. Stochastic-based Markov model has been selected to model the randomly changing communication process. The present study analyses the influence of arrival rate, short cycle, long cycle and timer value of short cycle on energy consumption. The present analysis is also focused to build up a systematic model to estimate latency and energy-saving utilized by DRX method for active mobile data traffic. The results indicate that short cycle period of DRX is very effective in reducing latency for active data traffic, and optimal selection of values for various operating parameters can maintain low latency in addition to considerable energy-saving.

Modeling Network-Controlled Device-to-Device Communications in SimuLTE.

In Long Term Evolution-Advanced (LTE-A), network-controlled device-to-device (D2D) communications allow User Equipments (UEs) to communicate directly, without involving the Evolved Node-B in data relaying, while the latter still retains control of resource allocation. The above paradigm allows reduced latencies for the UEs and increased resource efficiency for the network operator, and is therefore foreseen to support several services, from Machine-to-machine to vehicular communications. D2D communications introduce research challenges that might affect the performance of applications and upper-layer protocols, hence simulations represent a valuable tool for evaluating these aspects. However, simulating D2D features might pose additional computational burden to the simulation environment. To this aim, a careful modeling is required to reduce computational overhead. In this paper, we describe our modeling of network-controlled D2D communications in SimuLTE, a system-level LTE-A simulation library based on OMNeT++. We describe the core modeling choices of SimuLTE, and show how these allow an easy extension to D2D communications. Moreover, we describe in detail the modeling of specific problems arising with D2D communications, such as scheduling with frequency reuse, connection mode switching and broadcast transmission. We document the computational efficiency of our modeling choices, showing that simulation of D2D communications is not more complex than simulation of classical cellular communications of comparable scale. Results show that the heaviest computational burden of D2D communication lies in estimating the Sidelink channel quality. We show that SimuLTE allows one to evaluate the interplay between D2D communication and end-to-end performance of UDP- and TCP-based services. Moreover, we assess the accuracy of using a binary interference model for frequency reuse, and we evaluate the trade-off between speed of execution and accuracy in modeling the reception probability.