A refactoring approach for optimizing mobile networks

The purpose of the paper is to examine if public long term evolution (LTE) network is suitable for smart grid (SG) automatic metering usage, in a suburban scenario, without causing significant hindrance to typical LTE traffic. In addition, the same generalized suburban scenario is evaluated with a hybrid sensor - LTE network where wireless sensor network (WSN) cluster heads are also equipped with LTE remote terminal units (RTUs). The simulation topology is formed as a suburban area that consists of single RTU per apartment/house for SG traffic and an average 3.7 people per habitat, each with their user equipment, using one of the available carriers. Normal SG traffic is generated in both directions, uplink and downlink. The worst-case usage factor is considered as a power outage, where all SG meters in the area of influence simultaneously detect and attempt to report such a failure. Thus, emergency SG traffic is generated simultaneous by all RTUs. To alleviate the problem of the high momentary SG emergency traffic load, two distinct solutions are proposed and simulated. As the first solution, an artificial, up to one-second random delay is introduced in the transmission scheduling of emergency messages. The second solution applies low-power WSNs at the cluster level and cluster heads that contain both 802.15.4 and LTE communication interfaces, i.e., a hybrid sensor-LTE network. The simulation results show that both of the solutions that try to alleviate the high instantaneous traffic loads can reach the quality of service criteria for SG traffic without compromising other public LTE traffic. The deciding factor to use either pure LTE approach or a hybrid sensor-LTE network comes from non-functional requirements of the system.

One of the critical components of the Smart Grid (SG) is the Smart Meter (SM), which reports power consumption to a centralized control center at regular intervals. The Long Term Evolution (LTE) network has received much attention as a promising platform for SG communications due to its high bandwidth and low latency over a large coverage area. Nevertheless, when a huge number of SMs access the LTE network, the preamble collision becomes an issue. The negative effects of preamble collision are packet loss, delay, reestablishment attempts, and physical channel utilization, which degrade the performance of the LTE network. Hence, a Random Access (RA) mechanism is required for preamble signature to occupy Resource Blocks (RBs) for SMs. In this paper, we introduce a hybrid procedure that combines contention and non-contention based random access methods. The significance of this method is that, although SMs have fixed periodic communications, preamble signatures do not need to be reserved. The simulation results are compared with LTE standard. Packet loss is reduced by approximately 43%. SM latency is decreased by 11 ms at 90% of probability delay. Re-establishment attempts are lower than standard by proximately 76%. Finally, physical channel utilizations are reduced by around 3%.

In this paper, we investigate the performance of an alternative received signal filtering technique based on local averaging to improve the quality of handover decisions in Long Term Evolution (LTE) networks. The focus of LTE-Advance (LTE-A) networks is to provide enhanced capacity and reliability of radio access as well as broadband demand for mobile users. The necessity to maintain quality of service, especially for the delay sensitive data services and applications, has made mobility and handover decisions between the base stations in the LTE networks critical. Unfortunately, several handover decision algorithms in the LTE networks are based on the Reference Signal Received Power (RSRP) obtained as a linear averaging over the reference signals. The critical challenge with the linear averaging technique is that the limited reference signal available in the downlink packet introduces an estimation error. This estimation error is a result of the effects of linear averaging on propagation loss components in eliminating fast-fading from the received signals. Moreover, prompt and precise handover decisions cannot be based on inaccurate measurement. The standardized LTE layer 3 filtering technique is applied to the local averaged layer 1 signal to render it suitable for LTE handover decisions. The local averaging technique produces better handover than the linear averaging technique in terms of the reduced number of handover failures, improved high spectral efficiency and increased throughput, especially for cell-edge users with high speeds. The findings of this study suggest that the local averaging technique enhances mobility performance of LTE-Advance networks.

Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (RE) RE = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and RE = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and RE = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.

The dense deployment of Femtocells within the Macrocell's coverage is expected to dominate the future of Long Term Evolution (LTE) networks. While Mobile Femtocells (Mobile-Femtos) could be the solution for vehicular networks when there is a need to improve the vehicular User Equipment (UE) performance by mitigating the impact of penetration loss and path-loss issues. The deployed Femtocells have operated in a co-channel deployment due to the scarcity of spectrums. This issue causes interference between Femtocells and Macrocells as well it causes extra overhead on the LTE networks because of the co-tire interference between adjacent Femtocells. In this paper two interference scenarios are considered, the interference between Mobile-Femto and Macrocell, and the interference between the Mobile Femtos themselves. Therefore, to avoid the generated interference between Femtocells, the controlled transmission powers as well as the coverage planning techniques have been discussed. While in the worst-case scenarios, a frequency reuse scheme has been proposed to avoid the generated interference effectively and dynamically between the Mobile-Femtos as well as their UEs and between the Macrocell UEs.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

Research Question: This paper investigates into how machine learning can be applied for the purpose of detecting anomalies in the data describing transport component within the cellular network. Motivation: In the field of telecommunications, terabytes of data are generated each hour. This makes the manual analysis almost impossible to perform. There are thousands of components whose behaviour needs to be monitored, since anomalous behaviour could indicate a possible failure that can lead to network degradation, huge maintenance costs, and finally – a bad user experience. Our goal is to try to catch anomalous behaviour automatically, and thus help domain experts when performing drill down analysis of the degraded base stations and their key performance indicators (KPIs). Idea: The main idea of this paper is to empirically evaluate the application of machine learning for the problem of anomaly detection, in the field of telecommunications, specifically to long term evolution (LTE) networks. Data: Data used in the analysis contains information about base transceiver stations (BTS) behaviour through the time. The data are gathered from a cellular network provider located in Serbia. The data are collected on an hourly basis, for a period of two weeks, resulting in almost 700 thousand rows. The behaviour is assessed by 96 transport KPIs coming from BTS, describing the package losses, delays, transmission success rates, etc. Tools: Two main algorithms, ensemble-based Isolation Forest and autoencoder neural network, are elaborated and applied in order to identify patterns of anomalous behaviour. Findings: The results show that machine learning can be successfully applied in the field of LTE networks for the problem of anomaly detection. Machine learning can significantly reduce the time needed for the domain experts to identify anomalies within the network. In addition to time efficiency, one of the algorithms tested is able to identify anomalous KPIs separately, which is crucial when performing root cause analysis, by using drill-down approach, in order to identify which component is degraded. Contribution: This paper enriches existing research related to anomaly detection in LTE networks and provides an innovative approach to automated root-cause analysis of network degradation.

Wireless sensors may be connected directly to a cellular network. The power consumption and cost of these cellular devices need to be minimised. Sensors typically create small infrequent datagrams. A 3GPP Long Term Evolution (LTE) network needs to communicate many signalling messages in order to transfer these datagrams. This paper presents some novel techniques to reduce the power consumption and cost of LTE devices and to reduce the signalling associated with supporting sensor systems over an LTE network. (5 pages)

QoS-priority based load balancing algorithm for LTE systems with mixed users

Scalable priority-based resource allocation scheme for M2M communication in LTE/LTE-A network

ABSTRACTThe performance of 3GPP long term evolution (LTE) network can be significantly influenced by unbalanced user distribution. In heavily loaded cells, users cannot obtain satisfied services because of shortage of resources, whereas in lightly loaded cells, there are plenty of unoccupied resources. Load balancing (LB) is proposed to deal with this problem, through which the load distribution among nearby cells can be greatly balanced. However, because of the usage of full frequency reuse in LTE, the cell‐edge throughput is severely deteriorated by strong intercell interference. Meanwhile, LTE network usually serves users with heterogenous quality‐of‐service (QoS) requirements, which requires LB to be aware of it. Furthermore, call admission control (CAC) is indispensable in the process of LB, and it depends heavily on the long‐term average performance of the scheduling scheme. Thus, design of an efficient LB algorithm with the consideration of intercell interference, QoS, CAC and scheduling together becomes an important issue. To deal with the previous problem, a multi‐objective optimisation problem is formulated in this paper. Its complexity is analysed, and a practical algorithm, which includes hybrid scheduling, QoS‐guaranteed CAC, QoS‐aware intracell and intercell LB, is proposed. Extensive simulations are conducted. Results show that the proposed algorithm can approximate the optimal solution efficiently, thus leading to significantly better performance, that is, a lower new call blocking rate for users with QoS requirement, a larger total utility and a higher cell‐edge throughput for users without QoS requirement, although the cost is a bit degradation of the total throughput. Copyright © 2013 John Wiley & Sons, Ltd.

Supporting Machine-to-Machine (M2M) communications has been an essential requirement for the Long Term Evolution (LTE) networks. To cope with the massive access requests with stringent delay requirements, in this paper, a community-based random access scheme is proposed by exploiting the spatial correlation of Machine-Type Devices (MTDs). Specifically, a community detection algorithm is first proposed to form the community structure of the network, based on which one MTD is selected in each community to establish a connection with the base station upon each emergency event. Simulation results corroborate that by reducing the number of access requests, the proposed scheme can significantly improve the delay performance especially in the massive access scenarios with a limited number of preambles.

One of the challenges of the mobile industry is to cope with the growth of mobile traffic demand expected for the next years, primarily driven by the increasing usage of mobile video services. Indeed, the existence of increasingly powerful terminals is encouraging the consumption of high-quality video content. Usually, video services are identified with linear Television (TV) and scheduled broadcast (point-to-multipoint (p-t-m)) distribution. However, the consumption of video content over mobile networks is different from traditional fixed TV because contents are mainly consumed on-demand with unicast point-to-point (p-t-p) connections. Then, the convergence of linear TV and on-demand content delivery represents a challenge that requires a combined broadcast/unicast transmission model. This dissertation addresses the use of broadcasting technologies for the provision of mobile multimedia services in Fourth Generation (4G) mobile broadband networks and beyond. Specifically, the dissertation focuses on the broadcast technology included in 4G Long Term Evolution (LTE) and LTE Advanced (LTE-A) networks, known as Enhanced Multicast Broadcast Multimedia Services (eMBMS). It analyses the benefits of the eMBMS physical layer aspects regarding Multimedia Broadcast Multicast Services over a Single Frequency Network (MBSFN) deployments and identifies the current limitations of eMBMS at physical layer by comparing with the broadcast technology of the other 4G mobile system, the Institute of Electrical and Electronics Engineers (IEEE) 802.16m standard. Those limitations are the use of a dedicated carrier and Multiple-Input Multiple-Output (MIMO) techniques for broadcast transmissions. Our investigations employ a complete simulation platform including link-level and system-level simulations to evaluate the performance of broadcast transmissions in these real technologies. The research on eMBMS services is aimed at finding the optimum delivery of streaming and file download services focusing on the Radio Resource Management (RRM) problem and trade-off between Physical layer ? Forward Error Correction (PHY-FEC) and Application Layer - Forward Error Correction (AL-FEC). Concerning streaming services, results show that the use of AL-FEC increases the coverage level and, then, the maximum service data rate. The gain due to AL-FEC is greater in scenarios with high mobility users, although, this gain is limited if low zapping times are desired. Regarding file delivery services, this dissertation analyses the duration of the transmission required to guarantee the correct file reception and the reduction in the mean throughput of unicast users with different delivery modes. They are the unicast delivery, the eMBMS delivery and a hybrid approach, which combines a first eMBMS delivery with a post-delivery error repair phase with unicast transmissions. Our results show that the hybrid delivery is the most efficient configuration in terms of file download time, although it further reduces unicast performance. On the other hand, as an exemplary use case, this dissertation also investigates the use of LTE networks for the provision of vehicular safety services comparing both unicast and eMBMS delivery modes. Results highlight the significant benefits in terms of resource usage, end-to-end latency and cost delivery saving that can be achieved by using eMBMS for the delivery of road safety applications. In addition, research also addresses the problem associated with the support of road safety applications over the current eMBMS architecture, the configuration of the Intelligent Transportation Systems (ITS) server in charge of distributing safety messages as well as on its interaction with the mobile network operator. Finally, this dissertation analyzes feasible options of convergence between mobile and broadcast industry to ensure the success of the mobile broadcasting deployments in the future. A separated evolution of the broadcast technologies of both industries would lead to a scenario with two complete different industries, with different network infrastructures and business models competing for market and spectrum. This dissertation proposes an approach by which the definition of the future Fifth Generation (5G) mobile broadband communication system could bring together the cellular and broadcast industries to form a single fixed and mobile converged network and offer a full alternative to terrestrial TV broadcasting as a universal service

This paper evaluated the effects of third order Intermodulation Distortion (1MD3) on the Long Term Evolution (LTE) receiver due to coexistence between LTE and GSM networks. Amongst the various existing IMD orders which include first order, second order, third order, fifth order and seventh order. Third order is known to have the greatest distortion effects on a receiver due to its strength and its proximity to the frequency band of interest. It occurs as a result of the non-linear behavior of components or circuit at both the transmitter and receiver ends of wireless communication networks. IMD has potential negative effects on a victim receiver which majorly leads to increase in noise floor level and system capacity degradation. Deterministic approach was implemented in the work assuming worst case scenario. MATLAB software simulation was deployed to evaluate the capacity loss at the receiver end relative to a range of distances apart. Results obtained showed severe uplink capacity degradation when VISAFONE LTE network was interfered by INTERCELLULAR LTE downlink and ETISALAT GSM uplink. Various distances ranging from 500m to 3000m were varied between the ETISALAT GSM network and the VISAFONE LTE network. The results obtained showed that at 500 meters, the percentage capacity degradation was as high as 80. The least percentage capacity loss was obtained as 5.97 at 3000 meters.

Recently, there has been a tremendous increase in mobile data usage with widespread proliferation of smartphone like devices and increasing popularity of video streaming services such as YouTube. This is already causing overloading of 3G cellular access networks. It is estimated that even the 4G LTE (Long Term Evolution) technology which has significantly higher spectral efficiency than 3G will also face overloading in near future. As a result, it has become important to find out ways and means of offloading the traffic from the LTE network. One possible solution is offloading it to Wi-Fi to gain extra capacity and improve overall network performance. In this paper we have considered an outdoor macro cell LTE Network being offloaded by a Wi-Fi network. We study the loading of LTE Network in different types of user and traffic distribution and investigate how offload to Wi-Fi APs can help in easing the congestion in LTE network and thereby lead to better per user throughputs and better quality of experience to end users using NS-3. Also, among the various scheduling algorithms that have been defined for efficient resource allocation in LTE, three schedulers have been considered: Round Robin (RR), Proportional Fair (PF) and Priority Set Scheduler (PSS) schedulers and their performance has been evaluated in the offloading scenario.