BAHIRDAR UNIVERSITY BAHIRDAR INSTITUTE OF TECHNOLOGY SCHOOL OF RESEARCH AND POSTGRADUATE STUDIES FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING Performance Analysis of Coverage Prediction and Cell-Edge Throughput Improvement Techniques in LTE: Case for Amtel-Somalia MSc Thesis Proposal By: Said Ali Adan Program: Communication System Engineering Main advisor: DR: Amare Kassaw (PhD) March, 2022 Bahirdar, Ethiopia Bahir Dar Institute of Technology-Bahir Dar University School of Research and Graduate Studies Faculty of Electrical and Computer Engineering THESIS PROPOSAL Student: ________________________________________________________________________ Name Signature Date The following graduate faculty members certify that this student has successfully presented the necessary written thesis proposal and oral presentation of this proposal for partial fulfillment of the thesis-option requirements for the Degree of Master of Science in Communication System Engineering. Approved: Advisor: ________________________________________________________________________ Name Signature date Chair Holder: ________________________________________________________________________ Name Signature Date Faculty Dean: ________________________________________________________________________ Name Signature Date i List of acronyms BS Base Station CS Coordinated Scheduling COMP Coordinated Multi-point DL Downlink DPS-COMP Dynamic Point Selection Comp EPC Evolved packet core ENODEB Evolved Node B EUFS Edge User-Friendly Scheduler E-UTRAN Evolved universal terrestrial radio access network 1G First Generation 2G Second Generation 3G Third generation 4G Fourth Generation 3GPP 3rd Generation Partnership Project HSPA High-Speed Packet Access HETNETS Heterogenous Networks IMT-A International Mobile Telecommunication-Advanced ITU International Telecommunication Union IRAT Interrelated Radio Access Technologies IDW Inverse Distance Weighted JP-COMP Joint Processing Coordinated Multipoint ii JT-COMP Joint Transmission Coordinated Multipoint KPI Key Performance Indicators LTE Long-Term Evolution LTE-A Long-Term Evolution Advanced MIMO Multiple Input Multiple Output MME OK Mobility Management Entity Ordinary Kriging PF Proportional Fair QOS Quality of Service RF Radio Frequency RAT Radio Access Technology RSRP Received Signal Received Power SAE System Architecture Evolution S-GW Serving Gateway SSPS Semi-Static Point Selection SINR Signal to Interference Noise ratio SISO Single Input Single Output UL Uplink UMTS Universal Mobile Telecommunication System iii Table of contents List of acronyms ............................................................................................................................................ ii Table of contents ......................................................................................................................................... iv Abstract ......................................................................................................................................................... v 1. Introduction .............................................................................................................................................. 1 1.1Background .......................................................................................................................................... 1 1.2 Coverage prediction and cell edge throughput.................................................................................... 3 1.3 MIMO ................................................................................................................................................. 5 1.4 Coordinated multi-point(comp) .......................................................................................................... 6 1.4.1 Downlink comp transmission....................................................................................................... 8 1.4.1.1 Coordinated scheduling/beamforming ...................................................................................... 8 1.4.1.2 Joint processing comp ............................................................................................................... 8 2. Literature review ....................................................................................................................................... 9 3. Problem of Statement............................................................................................................................. 12 4. Objectives................................................................................................................................................ 12 4.1 General objective .............................................................................................................................. 12 4.2. Specific Objectives .......................................................................................................................... 12 5. Methodology ........................................................................................................................................... 13 6. Scope of the Study ................................................................................................................................. 14 7. Significance of the Study ......................................................................................................................... 15 8. Work Plan ................................................................................................................................................ 16 9. Budget Plan ............................................................................................................................................. 17 References .................................................................................................................................................. 18 iv Abstract Due to the advancement of telecommunications platforms, users are now demanding new applications such as gaming online, video streaming as well as mobile television. In long-term evolution focus is on higher capacity, higher spectral efficiency, increased peak data rate, increased number of simultaneously active subscribers, and improved performance at the cell edge. Due to these requirements, predicting coverage accurately and improving cell edge throughput has become extremely important. The received signal in a wireless cellular network degrades as the propagation distance increases. Consequently, the cell edge user experiences poor quality of service (QOS) specifically throughput, where the power efficiency and spectral efficiency are very low due to the low received signal and high fading effects. However, the cell-centric user experiences a good quality of service. In Somalia, there are several telecom service providers, whose deployed cellular networks like 2G,3G, and LTE. Preliminary survey and discussion show that the networks facing poor coverage and limited cell edge throughput in some areas. In this thesis, we will analyze the performance of coverage prediction and cell edge throughput improvement techniques in LTE to come up with effective and reliable coverage, as well as improved throughput. Particularly this work will be based on a selected area from Garowe city. Which is the capital city of Puntland state of Somalia. Similarly, the input data will be collected from AMTEL Telecom company which is one of the private service providers of Somalia. The main goal of this work is to analyze the performance of coverage prediction and cell edge throughput improvement techniques and then propose the most promising technique that help to avoid coverage related problems and met the demand of subscriber. Keywords: Coverage prediction, cell edge throughput, Comp and long-term evolution (LTE). v 1. Introduction 1.1 Background Wireless mobile communication is a rapidly growing technology to satisfy the increased demand of subscribers such as data and voice. Considering this, the network operators try to deploy new radio access technology (RAT), but as far as new technology is deployed the complexity of coverage prediction in the mobile cellular network will increase. Therefore, proper network design and planning is an obligation process to do. This is because the sophisticated planning tools used by the operators can not accurately predict the coverage of the desired area. Meanwhile, the cellular coverage estimation needs to be considered various network parameters and environmental-based conditions. In its easiest form, the LTE coverage area can be forecasted by using path loss prediction models, although this method is limited as it can not show the effect of traffic type distribution on the coverage. Nevertheless, it gives an adequate good first-hand estimate about the whole coverage which can be achieved with certain offered average capacity and quality. In the initial phase of the planning, a generalized link budget calculation can be produced to estimate the rough, average LTE cell range per area type (dense urban, urban, suburban, rural, and open area). By assuming a certain overlapping percentage for the adjacent cells, this exercise provides an initial estimate of the required number of the eNodeB sites over the planned area. However, in terms of coverage and capacity performance, the network subscribers may encounter some problems. These problems may include the cell edge problem which directly degrades the throughput of the cell edge subscribers. This is because coverage and throughput are two inter-related terms, whenever the required cell edge rate increased, the smaller cell coverage radius. Likewise, the smaller the cell edge rate, the higher the cell coverage radius. In addition, the cell edge user is always suffering from low signal-to-noise-plus interference -ratio (SINR) levels due to the farness from the base station (BS). Multipath fading, propagation distance, and path loss factors also cause the degradation and attenuation of the received signal in the cell boundaries. Increasing the power of the transmitter could not be suitable always, while it also increases intercell interference. Due to this, long-term evolution technology (LTE) is enabled by new technologies and features, and enhancements to existing technologies, like carrier aggregation, MIMO (multiple input, multiple output), beamforming, Coordinated Multi-Point (Comp), in-band Relaying (relay nodes), 1 and HetNets (heterogeneous networks) [1]. In addition, as the area becomes very interesting for research to enhance both the throughput and coverage to identify new improvement techniques. researchers proposed numerous techniques that can improve the performance of quality of service in general and specifically throughput for cell-edge users in a cellular network. But still, some challenges are not solved by the previous researchers and this is our motivation behind this research. therefore, in this thesis, we will analyse the performance of coverage and cell-edge throughput improvement techniques to enhance both coverage and throughput together. The target of the radio network planning could be a compromise between coverage, capacity, quality, and cost. Here are the steps of the planning and rollout of the LTE network. Preparation phase: coverage and capacity requirements are identified. Like traffic profile, cell edge throughput, and indoor and outdoor coverage probability. Nominal planning phase: link budget, Capacity dimensioning, and radio frequency prediction are conducted. Detailed planning: it verifies nominal planning by identifying site coordinates, conducting site surveys, and selecting a proper candidate to meet coverage targets. Rollout phase: based on the detailed planning the network rollout and site construction are conducted. Pre-optimization: the network is pre-optimized by validating the cell parameters, coverage target, and throughput. Soft launch: this is the final phase when the network has passed all required KPIs and service level agreements. The figure below shows end-to-end network deployment phases. 2 Figure1: End to end network deployment phases 1.2 Coverage prediction and cell edge throughput Analysing and discussing coverage holes in mobile networks is still an important problem that needs to be addressed, both deployed and future networks. However, each type of coverage hole has to be handled depending on the effect they have on the users. In particular, they are characterized by resulting abnormal disconnections or inter-Radio Access Technology (RAT) handovers when there is an underlying RAT available to maintain the connection released by the LTE network at the cost of reducing the service performance. The most susceptible system of coverage problems is the wireless communication system, due to channel fading characteristics which are decided based on the environment where radio propagation occurs. Radio propagation is the behaviour in which radio wave encounters while signals are transmitted from transmitter to receiver. In addition, many constraints like scattering, reflection, refraction, absorption, and among others will affect the radio wave. These problems can be reduced using a set of mathematical expressions and algorithms, known as propagation models (path loss models) which estimate the strength of the transmitted signal. Pathloss is the reduction of an electromagnetic wave power density. The following equation shows a free-space path loss 3 model. In [2] comprehensive survey of the various network coverage prediction techniques has been conducted. PL(dB) = 10Log Pt Pr Gt Gr ƛ^2 = −10Log[(4π)^2 d^2 ] Where Pt is the transmitted power Pr is the received power. Gt, Gr: antenna gains; λ: wavelength in meters Figure 2: The propagation phenomena. Similar to the other wireless communication system such as WiMAX and HSPA the LTE features, a rate layering feature that is the inter-relation of coverage radius and cell edge rate. This is due to the fixed power offered by user equipment which is normally 23dbm. The downlink cell edge rate can be calculated as follows: 4 Cell edge rate: NDS * NRBU * NAT * R * MML / TS. where NDS, is the number of a different data streams transmitted NRBU, is the number of resource blocks assigned to the user per frame NAT, is the number of available traffic carrying resource element per resource block R, is the coding rate, MML, is the modulation model level TS, is the Duration of each frame. In addition, depending on, the user mobility, location in the active cell, and cell load variety, the real throughput data is highly variable. To solve this problem, estimating and analysing cell load and cell throughput in a certain bandwidth and inter-cell distance is required. As stated by [3]. 1.3 MIMO Modern wireless communication networks use MIMO (multiple input multiple output) technology to improve high data rates and achieved throughput as a special MIMO technique, beamforming also permits targeted illumination of specific areas, making it possible to improve transmission to users at the far reaches of cell coverage [4]. MIMO communication sends the same data at several signals instantaneously through multiple antennas, while still using a single radio channel. This is a kind of antenna diversity that uses multiple antennas to develop signal quality and strength of radio frequency communication links. The data is divided into multiple streams at the transmitter side and gathered on the receiver side by another MIMO radio managed with the same number of antennas. In MIMO the receiver is designed to consider the very small-time difference between the reception of each signal, additional noise or interference, and lost signals. Multi-user-MIMO has an attractive feature that significantly improves the spectral efficiency of the system and the reliability of the communication link that is utilizing multiple antennas in both receiver and transmitter ends. Multiple MIMO have several advantages over single input single output (SISO). 5 1. MIMO radios can use the bounced and reflected RF transmissions (known as multipath propagation) due to various obstacles to improve signal strength with line-of-sight and, even without clear line-of-sight, 2. Total throughput can be increased, allocating for greater quality and quantity of video, voice or other data to be sent over the network. 3. By using multiple data streams, problems such as fading caused lost or dropped data packets can be degraded, resulting in better video or audio quality. Figure 3: 4X4 MIMO system, where four antennas from the transmit radio communicate with four antennas on the received radio to improve link connection strength and bandwidth. 1.4 Coordinated multi-point(comp) With the higher increase of user data rate and application demands, mobile operators and researchers have been aiming for a high quality of service in the last decade. Comp technology operation gives a significant solution to improved throughput and enhanced coverage performance by reducing interference, especially for cell-edge users [5]. In Comp operation, multiple Base Stations (BS) coordinate with each other in such a manner that the user's information signal from neighbouring evolved Node B (eNB) reduces interference or even can be combined to improve received signal quality. Comp transmission is relying on, the sharing of coordination information 6 via backhaul links, which usually, consists of the user's feedback that explains the channel condition. The fundamental principle of Comp is utilizing multiple transmit and receive antennas from multiple antenna site locations, which may or may not belong to the same physical cell, to enhance the received signal quality as well as to reduce interference, improve spectrum efficiency and enhance effective coverage area by exploiting the co-channel interferences. Comp mainly has been targeted to improve cell-edge UE experience, but regardless of the location, it is also used to enhance the system throughput to UEs those experience strong signals of different BSs/cells. Comp is mainly categorized as inter-site Comp and intra-site Comp. In inter-site Comp, the coordination is performed between BSs located in separated geographical areas. On the other hand, intra-site Comp enables the coordination between sectors of the same BS, where the coordination is performed through multiple Antenna Units (AUs) that allow coordination between the sectors. [ 3] the figure below illustrates both Comp categories. Figure4: Basic Comp Technology; Inter-site Comp and intra-site Comp 7 1.4.1 Downlink comp transmission Base station(BS) cooperation in the downlink can highly improve the average throughput in general and cell-edge throughput in particular. The third-generation partnership project (3 GPP) has divided the downlink coordinated multi-point into two categories. [6] 1.4.1.1 Coordinated scheduling/beamforming The data is only available in a serving cell while user beamforming and scheduling decisions are made with the coordination of the sectors. Semi-static point selection (SSPS) is used to make transmission decisions. Coordinated scheduling/beamforming gives fast strict coordination. but, at the same time, it uses the MIMO antenna capabilities through beamforming in a coordinated manner. 1.4.1.2 Joint processing comp This is the most advanced comp method which improves the spectral efficiency, as a whole and cell-edge user spectral efficiency in particular. In this mechanism, the user data to be transmitted is available in multiple sectors of the network. In terms of cooperation mechanism, JP-COMP is sub-divided into two main types, named joint transmission comp (JT-COMP) and dynamic point selection comp (DPS-COMP) . 8 Figure5: Downlink comp transmission 2. Literature review With continuous of mobile communication technology, wireless mobile network frame is more and more complicated, therefore, reasonable coverage and throughput enhancement techniques are the key to survival for the network. For this research, several kinds of literature on coverage prediction and cell edge throughput enhancement techniques for mobile networks were revied. To begin with [7], the author predicted the UMTS coverage network of a selected area in Addis Ababa city based on two spatial interpolation techniques, named IDW and OK. 9 In addition, the two methods were evaluated and selected the better one, taking CPCHRSCP collected from the drive test as a metric. The performance estimation of those algorithms was evaluated through the cross-validation, coefficient of determination(r^2), mean absolute error (MAE) and root mean square error (RMSE). Results declared that the two coverage prediction methods can predict coverage holes, but, based on the exponential model of semi variogram with an optimal of neighbours showed that the OK method estimated less error and accurate value than the IDW method. According [8], the authors innovated and developed a new scheduling technique named edge user-friendly scheduler (EUFS) and they aimed to increase the throughput experienced by the cell edge users by increasing the probability of assigned resource blocks without impacting of cell centric users. After setting up several experiments comparing the performance of the proposed new scheduling technique and several other methods. particularly, the proportional fair (pf) method it’s proven that the proposed algorithm has achieved better performance for cell-edge users than the other methods. In [9], an approach that detects cells with coverage holes and diagnoses their type severity was proposed. Furthermore, the author presented another method which can analyse the impact of each coverage hole has on the users both LTE and underlying RAT at the same time. As stated by the author, this was carried out by introducing a new inter-technology PI based on mobile traces which allows analysing the behaviour of the users who leave LTE by means of IRAT handovers. The proposed approach was evaluated in a live LTE network with assets of real data collected from the same network. According [10], disclosed, the possibility and accuracy of real-time mobile bandwidth and handoff predictions in 4G/LTE and 5G networks. It collected long series traces with rich bandwidth, channel, and context information from the public transportation systems. Furthermore, it developed recurrent neural network models to maintain temporal patterns of bandwidth evaluation in fixed-route mobility scenarios. For 4G and 5G co-existence networks, a new problem of handoff prediction between 4Gand 5G was proposed, which is important for low latency applications like self-driving strategy in realistic 5G scenarios. A classification and regression-based prediction model, which improve more than 80% of accuracy in predicting 4G and5G handoff in the recent 5G dataset were achieved. 10 In [11], propagation coverage prediction system based on the inversion method was presented. The method was suitable for most wireless environments and better than the traditional empirical prediction model as the authors stated in their work. In [12], the network coverage and network quality of coordinated LTE small cell radio points were compared to that of a cluster of independent standalone small cells. the conducted measurements were compared to theoretical predictions as measured by RSRP, SINR, and CQI. And it’s shown that the coordinated small cell provides significant improvement in the overall quality of LTE coverage. In [13], a 3D LTE signal coverage in a residential district were predicted, the 3D environment model of the target scenario is constructed. It’s used a ray-tracing model which have the ability to accurately estimate 3D signal coverage for urban scenarios. As result showed the transmission loss of the walls and windows decreased the indoor received power compared to the surrounded outdoor locations. In [14], a review on techniques to improve cell edge performance were conducted. The study was categorised into two parts which are techniques to improve cell edge performance over cellular networks and over wireless local area networks (WLAN) respectively. The work was defined that, there are existed several techniques to improve cell edge performance over cellular networks, proposed by different researchers. But, in contrast, its cleared the shortage of techniques to enhance cell edge performance over wireless local area networks. Due to this, the authors presented adaptive modulation and coding scheme technique which improves cell edge performance over wireless local area networks. The development of long-term evolution (LTE) with self-organizing network (SON) functions has been considered an effective way to overcome radio coverage problems and expenditure of operational tasks, as stated by [15]. 11 3. Problem of Statement The estimation of coverage and throughput in cellular network technologies is very challenged. Due to sophisticated planning tools used by the network operators and the limitation of coverage prediction path loss models. In particular, AMTEL Telecom which is our case study is facing many challenges including coverage holes, handover failure, sectors with the same physical cell identity, circuit-switched fallback problem, and limited throughput specifically for cell-edge users. Therefore, analysing the performance of coverage and throughput improvement techniques are extremely important to come up with good coverage and achieved throughput. Moreover, this work aims to propose valuable techniques which optimize the network quality of service. 4. Objectives 4.1 General objective The general objective of this thesis is to study and analyse the performance of coverage prediction and cell edge throughput improvement techniques and propose a promising technique that helps to avoid coverage-related problems and met the demand of subscribers. 4.2. Specific Objectives The specific objectives of this study are: • Collecting the data of the study case (AMTEL) by both drive test and walk test • Taking measurements of different LTE parameters • Solving cell edge problem using LTE coverage prediction improvement techniques • To take digital elevation model of the target area as an input and to construct coverage map • Improving cell edge throughput by enhancing whole throughput • To evaluate the performance of LTE enhancement techniques • Maintaining cell centric throughput to permanently achieve an acceptable level of performance 12 5. Methodology To address enough/efficient coverage and cell-edge throughput by analysing the performance of coverage prediction and cell-edge throughput improvement techniques in the LTE cellular network. This thesis will follow the following procedures. 1. Literature review: various literature were reviewed. 2. Data collection: the data used in this thesis will be based on AMTEL telecom LTE, specifically target area (Garowe city), and will be collected by drive test method using TEMS Pocket and TEMS investigation software while LTE (long-term evolution) key performance indicators are taken as metrices. 3. Analysing the performance of coverage and throughput improvement techniques with consideration of LTE key performance indicators. 4. Selecting the best promising technique to improve coverage, quality of service (QOS), and cell-edge throughput. The following figure illustrates the methodology used. 13 start Literature and Data Collection Input data to the Simulation Software with LTE Parameters Like SINR and RSRP Evaluate Coverage/throughput Improvement Techniques NO Throughput Improved NO Choose best Technique Coverage Improved Generate Coverage Map Map Classification Coverage Holes detected Figure6: Methodology 14 6. Scope of the Study The scope of this thesis is restricted to the performance analysis of coverage prediction and cell edge throughput improvement techniques in the LTE cellular network. Moreover, this study is performed by taking AMTEL telecom Somalia as a case study. 7. Significance of the Study The importance of this proposal is to fill an essential gap which is necessarily required to be addressed. That is analysing the performance of coverage prediction and cell-edge throughput improvement techniques aiming at the acceptable level of performance. The AMTEL telecom and its subscribers will benefit from this work. 15 8. Work Plan Month Tasks Week Februa March ry 1 2 3 4 1 2 3 April 4 1 2 3 May 4 1 2 July June 3 4 1 2 3 4 1 2 August 3 4 1 2 3 4 Septem ber Octobe r 1 1 2 3 4 1. Literature Review 2. Data Collection 3. Theoretical Analysis 4. Data Analysis 5. Documentation 6. Draft Report Submission 7. Final Report Submission 16 2 3 4 9. Budget Plan Number Category Quantity Unit cost Cost (birr) 1 Software’s for 1 7,000 7000 LS 6000 6000 5000 5000 LS 3000 3000 Communication LS 4000 4000 data analysis and simulation purpose 2 Transportation cost 3 Data collection cost 4 Thesis documentation cost 5 expenditure Total cost 25000 17 References [1] J. wiley, LTE backhaul planning and optimization, united kingdom, 2016. [2] E. O. Oluwafemi, A. M. Zungeru, C. K. Lebekwe, and J. M. Chuma, ""Cellular communications coverage prediction techniques: A survey and comparison."," IEEE Access, pp. 113052-113077, 8 (2020). [3] A. G. Flattie, "LTE Cell Load Analysis Using Live Network Data," J. Commun, vol. 14, pp. 580-586, 7(2019). [4] R. Schwarz, "LTE Transmission Modes and Beamforming," white paper, 2015. [5] M. S. Ali, "On the evolution of coordinated multi-point (CoMP) transmission in LTE-advanced," International Journal of Future Generation Communication and Networking, Vols. Vol.7, No.4, pp. pp.91-102, 2014. [6] R. Irmer, p. marsch and m. grieger, "Coordinated multipoint: Concepts, performance, and field trial results," IEEE Communications Magazine, pp. 102-111, 2011. [7] Z. Kassaw, Coverage Prediction Based on Spatial Interpolation Techniques: The Case of UMTS Network in Addis Ababa, Ethiopia, addiss ababa, 2020. [8] b. A. A. E.-M. c. M. S. c. S. M. N. a. H. M. E.-H. d. Wael S. Afifi a, "A novel scheduling technique for improving cell-edge performance in," Ain Shams Engineering Journal, p. 487–495, 2021. [9] G. Andrades, A. Raquel Barco and I. Serrano, "A method of assessment of LTE coverage holes," EURASIP Journal on Wireless Communications and Networking , pp. 1-12, 2016. [10] M. L, G. J, C. Y, C. H and . L. Y., "Realtime Mobile Bandwidth and Handoff Predictions in 4G/5G," Computer Networks, p. 108736, 27/4/2021. [11] B. L. a. B. K. Dakun Bao, "The Inversion-based Coverage Prediction Tool for Cellular Networks - Real City Experiments," JOURNAL OF NETWORKS, Vols. VOL. 8, NO. 8,, 2013. [12] W. 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