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Due to the advancement of
telecommunication platform, users are now demanding
new applications such as Online Gaming, mobile TV,
Web 2.0, and to meet this requirement operators
needed to design more flexible network. For the
deployment of this network, 3rd Generation
Partnership Project (3GPP) works on the Long Term
Evolution (LTE) and propose a s...
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Citations
... RELATED WORKS An analysis of practical coverage scenario in an urban area (i.e. Kolkata) in terms of received signal levels, total noise, interference, throughput, and quality factor for downlink signal level is shown in both [7] and [8]. In [9] a detailed LTE radio network dimensioning procedure i.e. capacity and coverage analysis has been performed in order to prepare a radio planning guideline considering possible network operation in Dhaka city through link budget preparation along with link and system level simulations. ...
... For the densely populated city Dhaka, nominal and detailed radio planning stage has been covered in [10]. But the current work is different from [7][8][9][10], as it not only takes a different city for planning; but the operating frequency has also been taken as the currently available one in India i.e. 2.3 GHz, which was 2.1 GHz for [9] and [10]. That makes the current analysis quite closer to the practical scenario. ...
Long Term Evolution (LTE) is a fourth generation technology which is expected to be the mobile broadband platform for services in innovation for the foreseeable future. Going on with LTE radio network planning is a well-chosen challenge and a certain hot topic in the current research arena. Again, efficient radio network planning for a densely populated city adds to certain level of complexity in the overall work in terms of proper resource management and capacity requirement fulfillment. In this paper, a detailed LTE radio network planning procedure has been elaborately presented which concentrates on nominal and detailed planning considering possible network implementation in the most populated Indian city Mumbai.
... Radio network planning for Dhaka city-coverage and capacity analysis approach has been suitably presented in [6][7]. A LTE coverage prediction scenario with Kolkata city is presented in [8]. Similar approach with Dhaka city using Link and System Level simulator [9] has been made in [13]; but that analysis didn't cover the UE mobility with near and far field region and its relationship with different performance metric considering different transmission modes. ...
This paper provides analyses of the performance of radio parameters necessary for efficient Long term evolution (LTE) radio planning: through numerous simulations in different transmission modes and network scenario. It mainly highlights the throughput, Block Error Rate (BLER) with respect to Signal-to-Noise Ratio (SNR) along with changed UE mobility on the physical layer and in network context covering different simulation environments
... Radio network planning for Dhaka city-coverage and capacity analysis approach has been suitably presented in [6][7]. A LTE coverage prediction scenario with Kolkata city is presented in [8]. Similar approach with Dhaka city using Link and System Level simulator [9] has been made in [13]; but that analysis didn't cover the UE mobility with near and far field region and its relationship with different performance metric considering different transmission modes. ...
The rapid usage of mobile phones by the people makes is necessary to seamlessly detect and analyze the signals of cellular networks for the users to identify the signal strength of their respective cellular network in the particular area or in user’s current location. The Software enables anyone can see the particular area’s signal strength of their respective cellular network. The Poor connectivity pockets can be found using the data from the users. The Authorities can use that information to assess the poor coverage areas and take necessary actions to address the issue of the poor coverage. The Network coverage can be viewed in the map where there it covers 500 m radius of the user’s location and it can be used to view the coverage in that area. Users will be provided a rich map-based UI to graphically view coverage stats. Government authorities and officials from different operators will be provided a separate portal where they will be provided access to the signal data along in a map-based UI so that they can have insights on improving connectivity in different regions. This can possibly increase competition in a healthy between competitive networks to improve connectivity in poor regions in order to gain market share and become a win–win situation for both the network operators and customers.
The provision of enhanced user experience with higher-data transmission speed over the radio frequency channels was one of the core objectives of evolving Long Term Evolution (LTE), which is a 4th generation of cellular broadband communication standard. LTE was developed to supports data transmission speed up to 300 Mbps and 75 Mbps in downlink and uplink, respectively, utilizing a robust multiplexing technique. For mobile radio networks operators to maintain high operability around the ever increasing and demanding subscribers, periodic evaluation, quantitative estimation and analysis of network performance is pivotal. There exist a quantum of previous research works on LTE system network performance that have been conducted both at spatial domain and temporal domain, but the authors concentrated their studies either on practical LTE radio coverage issues only or on general LTE network performance issues using analytical/simulation techniques. In this contribution, a combined statistical and machine learning approach is proposed and applied to provide an in-depth user data throughput performance of operational LTE networks in relation to signal coverage and signal quality parameters, using in typical microcellular built-up terrains as case study. Specifically, the impact of transmitter- receiver communication distance, received signal reference power, received Signal reference quality, Signal to Noise Ratio, Received Signal Strength and Channel Quality Indicator on user throughput over Radio Link Control, Physical Downlink Shared Channel and Packet Data Convergence Protocol layers have been shown using professional TEMS investigation tools. Particularly, for the purpose of effective LTE system network planning and management, the most influencing signal coverage and signal quality parameters on user throughput performance are also examined and shown in the framework of Self-organsing map (SOM) and statistical correlation stratagems.
Due to the advancement of telecommunication platform, users are now demanding new applications such as Online Gaming, mobile TV, Web 2.0, and to meet this requirement operators needed to design more flexible network. To fulfill the requirements, 3rd Generation Partnership Project 3GPP works on the Long Term Evolution LTE and propose a system which has larger bandwidths up to 20 MHz, low latency and packet optimized radio access technology having peak data rates of 100 Mbps in downlink and 50 Mbps in the uplink Magdalena, 2007; Motorola, 2007; Skold, 2009. Offering a greater coverage by providing higher data rates over wider areas and flexibility of use at existing and new frequency bands plan is a major challenge. In this paper, we are analyzing practical coverage scenario in an urban area i.e. Kolkata in terms of received signal levels, total noise, interference, throughput, and quality factor for downlink signal level.
Background/Objectives: In order to increase spectral efficiency and lower handover signaling overhead in long term evolution network, load balancing optimisation and ping-pong handover avoidance is important. Methods/Statistical Analysis: Here, an algorithm that uses an adaptive timer was developed to run on the network. The network comprises of seven cells numbered 1, 2, 3, 4, 5, 6 and 7 respectively. Each cell is powered by a centrally placed cell equipped with omni-directional antennas to covers its cell area and neighboring cell-edge users. Receive signal strength and cell load estimates were jointly used to model the handover adaptive timer for decision accuracy. Findings: Findings were made the validation of the Key Performance Indicators (KPIs) using computer simulations. The KPIs of attention in this research were load balancing index of the network, number of unsatisfied users, cumulative number of ping-pong handover request, cumulative number of non-ping-pong handover request and average throughput of the cell. The results of our proposal out perform two other references cited in literature. In terms of load distribution index specifically, a 95% level was achieved after only 150 load balancing cycles. Conclusion/Improvements: The propose solution proves great for its ability to effectively detect ping-pong handover request and non-ping-pong handover request while load balancing decision process is in progress.
