In the development of wireless networks, the three most critical and essential challenges are coverage, capacity, and interference. Most of the work during the initial stages of network construction and engineering optimization focuses on addressing coverage and interference issues, while capacity and interference problems are typically handled in later phases of network development. As network construction progresses, operators have increasingly demanded more refined planning, faster deployment, and efficient optimization. Effectively solving these three core issues has become the key to improving the overall quality of network construction.
Based on years of experience in wireless network deployment, ZTE introduced a new solution known as the Network Hard Emulation System (NES). This system is designed to enhance the accuracy of planning and design by using fixed receiver analog base stations and mobile transmitter analog terminals. Each fixed receiver simulates receiving signals from a specific cell, allowing for a reverse coverage analysis after implementation. The test results are then applied to wireless network planning and optimization, aiming to address coverage and interference issues as well as optimize wireless parameters.
The structure of the NES is illustrated in Figure 1. The propagation model testing and full-network hard simulation application of NES have been widely adopted and promoted in the field, significantly improving test efficiency and planning accuracy. This paper primarily discusses the practical application of the test results.
The extended applications of NES can be categorized into three main areas:
- Display and adjustment of road coverage prediction results – greatly reducing the workload for post-network engineering parameter adjustments.
- Elimination of pilot pollution – playing a crucial role in early-stage network optimization.
- Optimization of wireless parameters – mainly used to fine-tune wireless settings during the initial phase of network construction.
Road Coverage Prediction
Figure 2 shows the test data and road coverage prediction results from a full-network hard simulation test conducted in a city. The data collected through the NES test represents uplink information. By calculating the path loss based on this data, the PCCPCH RSCP is determined using the actual transmit power, azimuth, downtilt, and station height of the existing network design. This process enables accurate road coverage predictions, which reflect the real-world KPIs under current engineering parameters—essentially replicating the results of a road test before the network is deployed.
Using the PCCPCH RSCP obtained from the test, engineers can adjust the engineering parameters according to network construction goals to achieve optimal coverage. These optimized parameters can then be directly applied to the actual network, significantly reducing the need for post-deployment adjustments and minimizing the associated workload.
Pilot Pollution Adjustment
In real-world network optimization, when there are too many strong pilot signals but no dominant frequency signal, it leads to pilot pollution. In such areas, the C/I of the PCCPCH is usually poor, causing serious interference and frequent ping-pong handovers. After resolving basic coverage issues, it becomes necessary to address pilot pollution.
Figure 3 illustrates the pilot pollution identified through the NES test data and road coverage predictions. Before adjusting the engineering parameters, there were 18 pilot pollution points (indicated by green dots within the blue polygon). Using the NES, parameter recommendations were made, and after adjustments, only two pilot pollution points remained, effectively eliminating most of the interference. This resulted in more reasonable engineering parameters for implementation.
Wireless Parameter Optimization Application
Based on the NES test data, neighboring cells, frequency points, and scrambling codes can be optimized. By analyzing the overlapping of test data points from different cells, potential handover relationships can be identified, and appropriate neighboring cells can be added to the existing list. The strongest interfering neighboring cell and its frequency point can be identified and optimized, as well as the same-frequency, same-code cell that causes the most interference, which can also be adjusted.
These optimizations allow for better configuration of neighboring cells, frequency points, and scrambling codes. During the initial phase of network construction, with fewer users, wireless parameter optimization can be based on the actual signal distribution and interference levels between cells. A well-structured optimization plan provides significant guidance for the network’s early performance.
Solving wireless network coverage and interference issues forms the foundation and guarantee for high-quality network performance. It ensures the smooth operation of the network, supports business growth, and enhances user experience.
With the TD-SCDMA network entering its third phase of construction, the challenge lies in reducing optimization efforts, shortening the optimization cycle, and ensuring high-quality results that meet KPI requirements. ZTE has been actively working toward this goal. Traditional planning simulations often focus on verifying indicators after the network is built under existing parameters. However, under the concept of refined planning and optimization, the application of the Network Hard Simulation System (NES) emphasizes achieving KPIs, accelerating construction timelines, and addressing issues across all stages of the network.
The NES represents a significant evolution in traditional planning and optimization methods. Not only does it bring efficiency and cost savings for equipment manufacturers, but it also provides more time for operators and delivers substantial value.
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Kunshan Zeitech Mechanical & Electrical Technology Co., Ltd , https://www.zeithe.com