Comparative Analysis of Design Tool Prediction for 4T4R and 64T64R Massive MIMO in 5G Stadium Environment

Comparative Analysis of Design Tool Prediction for 4T4R and 64T64R Massive MIMO in 5G Stadium Environment

Tausif Ismail Patel (1), Syed Moazzam Pasha (2) and Saad Mohammed Farrukh (3)

Abstract – Accurate radio network planning is essential for the successful deployment of fth-generation (5G) New Radio (NR) networks, particularly in high-density stadium environments where thousands of users simultaneously access bandwidth- intensive services. During the network planning phase, radio frequency (RF) planning tools are widely employed to predict key performance indicators (KPIs), including coverage, Reference Signal Received Power (RSRP), and Signal-to-Interference-plus- Noise Ratio (SINR), enabling engineers to evaluate different deployment strategies before implementation. However, accu- rately predicting the performance of advanced Massive MIMO systems remains challenging because conventional planning tools primarily rely on propagation models and simplied antenna representations, while the dynamic characteristics of Massive MIMO, such as digital beamforming, adaptive beam steering, Multi-User MIMO (MU-MIMO), and scheduler-dependent spa- tial multiplexing, are only partially represented.

This paper presents a comparative analysis of the design-

stage KPI predictions for conventional 4T4R MIMO and 64T64R Massive MIMO in a 5G NR stadium environment using a commercial RF planning tool. The study is limited to the KPIs generated during the planning phase and does not include eld measurements or post-deployment performance evaluation. The predicted RSRP, SINR, coverage, and interference characteristics are analyzed to examine how conventional planning models represent different antenna congurations. The results indicate that while the 64T64R Massive MIMO conguration provides stronger predicted coverage through higher antenna gain, the predicted SINR may be comparable to, or in some scenarios lower than, that of the conventional 4T4R conguration. These observations suggest that design-stage SINR predictions should be interpreted with an understanding of the inherent limitations of conventional planning models when evaluating advanced Massive MIMO technologies. The study highlights the need for more realistic Massive MIMO modeling techniques to improve prediction accuracy and support more reliable design decisions for high-capacity 5G NR deployments.

Index Terms5G New Radio (NR), Massive MIMO, 4T4R MIMO, 64T64R Massive MIMO, RF Planning, Radio Network Planning, Signal-to-Interference-plus-Noise Ratio (SINR), Ref- erence Signal Received Power (RSRP), Beamforming, Stadium Networks.

1. INTRODUCTION

   Fifth-generation (5G) New Radio (NR) has transformed mo- bile communications by enabling enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). To meet the increasing demand for higher data rates, lower latency, and improved network capacity, 5G networks employ ad- vanced radio technologies such as Multiple-Input Multiple-

   Output (MIMO), beamforming, exible spectrum utilization, and network densication. As network complexity continues to increase, accurate radio network planning has become a critical step in ensuring that coverage, capacity, and quality-of-service requirements are achieved before commercial deployment.

   High-density stadiums represent one of the most demanding deployment scenarios for 5G NR networks due to the large concentration of users simultaneously accessing bandwidth- intensive applications such as live video streaming, social media, cloud services, and real-time multimedia sharing. These environments require radio networks to provide high capacity while effectively managing interference, maintaining adequate signal quality, and ensuring a consistent user experience. Consequently, advanced antenna technologies have become essential for supporting the trafc demands of modern stadium deployments.

   Conventional 4T4R MIMO has been widely deployed to improve network coverage and capacity through multiple transmit and receive antenna paths. More recently, 64T64R Massive MIMO has emerged as a key technology for enhanc- ing spectral efciency and increasing network capacity by em- ploying large antenna arrays, digital beamforming, and Multi- User Multiple-Input Multiple-Output (MU-MIMO). However, during the network planning stage, RF planning tools primar- ily estimate performance using propagation models, antenna radiation patterns, and static interference calculations. While these models provide reliable coverage predictions, they may not fully capture the dynamic behavior of Massive MIMO technologies, including adaptive beamforming, user-specic beam steering, channel state information (CSI)-based precod- ing, and scheduler-dependent spatial multiplexing. As a result, the predicted Key Performance Indicators (KPIs), particularly Signal-to-Interference-plus-Noise Ratio (SINR), may differ from the performance achieved in operational networks.

   This paper presents a comparative analysis of design-stage KPI predictions for conventional 4T4R MIMO and 64T64R Massive MIMO in a 5G NR stadium environment using a commercial RF planning tool. The study is limited to the analysis of predicted KPIs generated during the planning phase and does not include eld measurements or post- deployment validation. The predicted performance of both antenna congurations is evaluated using key planning met- rics, including Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), coverage, and interference. The objective is to examine how conventional

   planning tools represent different antenna technologies and to discuss the implications of these predictions for planning high- capacity 5G NR stadium deployments.

2. BACKGROUND AND RELATED WORK

   The evolution of fth-generation (5G) New Radio (NR) has introduced advanced antenna technologies and signal processing techniques to improve network coverage, capacity, and spectral efciency. Conventional Multiple-Input Multiple- Output (MIMO) systems have been widely deployed to en- hance wireless communication performance through spatial diversity and multiplexing. More recently, Massive MIMO has emerged as a key enabling technology for high-capacity 5G deployments by employing large-scale antenna arrays, digital beamforming, and Multi-User MIMO (MU-MIMO). During the network planning stage, radio frequency (RF) planning tools are commonly used to estimate key performance indicators (KPIs), including Reference Signal Received Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), cov- erage, and interference. Understanding the theoretical prin- ciples governing these KPIs is essential for interpreting the design-stage predictions generated by RF planning tools.

   1. Link Budget

      The link budget is a fundamental component of radio network planning that estimates the received signal strength between the transmitter and receiver. It accounts for the transmitted power, antenna gains, propagation losses, and ad- ditional system losses encountered during signal transmission. Accurate link budget analysis enables network planners to predict coverage, determine cell boundaries, and evaluate the feasibility of different deployment scenarios before network implementation.

      The received signal power can be expressed as

      Pr = Pt + Gt + Gr PL Lmisc (1) where Pr is the received power, Pt is the transmit power,

      Gt and Gr represent the transmitter and receiver antenna gains, PL denotes the propagation loss, and Lmisc repreents miscellaneous system losses. The predicted RSRP generated by RF planning tools is primarily derived from link budget calculations combined with the selected propagation model.

   2. Array Gain and Beamforming

      Massive MIMO improves radio performance by employing a large number of antenna elements to concentrate transmitted energy toward intended users through digital beamforming. Compared with conventional MIMO systems, larger antenna arrays provide higher array gain, improve signal quality, and increase spectral efciency by directing energy more ef- ciently while reducing unnecessary interference.

      The theoretical array gain can be approximated as

      where N represents the number of antenna elements and Nref is the reference antenna conguration. Although RF planning tools generally account for antenna gain through radiation patterns, they may not fully represent the adaptive beamforming behavior that occurs during live network opera- tion.

   3. Shannon Capacity

      The theoretical maximum data rate of a wireless communi- cation channel is governed by the Shannon-Hartley theorem, which establishes the relationship between channel bandwidth, signal quality, and achievable capacity.

      C = B log2(1 + SINR) (3)

      where C is the channel capacity, B is the channel band- width, and SINR is the Signal-to-Interference-plus-Noise Ra- tio. The equation illustrates that higher SINR values generally enable higher theoretical throughput. Consequently, accurate SINR prediction is an important objective during the radio planning stage.

   4. Spectral Efciency, RSRP, and SINR

   Reference Signal Received Power (RSRP) is widely used to evaluate network coverage by measuring the average received power of reference signals transmitted by the serving cell. In contrast, Signal-to-Interference-plus-Noise Ratio (SINR) reects the quality of the received signal by considering both interference and background noise. Together, RSRP and SINR provide complementary information regarding coverage and signal quality.

   Spectral efciency represents the amount of information successfully transmitted per unit bandwidth and is commonly expressed in bits/s/Hz. Higher spectral efciency indicates more effective utilization of available spectrum resources and is one of the primary performance objectives of Massive MIMO deployments.

   = C (4)

   B

   where denotes the spectral efciency, C is the achievable channel capacity, and B is the transmission bandwidth.

3. METHODOLOGY

   1. Objective and Scope

      The objective of this study is to evaluate and compare the design-stage Key Performance Indicator (KPI) predictions of conventional 4T4R MIMO and 64T64R Massive MIMO for a 5G New Radio (NR) stadium deployment using a commercial RF planning tool. The analysis focuses on the KPIs generated during the network planning phase under identical design assumptions, enabling a consistent and objective comparison of the two antenna congurations.

      The scope of this work is limited to the evaluation of design- stage predictions, including Reference Signal Received Power

      GArray

      = 10 log10

      N (2)

      Nref

      (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), cov- erage, and interference. No eld measurements, drive tests,

      or post-deployment network performance data are included in this study. The predicted results are interpreted using established wireless communication theory and relevant 3GPP specications to assess how conventional planning models represent different antenna technologies.

   2. Test Environment and System Setup

      The comparative assessment was performed using a com- mercial RF planning tool congured for a representative high- density 5G NR stadium deployment. Both the conventional 4T4R MIMO and the 64T64R Massive MIMO congurations were evaluated using identical network planning parameters to ensure a fair comparison. The planning assumptions in- cluded the same carrier frequency, channel bandwidth, antenna locations, antenna heights, sector layout, propagation model, and environmental conditions. The only intentional variation between the two scenarios was the antenna conguration.

      The planning scenario was congured to represent a high- capacity outdoor stadium environment operating in the 3.5 GHz frequency band with a channel bandwidth of 100 MHz. Multiple sectors were deployed to provide complete coverage across the stadium while maintaining consistent planning assumptions for both antenna congurations. The generated KPIs were used to compare the predicted radio performance under identical deployment conditions.

   3. Data Collection and Processing

   The RF planning tool was used to generate design-stage predictions for both antenna congurations. The primary KPIs evaluated in this study include Reference Signal Re- ceived Power (RSRP), Signal-to-Interference-plus-Noise Ratio (SINR), coverage distribution, and interference characteristics. These metrics were extracted directly from the planning tool following completion of the prediction process.

   The generated KPI distributions were analyzed and com- pared to identify differences between the 4T4R MIMO and 64T64R Massive MIMO congurations. The comparison fo- cuses exclusively on the planning-stage predictions without attempting to infer actual network performance after deploy- ment. The predicted results are interpreted in the context of established Massive MIMO principles, recognizing that conventional RF planning tools primarily rely on propagation- based models and may not fully capture dynamic features such as adaptive beamforming, Multi-User MIMO (MU- MIMO), channel state information (CSI)-based precoding, and scheduler-dependent spatial multiplexing.

4. RESULTS AND DISCUSSION

   The design-stage performance of conventional 4T4R MIMO and 64T64R Massive MIMO was evaluated using a com- mercial RF planning tool under identical network planning assumptions. The comparison focused on the predicted Key Performance Indicators (KPIs), including Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR), to assess how different antenna congurations are represented during the planning phase. The analysis is

   Fig. 1: Conventional 4T4R MIMO architecture used in the network planning study.

   limited to the predictions generated by the planning tool and does not include eld measurements or post-deployment network performance evaluation.

   1. Conventional 4T4R MIMO Architecture

      Conventional Multiple-Input Multiple-Output (MIMO) tech- nology improves wireless communication performance by employing multiple transmit and receive antenna elements to exploit spatial diversity and increase spectral efciency. In fth-generation (5G) New Radio (NR) systems, the 4-Transmit 4-Receive (4T4R) conguration is widely adopted because it provides a practical balance between coverage enhancement, implementation complexity, and deployment cost. Compared with earlier antenna congurations, 4T4R MIMO improves signal reliability, increases cell capacity, and enhances user throughput while maintaining relatively simple hardware ar- chitecture.

      Figure 1 illustrates the conceptual architecture of a con- ventional 4T4R MIMO system. The baseband processing unit generates four independent digital data streams, each of which is processed by an individual radio-frequency (RF) chain before transmission through four antenna elements. At the receiver, the incoming signals are processed using four corresponding receive RF chains, enabling spatial diversity and improved reception quality. Since each antenna element operates with a xed radiation pattern, the transmitted energy is distributed over a relatively broad coverage area without dynamically adapting to thelocations of individual users.

      The simplicity of the 4T4R architecture allows conventional RF planning tools to model its propagation characteristics with relatively high accuracy. Coverage prediction, Reference

      Signal Received Power (RSRP), and interference estimation are primarily determined by the antenna radiation pattern, transmit power, propagation model, and environmental char- acteristics. Consequently, the predicted design-stage KPIs pro- duced for conventional MIMO deployments generally exhibit good agreement with the expected coverage behaviour, making 4T4R a suitable baseline for comparison with more advanced Massive MIMO systems.

   2. 64T64R Massive MIMO Architecture

      Massive Multiple-Input Multiple-Output (Massive MIMO) extends the conventional MIMO concept by employing a signicantly larger number of transmit and receive antenna elements to improve spectral efciency, network capacity, and user experience. In 5G NR deployments, the 64-Transmit 64- Receive (64T64R) conguration enables advanced beamform- ing and spatial multiplexing techniques, allowing the radio sys- tem to concentrate transmission energy toward individual users while simultaneously serving multiple users within the same time-frequency resources. These capabilities are particularly benecial in high-density environments, such as stadiums, airports, and large public venues, where high trafc demand and severe interference conditions exist.

      Figure 2 illustrates the conceptual architecture of a 64T64R Massive MIMO system. Unlike the conventional 4T4R archi- tecture, the baseband processing unit is connected to sixty- four independent RF chains, each driving a dedicated antenna element. The large antenna array provides additional spatial degrees of freedom, enabling narrow beam generation, adap- tive beam steering, and simultaneous transmission to multiple users through Multi-User MIMO (MU-MIMO). Consequently, the transmitted energy can be dynamically focused toward active users rather than being uniformly distributed across the entire coverage area.

      Compared with conventional MIMO systems, Massive MIMO offers substantial improvements in array gain, beam- forming accuracy, interference suppression, and spectral ef- ciency. However, these performance enhancements rely on dynamic radio processing functions, including channel state in- formation (CSI)-based precoding, adaptive beamforming, user scheduling, and spatial multiplexing. Since conventional RF planning tools primarily perform static propagation analysis, many of these dynamic behaviours cannot be fully represented during the network planning stage.

      As a result, design-stage KPI predictions generated for Massive MIMO deployments should be interpreted with ap- propriate engineering consideration. While coverage-related metrics such as Reference Signal Received Power (RSRP) can generally be estimated with reasonable accuracy, performance indicators that depend heavily on real-time beam manage- ment and user distribution, such as Signal-to-Interference-plus- Noise Ratio (SINR), may not fully reect the operational behaviour observed after network deployment. This limitation forms the basis of the comparative analysis presented in the subsequent sections of this paper.

      Fig. 2: Conceptual architecture of the 64T64R Massive MIMO system.

   3. Test Environment and System Setup

   To ensure a fair and objective comparison between the conventional 4T4R MIMO and 64T64R Massive MIMO con- gurations, both deployment scenarios were developed using identical network planning assumptions within a commer- cial RF planning tool. The evaluation was performed for a representative high-density outdoor stadium environment, where network performance is primarily constrained by the large concentration of simultaneous users and the high trafc demand generated during major events.

   Figure 3 illustrates the network planning scenario adopted in this study. The stadium was divided into multiple sectors to provide complete radio coverage while maintaining over- lapping service areas for mobility and capacity enhancement. Each sector was congured using the same site locations, an- tenna heights, azimuth orientations, propagation model, carrier frequency, channel bandwidth, and environmental parameters. This approach ensured that any observed differences in the predicted Key Performance Indicators (KPIs) resulted solely from the antenna conguration rather than changes in the network design.

   The planning scenario was congured for operation in the 3.5 GHz frequency band using a channel bandwidth of 100 MHz, representing a typical enhanced Mobile Broadband (eMBB) deployment for high-capacity venues. The propaga- tion prediction model, terrain characteristics, clutter informa- tion, and simulation resolution were maintained consistently across both scenarios to eliminate external variables that could inuence the comparison.

   Two independent prediction cases were generated. In the

   Fig. 3: Network planning scenario for the high-density 5G NR stadium deployment.

   rst case, all sectors employed conventional 4T4R MIMO antennas, while in the second case the antenna conguration was replaced with 64T64R Massive MIMO. Apart from the antenna architecture, all remaining planning parameters were kept identical. This controlled methodology enabled a direct comparison of the predicted design-stage KPIs and isolated the impact of the antenna technology on network coverage and signal quality.

   The RF planning tool generated prediction maps for Reference Signal Received Power (RSRP) and Signal-to- Interference-plus-Noise Ratio (SINR), which form the basis of the comparative analysis presented in the following section. Since the study is limited to the network planning stage, the generated KPIs represent theoretical predictions derived from propagation modelling rather than measurements collected from an operational network. Consequently, the results should be interpreted as indicators of expected planning performance and not as validated eld performance after deployment.

5. RESULTS AND DISCUSSION

The predicted network performance of the conventional 4T4R MIMO and 64T64R Massive MIMO congurations was evaluated using the RF planning methodology described in the previous section. The comparison focuses on the design-stage Key Performance Indicators (KPIs) generated by the planning tool under identical deployment assumptions. The primary objective is to investigate how the two antenna architectures inuence the predicted coverage and signal quality within a high-density 5G NR stadium environment.

1. Design-Stage RSRP Analysis

   Reference Signal Received Power (RSRP) is one of the most important coverage indicators in cellular network plan- ning because it represents the average received power of the reference signals transmitted by the serving cell. During the planning stage, RSRP is extensively used to evaluate coverage continuity, determine cell boundaries, identify weak coverage regions, and verify whether the designed network satises

   Fig. 4: Predicted RSRP distribution for the conventional 4T4R MIMO and 64T64R Massive MIMO congurations.

   the required service objectives. Since RSRP is primarily determined by the link budget, antenna characteristics, and propagation environment, it is generally considered one of the most reliable KPIs generated by conventional RF planning tools.

   Figure 4 compares the predicted RSRP distributions for the conventional 4T4R MIMO and 64T64R Massive MIMO congurations under identical network planning assumptions. As illustrated in Fig. 4, the 64T64R Massive MIMO con- guration produces a noticeably stronger predicted RSRP dis- tribution across the stadium coverage area compared with the conventional 4T4R deployment. The improvement is observed not only in the regions immediately surrounding the serving sectors but also across the outer coverageboundaries, where higher received signal levels contribute to improved coverage uniformity. This behaviour is consistent with the theoretical advantages of larger antenna arrays, which provide increased array gain and more efcient utilization of transmitted energy. The planning results further indicate that the conventional 4T4R conguration exhibits a wider distribution of lower RSRP values near the sector boundaries, whereas the 64T64R deployment maintains stronger received signal levels through- out most of the service area. The higher predicted RSRP achieved by the Massive MIMO conguration can be attributed to the increased effective antenna gain provided by the larger antenna array. Consequently, the radio signal experiences lower propagation loss from the perspective of the receiving user equipment, resulting in improved predicted coverage

   performance.

   It is important to note that the planning tool estimates RSRP using deterministic propagation models, antenna radi- ation characteristics, terrain information, and network cong- uration parameters. Unlike dynamic performance indicators,

   Fig. 5: Predicted SINR distribution for the conventional 4T4R MIMO and 64T64R Massive MIMO congurations.

   RSRP prediction does not depend signicantly on real-time scheduling decisions or instantaneous user distribution. There- fore, conventional RF planning tools are generally capable of providing reliable design-stage RSRP predictions for both conventional MIMO and Massive MIMO deployments.

   Overall, the RSRP comparison demonstrates that the tran- sition from a conventional 4T4R architecture to a 64T64R Massive MIMO system signicantly enhances the predicted coverage performance within the stadium environment. These ndings conrm that larger antenna arrays provide measurable coverage benets during the planning stage and establish a strong foundation for evaluating the corresponding signal quality results discussed in the following subsection.

2. Design-Stage SINR Analysis

   Signal-to-Interference-plus-Noise Ratio (SINR) is a funda- mental performance indicator that reects the quality of the received radio signal by considering both interference from neighbouring cells and background noise. Unlike RSRP, which represents only the received signal strength, SINR directly in- uences the achievable modulation and coding scheme (MCS), spectral efciency, user throughput, and overall network capac- ity. Consequently, accurate SINR prediction is essential during the network planning stage to estimate the expected quality of service and user experience.

   Figure 5 presents the predicted SINR distributions for the conventional 4T4R MIMO and 64T64R Massive MIMO congurations under identical planning assumptions.

   As shown in Fig. 5, the planning tool predicts lower SINR values for the 64T64R Massive MIMO deployment compared

   with the conventional 4T4R conguration, despite the im- proved RSRP observed in the previous subsection. At rst glance, this result appears counterintuitive because Massive MIMO is widely recognized for improving signal quality, increasing network capacity, and enhancing spectral efciency in operational 5G networks.

   The observed difference can be explained by the methodol- ogy employed by conventional RF planning tools. During the planning stage, SINR is typically estimated using determinis- tic propagation models, antenna radiation patterns, transmit power, and static interference calculations. Although these models accurately represent large-scale propagation character- istics, they do not fully account for the dynamic radio resource management techniques that distinguish Massive MIMO from conventional antenna systems.

   In practical 5G deployments, Massive MIMO continuously adapts its transmission characteristics based on Channel State Information (CSI) obtained from active user equipment. Ad- vanced beamforming algorithms dynamically steer narrow beams toward individual users while minimizing interference to neighbouring users. In addition, Multi-User MIMO (MU- MIMO), adaptive precoding, and scheduler-controlled spatial multiplexing enable multiple users to be served simultaneously using the same time-frequency resources with reduced mutual interference. These real-time processing functions signicantly improve operational SINR but cannot be fully represented using static propagation-based planning models.

   Another contributing factor is that planning tools generally assume broad coverage predictions rather than user-specic beam allocation. Consequently, the predicted interference gen- erated by neighbouring sectors may appear higher than would actually occur in a live Massive MIMO network, where adaptive beamforming continuously suppresses inter-cell inter- ference and concentrates transmission energy toward intended users. As a result, the design-stage SINR prediction represents a conservative estimate of network performance rather than the expected operational behaviour after deployment.

   Therefore, the lower predicted SINR observed for the 64T64R conguration should not be interpreted as an in- dication of inferior radio performance. Instead, it high- lights an inherent limitation of conventional RF planning methodologies when evaluating advanced antenna systems. While propagation-based planning models effectively estimate coverage-related KPIs such as RSRP, they are less capable of accurately representing the dynamic interference mitigation and spatial processing techniques that dene Massive MIMO operation in commercial 5G networks.

   These ndings demonstrate that SINR predictions gener- ated during the planning stage should be interpreted together with engineering knowledge of Massive MIMO technology rather than being considered as absolute indicators of post- deployment network performance. Consequently, design-stage SINR results should be complemented by system-level sim- ulations, eld measurements, or operational network data whenever comprehensive performance evaluation is required.

   Fig. 6: Comparison of the predicted design-stage KPIs for the conventional 4T4R MIMO and 64T64R Massive MIMO congurations.

3. Comparative Discussion

   The comparative evaluation presented in Figs. 4, 5, and 6 provides valuable insights into the capabilities and limitations of conventional RF planning tools when assessing advanced antenna technologies. Although both deployment scenarios were developed using identical planning assumptions, the predicted KPIs reveal distinct performance characteristics for the conventional 4T4R MIMO and 64T64R Massive MIMO congurations.

   The RSRP prediction demonstrates a clear coverage advan- tage for the 64T64R Massive MIMO deployment. The larger antenna array provides higher array gain and enables more efcient utilization of transmitted energy, resulting in stronger received signal levels throughout the stadium coverage area. Since RSRP prediction primarily depends on deterministic propagation modelling and antenna characteristics, the plan- ning tool successfully captures the expected improvement in signal strength provided by the larger antenna conguration.

   From a network planning perspective, these ndings em- phasize that design-stage KPI predictions should be interpreted with appropriate engineering judgement rather than being con- sidered as absolute representations of operational performance. RF planning tools remain highly effective for site selection, coverage verication, and preliminary network dimensioning. However, the evaluation of advanced Massive MIMO features should be complemented by system-level simulations, eld measurements, or post-deployment performance analysis to fully capture the benets of adaptive beamforming and spatial multiplexing.

   Overall, the results conrm that the transition from con- ventional 4T4R MIMO to 64T64R Massive MIMO signif- icantly enhances predicted coverage performance while si- multaneously exposing the limitations of conventional plan-

   ning methodologes in estimating quality-related KPIs. These observations provide practical guidance for radio network planners and highlight the importance of combining design- stage predictions with operational performance evaluation when planning next-generation 5G networks.

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