The rapid advancement of communication technologies and the widespread adoption of mobile applications have driven exponential growth in mobile data consumption [1] . To accommodate this surge, Fourth Generation-Long Term Evolution (4G-LTE) networks were developed, offering enhanced capacity, higher throughput, and lower latency through packet-switched services [2] - [4] . LTE Release 8 introduced key technologies to improve spectrum utilization and increase data rates [5] - [8] . However, as mobile broadband traffic continues to rise—particularly in regions like Zambia, where data consumption grew by nearly 48.9% in the first half of 2022 [9] —further enhancements are required. In urban areas, high population densities and data-intensive applications contribute to increased LTE traffic and network congestion [10] . Figure 1 illustrates the relationship between data volume, user throughput, and PRB utilization, highlighting performance constraints at high PRB utilization levels.

To address these challenges, Carrier Aggregation (CA) was introduced in LTE Release 10, enhancing network capacity and throughput [12] - [16] . By combining multiple spectrum bands into wider channels, CA enables higher data rates and improved coverage. Studies conducted in South Korea have demonstrated significant improvements in user data rates following CA deployment [11] . However, its specific impact on LTE performance in Zambian urban areas remains largely unexplored. This study aims to assess the impact of CA on LTE networks in Chelstone, Lusaka, focusing on Operator X’s network.

The growing number of mobile devices and increasing data demand in Zambia are placing immense pressure on existing LTE networks [11] . While 5G deployment is underway, LTE and its advancements, including LTE-Advanced (LTE-A), remain vital. This research seeks to explore how technologies like CA can enhance LTE performance in urban Zambia, where network capacity and user experience are critical concerns.

The study aimed at:

i) Identifying the challenges faced by Mobile Network Operators (MNOs) in implementing CA.

ii) Determining the effect of CA on network capacity and throughput.

iii) Evaluating the performance improvements achieved through CA in LTE-A networks.

The findings of this research will provide valuable insights for LTE network planning and optimization in Zambia. By understanding CA’s effectiveness, network operators can enhance capacity, mitigate congestion, and improve user experience. Additionally, this study will guide MNOs in leveraging CA for optimal spectrum utilization and better service provision.

Cellular mobile communication systems have evolved through several generations: 1G analog, 2G digital, 3G broadband, 4G-LTE (or 3.9G in some publications), and the currently developing 5G [17] . LTE, like electricity, has become a necessity in modern society, representing an evolutionary step beyond 3G. It offers flexible deployment options and service offerings [18] . LTE utilizes an all-IP-based architecture, where mobile devices communicate via IP addresses. Figure 2 illustrates the LTE network architecture [18] .

LTE-Advanced (LTE-A), introduced in LTE Release 10, aimed to meet the International Mobile Telecommunications Advanced (IMT-Advanced) requirements by enhancing LTE with features like OFDMA/SC-FDMA, 20 MHz bandwidth support, MIMO, and advanced modulation, while maintaining backward compatibility [13] [16] . A key feature of LTE-A is Carrier Aggregation (CA), which increases bandwidth by aggregating multiple Component Carriers (CCs). CA enhances data rates, improves downlink coverage, and allows operators to utilize fragmented spectrum effectively. Multiple LTE carriers, each up to 20 MHz, can be transmitted in parallel, enabling wider bandwidths and higher data rates.

There are three modes of CA, allowing operators to exploit fragmented spectrum allocations. CCs do not need to be contiguous in frequency, and up to five CCs, possibly of different bandwidths up to 20 MHz, can be aggregated, resulting in overall transmission bandwidths up to 100 MHz. Terminals supporting CA can simultaneously receive or transmit on multiple CCs, which are backward compatible with earlier LTE releases. Figure 3 depicts the different modes of CA [19] - [21] .

A literature comparison matrix, as shown in Table 1 , provides a structured comparison of multiple research studies. It includes authors, publication dates, research questions, methodologies, findings, and conclusions. This matrix helps identify patterns, contradictions, knowledge gaps, and trends within the research area, facilitating the synthesis of existing literature and informing future research directions.

To address the research objectives, this study adopted a mixed-methods approach, integrating both quantitative and qualitative data collection and analysis to assess LTE performance and the impact of CA in urban Zambia.

Quantitative data was collected through drive tests, stationary tests, and statistical data from Operator X’s network sites in the Chelstone area. The drive tests followed a predefined route encompassing diverse terrain types—residential, commercial, and open spaces. Tests were conducted between 11:00 and 14:00 hours to ensure consistency in network conditions. During the tests, a 5 GB file was downloaded with CA initially disabled, while key performance indicators (KPIs) such as RSRP, SINR, and throughput were recorded using diagnostic tools. GPS logging was used to correlate network performance with specific geographic locations. After completing the first test, CA was enabled and verified, and the same route was driven again for a 30-minute period to allow accurate before-and-after comparisons. All test logs and screenshots were backed up for analysis.

Stationary tests involved downloading a 1 GB file at two fixed points within Chelstone—one near a cell tower and another in an indoor environment—to reflect different network conditions. As with the drive tests, these were conducted with CA both disabled and enabled, using a Xiaomi Note 4 smartphone (a CA-capable Category 6 UE) to ensure consistent device capability.

Qualitative data was gathered through stakeholder interviews and structured questionnaires. A modified Delphi approach was employed to gather expert insights on the deployment and performance of Carrier Aggregation in LTE networks [22] . Although only a single round of structured questionnaires and interviews was conducted, the method retained key Delphi principles such as expert selection, anonymity, and systematic data collection. This approach enabled the inclusion of informed perspectives from industry professionals, providing qualitative depth to complement the quantitative analysis. Participants were selected based on two criteria: i) at least five years of experience in the telecommunications sector, and ii) direct involvement in projects related to LTE and CA deployment. This strict selection criterion limited the pool to nine qualified personnel across three MNOs at the time of the study, representing a notable constraint. Completed questionnaires were reviewed, coded, and analyzed using Google Docs and Excel.

Challenges faced by MNOs in deploying CA were identified through questionnaires, interviews, and a literature review. Additionally, empirical data from Operator X’s network before and after CA activation were analyzed using testing tools and software to evaluate performance changes. The quantitative design was appropriate due to the study’s reliance on measurable variables and statistical data [20] .

The combined use of quantitative and qualitative methods was justified for the following reasons: i) the first objective did not require complex numerical analysis; ii) the research design was structured and naturally applicable; iii) the methodological procedures were value-free, minimizing researcher bias; and iv) the research utilized statistical data analysis.

Figure 4 illustrates the research process.

The sample size was calculated using Gogtay’s guidelines [23] [24] . Specifically, the following equation was used:

$n=\frac{2{\left(Z_{1-\frac{\alpha }{2}}+Z_{1-\beta }\right)}^2}{d^2}$

where:

n = sample size

$Z_{1-\frac{\alpha }{2}}$ = Z-score corresponding to the desired significance level (α)

$Z_{1-\beta }$ = Z-score corresponding to the desired power ( $1-\beta$)

d = effect size

Using this equation, with a 90% power (β = 0.10) and 5% significance level (α = 0.05), the calculated sample size was n = 44 days. Network KPI data were collected over 44 days (22 days pre-CA, 22 days post-CA), with daily analysis. Drive and stationary tests were conducted between 11:00 and 14:00 hours to maintain controlled conditions and avoid busy hours. Drive tests captured mobile network performance across Chelstone, while stationary tests assessed performance at selected locations. Both tests compared performance with and without CA.

The test location included drive test routes and stationary locations in Chelstone (see Figure 5 ), chosen for their CA-capable sites belonging to Operator X.

Measurements were conducted on Operator X’s commercial network using TEMS V.24.1 Drive Test Software, a professional tool for outdoor and indoor measurements. Logs were analyzed using TEMS Investigation software. Pre- and post-CA KPIs were collected over 44 days for comparison. Logs were also collected from the Ericsson Network Manager (ENM) and analyzed in Microsoft Excel. The Xiaomi Note 4, a CA-capable Category 6 UE, was used for testing.

To address the first research question, questionnaires were distributed to qualified personnel from three MNOs, with criteria ensuring respondents had substantial telecommunications experience and involvement in CA deployment. Human Resource departments verified respondent qualifications. Participants received introductory letters and were assured anonymity. The questionnaire, consisting of 45 multiple-choice questions across four sections (Technical, Finance, Legal/Regulatory, and General), was analyzed using percentages and Google Docs.

For the other research questions, CA RF KPI measurements were conducted in Chelstone. Drive tests covered diverse terrains, with data collected using testing tools while downloading a 5GB file with CA disabled and enabled. Data included LTE signal strength (RSRP), SINR, and throughput. Stationary tests involved downloading a 1 GB file at two locations, with tests conducted at similar times as drive tests, with CA disabled and enabled. OSS KPIs were downloaded from the ENM server for pre- and post-CA comparisons over 22 days.

Limitations of the study included: i) a small pool of interview respondents, mitigated by cross-verification with multiple data sources; ii) lack of calibration verification for tools and software, mitigated by using internationally calibrated and ZICTA-approved tools; and iii) single urban location, chosen for better control over confounding variables.

Although Category 6 UEs can theoretically achieve 300 Mbps downlink with 40 MHz aggregated bandwidth, reaching such speeds in live networks is difficult. However, this study demonstrated that CA can significantly enhance user experience, with observed peak data rates reaching 126.20 Mbps in a commercial setting.

The results confirm that CA boosts real-world data rates and network capacity by aggregating carriers, reducing PRB utilization, and maintaining robust coverage. These improvements are especially beneficial in urban areas facing increased data demand and congestion.

Overall, the study successfully met its objectives, showing that CA is an effective solution for optimizing LTE-Advanced networks in urban Zambia. It offers practical insights for network planning and improving service quality in high-traffic environments.

The authors would like to thank all stakeholders, MNOs, and individual respondents.