# A Comparison of Energy Consumption in Voice Call Services

Youssef Badra, Razvan Stanica

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Youssef Badra, Razvan Stanica. A Comparison of Energy Consumption in Voice Call Services. PIMRC 2025 - IEEE 36th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, Sep 2025, Istanbul, Turkey. hal-05308587

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# A Comparison of Energy Consumption in Voice Call Services

Youssef Badra*, Razvan Stanica*

*INSA Lyon, Inria, CITI, EA3720, 69621 Villeurbanne, France
{firstname.lastname}@insa-lyon.fr

***Abstract***—Voice call services remain the fundamental use case for mobile phones, even as modern smartphones have grown into sophisticated computing platforms. While prior work has addressed the general power behavior of mobile devices, little attention has been paid to the specific energy footprint of voice services at a component level. In this paper, we address this gap by performing fine-grained energy measurements, isolating the power rail of the radio frequency (RF) module within commercial smartphones. Our analysis compares total device and RF-only energy consumption across multiple voice call technologies under controlled conditions. We demonstrate that the accuracy of measuring energy consumption in voice call technology, when based only on the total device consumption, can distort conclusions and lead to incorrect analyses.

***Index Terms***—VoIP, VoLTE, VoWiFi, Circuit Switch, Voice Call, Energy Consumption.

## I. INTRODUCTION

Voice call services are the reason behind the existence of the telecommunications industry. They remain a fundamental use case for mobile phones, even as modern smartphones have grown into sophisticated computing platforms. As mobile networks evolved, so did voice call technologies, moving from a circuit switched (CS) approach towards a service running above the Internet Protocol (IP), so called Voice over IP (VoIP). With the increasing focus on optimizing battery life and improving energy efficiency, it is important to understand the energy impact of the different available voice call technologies. This is especially relevant in the context of evolving mobile network architectures and heterogeneous radio access technologies (RATs).

Smartphones today support multiple voice call technologies: legacy CS calling over 2G and 3G networks, Voice over LTE (VoLTE) over 4G, and Voice over WiFi (VoWiFi) over a WiFi local access network. VoLTE can operate over both 4G and 5G Non-Standalone (NAS) radio access network (RAN) configurations, using the IP Multimedia Subsystem (IMS) network for voice transport [4]. Similarly, VoWiFi also relies on IMS and, while VoIP data is exchanged over the WiFi local area network, it still requires the device to remain attached to a mobile RAN for mobility management and service continuity. In our study, VoWiFi is tested in such a configuration, with the phone connected to a 4G RAN during the call.

This work was partially funded by the CHIST-ERA ECOMOME project.

While prior work has addressed the general energy consumption behavior of mobile devices, little attention has been paid to the specific energy footprint of voice services at a component level. This paper addresses that gap by performing fine-grained energy measurements, isolating the radio-frequency (RF) module consumption using hardware-modified smartphones and a dedicated power monitoring platform. Our analysis compares total device and RF-only energy consumption across VoLTE, VoWiFi, and CS technologies under controlled conditions.

In this experimental study, we aim to challenge claims in the literature regarding the energy efficiency of voice call technologies. For instance, Ratul et al. [3] report that, in 4G networks, VoLTE reduces energy consumption by approximately 60% before and 40% after the service request phase compared to non-VoLTE voice modes. Their findings are based on theoretical estimates and field measurements using the XCAL drive test tool. On the other hand, Perrucci et al. [7] argued that GSM (2G) voice calls are significantly more efficient, requiring 46–50% less power on the user equipment (UE) side than UMTS (3G) calls. We look at these claims in the real world using our detailed, hardware-level measurements. This approach gives us a component-level understanding, especially at the RF module, of how different technologies use energy.

The remainder of this paper is structured as follows. Section II gives more details on the related works, while Section III provides some background information on voice call technologies. Section IV describes in detail our hardware-based measurement approach. Our main experimental results are summarized in Section V, while the conclusions of the study are discussed in Section VI.

## II. RELATED WORKS

Energy efficiency in mobile devices has been extensively studied across various applications and services, especially in light of the rapid evolution of cellular technologies [6]. However, the specific domain of energy consumption during voice call services has received comparatively limited attention. While earlier studies have evaluated the energy demands of different voice call technologies, such as VoLTE, CS, and VoWiFi, in real-world contexts, much of this work is now dated. This makes our research one of the few up-to-date investigations into this area. For example, Huang et
al. [4] assessed the performance and energy characteristics of 3G and 4G networks using a software-based approach, identifying VoWiFi as the most power-efficient, followed by CS and then VoLTE. While software-based power estimation methods remain popular due to their ease of use, they depend on modeling approaches that introduce inaccuracies, particularly when estimating device-wide energy usage.

More recently, Bezerra De Souza et al. [1] offered a detailed comparison of energy consumption between 4G VoLTE and 5G Standalone (SA) Voice over New Radio (VoNR) on IMS-based networks. Their results showed substantial energy savings (up to 38.88%) alongside improved call quality indicators, such as reduced jitter, in favor of 5G SA VoNR. This work utilized a hardware-based experimental configuration to assess the overall power usage of the device, encompassing elements such as the display and CPU, among others, which may adversely impact the accuracy of the results. While this method clearly provides a more authentic energy assessment than software-based solutions, it still falls short in precisely isolating the power consumption specific to the RF modules. Generally speaking, we argue that hardware-based measurement techniques provide more accurate, component-level energy insights. This paper will employ this method; studies that employ such methodologies, particularly those that focus on the RF module, remain relatively rare in the literature.

Going beyond measurements, some related studies explore optimization methods for call energy use. For example, Kapoor et al. [2] introduced techniques like Connected Mode Discontinuous Reception (CDRX) and Semi-Persistent Scheduling (SPS) for VoNR devices, effectively reducing energy consumption during voice calls.

A voice call consists of two phases: the call setup and the voice data exchange. These two phases were rarely studied separately from an energy consumption perspective. Ratul et al. [3] demonstrated that VoLTE significantly reduces call setup delay when compared to CS, by approximately 72%, primarily through minimizing signaling messages. This reduction also contributes directly to enhanced battery savings and improved overall energy efficiency. Similarly, De Aguiar et al. [5] showed that modem chipset technology and compliance with advanced 3GPP releases, particularly Release 16, can significantly reduce call setup time, achieving reductions up to 78% for VoNR scenarios. These improvements also imply potential modem power consumption savings due to fewer signaling interactions and optimized procedures [5]. However, these are only intuitions coming from delay-oriented studies and not direct energy consumption measurements.

Our study addresses this gap by utilizing a customized hardware measurement setup capable of isolating the power rail of the RF module in specific commercial smartphones. This provides a clearer and more precise analysis of energy consumption for voice call technologies (VoLTE, VoWiFi, and CS) under real-world operating conditions.

### III. CALL SERVICES TECHNOLOGY

Contemporary voice call services have progressed through four distinct technological paradigms, each characterized by unique network architectures and energy considerations.

CS networks, the traditional foundation of mobile telephony used in 2G and 3G technologies, establish dedicated physical connections between callers. This synchronous communication requires fixed and continuous radio resource allocation throughout voice calls and lacks native support for simultaneous voice and data transmission.

With the evolution towards VoIP, including in the world of fixed telephony, multiple technologies have evolved in the context of mobile networks. VoLTE is used in 4G and 5G NSA networks, which transitioned to all-IP architectures. VoLTE emerged as the packet-switched successor to CS. As key characteristics, VoLTE uses IMS to deliver voice as data flows over 4G and 5G networks, it enables high-definition (HD) voice transmission, and it allows concurrent voice/data sessions.

VoWiFi is an IP-based extension of VoLTE. VoWiFi routes calls through Wi-Fi access points to the cellular core network and IMS via encrypted tunnels. Through this, it benefits from Wi-Fi deployment and optimized power management at the physical and medium access control (MAC) layers. Finally, VoNR is the 5G SA evolution of VoLTE. VoNR leverages a dedicated 5G core and New Radio infrastructure. IMS is retained for service logic, ensuring interoperability with VoLTE. In all these technologies, a caller is a UE that initiates a call to another UE, which is referred to as the callee.

### IV. MEASUREMENT SETUP

Smartphone energy consumption can be measured using either software or hardware approaches [13]. However, software probes [12] cannot accurately measure the energy consumption of the different smartphone components, while, in our case, we want to focus on measuring the energy consumption of the wireless network component (isolate the RF module) in the context of voice call services. Our objective to focus on the RF module comes from our interest in comparing the different voice call technologies from a networking perspective.

Therefore, in order to conduct precise energy consumption measurements at the UE level, we build an experimental platform containing three parts: hardware-modified smartphones, a power monitoring instrument, and a data acquisition unit. On the smartphone side, we use three different devices: an iPhone 12 Mini A2399, an iPhone X A1865, and a Google Pixel 6 Pro. These specific models are popular commercial smartphones that are widely used today and were selected in this study for reasons that will be detailed below. For power monitoring, we use a PAC-1954 power monitor sensor. This sensor is a quad power and energy monitor with 16-bit voltage and current sensing resolution and a maximum sampling rate of 1024 Hz. This power monitoring sensor
is installed on a Mikroe Fusion development board with an
ARM STM32F407 microcontroller.

<figure><img src="image_2.png" /><figcaption>Fig. 1: Smartphone hardware modification: original board, reconnection of the two board parts, and final board setup.</figcaption></figure>

The FinFET manufacturing process, measured in nanometers (nm), directly impacts power consumption and energy efficiency of both modem and CPU components in smartphones. Smaller FinFET processes, such as the 5 nm used in the Apple A14 and Google Tensor chipsets, significantly reduce transistor size and leakage current, thereby decreasing power consumption and enhancing battery life. Consequently, devices equipped with smaller process nodes typically exhibit better energy efficiency compared to those using larger nodes (e.g., 10 nm and 14 nm), as observed in the power performance differences between the studied devices [14].

Finally, the data acquisition unit is a Linux computer that is connected to the Mikroe Fusion development board with the power monitor sensor through serial communication. The data acquisition unit sends the sensed data to the host at a sample rate of 64 samples per second. We have two different power-sensing channels for the iPhone models and three channels for the Pixel. This measurement setup is depicted in Fig. 2.

<figure><img src="image_3.png" /><figcaption>Fig. 2: Energy consumption measurement platform, iPhone scenario.</figcaption></figure>

However, modern smartphones have complex internal architecture and multi-layered power consumption patterns. Moreover, the internal smartphone architecture differs for different manufacturers or even for different smartphone models. Therefore, our work began by identifying smartphone models with hardware allowing us to separate the power consumption of the RF module from the overall power consumption. By modifying these smartphones, we can measure the consumption of the RF components (such as transceivers, baseband processors, power amplifiers, etc.) and eliminate the consumption of the screen, camera, processor, memory, etc.

<figure><img src="image_4.png" /><figcaption>Fig. 3: Power consumption for a Pixel 6 Pro on 3G RAN during 2 minutes of 3G CS call : P<sub>RF</sub> power consumed by the RF module and P<sub>Total</sub> overall power consumption.</figcaption></figure>

Indeed, these non-network components represent a significant portion of the power consumption for the device under test (DUT). To demonstrate this, in Fig. 3, we show a comparison between the overall power consumed by the DUT and the RF module in a 2-minute 3G CS voice call. The call session comprises two components: the call establishment or setup, which occurs prior to answering the call, and the call itself, which takes place after answering. We can clearly notice from this figure that the overall power consumption is dominated by other smartphone elements. Moreover, the total power consumption is not simply a shift of the RF power consumption, resulting in different consumption patterns. Since our goal is to analyze the power consumption of the different network technologies, this motivates us to propose a measurement setup capable of isolating these specific components.

A. Hardware modifications

As we want to obtain accurate power measurements for
a specific smartphone function (i.e., wireless connectivity),
we prefer hardware probes to software ones. We bring some
hardware modifications to specific smartphone models, with
the goal of gaining access to the internal power rail of the
logic board, which powers the RF module in the DUT. The
characteristics of the smartphone models used in this study
are summarized in Table I. Of course, we want to achieve
this objective without adding any component with an impact
on the power consumption we desire to measure.

The choice of smartphones capable of supporting such
hardware modifications is complex. In this study, we used
two iPhone models: the iPhone 12 mini A2399 EU version,
which can access 4G, 5G NSA sub6GHz, Wi-Fi 2.4Ghz, and
5Ghz networks, and the iPhone X A1865, which supports 3G,
4G, Wi-Fi 2.4Ghz and 5Ghz technologies. These two models
were selected because of their internal substrate-like printed
circuit board (SLP) architecture. After carefully studying the
internal architecture of these devices, we noticed that they
consist of two boards: the lower board of these two smart-
phones is mainly populated with RF network components,
and there are no RF components on the upper board.
More precisely, for the two selected iPhone models, the main logic board is split into two logic boards soldered together, known as the AP board (upper board) and RF board (lower board). The upper board is connected to the battery and has the main power management integrated circuit (PMIC), which then distributes power to all logic board components and supplies the lower board with the main power rail. The lower board is where all the RF module parts are grouped together. This includes baseband and all-band power amplifiers, antennas, transceivers, Wi-Fi and Bluetooth modules, the baseband power management unit (BB_PMU), modulators, the front-end module, etc. The BB_PMU generates all the power rails to supply the electronic components of the RF module on the lower board.

The hardware modification technique we employ involves disconnecting the vias and traces of the main power rail from the upper board to the lower board. This ensures that no power supply line reaches the lower board. To facilitate measurement, we extended the main power rail from the upper board to outside the phone using 0.1-mm copper wire, as illustrated in Figure 1. This extended rail is then connected to a power monitor instrument with a sense resistor before being routed back inside the phone and soldered back to the lower board. This supplies the lower board with power again. In order to boot the phone and make it functional, we need to solder back the lower and upper boards and reconnect the data line from the upper to the lower board. To summarize this modification, we conduct power consumption measurements on the lower board by interrupting the main power line trace, PP_VDD, which is responsible for delivering power to the lower board and is initially sourced from the upper board. PP_VDD functions as a 4.2V power rail, serving as the primary power supply on the logic board.

<table><caption>TABLE I: Specifications comparison between iPhone X, iPhone 12 mini, and Pixel 6 Pro.</caption><thead><tr><th></th><th>iPhone X A1865</th><th>iPhone 12 mini A2399</th><th>Pixel 6 Pro GLUOG</th></tr></thead><tbody><tr><th>Chipset</th><td>Apple A11 Bionic</td><td>Apple A14 Bionic</td><td>Google Tensor</td></tr><tr><th>CPU</th><td>2x 2.39 GHz Monsoon 4x 1.42 GHz Mistral (10 nm)</td><td>2x 3.1 GHz Firestorm 4x 1.8 GHz Icestorm (5 nm)</td><td>2x 2.80 GHz X1 2x 2.25 GHz A76 4x 1.80 GHz A55 (5 nm)</td></tr><tr><th>Modem-RF</th><td>Qualcomm X16 LTE (14 nm) [11]</td><td>Qualcomm X55 5G (7 nm) [10]</td><td>Samsung Exynos 5123 5G (7 nm) [9]</td></tr><tr><th>3GPP</th><td>Release 12</td><td>Release 15</td><td>Release 15</td></tr><tr><th>RAM</th><td>3GB</td><td>4GB</td><td>12GB</td></tr><tr><th>OS</th><td>iOS 16.7.10</td><td>iOS 17.5.7</td><td>Android 15</td></tr></tbody></table>

We also use a third model, an Android-based one, namely the Pixel 6 Pro. The specifications of the Pixel 6 Pro GLUOG model include access to 4G, 5G NSA sub-6GHz, and Wi-Fi networks at 2.4GHz and 5GHz. The device incorporates a distinct hardware modification compared to the previously discussed iPhone models. Indeed, Google integrated two

<table><caption>TABLE II: The table displays the measured components of both RF module channels. [8]</caption><thead><tr><th>RAN</th><th>Ch: PA FEM</th><th>Ch: BB TX-RX</th></tr></thead><tbody><tr><td>All RANs (2G, 3G, 4G, 5G)</td><td>Samsung Exynos SM 5800 Supply Modulator (2 pcs)</td><td>Samsung SHANNON A5123 5G Modem</td></tr><tr><td>All RANs (2G, 3G, 4G, 5G)</td><td>Samsung Shannon 5311A</td><td>Samsung SHANNON 5511 RF Transceiver</td></tr><tr><td>4G (LTE), 5G NR</td><td>Skyworks SKY53738 FEM (3 pcs)</td><td>-</td></tr><tr><td>2G (GSM), 3G (WCDMA), 4G</td><td>Skyworks SKY77652-31 PAM</td><td>-</td></tr><tr><td>4G (LTE), 5G NR</td><td>Skyworks SKY53737 FEM</td><td>-</td></tr><tr><td>4G (LTE), 5G NR</td><td>Skyworks SKY58260-11 FEM</td><td>-</td></tr><tr><td>4G (LTE), 5G NR, 3G</td><td>Qorvo QM77080 FEM</td><td>-</td></tr></tbody></table>

shunt resistors on the logic board of the Pixel device to monitor the power usage of network-associated components in the RF module. This allows us to delve deeply into the power consumption of the Pixel RF module. This hardware permits separate assessment of power amplification and front-end modules (PA FEM) as well as baseband and transceiver (BB TX-RX), with their sum equating to total RF module consumption, as indicated in Table II. This table details the components involved in the two channels, PA FEM and BB TX-RX, for the different RAN technologies. The third channel measures overall device consumption. Therefore, the Pixel measurements use the same power monitoring setup as described in Figure 2, but with a supplementary third channel of measurement.

## B. Experiment Description

The objective of our measurement campaign is to evaluate the energy consumption associated with different voice call technologies (CS, VoLTE, VoWiFi) across various cellular network conditions. Specifically, we measure the energy consumption for VoLTE and VoWiFi technologies on a 4G connection and the CS technology using EDGE (2G) and UMTS (3G) networks.

The conducted experiment involves voice calls with a controlled duration of two minutes per session to maintain consistent measurement conditions across tests. During each call session, background processes on the smartphones are minimized to eliminate interference from unrelated applications or tasks. The sessions are conducted in different configurations: the DUT can either be the caller initiating the call or the callee receiving the call, with configurations alternating between iPhone and Pixel devices as caller and callee. To maintain test repeatability, each call scenario is performed multiple times (3 iterations per scenario), and the smartphones are placed in an environment with stable cellular network conditions.

The results allow us to analyze the impact of the underlying voice call technology on: i) the power consumption of the smartphone RF module and ii) the total smartphone consumption. We do not include CS fallback scenarios in our
<figure><img src="image_5.png" /><figcaption>Fig. 4: Total device energy consumption during voice calls for a caller DUT.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5: RF module energy consumption only for a caller DUT.</figcaption></figure>

evaluation, i.e., calls initiated from a 4G RAN and falling back to 3G CS for voice service are excluded. Instead, CS calls are directly tested over native 2G and 3G RAN environments to provide a clean comparison between access technologies without handover-related complexity.

## V. RESULTS

In this section, we present the initial results obtained from our experiments conducted with the three DUT in a caller configuration, initiating calls to an iPhone equipment serving as the non-DUT receiving device.

Figure 4 presents the total device energy consumption, including CPU, RAM, LCD, NAND, RF module, and other components. We show this as the energy consumed over a one-minute interval for the three smartphone models (iPhone X, iPhone 12 Mini, and Pixel 6 Pro) acting as callers initiating calls to another (non-DUT) device. We do not observe any clear device-based trend: for some voice call technologies, the Pixel consumption is lower than that of iPhones, while in others, the reverse is true.

<figure><img src="image_7.png" /><figcaption>Fig. 6: Energy consumption for the iPhone DUTs during a complete call session, in both caller and callee configurations.</figcaption></figure>

For the Pixel phone, the differences among the four voice call technologies are rather low: 4G VoLTE has a 12.5% higher consumption than the other technologies. Conversely, the iPhones exhibit nearly identical energy consumption levels for 3G CS and 4G VoLTE calls, while 4G VoWiFi shows a 57% reduction in energy consumption.

Nevertheless, significant hardware and design differences between these smartphone models, including screen size, CPU, baseband modem, manufacturing year, and semiconductor fabrication technology (FinFET node), influence the overall energy consumption. These variations limit the accuracy and comparability of total energy measurements across different devices, requiring careful consideration when interpreting the results. To accurately distinguish the energy consumption specific to each voice call technology, relying solely on total device energy consumption is insufficient. Instead, a deeper analysis focusing specifically on the energy consumption of the RF modules is required.

As explained, our contribution allows us to measure only the RF module energy consumption. In Figure 5, we present the energy consumption of the RF module during the call sessions. On average, RF modules in DUTs consume 30% of the total device energy consumption. We note that this figure omits the 4G VoWiFi results because, for the Pixel 6 Pro, we could not integrate the energy consumption of the WiFi chip into the RF module measurements, unlike in the case of the iPhones.

Furthermore, the discrepancy in the consumption of all voice technologies on the iPhone is minimal, which makes us reconsider the results presented in Figure 4, where fluctuations in consumption are observed in the iPhone scenario. In the context of the Pixel case, although 5G NSA VoLTE is projected to consume the least energy overall, it actually exhibits the highest consumption in practice. These results demonstrate that focusing on the total device consumption can distort conclusions and lead to incorrect analyses.

The next step in our analysis, shown in Figure 6, is compar-
<figure><img src="image_8.png" /><figcaption>Fig. 7: Call setup and call power consumption in DUT caller configuration.</figcaption></figure>

ing the consumption of the RF modules across the four voice technologies in both call configurations, caller and callee. In the caller configuration, 4G VoLTE and 3G CS consume noticeably more energy than the callee configuration. We also notice that VoWiFi consumes 72% less energy than the other technologies in both configurations.

As discussed, the call session comprises two parts: the call establishment or setup, which occurs prior to answering the call, and the call itself, which takes place after answering. In Figure 7, the total energy consumption of the call is divided into these two parts. We note that we normalise these two energy consumptions as "energy consumed in a minute" to be able to compare them. However, in practice, the call setup phase generally has a much lower duration than the call itself.

For the Pixel DUT, we can go one step further with advancements in the specifics of RF module power consumption on two channels: PA FEM and BB TX-RX, as elaborated in Section IV. In Figure 8, we include an experiment on 2G technology (CS over Edge), as the device allows this. The first observation is that the modem and transceiver (BB TX-RX) energy dominates the call services energy consumption. On this DUT, VoLTE over 5G consumes more than VoLTE over 4G, as also observed in Figure 5. CS over 2G and 3G have similar overall consumption, but this is divided differently between the RF components.

## VI. CONCLUSION

This study offers a detailed component-level analysis of energy consumption for modern voice call technologies, utilizing innovative hardware modifications to isolate RF module power consumption in smartphones. Through the precise measurement of baseband-transceiver and power amplifier components, independent of total device consumption, we demonstrate that VoWiFi exhibits significantly enhanced energy efficiency, outperforming alternatives by 72%. In contrast, VoLTE and traditional CS calls present unexpectedly similar energy profiles. The RF module constitutes roughly

<figure><img src="image_9.png" /><figcaption>Fig. 8: Detailed RF module power consumption for the Pixel 6 Pro, as described in Table II.</figcaption></figure>

30% of the total energy consumption of the device during calls, with the modem and transceiver identified as the primary power consumers. The study reveals shortcomings in traditional device-level measurements, which frequently misrepresent accurate assessments of network energy consumption. This study introduces a novel methodology for precise benchmarking of voice technology and offers insights for the advancement of energy-efficient mobile networks.

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