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# Prediction-based Discontinuous Reception Mechanism for Extended Reality Applications

Sekinat Yahya, Razvan Stanica

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Sekinat Yahya, Razvan Stanica. Prediction-based Discontinuous Reception Mechanism for Extended Reality Applications. CCNC 2025 – IEEE Consumer Communications and Networking Conference, Las Vegas, January 2025., 2025, Las Vegas, United States. hal-04945405

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# Prediction-based Discontinuous Reception Mechanism for Extended Reality Applications

Sekinat Yahya*, Razvan Stanica*

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

***Abstract***—Extended reality (XR) applications are characterised by their unique multi-modal traffic pattern. However, XR user equipment (UE) presents significant battery-related challenges. Meanwhile, the discontinuous reception (DRX) mechanism was designed and standardised to extend the UE battery life on cellular networks. The DRX mechanism is based on a number of parameters, and studies focused on modelling its behaviour targeted diverse types of traffic on UMTS, LTE, and 5G NR networks. Some of these studies target generating analytical DRX models for specific traffic patterns, while others optimise the DRX mechanism with predictive models for specific applications. With evolving application types on the network, such as XR, there is a need to expand on these previous works to prepare current and future networks for the demands of new-age applications. In this paper, we examine prediction-based DRX optimisation tailored for XR applications. We design a dynamic DRX optimisation algorithm that utilises predictions of traffic inter-arrival times to select dynamic DRX parameters with two performance metrics: energy efficiency and delay. Using publicly available XR data, we utilised state-of-the-art forecasting tools such as Long Short Term Memory and Gaussian Processes models to design our dynamic DRX solution, and we report an improvement over the energy efficiency obtained by the standard DRX mechanism with minimal extra-delay.

***Index Terms***—Mobile Networks, Extended Reality, Radio Resource Control, Energy Optimisation

## I. INTRODUCTION

New-age mobile services, such as extended reality (XR) applications, are becoming more mainstream slowly but surely. Simultaneously, energy consumption is becoming a major research topic in the cellular network world, pushed by environmental concerns and increasing energy prices. Therefore, it seems important to prepare current and future networks for the impact of these new applications on the network, energy-wise. In the energy optimisation literature, different techniques and algorithms have been proposed to tackle the energy efficiency on the operator side, such as radio access network (RAN) switch-off schemes for channel carriers, sectors, or entire sites [3].

In a similar vein, on the user equipment (UE) part, the discontinuous reception (DRX) mechanism has been proposed (among other techniques) to reduce the energy consumption of mobile phones [7], [8]. The 3rd generation partnership project (3GPP) introduced this technique more than two decades ago, for the Universal Mobile Telecommunications System (UMTS) [8], but the DRX mechanism has gone

through several iterations for long-term evolution (LTE), LTE-advanced and 5G new radio (NR) technologies. However, next-generation mobile applications, such as XR, raise new challenges to the classical DRX approach, through their strict and heterogeneous quality of service (QoS) requirements [10].

Indeed, XR services can manifest solely in the digital realm, as virtual reality (VR) applications, or within the physical world in the form of mixed reality (MR) [2]. Delivering the XR experience involves the transport of various types of traffic (video, haptic, sensor data) simultaneously for a single UE. Indeed, through mechanisms such as split rendering and distributed computing, QoS of XR services is improved through decoupling uplink and downlink processing of sensors, camera, and haptic data of XR devices. These different types of data present different properties and QoS requirements that need to be tightly synchronized to enhance the immersive experience, bringing a unique and original challenge to the mobile network. This also translates into new definitions for performance metrics [2], [12]. In this paper, we provide arguments that support the fact that the conventional timer-based DRX approach needs to be revisited for modern XR applications. The contributions of this paper are the following: i) we propose, for the first time in the literature, a prediction-based DRX mechanism specifically conceived for XR traffic patterns; ii) we model future XR packet inter-arrival times using Gaussian Processes (GPs) and Long Short-Term Memory (LSTM) based on diverse XR traffic traces to evaluate the proposed dynamic prediction-based DRX mechanism; iii) we compare the two prediction techniques in terms of prediction error and overhead costs, alongside evaluating the dynamic DRX mechanism with the standard timer-based approach in terms of energy efficiency (EE) and delay. The rest of the paper is arranged as follows. Section II provides background information on the classical DRX mechanism. Section III presents our prediction-based DRX solution, evaluated on XR application data discussed in Section IV. Our evaluation results are shown in Section V, before wrapping up with a brief discussion of related works in Section VI and concluding remarks in Section VII.

## II. DRX MECHANISM

The DRX mechanism was defined to extend the UE battery life by periodically switching off the radio frequency (RF) elements of UEs, hence ceasing to monitor the physical downlink control channel (PDCCH) [5]. The DRX mechanism

<footnote>This work was partially funded by the CHIST-ERA ECOMOME project.</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1: DRX Mechanism</figcaption></figure>

occurs under the radio resource control (RRC) protocol of the cellular network stack. Figure 1 illustrates this mechanism in detail. The DRX mechanism occurs in both RRC connected and idle modes. In NR, an intermediate state (RRC inactive) is added between the connected and idle states of the RRC protocol. In this paper, we focus on the DRX mechanism in the RRC connected mode. In this mode, the UE listens to the PDCCCH for incoming signaling data. On data reception, the UE processes them and, when the reception stops, it starts an inactivity timer $T_{IN}$. If new data arrives before counting $T_{IN}$, the UE stays in an active state and the process repeats. If not, the UE goes to a sleep state. Technically speaking, the DRX mechanism has two sleep modes: the DRX short cycle (of duration $T_{DS}$) and the DRX long cycle (of duration $T_{DL}$). During a sleep period, the UE RF elements are intermittently turned on for a duration $T_{ON}$ to listen for incoming data on the channel and switched off for the rest of the DRX cycle. Therefore, at the expiration of the $T_{IN}$ timer, the UE first goes to the short sleep state. During short sleep, if an incoming or outgoing data packet is detected during the on period, the UE goes to a fully active state and serves the packet. If not, at the end of a predefined short sleep period, the UE enters the long sleep period, with a similar behavior, but a longer sleep cycle. Typically, there are multiple short and long sleep cycles in a DRX sleep period. These numbers are denoted by $N_{SC}$ and $N_{LC}$, respectively. Hence, the duration of the short sleep period is calculated by $T_{SC} = T_{DS} \cdot N_{SC}$, while the long sleep period $T_{LC} = T_{DL} \cdot N_{LC}$. Of course, the longer the sleep period, the lower the energy consumption of the UE. If the UE continues to have no activity, the RRC connection is stopped, and the UE goes into the RRC idle state. In case new traffic arrives, the UE needs to recreate the entire RRC connection, by going through the (long and energy-consuming) random access procedure. However, the inactivity period to move in RRC idle is currently in the orders of tens of seconds, and it is unlikely that such a situation would appear during the usage of an XR application. Therefore, in the following, we focus on the DRX parameters in the RRC connected state.

To summarise, there are 4 timers involved in the DRX mechanism: Inactivity Timer $T_{IN}$, On Duration Timer $T_{ON}$, Short Cycle Timer $T_{DS}$, Long Cycle Timer $T_{DL}$. These timers

are configured at the RAN level and transmitted on the control channel to the UE. They can be used to control the UE power activity [5]. Needless to say, the incoming packets that arrive during a sleep period of the UE are buffered at the base station until the next on period of the UE [8]. Over the years, analytical models have been developed to evaluate the performance of the DRX mechanism [8]. Some works take a generic approach, considering that most classes of applications on a cellular network exhibit a bursty nature [9]. Building on this property, the DRX cycle (i.e. the involved timers) can be analytically optimized [8]. Other studies look at the DRX mechanism from the point of view of specific applications, such as video streaming [9] and VoIP [4], building traffic-based prediction DRX algorithms for each service. However, none of these works have looked at XR services. In the following, we provide a complementary study to the existing prediction based DRX optimisation literature but with a focus on extended reality traffic.

### III. DYNAMIC DRX MECHANISM FOR XR

As explained, the current standard DRX mechanism is timer-based. Moreover, the timers are the same for all the UEs in the coverage area of a base station, regardless of the services they are actually using. We believe that this approach is unadapted to new types of applications, and especially to applications such as XR, where traffic with different QoS expectations coexists. Therefore, we propose a dynamic DRX cycle length optimisation solution based on forecasting traffic arrivals. Our proposal starts from a simple idea: when traffic stops, the UE makes a prediction of the next packet arrival. Based on this prediction, the UE decides whether to enter a short sleep cycle or a long sleep, depending on the expected duration of the inter-arrival time. The dynamic

**Data:** $T_{ON}$, $Th_{min}$ and $Th_{max}$
**Result:** UE state;
**while** UE in RRC connected do
check for data in buffer;
**if** buffer > 0 **then**
serve the packets;
**else**
predict $t_{DY}$ next packet arrival time;
**if** $Th_{min} \le t_{DY} < Th_{max}$ **then**
place UE in DRX short sleep
**else**
**if** $t_{DY} > Th_{max}$ **then**
place UE in DRX long sleep
**else**
end
end
end

**Algorithm 1:** The Dynamic DRX Mechanism.

DRX algorithm works on a three-state DRX mechanism: active (corresponding to the fully active RRC connected state), short
sleep (short DRX state), and long sleep (long DRX state). The algorithm takes four parameters as input: $T_{IN}$, $T_{ON}$, a minimum threshold value $Th_{min}$, and a maximum threshold value $Th_{max}$. After every packet transmission or reception, the UE examines the buffer for any incoming or outgoing packets. If the buffer is empty, we keep the behavior of the classical DRX mechanism: the UE waits during an inactivity period for the possibility of further packet arrival. However, we profit from this $T_{IN}$ timer to conduct a prediction of the next packet arrival time. This prediction process can be conducted by the UE, or it can be conducted by the base station, and the result transmitted back to the UE on the PDCCCH. If the predicted arrival time, $t_{DY}$, is less than a minimum threshold value, $Th_{min}$, the UE stays active. If $t_{DY}$ is greater than $Th_{min}$, but less than a maximum threshold $Th_{max}$, the UE enters DRX short sleep mode. Finally, if $t_{DY}$ is greater than $Th_{max}$, the UE enters the DRX long sleep state. The values of the thresholds $Th_{min}$ and $Th_{max}$ are chosen based on the delay budget of the service.

## A. XR Traffic Pattern Characteristics

XR applications have very challenging latency and power requirements. The end-to-end latency of an XR service is defined from motion information generation, the corresponding frame generation at the edge server set up in split rendering, network transmission, and on the head-mounted display (HMD). This motion-render-photon latency has been defined as 70 ms [1], [2], [12]. The network processing latency is defined in relation to the frame rate requirement (60 fps which translates to 16.67 ms delay, an inverse relationship) of current available XR devices [2]. By comparison, the observed silence time in an adaptive DRX optimisation study that targets VoIP service is between 50-100 ms [4], [6]. This highlights the necessity for XR service enhancement studies and has led to new definitions for performance metrics such as satisfaction [2]. Additionally, as part of XR enhancements on NR, XR traffic patterns are proposed to be represented as protocol data unit (PDU) sets and data bursts. This is to capture the multi-modal nature of its traffic pattern. Currently, an XR service setup consists of a split rendering mechanism, shown in Figure 2. In this mechanism, XR graphic processing is split between a powerful server and the XR device. This is to relax the latency requirement to maintain a full immersive experience. Time-critical adjustment to motion is done on the device [1], [2] while volumetric video processing is done at the edge server before delivery to the HMD.

## B. LSTM Neural Networks Architecture

For convenience in this section, the model used in the dynamic DRX algorithm described in Algorithm 1 uses LSTM to predict the next packet arrival time for an XR application, and it is called LSTM-DRX. LSTM contains a simple recurrent neural network (RNN), a cell state (the long-term memory, denoted as $c_t$), a hidden state (the short-term memory, denoted as $h_t$), a forget gate $f_t$, as well as input and output gates, $i_t$ and $o_t$, respectively. An LSTM cell retains memory

<figure><img src="image_3.png" /><figcaption>Fig. 2: Typical End-to-end Mechanism of Operation of XR Applications</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3: LSTM Memory Block</figcaption></figure>

from the previous cell [6]. During the training process, the weight matrices (denoted by $wfh$, $wih$, $wgh$, $wfx$, $wix$, and $wgx$) are learned. The prediction goal is to estimate the value at time $i$ in a time series $\{y_1, y_2, \dots\}$ based on historical data $\{y_{i-1}, y_{i-1}, \dots\}$. The input to our LSTM is the inter-arrival times of XR traffic and can be denoted as $\{x = \{y_{i-k}, y_{i-k+1}, y_{i-1}\}, i = \{k, \dots, n\}\}$. We aim to find values as close to the ground truth as possible. The difference between an LSTM and a normal RNN is its memory block. Through its input, output, and forget gates, LSTM conditionally decides what information to discard, input into the memory and output. In our study, the training hyper-parameters for the LSTM model include 200 hidden units in our neural network architecture, a learning rate of 0.004, a batch size of 128, and a number of training epochs of 600. We used the Adam optimizer and Root mean square error loss function (RMSE). The LSTM memory block is illustrated in Figure 3 and its notations formalized in the equations below:

$$f_t = \sigma[(wfh \cdot h_{t-1}) + (wfx + x_t) + b_t] \quad (1)$$

$$i_t = \sigma[(wih \cdot h_{t-1}) + (wix \cdot x_t) + b_i] \quad (2)$$

$$g_t = \tau[(wgh \cdot h_{t-1}) + (wgx \cdot x_t) + b_g] \quad (3)$$

$$c_t = f_t \otimes c_{t-1} + i_t \otimes g_t \quad (4)$$

$$h_t = o_t \otimes \tau(c_t) \quad (5)$$

where $\otimes$ represents element-wise multiplication. $\sigma$ is sigmoid activation function and $\tau$ is tanh activation function.
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## C. Gaussian Process Regression

Gaussian Processes (GPs) are non-parametric models that calculate the probability distribution over all admissible functions that fit a dataset, rather than calculating the probability distribution of parameters of a specific function [4]. This is a predictive modeling technique applicable to regression, classification, and general optimisation problems. In this work, we applied it to the traffic inter-arrival time forecasting problem.
We represent our problem as a regression problem according to $y(x) = f(x)+\eta$ where $f()$ is typically unknown. The goal is to evaluate the probability distribution of $y$ for some observation $x$ while also evaluating $f()$. The observations are obtained from a dataset and typically represented as input-output pairs $D = \{(x_i, y_i)\}_{i=1}^n$. In the case of this paper, the inputs $x_i$, representing the relative arrival time of packets to the buffer, are known precisely. Our goal is to forecast the output $y_*$ at a future input $x_*$. This predictive model is ultimately used in our dynamic DRX algorithm 1, dubbed GP-DRX. A GP is completely defined by its mean and covariance functions. This is represented in Equation 6

$$p(f(x)) = \mathcal{N}(\mu(x), k(x, x')) \quad (6)$$

where $\mu(x)$ is the mean function and $k(x, x')$ is the covariance function. In our work, we take the mean function to be 0. The covariance function is crucial for GP inference as it provides the correlation between any pair of observations $x_i$ and $x_j$ and allows for the definition of the covariance matrix for a set of observations $x = \{x_1, x_2, \dots, x_n\}$ [14]. In our work, we use a summation of Matérn kernel and white noise kernel. The latent function $f(x)$ is modelled as a Gaussian process according to Equation 6. The outputs are thus given as specified in Equation 7.

$$y(x) = f(x) + \eta_i, \eta_i \sim \mathcal{N}(0, \sigma_n^2) \quad (7)$$

where $\sigma_n^2$ is a noise factor or variance. We define the priors on $f(x)$ before seeing any data as $f = [f(x_1), \dots, f(x_n)]^\top \sim \mathcal{N}(0, K)$, where $K$ is the covariance matrix in the form $K_{ij} = k(x_i, x_j)$ and dimension $n \times n$. With the data, we update our belief about the function using Bayes theorem ref in Equation 8 to get the posterior distribution.

$$P(f(x)|y) = \frac{P(y|f(x) \cdot P(f(x))}{P(y)} \quad (8)$$

where $P(f(x)|y)$ is the posterior, the updated belief about the model after observing the data, $P(y|f(x)$ is the likelihood, which is the probability of the observed data given the model, $P(f(x))$ is the prior and $P(y)$ is the marginal likelihood. The marginal likelihood is represented as the integral of the likelihood and the prior. The learning process of a GP involves marginalizing the hyper-parameters of its mean and covariance functions (given as $\theta_m$ and $\theta_k$ respectively in Figure 4) by maximizing the log marginal likelihood through gradient updates as seen in Figure 4. This operation often requires $O(n^3)$ complexity, though the use of Cholesky decomposition reduces this to $O(n^3/6)$ [14]. The optimal parameters, once determined, are then used in the posterior process for prediction during inference [14].

## D. Performance Metrics

Our goal is to enhance the UE energy efficiency using a prediction-based DRX approach. For this, we consider the two prediction techniques discussed above, LSTM and GP. GPs are known for decent performance with limited training data, while neural networks excel at modeling complex non-linear

<figure><img src="image_5.png" /><figcaption>Fig. 4: Gaussian Process</figcaption></figure>

patterns with the right architecture and hyperparameters [13]. GPs are flexible but may struggle with complex data if the covariance function is not ideal [14]. Theoretically, both models have comparable computational complexity, with GPs typically requiring fewer hyperparameters.

The metrics used to quantify our proposed mechanism are the mean wake-up delay (Equation 9) and the sleep ratio/energy efficiency (Equation 10). We focus on energy consumed by the RF interface, which dominates during active transmission or reception. Our analysis considers only downlink reception, adopting an energy efficiency model from prior work [8], [9], which is based on the ratio of time the RF interface spends in sleep mode relative to total time.

$$E[D] = \frac{1}{N} \left[ N_S \cdot \frac{T_{off}^{ss}}{T_{ON} + T_{off}^{ss}} \cdot \frac{T_{off}^{ss}}{2} + N_L \cdot \frac{T_{off}^{ls}}{T_{ON} + T_{off}^{ls}} \cdot \frac{T_{off}^{ls}}{2} \right] \quad (9)$$

$$EE = \sum \frac{T_{off}}{T_A + T_{ON} + T_{off}} \quad (10)$$

Where $T_A$ is the time spent in active state. $T_{off} = T_{off}^{ss} + T_{off}^{ls}$ is the total time spent in the sleep states. $T_{off}^{ss}$ is the time spent in short sleep and $T_{off}^{ls}$ is the time spent in long sleep state. $N = N_S + N_L$ is the total number of times the UE entered a sleep state: $N_S$ and $N_L$ is the number of times the UE entered short sleep and long sleep respectively. These models are in accordance to what is found in the current literature [5], [8], [9]. The EE and mean delay values are summed across all instances of packet arrival.

## IV. DESCRIPTION OF THE DATA

We base our analysis on data collected by researchers at Bell Labs [11], describing a user's activity with 5 VR game applications on Steam, the game distribution service. Studies on XR applications mostly rely on game application data, as these are the most available sources for research purposes. As such, the data used for our analysis is based on 3 of the 5 VR games with different traffic requirements on the uplink and downlink. The data reflects the use of these applications in a residential environment and the user is connected to the Internet via WiFi. Opting to use traces collected on WiFi for our analysis is an adequate choice because many users switch between WiFi and cellular networks throughout the day. As such, for our purposes, we make the assumption that the behavior of the same user on WiFi and cellular networks is
<table><caption>TABLE I: Dataset Content [11]</caption><thead><tr><td>App</td><td>Total Duration (s)</td></tr></thead><tbody><tr><td>Half-life: Alyx</td><td>12041</td></tr><tr><td>Beatsaber</td><td>4310</td></tr><tr><td>Google Earth</td><td>1922</td></tr><tr><td>Rec Room</td><td>3563</td></tr><tr><td>VR chat</td><td>2083</td></tr></tbody></table>

similar. We also consider the fact that WiFi does not use DRX so the dataset is not biased by an existing mechanism. For this reason, as well as the reality of there being no publicly available XR traces collected over cellular networks (to the best of our knowledge), we decided to estimate our findings with the available traces collected over WiFi. The set-up used to collect the data is described in [11]. Practically, the user was installed at his residential lab during the measurement of the traces. The UEs were both personal computers and VR headsets (Meta Quest 2) connected to the Internet via a WiFi access point at 5GHz and 40MHz bandwidth [11] with a split rendering setup (for some of the games) for efficient uplink and downlink computation. In this study, we limit our scope to downlink DRX analysis. The public dataset contains data collected on 5 VR game applications. The basic information about the applications and the trace columns are available in Tables I. Due to space constraint, we selected only 3 of the applications. The columns of each trace of the data collected for each application contains the following fields: Time (s), Protocol (TLS, TCP, UDP etc), TCP length, UDP length, Link (uplink or downlink headset or PC) [11].

## V. RESULTS

In this section, we show the result of our analysis for XR applications based on the dataset described above. We carried out the analysis using power values defined for each DRX state in [5]. Sleep state power is 1060mW, active state power is 1680mW [5]. The RMSE scores of the inter-arrival times of both models are seen in Table II. With the same training and test data size, the LSTM shows lower RMSE scores than the GP, indicating that the GP struggles to model patterns as effectively as deep neural networks. The chosen hyper-parameters for the LSTM model were suitable for our problem. While the GP model underperformed, It could be viable in scenarios with limited training data and tailored mean and covariance functions, especially when minimizing overhead energy cost is crucial. Table III is an overview of the relative performance of both predictive models DRX and the 3GPP-DRX mechanism for the two performance metrics (EE and delay). We have normalised the values of all three models with the values of the 3GPP-DRX for both EE and mean delay. For this analysis we set the values of the thresholds $Thmin$ and $Thmax$ at 16.67ms and 70ms respectively. With less RMSE scores, the LSTM model consistently produced the highest values of EE across all games. We attribute this to the neural network properties discussed in section III. The delay metric is particularly interesting. Recall that we defined the model for estimating the delay as Equation 9. The mean delay for this

<table><caption>TABLE II: Inter-arrival time RMSE scores for the same size of training and test sets.</caption><thead><tr><td>Metric</td><td>LSTM</td><td>GP</td></tr></thead><tbody><tr><td>RMSE (VR chat)</td><td>0.057</td><td>0.141</td></tr><tr><td>RMSE (Beat saber)</td><td>0.079</td><td>0.042</td></tr><tr><td>RMSE (Half life)</td><td>0.1868</td><td>0.462</td></tr></tbody></table>

<table><caption>TABLE III: Metric Gains Relative to LTE - DRX for different values of $T_{ON}$ and $Thmin = 16.67ms$ and $Thmax = 70ms$</caption><thead><tr><td>Metric</td><td>GP - DRX</td><td>LSTM - DRX</td><td>3GPP - DRX</td></tr></thead><tbody><tr><td>EE (Beat saber)</td><td>20.8694</td><td>50.7665</td><td>1.0</td></tr><tr><td>D (Beat saber)</td><td>15.0129</td><td>1.2023</td><td>1.0</td></tr><tr><td>EE (Half-life)</td><td>2.6810</td><td>123.6058</td><td>1.0</td></tr><tr><td>D (s) (Half-life)</td><td>66.937</td><td>3.9544</td><td>1.0</td></tr></tbody></table>

metric is approximately 29ms (about 30fps) in Beat Saber, which remains within tolerable limits for XR service [1].

Figure 5 provides the trend in the values of the performance metrics as the value of $T_{ON}$ is increased while keeping the values of $Thmin$ and $Thmax$ at 16.67ms and 70ms respectively. LSTM-DRX generally showed superior performance in both performance metrics across the board. The EE on this scheme is the highest and it also shows the lowest wakeup delay. GP-DRX showed a decent performance on both metrics for some of the games while it however performed poorly in others. We attribute this to the need for careful selection of the mean and covariance functions for the GP. The 3GPP-DRX mechanism exhibits the lowest energy efficiency (EE) because it lacks the ability to adjust its sleep state duration according to traffic demands, unlike prediction-based models. Consequently, it has the lowest delay values. However, despite its minimal delay, the conventional DRX mechanism poses risks such as buffer overflow and synchronization issues. These arise when the traffic load exceeds the UE's processing capacity, potentially leading to packet losses, system crashes, and decreased user satisfaction.

The final question we ask is how XR applications fair in terms of the trend in both of the defined performance metrics as either one of the threshold values are increased. Figure 6 shows the results of this analysis. The decrease in EE values due to the increase in $Thmin$ value is attributable to the nature of the service and its traffic QoS demand as mentioned before. When $Thmin$ is kept at a value higher than 16.67, the UE goes to sleep less as the condition for entering short sleep (see Algorithm 1) is met less due to the fact that the threshold value is outside of the frequency of arrival of XR packets. This same reasoning is applicable to the trend when varying $Thmax$.

## VI. RELATED WORK

Adaptable DRX cycle length selection is an active research topic. [8] laid the foundations for DRX parameter modeling for bursty traffic using a Markovian set-up. It is based on their work that other similar approaches emerged, addressing other types of bursty traffic [9], or paying more attention to the energy efficiency problem and delay modeling for the same
<figure><img src="image_6.png" /><figcaption>Fig. 5: Energy Efficiency and mean wakeup delay for Beatsaber and Half-life: Alyx for variable values of $T_{ON}$ on all three DRX models with $Thmin = 16.67$ms and $Thmax = 70$ms</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6: Energy Efficiency and mean wake up delay for Half-life for variable values of $Thmax$ in (a) and (b) and $Thmin$ in (c) and (d) with $Thmin = 16.67$ms, $Thmax = 70$ms and $T_{ON} = 10$ms respectively.</figcaption></figure>

traffic type [5]. DRX-aware packet scheduling has been the focus of some studies [12], addressing metrics like fairness, energy, and QoS requirements of UEs and applications. The diversity of user applications is at the center of the scheduling decisions and analysis of this approach. Previous works have also addressed the DRX cycle lengths through data-driven approaches that include deep neural networks-based approaches [6] and Bayesian approaches [4]. In particular, LSTM was used to predict the value of the dynamic DRX cycle lengths [6]. Surveys on the DRX mechanism have emerged in a similar vein [7].

## VII. CONCLUSIONS

This study examined prediction-based dynamic DRX optimisation for XR applications. Optimisation models for the DRX mechanisms have been proposed for different applications types/services across UMTS, LTE and NR and this study is complementary to them. Our approach is unique in using publicly available XR data for prediction-based DRX optimization with LSTM and Gaussian Process models. Our models showed significant improvement over energy efficiency (EE) relative to the conventional DRX model with minimal delay impedance. LSTM has lower RMSE scores than GP for the same data size, though GP's performance is decent for when minimizing overhead energy costs is a priority although careful consideration needs to be made to select the best covariance function.

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