# Energy-Aware Meta Sleep Scheduler, an Alternative to DRX in 5G Networks

Loup Dorval, Cédric Gueguen, Guillaume Terrier

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Loup Dorval, Cédric Gueguen, Guillaume Terrier. Energy-Aware Meta Sleep Scheduler, an Alternative to DRX in 5G Networks. LCN 2025 - IEEE 50th Conference on Local Computer Networks, Oct 2025, Sydney, Australia. pp.1-6, 10.1109/LCN65610.2025.11146383. hal-05402488

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# Energy-Aware Meta Sleep Scheduler, an Alternative to DRX in 5G Networks

Loup Dorval

Univ Rennes, IRISA

Rennes, France

loup.dorval@irisa.fr

Cédric Gueguen

Univ Rennes, IRISA

Rennes, France

cedric.gueguen@irisa.fr

Guillaume Terrier

Univ Rennes, IRISA

Rennes, France

guillaume.terrier@irisa.fr

**Abstract**—Energy consumption reduction in 5G networks and beyond is a significant challenge. Discontinuous Reception (DRX) established by 3GPP is the current state of the art energy-saving strategy. With DRX, users that neither receive nor transmit for a certain duration are set in a sleep cycle. These users periodically wake up in order to consult the Physical Downlink Control Channel in case of potential transmissions. DRX timings are negotiated during the initial connection. Despite the inherent variability of user traffic patterns, DRX configuration parameters remain static, leading to energy waste. In addition, DRX does not consider delay constraints, which may incur QoS degradation due to inadequate sleep duration. This paper proposes Meta Sleep Scheduler (MSS) as an alternative to DRX. User sleep duration is dynamically adjusted to meet application time constraints while reducing the time users spend in high power states. Similarly to DRX, MSS is compatible with all radio resources allocation schedulers. MSS achieves an average reduction of up to 90% in user standby power consumption compared to DRX, while maintaining or surpassing DRX's Quality of Experience.

**Index Terms**—5G, Energy Consumption, DRX, Scheduling, Quality of Service (QoS).

## I. INTRODUCTION

With each new generation of wireless communication, increased networking capabilities enable applications to use higher data transfer rates, potentially increasing User Equipment (UE) power consumption [1]. 5G introduced the usage of higher frequency bands as the New Radio Frequency Range 2 (5GNR FR2), also called millimeter wave (mmWave) which spans from 30 GHz to 300 GHz. mmWave exhibits significantly higher power consumption at throughputs below 188 Mbps compared to sub-6 GHz (FR1) bands [1]. As more mmWave antennas get deployed, the increased energy consumption in common transmission scenarios is expected to negatively impact UE battery life. Modern UEs are often equipped with non-replaceable lithium-ion batteries, which have a limited number of charge cycles before they start degrading. Reducing energy consumption benefits the environment by decreasing electronic waste, as consumers can retain their devices longer. Furthermore, consumers benefit from longer device usage without frequent charging increasing user quality of experience. It is therefore crucial to develop new energy-saving mechanisms both for environmental concerns and to increase consumer satisfaction.

The primary power-saving strategy of 3rd Generation Partnership Project (3GPP) devices is Discontinuous Reception (DRX) [2]. DRX consists of UE entering sleep cycles after not receiving traffic and their inactivity timer running out. Every cycle, the UE wakes up, consults the Physical Downlink Control Channel (PDCH), and in the absence of scheduled traffic, UE goes back to sleep. This strategy allows great energy efficiency gains during sparse downlink traffic, but in dense traffic conditions, UEs using DRX as specified in [3] stay in high power states: Every packet received resets the DRX inactivity timer which leads to major energy wastage, as UEs are kept awake. DRX is a widely used power-saving strategy in 5G networks; however, it has limitations as it is not designed to handle UE time constraints, which can lead to potential quality of experience degradation under high traffic load conditions.

To improve on DRX, we propose Meta Sleep Scheduler (MSS) which dynamically adjusts UE sleep time while taking UE QoS time constraints into account. The novelty of this approach is to make the antenna responsible for deciding when the UE is set to sleep. The information available to the access point enables MSS to cut down on stand-by time and prolong sleep duration, which reduces UE energy consumption. In low load scenarios, gNB sets UE's radio frequency integrated circuit in a low power state until an adequate wake-up time which preserves quality of experience. Using UE traffic rate estimations, gNodeB (gNB) computes the adequate amount of bandwidth to reserve to transmit the accumulated amount of traffic during UE's sleep duration in order to satisfy its application delay constraint. Then, MSS sets UE to sleep as soon as UE's traffic queue is emptied. In high load conditions, little (or no) bandwidth capacity is available for reservation hence keeping UEs awake. This leads to higher network spectral Efficiency (SE) and system throughput when compared to DRX, allowing the network to reduce congestion.

In this paper, we present in Section II the state of the art, DRX, and the main acknowledged schedulers. In Section III, the system model and the energy consumption model are detailed. In Section IV, MSS algorithm is presented. Finally, performance evaluation study is presented in section V and we conclude in section VI.
<table><caption>TABLE I
LIST OF ACRONYMS.</caption><thead><tr><td>Acronym</td><td>Full Form</td></tr></thead><tbody><tr><td>3GPP</td><td>Third Generation Partnership Project</td></tr><tr><td>DRX</td><td>Discontinuous Reception</td></tr><tr><td>gNB</td><td>Next Generation NodeB (antenna / base station)</td></tr><tr><td>MaxSNR</td><td>Maximum Signal-to-Noise Ratio scheduler</td></tr><tr><td>MSS</td><td>Meta Sleep Scheduler</td></tr><tr><td>PDCCH</td><td>Physical Downlink Control Channel</td></tr><tr><td>PDSCH</td><td>Physical Downlink Shared Channel</td></tr><tr><td>PF</td><td>Proportional Fair scheduler</td></tr><tr><td>QoS</td><td>Quality of Service</td></tr><tr><td>RB</td><td>Resource Block</td></tr><tr><td>RR</td><td>Round Robin scheduler</td></tr><tr><td>SE</td><td>Spectral Efficiency</td></tr><tr><td>SINR</td><td>Signal-to-Interference-plus-Noise Ratio</td></tr></tbody></table>

<table><caption>TABLE II
DRX PARAMETER VALUES.</caption><thead><tr><td>Parameter</td><td>Value</td><td>Available Values</td></tr></thead><tbody><tr><td>DRX_short_ON_duration</td><td>4 ms</td><td>4 ms</td></tr><tr><td>DRX_short_cycles</td><td>16 [4]</td><td>/</td></tr><tr><td>DRX_short_time_per_cycle</td><td>40 ms</td><td>40, 160, 320 ms</td></tr><tr><td>DRX_short_inactivity_timer</td><td>25 ms</td><td>25, 10 ms</td></tr><tr><td>DRX_long_time_per_cycle</td><td>160 ms</td><td>40, 160, 320 ms</td></tr><tr><td>DRX_long_ON_duration</td><td>8 ms</td><td>8, 10 ms</td></tr></tbody></table>

## II. STATE OF THE ART

### A. Discontinuous Reception (DRX)

Discontinuous Reception (DRX) was first introduced in 3GPP Release 7 [5] and implemented in Release 8 [6]. DRX is a power-saving technique enabling UEs to enter different power states while retaining the context of the radio resource control connection. When entering a DRX cycle, UE alternates between consulting the PDCCH and entering low power states to conserve energy until traffic is allocated by the gNB. DRX parameters are negotiated with the gNB on connection and stay constant throughout the connection.

The DRX cycle consists of four main components: an inactivity timer where UE enters the sleep cycles upon expiration, shorter sleep periods, short wake-up periods defined by the On Duration where the UE listens to the PDCCH to check if traffic is scheduled, and deeper sleep periods after a certain number of shorter sleep periods have elapsed without scheduled traffic. UEs return to a sleep state after the On Duration expires (Fig. 1). The parameters controlling these four components are the On Duration Timer, Inactivity Timer, DRX Cycle Length, and optional Short DRX Cycle. These parameters define the timing and duration of active and sleep states. In this paper, we compare our solution to a DRX configuration from the 3GPP Technical Report [3].

### B. Radio Resources Allocation Schedulers

In this study, we compare DRX and MSS using three well-known and acknowledged schedulers: Round Robin (RR) and MaxSNR, Proportional Fair (PF).

Traditional resource allocation strategies used in wireless networks were originally and primarily designed for the wired

<figure><img src="image_2.png" /><figcaption>Fig. 1. DRX Power diagram.</figcaption></figure>

<table><caption>TABLE III
3GPP RADIO STATES POWER CONSUMPTION AND TRANSITION TIMES (FR1).</caption><thead><tr><th rowspan="2">Power state</th><th colspan="2">Power</th><th rowspan="2">Transition time (ms)</th></tr><tr><th>3GPP Relative Units</th><th>Translated (mW) [1]</th></tr></thead><tbody><tr><td>Deep Sleep</td><td>1</td><td>9</td><td>20</td></tr><tr><td>Light Sleep</td><td>20</td><td>180</td><td>6</td></tr><tr><td>Micro Sleep</td><td>45</td><td>405</td><td>0</td></tr><tr><td>PDCCH-only</td><td>100</td><td>900</td><td>/</td></tr><tr><td>PDCCH + PDSCH</td><td>300</td><td>2700</td><td>/</td></tr></tbody></table>

context. Consequently, these conventional access methods like Round Robin (RR) are not well adapted to the wireless environment. RR allocates radio resources turn by turn without taking into account the radio conditions. Consequently, it provides very poor throughput.

Intensive research efforts have focused on developing throughput efficient schedulers and channel dependent approaches (also called "opportunistic" schedulers) have emerged as the most effective solution [7]. They allocate radio resources dynamically using channel state information collected from UEs as opposed to RR which allocates resources in a fixed order.

The best known is called Maximum Signal to Noise Ratio (MaxSNR) scheduler [8]. For Each Resource Block (RB) n, MaxSNR allocates the resource to UE j with the highest Signal-to-Interference-plus-Noise Ratio (SINR) such as:

$$j = \underset{k}{\operatorname{argmax}}(\mathrm{SINR}_{k,n}). \qquad (1)$$

It preferably allocates the resources to the user with the most favourable channel conditions at a given time. It takes benefit of multiuser diversity in order to increase the system throughput. However users close to the access point have a better average throughput per RB than far users. As a result, with the MaxSNR scheduler, close users have statistically more chances to have access to the medium. In consequence, far users will often obtain radio resources last. This makes them exceed their QoS requirement when the network is close to congestion, leaving them unsatisfied. In addition, primarily focusing on close users, MaxSNR has a tendency
to exhaust users with higher mean SNR values. This results in suboptimal multiuser diversity usage and consequently to suboptimal overall SE.

In order to solve this issue, Proportional Fair (PF) and PF-based algorithms have been proposed [9]. The basic principle is to allocate resources to a user when its channel conditions are the most favourable with respect to its time average. For Each Resource Block (RB) n, PF allocates the resource to UE j with the highest SINR to average SINR ratio, such as:

$$j = \underset{k}{\operatorname{argmax}}\left(\frac{\mathrm{SINR}_{k,n}}{\mathrm{SINR}_{k}}\right). \qquad (2)$$

This approach is more fair than MaxSNR since all users have statistically the same probability to access radio resources. PF ensures more fairness than MaxSNR while offering higher system throughput than RR. Higher multiuser diversity leads to a higher average SE than MaxSNR, except under extreme load conditions where incoming traffic exceeds the network's total throughput, allowing MaxSNR's multiuser diversity to remain high despite its inherent unfairness.

### III. SYSTEM MODEL

#### A. Energy Consumption Model

The 3GPP power consumption model [3] categorizes UE radio power states into several distinct types, each with specific characteristics and relative power consumption values. In the 3GPP power consumption model, one 3GPP relative power unit corresponds to the power consumption of a UE's radio frequency integrated circuit in deep sleep mode. 3GPP partners design their 5G devices such that the power consumption of the different power states match the proportions given in Table III.

Transitioning to and from both Deep Sleep and Light Sleep requires a transition [3]. Power consumption values and transition times for Sub-6 GHz transmissions are presented in Table III. The translated power values are given by [1]. The power consumption of sleep states ranges from 1 unit for Deep Sleep to 300 units for PDCCH + PDSCH, with varying transition times for different sleep states.

The duration of deep sleep and light sleep must be longer than the total transition time required to enter and leave these states. Microsleep is considered to have immediate transitions between sleep and active states, designed for very short inactivity periods. In PDCCH-only state, the UE checks for the presence of paging messages. If a paging message is detected, UE demodulates the paging channel to see if the paging message is directed to it.

#### B. System description

The proposed system centralizes the control of sleep and resource allocation within the gNodeB (gNB). The system is considered to operate using the Orthogonal Frequency-Division Multiple Access framework, dividing the bandwidth into subcarriers, the time domain into frames with each frame subdivided into time slots. A Resource Block (RB) is defined as a combination of subcarriers and a time slot. Each RB

allocated uses the appropriate level of quadrature amplitude modulation depending on the channel state.

We consider downlink transmissions within a single frequency band shared by all UEs, excluding Carrier Aggregation to simplify resource allocation. We assume that channel states are sufficiently stable to utilize UEs' channel state information reports for scheduling.

The gNB estimates channel conditions for each UE on each subcarrier through signal-to-noise ratio (SINR) measurements. This enables adaptive modulation and coding, where each RB can be assigned a modulation order tailored to the current channel state between the gNB and the selected UE. By leveraging the channel state information, gNB can implement opportunistic scheduling techniques.

#### C. Channel Model

The channel model accounts for three primary interference types: path loss, shadowing, and multipath fading. The channel gain between gNB and UE k on subcarrier n is given by [10]:

$$G_{k,n} = h \cdot 10^{\frac{x \cdot \sigma}{10}} \cdot \left( \frac{d_{ref}}{d_k} \right)^{\tau}. \qquad (3)$$

Here, h represents Rayleigh multipath fading (modeled by an exponential distribution), X is a standard Gaussian random variable, σ is the shadowing standard deviation in dB, d ref is the reference distance, d k is the UE-gNB distance, and τ is the path loss exponent. The Signal to Noise Ratio (SINR) for UE k on subcarrier n is given by:

$$\gamma_{k,n} = \frac{P_n \cdot G_{k,n}}{B_{sub} \cdot N_0}, \qquad (4)$$

where P n is the transmitted power on subcarrier n, N 0 is the thermal noise power density, and B sub is the subcarrier spacing. Spectral efficiency η k,n for UE k on subcarrier n is computed using Shannon's formula:

$$\eta_{k,n} = \log_2(1 + \frac{\gamma_{k,n}}{\Gamma}). \qquad (5)$$

Γ is an SINR correction factor that accounts for the gap between theoretical and practical Modulation and Coding Scheme [9] performance:

$$\Gamma = -\frac{\ln(5E)}{1.5}, \qquad (6)$$

where E is the target Bit Error Rate.

### IV. META SLEEP SCHEDULER

Considering energy consumption minimisation, we face a complex challenge. If UE sleep duration is too short, energy consumption fails to be minimized. Indeed, as described in Table III, energy consumptions of the different power states are not linear, the energy consumption of the deepest sleep state allows the greatest energy-savings. Unfortunately, reaching these deepest sleep states requires longer sleep duration to have the time to do the power state transitions. Inversely, if the duration of UE sleep is too long, energy consumption is low but traffic might be delivered beyond UE time constraints. We consider the primary mission of the network is to satisfy users,
<figure><img src="image_3.png" /><figcaption>Fig. 2. MSS flowchart.</figcaption></figure>

and energy-saving cannot be at the expense of this criterion. Consequently, in this paper, we propose a solution called Meta Sleep Scheduler (MSS) which attempts to reduce UE power consumption as much as possible by making the gNB manage when connected UEs sleep while guaranteeing packet delivery within UE application time constraints.

The solution is summarized in Fig. 2 and described in detail in the following paragraphs. The main constraint of MSS is to transmit each packet of UE $k$ before its application time constraint $T_k$. When the UE has an empty buffer, we search the maximal $S_k$ duration value of sleep which is acceptable while respecting $T_k$. An acceptable $S_k$ value must respect the condition the system has enough resource blocks available on wake up to evacuate the number of bits accumulated during the sleep duration before $T_k$. MSS chooses the highest value among the set of possible sleep durations. In the worst case, $S_k = 0$, UE is kept awake. This happens in case of congestion. Else, MSS finds the highest value to reach its goal of energy consumption minimization.

MSS uses UE's channel state information reports to compute UE's average spectral efficiency as $\overline{SE_k}$. Using UE's mean traffic rate $R_k$, MSS adjusts it to account for inaccuracies as $R_{k,adjusted}$ due to traffic variance (see section IV-A). Using these values, MSS estimates $E_k$, the number of RBs required per time slot for UE $k$ as:

$$E_k = \frac{R_{k,adjusted}}{\overline{SE_k} \cdot M}, \qquad (7)$$

<figure><img src="image_4.png" /><figcaption>Fig. 3. UE traffic and power consumption over time.</figcaption></figure>

where $R_{k,adjusted}$ is the adjusted bitrate requirement for UE $k$ in bits/s (section IV-A), $\overline{SE_k}$ is UE's mean spectral efficiency in bits/RB and $M$ is the number of time slots per second. Fig. 3 describes different zones that impact the MSS strategy. Zone 1 represents Resource Blocks (RB) reserved for the currently considered User Equipment (UE) $k$. Zone 2 represents the RBs reserved for other UEs. Limit is the number of time slots after which UE $k$ can potentially begin to be woken up and scheduled. Before Limit are the time slots reserved for evacuating traffic accumulated by UEs unable to sleep, as the network approaches congestion. Limit is defined as the number of time slots required to transmit the traffic for each connected user $j$ which is the sum of the buffer occupancy $BO_j$ in bits, divided by the product of the mean spectral efficiency $\overline{SE_j}$ in bits/RB and the number of RBs per time slot $N$, with $C$ the set of connected UEs such as:

$$Limit = \sum_{j \in C} \frac{BO_j}{\overline{SE_j} \cdot N}. \qquad (8)$$

Zone 3 represents RBs available for reservation.

Using $RB_{avail}(i)$, the number of Resource Blocks (RB) available and free at offset $i$ time slots from the current time, $E_k$ as defined previously, and $T_k$ as UE $k$'s traffic delivery time constraint, we can determine the set $\Gamma$ of suitable sleep durations for UE $k$ as follows:

$$\Gamma = \left\{ x \in \mathbb{N} \mid Limit \le x \le T_k \land E_k \cdot x \le \sum_{i=x}^{T_k} RB_{avail}(i) \right\}. \qquad (9)$$

If the set is empty, the UE is kept awake. Otherwise, MSS chooses the longest sleep duration $S_k$ for UE $k$ as described in the flowchart (Fig. 2):

$$S_k = \max \Gamma. \qquad (10)$$

In a constant bitrate context, the $R_k$ would not vary and MSS would minimize UE sleep duration while satisfying all traffic constraints. However, with variable bitrate and realistic traffic, some prediction errors can occur. To address inaccuracies in traffic prediction, MSS incorporates an adjustment factor mechanism.
<figure><img src="image_5.png" /><figcaption>Fig. 4. Spectral Efficiency.</figcaption></figure>

<caption>Fig. 5. UE Average Time Spent in Sleep States.</caption>

<caption>Fig. 6. Standby energy consumption per second.</caption>

## A. Bitrate Estimation Adjustment Factor

As the required bitrate given by UE to gNB is subject to change, MSS implements a self-correcting traffic rate adjustment mechanism. After each transmission, an adjustment factor $\epsilon$ is calculated per UE as:

$$ \epsilon_k = \frac{\text{Actual Delivery Time}}{\text{Predicted Delivery Time}}, \quad (11) $$

where Actual Delivery Time is the time slot where UE $k$'s traffic buffer is emptied after a sleep cycle, and Predicted Delivery Time is the last time slot reserved for UE $k$. If $\epsilon_k > 1$, a prediction error occurred, potentially affecting the QoS, hence more caution is necessary.

MSS adjusts $R_{k,adjusted}$ using an asymmetric update mechanism. On connection, $R_{k,adjusted}$ of user $k$ is initialized to $R_k$. After waking from sleep, we compare $R_{k,adjusted}$ to $R_{k,instant}$, defined as:

$$ R_{k,instant} = R_k \cdot \epsilon_k. \quad (12) $$

This leads to the following adjustment rules for $R_{k,adjusted}$:

$$ R_{k,adjusted} = \begin{cases} R_{k,adjust\ up}, & \text{if } R_{k,instant} > R_{k,adjusted} \\ R_{k,adjust\ down}, & \text{otherwise} \end{cases} \quad (13) $$

$$ R_{k,adjust\ up} = (1 - \alpha) \cdot R_{k,adjusted} + \alpha \cdot R_{k,instant} \quad (14) $$

$$ R_{k,adjust\ down} = (1 - \beta) \cdot R_{k,adjusted} + \beta \cdot R_{k,instant} \quad (15) $$

Here, in terms of the required bitrate, $\alpha$ represents the upward adjustment celerity, $\beta$ the downward adjustment celerity. Calibration has shown $\alpha > \beta$ is suitable, as it ensures a quick response to increased resource needs and protects against delays beyond the traffic time constraints.

This bitrate adjustment mechanism enables the gNB to minimize delayed traffic while allowing MSS to adapt to changing traffic patterns and potential traffic bursts. Consequently, it improves the balance between energy efficiency and spectral efficiency, which is crucial for optimal network performance.

## V. PERFORMANCE EVALUATION

In this section, we evaluate the performance of MSS against 3GPP state of the art DRX solution. MSS is configured with $\alpha = 1$ and $\beta = 0.1$ based on results from calibration simulations.

<table><caption>TABLE IV
SIMULATION PARAMETERS.</caption><thead><tr><th>Parameters</th><th>Value</th></tr></thead><tbody><tr><td>Cell Radius</td><td>500 m</td></tr><tr><td>Number of subcarriers</td><td>128</td></tr><tr><td>Number of Time Slots</td><td>10</td></tr><tr><td>Remote radio head transmit power</td><td>20 W (43 dBm)</td></tr><tr><td>Standard deviation of shadowing</td><td>σ = 8 dB</td></tr><tr><td>Path-loss exponent τ</td><td>3.5 (urban context)</td></tr><tr><td>Target Bit Error Rate</td><td>5 × 10<sup>-5</sup></td></tr><tr><td>Subcarrier spacing</td><td>15 kHz</td></tr><tr><td>Thermal noise power density N<sub>0</sub></td><td>- 174 dBm/Hz</td></tr><tr><td>Simulation duration</td><td>100 000 time slots</td></tr></tbody></table>

### A. Simulation Setup

The system uses the parameters from Table IV to model the network. To accurately model a varying traffic pattern, we implemented a Group of Pictures video stream using inverse gamma distributions as described in [11]. These distributions effectively capture the variability in frame sizes observed in real-world video streams, enabling a more accurate evaluation of sleep scheduling algorithms under realistic network conditions. Delivery of packets for a video stream is subject to time constraints, allowing us to evaluate the QoS performance of different scheduling solutions. UEs have a 280 ms time constraint (shown in Fig. 7), as specified by 3GPP's 5G QoS Indicator for Non-Conversational Video (Buffered Streaming) [12], which corresponds to a 300 ms packet delay budget minus a 20 ms core network packet delay budget. This time constraint is used to evaluate the performance of the scheduling algorithms and power-saving strategies in terms of packet delay.

### B. Simulation results: Low traffic load

In Fig. 4, we observe that under low traffic load conditions, DRX has better spectral efficiency compared to MSS when using PF or MaxSNR scheduling, as more users remain awake concurrently (Fig. 5). This allows opportunistic schedulers using DRX to exploit increased multiuser diversity, which leverages channel state information. However, this spectral efficiency advantage serves no purpose since QoS is easily achieved when the system is far from congestion and schedulers experience no difficulties. Considering that an application would experience difficulties if packets arrive beyond time constraints, Fig. 8 shows the percentage of dissatisfied UEs (i.e., more than 5% of packets delivered beyond time con-
<figure><img src="image_6.png" /><figcaption>Fig. 7. Average Packet Delay.</figcaption></figure>

straints). Both DRX and MSS provide perfect user satisfaction. Note that with MSS, mean packet delay is higher than with DRX (Fig. 7). Indeed, contrary to DRX, MSS seeks to provide the longest sleep time (Fig. 5) to achieve lower energy consumption (Fig. 6) as long as packets arrive on time (Fig. 8), since the goal is not to deliver packets early but on time. Since RR is not opportunistic, multiuser diversity does not affect its spectral efficiency. In this case, DRX and MSS yield equivalent spectral efficiency. However, as with MaxSNR and PF, RR energy consumption with DRX is higher with no gain compared to MSS.

To conclude, the higher average sleep time translates to lower energy costs (see Fig. 6). At 4 users, using RR, MSS has an average energy cost of 0.061 Joules per second, while DRX has an average of 0.63 Joules per second, representing a 90% energy cost savings for MSS. Energy savings are similar with MaxSNR and PF schedulers.

## C. Simulation results: High traffic load

As traffic load increases, multiuser diversity increases; hence opportunistic schedulers allocate resources more efficiently in terms of spectral efficiency. However, when schedulers experience difficulties, MSS prevents UEs from going to sleep (Fig. 7), which increases energy consumption (Fig. 6) to the benefit of reduced delay. This results in significant spectral efficiency gains (Fig. 4), which explains the sudden average delay reduction (Fig. 7) since MSS attempts to save as many packets as possible from expiry. At high traffic load, the system is congested, leading to an exponential increase in packet delay. Overall, results using MSS are similar for RR and PF, or slightly better for MaxSNR, compared to DRX.

## VI. CONCLUSION

This paper presented Meta Sleep Scheduler (MSS), a novel energy-saving mechanism for 5G networks. This solution, compatible with all standard resources allocation schedulers, attempts to reduce UE power consumption as much as possible while guaranteeing packet delivery within UE application time constraints. MSS outperforms DRX in terms of energy efficiency, achieving up to a 90% reduction in standby energy consumption compared to DRX, while matching or exceeding DRX in quality of experience. There are several avenues for future work. Predicting traffic patterns using artificial

<figure><img src="image_7.png" /><figcaption>Fig. 8. UE Dissatisfaction.</figcaption></figure>

intelligence could enable further energy savings. Additionally, extending MSS to manage base station energy states could enable the Base Station to transition to lower power states at low traffic loads during scheduled UE sleep periods, enhancing overall network energy efficiency.

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