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REVIEW 4 major objections 5 minor 55 references

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that a deep reinforcement learning controller can jointly reduce Age of Information and energy consumption in C-V2X vehicle-to-vehicle communication, using multi-priority queues and NOMA with successive interference…

desk verdict A useful but rough simulation study combining multi-priority queues, NOMA, and MPDQN for C-V2X Mode 4; the headline AoI numbers are undermined by an internal contradiction in the AoI update rule. read the letter →

arxiv 2411.13104 v2 pith:W6DIRIQ6 submitted 2024-11-20 cs.LG cs.NI

Zheng Zhang , Qiong Wu , Pingyi Fan , Nan Cheng , Wen Chen , Khaled B. Letaief This is my paper · ORCID
classification cs.LGcs.NI
keywords AgeofInformationC-V2XNOMASuccessiveInterferenceCancellationDeepReinforcementLearningMulti-priorityqueueSemi-PersistentSchedulingVehicle-to-Vehiclecommunication
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

In C-V2X Mode 4, vehicles autonomously reserve radio resources, which causes collisions and degrades the freshness of safety-critical messages. This paper argues that adding multi-priority message queues and SIC-based NOMA, then letting a roadside unit learn each vehicle's transmission interval and power with a deep reinforcement learning algorithm, can cut both the average Age of Information and the average energy consumption. The proposed MPDQN scheme is shown in simulation to outperform genetic-algorithm and random baselines, while also keeping higher-priority messages fresher than lower-priority ones.

What carries the argument

The central object is MPDQN, a deep Q-network variant for hybrid discrete-continuous action spaces: a policy network maps each discrete RRI choice to the best continuous transmit power, and a Q-network scores the resulting action tuples against the state. Two analytical models carry the argument: a multi-priority FIFO queue per vehicle that tracks AoI separately for each message type, and a NOMA/SIC receiver model that decodes the strongest colliding signal first and cancels it before decoding weaker ones, thereby raising SINR and reducing transmission failure. The collision probability itself is taken from a Markov-chain model of C-V2X Mode 4 resource selection, which sets how often the SIC decoder is exercised.

What would settle it

Run a packet-level simulation of C-V2X Mode 4 with SIC-based NOMA and compare the measured fraction of collided resource reservations against the value predicted by Eq. (19) across the same vehicle densities and RRI values; a systematic discrepancy would invalidate the collision model on which the AoI and energy improvements rest.

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Extended reading notes

Core claim

The paper's central claim is that the resource reservation interval and transmit power of each vehicle can be selected online by a single DRL agent, trained with a multi-pass deep Q-network, to minimize the weighted sum of average receiver AoI and energy consumption in a C-V2X Mode 4 system. The agent observes per-vehicle state features, chooses among discrete RRI values (20, 50, or 100 ms) paired with a continuous power level, and receives a reward that is the negative weighted average of energy and AoI. Simulation results show the learned policy achieves lower average AoI and lower average energy consumption than genetic and random allocation, and that NOMA's SIC decoding reduces AoI most when the RRI is small and collisions are frequent.

Load-bearing premise

The load-bearing premise is that the collision probability formula borrowed from [51] remains accurate when NOMA and SIC change how interfering signals are decoded; if that formula is wrong, the AoI improvements attributed to NOMA and the deep reinforcement learning policy are not established.

Editorial extensions

If this is right

  • A roadside unit could periodically recompute per-vehicle RRI and transmit power without centralized scheduling, adapting to vehicle density and channel conditions in real time.
  • Multi-priority queues guarantee that high-priority safety messages (HPD and DENM) maintain lower AoI than lower-priority CAM and MHD traffic, even when the total message arrival rate exceeds the processing rate.
  • NOMA with SIC can be layered on existing Mode 4 resource selection to reduce the damage of collisions, with the largest gains at short reservation intervals where collisions are most frequent.
  • Energy consumption stays nearly flat as the number of vehicles grows under the learned policy, while AoI increases only gradually, suggesting the scheme decouples energy cost from network load.
  • Because the policy is learned from local state at the RSU, the same architecture could be retrained for other road geometries, traffic patterns, or message-size distributions without changing the model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The collision-probability model borrowed from [51] is assumed to remain valid when NOMA/SIC changes the interference structure; a more accurate SIC-aware collision model could shift the magnitude of the reported NOMA gains.
  • The fixed weighting factor ω1=0.6 implies a specific operator preference for energy savings over AoI; an adaptive weight that responds to the presence of high-priority traffic would be a natural extension of the reward design.
  • The discrete RRI set could be expanded or made continuous, which would let the agent trade off queue processing rate against collision frequency more finely than the current three choices allow.
  • The single-agent RSU architecture could be replaced by independent per-vehicle learners to test whether the performance degrades gracefully or requires coordination, a question the paper does not address.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper studies a C-V2X Mode 4 vehicular network with multiple message classes, multi-priority FIFO queues, and NOMA/SIC at the receiver. It develops analytical models for queueing AoI, receiver AoI, collision probability, SINR, and energy consumption, and formulates a joint optimization of the resource reservation interval (RRI) and transmit power to minimize a weighted sum of average AoI and energy consumption. The optimization is solved with a hybrid discrete-continuous action DRL algorithm (MPDQN), and simulation comparisons against GA-based and random policies are presented for various vehicle counts and message sizes.

Significance. The combination of multi-priority queues, NOMA/SIC, AoI, and DRL-based RRI/power control is timely and the authors release source code, which aids reproducibility. The conceptual direction—prioritizing safety-critical messages and using SIC to mitigate SPS collisions—is reasonable and potentially useful for C-V2X design. However, the paper's quantitative claims are conditional on resolving an internal contradiction in the receiver AoI update, a garbled collision-probability expression, and a physically inconsistent energy model; until these are fixed, the claimed AoI and energy benefits over baselines are not established.

major comments (4)
  1. [Section III-E, Eq. (15)] The receiver AoI update is internally inconsistent. Eq. (15) states that on transmission failure Φ_{t+1} = Φ_t + 1, but the paragraph immediately following states that because a failed C-V2X Mode 4 transmission forces a wait equal to the RRI size, the increase in Φ is Γ, with Γ ∈ {20, 50, 100}. These two updates differ by up to two orders of magnitude. Since Φ appears in the objective Eq. (25), the reward Eq. (31), and is the headline metric in Figs. 6, 9, and 10, the reported AoI values and the claimed superiority over baselines are not robust until this is resolved. Please correct Eq. (15) to match the implemented update or provide the code path that confirms which update is used.
  2. [Section III-F, Eq. (19)] The collision probability expression is garbled and its applicability under NOMA is not established. The product term has ambiguous indexing (the rendering of the product over i from 0 to Γ−1 of (1 − π/(1−π i)) is not clearly defined), and the overall formula lacks a clean derivation in this paper. More importantly, Eqs. (20) and (21) use different interference models for collisions with and without SIC, but Eq. (19) is borrowed from [51], which does not account for NOMA/SIC changing the set of undecodable interferers. If the collision probability is used to trigger transmission failures in the simulator, the AoI improvement attributed to NOMA may be an artifact of the model. Please clarify how Eq. (19) is applied in the NOMA case and provide a concrete validation, such as a comparison of SINR distributions with and without SIC for identical collision events.
  3. [Section III-G, Eqs. (23) and (24)] The energy consumption model has a unit consistency problem. Eq. (23) defines ε_i^t = p_i^t β_i^t, which has units of power (W) when p_i^t is in watts, not energy (J). Eq. (24) multiplies this by RC_i^0, a dimensionless count, so the resulting E_i^t is still not an energy. The energy values plotted in Figs. 9 and 10 are therefore not physically meaningful without an explicit time duration or normalization factor. This is load-bearing because energy is one of the two objectives in Eq. (25) and the reward (31). Please redefine ε_i^t as p_i^t times the transmission duration (e.g., one subframe) or clearly state that a normalized energy unit is used.
  4. [Section V-B, Figs. 4–10] The simulation results do not include error bars, confidence intervals, or statistical tests, and the reported curves appear to be point estimates from single runs. Since the baseline comparisons in Figs. 9 and 10 are the central evidence for the claim that MPDQN 'outperforms' GA and random policies, the absence of variance information makes it impossible to judge whether the differences are significant. Additionally, the reward weight ω_1 is selected from the training curves in Fig. 7, and the trained policy is evaluated on the same simulator used for training, so there is no out-of-sample test. Please add multiple-seed results with error bars or statistical significance measures, and describe how the reported curves are averaged.
minor comments (5)
  1. [Section IV-B, Eq. (38)] The action set in the loss function is written as {20, 50, 10}; this should be {20, 50, 100} to match Eq. (25b).
  2. [Section V-B, Fig. 7(b)] The x-axis labels in Fig. 7(b) are malformed ('0.3 0.4 0.5 0.6 .7'); they should be a proper numeric axis with values 0.3 through 0.7.
  3. [Section III-C, Eqs. (3)–(7)] The resource reservation model is hard to follow: the notation m_i^t, t_rk, and the condition for using Eq. (3) versus Eq. (4) is not clearly defined, and Eq. (7) contains an ambiguous bracket expression. Please rewrite these definitions more formally.
  4. [Section III-D, Eq. (12)] The multi-priority transmission rule is only explicitly written for HPD and CAM; the corresponding conditions for DENM and MHD queues are not shown, even though the text says there are four queues. Please give the general expression or the full cases.
  5. [Section III-D, Eq. (10)] The Poisson arrival probability P(arr_{i,n}^t = 1) = λ_n e^{−λ_n} is dimensionally unclear; if λ_n is a rate per time slot, the right-hand side should be 1 − e^{−λ_n} for the probability of at least one arrival. Please define λ_n and the time unit explicitly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the simulated priority/queue and NOMA effects follow from the model definitions rather than from fitted predictions, and the self-citations are not load-bearing.

full rationale

This paper does not exhibit a circular derivation in the defined sense. The system model (queue equations (8)-(14), AoI definition (15), SINR models (18)-(21), energy model (23)-(24)) is stated as assumptions, and the DRL objective (25) is matched to the reward (31) by design; that is an optimization setup, not a fitted input renamed as a prediction. The multi-priority queue behavior shown in Fig. 4 follows from the explicit priority scheduling in Eq. (12), and the NOMA gain in Fig. 6 follows from the SIC SINR expression (21); these are model consequences rather than independent empirical predictions, so they are not circular. The self-citations, including [52] used to justify the MPDQN action representation, are method citations and are not invoked as a uniqueness theorem or as evidence for the paper's main result. There is a serious internal inconsistency in Eq. (15): the receiver AoI is defined as incrementing by 1 on failure, while the accompanying text states that a failure forces a wait equal to the RRI size and thus an increase by Γ. That inconsistency threatens reproducibility and the quantitative AoI claims, but it is a correctness/consistency issue rather than a circularity, because it does not make the output equal to an input by construction. Accordingly, no specific circular step can be quoted, and the circularity score is 0.

Assumptions & free parameters 5 free parameters · 7 assumptions · 0 invented entities

The central results rest on several unverified modeling choices: the borrowed collision probability formula, Poisson arrivals with a fixed lambda, strict priority queueing, perfect SIC, and the choice of omega_1 = 0.6 tuned on the same evaluation data.

free parameters (5)
  • omega_1 (energy weight in reward) = 0.6
    Chosen as the value giving highest average reward in Fig. 7, then fixed for all other simulations; effectively tuned on the evaluation metric.
  • omega_2 (AoI weight in reward) = 0.4 (implied, assuming omega_1 + omega_2 = 1)
    Not explicitly stated; the paper only varies omega_1 from 0.3 to 0.7, so omega_2 appears to be 1 - omega_1. This balance is a design choice.
  • lambda (Poisson arrival rate for non-CAM messages) = 0.0001
    Used for DENM and MHD message generation; no sensitivity analysis is provided, and this low value may make queue buildup minimal.
  • K_H, K_D (retransmission counts) = 8 and 5
    Retransmission parameters for high-priority data and DENM; chosen without justification or sensitivity analysis.
  • Queue capacity L = 10
    Maximum queue length; chosen without justification; overflow behavior affects AoI.
assumptions (7)
  • domain assumption C-V2X Mode 4 SPS resource selection with 20% lowest-RSSI candidates
    Section III-C describes resource reservation using SPS; this is a 3GPP model, but the paper treats it as given and does not validate the 20% rule in the simulation.
  • domain assumption Collision probability formula Eq. (19) from [51]
    The paper borrows this expression for P_col and uses it to model collisions; it is not derived in this paper and its validity under NOMA/SIC is unverified.
  • domain assumption Poisson message arrivals with P(arr=1) = lambda * exp(-lambda)
    Eq. (10) models generation of non-CAM messages; this is a simplifying assumption with no empirical basis, and the probability expression omits the factorial for k=1.
  • ad hoc to paper Strict priority service in four FIFO queues
    Eq. (12) enforces that high-priority queues are served first; this is a modeling choice introduced here and is the direct cause of the claimed AoI advantage for high-priority messages.
  • domain assumption Perfect SIC decoding ordered by received power
    Eq. (21) assumes the receiver can perfectly cancel stronger signals; in practice SIC errors would reduce the NOMA gain.
  • ad hoc to paper MPDQN converges to a near-optimal policy
    No convergence guarantee or optimality bound is given; the paper relies on training curves in Fig. 8 to assert the strategy is approaching optimality.
  • domain assumption LTEV2Vsim simulator [55] with paper-specific modifications
    The simulation is built on the open LTEV2Vsim, but the modifications are not specified, so results depend on the simulator's channel and PHY models.

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Cite this review

Pith. "Pith review of DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV." pith.science (2026). https://pith.science/paper/W6DIRIQ6

@misc{pith2026241113104,
  author       = {Pith},
  title        = {Pith review of: DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W6DIRIQ6}},
  note         = {Machine review of arXiv:2411.13104}
}
read the original abstract

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI.

Figures

Figures reproduced from arXiv: 2411.13104 by the authors.

Figure 1
Figure 1. C-V2X IoV system to decode messages in descending order of power. Lower￾power messages are treated as interference and excluded after decoding the highest power message, thereby improving the SINR of each message and reducing the AoI resulting from communication failures. AoI reflects the time spent in queuing and transmission processes, where the queuing process is affected by packet generation rate or RRI, and the… view at source ↗
Figure 2
Figure 2. Schematic diagram of communication resource reser [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Changes in AoI. The changes in AoI are shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The AoI in queue with low processing rates. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: The changes of average AoI with different [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: (a) illustrates the influence of different RRI chosen by vehicles on the average AoI at the receiver for vary￾ing numbers of vehicles. It can be observed that the AoI is consistently higher when the Γ is 100ms, attributed to inadequate processing rates leading to exces…
Figure 8
Figure 8. Figure 8: Reward curves for different ilhdh [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 7
Figure 7. Figure 7: The simulation results for different values of [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 9
Figure 9. Figure 9: The simulation results of each scheme for different [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.