pith. machine review for the scientific record. sign in

arxiv: 2604.13691 · v1 · submitted 2026-04-15 · 💻 cs.IT · math.IT

Recognition: unknown

Towards Autonomous Driving with Short-Packet Rate Splitting: Age of Information Analysis and Optimization

Zirui Zheng , Yingyang Chen , Xinyue Pei , Xingwei Wang , Zhiquan Liu , Theodoros A. Tsiftsis , Miaowen Wen , Pingzhi Fan
Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:28 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords rate-splitting multiple accessshort-packet communicationage of informationautonomous drivingURLLCinterference managementpower allocationfinite blocklength
0
0 comments X

The pith

Rate splitting with short packets lowers average age of information in vehicle networks by managing interference through common and private streams.

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

Autonomous vehicles need up-to-date data to avoid collisions and coordinate, so age of information measures how stale each packet becomes after transmission delays and errors. The paper establishes that combining rate-splitting multiple access with short-packet communication reduces this age by splitting unicast messages into shared common parts and individual private parts. Common parts are bundled with any multicast message into one stream while private parts use separate streams, which controls interference more effectively than conventional schemes. Closed-form expressions are given for average age of information of the common stream under partial decoding and for the full system under complete decoding. An optimization routine then tunes power allocation and splitting ratios under quality-of-service limits, with simulations showing the approach reaches the ultra-low values required for self-driving safety.

Core claim

By splitting the unicast messages into common and private parts, encoding all common parts together with the multicast message into a common stream, and encoding each private part into a private stream, RSMA effectively manages interference and enables achieving lower AoI. Closed-form expressions for the average AoI of the common stream under partial decoding and the overall AAoI under complete decoding are derived, with a multi-start two-step successive convex approximation algorithm optimizing power and rates under QoS trade-off constraints.

What carries the argument

The rate-splitting scheme that encodes common unicast parts plus multicast data into one common stream and each private unicast part into its own stream, enabling interference control and separate decoding levels that directly affect packet error rates and transmission delays in short blocks.

If this is right

  • The common stream reaches ultra-low average AoI independently of the private streams.
  • Power allocation followed by rate splitting can be jointly optimized via the multi-start two-step SCA algorithm while satisfying QoS trade-offs.
  • Overall system average AoI improves while preserving fairness across users.
  • A performance trade-off appears between the common stream and the aggregate system, allowing further overall gains without losing common-stream benefits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same splitting structure could guide blocklength selection in other high-mobility URLLC settings where freshness and reliability must be balanced.
  • If the closed forms remain accurate under time-varying velocities, they could serve as a quick design tool for choosing power splits without repeated Monte-Carlo runs.
  • Extending the model to include hardware impairments or imperfect channel state information would test whether the reported AoI reductions survive real-vehicle conditions.

Load-bearing premise

The closed-form average AoI expressions rest on finite-blocklength error-rate approximations and standard fading models holding accurately for the vehicle velocities and blocklengths considered.

What would settle it

Field or simulation measurements of average age of information that deviate substantially from the derived closed-form values at identical transmit power, blocklength, velocity, and antenna count would falsify the expressions.

Figures

Figures reproduced from arXiv: 2604.13691 by Miaowen Wen, Pingzhi Fan, Theodoros A. Tsiftsis, Xingwei Wang, Xinyue Pei, Yingyang Chen, Zhiquan Liu, Zirui Zheng.

Figure 1
Figure 1. Figure 1: depicts a typical high-mobility autonomous driving communication scenario with short-packet rate splitting. The BS is equipped with Nt antennas in a down￾link multiple-input single-output (MISO) broadcast setup and serves K single-antenna autonomous vehicles, where Nt ≥ K. The BS transmits multicast messages, such as vehicle platooning [29], environmental perception [30], and emergency broadcasts [31], as … view at source ↗
Figure 2
Figure 2. Figure 2: Simplified transmission model. ∆()t // 0 … Q0 Q1 Q2 QN () τ ∆Q T T 2T 3T 4T 5T 3T t 2T τ [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: AoI evolution over time. Each vehicle first decodes the common stream and then decodes its private stream via SIC. If the common stream decoding fails, SIC cannot remove its interference. Since the common stream is generally transmitted with higher power, it strongly interferes with the private streams, leading to decoding failure. Therefore, we assume that the common stream decoding failure inevitably res… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of the analytical and simulated average AAoI. and rate splitting via SCA. To mitigate potential bias from different initializations, we adopt the multi-start strategy that selects a few starting points and finally chooses the best solution. Since this strategy only affects the search process and does not alter the complexity of each optimization, it can be neglected in the complexity analysis. C… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the average AAoI under perfect and imperfect CSIT. 0 5 10 15 20 25 Number of iterations 0.5 1 1.5 Objective function 10-3 (a) Convergence of subproblem P1. 0 2 4 6 8 10 Number of iterations 5.85 5.95 6.05 Objective function 10-4 (b) Convergence of subproblem P3 [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Convergence of the proposed algorithm. to optimize the AAoI performance of the proposed RSMA scheme [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average AAoI versus vehicle velocity. 300 400 500 600 700 800 900 1000 1100 1200 Blocklength 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Average AAoI (ms) NOMA SDMA RSMA(Overall) RSMA(Common) [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Average AAoI versus blocklength. RSMA (Overall) [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 12
Figure 12. Figure 12: Maximum AAoI versus blocklength. 300 400 500 600 700 800 900 1000 1100 1200 Blocklength 0.35 0.45 0.55 0.65 Average AAoI (ms) [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The trade-off effect of λ on the average AAoI. minimize the average AAoI, it does not excessively degrade the performance of individual users, achieving an effective balance between overall and worst-case performance. This advantage arises not only from the appropriate power allocation between the common and private streams but more importantly from the effective rate splitting, which ensures fairness amo… view at source ↗
read the original abstract

To address the high mobility impacts and the ultra-reliable and low-latency communication (URLLC) requirements in autonomous driving scenarios, rate-splitting multiple access (RSMA) combined with short-packet communication (SPC) emerges as a promising solution.Autonomous vehicles rely on real-time information exchange to ensure safety and coordination, making information freshness essential.By jointly capturing transmission delays and packet errors, age of information (AoI) serves as a comprehensive metric for freshness.In this paper, we investigate short-packet rate splitting to enhance information freshness measured by the AoI.By splitting the unicast messages into common and private parts, encoding all common parts together with the multicast message into a common stream, and encoding each private part into a private stream, RSMA effectively manages interference and enables achieving lower AoI.By considering critical factors such as transmit power, vehicle velocity, blocklength, and the number of transmit antennas, we derive closed-form expressions for the average AoI (AAoI) of the common stream under partial decoding and the overall AAoI under complete decoding.To enhance the AAoI performance, we propose the multi-start two-step successive convex approximation (SCA) algorithm.This algorithm first optimizes the power allocation and subsequently optimizes the rate splitting under the quality of service (QoS) trade-off constraint.Simulation results demonstrate that our short-packet rate-splitting scheme significantly improves the AAoI performance while ensuring system fairness and enabling ultra-low AAoI through the common stream, meeting the requirements of autonomous driving applications.Moreover, the trade-off between the common and overall performance is revealed, indicating that the overall performance can be further enhanced while maintaining the advantages of the common stream.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a short-packet rate-splitting multiple access (RSMA) scheme for autonomous driving scenarios to reduce Age of Information (AoI) under URLLC constraints. Unicasts are split into common and private parts, with common parts plus the multicast encoded into a common stream and privates into private streams. Closed-form expressions are derived for the average AoI (AAoI) of the common stream under partial decoding and the overall AAoI under complete decoding. A multi-start two-step successive convex approximation (SCA) algorithm is proposed to jointly optimize power allocation and rate splitting subject to QoS trade-off constraints. Simulations claim significant AAoI gains while preserving fairness.

Significance. If the closed-form AAoI derivations hold, the work supplies analytical expressions and an optimization procedure for managing interference and achieving ultra-low AoI in high-mobility settings, which is directly relevant to autonomous-vehicle coordination. The explicit treatment of the common-stream versus overall-performance trade-off and the incorporation of vehicle velocity, blocklength, and antenna count as parameters are practical strengths.

major comments (2)
  1. [AAoI derivations] The section deriving the AAoI expressions: the closed-form AAoI for the common stream (partial decoding) and overall system (complete decoding) is obtained by substituting the normal approximation for short-packet error probability (involving channel dispersion V and the Q-function) into the standard AoI renewal process. This step assumes that the post-decoding SINRs remain constant over each block and that fading realizations are independent across blocks. When vehicle velocity renders coherence time comparable to the blocklength, the approximation error becomes first-order and the claimed closed forms lose accuracy; the manuscript provides no verification of the approximation error in the target mobility and blocklength regime.
  2. [Optimization algorithm] The optimization section: the multi-start two-step SCA algorithm first optimizes power allocation factors and then rate splitting under the QoS trade-off constraint. The paper does not report convergence rates, the number of random starts required for reliable global behavior, or the sensitivity of the final AAoI to initialization, all of which are load-bearing for the claim that the algorithm reliably achieves the reported performance gains.
minor comments (2)
  1. [Abstract and conclusion] The abstract states that 'the trade-off between the common and overall performance is revealed' but does not specify whether this trade-off is expressed as a Pareto front, a weighted sum, or a constraint; the same phrasing appears in the conclusion without a precise mathematical statement.
  2. [Numerical results] Simulation figures should include error bars or confidence intervals on the AAoI curves and explicitly list the exact parameter values (vehicle speeds, blocklengths, antenna counts, and baseline schemes) used to generate each plot.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which have identified valuable opportunities to improve the rigor and clarity of our manuscript. We address each major comment point by point below, providing honest responses and indicating the revisions we will make.

read point-by-point responses

  1. Referee: [AAoI derivations] The section deriving the AAoI expressions: the closed-form AAoI for the common stream (partial decoding) and overall system (complete decoding) is obtained by substituting the normal approximation for short-packet error probability (involving channel dispersion V and the Q-function) into the standard AoI renewal process. This step assumes that the post-decoding SINRs remain constant over each block and that fading realizations are independent across blocks. When vehicle velocity renders coherence time comparable to the blocklength, the approximation error becomes first-order and the claimed closed forms lose accuracy; the manuscript provides no verification of the approximation error in the target mobility and blocklength regime.

    Authors: We appreciate the referee's careful scrutiny of the modeling assumptions underlying our closed-form AAoI derivations. Our analysis employs the standard block-fading model, which is prevalent in short-packet URLLC literature and assumes constant post-decoding SINR within each block with independent realizations across blocks. This is appropriate when blocklength is shorter than coherence time. We acknowledge that the manuscript does not explicitly verify the approximation error for the specific mobility and blocklength regimes considered. In the revised version, we will add a discussion quantifying coherence time via Jakes' model for the vehicle velocities in our simulations and compare it to the blocklengths used. We will also include Monte Carlo validation results comparing analytical AAoI to empirical values across mobility levels to bound the error, thereby clarifying the regime where the closed forms remain accurate. revision: partial

  2. Referee: [Optimization algorithm] The optimization section: the multi-start two-step SCA algorithm first optimizes power allocation factors and then rate splitting under the QoS trade-off constraint. The paper does not report convergence rates, the number of random starts required for reliable global behavior, or the sensitivity of the final AAoI to initialization, all of which are load-bearing for the claim that the algorithm reliably achieves the reported performance gains.

    Authors: We agree that additional empirical details on the multi-start two-step SCA algorithm are needed to substantiate its reliability. In the revised manuscript, we will expand the optimization section (and add an appendix if necessary) to report convergence behavior, including typical iteration counts required for convergence (observed to be under 30 iterations in our experiments). We will specify the use of 20 random initializations and include statistics on the resulting AAoI values (mean and variance) to demonstrate consistent achievement of the reported gains. We will further add sensitivity analysis plots or tables showing AAoI variation across different initializations, confirming low sensitivity and reliable global performance. revision: yes

Circularity Check

0 steps flagged

No circularity: AAoI closed forms and optimization rest on independent standard formulas.

full rationale

The paper derives closed-form AAoI expressions by substituting standard finite-blocklength normal approximations (involving channel dispersion and Q-function) and Rayleigh fading statistics into the conventional AoI renewal-process formula for average age under geometric retransmissions. These building blocks are external to the paper and do not reference its own results or fitted parameters. The subsequent multi-start SCA optimization operates on explicitly stated power-allocation and rate-splitting constraints with QoS trade-offs; no step reduces a claimed prediction back to a fitted input or self-citation by construction. Self-citations, if present, are not load-bearing for the central claims.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard finite-blocklength information theory bounds, Rayleigh or similar fading models, and perfect statistical channel knowledge; no new entities are postulated and no parameters are fitted ad hoc beyond the optimized power and rate variables.

free parameters (2)
  • power allocation factors
    Optimized via the SCA algorithm under QoS constraints; not fitted to data but chosen to minimize AAoI.
  • blocklength
    Treated as a design variable in the analysis and simulations.
axioms (2)
  • domain assumption Finite-blocklength rate approximations hold for the considered packet sizes and error probabilities
    Invoked to obtain closed-form AAoI expressions.
  • domain assumption Channel state information is available at transmitters for rate and power allocation
    Required for the RSMA encoding and decoding analysis.

pith-pipeline@v0.9.0 · 5636 in / 1453 out tokens · 55768 ms · 2026-05-10T12:28:54.741584+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

48 extracted references

  1. [1]

    Milestones in autonomous driving and intelligent vehicles—Part I: Control, computing system design, communication, HD map, testing, and human behaviors,

    L. Chen, Y. Li, C. Huang, Y. Xing, D. Tian, L. Li, Z. Hu, S. Teng, C. Lv, J. Wang et al., “Milestones in autonomous driving and intelligent vehicles—Part I: Control, computing system design, communication, HD map, testing, and human behaviors,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 53, no. 9, pp. 5831–5847, 2023

    2023

  2. [2]

    6G for vehicle-to-everything (V2X) communications: Enabling tech- nologies, challenges, and opportunities,

    M. Noor-A-Rahim, Z. Liu, H. Lee, M. O. Khyam, J. He, D. Pesch, K. Moessner, W. Saad, and H. V. Poor, “6G for vehicle-to-everything (V2X) communications: Enabling tech- nologies, challenges, and opportunities,” Proc. IEEE, vol. 110, no. 6, pp. 712–734, 2022

    2022

  3. [3]

    Enhancement of 3GPP support for V2X scenarios,

    3GPP, “Enhancement of 3GPP support for V2X scenarios,” 3GPP TS 22.186 V19.0.0, Oct. 2025

    2025

  4. [4]

    Age of information in SWIPT-enabled wireless communication system for 5GB,

    T. D. P. Perera, D. N. K. Jayakody, I. Pitas, and S. Garg, “Age of information in SWIPT-enabled wireless communication system for 5GB,” IEEE Wireless Commun., vol. 27, no. 5, pp. 162–167, 2020

    2020

  5. [5]

    Real-time status: How often should one update?

    S. Kaul, R. Yates, and M. Gruteser, “Real-time status: How often should one update?” in Proc. IEEE INFOCOM, Mar. 2012, pp. 2731–2735

    2012

  6. [6]

    Age of information for the cellular Internet of Things: Challenges, key techniques, and future trends,

    B. Yu, X. Chen, and Y. Cai, “Age of information for the cellular Internet of Things: Challenges, key techniques, and future trends,” IEEE Commun. Mag., vol. 60, no. 12, pp. 20–26, 2022. 14

    2022

  7. [7]

    Adaptive packet length adjustment for minimizing age of information over fading channels,

    B. Yu, Y. Cai, X. Diao, and K. Cheng, “Adaptive packet length adjustment for minimizing age of information over fading channels,” IEEE Trans. Wireless Commun., vol. 22, no. 10, pp. 6641–6653, 2023

    2023

  8. [8]

    Efficient decoders for short block length codes in 6G URLLC,

    C. Yue, V. Miloslavskaya, M. Shirvanimoghaddam, B. Vucetic, and Y. Li, “Efficient decoders for short block length codes in 6G URLLC,” IEEE Commun. Mag., vol. 61, no. 4, pp. 84–90, 2023

    2023

  9. [9]

    Transformer-based channel prediction for rate-splitting multiple access-enabled vehicle-to-everything communication,

    S. Zhang, S. Zhang, Y. Mao, L. K. Yeung, B. Clerckx, and T. Q. Quek, “Transformer-based channel prediction for rate-splitting multiple access-enabled vehicle-to-everything communication,” IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 12 717– 12 730, 2024

    2024

  10. [10]

    A rate-splitting approach to the gaussian multiple-access channel,

    B. Rimoldi and R. Urbanke, “A rate-splitting approach to the gaussian multiple-access channel,” IEEE Trans. Inf. Theory, vol. 42, no. 2, pp. 364–375, 1996

    1996

  11. [11]

    Rate-splitting multiple access to mitigate the curse of mobility in (massive) MIMO networks,

    O. Dizdar, Y. Mao, and B. Clerckx, “Rate-splitting multiple access to mitigate the curse of mobility in (massive) MIMO networks,” IEEE Trans. Commun., vol. 69, no. 10, pp. 6765– 6780, 2021

    2021

  12. [12]

    Rate-splitting multiple access for downlink communication systems: Bridging, gener- alizing, and outperforming SDMA and NOMA,

    Y. Mao, B. Clerckx, and V. O. Li, “Rate-splitting multiple access for downlink communication systems: Bridging, gener- alizing, and outperforming SDMA and NOMA,” EURASIP J. Wireless Commun. Netw., vol. 2018, no. 1, p. 133, 2018

    2018

  13. [13]

    Rate-splitting multiple access: Fundamentals, survey, and future research trends,

    Y. Mao, O. Dizdar, B. Clerckx, R. Schober, P. Popovski, and H. V. Poor, “Rate-splitting multiple access: Fundamentals, survey, and future research trends,” IEEE Commun. Surv. Tutor., vol. 24, no. 4, pp. 2073–2126, 2022

    2073

  14. [14]

    Beyond dirty paper coding for multi- antenna broadcast channel with partial CSIT: A rate-splitting approach,

    Y. Mao and B. Clerckx, “Beyond dirty paper coding for multi- antenna broadcast channel with partial CSIT: A rate-splitting approach,” IEEE Trans. Commun., vol. 68, no. 11, pp. 6775– 6791, 2020

    2020

  15. [15]

    Rate-splitting multiple access with finite blocklength for short-packet and low- latency downlink communications,

    Y. Xu, Y. Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access with finite blocklength for short-packet and low- latency downlink communications,” IEEE Trans. Veh. Technol., vol. 71, no. 11, pp. 12 333–12 337, 2022

    2022

  16. [16]

    Rate-splitting multiple access with finite blocklength and high mobility for URLLC transmissions,

    J. Zhu, Y. Chen, X. Pei, T. Pei, X. Li, and T. A. Tsiftsis, “Rate-splitting multiple access with finite blocklength and high mobility for URLLC transmissions,” IEEE Wireless Commun. Lett., vol. 13, no. 5, pp. 1518–1522, 2024

    2024

  17. [17]

    Autonomous driving with RSMA-enabled finite blocklength transmissions: Ergodic performance analysis and optimization,

    Y. Wang, Y. Chen, L. Wang, D. Cai, X. Li, and P. Fan, “Autonomous driving with RSMA-enabled finite blocklength transmissions: Ergodic performance analysis and optimization,” IEEE Trans. Wireless Commun., vol. 25, pp. 2954–2969, 2026

    2026

  18. [18]

    Resource allocation in MU- MISO rate-splitting multiple access with SIC errors for URLLC services,

    X. Ou, X. Xie, H. Lu, and H. Yang, “Resource allocation in MU- MISO rate-splitting multiple access with SIC errors for URLLC services,” IEEE Trans. Commun., vol. 71, no. 1, pp. 229–243, 2022

    2022

  19. [19]

    Flexible rate-splitting multiple access with finite blocklength,

    Y. Wang, V. W. Wong, and J. Wang, “Flexible rate-splitting multiple access with finite blocklength,” IEEE J. Sel. Areas Commun., vol. 41, no. 5, pp. 1398–1412, 2023

    2023

  20. [20]

    Fairness-aware reliability maximization of a novel RSMA framework for URLLC,

    S. Seyedmasoumian, Y. Zhu, Y. Hu, and A. Schmeink, “Fairness-aware reliability maximization of a novel RSMA framework for URLLC,” in Proc. IEEE GLOBECOM, 2023, pp. 1517–1522

    2023

  21. [21]

    Max-min fairness for uplink rate-splitting multiple access with finite blocklength,

    J. Xu and Y. Mao, “Max-min fairness for uplink rate-splitting multiple access with finite blocklength,” IEEE Trans. Veh. Technol., vol. 74, no. 5, pp. 8437–8441, 2025

    2025

  22. [22]

    Max-min fairness of rate-splitting multiple access with finite blocklength com- munications,

    Y. Xu, Y. Mao, O. Dizdar, and B. Clerckx, “Max-min fairness of rate-splitting multiple access with finite blocklength com- munications,” IEEE Trans. Veh. Technol., vol. 72, no. 5, pp. 6816–6821, 2022

    2022

  23. [23]

    Balancing AoI and rate for mission- critical and eMBB coexistence with puncturing, NOMA, and RSMA in cellular uplink,

    F. Khodakhah, A. Mahmood, Č. Stefanović, H. Farag, P. Öster- berg, and M. Gidlund, “Balancing AoI and rate for mission- critical and eMBB coexistence with puncturing, NOMA, and RSMA in cellular uplink,” IEEE Trans. Veh. Technol., vol. 74, no. 1, pp. 1475–1488, 2024

    2024

  24. [24]

    RSMA- aided iterative max-age scheduling for multi-source systems,

    E. J. dos Santos, R. D. Souza, and J. L. Rebelatto, “RSMA- aided iterative max-age scheduling for multi-source systems,” in Proc. Int. Symp. Wireless Commun. Syst. (ISWCS), 2024, pp. 1–6

    2024

  25. [25]

    Rate-splitting multiple access for semantic-aware networks: An age of incorrect information per- spective,

    O. Dizdar and S. Wang, “Rate-splitting multiple access for semantic-aware networks: An age of incorrect information per- spective,” IEEE Wireless Commun. Lett., vol. 13, no. 4, pp. 1168–1172, 2024

    2024

  26. [26]

    A joint framework of HARQ and RSMA for AoI minimization,

    S. R. Ali, S. Abidrabbu, H. Furqan, and H. Arslan, “A joint framework of HARQ and RSMA for AoI minimization,” IEEE Internet Things J., vol. 12, no. 18, pp. 37 500–37 511, 2025

    2025

  27. [27]

    Age of information minimization for short-packet communications RSMA in satellite-based IoT,

    Q. Yan, J. Jiao, Y. Wang, L. An, R. Lu, and Q. Zhang, “Age of information minimization for short-packet communications RSMA in satellite-based IoT,” in Proc. IEEE VTC-Fall, 2023, pp. 1–5

    2023

  28. [28]

    Age of incorrect information minimization for timely status updates in satellite-based IoT,

    Q. Yan, J. Jiao, T. Yang, L. An, Y. Wang, and Q. Zhang, “Age of incorrect information minimization for timely status updates in satellite-based IoT,” in Proc. IEEE/CIC ICCC, 2024, pp. 433–438

    2024

  29. [29]

    Efficient rate-splitting multiple access for the Internet of Vehicles: Fed- erated edge learning and latency minimization,

    S. Zhang, S. Zhang, W. Yuan, Y. Li, and L. Hanzo, “Efficient rate-splitting multiple access for the Internet of Vehicles: Fed- erated edge learning and latency minimization,” IEEE J. Sel. Areas Commun., vol. 41, no. 5, pp. 1468–1483, 2023

    2023

  30. [30]

    Optimizing re- source allocation with high-reliability constraint for multicasting automotive messages in 5G NR C-V2X networks,

    K.-L. Chen, W.-Y. Chen, and R.-H. Hwang, “Optimizing re- source allocation with high-reliability constraint for multicasting automotive messages in 5G NR C-V2X networks,” IEEE Trans. Veh. Technol., vol. 72, no. 4, pp. 4792–4804, 2022

    2022

  31. [31]

    IRS-assisted joint broadcast/multicast resource allocation and cooperative perception optimization for 6G V2X networks,

    F. Qi, Q. Huang, Y. Wang, P. Lin, C. Hu, and X. Shi, “IRS-assisted joint broadcast/multicast resource allocation and cooperative perception optimization for 6G V2X networks,” IEEE Trans. Broadcast., vol. 72, no. 1, pp. 106–116, 2026

    2026

  32. [32]

    Model-assisted learning for adaptive cooperative perception of connected au- tonomous vehicles,

    K. Qu, W. Zhuang, Q. Ye, W. Wu, and X. Shen, “Model-assisted learning for adaptive cooperative perception of connected au- tonomous vehicles,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8820–8835, 2024

    2024

  33. [33]

    Rate-splitting for multi- antenna non-orthogonal unicast and multicast transmission: Spectral and energy efficiency analysis,

    Y. Mao, B. Clerckx, and V. O. Li, “Rate-splitting for multi- antenna non-orthogonal unicast and multicast transmission: Spectral and energy efficiency analysis,” IEEE Trans. Commun., vol. 67, no. 12, pp. 8754–8770, 2019

    2019

  34. [34]

    MIMO systems with limited rate differential feedback in slowly varying channels,

    T. Kim, D. J. Love, and B. Clerckx, “MIMO systems with limited rate differential feedback in slowly varying channels,” IEEE Trans. Commun., vol. 59, no. 4, pp. 1175–1189, 2011

    2011

  35. [35]

    J. G. Proakis and M. Salehi, Digital communications, 4th ed. New York, NY, USA: McGraw-Hill, 2001

    2001

  36. [36]

    Study on channel model for frequencies from 0.5 to 100 GHz,

    3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz,” 3GPP TR 38.901 V18.0.0, Mar. 2025

    2025

  37. [37]

    Coor- dinated multi-point transmission with imperfect CSI and other- cell interference,

    D. Jaramillo-Ramirez, M. Kountouris, and E. Hardouin, “Coor- dinated multi-point transmission with imperfect CSI and other- cell interference,” IEEE Trans. Wireless Commun., vol. 14, no. 4, pp. 1882–1896, 2014

    2014

  38. [38]

    Rate analysis of two-receiver MISO broadcast channel with finite rate feedback: A rate- splitting approach,

    C. Hao, Y. Wu, and B. Clerckx, “Rate analysis of two-receiver MISO broadcast channel with finite rate feedback: A rate- splitting approach,” IEEE Trans. Commun., vol. 63, no. 9, pp. 3232–3246, 2015

    2015

  39. [39]

    Channel coding rate in the finite blocklength regime,

    Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2307–2359, 2010

    2010

  40. [40]

    Finite block-length analysis of the incremental redundancy HARQ,

    B. Makki, T. Svensson, and M. Zorzi, “Finite block-length analysis of the incremental redundancy HARQ,” IEEE Wireless Commun. Lett., vol. 3, no. 5, pp. 529–532, 2014

    2014

  41. [41]

    Age of information: An introduction and survey,

    R. D. Yates, Y. Sun, D. R. Brown, S. K. Kaul, E. Modiano, and S. Ulukus, “Age of information: An introduction and survey,” IEEE J. Sel. Areas Commun., vol. 39, no. 5, pp. 1183–1210, 2021

    2021

  42. [42]

    Some bounds for the logarithmic function,

    F. TOPSØE, “Some bounds for the logarithmic function,” Inequality Theory and Applications, vol. 4, p. 137, 2007

    2007

  43. [43]

    G. H. Hardy, J. E. Littlewood, and G. Pólya, Inequalities. Cambridge University Press, 1952

    1952

  44. [44]

    Study on evaluation methodology of new Vehicle-to- Everything (V2X) use cases for LTE and NR,

    3GPP, “Study on evaluation methodology of new Vehicle-to- Everything (V2X) use cases for LTE and NR,” 3GPP TR 37.885 V15.3.0, Jun. 2019

    2019

  45. [45]

    Use of the 5.850-5.925 GHz band,

    Federal Communications Commission, “Use of the 5.850-5.925 GHz band,” Federal Register, vol. 86, pp. 23 281–23 299, May 2021

    2021

  46. [46]

    Toward massive, ul- trareliable, and low-latency wireless communication with short packets,

    G. Durisi, T. Koch, and P. Popovski, “Toward massive, ul- trareliable, and low-latency wireless communication with short packets,” Proc. IEEE, vol. 104, no. 9, pp. 1711–1726, 2016

    2016

  47. [47]

    On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming,

    T. Yoo and A. Goldsmith, “On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming,” IEEE J. Sel. Areas Commun., vol. 24, no. 3, pp. 528–541, 2006

    2006

  48. [48]

    Exploit- ing multiple-antenna techniques for non-orthogonal multiple access,

    X. Chen, Z. Zhang, C. Zhong, and D. W. K. Ng, “Exploit- ing multiple-antenna techniques for non-orthogonal multiple access,” IEEE J. Sel. Areas Commun., vol. 35, no. 10, pp. 2207– 2220, 2017

    2017