{"id":"06a96581-28b3-4123-b9ea-fca9853415b4","arxiv_id":"2607.06169","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":7,"one_line_summary":"An analytical DVP upper bound for slot-based 5G HARQ with parallel processes, decoding/feedback delays, and periodic control signaling is derived and validated against ns-3 5G-LENA simulations.","lead":"This paper derives an upper bound on the delay violation probability for 5G HARQ systems that accounts for parallel HARQ processes, decoding/feedback delays, and control signaling blockage. It matters for engineers designing ultra-reliable low-latency 5G links who need tractable DVP estimates without running full packet-level simulations.","discovery_kind":"unclear","skeptic_critique":{"model":"glm-5.2","headline":"The DTMC in §III.A (Table II) assumes same-slot feedback (transmission outcomes known immediately), omitting the RTT = 1+τd+τf gap during which packets occupy HARQ processes but are neither queued nor retransmittable. This may cause π_q to underestimate true backlog, potentially invalidating the DVP","rationale":"The reader correctly identified that the CS blockage approximation (Eqs. 9, 15, 19) is a load-bearing simplification whose validity is regime-dependent. However, I identify a more fundamental concern: the DTMC itself does not account for the non-zero decoding and feedback delays (τd, τf) that the paper explicitly models in the service-delay component (§III.C). The DTMC transitions in Table II assume same-slot feedback, which means the stationary queue-length distribution π_q — and hence the entire waiting-delay distribution and the final DVP bound — may not correctly reflect the true system dynamics.\n\nThe key distinction is: the CS blockage concern is about whether a specific approximation remains valid in certain parameter regimes (large d̄ relative to ξ), while the DTMC concern is about whether the core model correctly captures the system's queue dynamics at all. If the DTMC's π_q is wrong, the CS blockage approximation is moot.\n\nThat said, the ns-3 5G-LENA validation provides real evidence that the overall bound holds in practice for the tested configurations. The tested parameters (f ≤ 1/3, τd = τf = 0.5, RTT = 2 slots) represent a regime where in-flight packets have minimal impact on queue dynamics — with RTT = 2 and moderate arrival rates, at most 1–2 packets are in-flight at any time, well within the 8+ HARQ processes typical of 5G. The concern is specifically about whether the bound remains valid when this condition is stressed.\n\nThe paper would be strengthened by: (1) explicitly stating the assumption that the number of HARQ processes is sufficiently large relative to the RTT and arrival rate, (2) specifying the number of HARQ processes used in the ns-3 simulation, and (3) testing the bound in a regime where the feedback delay has a more pronounced impact on queue dynamics.\n\nThe verdict remains CONDITIONAL — the bound appears valid in tested regimes, but the theoretical claim of an upper bound rests on an unstated assumption about the relationship between RTT, arrival rate, and the number of HARQ processes. The condition should be broadened beyond the CS blockage approximation to include verification of the DTMC approximation under stressed feedback-delay regimes.","tokens_in":18383,"tokens_out":21417,"duration_ms":1305434,"concrete_test":"Re-run the ns-3 5G-LENA simulation with τd = τf = 2 slots (RTT = 5) and f = 1/2, keeping other parameters at default (Table III). Compare the simulated DVP against the Theorem 2 bound. If the simulated DVP exceeds the bound, the DTMC's implicit zero-feedback-delay assumption invalidates the upper bound. Additionally, extract the empirical queue-length distribution from ns-3 and compare it directly with π_q from Eq. (8); a significant discrepancy would confirm that the DTMC does not correctly capture the queue dynamics under non-negligible RTT.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The DTMC in Section III.A (Table II) models the system at slot boundaries with state (q, m, k_ξ). The transitions assume that transmission outcomes (ACK/NACK) are known within the same slot. For example, (q, m) → (q, m+1) with probability I·f'·p_m indicates that a packet transmitted in an available slot fails and is immediately ready for retransmission at attempt m+1 in the next slot. Similarly, (q, m) → (q−1, 1) with probability I·f·p'_m indicates that a packet succeeds and a new packet is immediately scheduled, all within one slot.\n\nWith non-zero τd and τf (RTT = 1 + τd + τf ≥ 2 slots per Eq. 12), feedback arrives RTT−1 slots after transmission. During this gap, the transmitted packet occupies a HARQ process but is neither in the queue nor available for retransmission. The DTMC does not track these in-flight packets or the constraint they impose on the number of available HARQ processes. This has two consequences:\n\n1. The stationary queue-length distribution π_q (Eq. 8) may underestimate the true backlog, particularly when the arrival rate is high enough that in-flight packets saturate available HARQ processes, forcing new packets to wait even when the queue appears short.\n\n2. The DTMC's attempt index m tracks only the packet currently being transmitted, not the states of other packets awaiting feedback in parallel HARQ processes. The scheduler's next action depends on which processes have pending retransmissions (the paper states retransmissions have priority), but this information is not in the state space.\n\nThe paper does not specify the number of HARQ processes, and the DTMC does not include it as a parameter. The ns-3 5G-LENA validation (which does model feedback delay and parallel processes) shows the bound holds in tested regimes (f ≤ 1/3, τd = τf = 0.5 slots, ξ ≥ 80). However, these regimes have RTT = 2 slots and low arrival rates, where the impact of in-flight packets on queue dynamics is minimal. The concern is about untested regimes with higher load or","agreement_with_reader":"partial"},"referee_report":{"model":"glm-5.2","summary":"This paper proposes a tractable upper bound on the delay violation probability (DVP) for slot-based 5G systems with HARQ. The framework decomposes total delay into waiting delay (from arrival to first transmission) and service delay (from first transmission to success or discard). The waiting-delay PMF is derived from a discrete-time Markov chain (DTMC) over queue length, HARQ attempt index, and control signal periodicity (CSP) phase, combined with a recursive algorithm (Algorithm 1). The service-delay bound accounts for decoding delay, feedback delay, retransmission attempts, and periodic CS blockage. The final DVP bound (Theorem 2) combines both components and is validated against a MATLAB queueing simulation and ns-3 5G-LENA packet-level simulations across arrival rates, SNRs, CSPs, and delay targets.","tokens_in":18701,"tokens_out":7525,"duration_ms":533023,"significance":"The paper addresses a practically important problem for URLLC DVP prediction in 5G HARQ systems. The derivation from the DTMC steady-state distribution through the waiting-delay PMF to the service-DVP bound is logically coherent, with clean proofs in Appendices A and B. The bound is parameter-free given system inputs (f, Qmax, xi, tau_d, tau_f, M, p). The ns-3 5G-LENA validation is comprehensive (Figs. 4-6, Appendix C) and the bound is consistently tight across tested scenarios. Reproducible code is provided via GitHub. The inclusion of decoding/feedback timing and CS blockage in a unified DVP framework is a useful contribution over prior work that assumes instantaneous feedback or omits these components.","major_comments":[{"comment":"Section III.A, Table II, Contribution 1: The paper claims to 'model the parallel HARQ processes using a multi-server queue,' but the DTMC state (q, m, k_xi) tracks a single packet's attempt index m, not the states of multiple in-flight HARQ processes. The transitions (e.g., (q,m) -> (q, m+1) with probability I*f'*p_m) assume that a failed packet is immediately ready for retransmission in the next slot, which corresponds to same-slot (instantaneous) feedback. This contradicts the non-zero RTT = 1 + tau_d + tau_f (Eq. 12) that is explicitly modeled in the service delay (Section III.C). The parallel HARQ behavior is captured only in the service-delay timing analysis, not in the queue dynamics that determine the waiting-delay distribution. The paper should clarify what the 'multi-server queue' refers to, explicitly state this simplification, and discuss its implications for the DVP bound's紧度","section":null},{"comment":"Section IV: The ns-3 validation covers arrival rates f^{-1} in {3, 6, 8, 10}, all relatively low. With RTT = 2 slots (tau_d = tau_f = 0.5 ms), the expected number of in-flight packets at f = 1/3 is approximately f * RTT = 2/3, which is insufficient to test the regime where in-flight packets saturate available HARQ processes and constrain new transmissions. Since the DTMC does not track in-flight packets, the bound's validity in higher-load regimes (e.g., f = 1/2 or f = 1) is untested. Additional validation at higher arrival rates would strengthen the generality claim.","section":null},{"comment":"Section III.A, Table II: The DTMC's attempt index m tracks only the packet currently being transmitted, not the states of other packets awaiting feedback in parallel HARQ processes. The scheduler's next action depends on which processes have pending retransmissions (the paper states retransmissions have priority), but this scheduling dependency on multiple in-flight process states is not captured. The paper should discuss whether and how this affects the stationary queue-length distribution pi_q (Eq. 8) and the resulting waiting-delay PMF.","section":null}],"minor_comments":[{"comment":"Eq. (9): The text states the bound is 'accurate when q << xi,' but beta_k depends on k (the waiting interval length), not q. This should read 'accurate when k << xi.'","section":null},{"comment":"The paper should specify the number of HARQ processes assumed in the ns-3 5G-LENA simulations, as this directly affects the parallel HARQ behavior being modeled.","section":null},{"comment":"Figure 2 is difficult to read; consider enlarging or simplifying the timing diagram.","section":null},{"comment":"References [40], [44], [45] are to very recent or forthcoming work (2025); ensure final versions are properly cited.","section":null},{"comment":"In Table II, the notation f' and p'_m are defined but the prime notation for p'_m = 1 - p_m could be confused with the attempt index; a brief reminder in the table caption would help.","section":null},{"comment":"Section IV.A states the MATLAB validation 'focuses on the modeled queueing dynamics rather than the detailed 5G protocol stack.' It would be helpful to explicitly state that the MATLAB simulation implements the same timing assumptions as the DTMC (including instantaneous feedback), so readers understand that the ns-3 validation is the independent test.","section":null}],"recommendation":"major_revision","confidential_remarks":"The paper's claim to 'model parallel HARQ processes using a multi-server queue' is not substantiated by the DTMC as presented. The DTMC is a single-server model with an attempt-index state variable. The parallel HARQ behavior is only partially captured (in the service-delay timing). This gap between the claimed contribution and the actual model is the primary concern. The ns-3 validation is reassuring but is conducted only in low-load regimes where the simplification is unlikely to matter. The authors should either (a) revise the contribution claims to accurately reflect what is modeled, or (b) extend the DTMC to track multiple in-flight HARQ processes. Option (a) is likely sufficient for publication given the strong empirical validation, but the overstatement needs to be corrected."},"author_rebuttal":{"model":"glm-5.2","summary":"We thank the referee for a careful and substantive review. The referee's three major comments center on a common and valid observation: the DTMC in Section III.A does not track the states of multiple in-flight HARQ processes, and the transition structure implicitly assumes same-slot feedback, which is inconsistent with the non-zero RTT modeled in the service-delay analysis. We agree that the terminology 'multi-server queue' is imprecise and that this simplification must be explicitly stated and discussed. We also agree that the validation is limited to relatively low arrival rates. Below we respond to each comment in detail.","responses":[{"response":"The referee is correct on all counts. We acknowledge the following: (1) The DTMC state (q, m, k_ξ) tracks the attempt index of the single packet currently being served by the scheduler, not the states of all in-flight HARQ processes. (2) The transitions in Table II, e.g., (q, m) → (q, m+1) with probability I(k_ξ)·f'·p_m, implicitly assume that the outcome of the m-th attempt is known by the next slot, i.e., same-slot feedback. This is inconsistent with the non-zero RTT = 1 + τ_d + τ_f modeled in Section III.C. (3) The term 'multi-server queue' in Contribution 1 is therefore misleading. The parallel HARQ behavior—specifically, the fact that new packets can be transmitted while earlier ones await feedback—is captured only in the service-delay timing analysis (Section III.C), not in the queue dynamics that determine the waiting-delay distribution π_q and the PMF f_τw(k). We will revise the manuscript as follows: (a) Replace the 'multi-server queue' terminology with an accurate description of what the DTMC models—a single-server queue with an attempt-index-augmented state that approximates pipelined service by allowing the attempt index to advance without blocking on feedback. (b) Add an explicit paragraph in Section III.A stating the simplification: the DTMC assumes same-slot feedback for queue dynamics, while the full RTT is accounted for in the service-delay bound. (c) Discuss the implications: at low arrival rates (f ≪ 1/RTT), the expected number of in-flight packets is small, so the queue dynamics are dominated by arrivals and occasional retransmissions rather than by pipeline saturation. In this regime, the simplification has minimal impact on π_q and hence on f_τw(k), and the service-delay analysis (which does account for the full RTT) dominates the DVP bound. At更高载s","revision_made":"yes","referee_comment":"Section III.A, Table II, Contribution 1: The paper claims to 'model the parallel HARQ processes using a multi-server queue,' but the DTMC state (q, m, k_xi) tracks a single packet's attempt index m, not the states of multiple in-flight HARQ processes. The transitions assume that a failed packet is immediately ready for retransmission in the next slot, which corresponds to same-slot (instantaneous) feedback. This contradicts the non-zero RTT = 1 + tau_d + tau_f (Eq. 12) that is explicitly modeled in the service delay (Section III.C). The parallel HARQ behavior is captured only in the service-delay timing analysis, not in the queue dynamics that determine the waiting-delay distribution. The paper should clarify what the 'multi-server queue' refers to, explicitly state this simplification, and discuss its implications for the DVP bound's tightness."},{"response":"The referee is correct that the tested arrival rates correspond to low in-flight packet counts and that higher-load validation would strengthen the paper. We will add ns-3 5G-LENA simulation results for f^{-1} = 2 (i.e., f = 1/2) and, if feasible within the revision timeline, f^{-1} = 1 (i.e., f = 1, every slot has an arrival). At f = 1/2 with RTT = 2, the expected in-flight count is approximately 1, which begins to exercise the pipeline. At f = 1, the pipeline is fully saturated and the simplification in the DTMC is most stressed. We expect the bound to remain valid (as an upper bound) because the service-delay analysis conservatively accounts for RTT and CS blockage, but we will report the actual gap between the bound and simulation at these higher loads. If the bound becomes loose at high load, we will explicitly state the load regime where the model is recommended. We will also add a remark in Section IV acknowledging that the model's tightness is expected to degrade as the arrival rate approaches 1/RTT, since the DTMC simplification becomes more impactful when multiple packets are simultaneously in flight.","revision_made":"partial","referee_comment":"Section IV: The ns-3 validation covers arrival rates f^{-1} in {3, 6, 8, 10}, all relatively low. With RTT = 2 slots (tau_d = tau_f = 0.5 ms), the expected number of in-flight packets at f = 1/3 is approximately f * RTT = 2/3, which is insufficient to test the regime where in-flight packets saturate available HARQ processes and constrain new transmissions. Since the DTMC does not track in-flight packets, the bound's validity in higher-load regimes (e.g., f = 1/2 or f = 1) is untested. Additional validation at higher arrival rates would strengthen the generality claim."},{"response":"This comment is closely related to Comment 1 and identifies the same underlying issue from the scheduling perspective. The referee is correct: the scheduler's retransmission-priority policy depends on the states of all in-flight HARQ processes, but the DTMC collapses this into a single attempt index m for the currently served packet. This means the DTMC cannot distinguish between, for example, a slot where a retransmission is prioritized because a NACK arrived for an earlier packet versus a slot where a new packet is transmitted because no retransmission is pending. The practical effect on π_q is as follows: when the system has few in-flight packets (low f), retransmissions are infrequent and the priority policy rarely changes the scheduling decision, so the impact on π_q is negligible. As f increases and multiple packets are in flight, the retransmission-priority policy can delay new transmissions, increasing the queue backlog beyond what the DTMC predicts. This would cause the DTMC to underestimate π_q at larger q values, which in turn underestimates the waiting delay and could make the bound tighter (i.e., lower) than it should be—potentially compromising the upper-bound property. We will add a discussion of this effect in Section III.A, explicitly stating that the bound's validity as an upper bound is expected to hold in the low-to-moderate load regime (f · RTT ≪ number of HARQ processes) and that the simplification becomes increasingly conservative at higher loads. We will also note that the ns-3 validation (which uses the full retransmission-priority scheduler) serves as an empirical check: if the bound remains above the simulation DVP at higher arrival rates (per Comment 2), this provides evidence that the simplification does not violate the upper-bound property.","revision_made":"yes","referee_comment":"Section III.A, Table II: The DTMC's attempt index m tracks only the packet currently being transmitted, not the states of other packets awaiting feedback in parallel HARQ processes. The scheduler's next action depends on which processes have pending retransmissions (the paper states retransmissions have priority), but this scheduling dependency on multiple in-flight process states is not captured. The paper should discuss whether and how this affects the stationary queue-length distribution pi_q (Eq. 8) and the resulting waiting-delay PMF."}],"tokens_in":18091,"tokens_out":2922,"duration_ms":211262,"standing_objections":[]},"desk_editor":{"model":"glm-5.2","letter":"This paper derives a parameter-free upper bound on delay violation probability for slot-based 5G HARQ, combining a DTMC backlog model with explicit decoding delay, feedback delay, and periodic control-signaling blockage. The bound is validated against both MATLAB queueing simulation and ns-3 5G-LENA, with publicly available code. That is the headline, and it is a real contribution — prior work on HARQ DVP either assumed instantaneous feedback or used sequential FIFO abstractions, and this paper genuinely moves past both simplifications in a single framework. The derivation from DTMC steady-state through the recursive waiting-delay PMF (Algorithm 1) to the service-DVP bound (Lemma 1) and total DVP (Theorem 2) is logically coherent. The proofs in Appendices A and B are short and correct. The validation is the strongest part: the proposed bound tracks ns-3 5G-LENA results closely across arrival rates, SNR, CSP, and delay targets, and the two benchmarks (Max Throughput, Fixed Tx Rate) are well-chosen to isolate what each modeling component buys you. The code being public is a real plus. The soft spot is the CS blockage approximation. The waiting-delay bound uses a union bound β_k = min(1, k/ξ) that is accurate when q ≪ ξ, and the service-delay bound assumes at most one blocked slot during a service window (Eq. 15). When the delay target is large relative to ξ, or when queue buildup makes q comparable to ξ, multiple CS slots can fall within a single service window and the single-slot reduction may underestimate the true impact. The paper does not test these regimes explicitly. The stress-test note raises a separate concern about the DTMC assuming same-slot feedback and not tracking in-flight packets across parallel HARQ processes. On reading the paper, this concern is partially valid but overstated for the tested regimes. The DTMC does model the attempt index m and the paper does incorporate τd and τf in the service-delay analysis (Eq. 12). The real gap is that the DTMC does not track how many HARQ processes are in flight, which matters when arrival rates are high enough to saturate available processes. But the tested regimes (f ≤ 1/3, RTT = 2 slots, ξ ≥ 80) are exactly where this effect is minimal, and the ns-3 validation — which does model parallel processes and feedback delay — confirms the bound holds. The concern is about untested high-load regimes, not a flaw in what was tested. Neither concern is fatal. The bound is an upper bound, so even if the approximations loosen in extreme regimes, the result remains valid — just potentially less tight. The paper would benefit from one paragraph acknowledging the scope of the CS approximation and one experiment pushing q closer to ξ. This paper is for researchers and engineers working on URLLC QoS in 5G who need a tractable DVP estimate without full simulation. It deserves a serious referee. The main question for review is whether the CS approximation needs explicit tightening or whether the current scope statement is sufficient.","headline":"Tractable DVP bound for 5G HARQ with practical timing — mostly solid, with one load-bearing approximation worth probing","tokens_in":19634,"tokens_out":709,"would_cite":true,"duration_ms":184365,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"glm-5.2","headline":"Bound Predicts 5G Delay Violations Under Real HARQ Timing","keywords":["delay violation probability","HARQ","5G NR","queueing theory","Markov chain","ultra-reliable low-latency communication","control signaling","quality of service"],"falsifier":"If ns-3 5G-LENA simulations under configurations with small control-signal periodicity ξ relative to the delay target show DVP values consistently exceeding the proposed upper bound, the bound would be violated and the single-slot blockage approximation invalidated.","tokens_in":18469,"feed_emoji":"📡","tokens_out":1138,"duration_ms":2021891,"temperature":0.7,"pith_summary":"This paper derives a tractable upper bound on the delay violation probability (DVP) for 5G systems using HARQ retransmissions, where DVP is the probability that a packet's end-to-end delay exceeds a target threshold. The central contribution is a decomposition of total delay into a waiting component (from queueing and periodic control-signal blockage) and a service component (from transmissions, decoding delay, feedback delay, and retransmissions), combined into a single bound via a discrete-time Markov chain that captures the stationary queue backlog. The key mechanism is a recursive algorithm that computes the waiting-delay distribution conditioned on the number of packets ahead, weighted by the Markov chain's stationary distribution, and then convolved with a service-delay violation probability that explicitly accounts for the round-trip time between transmission attempts (one transmission slot plus decoding and feedback delays) and the periodic loss of data slots to control signaling. The resulting bound in Theorem 2 is parameterized by arrival rate, queue size, control-signal periodicity, decoding and feedback delays, maximum retransmission attempts, and per-attempt packet error rates, and is validated against both MATLAB queueing simulations and ns-3 5G-LENA packet-level simulations across varying delay targets, SNRs, arrival rates, and control-signal periodicities.","feed_headline":"Realistic HARQ Timing Raises 5G Delay Violation Estimates","feed_subtitle":"A new upper bound on 5G delay violation probability accounts for decoding, feedback, and control-signal delays that prior models ignored,揭示了","key_machinery":"Discrete-time Markov chain over (queue backlog, HARQ attempt index, control-signal phase); recursive waiting-delay PMF computation (Algorithm 1); decomposition of DVP into waiting and service components; control-signal blockage approximated by union bound β_k = min(1, k/ξ) for waiting and α = min(1, M/ξ) for service","core_discovery":"The paper's central result is Theorem 2 (Eq. 21), which provides a closed-form upper bound on the total DVP by summing over all possible waiting-delay values k, weighting each by the waiting-delay PMF f_{τw}(k) computed from the Markov chain stationary distribution and Algorithm 1, and multiplying by a service-delay violation probability that splits into a no-blockage case and a control-signal-blockage case. The bound reveals that under stringent latency targets, DVP exhibits a floor dominated by protocol timing (decoding, feedback, control-signal blockage) rather than physical-layer reliability, meaning that increasing signal power or radio resources beyond a certain point yields no further","pith_inferences":["The single-slot reduction for control-signal blockage during service (Eq. 15) could be tightened by allowing multiple blocked slots, which would improve the bound's accuracy when the delay target is large relative to ξ or when multiple HARQ retransmissions extend the service window across several control-signal cycles.","The framework could be extended to non-Bernoulli arrivals (e.g., periodic or bursty traffic typical of industrial control loops) by modifying the Markov chain transition probabilities, potentially revealing different DVP floors for different traffic patterns.","The delay floor suggests a natural design principle: there exists a minimum achievable DVP for a given protocol timing configuration, and below that floor, no amount of physical-layer improvement helps — a regime boundary that could be computed in closed form from the model parameters."],"forward_implications":["Network operators can use the bound to determine minimum radio resource allocations (resource blocks) needed to meet a target DVP without running full system simulations, enabling faster QoS-aware provisioning.","The delay floor phenomenon identified by the model implies that for ultra-low-latency URLLC services, reducing decoding or feedback delays (e.g., via faster processing or shorter feedback timing) may be more effective than improving channel coding or SNR.","The framework can inform control-signal periodicity configuration (ξ): the model quantifies exactly how much DVP degrades when control signals are more frequent, supporting a trade-off between signaling overhead and latency guarantees.","The Markov chain structure is extensible to other periodic blockage patterns (e.g., TDD guard periods or measurement gaps) by modifying the phase variable, broadening applicability beyond control signaling."],"fun_headline_variants":["Protocol Timing, Not Signal Power, Limits 5G Delay Violation Probability","Closed-Form Bound Captures Protocol Timing Effects on 5G DVP","Realistic 5G HARQ Timing Exposes Delay Violation Floor","5G Delay Violation Bound Accounts for Control and Feedback Delays","Accurate 5G HARQ Timing Reveals Protocol-Limited Delay Violation"],"cache_read_input_tokens":0,"weakest_assumption_plain":"The bound assumes that at most one data slot is lost to control signaling during a packet's service period, reducing the effective delay budget by one slot. When the service window spans multiple control-signal cycles (which can happen with several retransmissions or a large delay target), more than one blocked slot may occur, and the single-slot reduction may underestimate the true delay impact.","fun_headline_variants_meta":{"raw":{"variants":["Protocol Timing, Not Signal Power, Limits 5G Delay Violation Probability","Closed-Form Bound Captures Protocol Timing Effects on 5G DVP","Realistic 5G HARQ Timing Exposes Delay Violation Floor","5G Delay Violation Bound Accounts for Control and Feedback Delays","Accurate 5G HARQ Timing Reveals Protocol-Limited Delay Violation"]},"model":"glm-5.2","effort":"high","cost_usd":0.0,"raw_usage":{"total_tokens":1120,"prompt_tokens":574,"completion_tokens":546,"prompt_tokens_details":null},"tokens_in":574,"tokens_out":546,"duration_ms":38879,"temperature":1.0,"reasoning_tokens":458,"cache_read_input_tokens":0,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-08T14:23:45.506527+00:00","model_set":{"reader":"glm-5.2"},"falsifier":"If ns-3 5G-LENA simulations under configurations with small control-signal periodicity ξ relative to the delay target show DVP values consistently exceeding the proposed upper bound, the bound would be violated and the single-slot blockage approximation invalidated.","supporting_citations":[],"review_version":1}