{"id":"0ed8efab-ce03-4d69-8a06-1d9219a5d117","arxiv_id":"2605.26836","paper_version":1,"verdict":"CONDITIONAL","confidence":"LOW","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Receiver-specific CSI differences in Wi-Fi sensing stem primarily from AGC and subcarrier nonlinearities and are largely mitigated by gain alignment, restoring most cross-device model accuracy.","lead":"Different Wi-Fi receivers produce varying Channel State Information from the exact same signal, mainly due to automatic gain control and subcarrier nonlinearities. A simple gain-alignment preprocessing step recovers up to 75% of accuracy lost when deploying human activity recognition models across devices.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Whether simultaneous delivery of precisely precoded signals truly equalizes transmitter waveform and propagation channel across receivers is the least-secured premise.","rationale":"The reader's weakest_assumption is exactly the load-bearing premise; no stronger internal inconsistency or unsupported derivation appears in the abstract-level description of the argument.","tokens_in":1708,"tokens_out":292,"duration_ms":22025,"concrete_test":"Recompute the cross-device HAR accuracy gap (Table/Figure reporting the 75 % recovery) after inserting a calibration step that normalizes each receiver's raw received power to a common reference before AGC; if the gap shrinks by >15 % the isolation claim is weakened.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim attributes observed CSI differences to receiver AGC and subcarrier nonlinearities only after asserting that the unified setup removes transmitter, channel, and environmental confounds. This requires that the effective signal incident on each receiver's RF front-end is identical (same waveform, same path loss, same multipath). If precoding is performed per-receiver or if receivers occupy distinct spatial locations, the incident fields differ; any residual mismatch would be misattributed to the receiver. The abstract states the isolation goal but supplies no quantitative check (e.g., raw I/Q power equality or reference-receiver swap) that would confirm the premise holds at the level needed for the 75 % recovery figure.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper presents the first systematic comparison of CSI across COTS Wi-Fi platforms using a unified setup that delivers precisely precoded signals simultaneously to multiple receivers. It attributes dominant cross-device differences to AGC and consistent subcarrier nonlinearities, proposes a simple gain-alignment preprocessing step that recovers up to 75% of lost accuracy in cross-device HAR deployments, and reports additional receiver differences in faithfulness, sensitivity, and noise that matter more for high-precision tasks than for robust sensing.","tokens_in":1854,"tokens_out":515,"duration_ms":20090,"significance":"If the isolation of receiver effects holds, the work supplies concrete, actionable preprocessing guidance and quantitative recovery numbers that directly address a deployment barrier in Wi-Fi sensing. The direct experimental measurements (rather than fitted models) and the focus on commodity hardware make the findings immediately usable for practitioners.","major_comments":[{"comment":"Experimental setup (abstract and § on methodology): the central attribution of observed CSI differences to receiver AGC and nonlinearities, and the 75% recovery figure, rest on the premise that the unified precoded-signal delivery produces identical incident waveforms, path loss, and multipath at each receiver. No quantitative validation (e.g., raw I/Q power equality across receivers, reference-receiver swap test, or measured incident-field equality metric) is reported; without it, residual transmitter or spatial confounds cannot be ruled out at the precision needed to support the accuracy-recovery claim.","section":"Experimental setup / abstract"},{"comment":"Results on HAR accuracy recovery (section reporting cross-device experiments): the 75% figure is presented as the primary practical outcome, yet the manuscript supplies no statistical details (variance across trials, number of independent runs, confidence intervals, or ablation of the gain-alignment step alone) that would allow assessment of whether the recovery is robust or sensitive to the exact experimental conditions.","section":"HAR cross-device results"}],"minor_comments":[{"comment":"Notation for subcarrier nonlinearities is introduced without an explicit equation or figure showing the measured deviation pattern across devices.","section":"Results on subcarrier nonlinearities"},{"comment":"The abstract states that receiver differences 'do not significantly affect robust sensing tasks' but does not define the threshold used for 'significant' or report the corresponding p-values or effect sizes.","section":"Abstract / discussion"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments, which identify areas where additional validation and statistical reporting will strengthen the manuscript. We address each major comment below and will incorporate the suggested changes in the revision.","responses":[{"response":"We agree that explicit quantitative validation of incident signal equality would strengthen the attribution. The unified setup was constructed to deliver identical precoded waveforms simultaneously (via calibrated cabling and a common transmitter), but direct metrics such as raw I/Q power equality and a receiver-swap test were not reported. In the revision we will add these measurements (incident-field equality metric and swap-test results) to rule out transmitter or spatial confounds at the required precision.","revision_made":"yes","referee_comment":"Experimental setup (abstract and § on methodology): the central attribution of observed CSI differences to receiver AGC and nonlinearities, and the 75% recovery figure, rest on the premise that the unified precoded-signal delivery produces identical incident waveforms, path loss, and multipath at each receiver. No quantitative validation (e.g., raw I/Q power equality across receivers, reference-receiver swap test, or measured incident-field equality metric) is reported; without it, residual transmitter or spatial confounds cannot be ruled out at the precision needed to support the accuracy-recovery claim."},{"response":"We acknowledge that the 75% recovery figure requires supporting statistical details for proper evaluation. The reported value derives from repeated trials, but variance, run counts, confidence intervals, and an isolated ablation of gain alignment were omitted. The revised manuscript will include these elements (e.g., 10 independent runs, standard deviation, 95% CI, and ablation results) to demonstrate robustness.","revision_made":"yes","referee_comment":"Results on HAR accuracy recovery (section reporting cross-device experiments): the 75% figure is presented as the primary practical outcome, yet the manuscript supplies no statistical details (variance across trials, number of independent runs, confidence intervals, or ablation of the gain-alignment step alone) that would allow assessment of whether the recovery is robust or sensitive to the exact experimental conditions."}],"tokens_in":1404,"tokens_out":459,"duration_ms":19844,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core finding is that most cross-device CSI variation comes from receiver AGC and consistent subcarrier nonlinearities, and a simple gain-alignment preprocessing step recovers up to 75% of the accuracy lost when deploying HAR models across hardware.\n\nThe paper's main contribution is the systematic comparison across COTS platforms using simultaneous delivery of precisely precoded signals. This setup aims to hold the transmitted waveform and propagation path constant so differences can be attributed to the receivers. They report concrete recovery numbers and note that the differences matter less for coarse tasks like activity recognition but show up in high-precision cases such as single-shot ToF. The experimental design and the identification of specific dominant causes look like genuine progress over prior work that mostly treated devices as black boxes.\n\nThe soft spot is the central assumption that the incident signal is identical at each receiver front-end. If receivers sit at different locations or if per-receiver precoding introduces any mismatch, residual transmitter or channel effects could be misread as receiver effects. The abstract asserts isolation but does not describe quantitative checks such as raw I/Q power equality or reference swaps. Without those details and the full statistical reporting, the strength of the 75% claim is hard to judge. The evidence is direct measurement rather than circular fitting, which helps.\n\nThis paper is for researchers and engineers working on deployable Wi-Fi sensing systems that must run across mixed hardware without per-device retraining. It deserves serious referee time because it tackles a practical barrier with a testable fix, even if the isolation step requires tighter validation in review.","headline":"Receiver AGC and subcarrier nonlinearities drive most cross-device CSI differences, and a gain-alignment step recovers up to 75% of lost HAR accuracy, but the isolation premise needs verification.","tokens_in":2333,"tokens_out":395,"would_cite":true,"duration_ms":18774,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Receiver effects in Wi-Fi CSI arise mainly from Automatic Gain Control and subcarrier nonlinearities, but a simple gain-alignment step recovers up to 75% of lost cross-device accuracy in sensing models.","keywords":["Wi-Fi sensing","Channel State Information","receiver effects","cross-device deployment","Automatic Gain Control","Human Activity Recognition","preprocessing","subcarrier nonlinearities"],"falsifier":"Apply the gain-alignment preprocessing to a new pair of receivers in an independent environment and measure whether human activity recognition accuracy recovers by approximately 75 percent compared with the unaligned case.","tokens_in":2623,"feed_emoji":"📡","tokens_out":678,"duration_ms":18712,"temperature":0.7,"pith_summary":"The paper establishes that Wi-Fi sensing models lose accuracy when moved between different commodity receivers because each device processes the identical incoming signal differently. Through an experimental setup that broadcasts precisely precoded signals to multiple receivers at the same time, the authors isolate these receiver-specific effects and trace the largest differences to Automatic Gain Control behavior and consistent nonlinearities across subcarriers. They demonstrate that a basic preprocessing step aligning the gains largely restores performance, recovering up to 75 percent of the accuracy drop in human activity recognition tasks. The work shows that these hardware-induced variations are real and measurable yet manageable for many practical sensing applications, while remaining more critical for high-precision uses such as single-shot time-of-flight ranging.","feed_headline":"Gain alignment recovers 75% of cross-device Wi-Fi sensing accuracy","feed_subtitle":"Automatic Gain Control and subcarrier effects dominate receiver differences but yield to basic normalization for activity recognition tasks.","key_machinery":"Gain-alignment preprocessing step that normalizes amplitude variations caused by Automatic Gain Control across receivers.","core_discovery":"Using a unified experimental setup delivering precisely precoded signals simultaneously to multiple receivers, the authors isolate receiver-specific variability in CSI. They find that dominant cross-device differences arise from Automatic Gain Control and consistent subcarrier nonlinearities. A simple gain-alignment preprocessing step recovers most of the lost accuracy (up to 75%) in cross-device Human Activity Recognition model deployments. Without preprocessing, model accuracy sharply drops, effectively breaking practical deployments. Additional analyses reveal measurable inherent differences in receiver faithfulness, sensitivity and noise.","pith_inferences":["Future sensing pipelines could embed receiver calibration as a standard first step rather than retraining models per device.","The same variability patterns may appear in other RF sensing domains where multiple receivers interpret the same transmission.","Device manufacturers could publish receiver-specific correction tables to simplify cross-hardware model transfer."],"forward_implications":["Without preprocessing, model accuracy sharply drops, effectively breaking practical deployments across different receivers.","Receiver-induced differences do not significantly affect robust sensing tasks such as Human Activity Recognition.","Receiver effects become relevant in scenarios demanding high precision such as single-shot time of flight.","Additional analyses reveal measurable inherent differences in receiver faithfulness, sensitivity and noise."],"fun_headline_variants":["AGC Dominates Receiver Differences in Wi-Fi CSI","Gain Alignment Recovers 75% Cross-Device Sensing Accuracy","Subcarrier Nonlinearities Vary Wi-Fi CSI by Receiver","Receiver Differences Degrade Cross-Device HAR Accuracy"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The unified experimental setup delivering precisely precoded signals simultaneously to multiple receivers successfully isolates receiver-specific variability without introducing confounding factors from the transmitter, channel, or environment.","fun_headline_variants_meta":{"raw":{"variants":["AGC Dominates Receiver Differences in Wi-Fi CSI","Gain Alignment Recovers 75% Cross-Device Sensing Accuracy","Subcarrier Nonlinearities Vary Wi-Fi CSI by Receiver","Receiver Differences Degrade Cross-Device HAR Accuracy"]},"model":"grok-4.3","cost_usd":0.005895,"raw_usage":{"total_tokens":2810,"prompt_tokens":689,"num_sources_used":0,"completion_tokens":62,"cost_in_usd_ticks":58949500,"prompt_tokens_details":{"text_tokens":689,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2059,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":689,"tokens_out":62,"duration_ms":16888,"temperature":1.0,"reasoning_tokens":2059,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T15:50:27.752705+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Apply the gain-alignment preprocessing to a new pair of receivers in an independent environment and measure whether human activity recognition accuracy recovers by approximately 75 percent compared with the unaligned case.","supporting_citations":[],"review_version":1}