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arxiv: 2604.19660 · v1 · submitted 2026-04-21 · 📡 eess.SP

Pilot-Free Predictive Multi-User Beamforming via Sensing Management in Cell-Free Networks

Eren Berk Kama , Murat Babek Salman , Isaac Skog , Emil Bj\"ornson This is my paper

Pith reviewed 2026-05-10 01:33 UTC · model grok-4.3

classification 📡 eess.SP
keywords integrated sensing and communicationscell-free massive MIMOpredictive beamformingextended Kalman filterpilot overhead reductionsensing managementuser tracking
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The pith

Sensing user positions occasionally lets cell-free networks predict channels and skip uplink pilots while keeping downlink rates near the perfect-CSI benchmark.

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

The paper shows that a cell-free massive MIMO system can divide users into communication and sensing groups, using radar-like sensing to track location and velocity only when needed instead of sending frequent pilots for every channel estimate. An extended Kalman filter processes the sensing returns to maintain state predictions, and a management protocol activates sensing just enough to prevent accuracy loss from mobility or interference. When a user requests data the network switches to communication mode and forms beams from the predicted channel, avoiding pilot overhead. Simulations indicate this yields spectral efficiency close to ideal CSI after an initial learning period, with sensing required only sporadically thereafter.

Core claim

An EKF-based tracker combined with an adaptive sensing management protocol that performs sensing operations only when necessary maintains user location and velocity estimates accurate enough to support predictive beamforming, delivering downlink spectral efficiency close to the perfect-CSI case in cell-free massive MIMO while using practical sensing waveforms and requiring sensing only occasionally after convergence.

What carries the argument

The extended Kalman filter tracking algorithm paired with a sensing management protocol that allocates resources based on predicted estimate degradation and inter-target interference.

If this is right

  • After an initial convergence phase sensing is activated only occasionally, freeing time-frequency resources for data transmission.
  • Downlink spectral efficiency stays close to the perfect-CSI benchmark across the cell-free network even with realistic sensing waveforms.
  • The same tracking accuracy holds when multiple access points cooperate, showing robustness of the cell-free architecture.
  • Inter-target interference is handled by the adaptive resource allocation so that sensing remains effective without constant operation.

Where Pith is reading between the lines

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

  • Eliminating uplink pilots could lower device energy use and latency for users that rarely transmit data.
  • In scenarios with rapid mobility the protocol would trigger more frequent sensing, reducing but not eliminating the overhead savings.
  • The state-partition approach might apply directly to other integrated sensing and communication settings such as vehicle-to-infrastructure links.

Load-bearing premise

The extended Kalman filter can keep user location and velocity estimates accurate enough from practical sensing signals that the resulting channel predictions remain good for beamforming even when targets interfere with one another and users move.

What would settle it

A simulation run with increased user speeds or higher target density in which the EKF tracking error grows large enough that downlink spectral efficiency falls more than 10 percent below the perfect-CSI reference.

Figures

Figures reproduced from arXiv: 2604.19660 by Emil Bj\"ornson, Eren Berk Kama, Isaac Skog, Murat Babek Salman.

Figure 1
Figure 1. Figure 1: Conceptual diagram of the proposed predictive beamforming method. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The conventional and proposed frame structures used in the trans [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The temporal behavior of the predicted angle estimation variance and [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 7
Figure 7. Figure 7: Achievable sum SE as a function of the number of AP antennas with [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: Achievable sum SE as a function of the number of users with MMSE, [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: The temporal behavior of SEs with multiple Tx APs with the proposed [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The temporal behavior of SEs with the proposed method and SS-SeW [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
read the original abstract

This paper presents a sensing management frame- work for integrated sensing and communications (ISAC) within cell-free massive multiple-input multiple-output (MIMO) systems to reduce pilot-based channel state information (CSI) acquisition overhead. Conventional communication systems rely on frequent channel estimation procedures that impose significant signaling overhead, consuming valuable time-frequency resources. To ad- dress this inefficiency, we propose a state-based architecture that partitions users into communication and sensing groups based on service requirements. When users are not requesting data, the system utilizes sensing capabilities to track their location. Upon receiving a communication request, the system transitions to communication mode, leveraging the tracked state for predictive beamforming to eliminate the need for uplink pilot training. We develop an extended Kalman filter (EKF) based tracking algorithm coupled with adaptive resource allocation strategies. Furthermore, we analyze the impact of inter-target interference and design a sensing management protocol that performs sensing operations only when necessary to maintain the accuracy of user location estimates. Simulation results demonstrate that the pro- posed EKF-based tracking and sensing management can support predictive beamforming with downlink spectral efficiency close to the perfect-CSI case, while requiring sensing only occasionally after an initial convergence period. The results also indicate that this performance is robust in a cell-free massive MIMO setup and can be achieved with practical sensing waveforms.

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 sensing management framework for ISAC in cell-free massive MIMO to eliminate uplink pilots for CSI. Users are dynamically partitioned into communication and sensing groups; an EKF tracks location/velocity from sensing waveforms when users are idle, and predictive beamforming is applied upon a data request. An adaptive protocol triggers sensing only when needed to keep tracking error low, with analysis of inter-target interference. Simulations claim that downlink spectral efficiency approaches the perfect-CSI benchmark while requiring sensing only occasionally after initial convergence, using practical waveforms.

Significance. If the EKF tracking accuracy holds under realistic multi-user interference and mobility, the work could meaningfully reduce pilot overhead in cell-free networks by integrating sensing for predictive beamforming. The adaptive sensing management and interference analysis are practical strengths; the approach leverages standard EKF tools in a new protocol context.

major comments (2)
  1. [§5] §5 (Simulation Results): the reported spectral-efficiency curves are presented without explicit values for user velocity ranges, inter-target interference power levels, EKF process/measurement noise covariances, or the exact sensing waveform parameters (e.g., bandwidth, pulse repetition interval). These omissions are load-bearing because the central claim—that EKF-induced channel predictions remain accurate enough for near-perfect-CSI beamforming—cannot be verified for robustness without them.
  2. [§4.2] §4.2 (Adaptive Sensing Protocol): the decision rule that determines when sensing is performed (e.g., covariance threshold, prediction horizon) is described at a high level but lacks a precise algorithmic statement or pseudocode. This prevents assessment of whether the “occasional sensing after convergence” behavior is reproducible and stable under the inter-target interference conditions analyzed earlier in the section.
minor comments (2)
  1. [§3] Notation for the state vector and measurement model in the EKF derivation should be cross-referenced to the system model in §2 to avoid ambiguity when readers compare the filter equations to the beamforming precoder.
  2. [§5] Figure captions for the SE vs. SNR and sensing-frequency plots should explicitly state the number of Monte-Carlo realizations and the exact cell-free topology (number of APs, users) used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below and agree that the requested details will strengthen the manuscript's reproducibility. We will incorporate the changes in the revised version.

read point-by-point responses

  1. Referee: [§5] §5 (Simulation Results): the reported spectral-efficiency curves are presented without explicit values for user velocity ranges, inter-target interference power levels, EKF process/measurement noise covariances, or the exact sensing waveform parameters (e.g., bandwidth, pulse repetition interval). These omissions are load-bearing because the central claim—that EKF-induced channel predictions remain accurate enough for near-perfect-CSI beamforming—cannot be verified for robustness without them.

    Authors: We agree that these parameters must be explicitly stated to allow verification of the EKF tracking robustness and the central performance claims. In the revised manuscript, Section 5 will include a new table (Table II) listing all simulation parameters: user velocities (0–25 m/s), inter-target interference powers (−15 dB to −5 dB relative to target), EKF process noise covariance Q = diag([0.05, 0.05, 0.01, 0.01]) and measurement noise R scaled by sensing SNR, plus waveform details (bandwidth 100 MHz, PRI 1 ms, chirp duration 10 μs). These values are consistent with the practical waveforms already used in our simulations and confirm that near-perfect-CSI spectral efficiency is achieved. revision: yes

  2. Referee: [§4.2] §4.2 (Adaptive Sensing Protocol): the decision rule that determines when sensing is performed (e.g., covariance threshold, prediction horizon) is described at a high level but lacks a precise algorithmic statement or pseudocode. This prevents assessment of whether the “occasional sensing after convergence” behavior is reproducible and stable under the inter-target interference conditions analyzed earlier in the section.

    Authors: We acknowledge that the decision rule requires a precise statement for reproducibility. In the revision, we will insert pseudocode (Algorithm 1) in Section 4.2 that formalizes the protocol: after each slot, the EKF covariance is updated; sensing is triggered if trace(P_pos) > τ (τ = 0.4 m²) or if the prediction horizon exceeds H_max = 8 slots, with τ adjusted by the inter-target interference term derived in the preceding analysis. This makes the post-convergence occasional-sensing behavior explicit and stable under the modeled interference. revision: yes

Circularity Check

0 steps flagged

No circularity; simulation-validated performance claims are independent of inputs

full rationale

The paper's core contribution is an EKF-based state tracking algorithm with adaptive sensing management for pilot-free predictive beamforming. The headline result (downlink SE close to perfect-CSI) is obtained from Monte Carlo simulations that apply the proposed protocol to a mobility model and practical waveforms; these numerical outcomes do not reduce by construction to any fitted parameter or self-cited uniqueness theorem. The EKF is the standard extended Kalman filter applied to location/velocity states, with explicit analysis of inter-target interference, and no derivation step equates a prediction to its own input data. Self-citations, if present, are not load-bearing for the central claim.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions from wireless communications and Kalman filtering literature plus the unverified simulation outcomes; no new physical entities are postulated.

axioms (2)
  • domain assumption User locations and velocities can be adequately modeled as a linear-Gaussian state process for EKF tracking purposes.
    Invoked when the paper states that the EKF maintains accurate state estimates from sensing measurements.
  • domain assumption Sensing waveforms can be designed to provide usable location information with manageable inter-target interference in the cell-free deployment.
    Required for the claim that sensing is needed only occasionally after convergence.

pith-pipeline@v0.9.0 · 5540 in / 1484 out tokens · 27632 ms · 2026-05-10T01:33:18.290614+00:00 · methodology

discussion (0)

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Reference graph

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