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arxiv: 2606.18488 · v1 · pith:D2KPCBTSnew · submitted 2026-06-16 · 📡 eess.SP

Cell-Free Integrated Sensing and Communication

Diluka Galappaththige , Chintha Tellambura This is my paper

Pith reviewed 2026-06-26 22:24 UTC · model grok-4.3

classification 📡 eess.SP
keywords cell-freeintegrated sensing and communicationISACdistributed access pointsmulti-static sensingresource allocationsynchronizationperformance analysis
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The pith

Cell-free ISAC merges distributed access points with sensing and communication to remove cell boundaries and improve coverage, efficiency, and reliability.

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

This monograph establishes the foundational principles of cell-free integrated sensing and communication and shows how the architecture supports cooperative transmission, target estimation, and multi-static sensing. A sympathetic reader would care because the approach promises higher spectral and energy efficiency along with seamless resource optimization for multi-user systems. The paper surveys integration levels, sensing metrics, performance analysis, resource allocation, security, and applications while addressing practical issues such as synchronization and fronthaul limits. It also examines advanced antenna technologies, near-field operation, and machine learning methods as extensions.

Core claim

Cell-free ISAC leverages distributed access points, removes cell boundaries, and enhances coverage, spectral efficiency, and reliability while improving energy efficiency, enabling robust multi-user communication, distributed multi-static sensing, and seamless resource optimization.

What carries the argument

The cell-free architecture merged with ISAC functionalities, which distributes access points to eliminate cell boundaries and support multi-static sensing plus joint resource optimization.

If this is right

  • Performance analysis and resource allocation become essential tools for optimizing multi-user communication and sensing in distributed setups.
  • Security mechanisms and user- or target-centric designs emerge as key considerations for practical deployment.
  • Solutions for synchronization, multi-target detection, interference management, and fronthaul constraints must be developed to realize full system potential.
  • Advanced antenna technologies, network-assisted operation, near-field sensing, and machine learning methods can extend the capabilities of CF-ISAC.

Where Pith is reading between the lines

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

  • The survey could guide the design of 6G systems that jointly handle communication and environmental sensing at scale.
  • Cross-technology integration might allow CF-ISAC to interoperate with existing cellular and radar standards.
  • Near-field CF-ISAC could enable high-precision sensing for indoor or short-range applications where far-field assumptions fail.
  • Machine learning approaches highlighted may support real-time adaptation to changing user and target dynamics.

Load-bearing premise

The surveyed topics provide a comprehensive and unbiased coverage of the cell-free ISAC field without major omissions from the current literature.

What would settle it

Discovery of a major topic in cell-free ISAC, such as an unaddressed application domain or performance metric central to the field, that is missing from the monograph.

Figures

Figures reproduced from arXiv: 2606.18488 by Chintha Tellambura, Diluka Galappaththige.

Figure 1.1
Figure 1.1. Figure 1.1: Pareto boundary between the communication and sensing performance in an ISAC system under a fixed BS transmit power constraint. 1.0.3 Technical Challenges ISAC systems must balance conflicting performance goals: • High Data Rate: Essential for communication services. • High Sensing Accuracy: Required for precise environmental awareness. This dual functionality introduces a fundamental trade-off in resour… view at source ↗
Figure 1
Figure 1. Figure 1: illustrates the Pareto boundary for an ISAC system, highlighting the [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: illustrates a wide range of potential and largely unexplored research [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 1.2
Figure 1.2. Figure 1.2: Potential research directions of CF-ISAC for supporting future wireless networks. 1.2 Roadmap of this Monograph Chapter 2 introduces the fundamentals of CF mMIMO (CFMM) systems. Chap￾ter 3 explores key concepts in radar sensing, including signal transmission, system configurations, and target parameter estimation techniques. Chapter 4 reviews the principles of conventional ISAC systems along with recent … view at source ↗
Figure 2.1
Figure 2.1. Figure 2.1: A cell-based cellular system. users and fixed BSs, CFMM improves user fairness and performance at the net￾work edge, making it an especially appealing solution for ISAC with multi-static sensing applications that require reliable service and spatial diversity. 2.2 Lack of Cell Boundaries Since there are no cell borders, multiple APs collaboratively cover a large geo￾graphic area without dividing it into … view at source ↗
Figure 2.2
Figure 2.2. Figure 2.2: An architectural comparison between co-located mMIMO and CFMM systems. (CSI) acquisition and user data exchange among APs, leading to higher commu￾nication overhead. Second, the processing complexity increases as coordination among many APs and users becomes computationally intensive. For instance, fronthaul bandwidth demands and processing load scale linearly (or faster) with the number of APs and serve… view at source ↗
Figure 2.3
Figure 2.3. Figure 2.3: Channel hardening and favorable propagation versus the number of AP antennas, N, assuming i.i.d. Rayleigh fading. large-scale path-loss and shadowing. Then, the coefficient of variation of the in￾stantaneous channel gain, i.e., ∥hmk∥ 2 , which is a measure of the variability relative to the mean, is given as [25] CV = ∥hmk∥ 2 E {∥hmk∥ 2} → 1 as N → ∞. (2.1) As N → ∞, the coefficient of variation approach… view at source ↗
Figure 2.4
Figure 2.4. Figure 2.4: Sum SE (left y-axis) and per-user SE (right y-axis) comparison be￾tween CFMM and co-located mMIMO systems with 100 antennas in a coverage area of 1 km2 . In the CF system, the 100 single-antenna APs are uniformly distributed, whereas in the co-located system, a 100-antenna BS is placed in the cell center. The DL SEs are achieved by assuming conjugate beamforming and statistical CSI knowledge at the users… view at source ↗
Figure 3.1
Figure 3.1. Figure 3.1: Types of radar/sensing. and its reflecting and scattering characteristics [59]. Let’s consider the following radar equation: Pr = PtGtGrλ 2σ (4π) 3r 4 , (3.1) where Pr is the power received back from the target by the radar (watt), Pt is the transmitter’s input power (watt), Gt is the gain of the radar transmit antenna (dimensionless), Gr is the gain of the radar receiver antenna (dimensionless), and r i… view at source ↗
Figure 4.1
Figure 4.1. Figure 4.1: The sensing SE as a function of the number of BS antennas (M). where pmax is the maximum allowable transmit power at the ISAC BS. In practice, this metric can be approximated using the sensing signal-to-interference￾plus-noise ratio (SINR) as S Sen ≈ log2 [PITH_FULL_IMAGE:figures/full_fig_p043_4_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: shows sensing SE as a function of the number of BS antennas ( [PITH_FULL_IMAGE:figures/full_fig_p043_4.png] view at source ↗
Figure 4.2
Figure 4.2. Figure 4.2 [PITH_FULL_IMAGE:figures/full_fig_p045_4_2.png] view at source ↗
Figure 4.3
Figure 4.3. Figure 4.3: The directional beampattern gain profiles over a ±90◦ angular spread. 4.4 Near-Filed ISAC Recent advances in wireless technologies, such as the use of extremely large-scale antenna arrays (ELAAs) with hundreds to thousands of elements (typically more than 100 antennas) and the adoption of higher-frequency bands (e.g., mmWave at 10 GHz to 100 GHz and THz at 100 GHz to 10 000 GHz), have led to a fundamenta… view at source ↗
Figure 4.4
Figure 4.4. Figure 4.4: FF planar wavefront versus NF spherical wavefront. Despite the more complex fading behavior, NF propagation introduces several advantages for ISAC: • Spherical wave modeling enables joint estimation of range and angle from a single observation, enhancing sensing capabilities. • Beam focusing in both angular and radial domains increases echo SNR, im￾proving estimation precision. • Higher spatial resolutio… view at source ↗
Figure 4.5
Figure 4.5. Figure 4.5: ISAC applications. (distance) and angle information. This dual dependency enables range-aware re￾ception, facilitating selective signal capture from specific sources while suppressing interference from others. As a result, NF reception not only boosts sensing accu￾racy but also enhances interference mitigation through spatial filtering [104]. By combining NF reception with advanced parameter estimation a… view at source ↗
Figure 7.1
Figure 7.1. Figure 7.1: A CF-ISAC system setup with UL and DL APs. independently change across intervals. A unified representation of all channels is given as a = ζ 1/2 a a˜, (7.1) where a ∈ {hmk, g d mt, g u mt}, a˜ ∼ CN (0, IL) captures the small-scale Rayleigh fad￾ing, which is static during one coherence interval, and ζa accounts for the large-scale path-loss and shadowing. Since large-scale fading coefficients remain const… view at source ↗
Figure 7
Figure 7. Figure 7: and Figure 7.3 illustrate the communication and sensing SEs, respec [PITH_FULL_IMAGE:figures/full_fig_p078_7.png] view at source ↗
Figure 7.2
Figure 7.2. Figure 7.2 [PITH_FULL_IMAGE:figures/full_fig_p079_7_2.png] view at source ↗
Figure 7.3
Figure 7.3. Figure 7.3: Sensing SE versus the number of AP antennas. 79 [PITH_FULL_IMAGE:figures/full_fig_p079_7_3.png] view at source ↗
Figure 8
Figure 8. Figure 8: considers a CF-ISAC setup that consists of [PITH_FULL_IMAGE:figures/full_fig_p081_8.png] view at source ↗
Figure 8.1
Figure 8.1. Figure 8.1: A CF-ISAC system setup. follow a LoS model [72]. Accordingly, the transmit array steering vector pointing toward the direction θmt is expressed as a(θmt) = 1 √ L [PITH_FULL_IMAGE:figures/full_fig_p082_8_1.png] view at source ↗
Figure 8.2
Figure 8.2. Figure 8.2: Average running time versus number of users and per-AP antennas for CCPA and ALMCI algorithms [PITH_FULL_IMAGE:figures/full_fig_p092_8_2.png] view at source ↗
Figure 8.3
Figure 8.3. Figure 8.3: Communication SE compression between CCPA and ALMCI algo￾rithms as a function of the number of per-AP antennas. its search within a manifold that has (L + 1)KM dimensions, in contrast to the larger Euclidean space of MLKT dimensions employed by conventional methods like CCPA. This dimensionality reduction streamlines gradient computations, step size adjustments, memory usage, and ensures numerical stabil… view at source ↗
Figure 8
Figure 8. Figure 8: shows the communication SE as a function of the number of AP [PITH_FULL_IMAGE:figures/full_fig_p093_8.png] view at source ↗
Figure 8.4
Figure 8.4. Figure 8.4: Communication SE versus the number of per-AP antennas for K = 2 and T = 3. increases as the number of AP antennas grows. Moreover, for a fixed L, a higher value of M leads to a greater communication SE. For instance, when M = 16 and L = 12, the communication SE is 53.9 % and 25.2 % higher compared to the M = 4 and M = 9 cases, respectively. This performance improvement emphasizes the scalability and vers… view at source ↗
Figure 8
Figure 8. Figure 8: presents the effects of beamforming gains utilizing [PITH_FULL_IMAGE:figures/full_fig_p094_8.png] view at source ↗
Figure 8.5
Figure 8.5. Figure 8.5: Beampattern gain profiles over a ±90◦ angular spread at different APs, illustrating the gain variations and directivity in a color-coded scale for L = 8, M = 4, K = 2, and T = 3. gain, where red indicates strong gains due to focused energy, and blue represents ar￾eas with minimal radiated power. The intersection of beampattern gain directions across multiple APs enables precise target localization, a sig… view at source ↗
Figure 8
Figure 8. Figure 8: compares the communication SE between CF-ISAC and co-located [PITH_FULL_IMAGE:figures/full_fig_p095_8.png] view at source ↗
Figure 8.6
Figure 8.6. Figure 8.6: Communication SE comparison between CF-ISAC and co-located ISAC systems for M × L = 64. loss and shadowing effects. The distributed nature of CF-ISAC ensures more uni￾form coverage and better signal quality, providing a significant advantage over co-located ISAC systems. Additionally, [PITH_FULL_IMAGE:figures/full_fig_p096_8_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: compares beampattern gains between CF-ISAC and co-located ISAC [PITH_FULL_IMAGE:figures/full_fig_p096_8.png] view at source ↗
Figure 8.7
Figure 8.7. Figure 8.7: A comparison of directional beampattern gain profiles over a ±90◦ angular spread between CF-ISAC and co-located ISAC systems. As shown in [PITH_FULL_IMAGE:figures/full_fig_p097_8_7.png] view at source ↗
Figure 9.1
Figure 9.1. Figure 9.1: Communication SE and maximum leakage SE as functions of the num￾ber of APs. standard semidefinite programming (SDP) problem, which can be solved using the CVX MATLAB tool [160]. If the SDR solutions meet the conditions Rank(Wk) = 1 the optimal trans￾mit communication beamforming can be derived through eigenvalue decomposi￾tion (EVD) [161]. Let the eigenvalue decomposition of Wk be represented as Wk = UkΣ… view at source ↗
Figure 9
Figure 9. Figure 9: illustrates how the number of APs ( [PITH_FULL_IMAGE:figures/full_fig_p102_9.png] view at source ↗
Figure 9.2
Figure 9.2. Figure 9.2: Directional beampattern gain profiles over a ±90◦ angular spread at different APs. tial diversity, enhanced beamforming gains, reduced path loss and shadowing, and more effective interference mitigation. The CF-ISAC system demonstrates strong scalability with increasing user numbers, capitalizing on spatial multiplexing and the distributed nature of the APs. For example, with M = 16, the system config￾ur… view at source ↗
Figure 9
Figure 9. Figure 9: illustrates the directional beampattern gain profiles of the secure [PITH_FULL_IMAGE:figures/full_fig_p103_9.png] view at source ↗
Figure 10.1
Figure 10.1. Figure 10.1: A NAFD CF ISAC system setup with multiple users and targets. where c ∈ {a, b}, d ∈ {m, n} and, θdt is the t-th target’s direction from the d-th AP (i.e., the m-th DL AP or the n-th UL AP) with respect to the x-axis of the coordinate system. 10.2 Transmission Model It is assumed that the APs are able to switch between the UL and DL modes. The operation mode of an AP is decided based on the system’s SE as… view at source ↗
Figure 10
Figure 10. Figure 10: investigates the communication SE as a function of the number of [PITH_FULL_IMAGE:figures/full_fig_p115_10.png] view at source ↗
Figure 10.2
Figure 10.2. Figure 10.2: Communication SE versus the number of per-AP antennas for M = 9, K = 2, and T = 3. pronounced as L increases, suggesting that the NAFD scheme better capitalizes on additional antenna resources by allocating them more judiciously. For example, with L = 12, the NAFD CF scheme provides approximately a 2 % communication SE gain compared to the random AP assignment scheme. This gain reflects not only better … view at source ↗
Figure 10.3
Figure 10.3. Figure 10.3: Sensing SE versus the number of per-AP antennas for M = 9, K = 2, and T = 3. ficiency, i.e., the system requires high antenna resources to meet the same sensing SE target achieved effortlessly by NAFD CF. For instance, the random strategy requires at least L = 9 antennas per AP to exceed the minimum acceptable sensing SE, whereas NAFD CF meets it consistently even with the smallest L. This is be￾cause t… view at source ↗
Figure 10
Figure 10. Figure 10: illustrates the directional beampattern gain profiles of the NAFD [PITH_FULL_IMAGE:figures/full_fig_p117_10.png] view at source ↗
Figure 10.4
Figure 10.4. Figure 10.4: Directional beampattern gain profiles over a ±90◦ angular spread at different APs. while maintaining spectral coexistence with communication users. In the case of AP 2 (UL mode), a composite beampattern is plotted that accounts for both the receive combining and the transmit beamforming. The presence of clearly defined lobes aligned with the target angles validates the NAFD CF’s ability to generate effe… view at source ↗
read the original abstract

Cell-free (CF) integrated sensing and communication (ISAC) merges the CF architecture with ISAC functionalities. CF-ISAC leverages distributed access points, removes cell boundaries, and enhances coverage, spectral efficiency, and reliability. It also improves energy efficiency, enabling robust multi-user communication, distributed multi-static sensing, and seamless resource optimization. A comprehensive survey on CF-ISAC has been lacking. This monograph addresses that gap by covering the foundational principles, cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, and key applications. It also explores the advantages of multi-static sensing. Performance analysis, resource allocation, security, and user/target-centric designs are discussed. Finally, synchronization, multi-target detection, interference management, and fronthaul limitations are discussed. Advanced antenna technologies, network-assisted systems, near-field CF-ISAC, cross-technology integration, and machine learning approaches are presented.

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

0 major / 1 minor

Summary. The manuscript is a survey monograph on cell-free integrated sensing and communication (CF-ISAC). It claims to fill a gap in the literature by covering foundational principles of CF and ISAC, cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, key applications, multi-static sensing advantages, performance analysis, resource allocation, security, user/target-centric designs, synchronization, multi-target detection, interference management, fronthaul limitations, advanced antenna technologies, network-assisted systems, near-field CF-ISAC, cross-technology integration, and machine learning approaches.

Significance. If the coverage proves complete and accurate, the monograph would serve as a useful reference synthesizing cell-free massive MIMO architectures with ISAC functionalities, highlighting potential gains in coverage, spectral and energy efficiency, and distributed multi-static sensing. It could aid researchers working on resource optimization and emerging applications in wireless systems.

minor comments (1)
  1. [Abstract] Abstract: the listed topics are enumerated but the abstract provides no indication of the number of references, depth of treatment, or verification method used to ensure coverage of the current literature on each topic.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our survey monograph on cell-free integrated sensing and communication and for recommending minor revision. The provided summary accurately captures the manuscript's scope and contributions. No specific major comments appear in the report, so we have no individual points requiring point-by-point rebuttal. We will incorporate any minor adjustments needed to confirm completeness and accuracy in the revised version.

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a survey monograph whose central claim is coverage of listed CF-ISAC topics drawn from prior literature. No original derivations, equations, predictions, or fitted models are advanced, so no derivation chain exists that could reduce to self-definition, fitted inputs, or self-citation load-bearing steps. The work is self-contained as a review and receives the default non-circularity outcome.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper and introduces no free parameters, axioms, or invented entities of its own.

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Reference graph

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