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arxiv: 2605.05059 · v1 · submitted 2026-05-06 · 💻 cs.IT · eess.SP· math.IT

A Comparison Between Co-Located and Distributed MIMO Deployments in OFDM-ISAC Networks

Maryam Darabi , Sergi Liesegang , Emanuele Grossi , Stefano Buzzi This is my paper

Pith reviewed 2026-05-08 16:09 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords ISACcell-free massive MIMOOFDMsensing performancedistributed MIMOmulti-cell MIMOGLRT detectorsensing SNR
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The pith

Cell-free massive MIMO with distributed access points delivers higher and more robust sensing performance than co-located multi-cell MIMO in OFDM-based ISAC networks.

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

The paper compares two network topologies for integrated sensing and communication that both use the same OFDM waveform: a cell-free massive MIMO system that spreads access points across the coverage area and operates in a user-target-centric way, versus a multi-cell massive MIMO system that keeps transmit-receive arrays together inside conventional cells. It derives a generalized likelihood ratio test detector and closed-form sensing signal-to-noise ratio expressions for each architecture, then runs case studies that vary the number of subcarriers, how transceivers are allocated, and how antennas or nodes are placed. The evaluations show the distributed cell-free approach consistently produces better sensing results, especially when resources are spread out rather than concentrated. A sympathetic reader would care because future wireless networks are expected to perform both communication and radar-style sensing with the same signals, so the choice of topology directly affects how well the sensing part works.

Core claim

In OFDM-based ISAC networks the cell-free massive MIMO architecture, which uses distributed access points and a scalable user-target-centric operation, achieves higher sensing signal-to-noise ratios and more robust detection than the multi-cell massive MIMO architecture that relies on co-located arrays and conventional cell-centric deployment; this advantage is established through the derived GLRT detector and the corresponding closed-form sensing SNR expressions, then confirmed by case studies that vary subcarrier count, transceiver allocation, and spatial distribution of antennas or nodes.

What carries the argument

The GLRT-based sensing detector and the closed-form sensing SNR expressions derived separately for the cell-free and multi-cell topologies.

If this is right

  • Sensing performance improves when transmit resources and antenna elements are spread across more nodes instead of being concentrated inside fewer cells.
  • The relative advantage of the cell-free topology grows as the spatial distribution of resources increases.
  • Both architectures benefit from more OFDM subcarriers, yet the distributed setup maintains higher and more stable sensing SNR across the tested range.
  • User-target-centric processing yields more consistent sensing outcomes than cell-centric operation when targets are not aligned with cell centers.

Where Pith is reading between the lines

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

  • Network operators could achieve comparable or better integrated sensing by deploying fewer total antennas if they are distributed rather than clustered in co-located arrays.
  • The comparison suggests that standards for future ISAC systems may need to move away from strict cell boundaries toward cell-free coordination to maximize sensing utility.
  • Real-world validation would require extending the model to include mobility and multi-target scenarios to check whether the reported robustness holds under time-varying conditions.

Load-bearing premise

The analysis assumes that the derived detector and SNR expressions remain valid under ideal synchronization and without hardware impairments, phase noise, or channel estimation errors.

What would settle it

A controlled measurement campaign that records actual sensing detection rates or empirical SNR values from a real distributed access-point deployment versus a co-located array deployment, both using the same OFDM waveform and transmit power, would confirm or refute the reported performance gap.

Figures

Figures reproduced from arXiv: 2605.05059 by Emanuele Grossi, Maryam Darabi, Sergi Liesegang, Stefano Buzzi.

Figure 1
Figure 1. Figure 1: (b) depicts the MC-mMIMO system where MMC macro base stations (BSs) are deployed, each located at the center of a cell in the multi-cell environment. Every BS is equipped with a co-located transmit–receive antenna array, comprising NMC−tx a transmit and NMC−rx a receive elements, arranged either as a ULA or a UPA. This co-located archi￾tecture enables each BS to simultaneously serve its associated K UEs an… view at source ↗
Figure 2
Figure 2. Figure 2: Sensing SNR CDF w.r.t Nc in both ISAC network frameworks for transceiver split |Mtx| = 32 and |Mrx| = 2. this array provides strong coherent gain, its performance is more sensitive to unfavorable target–BS geometries, resulting in larger variation. B. Distributed vs. Co-Located: Transceiver Allocation To investigate how the distribution of transmit and receive resources affects sensing, we fixed MCF = 32, … view at source ↗
Figure 5
Figure 5. Figure 5: Sensing SNR CDF comparison for CF and MC under view at source ↗
Figure 4
Figure 4. Figure 4: Sensing SNR CDF for different transceiver allocation view at source ↗
read the original abstract

This paper investigates network-level integrated sensing and communication (ISAC) under two fundamentally different topology configurations: cell-free massive MIMO (CF-mMIMO) and multi-cell massive MIMO (MC-mMIMO). A unified OFDM-based waveform is adopted for both architectures as the key enabler for ISAC functionalities. The CF system exploits distributed access points (APs) and a scalable user-target-centric operation, whereas the MC system relies on co-located transmit-receive arrays with conventional cell-centric deployment. For both architectures, we derive a GLRT-based sensing detector and the corresponding sensing SNR expressions. We then examine a series of case studies investigating how the number of OFDM subcarriers, the transceiver allocation strategy, and the antenna/node distribution across the network affect the sensing performance. The results consistently demonstrate that CF-mMIMO provides more robust and higher sensing performance across most tested scenarios, particularly when transmit resources or antenna elements are spatially distributed. These findings highlight the inherent advantages of CF deployments for next-generation ISAC networks.

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

1 major / 1 minor

Summary. The paper investigates network-level ISAC under CF-mMIMO (distributed APs, user-target-centric) versus MC-mMIMO (co-located arrays, cell-centric) topologies using a unified OFDM waveform. It derives a GLRT-based sensing detector and closed-form sensing SNR expressions for both architectures, then presents case studies varying the number of OFDM subcarriers, transceiver allocation strategy, and spatial distribution of antennas/nodes. The central claim is that CF-mMIMO yields more robust and higher sensing performance across most scenarios, particularly when resources are distributed.

Significance. If the results hold, the work highlights architectural advantages of distributed CF-mMIMO for ISAC networks, with potential implications for 6G design. Credit is due for the analytical derivations of the GLRT detector and sensing SNR expressions under a common waveform model, as well as the systematic case studies that vary key parameters to isolate performance differences. These elements provide a foundation for quantitative comparison without sole reliance on simulation.

major comments (1)
  1. [Derivations of GLRT detector and sensing SNR expressions] Analytical derivations of GLRT detector and sensing SNR expressions: these start from standard MIMO-OFDM signal models and assume perfect synchronization, ideal hardware, and accurate channel knowledge. Distributed APs in CF-mMIMO introduce inter-AP phase/timing offsets absent in co-located MC-mMIMO; without modeling these impairments, the reported robustness advantage of CF-mMIMO is not guaranteed to hold and is load-bearing for the central comparative claim.
minor comments (1)
  1. The abstract and system model should explicitly list all modeling assumptions (perfect synchronization, ideal channel knowledge) to clarify the scope of the closed-form results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We are pleased that the referee recognizes the significance of the analytical contributions and case studies. Below, we provide a point-by-point response to the major comment.

read point-by-point responses

  1. Referee: [Derivations of GLRT detector and sensing SNR expressions] Analytical derivations of GLRT detector and sensing SNR expressions: these start from standard MIMO-OFDM signal models and assume perfect synchronization, ideal hardware, and accurate channel knowledge. Distributed APs in CF-mMIMO introduce inter-AP phase/timing offsets absent in co-located MC-mMIMO; without modeling these impairments, the reported robustness advantage of CF-mMIMO is not guaranteed to hold and is load-bearing for the central comparative claim.

    Authors: We thank the referee for highlighting this important aspect. The analytical derivations indeed rely on standard MIMO-OFDM signal models under the assumptions of perfect synchronization, ideal hardware, and accurate channel knowledge. This is a common approach in theoretical comparisons to focus on the architectural differences between co-located and distributed deployments. Our case studies demonstrate the sensing performance advantages of CF-mMIMO under these ideal conditions. However, we acknowledge that inter-AP phase and timing offsets in practical CF-mMIMO systems could influence the results and that the robustness advantage may not hold universally without accounting for these impairments. In the revised version, we will add a dedicated paragraph in the discussion section addressing these practical considerations, including references to existing work on synchronization in cell-free networks. This addition will temper the claims appropriately while preserving the core analytical contributions. revision: partial

Circularity Check

0 steps flagged

Derivations start from standard GLRT and OFDM signal models with no reduction by construction

full rationale

The paper explicitly states that it derives the GLRT-based sensing detector and closed-form sensing SNR expressions for both architectures from a unified OFDM waveform and the respective MIMO signal models. These steps begin from first-principles signal processing rather than from fitted parameters, self-citations, or ansatzes that presuppose the final performance comparison. No equation or claim reduces to its own inputs by definition, and the reported CF-mMIMO advantages emerge from the subsequent case studies and simulations rather than being forced by the derivation structure itself.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard statistical signal processing assumptions (Gaussian noise, known channel statistics for GLRT) and on the validity of the OFDM waveform model for joint sensing and communication. No new free parameters or invented entities are introduced beyond conventional MIMO-OFDM parameters.

axioms (2)
  • standard math Received signal follows standard MIMO-OFDM model with additive white Gaussian noise and known second-order statistics for GLRT derivation.
    Invoked when deriving the GLRT-based sensing detector and SNR expressions.
  • domain assumption Perfect synchronization and ideal channel state information are available for both communication and sensing.
    Implicit in the closed-form SNR expressions and case-study comparisons.

pith-pipeline@v0.9.0 · 5488 in / 1367 out tokens · 33381 ms · 2026-05-08T16:09:06.365935+00:00 · methodology

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Reference graph

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