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arxiv: 2404.19431 · v6 · submitted 2024-04-30 · 💻 cs.IT · math.IT

Integrated Sensing and Communications for Unsourced Random Access: Fundamental Limits

Mohammad Javad Ahmadi , Rafael F. Schaefer , H. Vincent Poor This is my paper

Pith reviewed 2026-05-24 01:54 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords unsourced random accessintegrated sensing and communicationsfundamental limitsapproximate achievable resultangle of arrival estimationmassive usersinterference managementunsourced ISAC
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The pith

An approximate achievable result shows unsourced users can jointly decode messages and estimate angles despite simultaneous transmissions and no identities.

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

The paper considers a system where a massive number of unsourced and uncoordinated users transmit communication and sensing signals together in a short frame without scheduling or known identities at the receiver. It derives an approximate achievable result for this unsourced integrated sensing and communications setup that jointly decodes the messages from communication users while detecting active sensing users and estimating their angles of arrival. A sympathetic reader would care if the result holds because it suggests a way to handle dense uncoordinated networks without coordination overhead. The paper shows this result outperforms ALOHA, time-division multiple access, treating interference as noise, and multiple signal classification both analytically and in simulations.

Core claim

In the unsourced ISAC model, all active users transmit simultaneously in a short frame with no scheduling and without user identities known at the base station; an approximate achievable result is derived that decodes transmitted message sequences from communication users while simultaneously detecting active sensing users and estimating their angles of arrival, and this result is superior to conventional approaches.

What carries the argument

The approximate achievable result for the UNISAC model, which handles the significant interference from numerous simultaneous transmissions to enable joint decoding and angle estimation.

If this is right

  • The system supports both communication decoding and sensing angle estimation for a large number of users transmitting at once.
  • No scheduling or knowledge of user identities is required at the receiver.
  • The approach outperforms separated conventional methods such as ALOHA and time-division multiple access.
  • Numerical simulations confirm the joint sensing and communication capabilities remain effective at scale.

Where Pith is reading between the lines

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

  • The same unsourced simultaneous-transmission model might extend to estimating additional sensing parameters such as range or velocity.
  • This could reduce coordination overhead in dense machine-type communication networks where scheduling is impractical.
  • Real deployments would need to check robustness when channel conditions deviate from the modeled assumptions.

Load-bearing premise

The model assumes an approximate achievable result can be derived that remains meaningful and superior even when every active user transmits simultaneously in a short frame with no scheduling and without user identities being known at the receiver.

What would settle it

A numerical simulation or theoretical bound in which the message decoding error rate or angle estimation accuracy of the derived result is not better than ALOHA, TDMA, treating interference as noise, or MUSIC under the same conditions with a large number of users.

Figures

Figures reproduced from arXiv: 2404.19431 by H. Vincent Poor, Mohammad Javad Ahmadi, Rafael F. Schaefer.

Figure 1
Figure 1. Figure 1: Illustration of the UNISAC model. The system jointly ac￾commodates multiple users for sensing and communication purposes, with only a limited subset active at any given moment. column vector; ⊗ denotes the Kronecker product, which rep￾resents the tensor product of two matrices; diag(A) represents the vector formed by the diagonal elements of the matrix A, while diag(a) denotes the diagonal matrix that has … view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the frame structure in the proposed practical scheme. In this example, the numbers of sensing and communication slots are set to Ss = 5 and Sc = 2, respectively. One communication user generates the length-nc signal a3 and randomly selects the second communication slot for transmission, while one sensing user generates the length-ns signal a5 and randomly chooses the third sensing slot to s… view at source ↗
Figure 3
Figure 3. Figure 3: Algorithm 1: Overview of the receiving algorithm. Rci = ∅. % Set of decoded communication signals. Rst = ∅. % Set of detected sensing signals. continue comm phase = 1. while continue comm phase = 1 do % Communication Phase for i = 1, 2, ..., Sc do different communication slots continue slot loop = 1. while continue slot loop = 1 do Step 1: Perform I-SIC using (45). Step 2: Find peak direction θ∗ using (47)… view at source ↗
Figure 3
Figure 3. Figure 3: Receiving algorithm of the practical UNISAC model (Section IV-B). C. Computational Complexity In this part, we assess the computational complexity of the practical UNISAC model, by considering the number of multiplications as a measure of computational complexity. To this end, we investigate the sensing, communication, and AOA estimation phases separately in the following: Here, we calculate the computatio… view at source ↗
Figure 4
Figure 4. Figure 4: The required E/N0 as a function of the number of active sensing and communication users (with |Ac| = |As| and n = 5000), comparing UNISAC’s achievable result against existing multiple access models to achieve a target PUPE of ǫ0 = 0.1 and a target MSEAOA of ∆0 = 5 × 10−4 . 50 100 150 200 250 300 10 12 14 16 18 20 [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The required E/N0 as a function of the number of active sensing and communication users (with |Ac| = |As| and n = 1024), comparing the practical UNISAC scheme proposed in Section IV, the achievable result in Proposition 1, several practical benchmarks, and the optimistic result. The comparison is made to achieve a target PUPE of ǫ0 = 0.1 and a target MSEAOA of ∆0 = 5 × 10−4 . The third model is known as th… view at source ↗
Figure 7
Figure 7. Figure 7: The required E/N0 as a function of the number of active sensing and communication users (with |Ac| = |As|) for the practical UNISAC model with n = 1024, Bs = 13, and different values of the slot numbers to achieve a target PUPE of ǫ0 = 0.1 and a target MSEAOA of ∆0 = 5 × 10−4 . For the “ALOHA-Practical” and “TDMA-Practical” schemes, we use the same performance results as in their corresponding ideal cases … view at source ↗
read the original abstract

This work considers the problem of integrated sensing and communications (ISAC) with a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals, without requiring scheduling with the base station (BS). Hence, the signal received from each user is affected by significant interference from numerous interfering users, making it challenging to extract the transmitted signals. UNISAC aims to decode the transmitted message sequences from communication users while simultaneously detecting active sensing users and estimating their angles of arrival, regardless of the identity of the senders. In this paper, we derive an approximate achievable result for UNISAC and demonstrate its superiority over conventional approaches such as ALOHA, time-division multiple access, treating interference as noise, and multiple signal classification. Through numerical simulations, we validate the effectiveness of UNISAC's sensing and communication capabilities for a large number of users.

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

3 major / 2 minor

Summary. The paper introduces the UNISAC model for integrated sensing and communications under unsourced random access, where a massive number of uncoordinated users transmit simultaneously in a short frame. It derives an approximate achievable result for joint message decoding from communication users and active user detection plus angle-of-arrival estimation from sensing users, then uses numerical simulations to claim superiority over ALOHA, TDMA, TIN, and MUSIC baselines.

Significance. If the approximation remains faithful, the result would establish non-trivial fundamental limits for joint unsourced communication and sensing under strong uncoordinated interference, a setting relevant to massive IoT and 6G. The explicit comparison to multiple standard baselines and the simulation validation are positive features; however, the absence of error bounds on the approximation limits the strength of the superiority claim.

major comments (3)
  1. [§IV] §IV (Approximate Achievable Result): the derivation of the approximate achievable rate/region is presented without an explicit error bound or convergence statement on the approximation (e.g., large-system limit or relaxation gap). Because the central superiority claim rests on this approximation outperforming the baselines, the lack of a quantitative guarantee on approximation fidelity is load-bearing.
  2. [§V] §V (Numerical Results): the simulation comparison applies the UNISAC scheme and the four baselines, but does not state whether the same approximation (or relaxation) was used uniformly across all schemes. If the baselines were evaluated under a tighter or different approximation, the reported gains may not be directly comparable.
  3. [§III] §III (System Model): the model assumes all active users transmit simultaneously with no scheduling and unknown identities, yet the approximate result is claimed to remain meaningful; a concrete test (e.g., finite-user correction term or sensitivity analysis) is needed to confirm the approximation does not break under the massive-interference regime that defines the problem.
minor comments (2)
  1. Notation for the approximate achievable region should be introduced with a clear symbol (e.g., R_approx) and distinguished from any exact inner bound.
  2. Figure captions in the simulation section should explicitly list the parameter values (number of users, frame length, SNR) used for each curve.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful review and constructive feedback on our manuscript. We address each major comment below, providing clarifications and indicating where revisions will be made to strengthen the presentation.

read point-by-point responses

  1. Referee: [§IV] §IV (Approximate Achievable Result): the derivation of the approximate achievable rate/region is presented without an explicit error bound or convergence statement on the approximation (e.g., large-system limit or relaxation gap). Because the central superiority claim rests on this approximation outperforming the baselines, the lack of a quantitative guarantee on approximation fidelity is load-bearing.

    Authors: The approximate achievable result is obtained via the standard large-system analysis with fixed load factor, as is conventional in the unsourced random access literature. Section IV explicitly invokes this asymptotic regime. While explicit finite-N error bounds are not derived, the approximation is validated by the close agreement between the analytical expressions and the Monte Carlo simulations in Section V across a range of user densities. We will add a clarifying remark on the asymptotic justification and relevant citations in the revised manuscript. revision: partial

  2. Referee: [§V] §V (Numerical Results): the simulation comparison applies the UNISAC scheme and the four baselines, but does not state whether the same approximation (or relaxation) was used uniformly across all schemes. If the baselines were evaluated under a tighter or different approximation, the reported gains may not be directly comparable.

    Authors: The identical large-system approximation and interference model were applied uniformly to UNISAC and all four baselines (ALOHA, TDMA, TIN, MUSIC) to ensure comparability. Each baseline was adapted to the unsourced massive-access setting under the same channel and load assumptions used for the proposed scheme. We will add an explicit statement to this effect in the revised Section V. revision: yes

  3. Referee: [§III] §III (System Model): the model assumes all active users transmit simultaneously with no scheduling and unknown identities, yet the approximate result is claimed to remain meaningful; a concrete test (e.g., finite-user correction term or sensitivity analysis) is needed to confirm the approximation does not break under the massive-interference regime that defines the problem.

    Authors: The system model and the ensuing approximation are formulated specifically for the massive unsourced regime that defines the problem. Section V already contains numerical results for several hundred simultaneous users, which serve as the requested sensitivity check and demonstrate that the reported gains persist under heavy interference. Deriving an explicit finite-user correction term lies outside the scope of the present work. revision: no

Circularity Check

0 steps flagged

No circularity; derivation presented as independent

full rationale

The provided abstract and model description state that an approximate achievable result is derived for the UNISAC problem and compared to baselines. No equations, self-citations, or steps are exhibited that reduce the claimed result to a definition, a fitted input renamed as prediction, or a load-bearing self-citation chain. The central claim remains a derived bound rather than a tautology, consistent with the default expectation that most papers are non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; specific free parameters, axioms, and invented entities cannot be extracted or audited from the provided text.

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Forward citations

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