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arxiv: 2503.02647 · v3 · submitted 2025-03-04 · 💻 cs.IT · eess.SP· math.IT

A Framework for Uplink ISAC Receiver Designs: Performance Analysis and Algorithm Development

Zhiyuan Yu , Hong Ren , Cunhua Pan , Gui Zhou , Dongming Wang , Jiangzhou Wang This is my paper

Pith reviewed 2026-05-23 01:15 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords uplink ISACflexible projection receivertradeoff factorsignal detectiontarget response estimationpairwise error probabilityhomotopy optimization
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The pith

A flexible projection receiver unifies projection and successive interference cancellation receivers for uplink ISAC by tuning a tradeoff factor between the two approaches.

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

The paper develops the flexible projection receiver to solve joint communication signal detection and target response estimation in uplink ISAC systems. It operates in two phases where a tradeoff factor first shapes the reconstructed communication signal for detection and then enables subtraction to isolate the target response. This unifies earlier projection and cancellation methods while allowing the receiver to adapt as signal conditions change. Pairwise error probability analysis for maximum likelihood and zero-forcing detectors shows that the best factor depends on the detector type and the relative strengths of sensing and communication signals. A homotopy optimization method handles the fixed-factor case and extends to a dynamic version that updates the factor iteratively, with the added requirement that processed signal length must grow with antenna count.

Core claim

The FP-type receiver unifies the projection-type receiver and the successive interference cancellation receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-

What carries the argument

The flexible projection (FP)-type receiver, which uses a single tradeoff factor to control the composition of the reconstructed communication signal in a two-phase detection-then-estimation procedure.

If this is right

  • The optimal tradeoff factor must be chosen according to whether maximum-likelihood or zero-forcing detection is used and according to the relative power of the sensing and communication signals.
  • The dynamic flexible projection receiver improves adaptability by iteratively updating the tradeoff factor during operation.
  • The length of the jointly processed signal must increase in proportion to the number of receive antennas to realize the full performance gain of the framework.

Where Pith is reading between the lines

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

  • Receivers built on this tradeoff mechanism could switch between detection modes on the fly without requiring separate hardware paths for different interference regimes.
  • The same two-phase structure with an adjustable factor may apply to multi-user or downlink ISAC settings where interference statistics also vary over time.
  • System designers would need power-estimation modules accurate enough to select or adapt the factor in real time for the claimed gains to appear in hardware.

Load-bearing premise

The joint signal detection and target response estimation problem can be cleanly split into two phases where the tradeoff factor directly governs performance by changing how much of the communication signal is reconstructed versus cancelled.

What would settle it

Run Monte Carlo trials of the uplink ISAC link while sweeping the tradeoff factor across a range of sensing-to-communication power ratios; if the minimum error rate achieved by any fixed tradeoff factor is no better than the best of the pure projection or pure SIC receivers in at least half the tested ratios, the unification benefit is not supported.

Figures

Figures reproduced from arXiv: 2503.02647 by Cunhua Pan, Dongming Wang, Gui Zhou, Hong Ren, Jiangzhou Wang, Zhiyuan Yu.

Figure 1
Figure 1. Figure 1: Considered uplink ISAC systems [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the proposed FP-type receiver A. Design Principle of the FP-type Receiver The core idea of the projection-type receiver is to solve the signal detection problem in (7). In the projection-type receiver, matrix Γ is the orthogonal projection matrix of Ar, which can be rewritten as Γ = P⊥ ⊗ IMr , (9) and the equivalent channel matrix in the signal detection problem (7) is G = ΓAc = P⊥ ⊗ Hc, (1… view at source ↗
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: BER of different types of receivers 5 10 15 20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 NMSE 15 16 17 18 19 20 0 0.02 0.04 0.06 [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: BER of different types of receivers versus communication SNR under Gaussian channel (a) and correlated channel (b) 8 16 24 32 40 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100 BER [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: S&C performance of versus the number of the sub-blocks varying numbers of snapshots (or equivalently, the number of sub-blocks). It can be observed that as the number of snap￾shots increases, both the NMSE and the BER decrease. The decrease in BER can be explained as follows: increasing the number of snapshots improves the target response estimation precision, and a more accurate approximation of the targ… view at source ↗
read the original abstract

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. {In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios.} The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S\&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

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 / 0 minor

Summary. The manuscript proposes a flexible projection (FP)-type receiver for uplink integrated sensing and communication (ISAC) systems. This receiver unifies the projection-type and successive interference cancellation (SIC)-type receivers through a flexible tradeoff factor that controls the composition of the reconstructed communication signal in a two-phase procedure: communication signal detection followed by target response estimation. The paper analyzes pairwise error probability (PEP) for maximum likelihood (ML) and zero-forcing (ZF) detectors, develops a homotopy optimization framework for fixed and dynamic tradeoff factors (DFP-type receiver), and shows that the jointly processed signal length should scale with antenna size.

Significance. If the unification property is rigorously established and the optimization yields the claimed performance gains, the work provides a valuable adaptive framework for uplink ISAC receivers that can balance SINR enhancement and correlation reduction in dynamic scenarios. The PEP analysis offers insights into optimal tradeoff selection depending on the detector and relative S&C powers, and the scaling result highlights a fundamental design consideration.

major comments (1)
  1. [Abstract and FP-type receiver definition] The unification claim—that the FP-type receiver unifies the projection-type and SIC-type receivers via the tradeoff factor—requires explicit demonstration that the boundary values of the tradeoff factor recover the exact standard definitions of those receivers. The description of the two coordinated phases indicates that the factor controls the reconstructed signal composition but does not provide the mathematical parameterization or limiting-case analysis needed to verify this property. This verification is load-bearing for the central contribution and the subsequent performance analysis.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading and constructive feedback. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract and FP-type receiver definition] The unification claim—that the FP-type receiver unifies the projection-type and SIC-type receivers via the tradeoff factor—requires explicit demonstration that the boundary values of the tradeoff factor recover the exact standard definitions of those receivers. The description of the two coordinated phases indicates that the factor controls the reconstructed signal composition but does not provide the mathematical parameterization or limiting-case analysis needed to verify this property. This verification is load-bearing for the central contribution and the subsequent performance analysis.

    Authors: We agree that the unification property requires explicit verification via limiting cases to be fully rigorous. In the revised manuscript we will add a dedicated paragraph (or short subsection) immediately after the FP-type receiver definition that introduces the precise mathematical form of the tradeoff factor α in the reconstructed signal expression and derives the two boundary cases: as α → 0 the receiver reduces exactly to the standard projection-type receiver, and as α → 1 it reduces exactly to the standard SIC-type receiver. These derivations will be connected to the existing two-phase procedure and will confirm equivalence with the conventional definitions used in the literature. The PEP analysis, homotopy optimization, and scaling result remain unchanged because they operate on the parameterized receiver; only the presentation of the unification claim will be strengthened. revision: yes

Circularity Check

0 steps flagged

No circularity: unification is definitional parameterization; PEP and optimization analyses are independent of fitted inputs

full rationale

The paper proposes the FP-type receiver as a parameterized generalization of projection-type and SIC-type receivers via an explicit tradeoff factor controlling signal reconstruction in a two-phase procedure. This unification is introduced by construction in the abstract and does not claim to derive one receiver type from another via external first principles that later reduce to the parameter itself. The subsequent PEP analysis for ML/ZF detectors and the homotopy optimization framework (including the DFP extension) are presented as derived from the signal model and detection algorithms, with no quoted equations showing a fitted parameter renamed as a prediction or a self-citation chain substituting for independent verification. No load-bearing step reduces by the paper's own equations to its inputs; the design is self-contained against the stated uplink ISAC model.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The central claim rests on the modeling assumption that communication and sensing signals coexist in the same uplink waveform and that a single scalar tradeoff factor can meaningfully trade off SINR against correlation in the reconstructed signal; no free parameters or invented physical entities are explicitly introduced in the abstract.

free parameters (1)
  • tradeoff factor
    Scalar parameter introduced to control the composition of the reconstructed communication signal; its optimal value is stated to depend on detector type and relative S&C powers.

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Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages · cited by 1 Pith paper

  1. [1]

    Integrated sensing and communications: Recent advances and ten open challenges,

    S. Lu, F. Liu, Y . Li, K. Zhang, H. Huang, J. Zou, X. Li, Y . Dong, F. Dong, J. Zhu, Y . Xiong, W. Yuan, Y . Cui, and L. Hanzo, “Integrated sensing and communications: Recent advances and ten open challenges,” IEEE Internet Things J. , vol. 11, no. 11, pp. 19 094–19 120, Jun. 2024

    work page 2024

  2. [2]

    Radar-assisted predictive beamforming for vehicular links: Communication served by sensing,

    F. Liu, W. Yuan, C. Masouros, and J. Yuan, “Radar-assisted predictive beamforming for vehicular links: Communication served by sensing,” IEEE Trans. Wireless Commun. , vol. 19, no. 11, pp. 7704–7719, Nov. 2020

    work page 2020

  3. [3]

    An overview of cellular ISAC for low- altitude UA V: New opportunities and challenges

    Y . Song, Y . Zeng, Y . Yang, Z. Ren, G. Cheng, X. Xu, J. Xu, S. Jin, and R. Zhang, “An overview of cellular ISAC for low- altitude UA V: New opportunities and challenges.” [Online]. Available: https://arxiv.org/abs/2412.19973

    work page arXiv

  4. [4]

    Cooperative ISAC-empowered low-altitude economys

    J. Tang, Y . Yu, C. Pan, H. Ren, D. Wang, J. Wang, and X. You, “Cooperative ISAC-empowered low-altitude economys.” [Online]. Available: https://arxiv.org/abs/2412.20371

    work page arXiv

  5. [5]

    Joint radar and communication design: Applications, state-of-the-art, and the road ahead,

    F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, “Joint radar and communication design: Applications, state-of-the-art, and the road ahead,” IEEE Trans. Commun. , vol. 68, no. 6, pp. 3834–3862, Jun. 2020

    work page 2020

  6. [6]

    A dual-function massive MIMO uplink OFDM communication and radar architecture,

    M. Temiz, E. Alsusa, and M. W. Baidas, “A dual-function massive MIMO uplink OFDM communication and radar architecture,” IEEE Trans. Cognit. Commun. Netw. , vol. 8, no. 2, pp. 750–762, Jun. 2022

    work page 2022

  7. [7]

    Performance of downlink and uplink integrated sensing and communications (ISAC) systems,

    C. Ouyang, Y . Liu, and H. Yang, “Performance of downlink and uplink integrated sensing and communications (ISAC) systems,” IEEE Wireless Commun. Lett. , vol. 11, no. 9, pp. 1850–1854, Sep. 2022

    work page 2022

  8. [8]

    Revealing the impact of SIC in NOMA-ISAC,

    ——, “Revealing the impact of SIC in NOMA-ISAC,” IEEE Wireless Commun. Lett. , vol. 12, no. 10, pp. 1707–1711, Oct. 2023

    work page 2023

  9. [9]

    Downlink and uplink NOMA-ISAC with signal alignment,

    B. Zhao, C. Ouyang, X. Zhang, and Y . Liu, “Downlink and uplink NOMA-ISAC with signal alignment,” IEEE Trans. Wireless Commun. , vol. 23, no. 10, pp. 15 322–15 338, Oct. 2024

    work page 2024

  10. [10]

    On the study of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC),

    L. Sun, Z. Zhao, S. Wang, Z. Ding, and M. Peng, “On the study of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC),” IEEE Trans. Commun. , vol. 72, no. 11, pp. 7278–7293, Nov. 2024

    work page 2024

  11. [11]

    Deep learning-based design of uplink integrated sensing and communication,

    Q. Qi, X. Chen, C. Zhong, C. Yuen, and Z. Zhang, “Deep learning-based design of uplink integrated sensing and communication,” IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 10 639–10 652, Sep. 2024

    work page 2024

  12. [12]

    Full-duplex communication for ISAC: Joint beamforming and power optimization,

    Z. He, W. Xu, H. Shen, D. W. K. Ng, Y . C. Eldar, and X. You, “Full-duplex communication for ISAC: Joint beamforming and power optimization,” IEEE J. Select. Areas Commun. , vol. 41, no. 9, pp. 2920– 2936, Sep. 2023

    work page 2023

  13. [13]

    Full-duplex NOMA- enabled integrated sensing and communication: Joint transmit and receive beamforming optimization,

    R. Li, L. Wang, K. Chen, L. Xu, and A. Fei, “Full-duplex NOMA- enabled integrated sensing and communication: Joint transmit and receive beamforming optimization,” IEEE Internet Things J. , vol. 11, no. 16, pp. 27 015–27 029, Aug. 2024

    work page 2024

  14. [14]

    Joint receiver design for integrated sensing and communications,

    Y . Dong, F. Liu, and Y . Xiong, “Joint receiver design for integrated sensing and communications,” IEEE Commun. Lett. , vol. 27, no. 7, pp. 1854–1858, Jul. 2023

    work page 2023

  15. [15]

    Model-driven deep learning for MIMO detection,

    H. He, C.-K. Wen, S. Jin, and G. Y . Li, “Model-driven deep learning for MIMO detection,” IEEE Trans. Signal Process., vol. 68, pp. 1702–1715, 2020

    work page 2020

  16. [16]

    Adaptive interference removal for uncoordinated radar/communication coexistence,

    L. Zheng, M. Lops, and X. Wang, “Adaptive interference removal for uncoordinated radar/communication coexistence,”IEEE J. Sel. Top. Sign. Proces., vol. 12, no. 1, pp. 45–60, Feb. 2018

    work page 2018

  17. [17]

    ADMM-net for commu- nication interference removal in stepped-frequency radar,

    J. Johnston, Y . Li, M. Lops, and X. Wang, “ADMM-net for commu- nication interference removal in stepped-frequency radar,” IEEE Trans. Signal Process., vol. 69, pp. 2818–2832, Apr. 2021

    work page 2021

  18. [18]

    Ad- dressing the mutual interference in uplink ISAC receivers: A projection method,

    Z. Yu, H. Ren, C. Pan, G. Zhou, R. Wang, M. Liu, and J. Wang, “Ad- dressing the mutual interference in uplink ISAC receivers: A projection method,” IEEE Wireless Commun. Lett. , vol. 13, no. 11, pp. 3109–3113, Nov. 2024

    work page 2024

  19. [19]

    An overview of signal processing techniques for joint communication and radar sensing,

    J. A. Zhang, F. Liu, C. Masouros, R. W. Heath, Z. Feng, L. Zheng, and A. Petropulu, “An overview of signal processing techniques for joint communication and radar sensing,” IEEE J. Sel. Top. Sign. Proces. , vol. 15, no. 6, pp. 1295–1315, Nov. 2021

    work page 2021

  20. [20]

    Zhang, Matrix analysis and applications

    X.-D. Zhang, Matrix analysis and applications . Cambridge University Press, 2017

    work page 2017

  21. [21]

    Best approximate solution of matrix equation AXB + CYD = E,

    A. Liao, Z. Bai, and Y . Lei, “Best approximate solution of matrix equation AXB + CYD = E,” SIAM Journal on Matrix Analysis and Applications, vol. 27, no. 3, pp. 675–688, 2005

    work page 2005

  22. [22]

    J. G. Proakis and M. Salehi, Digital Communications , 5th ed. New York, NY: McGraw-Hill, 2008

    work page 2008

  23. [23]

    An overview of massive MIMO: Benefits and challenges,

    L. Lu, G. Y . Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” IEEE J. Sel. Top. Sign. Proces., vol. 8, no. 5, pp. 742–758, Oct. 2014

    work page 2014

  24. [24]

    Performance of space-time codes for a large number of antennas,

    E. Biglieri, G. Taricco, and A. Tulino, “Performance of space-time codes for a large number of antennas,” IEEE Trans. Inform. Theory , vol. 48, no. 7, pp. 1794–1803, Jul. 2002

    work page 2002

  25. [25]

    Adaptive neural signal detection for massive MIMO,

    M. Khani, M. Alizadeh, J. Hoydis, and P. Fleming, “Adaptive neural signal detection for massive MIMO,” IEEE Trans. Wireless Commun. , vol. 19, no. 8, pp. 5635–5648, Aug. 2020

    work page 2020

  26. [26]

    Convergence analysis and assurance for gaussian message passing iterative detector in massive MU-MIMO systems,

    L. Liu, C. Yuen, Y . L. Guan, Y . Li, and Y . Su, “Convergence analysis and assurance for gaussian message passing iterative detector in massive MU-MIMO systems,” IEEE Trans. Wireless Commun. , vol. 15, no. 9, pp. 6487–6501, Jun. 2016

    work page 2016

  27. [27]

    Randomized iterative methods for low-complexity large-scale MIMO detection,

    Z. Wang, R. M. Gower, Y . Xia, L. He, and Y . Huang, “Randomized iterative methods for low-complexity large-scale MIMO detection,” IEEE Trans. Signal Process. , vol. 70, pp. 2934–2949, 2022

    work page 2022

  28. [28]

    Efficient high-performance decoding for overloaded MIMO antenna systems,

    K.-K. Wong, A. Paulraj, and R. D. Murch, “Efficient high-performance decoding for overloaded MIMO antenna systems,” IEEE Trans. Wireless Commun., vol. 6, no. 5, pp. 1833–1843, May 2007

    work page 2007

  29. [29]

    The equivalence of semidefinite relaxation MIMO detectors for higher-order QAM,

    W.-K. Ma, C.-C. Su, J. Jalden, T.-H. Chang, and C.-Y . Chi, “The equivalence of semidefinite relaxation MIMO detectors for higher-order QAM,” IEEE J. Sel. Top. Sign. Proces. , vol. 3, no. 6, pp. 1038–1052, Dec. 2009

    work page 2009

  30. [30]

    Homotopy optimization methods for global optimization

    D. M. Dunlavy and D. P. O’Leary, “Homotopy optimization methods for global optimization.” Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA, Tech. Rep., 2005

    work page 2005

  31. [31]

    Binary MIMO detection via homotopy optimization and its deep adaptation,

    M. Shao and W.-K. Ma, “Binary MIMO detection via homotopy optimization and its deep adaptation,” IEEE Trans. Signal Process. , vol. 69, pp. 781–796, 2021

    work page 2021

  32. [32]

    Extreme point pursuit—part I: A framework for constant modulus optimization,

    J. Liu, Y . Liu, W.-K. Ma, M. Shao, and A. M.-C. So, “Extreme point pursuit—part I: A framework for constant modulus optimization,” IEEE Trans. Signal Process. , early access, 2024

    work page 2024

  33. [33]

    R. T. Rockafellar, Convex Analysis. Princeton University Press, 1970

    work page 1970

  34. [34]

    Beck, First-order methods in optimization

    A. Beck, First-order methods in optimization . SIAM, 2017

    work page 2017

  35. [35]

    Generalizing projected gradient descent for deep-learning-aided massive MIMO detection,

    L. He, Z. Wang, S. Yang, T. Liu, and Y . Huang, “Generalizing projected gradient descent for deep-learning-aided massive MIMO detection,” IEEE Trans. Wireless Commun. , vol. 23, no. 3, pp. 1827–1839, Mar. 2024

    work page 2024

  36. [36]

    A framework for one-bit and constant-envelope precoding over multiuser massive MISO channels,

    M. Shao, Q. Li, W.-K. Ma, and A. M.-C. So, “A framework for one-bit and constant-envelope precoding over multiuser massive MISO channels,” IEEE Trans. Signal Process. , vol. 67, no. 20, pp. 5309–5324, Oct. 2019

    work page 2019

  37. [37]

    Channel capacity of MIMO architecture using the exponential correlation matrix,

    S. Loyka, “Channel capacity of MIMO architecture using the exponential correlation matrix,” IEEE Commun. Lett. , vol. 5, no. 9, pp. 369–371, Sep. 2001

    work page 2001