Pith. sign in

REVIEW 3 major objections 6 minor 32 references

Cognitive-Radio Functionality: A Novel Configuration for STAR-RIS assisted RSMA Networks

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Partitioning a STAR-RIS into reflecting and transmitting regions with separate power control lets a cognitive-radio network protect the primary user and speed up the secondary user, and the paper backs this with closed-form rate formulas.

desk verdict New STAR-RIS/CR-RSMA configuration with solid SU rate derivations, but the PU outage metric is internally inconsistent and overstates failures. read the letter →

arxiv 2505.24583 v1 pith:LRZUTCAP submitted 2025-05-30 cs.ET eess.SP

Saeed Ibrahim , Yue Xiao , Dimitrios Tyrovolas , Sotiris A. Tegos , Panagiotis D. Diamantoulakis , Zheng Ma , George K. Karagiannidis , Pinghzi Fan This is my paper
classification cs.ETeess.SP
keywords STAR-RIScognitiveradiorate-splittingmultipleaccesselementsplittingpowerergodicrateoutageprobabilityNakagami-mfading
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Radio spectrum is often shared between a protected primary user and a best-effort secondary user, and the hard part is controlling interference without starving either side. This paper proposes to do that control inside the propagation environment itself: a STAR-RIS — a reconfigurable intelligent surface that can both reflect and transmit signals — is split into two regions, one serving each user, with the power each region uses tuned independently. Depending on the primary user's channel, the surface follows one of three policies: lower the primary's interference to speed up the secondary user when the primary's target is already met; attenuate the secondary signal to rescue the primary when the target is barely missed; or reconfigure the entire surface to transmit for the secondary user when the primary is in unavoidable outage. The paper derives closed-form expressions for the secondary user's ergodic rate and the primary user's outage rate under Nakagami-m fading — a flexible fading model spanning scattered and line-of-sight regimes — and verifies them with Monte Carlo simulation. The payoff of the scheme is that the responsibility for interference control shifts from the users' rate-splitting parameter to the surface itself, which the paper argues makes the service agreement easier to fulfill.

What carries the argument

The load-bearing mechanism is the CR functionality itself: the element-splitting parameter $\beta$ decides how many STAR-RIS elements reflect toward the primary user versus transmit toward the secondary user, while the power-splitting factors $\rho_r$ and $\rho_t$ decide what fraction of incident power each region actually uses, giving the surface a third role beyond reflection and transmission — absorption. These parameters shape the effective channel gains $g_p = \rho_r^2 \left|\sum_{i=1}^{N_r} |h_i^H|\,|h_{pi}|\right|^2$ and $g_s = \rho_t^2 \left|\sum_{j=1}^{N_t} |h_j^H|\,|h_{sj}|\right|^2$, which the analysis converts into Gamma-distributed random variables by matching the first two moments of the coherent sum (Proposition 1). On top of that statistical approximation sits a three-case SINR policy that maps each channel state to a prescription for $\rho_r$, $\rho_t$, or full reconfiguration, and the closed forms in Propositions 2–5 — assembled from incomplete Gamma functions, exponential integrals, and one hypergeometric identity — give the SU ergodic rate and PU outage rate that the simulations confirm.

What would settle it

Re-run the Monte Carlo validation with the surface-to-base-station channel magnitudes drawn from a fading distribution, for example Rayleigh or Nakagami-m with the same shape parameter as the user links, instead of fixed at one: the analytical curves for the SU ergodic rate and PU outage rate — equations (28), (33), (34), and (39) — should separate from simulation by an amount governed by the variance of that second hop. A second, independent check is to shrink the number of surface elements $N$ to small values such as 4–8, where the Gamma moment-matching approximation loosens, and compare the closed forms against exact simulation.

Watch

Extended reading notes

Core claim

The paper's claim is that a STAR-RIS configured with what it calls the 'CR functionality' can serve an uplink cognitive-radio rate-splitting multiple-access (CR-RSMA) network in which a primary user (PU) has guaranteed access and a secondary user (SU) splits its message into two sub-messages decoded in the order $x_{s1} \rightarrow x_p \rightarrow x_{s2}$. The configuration is the joint application of element splitting — a parameter $\beta$ dividing the $N$ surface elements into $N_r = \beta N$ reflecting elements for the PU and $N_t = (1-\beta) N$ transmitting elements for the SU — and power splitting, modeled by factors $\rho_r$ and $\rho_t$ that set how much of the incident power each region uses, with the STAR-RIS acting partly as an absorber. From the PU's SINR under full power, the scheme classifies three cases: the PU target $\hat{\gamma}_p$ is already met, so $\rho_r$ is lowered to reduce PU interference on the SU; the PU misses its target but can be rescued by lowering $\rho_t$ to attenuate the SU; or the PU is in persistent outage, in which case all elements switch to transmission and the SU is decoded without PU interference — a regime the authors emphasize conventional CR-RSMA cannot reach, where the RS factor would simply be set to $\alpha = 1$. The mathematical core of the performance claim is that the effective channel gains, coherent sums of Nakagami-m distributed amplitudes, can be approximated as Gamma random variables by moment matching, and that with these approximations the SU's ergodic rate and the PU's outage rate have closed forms that match Monte Carlo simulations across the element-allocation, RS, and geometric parameters.

Load-bearing premise

The closed-form analysis rests on assuming the link from the smart surface to the base station never fades — its channel magnitude is fixed at one — so only the users' links are random; if that leg of the path fades, the derived rate and outage expressions no longer hold.

Editorial extensions

If this is right

  • Network operators can compute the secondary user's ergodic rate and the primary user's outage rate directly from $\beta$, $\alpha$, $\rho_r$, and $\rho_t$, replacing Monte Carlo runs with the closed-form expressions.
  • The surface becomes self-classifying: it detects whether the primary user's target is met, recoverable, or hopeless, and the simulated rate curves show sharp transitions exactly at those case boundaries when geometry or the target threshold varies.
  • Because interference control shifts from the secondary user's rate-splitting factor to the surface's power splits, the paper shows that $\alpha$ can be tuned for secondary throughput over a wider range while the PU's QoS is still held.
  • When the primary user falls into persistent outage, the entire surface converts to transmission and the secondary user is decoded without primary interference — a regime the authors identify as unavailable in conventional CR-RSMA.
  • The element-allocation trade-off is quantified: larger $\beta$ (more reflecting elements) widens the range of $\alpha$ and of the PU target threshold over which the PU's QoS holds, at the cost of a lower ceiling on the secondary user's ergodic rate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the closed forms hinge on treating the surface-to-base-station hop as non-fading, so a natural stress test is to let that hop fade and check whether the Gamma approximation survives; the effective gains would become products of random variables, and the exact expressions given here would not be expected to hold.
  • Editorial inference: the paper performs analysis rather than optimization; the closed forms make it feasible to optimize $\beta$, $\rho_r$, $\rho_t$, and $\alpha$ jointly under a PU outage constraint, which would likely sharpen the claimed secondary-user gains.
  • Editorial inference: the CR functionality reverses the usual direction of interference control — the environment adapts rather than the transmitters — so the same element-plus-power-splitting idea transfers to other coexistence problems, such as underlay IoT with a protected incumbent or integrated sensing and communication, where one link must be throttled to protect another.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper proposes a 'cognitive-radio functionality' for a STAR-RIS in an uplink CR-RSMA network. The STAR-RIS is partitioned into two regions with independent power-splitting factors, one serving the primary user and one serving the secondary user, and the authors derive closed-form expressions for the secondary user's ergodic rate and the primary user's outage rate under Nakagami-m fading. The central claim is that this configuration simultaneously maintains the primary user's QoS while improving the secondary user's throughput, with analytical results validated by Monte Carlo simulations.

Significance. If the derivations were correct as stated, the paper would offer a useful analytical framework for STAR-RIS-assisted CR-RSMA, and the Gamma moment-matching approach is a plausible route to tractable closed forms. The case-based treatment (Cases I–III) and the use of element splitting to decouple user-specific QoS are attractive ideas. However, the current manuscript contains a fundamental inconsistency in the primary-user outage metric and a parameterization error in the Gamma approximation that directly affect the numerical claims; these issues must be resolved before the results can be accepted.

major comments (3)
  1. [Section III.B, Eqs. (28), (33), (34), (39)] The Gamma approximation in Section III.B assigns the same shape parameter k and scale parameter θ_e to both g_p and g_s, although g_p sums N_r = βN Nakagami variables and g_s sums N_t = (1−β)N variables. Since the moment-matching parameters in Proposition 1 depend on the number of summed elements, the two channels must have different (k_p, θ_p) and (k_s, θ_s) unless β = 0.5. As written, the closed-form expressions (28), (33), (34), and (39) contain no β dependence, yet Figs. 2–4 show strong β dependence. This indicates either that the derivations are incorrectly parameterized or that the plotted theoretical curves are generated by a model different from the one in the text. The analysis needs to be redone with distinct Gamma parameters for the PU and SU branches.
  2. [Section III.C, Eqs. (38)–(40), Proposition 5] The primary-user outage probability is evaluated as P_out = Pr(γ_p ≤ γ̂_p) using the unadjusted SINR (4) with ρ_r = ρ_t = 1. This event is the union of Case II and Case III. However, Section II.B explicitly states that in Case II the PU QoS is restored by adjusting ρ_t, so realizations in Case II should not be counted as outages after the CR functionality is applied. The actual outage event after applying the CR functionality is Case III only, i.e., L_p g_p ≤ γ̂_p. Proposition 5 therefore overstates P_out, and the numerical results in Figs. 2b–4b do not demonstrate the claimed QoS guarantee with the metric as defined.
  3. [Section II.B, Case III, Eqs. (13)–(14)] The text states that in Case III the reflecting elements are reassigned as transmitting elements, effectively converting the entire STAR-RIS surface into a transmission-focused mode. However, the rate expression R_c3 = log2(1 + L_s g_s) uses g_s as defined over N_t = (1−β)N elements. If the entire surface is used for transmission, the effective SU channel gain should be recomputed over all N elements. Please clarify whether the analytical and numerical results reflect this reconfiguration or whether the notation is intentionally retaining the original N_t.
minor comments (6)
  1. [Section I.C] The phrase 'The remaining of the paper' should be 'The remainder of the paper.'
  2. [Section II.A] The notation h_pi ∈ C^{N×1} and h_sj ∈ C^{N×1} is misleading: these are scalar channel coefficients from the users to individual RIS elements, not N-dimensional vectors.
  3. [Section III.A] The deterministic RIS-to-BS channel assumption |h_i^H| = |h_j^H| = 1 is a strong modeling condition. It should be explicitly stated as a limitation, because if the second hop fades, the effective channel gains become products of random variables and the Gamma approximation and all subsequent closed forms no longer hold.
  4. [Table I] The entry 'Pathloss @ reference distance C_o 10 mW' is dimensionally unclear; C_0 is normally dimensionless or expressed in dB, not in mW.
  5. [Eq. (21)] The last two terms in the expression for I_2 are not typeset cleanly; the factorization involving (N−1)(N−2) and (N−1)(N−2)(N−3) should be checked and reformatted.
  6. [Appendix A, Eqs. (59), (61), (65)] There are several typographical issues with subscripts and exponents in the intermediate integrals; for example, the exponent of θ_e in Eq. (65) appears as k+f without the subscript, and the placement of subscripts in Eqs. (59) and (61) should be corrected.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular step found; the closed-form ergodic and outage expressions are derived self-containedly under an explicitly labeled Gamma approximation, and the self-citations are not load-bearing.

full rationale

The central derivation chain is self-contained. Proposition 1 obtains the Gamma approximation for the coherent sum of Nakagami-m magnitudes with explicit first and second moments (Eqs. 19-21), and Propositions 2-4 integrate that approximate distribution over the case regions defined in Section II.B (Eqs. 28, 33, 34); no parameter in these expressions is fitted to the target ergodic rate, and the Monte Carlo validation is independent of the closed forms. Similarly, the PU outage expression in Proposition 5 (Eq. 39) follows algebraically from the definition P_out = Pr(γ_p <= γ_hat_p) in Eq. 38, so it is a definition-based calculation, not a fitted prediction. The self-citations to [7] for the CR-SIC decoding concept and to [29,30] for the moment-matching technique are not load-bearing: the present paper restates the moments in its proof and verifies the approximation numerically. A separate correctness concern, not a circularity, is that Eq. 40 counts Case II realizations as PU outages although Section II.B states that in Case II the STAR-RIS adjusts rho_t to restore PU service; this weakens the QoS-guarantee claim but does not make any prediction equivalent to its input by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters are fitted to data; all quantities are system parameters or computed moments. The 'CR functionality' is a configuration policy, not a new physical entity, so no invented entities are listed. The main axioms are the fading and hardware assumptions that make the analytical framework tractable.

assumptions (4)
  • domain assumption User-to-RIS channels follow Nakagami-m fading with shape m and scale Omega, while RIS-to-BS channels are deterministic with |h^H|=1.
    Invoked in Section III.A to make the effective channel gains (16)-(17) sums of Nakagami-m magnitudes, enabling the Gamma approximation.
  • domain assumption For sufficiently large N, the squared coherent sum of Nakagami-m magnitudes can be approximated as a Gamma random variable via moment matching.
    Used in Proposition 1 and throughout the rate derivations; this approximation is taken from prior work [29,30] and is not proven tight for the parameter ranges considered.
  • domain assumption The decoding order x_s1 -> x_p -> x_s2 is adopted.
    Section II.B states this order is inherited from [28] and is essential for the SINR expressions (3)-(5).
  • ad hoc to paper The STAR-RIS can be partitioned into two independent regions with independent power splitting factors rho_r and rho_t, including the ability to act as an absorber.
    This is the proposed 'CR functionality' mechanism (Section II.A). It is a hardware capability that is assumed without physical validation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Cognitive-Radio Functionality: A Novel Configuration for STAR-RIS assisted RSMA Networks." pith.science (2026). https://pith.science/paper/LRZUTCAP

@misc{pith2026250524583,
  author       = {Pith},
  title        = {Pith review of: Cognitive-Radio Functionality: A Novel Configuration for STAR-RIS assisted RSMA Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LRZUTCAP}},
  note         = {Machine review of arXiv:2505.24583}
}
read the original abstract

Cognitive radio rate-splitting multiple access (CR-RSMA) has emerged as a promising multiple access framework that can efficiently manage interference and adapt dynamically to heterogeneous quality-of-service (QoS) requirements. To effectively support such demanding access schemes, programmable wireless environments have attracted considerable attention, especially through simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), which can enable full-space control of signal propagation in asymmetric user deployments. In this paper, we propose the cognitive radio (CR) functionality for STAR-RIS-assisted CR-RSMA systems, leveraging the unique capability of the STAR-RIS to combine element and power splitting for adaptive control of transmission and reflection in CR scenarios. Specifically, the proposed CR functionality partitions the STAR-RIS into two regions independently controlling the transmission and reflection of signals, simultaneously ensuring the required QoS for the primary user and enhancing the performance of the secondary user. To accurately characterize the system performance, we derive analytical expressions for the ergodic rate of the secondary user and the outage rate of the primary user under Nakagami-m fading. Finally, simulation results show that the proposed approach effectively manages interference, guarantees the QoS of the primary user, and significantly improves the throughput of the secondary user, highlighting STAR-RIS as an efficient solution for CR-RSMA-based services.

Figures

Figures reproduced from arXiv: 2505.24583 by the authors.

Figure 1
Figure 1. System overview. to the PU’s and SU’s different requirements. This allows the proportion of power reflected or transmitted by each part to be controlled, thus modifying the channel gains of both the PU and SU and maximizing the efficiency of the CR￾RSMA scheme. In more detail, the use of the STAR-RIS as an absorber is introduced and modeled by the parameters ρr and ρt, which are power splitting factors indicating th… view at source ↗
Figure 2
Figure 2. Ergodic rate and outage rate versus α for various β values with ω = 0.2. balance between transmission and reflection achieved through the CR functionality of the STAR-RIS. Figs. 3a and 3b illustrate the impact of the PU’s target threshold γˆp on the performance of the proposed STAR-RIS￾assisted CR-RSMA system, evaluated for different values of the element allocation parameter β, under a deployment where the STAR-RIS… view at source ↗
Figure 4
Figure 4. Ergodic rate and outage rate versus ω for various β values. Finally, Figs. 4a and 4b illustrate the influence of the spatial deployment parameter ω on the performance of the proposed STAR-RIS-assisted CR-RSMA system, evaluated for different values of the element allocation parameter β. Specifically, Fig. 4a shows the ergodic rate of the SU, while Fig. 4b presents the corresponding outage rate of the PU. As it can be… view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

32 extracted references · 29 canonical work pages

  1. [1]

    A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends,

    Z. Ding, X. Lei, G. K. Karagiannidis, R. Schober, J. Yuan, and V . K. Bhargava, “A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends,”IEEE Trans. V eh. Technol., vol. 35, no. 10, pp. 2181–2195, 2017

    work page 2017

  2. [2]

    Rate-splitting multiple access for 6G—Part I: Principles, applications and future works,

    A. Mishra, Y . Mao, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for 6G—Part I: Principles, applications and future works,”IEEE Commun. Lett., vol. 26, no. 10, pp. 2232–2236, 2022

    work page 2022

  3. [3]

    Enhancing the physical layer security of non-orthogonal multiple access in large-scale networks,

    Y . Liu, Z. Qin, M. Elkashlan, Y . Gao, and L. Hanzo, “Enhancing the physical layer security of non-orthogonal multiple access in large-scale networks,”IEEE Trans. Wirel. Commun., vol. 16, no. 3, pp. 1656–1672, 2017

    work page 2017

  4. [4]

    Multiple Access in the Era of Distributed Computing and Edge Intelligence,

    N. G. Evgenidis, N. A. Mitsiou, V . I. Koutsioumpa, S. A. Tegos, P. D. Diamantoulakis, and G. K. Karagiannidis, “Multiple Access in the Era of Distributed Computing and Edge Intelligence,”Proc. IEEE, vol. 112, no. 9, pp. 1497–1526, 2024

    work page 2024

  5. [5]

    On the performance of uplink rate-splitting multiple access,

    S. A. Tegos, P. D. Diamantoulakis, and G. K. Karagiannidis, “On the performance of uplink rate-splitting multiple access,”IEEE Commun. Lett., vol. 26, no. 3, pp. 523–527, Mar. 2022

    work page 2022

  6. [6]

    A new rate splitting strategy for uplink CR-NOMA systems,

    H. Liu, Z. Bai, H. Lei, G. Pan, K. J. Kim, and T. A. Tsiftsis, “A new rate splitting strategy for uplink CR-NOMA systems,”IEEE Trans. V eh. Technol., vol. 71, no. 7, pp. 7947–7951, 2022

    work page 2022

  7. [7]

    On the ergodic rate of cognitive radio inspired uplink multiple access,

    Y . Xiao, S. A. Tegos, P. D. Diamantoulakis, Z. Ma, and G. K. Karagian- nidis, “On the ergodic rate of cognitive radio inspired uplink multiple access,”IEEE Commun. Lett., vol. 27, no. 1, pp. 95–99, 2023

    work page 2023

  8. [8]

    Reconfigurable intelligent surface aided multi-user communications: State-of-the-art techniques and open issues,

    M. Munochiveyi, A. C. Pogaku, D.-T. Do, A.-T. Le, M. V oznak, and N. D. Nguyen, “Reconfigurable intelligent surface aided multi-user communications: State-of-the-art techniques and open issues,”IEEE Access, vol. 9, pp. 118 584–118 605, 2021

    work page 2021

Show all 32 references
  1. [9]

    A new wireless communication paradigm through software- controlled metasurfaces,

    C. Liaskos, S. Nie, A. Tsioliaridou, A. Pitsillides, S. Ioannidis, and I. Akyildiz, “A new wireless communication paradigm through software- controlled metasurfaces,”IEEE Commun. Mag., vol. 56, no. 9, pp. 162– 169, 2018

    2018

  2. [10]

    Communication mod- els for reconfigurable intelligent surfaces: From surface electromagnetics to wireless networks optimization,

    M. Di Renzo, F. H. Danufane, and S. Tretyakov, “Communication mod- els for reconfigurable intelligent surfaces: From surface electromagnetics to wireless networks optimization,”Proceedings of the IEEE, vol. 110, no. 9, pp. 1164–1209, 2022

    2022

  3. [11]

    On the distribution of the sum of double- Nakagami-mrandom vectors and application in randomly reconfigurable surfaces,

    S. A. Tegos, D. Tyrovolas, P. D. Diamantoulakis, C. K. Liaskos, and G. K. Karagiannidis, “On the distribution of the sum of double- Nakagami-mrandom vectors and application in randomly reconfigurable surfaces,”IEEE Trans. V eh. Technol., vol. 71, no. 7, pp. 7297–7307, 2022

    2022

  4. [12]

    Reconfigurable intelligent surfaces: Principles and opportunities,

    Y . Liu, X. Liu, X. Mu, T. Hou, J. Xu, M. Di Renzo, and N. Al-Dhahir, “Reconfigurable intelligent surfaces: Principles and opportunities,”IEEE Commun. Surv. Tutor ., vol. 23, no. 3, pp. 1546–1577, 2021

    2021

  5. [13]

    STAR: Simultaneous transmission and reflection for 360° coverage by intelligent surfaces,

    Y . Liu, X. Mu, J. Xu, R. Schober, Y . Hao, H. V . Poor, and L. Hanzo, “STAR: Simultaneous transmission and reflection for 360° coverage by intelligent surfaces,”IEEE Wirel. Commun., vol. 28, no. 6, pp. 102–109, 2021

    2021

  6. [14]

    Zero-energy reconfigurable intelligent surfaces (zeRIS),

    D. Tyrovolas, S. A. Tegos, V . K. Papanikolaou, Y . Xiao, P.-V . Mekikis, P. D. Diamantoulakis, S. Ioannidis, C. K. Liaskos, and G. K. Karagian- nidis, “Zero-energy reconfigurable intelligent surfaces (zeRIS),”IEEE Trans. Wirel. Commun., vol. 23, no. 7, pp. 7013–7026, 2024

    2024

  7. [15]

    Empowering programmable wireless environments with optical anchor- based positioning,

    D. Tyrovolas, D. Bozanis, S. A. Tegos, V . K. Papanikolaou, P. D. Diamantoulakis, C. K. Liaskos, R. Schober, and G. K. Karagiannidis, “Empowering programmable wireless environments with optical anchor- based positioning,”IEEE Netw., vol. 39, no. 1, pp. 14–20, 2025

    2025

  8. [16]

    Active reconfigurable intelligent surface-aided wireless communications,

    R. Long, Y .-C. Liang, Y . Pei, and E. G. Larsson, “Active reconfigurable intelligent surface-aided wireless communications,”IEEE Trans. Wirel. Commun., vol. 20, no. 8, pp. 4962–4975, 2021

    2021

  9. [17]

    Intelligent reflecting surface-assisted cognitive radio system,

    J. Yuan, Y .-C. Liang, J. Joung, G. Feng, and E. G. Larsson, “Intelligent reflecting surface-assisted cognitive radio system,”IEEE Trans. Com- mun., vol. 69, no. 1, pp. 675–687, 2021

    2021

  10. [18]

    Reconfigurable intelligent surface-aided cognitive noma networks: Performance analysis and deep learning evaluation,

    T.-H. Vu, T.-V . Nguyen, D. B. d. Costa, and S. Kim, “Reconfigurable intelligent surface-aided cognitive noma networks: Performance analysis and deep learning evaluation,”IEEE Trans. Wirel. Commun., vol. 21, no. 12, pp. 10 662–10 677, 2022

    2022

  11. [19]

    Joint transmit precoding and reflect beamforming design for IRS-assisted mimo cog- nitive radio systems,

    W. Jiang, Y . Zhang, J. Zhao, Z. Xiong, and Z. Ding, “Joint transmit precoding and reflect beamforming design for IRS-assisted mimo cog- nitive radio systems,”IEEE Trans. Wirel. Commun., vol. 21, no. 6, pp. 3617–3631, 2022

    2022

  12. [20]

    Reliability performance of RIS- CR-NOMA based short-packet communications,

    C. Xia, Z. Xiang, H. Liu, and J. Meng, “Reliability performance of RIS- CR-NOMA based short-packet communications,” in2024 IEEE 16th 13 International Conference on Advanced Infocomm Technology (ICAIT), 2024, pp. 60–67

    2024

  13. [21]

    Ris-assisted wireless powered mec: Multiple access design and resource allocation,

    L. Lv, H. Luo, L. Yang, Z. Ding, A. Nallanathan, N. Al-Dhahir, and J. Chen, “Ris-assisted wireless powered mec: Multiple access design and resource allocation,”IEEE Trans. Wirel. Commun., vol. 24, no. 2, pp. 984–1000, 2025

    2025

  14. [22]

    Intelligent reflecting surface-assisted cognitive radio-inspired rate- splitting multiple access systems,

    P. Liu, G. Jing, H. Liu, L. Yang, and T. A. Tsiftsis, “Intelligent reflecting surface-assisted cognitive radio-inspired rate- splitting multiple access systems,”Digit. Commun. Netw., vol. 9, no. 3, pp. 655–666, 2023. [Online]. Available: https://www.sciencedirect.com/science/a...

    2023

  15. [23]

    STAR-RIS in cognitive radio networks,

    H. Li, Y . Liu, X. Mu, Y . Chen, P. Zhiwen, and X. You, “STAR-RIS in cognitive radio networks,”IEEE Trans. Wireless Commun., 2024

    2024

  16. [24]

    Outage performance analysis of STAR-RIS assisted CR-NOMA networks,

    S. Wang, B. Lian, X. Gao, X. Li, and M. Zeng, “Outage performance analysis of STAR-RIS assisted CR-NOMA networks,” inGLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 4411– 4417

    2023

  17. [25]

    Reliability and security of cr-star-ris-noma-assisted iot networks,

    X. Li, J. Zhang, C. Han, W. Hao, M. Zeng, Z. Zhu, and H. Wang, “Reliability and security of cr-star-ris-noma-assisted iot networks,”IEEE Internet Things J., vol. 11, no. 17, pp. 27 969–27 980, 2024

    2024

  18. [26]

    Performance Analysis of STAR-RIS-CR-NOMA-Based Consumer IoT Networks for Resilient Industry 5.0,

    X. Li, X. Gao, L. Yang, H. Liu, J. Wang, and K. M. Rabie, “Performance Analysis of STAR-RIS-CR-NOMA-Based Consumer IoT Networks for Resilient Industry 5.0,”IEEE Trans. Consum. Electron., vol. 70, no. 1, pp. 1380–1391, 2024

    2024

  19. [27]

    Improved spectral efficiency in STAR-RIS aided uplink communication using rate splitting multiple access,

    M. Katwe, K. Singh, B. Clerckx, and C.-P. Li, “Improved spectral efficiency in STAR-RIS aided uplink communication using rate splitting multiple access,”IEEE Trans. Wirel. Commun., vol. 22, no. 8, pp. 5365– 5382, 2023

    2023

  20. [28]

    A new rate splitting strategy for uplink CR-NOMA systems,

    H. Liu, Z. Bai, H. Lei, G. Pan, K. J. Kim, and T. A. Tsiftsis, “A new rate splitting strategy for uplink CR-NOMA systems,”IEEE Trans. V ehicular Tech., vol. 71, no. 7, pp. 7947–7951, 2022

    2022

  21. [29]

    On the performance of HARQ in IoT networking with UA V-mounted reconfigurable intelligent surfaces,

    D. Tyrovolas, P.-V . Mekikis, S. A. Tegos, P. D. Diamantoulakis, C. K. Liaskos, and G. K. Karagiannidis, “On the performance of HARQ in IoT networking with UA V-mounted reconfigurable intelligent surfaces,” in 2022 IEEE 95th V ehicular Technology Conference: (VTC2022-Spring), ...

    2022

  22. [30]

    On the per- formance of RIS-assisted networks with HQAM,

    T. K. Oikonomou, D. Tyrovolas, S. A. Tegos, P. D. Diamantoulakis, P. Sarigiannidis, C. Liaskos, and G. K. Karagiannidis, “On the per- formance of RIS-assisted networks with HQAM,” in2024 Joint Eu- ropean Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), ...

    2024

  23. [31]

    I. S. Gradshteyn and I. M. Ryzhik,Table of integrals, series, and products. Academic press, 2014

    2014

  24. [32]

    A. P. Prudnikov, Y . A. Brychkov, and O. I. Marichev,Integrals and Series. V olume 1: Elementary Functions. Moscow: Taylor & Francis, 1986

    1986

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.