Multiple D2D Multicasts in Underlay Cellular Networks

Ajay Bhardwaj; Samar Agnihotri

REVIEW 4 major objections 5 minor 45 references

Multiple D2D Multicasts in Underlay Cellular Networks

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper claims that splitting the joint resource allocation problem into channel assignment and power control lets multiple D2D multicast groups share a cellular uplink while maximizing sum throughput under QoS and power limits.

desk verdict A useful D2D multicast heuristic with unsupported optimality claims; the Gk=2 corner-search proof fails, but the two-step scheme is worth refereeing as a major revision. read the letter →

arxiv 1908.08866 v2 pith:HDYS353C submitted 2019-08-23 cs.NI cs.ITmath.IT

Ajay Bhardwaj , Samar Agnihotri This is my paper · ORCID

classification cs.NIcs.ITmath.IT

keywords device-to-devicemulticastunderlaycellularnetworkschannelallocationpowersumthroughputmaximizationMINLPQoSconstraintsinterferencemanagement

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

Underlay device-to-device (D2D) multicast lets nearby phones broadcast popular content directly to a group while reusing a cellular uplink channel, but the mutual interference between cellular users and multicast groups can erase the throughput gain. The paper studies the general setting in which any number of multicast groups can share any cellular channel, and tries to maximize total system throughput under per-user QoS constraints and per-transmitter power caps. Because the joint problem is a mixed-integer nonlinear program and therefore computationally intractable in general, the authors split it into a channel-allocation stage and a power-allocation stage. They claim that their two-step schemes—interference-aware and outage-aware channel allocation, each followed by the STIM power-allocation algorithm—outperform existing random, greedy, and bipartite-matching resource allocations in sum throughput, and that for one or two multicast groups per channel the power-allocation rules are optimal or near-optimal. If these claims hold, operators can support multicast video and file distribution in dense cells without sacrificing cellular QoS.

What carries the argument

The load-bearing object is the geometry of the feasible power region for a channel shared by a CU and one or two multicast groups. For one group per channel, Lemmas 2 and 3 place the optimum on the boundary of the region, at a corner where either the CU or the group transmitter transmits at maximum power, making bipartite matching plus a corner check sufficient. For two groups per channel, Lemma 5 asserts that the sum-rate function is quasi-convex on the boundary of the feasible region, so the maximum lies among the corner points generated by intersecting the three SINR planes (equations (16a)-(16c)) with the faces of the power cube; the paper lists seven candidate regions and evaluates those points. For the general many-group case, STIM replaces geometric corner search with a fixed-point iteration adapted from the GDCPC algorithm, updating each transmitter's power by the ratio of its target SINR to its current SINR and capping interference to the shared CU. This combination reduces an NP-hard MINLP to a polynomial-time channel assignment followed by a small corner search or an iterative power update.

What would settle it

Pick a random channel realization with two multicast groups and one cellular user, compute the sum rate on a fine grid over the boundary faces of the feasible power region, and compare the best interior boundary point with the best of the seven corner-region candidates from Section V-B; if any interior boundary point beats the corner candidates, Lemma 5's quasi-convexity is false and the corner-search optimality claim fails.

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Extended reading notes

Core claim

The central claim is that the intractable joint channel-and-power allocation problem for multiple D2D multicast groups sharing uplinks can be decomposed without losing much throughput. For each channel, the base station first admits the set of multicast groups that will be allowed to share that channel, using either an interference-aware rule (keep mutually interfering groups apart, admit only groups that add positive throughput) or an outage-aware rule (choose channels that minimize outage probabilities subject to an aggregate interference budget). The STIM power-allocation stage then updates each group transmitter's power through a modified GDCPC iteration so that SINR targets are met and the shared CU is protected. For the special cases of one or two groups per channel, the paper proves that the optimal power vector lies on the boundary of the feasible power region and, for two groups, that the sum-rate function is quasi-convex on that boundary, so the maximum can be located by checking only corner points. The numerical section concludes that the proposed schemes outperform existing resource allocation schemes in sum throughput.

Load-bearing premise

The claim that the corner search is optimal for two multicast groups per channel rests on the sum-rate function being quasi-convex on the boundary of the feasible power region; if that property fails on realistic channel gains, the corner search is only an uncalibrated heuristic and the paper's strongest optimality claim collapses.

Editorial extensions

If this is right

For exactly one multicast group per channel, the optimal power allocation is the corner solution used with bipartite matching, so no continuous power search is needed at all.
For exactly two groups per channel, testing the seven listed corner candidates gives the maximum sum rate, provided the quasi-convexity premise holds; this makes the two-group case computationally cheap.
The STIM iteration makes the general problem tractable and, in simulations, yields sum throughput that first increases and then decreases with the maximum D2D transmit power, with the best point near 15 dBm in the tested configuration.
As the number of multicast groups grows, sum throughput saturates once co-channel interference blocks further admission, and both IA-STIM and OA-STIM beat the random, greedy, and bipartite baselines in most simulated regimes.
Higher CU QoS thresholds reduce the room for D2D sharing, so the achievable gain shrinks as the CU rate requirement rises; the proposed schemes still outperform the baselines across the reported range.

Reading between the lines

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

A natural testable extension is to run the same two-step scheme in a multi-cell layout with inter-cell interference, since the paper only treats a single cell and lists multi-cell operation as future work.
Because Lemma 5's proof simplifies the SINR expression to an interference-limited form and drops the noise term at one step, one can stress-test it numerically by fine-grid search on the boundary; if a non-corner boundary point wins, the corner-search claim is only heuristic.
The simulation peak near 15 dBm suggests a system-design rule the authors do not state: even when hardware maximum power is higher, capping D2D multicast transmitters near that level can maximize sum throughput by limiting co-channel interference.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a plausible engineering paper whose formal optimality claims don't survive contact with the appendices. The reader's report is right about the proofs, and the stress-test on Lemma 5 hits the load-bearing flaw.

What's actually new: the formulation lets an arbitrary number of D2D multicast groups share one CU uplink channel under individual QoS constraints, and the paper proposes a two-step channel/power allocation framework (IA-STIM and OA-STIM). That is a reasonable extension of existing D2D resource allocation work, and the simulations show consistent gains over the baselines. The Hungarian matching for the single-MG case is standard but correctly applied; the corner-search idea for two MGs is a useful practical heuristic.

The soft spots are concentrated in the proofs. Appendix G is the serious one. Lemma 5 asserts the sum-rate function is quasi-convex on the feasible boundary so the maximum is at corners, but the proof uses condition (G.1) as if it characterized quasi-convexity, then assumes quasi-convexity is preserved under addition. Both steps are false: the individual SINR terms are quasi-convex, but sums of quasi-convex functions need not be. So the corner-search result is unproven as an optimality claim. The paper sometimes calls it an approximate solution, which is the honest description, but the abstract and Lemma 5 say more.

Appendix C has a scaling error: multiplying both powers by alpha > 1 can leave the feasible box, and the rate comparison ignores that both signal and interference scale together. Appendix A's bound for Proposition 1 drops the noise term and then replaces a sum of logs of sums with a sum of logs of maxima; the inequality direction doesn't follow from the text. None of these kill the heuristic, but they kill the claim that the proposed power allocation maximizes system throughput.

One additional concern: the outage-aware allocation relies on a PPP-based interference formula (Lemma 1), but the simulation model is a fixed finite cell with uniformly distributed nodes. That formula is not obviously valid for the simulated topology, so the OA-STIM numerical results are shakier than the presentation suggests. The citation pattern is fine; the self-citation to the authors' earlier exclusion-zone paper is not load-bearing.

Bottom line: readers building practical D2D multicast resource allocation systems will find useful algorithms and benchmarks here. The paper deserves a serious referee, but it needs major revision: either the optimality claims must be properly proven, or they must be honestly downgraded to heuristics throughout, including the abstract.

Referee Report

4 major / 5 minor

Summary. The paper studies uplink underlay D2D multicast in a single cell, where multiple multicast groups may share a cellular user's channel. It formulates a sum-throughput maximization problem as a mixed-integer nonlinear program (P1), then proposes a two-stage solution: channel allocation algorithms (interference-aware IA-STIM and outage-aware OA-STIM) followed by power allocation. For the special cases of one or two multicast groups per channel, it claims optimal corner-based power allocation; for the general case, it proposes an iterative STIM power-control algorithm. The numerical section compares the proposed schemes with bipartite matching, random allocation, and greedy heuristics, reporting higher sum throughput under the chosen scenarios.

Significance. If the optimality claims were correct, the paper would provide a useful decomposition of a hard MINLP into a polynomial-time channel allocation step and provably optimal power allocation for small channel-sharing groups, together with a stochastic-geometry outage expression and extensive simulations. The paper's strengths include a clearly stated system model, a standard outage derivation in Lemma 1, and a broad set of numerical comparisons. However, the central load-bearing proofs for the claimed optimality of the corner-search power allocation are invalid, and the general STIM algorithm does not optimize the stated objective. These issues undermine the paper's main theoretical contribution, so the current manuscript does not meet the standard for publication.

major comments (4)

[Section V.B, Lemma 5 and Appendix G] The proof of Lemma 5 does not establish that the sum-rate function is quasi-convex on the boundary of the feasible power region. Appendix G first invokes a first-order condition (G.1) that is, in fact, a valid characterization for differentiable quasi-convex functions, but the proof then makes two unjustified steps: it assumes that a sum of quasi-convex functions is quasi-convex, which is false in general, and it analyzes a sum of SINR ratios rather than the actual objective, which is a sum of log(1+SINR) terms. The derivation from (G.4) to (G.5) essentially restates the target inequality without proving it. Consequently, the claim that the maximum of the sum rate over the polytope defined by (16a)–(16c) and the power bounds occurs at a corner point is unproven. This is load-bearing because the corner-search algorithm for Gk=2 is presented as optimal. The paper's own Section VI.B later calls corner search 'a good heuristic' and 'a lower bound to the optimal throughput,' which directly contradicts Lemma 5 and the abstract's optimality claim.
[Section V.A, Lemmas 2 and 3, Appendices C and D] The proofs of Lemmas 2 and 3, which assert optimality at the corners for the Gk=1 case, are invalid as written. Appendix C scales both transmit powers by α>1 and claims C(αP_i,αP_j) > C(P_i,P_j). In an interference-limited setting (which the paper assumes in Proposition 1 and Appendix A), a common scaling of both powers leaves all SINRs invariant up to the noise term, so the displayed expressions for β1 and β2 do not imply the claimed inequality. The subsequent argument then asserts convexity of a product-like quantity T(P_i,P_j) in Appendix D without deriving it from the actual sum-rate objective. Thus the corner-optimality result for the single-MG case is not proven by the material supplied.
[Section V.C, Algorithm 3, and equations (17)–(19)] The STIM power-allocation algorithm for the general case does not maximize the throughput objective of Problem P1. Equation (17) merely caps each MGTX's power by an evenly split interference budget, and the update rule (18)–(19) iterates toward meeting SINR thresholds. No objective function appears in this procedure, so there is no mechanism by which it 'maximizes the system throughput,' as claimed in the abstract and in Section I.B. The numerical results show that the proposed scheme outperforms the chosen baselines under certain parameters, but outperforming heuristics does not establish throughput optimality or provide a performance guarantee relative to the optimum of P1.
[Section VI.B, Figure 4 and surrounding text] The manuscript is internally inconsistent about the status of the corner-search power allocation. Section V.B and the abstract present it as optimal for Gk=2, while Section VI.B states that the corner-search method 'tries to support minimum SINR thresholds' and 'may serve as a good heuristic to efficiently provide a lower bound to the optimal throughput.' A heuristic lower bound is not an optimal solution. The authors need to either provide a valid proof of optimality or explicitly reframe all such claims as heuristic, with appropriate empirical validation against exhaustive search for small instances.

minor comments (5)

[Section IV, Algorithms 1 and 2] The pseudocode contains notation that is difficult to follow: in Algorithm 1, the set G_k of multicast groups sharing channel k is updated using objects that appear to be MGs, while in Algorithm 2 the update 'G_k = G_k ∪ k' mixes a set of MGs with a CU index k. These lines should be rewritten with explicit indices so that the set-membership operations are unambiguous.
[Appendix H, first paragraph] The phrase 'γth_r = γth_{r'} = γth_c = 3 watt or 5 dB' is dimensionally inconsistent: 3 watts is a linear power value and 5 dB is a logarithmic ratio; they cannot be equal. The intended units and conversion should be stated correctly.
[Section II.B and Lemma 1 (Appendix B)] The outage probability formula in (10) uses intensities λ_c and λ_g, but these are not defined in the system model in Section II. The relation of these intensities to the PPP models of CUs and MGs should be stated explicitly before Lemma 1 is used.
[Section II.C, equations (12)–(14)] In the Gk=2 SINR constraints, the receivers r and r' are introduced without specifying that they are the worst-case receivers of MG 1 and MG 2, respectively; this should be clarified to match the definition in (3).
[Section I.B and Section IV] The novelty claim 'for the first time considers the general problem of optimal resource allocation for multiple D2D multicasts' should be tempered or qualified, since references [10], [28], and [29] address closely related underlay D2D multicast resource allocation, and the authors should state explicitly what is new beyond those works and beyond their own conference papers (WCNC 2016/2017) and preprint [32].

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the only self-citation is background and the central derivations rest on external models and baselines.

full rationale

I find no circular reduction in the claimed derivation chain. The MINLP formulation in (9) follows from the stated system model and QoS constraints, and the proposed channel-allocation and power-allocation schemes are evaluated against external baselines rather than fitted to the authors' own outputs. The outage probability in Lemma 1 is derived from stochastic geometry tools with an external citation [45], the G_k=1 power-allocation lemmas are argued from feasible-region geometry, and the general STIM power update is based on the external GDCPC algorithm [42]. The single self-citation [32] to the authors' prior work on exclusion zones appears only as background in the related-work discussion and is not used to justify the paper's optimality or throughput claims. One mathematical concern is that Appendix G's proof of Lemma 5 does not rigorously establish quasi-convexity of the sum-rate function, since quasi-convexity of a sum does not follow from quasi-convexity of its terms; however, this is a proof gap and correctness risk, not a circular step, because the claimed conclusion is not assumed as an input or made equivalent to an earlier fitted parameter. Therefore, the paper does not exhibit self-definitional, fitted-input-as-prediction, or self-citation-load-bearing circularity, and the appropriate score is low. The most notable weakness is the unproven Lemma 5, which affects the strength of the G_k=2 corner-search optimality claim, but it is not circularity under the specified criteria.

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

The schemes depend on several hand-chosen thresholds that are not specified, and the outage formula is derived under a Poisson field assumption that does not match the simulation model. The optimality lemmas are asserted with invalid proofs, and the general power allocation does not maximize throughput. No parameters are fitted to data in the sense of calibration to measurements; the unspecified thresholds are ad hoc choices.

free parameters (4)

gamma_th (interference-aware channel gain ratio threshold)
In Algorithm 1, two MGs are allowed to share a channel only if the ratio of their channel gains exceeds this threshold. Its value is not given in the paper and directly controls how many MGs share a channel.
Theta_th_g (outage probability threshold)
In Algorithm 2, an MG is admitted to a channel only if its outage probability is below this threshold; no value is specified.
I_k^th (CU interference threshold)
Used in Algorithm 2 and STIM power derivation as the maximum aggregate interference the CU can tolerate; the paper does not state how it is set or calibrated.
lambda_c, lambda_g (PPP intensities in outage formula)
Lemma 1 assumes interfering CUs and MGs form Poisson point processes with intensities lambda_c and lambda_g; the simulation model is a finite cell with uniform placement, so these intensities are ad hoc and not mapped to simulation parameters.

assumptions (4)

domain assumption Interfering CUs and MGs form independent homogeneous Poisson point processes over the plane
Used in the derivation of the outage probability in Lemma 1 (Appendix B), but the simulations use a finite circular cell with uniformly distributed nodes; the PPP approximation is not validated for this scenario.
domain assumption The throughput contribution Delta R_g,k of an MG can be evaluated independently of the co-channel MG set in channel allocation
Algorithm 1 sorts MGs by individual gains and only checks pairwise channel-gain ratios, ignoring the joint SINR of the assembled group. This keeps the problem tractable but is an approximation.
ad hoc to paper The sum-rate function is quasi-convex on the boundary of the feasible power region
The corner-search optimality for Gk=2 depends on this lemma; the proof in Appendix G is invalid because sums of linear-fractional SINR functions are not necessarily quasi-convex.
domain assumption For two MGs per channel, the augmented matrix of the SINR inequalities (12)-(14) is full rank
Stated in Section V.B to guarantee a unique intersection of the power planes; not shown to hold for all channel realizations.

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Cite this review

Pith. "Pith review of Multiple D2D Multicasts in Underlay Cellular Networks." pith.science (2026). https://pith.science/paper/HDYS353C

@misc{pith2026190808866,
  author       = {Pith},
  title        = {Pith review of: Multiple D2D Multicasts in Underlay Cellular Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HDYS353C}},
  note         = {Machine review of arXiv:1908.08866}
}

read the original abstract

Multicasting for disseminating popular data is an interesting solution for improving the energy and spectral efficiencies of cellular networks. To improve the achievable performance of such networks, underlay device-to-device (D2D) multicast communication offers a practical solution. However, despite significant potential for providing higher throughput and lower delay, implementing underlay D2D multicast communication poses several challenges, such as mutual interference among cellular users (CUs) and D2D multicast groups (MGs), and overhead signaling to provide channel state information, that may limit potential gains. We study a scenario where multiple D2D multicast groups may share a CU's uplink channel. We formulate an optimization problem to maximize the achievable system throughput while fulfilling quality of service (QoS) requirements of every CU and D2D MGs, subject to their corresponding maximum transmit power constraints. The formulated optimization problem is an instance of mixed integer non-linear programming (MINLP) problem, which is computationally intractable, in general. Therefore, to find a feasible solution, we propose a pragmatic two-step process of channel allocation and power allocation. In the first-step, we propose a channel allocation algorithm, which determines the subset of MGs that may share a channel subject to criteria based on two different parameters: interference and outage probabilities. Then, we propose an algorithm to allocate power to these MG subsets that maximizes the system throughput, while satisfying transmit power constraint. Numerical results show the efficacy of proposed approach in terms of higher achievable sum throughput and better spectrum efficiency with respect to various existing schemes.

Figures

Figures reproduced from arXiv: 1908.08866 by the authors.

**Figure 1.** Illustration of the D2D integrated LTE-A network. Cellular eNB, CUs and D2D users are uniformly distributed in network, the [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗

**Figure 2.** Power region when two multicast group shares the resources with one cellular user. [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗

**Figure 3.** The sum throughput variation with number of receivers in a MG, when exactly one MG share the channel with a CU, [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗

Figures from the paper (13 more)

**Figure 4.** Figure 4: The sum throughput variation with number of receivers in a MG, when exactly two multicast groups share resources with a cellular [PITH_FULL_IMAGE:figures/full_fig_p023_4.png]

**Figure 5.** Figure 5: The sum throughput variation with geographical spread when when exactly two multicast groups share resources with a cellular [PITH_FULL_IMAGE:figures/full_fig_p024_5.png]

**Figure 6.** Figure 6: The sum throughput with varying no. of D2D multicast groups, C = 5. [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]

**Figure 7.** Figure 7: The sum throughput with varying geographical spread of MGs, C=5, G=20. [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]

**Figure 8.** Figure 8: The sum throughput as a function of each CU’s QoS requirement, C=5, G=20. [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]

**Figure 9.** Figure 9: The sum Throughput as a function of maximum transmission power, C = 5 , G=20, R=500m. [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]

**Figure 10.** Figure 10: Feasible regions: (a) feasible region C1, (b) feasible region C2, (c) the optimal power allocation P ∗ i , P∗ j achieved on the boundary of W [PITH_FULL_IMAGE:figures/full_fig_p029_10.png]

**Figure 11.** Figure 11: Feasible region W for optimal power allocation P ∗ i , P∗ j With increasing α, C [PITH_FULL_IMAGE:figures/full_fig_p029_11.png]

**Figure 12.** Figure 12: The excess matrix and corresponding equality subgraph. [PITH_FULL_IMAGE:figures/full_fig_p031_12.png]

**Figure 13.** Figure 13: The excess matrix and corresponding equality subgraph with [PITH_FULL_IMAGE:figures/full_fig_p031_13.png]

**Figure 14.** Figure 14: The excess matrix and corresponding equality subgraph with [PITH_FULL_IMAGE:figures/full_fig_p031_14.png]

**Figure 15.** Figure 15: The original weighted matrix with optimal matching. [PITH_FULL_IMAGE:figures/full_fig_p031_15.png]

**Figure 16.** Figure 16: Example for Bipartite Graph matching algorithm with G = 5 and C = 5. [PITH_FULL_IMAGE:figures/full_fig_p032_16.png]

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

45 extracted references · 44 canonical work pages

[1]

Forecast and Trends, 2017-2022,

C. V . N. Index, “Forecast and Trends, 2017-2022,” Feb. 2019

work page 2017
[2]

Fifth report on international broadband data report,

F. C. Commission, “Fifth report on international broadband data report,” Jan. 2016

work page 2016
[3]

New physical layer features of 3GPP LTE Release-13 [Industry Perspectives],

P.-H. Kuo, “New physical layer features of 3GPP LTE Release-13 [Industry Perspectives],” IEEE Wireless Commun. , vol. 22, no. 4, 2015

work page 2015
[4]

Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey,

R. O. Afolabi, A. Dadlani, and K. Kim, “Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey,” IEEE Commun. Surveys & Tutorials , vol. 15, no. 1, 2013

work page 2013
[5]

Analyzing assisted ofﬂoading of cellular user sessions onto D2D links in unlicensed bands,

S. Andreev, O. Galinina, A. Pyattaev, K. Johnsson, and Y . Koucheryavy, “Analyzing assisted ofﬂoading of cellular user sessions onto D2D links in unlicensed bands,” IEEE JSAC, vol. 33, no. 1, 2015

work page 2015
[6]

D2D task ofﬂoading: A dataset-based Q&A,

D. Chatzopoulos, C. Bermejo, E. ul Haq, Y . Li, and P. Hui, “D2D task ofﬂoading: A dataset-based Q&A,” IEEE Commun. Magazine , vol. 57, no. 2, 2019

work page 2019
[7]

Device-to-Device Communications over 5G Systems: Standardization, Challenges and Open Issues,

G. Araniti, A. Raschell `a, A. Orsino, L. Militano, and M. Condoluci, “Device-to-Device Communications over 5G Systems: Standardization, Challenges and Open Issues,” in 5G Mobile Communications , Springer, 2017

work page 2017
[8]

A survey on predicting the popularity of web content,

A. Tatar, M. D. de Amorim, S. Fdida, and P. Antoniadis, “A survey on predicting the popularity of web content,” Journal of Internet Services and Applications , vol. 5, no. 1, 2014

work page 2014

Show all 45 references

[9]

Modeling, analysis, and optimization of multicast device-to-device transmissions,

X. Lin, R. Ratasuk, A. Ghosh, and J. G. Andrews, “Modeling, analysis, and optimization of multicast device-to-device transmissions,” IEEE Trans. on Wireless Commun. , vol. 13, no. 8, 2014

2014
[10]

A resource allocation scheme for D2D multicast with QoS protection in OFDMA-based systems,

B. Peng, C. Hu, T. Peng, Y . Yang, and W. Wang, “A resource allocation scheme for D2D multicast with QoS protection in OFDMA-based systems,” in Proc. IEEE PIMRC , London, UK, Sept. 2013

2013
[11]

Device-to-device communication in LTE-Advanced networks: A survey,

J. Liu, N. Kato, J. Ma, and N. Kadowaki, “Device-to-device communication in LTE-Advanced networks: A survey,” IEEE Commun. Surveys & Tutorials , vol. 17, no. 4, 2015

2015
[12]

In-band device-to-device communication in OFDMA cellular networks: A survey and challenges,

P. Mach, Z. Becvar, and T. Vanek, “In-band device-to-device communication in OFDMA cellular networks: A survey and challenges,” IEEE Commun. Surveys & Tutorials , vol. 17, no. 4, 2015

2015
[13]

Resource allocation for device-to-device Communication Underlaying Cellular Networks: An alternating optimization method,

W. Zhao and S. Wang, “Resource allocation for device-to-device Communication Underlaying Cellular Networks: An alternating optimization method,” IEEE Commun. Letters , vol. 19, no. 8, 2015

2015
[14]

Game-theoretic resource allocation methods for device-to-device communication,

L. Song, D. Niyato, Z. Han, and E. Hossain, “Game-theoretic resource allocation methods for device-to-device communication,” IEEE Wireless Commun., vol. 21, no. 3, 2014

2014
[15]

Performance analysis and resource allocation for cooperative D2D communication in cellular networks with multiple D2D pairs,

J. Lee and J. H. Lee, “Performance analysis and resource allocation for cooperative D2D communication in cellular networks with multiple D2D pairs,” IEEE Commun. Lett. , vol. 23, no. 5, 2019

2019
[16]

Resource allocation for 5G D2D multicast content sharing in social-aware cellular networks,

L. Feng, P. Zhao, F. Zhou, M. Yin, P. Yu, W. Li, and X. Qiu, “Resource allocation for 5G D2D multicast content sharing in social-aware cellular networks,” IEEE Commun. Magazine , vol. 56, no. 3, 2018

2018
[17]

Interference mitigation for multicast D2D communications underlay cellular networks,

D. D. Ningombam, C. G. Lee, and S. Shin, “Interference mitigation for multicast D2D communications underlay cellular networks,” in Proc. IEEE ICAIIC , Okinawa, Japan, March 2019

2019
[18]

Downlink resource sharing in multichannel device-to-device communication,

L. Ma, X. Deng, J. Wang, Y . Huang, and F. Shi, “Downlink resource sharing in multichannel device-to-device communication,” IEEE Wireless Commun. Lett. , 2019. 36

2019
[19]

CDMA2000 R© High rate broadcast packet data air interface design,

P. Agashe, R. Rezaiifar, and P. Bender, “CDMA2000 R© High rate broadcast packet data air interface design,” IEEE Commun. Magazine , vol. 42, no. 2, 2004

2004
[20]

Efﬁcient resource allocation for OFDMA multicast systems with fairness consideration,

D. T. Ngo, C. Tellambura, and H. H. Nguyen, “Efﬁcient resource allocation for OFDMA multicast systems with fairness consideration,” in Proc. on IEEE Radio and Wireless Symposium , CA, USA, Jan. 2009

2009
[21]

Multicast scheduling in cellular data networks,

H. Won, H. Cai, D. Y . Eun, K. Guo, A. Netravali, I. Rhee, and K. Sabnani, “Multicast scheduling in cellular data networks,” IEEE Trans. on Wireless Commun. , vol. 8, no. 9, 2009

2009
[22]

Layered multicast with inter-layer network coding for multimedia streaming,

M. Shao, S. Dumitrescu, and X. Wu, “Layered multicast with inter-layer network coding for multimedia streaming,” IEEE Trans. on Multimedia, vol. 13, no. 2, 2011

2011
[23]

Two-stage cooperative multicast transmission with optimized power consumption and guaranteed coverage,

Y . Zhou, H. Liu, Z. Pan, L. Tian, J. Shi, and G. Yang, “Two-stage cooperative multicast transmission with optimized power consumption and guaranteed coverage,” IEEE JSAC, vol. 32, no. 2, 2014

2014
[24]

Multimedia broadcast/multicast service (MBMS); Architecture and functional description,

3GPP TS 23.246 V13.3.0, “Multimedia broadcast/multicast service (MBMS); Architecture and functional description,” Mar. 2015

2015
[25]

Evolved multimedia broadcast/multicast service (eMBMS) in LTE-Advanced: overview and Rel-11 enhancements,

D. Lecompte and F. Gabin, “Evolved multimedia broadcast/multicast service (eMBMS) in LTE-Advanced: overview and Rel-11 enhancements,” IEEE Commun. Magazine , vol. 50, no. 11, 2012

2012
[26]

Efﬁcient incremental raptor decoding over BEC for 3GPP MBMS and DVB IP-Datacast Services,

T. Mladenov, S. Nooshabadi, and K. Kim, “Efﬁcient incremental raptor decoding over BEC for 3GPP MBMS and DVB IP-Datacast Services,” IEEE Trans. on Broadcasting , vol. 57, no. 2, 2011

2011
[27]

Joint delivery of unicast and E-MBMS services in LTE networks,

J. F. Monserrat, J. Calabuig, A. Fernandez-Aguilella, and D. Gomez-Barquero, “Joint delivery of unicast and E-MBMS services in LTE networks,” IEEE Trans. on Broadcasting , vol. 58, no. 2, 2012

2012
[28]

Particle swarm optimization based power allocation schemes of device-to-device multicast communication,

W. Gong and X. Wang, “Particle swarm optimization based power allocation schemes of device-to-device multicast communication,” Wireless Personal Communications, vol. 85, no. 3, 2015

2015
[29]

Optimal resource allocation in multicast device-to-device communications underlaying LTE networks,

H. Meshgi, D. Zhao, and R. Zheng, “Optimal resource allocation in multicast device-to-device communications underlaying LTE networks,” IEEE Trans. on V ehicular Techno., vol. 66, no. 9, 2017

2017
[30]

Network controlled D2D communications: Licensed or unlicensed spectrum?

F. Wu, H. Zhang, B. Di, J. Wu, and L. Song, “Network controlled D2D communications: Licensed or unlicensed spectrum?” in Proc. IEEE ICC . Shanghai, China: IEEE, May 2019

2019
[31]

Full-duplex device-to-device cellular networks: Power control and performance analysis,

H. V . Vu, N. H. Tran, and T. Le-Ngoc, “Full-duplex device-to-device cellular networks: Power control and performance analysis,” IEEE Trans. on V ehicular Techno., vol. 68, no. 4, 2019

2019
[32]

D2D multicast in underlay cellular networks with exclusion zones,

A. Bhardwaj and S. Agnihotri, “D2D multicast in underlay cellular networks with exclusion zones,” arXiv preprint arXiv:1905.00300 , 2019

1905 arXiv
[33]

Efﬁciency resource allocation for device-to-device underlay communication systems: A reverse iterative combinatorial auction based approach,

C. Xu, L. Song, Z. Han, Q. Zhao, X. Wang, X. Cheng, and B. Jiao, “Efﬁciency resource allocation for device-to-device underlay communication systems: A reverse iterative combinatorial auction based approach,” IEEE JSAC, vol. 31, no. 9, 2013

2013
[34]

Energy-aware resource allocation for device-to-device underlay communication,

F. Wang, C. Xu, L. Song, Q. Zhao, X. Wang, and Z. Han, “Energy-aware resource allocation for device-to-device underlay communication,” in Proc. IEEE ICC , Budapest, Hungary, Jun. 2013

2013
[35]

Single frequency-based device-to-device-enhanced video delivery for evolved multimedia broadcast and multicast services,

L. Militano, M. Condoluci, G. Araniti, A. Molinaro, A. Iera, and G.-M. Muntean, “Single frequency-based device-to-device-enhanced video delivery for evolved multimedia broadcast and multicast services,” IEEE Trans. on Broadcasting , vol. 61, no. 2, 2015

2015
[36]

Resource allocation for multicell device-to-device communications in cellular network: a game theoretic approach,

J. Huang, Y . Sun, J. Li, and Y . Zhao, “Resource allocation for multicell device-to-device communications in cellular network: a game theoretic approach,” in Proc. ICST MOBIMEDIA , China, May 2015

2015
[37]

Optimized smart grid energy procurement for LTE networks using evolutionary algorithms,

H. Ghazzai, E. Yaacoub, M.-S. Alouini, and A. Abu-Dayya, “Optimized smart grid energy procurement for LTE networks using evolutionary algorithms,” IEEE Trans. on V ehicular Techno., vol. 63, no. 9, 2014

2014
[38]

Resource allocation under channel uncertainties for relay-aided device-to-device communication underlaying LTE-A cellular networks,

M. Hasan, E. Hossain, and D. I. Kim, “Resource allocation under channel uncertainties for relay-aided device-to-device communication underlaying LTE-A cellular networks,” IEEE Trans. on Wireless Commun. , vol. 13, no. 4, 2014

2014
[39]

On the computational complexity of integer programming problems,

R. Kannan and C. L. Monma, “On the computational complexity of integer programming problems,” Optimization and Operations Research, Springer, 1978

1978
[40]

An nˆ5/2 algorithm for maximum matchings in bipartite graphs,

J. E. Hopcroft and R. M. Karp, “An nˆ5/2 algorithm for maximum matchings in bipartite graphs,” SIAM Journal on computing , vol. 2, no. 4, 1973

1973
[41]

The Hungarian method for the assignment problem,

H. W. Kuhn, “The Hungarian method for the assignment problem,” Naval Research Logistics (NRL) , vol. 52, no. 1, 2005

2005
[42]

Autonomous distributed power control for cognitive radio networks,

S. Im, H. Jeon, and H. Lee, “Autonomous distributed power control for cognitive radio networks,” in Proc. IEEE VTC , Canada, Sept. 2008

2008
[43]

Device-to-device communications underlaying cellular networks,

D. Feng, L. Lu, Y . Yuan-Wu, G. Li, G. Feng, and S. Li, “Device-to-device communications underlaying cellular networks,” IEEE Trans. on Commun. , vol. 61, no. 8, 2013

2013
[44]

Efﬁcient resource allocation for device-to-device communication underlaying LTE network,

M. Zulhasnine, C. Huang, and A. Srinivasan, “Efﬁcient resource allocation for device-to-device communication underlaying LTE network,” in Proc. IEEE WiMob , China, Oct. 2010

2010
[45]

Haenggi, Stochastic geometry for wireless networks

M. Haenggi, Stochastic geometry for wireless networks . Cambridge University Press, 2012

2012

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Forecast and Trends, 2017-2022,

C. V . N. Index, “Forecast and Trends, 2017-2022,” Feb. 2019

work page 2017

[2] [2]

Fifth report on international broadband data report,

F. C. Commission, “Fifth report on international broadband data report,” Jan. 2016

work page 2016

[3] [3]

New physical layer features of 3GPP LTE Release-13 [Industry Perspectives],

P.-H. Kuo, “New physical layer features of 3GPP LTE Release-13 [Industry Perspectives],” IEEE Wireless Commun. , vol. 22, no. 4, 2015

work page 2015

[4] [4]

Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey,

R. O. Afolabi, A. Dadlani, and K. Kim, “Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey,” IEEE Commun. Surveys & Tutorials , vol. 15, no. 1, 2013

work page 2013

[5] [5]

Analyzing assisted ofﬂoading of cellular user sessions onto D2D links in unlicensed bands,

S. Andreev, O. Galinina, A. Pyattaev, K. Johnsson, and Y . Koucheryavy, “Analyzing assisted ofﬂoading of cellular user sessions onto D2D links in unlicensed bands,” IEEE JSAC, vol. 33, no. 1, 2015

work page 2015

[6] [6]

D2D task ofﬂoading: A dataset-based Q&A,

D. Chatzopoulos, C. Bermejo, E. ul Haq, Y . Li, and P. Hui, “D2D task ofﬂoading: A dataset-based Q&A,” IEEE Commun. Magazine , vol. 57, no. 2, 2019

work page 2019

[7] [7]

Device-to-Device Communications over 5G Systems: Standardization, Challenges and Open Issues,

G. Araniti, A. Raschell `a, A. Orsino, L. Militano, and M. Condoluci, “Device-to-Device Communications over 5G Systems: Standardization, Challenges and Open Issues,” in 5G Mobile Communications , Springer, 2017

work page 2017

[8] [8]

A survey on predicting the popularity of web content,

A. Tatar, M. D. de Amorim, S. Fdida, and P. Antoniadis, “A survey on predicting the popularity of web content,” Journal of Internet Services and Applications , vol. 5, no. 1, 2014

work page 2014