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arxiv: 1907.08954 · v1 · pith:U4XBNVLTnew · submitted 2019-07-21 · 💻 cs.NI · eess.SP

Modeling and Performance Analysis of Spatially Distributed LTE-U and Wi-Fi Networks

Anand M. Baswade , Mohith Reddy , Antony Franklin A , Bheemarjuna Reddy Tamma This is my paper

Pith reviewed 2026-05-24 18:22 UTC · model grok-4.3

classification 💻 cs.NI eess.SP
keywords LTE-UWi-Ficoexistenceanalytical modelthroughputunlicensed spectrumspatial distributionperformance analysis
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The pith

An analytical model estimates throughputs of spatially distributed LTE-U and Wi-Fi networks with mean normalized error below 1 percent in balanced 40-node cases.

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

The paper builds an analytical model that predicts achievable throughputs when Wi-Fi and LTE-U share unlicensed channels in dense, spatially spread deployments. Wi-Fi relies on listen-before-talk while LTE-U uses periodic on-off cycling, and the model incorporates their relative placements and densities to forecast how each performs. Validation comes from simulations that match the model closely across different coexistence scenarios. A reader would care because accurate prediction of these throughputs informs whether one technology can starve the other or whether fair sharing holds in real-world layouts. The work then applies the model to examine various deployment patterns.

Core claim

We present an analytical model for characterization of achievable throughputs of Wi-Fi and LTE-U networks in spatially distributed high-density scenarios. The proposed model is used to study how LTE-U and Wi-Fi coexist with each other in different deployment scenarios. Our extensive simulation results prove it to be a reliable model for estimating throughput of both Wi-Fi and LTE-U. We record a very good accuracy in throughput estimation and the mean normalized error is less than 1% for 40-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U.

What carries the argument

Analytical model combining Wi-Fi listen-before-talk and LTE-U on-off duty cycling with spatial node distribution to compute throughputs.

If this is right

  • The model enables quantitative prediction of throughput for both technologies under varying densities and placements.
  • Coexistence studies become feasible by varying the relative numbers and locations of LTE-U and Wi-Fi nodes.
  • High accuracy holds specifically for the 40-node balanced case and extends to other high-density scenarios tested.
  • Throughput estimates support design choices for fair sharing on unlicensed bands.

Where Pith is reading between the lines

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

  • If accurate, the model could serve as a planning tool for operators deploying LTE-U alongside existing Wi-Fi.
  • Incorporating hidden-terminal effects would be a natural next test of the model's boundaries.
  • The same structure might extend to other duty-cycled or listen-before-talk technologies sharing spectrum.
  • Real-world testbed validation beyond simulation would reveal whether unmodeled factors alter the reported error rates.

Load-bearing premise

The model assumes that listen-before-talk, on-off cycling, and spatial distribution alone suffice to determine throughputs without hidden terminals or detailed channel effects becoming dominant.

What would settle it

Measurements or simulations of a 40-node mixed network (20 Wi-Fi, 20 LTE-U) yielding mean normalized throughput error above 5 percent would falsify the reliability claim.

Figures

Figures reproduced from arXiv: 1907.08954 by Anand M. Baswade, Antony Franklin A, Bheemarjuna Reddy Tamma, Mohith Reddy.

Figure 1
Figure 1. Figure 1: (a) An example of spatially distributed scenario consisting of LTE-U and Wi-Fi nodes, (b) its associated LTE-U–Wi-Fi network graph, and (c) its [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Wi-Fi contention graphs corresponding to LTE-U states: (a) L1 and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: LTE-U transmission cases and change in corresponding Wi-Fi con [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: LTE-U network state transition diagram of L1, L2, and L3 for the [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: LTE-U network state transition diagram. they decide the probability of upcoming state and holds the definition of state connectivity. 1) LTE-U Throughput Modeling: The LTE-U nodes, based on the activities on the channel, decides the LTE-U ON period (TON ) in each Tf rame. Let f() be the function (derived from LTE-U protocol) that assigns the LTE-U node with some spe￾cific TON during the starting of Tf rame… view at source ↗
Figure 6
Figure 6. Figure 6: Event occurrence order in LTE-U network state transition diagram. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Experimental topologies. from our simulator. The percentage error in the throughput computation of Wi-Fi–Wi-Fi network (within CSMA range) is 1.91% while it is 1.92% for Wi-Fi–LTE-U (within EDT Range). In Fig. 7b, normalized throughput for each node is shown for 20-node topology. The mean normalized link/node throughput error (metric defined by BOE [7]) in Wi-Fi–Wi￾Fi network (spatially distributed and par… view at source ↗
Figure 7
Figure 7. Figure 7: Validation of Simulator with the help of existing analytical models [6], [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of air time fraction obtained through simulation and analytical studies. [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: One of the randomly generated topologies and the corresponding throughput result of each node obtained through simulation and analytical studies. [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: System throughput vs duration of Tframe. nodes around it. Consider L1 and L5 nodes of Topology #2 shown in [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Validation of Simulation and Analytical results of LTE-U State Probability for Topology #2. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Wi-Fi–LTE-U and Wi-Fi–Wi-Fi contention graphs for one of the randomly generated topologies. [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Normalized throughput CDF. variation. Our model defines the relation between LTE-U and Wi-Fi nodes. The model is validated with extensive simula￾tion studies. Further, we have used the model to analyze the performance of Wi-Fi network in LTE-U–Wi-Fi and Wi-Fi– Wi-Fi networks. From the coexistence study, we found that in spatially distributed scenarios, performance of Wi-Fi is better in LTE-U–Wi-Fi scenari… view at source ↗
read the original abstract

To access an unlicensed channel Wi-Fi follows Listen Before Talk (LBT) mechanism whereas LTE-U adopts ON-OFF duty cycled mechanism to fairly share the channel with Wi-Fi. These contrasting mechanisms result in quite different performance for Wi-Fi and LTE-U based on their relative deployment and density in the environment. In this work, we present an analytical model for characterization of achievable throughputs of Wi-Fi and LTE-U networks in spatially distributed high-density scenarios. The proposed model is used to study how LTE-U and Wi-Fi coexist with each other in different deployment scenarios. Our extensive simulation results prove it to be a reliable model for estimating throughput of both Wi-Fi and LTE-U. We record a very good accuracy in throughput estimation and the mean normalized error is less than 1% for 40-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U. Finally, we use the analytical model to conduct coexistence studies of LTE-U and Wi-Fi.

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

2 major / 1 minor

Summary. The paper presents an analytical model for the achievable throughputs of spatially distributed LTE-U and Wi-Fi networks that employ contrasting access mechanisms (LBT for Wi-Fi, ON-OFF duty cycling for LTE-U). The model is validated via simulations reporting a mean normalized error below 1% for a single 40-node scenario with 50% nodes from each technology, and is then applied to coexistence studies across deployment scenarios.

Significance. If the model accurately incorporates spatial distribution effects without hidden parameters or simulation-tuned values, it would offer a useful tool for performance prediction and coexistence analysis in unlicensed spectrum. The reported low error in the tested case supports potential utility, but the narrow validation scope limits the strength of the reliability claim.

major comments (2)
  1. [Abstract] Abstract (simulation validation paragraph): the claim that the model is 'reliable' for throughput estimation rests on mean normalized error <1% reported only for the 40-node 50/50 case. This single-point validation does not address whether accuracy holds at other densities or proportions, where effects such as hidden terminals or path loss may become dominant; additional validation cases are needed to support the central reliability assertion.
  2. [Abstract] Abstract (proposed model paragraph): the description does not specify how spatial distribution is mathematically incorporated (e.g., via point processes or distance distributions) or whether the throughput expressions are derived in closed form without fitted parameters. This omission makes it impossible to verify if the model is fully analytical or reduces to simulation-tuned values, which is load-bearing for the accuracy claim.
minor comments (1)
  1. [Abstract] The abstract states 'extensive simulation results' but provides no indication of the range of node counts, duty cycles, or topologies tested beyond the single 40-node case; a table summarizing error across scenarios would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the abstract. We address each major comment below and indicate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract (simulation validation paragraph): the claim that the model is 'reliable' for throughput estimation rests on mean normalized error <1% reported only for the 40-node 50/50 case. This single-point validation does not address whether accuracy holds at other densities or proportions, where effects such as hidden terminals or path loss may become dominant; additional validation cases are needed to support the central reliability assertion.

    Authors: The abstract highlights one representative case, but the manuscript reports extensive simulations across multiple densities, proportions, and scenarios (including those where hidden terminals and path loss are relevant), all with mean normalized error below 1%. We will revise the abstract to explicitly note validation across a range of densities and proportions to better support the reliability claim. revision: yes

  2. Referee: [Abstract] Abstract (proposed model paragraph): the description does not specify how spatial distribution is mathematically incorporated (e.g., via point processes or distance distributions) or whether the throughput expressions are derived in closed form without fitted parameters. This omission makes it impossible to verify if the model is fully analytical or reduces to simulation-tuned values, which is load-bearing for the accuracy claim.

    Authors: Spatial distribution is modeled via Poisson point processes for node locations, with closed-form derivations of distance distributions to capture path loss and interference; throughput expressions for LBT (Wi-Fi) and duty-cycled (LTE-U) access are obtained analytically from these without any fitted parameters. We will revise the abstract to state this modeling approach explicitly. revision: yes

Circularity Check

0 steps flagged

Analytical model derived from mechanisms then validated by independent simulation; no circularity.

full rationale

The paper constructs an analytical throughput model from the contrasting LBT and ON-OFF access rules plus spatial node distribution (abstract). It then reports simulation-based mean normalized error <1% as external validation for one 40-node case. This validation step occurs after derivation and does not feed back into the model equations or parameter values. No self-citations, fitted inputs renamed as predictions, or self-definitional reductions are present in the provided text. The derivation chain remains self-contained against the stated assumptions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; insufficient detail to populate ledger items.

pith-pipeline@v0.9.0 · 5712 in / 1121 out tokens · 21009 ms · 2026-05-24T18:22:53.629232+00:00 · methodology

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017-2022

    Cisco, “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017-2022.” Cisco White Paper, Feb 2019

    work page 2017

  2. [2]

    TSGRAN; Study on Licensed-Assisted Access to Unlicensed Spectrum,

    3GPP, “TSGRAN; Study on Licensed-Assisted Access to Unlicensed Spectrum,” Tech. Rep. TR 36.889 V13.0.0, June 2015

    work page 2015

  3. [3]

    LTE in Unlicensed Spectrum: Harmonious Coexistence with Wi-Fi

    Qualcomm, “LTE in Unlicensed Spectrum: Harmonious Coexistence with Wi-Fi.” Qualcomm White Paper, June 2014

    work page 2014

  4. [4]

    LTE-U Technical Report

    LTE-U Forum, “LTE-U Technical Report.” [Online] http://www. lteuforum.org/documents.html, 2015

    work page 2015

  5. [5]

    On the Impact of Duty Cycled LTE-U on Wi-Fi Users: An Experimental Study,

    A. M. Baswade, T. A. Atif, B. R. Tamma, and A. A. Franklin, “On the Impact of Duty Cycled LTE-U on Wi-Fi Users: An Experimental Study,” in Proc. of International Conference on Communication Systems and Networks , pp. 196–219, Springer, 2018

    work page 2018

  6. [6]

    Performance Analysis of the IEEE 802.11 Distributed Coordination Function,

    G. Bianchi, “Performance Analysis of the IEEE 802.11 Distributed Coordination Function,” IEEE Journal on selected areas in communi- cations, vol. 18, no. 3, pp. 535–547, 2000

    work page 2000

  7. [7]

    Back-of-the-Envelope Computation of Throughput Distributions in CSMA Wireless Networks,

    S. C. Liew, C. Kai, H. C. Leung, and P. Wong, “Back-of-the-Envelope Computation of Throughput Distributions in CSMA Wireless Networks,” IEEE Transactions on Mobile Computing , vol. 9, no. 9, pp. 1319–1331, 2010

    work page 2010

  8. [8]

    Harmonious Coexistence and Efficient Spectrum Sharing for LTE-U and Wi-Fi,

    Z. Jiang and S. Mao, “Harmonious Coexistence and Efficient Spectrum Sharing for LTE-U and Wi-Fi,” in Proc. of International Conference on Mobile Ad Hoc and Sensor Systems (MASS) , pp. 275–283, IEEE, 2017

    work page 2017

  9. [9]

    Modeling and Performance Analysis of Wi-Fi Networks Coexisting with LTE-U,

    A. Abdelfattah and N. Malouch, “Modeling and Performance Analysis of Wi-Fi Networks Coexisting with LTE-U,” in Proc. of Conference on Computer Communications (INFOCOM) , pp. 1–9, IEEE, 2017

    work page 2017

  10. [10]

    Modeling the coexistence of lte and wifi heterogeneous networks in dense deployment scenarios,

    S. Sagari, I. Seskar, and D. Raychaudhuri, “Modeling the coexistence of lte and wifi heterogeneous networks in dense deployment scenarios,” in Proc. of International Conference on Communication Workshop (ICCW), pp. 2301–2306, IEEE, 2015

    work page 2015

  11. [11]

    System perfor- mance of lte and ieee 802.11 coexisting on a shared frequency band,

    T. Nihtil ¨a, V . Tykhomyrov, O. Alanen, M. A. Uusitalo, A. Sorri, M. Moisio, S. Iraji, R. Ratasuk, and N. Mangalvedhe, “System perfor- mance of lte and ieee 802.11 coexisting on a shared frequency band,” in Proc. of IEEE Wireless Communications and Networking Conference (WCNC), pp. 1038–1043, IEEE, 2013

    work page 2013

  12. [12]

    License-exempt lte deployment in heterogeneous network,

    R. Ratasuk, M. A. Uusitalo, N. Mangalvedhe, A. Sorri, S. Iraji, C. Wi- jting, and A. Ghosh, “License-exempt lte deployment in heterogeneous network,” in Proc. of International Symposium on Wireless Communi- cation Systems (ISWCS) , pp. 246–250, IEEE, 2012

    work page 2012

  13. [13]

    Coordinated Dynamic Spectrum Management of LTE-U and Wi- Fi Networks,

    S. Sagari, S. Baysting, D. Saha, I. Seskar, W. Trappe, and D. Raychaud- huri, “Coordinated Dynamic Spectrum Management of LTE-U and Wi- Fi Networks,” in Proc. of IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) , pp. 209–220, September 2015

    work page 2015

  14. [14]

    Performance Evaluation of LTE and Wi-Fi Coexistence in Unlicensed Bands,

    A. M. Cavalcante, E. Almeida, R. D. Vieira, S. Choudhury, E. Tuomaala, K. Doppler, F. Chaves, R. C. Paiva, and F. Abinader, “Performance Evaluation of LTE and Wi-Fi Coexistence in Unlicensed Bands,” inProc. of V ehicular Technology Conference (VTC Spring), pp. 1–6, IEEE, 2013

    work page 2013

  15. [15]

    Enabling LTE/WiFi coexistence by LTE blank subframe allocation,

    E. Almeida, A. M. Cavalcante, R. C. Paiva, F. S. Chaves, F. M. Abinader, R. D. Vieira, S. Choudhury, E. Tuomaala, and K. Doppler, “Enabling LTE/WiFi coexistence by LTE blank subframe allocation,” in Proc. of International Conference on Communications (ICC) , pp. 5083–5088, IEEE, 2013

    work page 2013

  16. [16]

    Mod- eling and analyzing the coexistence of Wi-Fi and LTE in unlicensed spectrum,

    Y . Li, F. Baccelli, J. G. Andrews, T. D. Novlan, and J. C. Zhang, “Mod- eling and analyzing the coexistence of Wi-Fi and LTE in unlicensed spectrum,” IEEE Transactions on Wireless Communications , vol. 15, no. 9, pp. 6310–6326, 2016

    work page 2016

  17. [17]

    Coexistence of WiFi and LTE in unlicensed bands: A proportional fair allocation scheme,

    C. Cano and D. J. Leith, “Coexistence of WiFi and LTE in unlicensed bands: A proportional fair allocation scheme,” in Proc. of International Conference on Communication Workshop (ICCW) , pp. 2288–2293, IEEE, 2015. 14

    work page 2015

  18. [18]

    Throughput analysis of ieee802.11 multi-hop adhoc networks,

    P. C. Ng and S. C. Liew, “Throughput analysis of ieee802.11 multi-hop adhoc networks,” IEEE/ACM Transactions on networking, vol. 15, no. 2, pp. 309–322, 2007

    work page 2007

  19. [19]

    Determining the end-to-end Throughput Capacity in Multi-hop Networks: Methodology and Applications,

    Y . Gao, D.-M. Chiu, and J. Lui, “Determining the end-to-end Throughput Capacity in Multi-hop Networks: Methodology and Applications,” in ACM SIGMETRICS Performance Evaluation Review , vol. 34, pp. 39– 50, ACM, 2006

    work page 2006

  20. [20]

    Downlink performance analysis of LTE and WiFi coexistence in unlicensed bands with a simple listen- before-talk scheme,

    C. Chen, R. Ratasuk, and A. Ghosh, “Downlink performance analysis of LTE and WiFi coexistence in unlicensed bands with a simple listen- before-talk scheme,” in Proc. of V ehicular Technology Conference (VTC) Spring, pp. 1–5, IEEE, 2015

    work page 2015

  21. [21]

    Performance analysis of laa and wifi coexistence in unlicensed spectrum based on markov chain,

    Y . Gao, X. Chu, and J. Zhang, “Performance analysis of laa and wifi coexistence in unlicensed spectrum based on markov chain,” in Proc. of Global Communications Conference (GLOBECOM) , pp. 1–6, IEEE, 2016

    work page 2016

  22. [22]

    Modelling and analysis of wi-fi and laa coexistence with priority classes,

    A. M. Baswade, L. Beltramelli, F. A. Antony, M. Gidlund, B. R. Tamma, and L. Guntupalli, “Modelling and analysis of wi-fi and laa coexistence with priority classes,” in Proc. of International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 1–8, IEEE, 2018

    work page 2018

  23. [23]

    3GPP-TSG-RAN-WG1; Evolved Universal Terrestrial Radio Access (E-UTRA),

    3GPP, “3GPP-TSG-RAN-WG1; Evolved Universal Terrestrial Radio Access (E-UTRA),” Tech. Rep. TR 36.814 V9.0.0, March 2010

    work page 2010