Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks

Erik Blasch; Liang C Chu; Sambrama; Shih-Chun Lin; Venkata Srirama Rohit Kantheti

REVIEW 4 major objections 5 minor 13 references

Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that a self-healing LEO satellite swarm with a three-rule switching combiner always beats every individual receiver's datarate.

desk verdict A useful engineering demonstration of adaptive combining on SDR hardware, but the self-healing claim rests on an uncharacterized inter-device link and a misstated BER threshold rule. read the letter →

arxiv 2506.01210 v1 pith:PHVZBLNQ submitted 2025-06-01 eess.SP

Sambrama , Venkata Srirama Rohit Kantheti , Liang C Chu , Erik Blasch , Shih-Chun Lin This is my paper

classification eess.SP

keywords self-healingnetworksLEOsatelliteswarmsdistributedmaximalratiocombiningLMMSEselectionsoftware-definedradiotestbedanti-jammingSIMO

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

Low Earth Orbit satellites provide low-latency links but are easy to jam. This paper argues that a swarm of satellite receivers can heal itself by sharing data over inter-device links and letting a rotating leader node pick one of three combining rules based on how many receivers have error rates above a threshold. The rules are distributed maximal-ratio combining, distributed linear minimum mean-squared error estimation, and selection combining. On a software-defined-radio hardware testbed with three receivers and an external interferer, plus an outdoor moving-receiver field test, the combined datarate is reported to exceed every individual receiver's datarate in all tested regimes. If true, the scheme offers an autonomous, jam-resistant path for critical satellite communications without a ground station in the loop.

What carries the argument

The load-bearing mechanism is the BER-count state machine together with the three combining formulas. The state machine maps the number $N_s$ of receivers whose BER exceeds a preset threshold to one of three rules: $N_s = N$ selects d-MRC, $1 < N_s < N$ selects d-LMMSE, and $N_s = 1$ selects SC. d-MRC weights each branch by the conjugate channel vector $h^H$ to maximise received SNR; d-LMMSE replaces this with the interference-aware weight vector $u = \sqrt{p_g}(p_g hh^H + p_j h_j h_j^H + \sigma^2 I)^{-1}h$; SC keeps only the best branch. Around this, the inter-device TCP links deliver per-receiver channel estimates and payloads to a rotating leader node, which is what makes the combining distributed and autonomous. The argument is carried by the claim that this selection logic always yields the highest datarate among the three candidates.

What would settle it

Measure packet loss and one-way latency on the inter-device TCP link while running the three transmit-power regimes; if a receiver's channel estimate reaches the leader later than one coherence time, or is dropped, the combined datarate will fall to the best single receiver, contradicting the reported result that the combined output is always better than the individual datarates.

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Extended reading notes

Core claim

The paper's central claim is that a self-healing distributed SIMO receiver can always outperform its best individual branch. The state machine counts the number of receivers $N_s$ whose bit error rate exceeds a threshold: when all $N$ receivers are good it chooses d-MRC, which multiplies each branch by the conjugate of its channel to maximize SNR; when only one receiver is good it chooses SC, which zeroes the other weights; in between it chooses d-LMMSE, whose weight vector incorporates the jammer channel as $\sqrt{p_g}[p_g hh^H + p_j h_j h_j^H + \sigma^2 I]^{-1}h$. The leader node collects channel estimates and payloads over device-to-device TCP links, applies the selected rule, and hands leadership to the next receiver after each cycle. In the $1\times3$ hardware experiments with an external jammer near one receiver, and in the $1\times2$ outdoor field test with moving receivers, the combined datarate is reported to be strictly higher than every individual datarate, and the reconstructed images are visibly cleaner than any single receiver's image.

Load-bearing premise

The whole scheme assumes the Wi-Fi/TCP inter-device link between receivers and the leader delivers channel estimates and payloads reliably and fast enough for coherent combining; the paper gives no packet-loss, delay, or throughput measurements for that link.

Editorial extensions

If this is right

In the tested $1\times3$ configuration, the switching rule produces a combined datarate above every individual receiver in all three transmit-power regimes: d-MRC when all branches are healthy, d-LMMSE when one branch is jammed, and SC when only one branch survives.
The distributed architecture removes the need for a ground-side combiner: the rotating leader computes BERs, chooses the algorithm, and combines locally, so no single node is a permanent point of failure.
Image transmission shows that the benefit is not just datarate: the combined image is visibly less corrupted than any single receiver's reconstruction when one or two receivers are jammed.
Outdoor tests with two moving receivers at different altitudes confirm that combined datarates beat individual datarates, and that d-LMMSE beats d-MRC when one receiver is very bad.

Reading between the lines

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

Inference: the state machine could be made continuous rather than threshold-based by feeding instantaneous SINR or datarate estimates into the selection, which would likely smooth handovers between d-MRC and d-LMMSE.
Inference: because the inter-device link is a shared Wi-Fi TCP channel, scaling to larger swarms will eventually be bottlenecked by the leader's inbound traffic; a testable limit is the number of receivers at which combining latency exceeds a channel coherence time.
Inference: the same BER-count rule applies to any distributed SIMO setting, including terrestrial UAV relays or vehicle swarms, so the result is not tied to orbital mechanics.

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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

This paper is a hardware-in-loop testbed for a self-healing distributed SIMO receiver system that switches among d-MRC, d-LMMSE, and selection combining based on a BER threshold. What's new is the integration: the state machine on live USRP/RPi hardware, image payloads, and the AERPAW UAV field validation. The combining rules are textbook and from the authors' prior work [6], so the contribution is the engineering demonstration.

What it does well: the main empirical claim—combined datarate above every individual receiver—is plausible, consistent with diversity combining, and the plots support it. The AERPAW results, though processed offline, do validate the combining formulas on real outdoor captures. The rotating leader idea with fault tolerance is sensible, and the authors are upfront about which pieces come from their earlier papers.

Soft spots. First, the state machine in Section III-C is misstated. It says receivers with BER greater than the threshold are high quality and N_S counts those. Lower BER is normally better. The text and figure need correcting, and no threshold value is given, so the switching behavior can't be independently reconstructed. That's a real, fixable flaw. Second, the inter-device link in Section IV is a TCP connection over shared Wi-Fi on Raspberry Pis. The whole leader-based combining and self-healing depend on that link, but there are no packet-loss, latency, jitter, or synchronization measurements, and no description of how per-receiver timing/frequency/phase-compensated streams are aligned at the leader. Without that, the 'always better' result is a system claim on shaky ground. Third, the AERPAW test processes data offline, so it validates combining but not the real-time self-healing or inter-device link. Minor: no error bars or trial counts, and the LEO relevance is asserted rather than demonstrated.

Bottom line: a useful engineering demonstration with a load-bearing uncharacterized link and a misstated state machine. A serious referee could ask for revision. It's not a breakthrough, but it's a concrete testbed that others can build on. I'd accept it for peer review.

Who: researchers building SDR testbeds for distributed combining or swarm comms. Reading group maybe, not because it's deep theory but because it's a good case study in what hardware-in-loop demos need to report.

Recommendation: accept for review, with the expectation of major revision. The authors should correct the BER logic, report inter-device link statistics and synchronization method, and add error bars.

Referee Report

4 major / 5 minor

Summary. The paper proposes a self-healing distributed SIMO architecture for LEO satellite swarms, implemented on a hardware-in-the-loop testbed with USRP B210 radios and Raspberry Pi nodes. A leader node collects channel estimates and payloads from receivers over a TCP/Wi-Fi inter-device link, computes bit error rates (BERs), and selects among d-MRC, d-LMMSE, and SC. Experiments on bitstream and image data, plus offline AERPAW field data, report that the combined datarate always exceeds each individual receiver's datarate. The manuscript claims this improves reliability and interference resilience for LEO satellite networks.

Significance. If substantiated, the paper's main contribution is a working hardware testbed demonstrating that distributed combining with rotating leader selection can improve end-to-end datarate in a 1x3 SIMO setup over a wide range of transmit powers and interference conditions. Strengths include the use of actual SDR hardware, a rotating leader mechanism, bitstream and image experiments, and validation on an outdoor UAV testbed. The combining algorithms are standard and taken from prior work, so the novelty is experimental integration rather than new theory. The central claim nevertheless rests on uncharacterized assumptions about the inter-device link and on a BER-based state machine that is not fully specified.

major comments (4)

[IV, Fig. 4] The inter-device link is implemented as TCP sockets over a shared Wi-Fi network on Raspberry Pis, yet the paper reports no measurements of packet loss, delay, jitter, or throughput for this link, and it does not describe how the per-receiver timing/frequency/phase-compensated payload streams are synchronized or aligned at the leader before weighting. Because d-MRC and d-LMMSE weighting and BER-based algorithm selection all execute at the leader, a degraded or delayed inter-device link would directly invalidate the measured 'always better' combined datarates. Please characterize this link (e.g., latency/loss statistics, synchronization mechanism, behavior under load) and state any assumptions about its reliability.
[III-C, Fig. 3] The state-machine description is internally inconsistent and underspecified. It defines N_s as receivers 'having bit error rates (BER) greater than the threshold' and then treats higher BER as indicating higher quality ('When all the RXs receive high quality signal, the BER for each of the RX units will be greater than the given threshold'), whereas lower BER normally indicates better reception. No threshold value is given, and the mapping from BER comparisons to d-MRC/d-LMMSE/SC selection is therefore not reproducible. Please correct the BER condition, report the threshold or how it is set, and state how each of the three regions N_s=N, 1<N_s<N, N_s=1 is determined.
[V-A, Fig. 5, and V-C, Fig. 8] The central empirical claim that 'the combined output is always better than the individual datarate values' is supported only by plots of what appear to be single runs, with no error bars, trial counts, or confidence intervals, and no definition of how datarate is measured per time point. Without a statistical characterization, the 'always' claim is not established. The AERPAW results in Section V-C are additionally processed offline, as stated in the text, so they validate offline combining but not the real-time inter-device-link architecture or the self-healing state machine. Please add quantitative results (multiple trials, error bars, statistical summary) and clearly separate real-time self-healing validation from offline algorithm validation.
[V-A, Fig. 5] The text infers which algorithm was implemented from the transmit-power setting ('this suggests the d-MRC algorithm was implemented'), but the paper reports no direct log of the selected algorithm or the BER values that triggered the switch. Since the central claim depends on the self-healing state machine operating as described, please provide the algorithm-selection time series or BER measurements alongside the datarate plots.

minor comments (5)

[Abstract, V-C] There is a typographical inconsistency: 'AERPA W' appears in place of 'AERPAW' in the abstract and in the Section V-C heading.
[III-C] The notation alternates between N_s and N_S, and 'inorder' should be 'in order'; please unify the notation and proofread.
[III-B, Table I] The standalone algorithm results use a 1x2 SIMO testbed with USRP X310 radios, while the self-healing evaluation uses a 1x3 testbed with B210s; please clarify whether and how the standalone results transfer to the B210 configuration.
[V-A] The experiment places the interference antenna close to RX1, but in the lowest transmit-power case 'both RX1 and RX2 are highly impacted'; please explain the geometry or propagation path that causes RX2 to also suffer strong interference, since a reader cannot infer this from the described setup.
[V-C, Fig. 8] The caption says 'Each point in x-axis corresponds to the average datarate for 500 messages' but the axis label and averaging procedure are not shown in the figure; please state the exact definition and include the averaging window in the plot.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the combining algorithms are standard and restated in the paper, and the central datarate claims are measured against individual receiver baselines; self-citations are descriptive rather than load-bearing.

full rationale

The paper's derivation chain does not reduce to its own inputs. The d-MRC, d-LMMSE, and SC combining rules are standard techniques restated with explicit equations in Section III-A, and the paper does not fit parameters to the reported data and then relabel them as predictions. The central empirical claim in Section V-A is that the leader-node combined datarate exceeds the individual receivers; this is a measured comparison on a 1x3 USRP/RPi testbed, so it is not constructed from the same datarates. For the SC portions, the paper explicitly acknowledges that the combined output equals the datarate of the best receiver, which is a definitional property of selection combining; however, the algorithm selection decision is driven by measured BER relative to a threshold, not by the combined datarate, so the self-healing behavior retains empirical content. The algorithms are attributed to prior same-author work [6] and the rotating TCP leader mechanism cites [13], but the paper restates the equations and benchmarks against individual receiver baselines, so these self-citations are not load-bearing. No uniqueness theorem is imported from prior work to force the design choice. The AERPAW field test in Section V-C is described as offline processing of external UAV-collected data; this limits the scope of what is validated (the combining formulas, not the real-time inter-device link), but it is not circular because the algorithms are fixed and applied to independent data. The skeptical concerns about uncharacterized TCP/Wi-Fi inter-device latency or loss and the inconsistent BER-threshold wording in Section III-C are correctness, completeness, or reporting limitations rather than circular derivation steps. Overall, the central result is self-contained against direct baselines, so circularity is minimal.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The framework rests on standard diversity-combining assumptions (independent noise, known channel state, interference manageable at the algorithm selection level) and on an uncharacterized Wi-Fi/TCP inter-device link. The only hand-chosen numeric parameter is the BER threshold in the state machine. No new physical or logical entities are introduced.

free parameters (1)

BER threshold for state machine = not specified
Section III-C selects the combining algorithm based on the number of receivers whose BER exceeds a 'predefined threshold'. The threshold value is never given, and no sensitivity analysis is provided. It directly determines which algorithm is used in each interference regime.

assumptions (4)

domain assumption Signal model with independent transmitter, jammer, and iid noise (Eq. 1)
Section III-A1 assumes the transmitter and jammer signals are independent and noise is iid, enabling the d-MRC and d-LMMSE weight derivations.
domain assumption Interference is negligible for d-MRC
Section III-A1 states 'The interference is assumed to be negligible' before writing the SNR expression for d-MRC, which is questionable under jamming.
domain assumption Inter-device TCP links deliver data reliably and timely
Section IV assumes the Wi-Fi/TCP link between RPis can carry channel estimates and payload to the leader node for combining, but no latency, loss, or throughput measurements are provided.
domain assumption UAV field test approximates LEO satellite conditions
Section V-C claims UAVs provide environments 'close to that of LEO satellites', but the altitude, speed, Doppler, and propagation channel differ by orders of magnitude from LEO.

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Cite this review

Pith. "Pith review of Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks." pith.science (2026). https://pith.science/paper/PHVZBLNQ

@misc{pith2026250601210,
  author       = {Pith},
  title        = {Pith review of: Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PHVZBLNQ}},
  note         = {Machine review of arXiv:2506.01210}
}

read the original abstract

The use of Low Earth Orbit (LEO) satellites in the next generation (Next-G) communication systems has been gaining traction over the last few years due to their potential for providing global connectivity with low latency. Since they are the closest to the earth they come with their own set of disadvantages including high vulnerability to jamming and interference. To address these issues, this paper introduces a resilient, self-healing network designed to optimize signal quality under dynamic interference and adversarial conditions. The network leverages inter-satellite communication and an intelligent algorithm selection process, incorporating combining techniques like distributed-Maximal Ratio Combining (d-MRC), distributed-Linear Minimum Mean Squared Error Estimation (d-LMMSE), and Selection Combining (SC). These algorithms are selected to improve performance by adapting to changing network conditions. To evaluate the effectiveness of the proposed solution, we develop a software-defined radio (SDR)-based hardware testbed and perform detailed performance evaluations. Additionally, we present results from field tests conducted on the AERPAW testbed, which validate the proposed combining solutions in real-world scenarios. The results show that our approach makes LEO satellite networks more reliable and better able to handle interference, making them suitable for critical communications.

Figures

Figures reproduced from arXiv: 2506.01210 by the authors.

**Figure 2.** shows the testbed setup used to implement the system described above. The setup includes three USRP B210 software-defined radios, each connected to a RPi, functioning as the receivers. An additional USRP B210 is connected to [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** State machine for self-healing. C. State Machine Model for Self-Healing Self-healing is implemented to make the system independent and have the best possible combining algorithm implemented at all times inorder to obtain superior quality signal [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 5.** Datarates for bitstream data. modulated using QPSK modulation and then, appended with Zadoff-Chu preamble sequence. We use a centre frequency of 2.55 GHz and a bandwidth of 1 MHz. The data is continuously transmitted from the transmitter. The entire reception is automated to work with self-healing. The individual B210s capture this data, save it on the RPi which also performs signal processing including timing, fr… view at source ↗

Figures from the paper (4 more)

**Figure 6.** Figure 6: Datarates for image data. implement self-healing on image data. We make use of a small airplane figure as the signal to be transmitted. The image is converted to grayscale to reduce the overall size of the signal and is then vectorized and converted into bitstream befo…

**Figure 7.** Figure 7: Received Images (a) UAVs taking different paths (b) Both UAVs taking path1 (c) Both UAVs taking path2 [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: Datarates for individual radios, d-MRC and d-LMMSE [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

**Figure 9.** Figure 9: Path for AERPAW experiments. [3] S.-C. Lin, C.-H. Lin, L. C. Chu, and S.-Y. Lien, “Enabling resilient access equality for 6g leo satellite swarm networks,” IEEE Internet of Things Magazine, vol. 6, no. 3, pp. 38–43, 2023. [4] M. S. Shahriar, S. Subramaniam, M. Matsuura…

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Reference graph

Works this paper leans on

13 extracted references · 12 canonical work pages

[6]

Anti-jamming resilient leo satellite swarms,

V . S. R. Kantheti, C.-H. Lin, S.-C. Lin, and L. C. Chu, “Anti-jamming resilient leo satellite swarms,” inMILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), 2023, pp. 77–82

work page 2023
[1]

Satellite communi- cations in the new space era: A survey and future challenges,

O. Kodheli, E. Lagunas, N. Maturo, S. K. Sharma, B. Shankar, J. F. M. Montoya, J. C. M. Duncan, D. Spano, S. Chatzinotas, S. Kisseleff, J. Querol, L. Lei, T. X. Vu, and G. Goussetis, “Satellite communi- cations in the new space era: A survey and future challenges,”IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 70–109, 2021

2021
[2]

Leo satellites in 5g and beyond networks: A review from a standardization perspective,

T. Darwish, G. K. Kurt, H. Yanikomeroglu, M. Bellemare, and G. La- montagne, “Leo satellites in 5g and beyond networks: A review from a standardization perspective,”IEEE Access, vol. 10, pp. 35 040–35 060, 2022. (a) Image for d-MRC Combining (b) Image for d-LMMSE Combining (c) Image for Selection Combining Fig. 7: Received Images (a) UA Vs taking differen...

work page 2022
[3]

Enabling resilient access equality for 6g leo satellite swarm networks,

S.-C. Lin, C.-H. Lin, L. C. Chu, and S.-Y . Lien, “Enabling resilient access equality for 6g leo satellite swarm networks,”IEEE Internet of Things Magazine, vol. 6, no. 3, pp. 38–43, 2023

work page 2023
[4]

Digital twin enabled data-driven approach for traffic efficiency and software-defined vehicular network optimization,

M. S. Shahriar, S. Subramaniam, M. Matsuura, H. Hasegawa, and S.-C. Lin, “Digital twin enabled data-driven approach for traffic efficiency and software-defined vehicular network optimization,” in2024 IEEE 100th V ehicular Technology Conference (VTC2024-Fall), 2024, pp. 1–7

work page 2024
[5]

Spels: Scalable and programmable testbed for evaluating leo satellite swarm communica- tions,

V . S. R. Kantheti, S.-C. Lin, and L. C. Chu, “Spels: Scalable and programmable testbed for evaluating leo satellite swarm communica- tions,” inIEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023, pp. 1–6

work page 2023
[7]

A unified framework for self-healing in wireless networks,

R. Barco, P. Lazaro, and P. Munoz, “A unified framework for self-healing in wireless networks,”IEEE Communications Magazine, vol. 50, no. 12, pp. 134–142, 2012

work page 2012
[8]

Dynamic power allocation assisted by navigation message for inter-satellite links in leo constellation,

S. Shi, G. Li, Z. Li, and Z. Xiang, “Dynamic power allocation assisted by navigation message for inter-satellite links in leo constellation,” in2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, pp. 3000–3004

work page 2016

Show all 13 references

[9]

Optimization design of inter-satellite link (isl) assignment parameters in gnss based on genetic algorithm,

J. Huang, Y . Su, W. Liu, and F. Wang, “Optimization design of inter-satellite link (isl) assignment parameters in gnss based on genetic algorithm,”Advances in Space Research, vol. 60, no. 12, pp. 2574–2580, 2017, bDS/GNSS+: Recent Progress and New Applications - Part 2. [Onli...

2017
[10]

Advanced wireless for unmanned aerial systems: 5g standardization, research challenges, and aerpaw architecture,

V . Marojevic, I. Guvenc, R. Dutta, M. L. Sichitiu, and B. A. Floyd, “Advanced wireless for unmanned aerial systems: 5g standardization, research challenges, and aerpaw architecture,”IEEE V ehicular Technol- ogy Magazine, vol. 15, no. 2, pp. 22–30, 2020

2020
[11]

R. W. Heath Jr,Introduction to wireless digital dommunication: A signal processing perspective. Prentice Hall, 2017

2017
[12]

Linear diversity combining techniques,

D. G. Brennan, “Linear diversity combining techniques,”Proceedings of the IRE, vol. 47, no. 6, pp. 1075–1102, 1959

1959
[13]

Hardware-in-the-loop implementation of distributed sdr systems and throughput-optimal coordinated transmissions,

Sambrama, V . S. R. Kantheti, S.-C. Lin, L. C. Chu, and E. Blasch, “Hardware-in-the-loop implementation of distributed sdr systems and throughput-optimal coordinated transmissions,” inProceedings of the IEEE DySPAN Workshop, May 2024

2024

Pith tools

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

[1] [6]

Anti-jamming resilient leo satellite swarms,

V . S. R. Kantheti, C.-H. Lin, S.-C. Lin, and L. C. Chu, “Anti-jamming resilient leo satellite swarms,” inMILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), 2023, pp. 77–82

work page 2023

[2] [1]

Satellite communi- cations in the new space era: A survey and future challenges,

O. Kodheli, E. Lagunas, N. Maturo, S. K. Sharma, B. Shankar, J. F. M. Montoya, J. C. M. Duncan, D. Spano, S. Chatzinotas, S. Kisseleff, J. Querol, L. Lei, T. X. Vu, and G. Goussetis, “Satellite communi- cations in the new space era: A survey and future challenges,”IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 70–109, 2021

2021

[3] [2]

Leo satellites in 5g and beyond networks: A review from a standardization perspective,

T. Darwish, G. K. Kurt, H. Yanikomeroglu, M. Bellemare, and G. La- montagne, “Leo satellites in 5g and beyond networks: A review from a standardization perspective,”IEEE Access, vol. 10, pp. 35 040–35 060, 2022. (a) Image for d-MRC Combining (b) Image for d-LMMSE Combining (c) Image for Selection Combining Fig. 7: Received Images (a) UA Vs taking differen...

work page 2022

[4] [3]

Enabling resilient access equality for 6g leo satellite swarm networks,

S.-C. Lin, C.-H. Lin, L. C. Chu, and S.-Y . Lien, “Enabling resilient access equality for 6g leo satellite swarm networks,”IEEE Internet of Things Magazine, vol. 6, no. 3, pp. 38–43, 2023

work page 2023

[5] [4]

Digital twin enabled data-driven approach for traffic efficiency and software-defined vehicular network optimization,

M. S. Shahriar, S. Subramaniam, M. Matsuura, H. Hasegawa, and S.-C. Lin, “Digital twin enabled data-driven approach for traffic efficiency and software-defined vehicular network optimization,” in2024 IEEE 100th V ehicular Technology Conference (VTC2024-Fall), 2024, pp. 1–7

work page 2024

[6] [5]

Spels: Scalable and programmable testbed for evaluating leo satellite swarm communica- tions,

V . S. R. Kantheti, S.-C. Lin, and L. C. Chu, “Spels: Scalable and programmable testbed for evaluating leo satellite swarm communica- tions,” inIEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023, pp. 1–6

work page 2023

[7] [7]

A unified framework for self-healing in wireless networks,

R. Barco, P. Lazaro, and P. Munoz, “A unified framework for self-healing in wireless networks,”IEEE Communications Magazine, vol. 50, no. 12, pp. 134–142, 2012

work page 2012

[8] [8]

Dynamic power allocation assisted by navigation message for inter-satellite links in leo constellation,

S. Shi, G. Li, Z. Li, and Z. Xiang, “Dynamic power allocation assisted by navigation message for inter-satellite links in leo constellation,” in2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, pp. 3000–3004

work page 2016