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arxiv: 2606.04849 · v1 · pith:YOC4FPXMnew · submitted 2026-06-03 · 💻 cs.IT · eess.SP· math.IT

Dynamic FDD for Spectrum Sharing in Non-Terrestrial Networks

Sourav Mukherjee , Bho Matthiesen , Armin Dekorsy , Petar Popovski This is my paper

Pith reviewed 2026-06-28 04:24 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords dynamic FDDspectrum sharingnon-terrestrial networksLEO satellitesinterference managementthroughput optimizationjoint resource allocation6G networks
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The pith

Dynamic reassignment of FDD bands for uplink and downlink reduces interference and raises throughput up to 30 percent in dense LEO satellite deployments.

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

The paper proposes dynamic reassignment of FDD bands to manage interference among satellites in dense low-Earth-orbit mega-constellations. It formulates a joint optimization problem covering dynamic band assignment, user scheduling, and power allocation in both directions. The non-convex mixed-integer problem is addressed through equivalence transforms, alternating optimization, and mixed-integer solvers. Numerical results show this yields up to 30 percent higher throughput than conventional fixed-band FDD, especially in dense setups. A reader would care because spectrum sharing becomes critical as 6G networks add satellite layers for global coverage.

Core claim

The central claim is that dynamic FDD band assignment, when jointly optimized with user scheduling and power allocation, significantly enhances system performance over conventional FDD by improving interference management, achieving up to 30 percent improvement in throughput in dense deployments.

What carries the argument

The joint optimization problem for dynamic band assignment, user scheduling, and power allocation solved via equivalence transforms, alternating optimization, and mixed-integer solvers.

If this is right

  • Dynamic band assignment enables better interference management than fixed FDD bands in both downlink and uplink.
  • The joint optimization delivers up to 30 percent throughput gain specifically in dense deployments.
  • The approach applies directly to spectrum sharing among satellites operating over shared frequency bands.
  • Solving the problem with the described transforms and solvers is presented as sufficient to obtain the reported gains.

Where Pith is reading between the lines

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

  • The same band-reassignment logic could be tested in hybrid terrestrial-satellite networks where ground stations also share spectrum.
  • Orbital motion and time-varying visibility might require periodic re-optimization whose computational cost is not quantified here.
  • If the 30 percent gain holds under realistic channel models that include Doppler and atmospheric effects, regulators could consider dynamic FDD as a spectrum-sharing tool for future mega-constellations.

Load-bearing premise

The non-convex mixed integer optimization problem can be solved to a quality sufficient to realize the reported throughput gains using equivalence transforms, alternating optimization, and mixed integer solvers.

What would settle it

A simulation run on the same dense-deployment scenario that shows the dynamic-assignment throughput gain falling below 5 percent or disappearing entirely when the solver is replaced by a standard fixed-band baseline would falsify the performance claim.

Figures

Figures reproduced from arXiv: 2606.04849 by Armin Dekorsy, Bho Matthiesen, Petar Popovski, Sourav Mukherjee.

Figure 1
Figure 1. Figure 1: Illustration of dynamic FDD in a two-satellite scenario. Two FDD [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A system of J LEO satellites in orbit serving K UEs over region. Frequency UL DL DL UL rj = 1 rj = 0 Ω2 Ω1 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of sequence of reformulations to solve (9). [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: CDF of the objective value f0 for J = 3 satellites and K = 25 users. 10 15 20 25 30 35 40 20 30 40 50 Number of users K Average sum-rate (bits/sec/Hz) All spin 0 All spin 1 Optimized spin (dynamic FDD) [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average sum-rate f0 versus number of users K for fixed J = 2 satellites. each UE transmits with a maximum power of 2 W in the UL direction, i.e., p max k = 2, ∀k [44]. We consider two frequency bands Ω2 = 1.6 GHz and Ω1 = 2.4 GHz. Furthermore, equal bandwidths of B1 = B2 = 10 MHz are assumed for both frequency bands. Corresponding to these system parameters, the big-M parameter in Algorithm 1 is M = 5. In … view at source ↗
Figure 7
Figure 7. Figure 7: Average sum-rate f0 versus J for fixed number of users (K = 40). 10 15 20 25 30 35 40 5 10 15 20 Number of users K Relative improvement (%) w.r.t. all spin 0 w.r.t. all spin 1 [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Relative improvement of the optimized spin objective versus number [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
read the original abstract

Future 6G networks are envisioned to integrate low Earth orbit satellite mega-constellations to enable seamless global connectivity, particularly in underserved and remote areas. However, the deployment of dense mega-constellations introduces interference among satellites operating over shared frequency bands. This represents a rather new setup for studying spectrum sharing, which exacerbates the limited flexibility of conventional FDD systems based on fixed bands for downlink and uplink transmissions. We address this spectrum-sharing problem and propose dynamic re-assignment of FDD bands for improved interference management in dense deployments, as well as evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. Numerical results demonstrate that the proposed approach of dynamic FDD band assignment significantly enhances system performance over conventional FDD, achieving up to 30\% improvement in throughput in dense deployments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript proposes dynamic FDD band reassignment for interference management in dense LEO satellite mega-constellations. It formulates a joint non-convex mixed-integer optimization over band assignment, scheduling, and power allocation in both link directions, solved via equivalence transforms, alternating optimization, and industrial MINLP solvers. Numerical results claim up to 30% throughput gain versus conventional fixed FDD in dense deployments.

Significance. If the reported gains are reproducible and the solver solutions are verifiably near-optimal, the work would provide a concrete resource-allocation technique for spectrum sharing in 6G non-terrestrial networks. The explicit use of commercial-grade MINLP solvers for a joint assignment-scheduling-power problem is a methodological strength that could be leveraged by follow-on studies.

major comments (1)
  1. [Abstract] Abstract: the headline claim of 'up to 30% improvement in throughput in dense deployments' is presented without any description of the simulation setup (satellite/user counts, channel models, traffic assumptions), the conventional-FDD baseline, Monte-Carlo statistics, error bars, or any diagnostic on solver quality (optimality gaps, convergence curves, or exhaustive-search comparisons on small instances). Because the 30% figure is produced by the same alternating-optimization + MINLP procedure whose global optimality is not established, this omission directly undermines assessment of the central performance claim.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'a rather new setup' is informal; a more precise statement of the modeling novelty would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that additional context is needed to support the performance claims and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of 'up to 30% improvement in throughput in dense deployments' is presented without any description of the simulation setup (satellite/user counts, channel models, traffic assumptions), the conventional-FDD baseline, Monte-Carlo statistics, error bars, or any diagnostic on solver quality (optimality gaps, convergence curves, or exhaustive-search comparisons on small instances). Because the 30% figure is produced by the same alternating-optimization + MINLP procedure whose global optimality is not established, this omission directly undermines assessment of the central performance claim.

    Authors: We agree the abstract is too terse and will expand it in revision to include key simulation parameters (LEO satellite count, user density, 3GPP NTN channel models, full-buffer traffic), the fixed-FDD baseline definition, and averaging over Monte-Carlo runs. The body already reports these details in Section IV; the abstract revision will summarize them concisely. On solver quality, the commercial MINLP solver returns optimality gaps and we will add a brief statement that the reported gains are obtained under the same procedure for both schemes, with consistent relative improvement across random seeds. Global optimality is not claimed; the contribution is a practical, reproducible resource-allocation method whose gains are verified numerically. revision: yes

Circularity Check

0 steps flagged

No circularity; optimization outputs are independent of inputs

full rationale

The paper formulates a joint non-convex mixed-integer optimization over band assignment, scheduling and power, then reports throughput gains obtained by solving that program with equivalence transforms, alternating optimization and MINLP solvers. No step renames a fitted parameter as a prediction, invokes a self-citation as the sole justification for a uniqueness claim, or defines the performance metric in terms of itself. The 30% improvement figure is presented strictly as the numerical output of the solver on the stated objective; it is not constructed by construction from the inputs. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities are identifiable from the provided text.

pith-pipeline@v0.9.1-grok · 5720 in / 971 out tokens · 30954 ms · 2026-06-28T04:24:29.684901+00:00 · methodology

discussion (0)

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Reference graph

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