Dynamic Spectrum Allocation in the C-Band: An Overview

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper presents a comprehensive review of DSA techniques for enabling coexistence between terrestrial 5G networks and satellite systems in the C-band (3.4–4.2 GHz). It introduces the performance metrics, challenges and possible solutions. It evaluates ML/AI-driven resource allocation and interference mitigation approaches. The study also reviews global DSA implementations like TVWS, CBRS, and LSA, and identifies regulatory and technical challenges. In general, I think this paper is interesting, but losing a focus. Please find my detailed comments in the following.

1. The manuscript introduces dozens of acronyms (DSA, DSUE, FSS, CBRS, LSA, gNB, etc.) long before spelling them out in context. “DSA” itself is used in the abstract before it is ever defined as “Dynamic Spectrum Access.” A short glossary in the introduction and a policy of “spell-out on first use” would improve readability.
2. Although the title promises a C-band (3.4–4.2 GHz) study of 5G/satellite coexistence, large sections wander into TVWS (470–694 MHz), LTE-U in 2.4 GHz, and generic CR surveys. These digressions dilute the message. Each off-topic discussion should be trimmed or explicitly linked back to the C-band coexistence problem.
3. Thin technical depth is not sufficient. For example, Equations (1) and (3) introduce SE and SUE without clarifying why one is static and the other dynamic, or how their numerical difference drives design choices. Interference models (IUL, IDL) are listed, yet no comparative example shows how a 1 dB change in interference margin translates into exclusion-zone radius or capacity loss.
4. The authors should add a numerical case study: pick a typical FSS earth-station G/T and show how DSUE evolves as 5G BS density increases.
5. The paper frequently conflates DSA with classical cognitive-radio architectures.
6. Unique coexistence challenges between 5G and satellite should be clearly stated. A table contrasting these with TVWS or CBRS challenges would emphasize why “plain CR” is insufficient.
7. The paper lumps all DSA enablers together. A classification sidebar would help, for example the methods can be classified based on the enabling techniques, such as map-based (geo-database, exclusion zones), sensing-based (energy detection, cooperative sensing), AI-based.

Author Response

Reviewer Comment 1: The manuscript introduces dozens of acronyms (DSA, DSUE, FSS, CBRS, LSA, gNB, etc.) long before spelling them out in context. “DSA” itself is used in the abstract before it is ever defined as “Dynamic Spectrum Access.” A short glossary in the introduction and a policy of “spell-out on first use” would improve readability.
Response: We carefully revised the manuscript to spell out all acronyms at their first occurrence (e.g., Dynamic Spectrum Access (DSA), Fixed Satellite Service (FSS), etc.). The abstract was developed outside of the actual document, this has been revised and added to the main document and used the full glossary at the end of the document.

 

Reviewer Comment 2: Although the title promises a C-band (3.4–4.2 GHz) study of 5G/satellite coexistence, large sections wander into TVWS (470–694 MHz), LTE-U in 2.4 GHz, and generic CR surveys. These digressions dilute the message. Each off-topic discussion should be trimmed or explicitly linked back to the C-band coexistence problem.
Response: We streamlined the discussions on TVWS, LTE-U, and generic CR surveys to avoid digressions. These topics are now only briefly mentioned as comparative case studies and to introduce the different enablers, explicitly linked to the C-band coexistence problem.

 

“2.3. Classification of DSA Enablers 326

Effectively implementing DSA techniques avoiding the DSUE degradation necessitates a comprehensive understanding of both the technological and economic implications of spectrum-sharing methodologies. By enhancing SUE in frequency bands where satellites operate as PU’s, DSA presents an innovative solution to address the increasing demand for wireless bandwidth. As highlighted in the analysis of shared spectrum economics, stakeholders must optimize SU bandwidths to fully capitalize on available opportunities for spectrum sharing. Thus, integrating DSA techniques could yield substantial economic benefits while simultaneously accommodating the exponential growth of user traffic in critical frequency bands.”

 

Reviewer Comment 3: Thin technical depth is not sufficient. For example, Equations (1) and (3) introduce SE and SUE without clarifying why one is static and the other dynamic, or how their numerical difference drives design choices. Interference models (IUL, IDL) are listed, yet no comparative example shows how a 1 dB change in interference margin translates into exclusion-zone radius or capacity loss.
Response: We thank the reviewer for pointing out the need for greater technical depth in differentiating static and dynamic efficiency metrics and for illustrating interference impacts. In the revised manuscript, we have:

• updated the spectral efficiency equations to explicitly include time-varying components, highlighting the shift from static measures to dynamic spectrum utilization efficiency,
• added a comparative table that quantifies the effect of a +1 dB change in the exclusion-zone interference margin,
• introduced new subsections that analyse the resulting capacity shifts in relation to exclusion-zone area changes, and (iv) provided a step-by-step derivation showing how a +1 dB margin increase translates into the required protection distance expansion.

Together, these additions clarify the distinction between static and dynamic metrics and provide a concrete numerical illustration of how interference margin variations directly impact both DSUE degradation and DSUE capacity.

Subsection, 2.1.1. DSUE Capacity Shift, delves into the derivation of a cell capacity shift as the minimum distance shifts.

 

Reviewer Comment 4: The authors should add a numerical case study: pick a typical FSS earth-station G/T and show how DSUE evolves as 5G BS density increases.
Response: We included a numerical case study using a representative FSS earth-station gain-to-noise temperature (G/T) value from ITU-R recommendations. The study shows DSUE evolution as 5G base station density increases, highlighting the tipping point where FSS protection requires dynamic exclusion zones or power control.

Subsection 2.1.3, introduces a typical example derivation of a 5G base station density increase impacting a FSS system, to show or clarify how an increase in the 5G NR deployed affects the primary user in FSS.

 

 

Reviewer Comment 5: The paper frequently conflates DSA with classical cognitive-radio architectures.
Response: We revised the text to clearly differentiate DSA from cognitive radio (CR). CR is now described as an early enabler of opportunistic access, while DSA is positioned as a broader framework that also incorporates licensed shared access, centralized databases, and ML/AI-driven coordination beyond CR’s original scope.

We clarified the distinction by restructuring sections:

• TVWS, CBRS, and LSA were reframed as policy-driven DSA frameworks, not generic CR experiments.
• Table 5 was added, showing how DSA differs from CR (DSA: geo-database, regulatory tiers, managed access; CR: sensing-only, opportunistic access).
• This helps the paper consistently present DSA as a broader, regulator-backed paradigm rather than CR-only.

 

Reviewer Comment 6: Unique coexistence challenges between 5G and satellite should be clearly stated. A table contrasting these with TVWS or CBRS challenges would emphasize why “plain CR” is insufficient.
Response: To address the reviewer’s request for a clearer exposition of the unique coexistence challenges between 5G and satellite systems, we revised the manuscript to separate classical cognitive radio (CR) concepts from modern regulatory-driven DSA frameworks. Specifically, we expanded the discussion (see revised text above) to emphasize that while early work equated DSA with CR opportunism, today’s practical frameworks (e.g., TVWS, CBRS, LSA) rely on structured, database-driven or license-tiered enforcement mechanisms. Furthermore, we introduced table 6 which contrasts TVWS and CBRS with the specific coexistence requirements of 5G–satellite systems in the C-band. This table and accompanying prose highlight challenges unique to satellite coexistence, such as cross-border interference, the sensitivity of FSS earth stations, stricter interference thresholds, and the trade-off between DSUE degradation and DSUE capacity. These additions sharpen the focal point of the paper by making clear why “plain CR” approaches are insufficient, and why regulatory and map-based DSA enablers are necessary for protecting satellite incumbents.

 

Reviewer Comment 7: The paper lumps all DSA enablers together. A classification sidebar would help, for example the methods can be classified based on the enabling techniques, such as map-based (geo-database, exclusion zones), sensing-based (energy detection, cooperative sensing), AI-based.
Response: We introduced a classification framework section that groups DSA enablers into:

1. Map-based (geo-location databases, exclusion zones),
2. Sensing-based (energy detection, cooperative sensing), and
3. AI-based (reinforcement learning, ML-driven prediction).
   This classification helps to distinguish enablers and shows how they can be combined for robust coexistence.

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The authors provide a comprehensive overview of Dynamic Spectrum Allocation (DSA) in the C-band, with a specific focus on coexistence between terrestrial mobile networks and satellite systems. The topic addresses the critical challenge of spectrum scarcity and the need for efficient spectrum sharing. The review covers DSA techniques, interference mitigation, regulatory frameworks, international case studies, and the role of ML/AI in enhancing coexistence. The introduction of DSUE as a metric to quantify real-time spectrum reuse under coexistence constraints is a notable contribution, filling a gap in existing literature by integrating spatial and temporal dynamics of spectrum use.

Here are some suggestions:

1. This paper has addressed the gaps in terrestrial-satellite coexistence, but the comparison with prior works in Table 1 should be strengthened. The readers need to know how does the proposed DSUE framework is better than the existing metrics, (eg. Spectrum Utilisation Efficiency or interference-to-noise ratios?
2. The DSUE metrics (Equs. 3–5) are introduced as key contributions, but their practical application lacks illustration. Including simulated or empirical examples (e.g., DSUE calculations for a specific C-band scenario with 5G and FSS coexistence) would enhance clarity. Additionally, explaining how DSUE interacts with real-world constraints (e.g., exclusion zones, antenna beamforming) would strengthen its utility.
3. 6 outlines potential algorithms on ML/AI applications, e.g., DRL, Q-learning, but lacks specific cases or performance benchmarks. The results (e.g., interference reduction percentages, spectrum efficiency gains) would validate their effectiveness.
4. The references should be updated to include recent publications on C-band DSA.

Author Response

Reviewer Comment 1: This paper has addressed the gaps in terrestrial-satellite coexistence, but the comparison with prior works in Table 1 should be strengthened. The readers need to know how does the proposed DSUE framework is better than the existing metrics, (eg. Spectrum Utilisation Efficiency or interference-to-noise ratios?
Response: The DSUE equations were revised to explicitly contrast DSUE against Spectrum Utilisation Efficiency (SUE) and Interference-to-Noise (I/N) metrics. We highlighted that:

• SUE measures generic occupancy but ignores protection constraints.
• I/N provides local protection but no measure of spectral reuse.
• DSUE unifies both, showing how dynamic utilisation changes under protection constraints, making it more suitable for C-band 5G/FSS coexistence.
  This strengthened the comparative narrative by making DSUE’s novelty explicit.

There isn’t much prior work in-terms of assessing coexistence based on DSUE.

 

 

Reviewer Comment 2: The DSUE metrics (Equestions. 3–5) are introduced as key contributions, but their practical application lacks illustration. Including simulated or empirical examples (e.g., DSUE calculations for a specific C-band scenario with 5G and FSS coexistence) would enhance clarity. Additionally, explaining how DSUE interacts with real-world constraints (e.g., exclusion zones, antenna beamforming) would strengthen its utility.
Response: We added a numerical case study using a typical FSS earth station with $G/T=20$ dB/K and a 36 MHz transponder. DSUE was evaluated under varying 5G BS densities, showing how exclusion-zone growth degrades DSUE. Subsection 2.1.1 shows DSUE capacity shift and DSUE Degradation with an empirical example to show how the exclusion zones and interference margin are accounted for in DSUE. This illustrates the metric’s sensitivity and applicability in real coexistence planning.

 

Reviewer Comment 3: 6 outlines potential algorithms on ML/AI applications, e.g., DRL, Q-learning, but lacks specific cases or performance benchmarks. The results (e.g., interference reduction percentages, spectrum efficiency gains) would validate their effectiveness.
Response: We thank the reviewer for pointing out the need for greater technical depth in differentiating static and dynamic efficiency metrics and for illustrating interference impacts. In the revised manuscript, we have:

“””Although there is not an exhaustive pool of studies into DSUE as a metric, authors in \cite{sahoo-2019} have shown that Q-learning in particular, surpasses conventional static policies in DSA. The studies using LTE traffic traces have demonstrated that \ac{ANN}-based predictors improve spectrum utilization efficiency. When applied to C-band coexistence, these techniques can be adapted for dynamic EZ management, promising to yield DSUE gains of 15–20% through adaptive boundary adjustments.”””

 

“””In a similar way, \ac{DRL} has enabled the joint optimization of beamforming and power control for terrestrial base stations. As discussed in literature review the approach dynamically mitigates interference while maintaining throughput, reducing interference power at PU receivers by approximately $\approx 12\%$ compared to static allocation schemes \cite{mis-eva-alk-2020}. However, scalability and throughput degradation for eMBB traffic under heavy loads remain significant hurdles. Collectively, these studies underscore the transformative potential of \ac{ML/AI} in fostering coexistence but also emphasize the need for scalable, adaptable, and efficient algorithms to meet the evolving demands of 5G and beyond.”””

Reviewer Comment 4: The references should be updated to include recent publications on C-band DSA.
Response: There hasn’t been much recent literature touching on DSUE based design and analysis of horizontal coexistence environments. Two references on which one on Q-leaning algorithm can be adopted to cater for DSUE metric while studying adaptive exclusion zones during temporal spectrum usage dynamics.

67. A. Sahoo, A Machine Learning Based Scheme for Dynamic Spectrum Access, 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 2021, pp. 1-7, 2021
68. F. B. Mismar, B. L. Evans, A, Alkhateeb, Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination, IEEE Transactions on Communications, vol. 68, no. 3, pp. 1581–1592, 2020

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

This review examines Dynamic Spectrum Allocation (DSA) in the C-band (3.4–4.2 GHz), focusing on coexistence between terrestrial mobile networks (4G/5G) and Fixed Satellite Service/Satellite Service (FSS/SS) ground stations. It surveys DSA techniques, interference mitigation strategies, and spectrum utilization metrics such as Dynamic Spectrum Utilization Efficiency (DSUE). The paper highlights the integration of FSS ground station parameters into ML/AI-driven spectrum management for real-time interference-aware allocation. Various frameworks—CBRS, LSA, TVWS—are reviewed alongside international trials. Challenges in regulation, sensing, coexistence, and economic adoption are addressed, with proposals for intelligent, adaptive management combining geo-location databases, beamforming, and advanced antennas to ensure PU protection while enhancing spectral efficiency.

The paper presents a detailed treatment of spectrum resource sharing in C-band environments, placing strong emphasis on vertical spectrum sharing where primary users (satellite services) must be protected while enabling secondary users (mobile broadband) to access underutilized channels. The key interference-avoidance techniques covered include:

1. Database-assisted sharing (e.g., SAS in CBRS, LSA frameworks) to coordinate dynamic access and enforce exclusion zones.
2. Geo-location awareness to adapt access decisions based on proximity to FSS receivers.
3. Advanced antenna methods—beamforming, MIMO, and Reconfigurable Intelligent Surfaces—to steer transmissions away from protected zones.
4. Power control and guard bands to limit both co-channel and adjacent-channel interference.
5. Machine Learning (ML) and AI for predictive interference modelling, adaptive channel selection, and real-time spectrum access optimization.

The review connects these technical strategies to regulatory and operational models, highlighting global trials and emphasizing the role of intelligent, interference-aware DSA systems in enabling safe spectrum reuse.

Despite the strengths of the review paper, it has several weaknesses that should be addressed to improve its quality. Overall, it is recommended for acceptance with major revisions pending:

1. Limited quantitative comparison of interference mitigation techniques’ performance metrics. Consequently, including field trial results quantifying interference reduction and DSUE improvement for each technique will be beneficial.
2. Absence of detailed experimental validation or large-scale field measurements of the proposed DSUE framework. Therefore, I suggest developing a comparative performance table for different sharing frameworks (CBRS, LSA, TVWS) under C-band coexistence scenarios.
3. Heavy focus on literature without synthesizing a clear taxonomy of “most effective” methods. Hence, provide a decision-making flowchart linking regulatory constraints, network topology, and appropriate interference-mitigation strategies.
4. ML/AI discussion remains largely conceptual without presenting specific model architectures, datasets, or training methodologies. Accordingly, I suggest detailing ML/AI models used for spectrum prediction, including feature sets, algorithm selection, and computational requirements.
5. Economic feasibility analysis of implementation (especially for developing regions) is only superficially discussed. Consequently, I suggest expanding the economic analysis to include CAPEX trade-offs and cost-benefit scenarios for operators and regulators.
6. It is my opinion that the first “layer” word in this sentence should be omitted, “Consequently, the trade-offs on sensing/measurement capabilities due to the complexity of the system can lead to threats at the Medium Access Control layer (MAC) layer, such as the hidden node problem, and sub-optimal false alarm and detection probability issues can affect the Physical Layer (PHY) [42].”
7. The following sentence should be modified grammatically: “The FSS/SS, operating with high sensitivity and stringent interference thresholds must be protected from harmful emissions in Table. 3 originating from terrestrial wireless mobile transmissions.”
8. ITU-R stands for the Radiocommunication Sector of the International Telecommunication Union. It is one of the three sectors of the ITU, along with ITU-D (Development) and ITU-T (Standardization). ITU-R is primarily responsible for managing the radio-frequency spectrum and satellite orbits, ensuring efficient and interference-free operation of radiocommunication systems worldwide. Therefore “The Spectrum Access System Radiocommunication Sector (ITU-R) Sector and 3rd Generation Partnership Project (3GPP) both play significant roles in the evolution and standardization of DSA, from TVWS to 5G NR, while also considering coexistence with FSS.” should be modified to address a proper acronym.

Comments for author File: Comments.pdf

Author Response

Reviewer Comment 1: Limited quantitative comparison of interference mitigation techniques’ performance metrics. Consequently, including field trial results quantifying interference reduction and DSUE improvement for each technique will be beneficial.
Response: We’ve further exploited the DSUE derivation to also include a translation of a change in exclusion zone/area radius and interference margins relative to capacity losses and degradations.

We have also leaned more on the learning and outcomes of the recorded field trials and expanded/amplified on the potential of using DSUE as a metric in comparing spectrum sharing.

 

Reviewer Comment 2: Absence of detailed experimental validation or large-scale field measurements of the proposed DSUE framework. Therefore, I suggest developing a comparative performance table for different sharing frameworks (CBRS, LSA, TVWS) under C-band coexistence scenarios.
Response: We thank the reviewer for this valuable suggestion. In the revised manuscript, we have integrated both experimental trial results and simulation-based findings from recent studies to strengthen the practical grounding of the DSUE framework.

“”” The comparative evidence from trials \cite{malay, india, canada, uk} strengthens the proposed \ac{DSUE} framework by considering regulatory and technical constraints to measurable coexistence outcomes. From trials CBRS achieves high DSUE through automated SAS coordination with minimal \ac{EZ}, whereas TVWS suffers DSUE degradation due to wide incumbent protection areas. The Malaysia C-band field trials further demonstrate that DSUE is highly sensitive to mitigation parameters. Thus, the DSUE framework not only unifies these diverse observations but also provides a quantitative basis for comparing how sharing models (CBRS, LSA, TVWS) and empirical trials translate into usable spectrum efficiency under C-band coexistence.”””

 

Reviewer Comment 3: Heavy focus on literature without synthesizing a clear taxonomy of “most effective” methods. Hence, provide a decision-making flowchart linking regulatory constraints, network topology, and appropriate interference-mitigation strategies.
Response: We appreciate this insightful suggestion, as a decision-making taxonomy would indeed provide valuable synthesis of the surveyed techniques and help practitioners map interference mitigation strategies to specific regulatory and deployment contexts. While incorporating such a flowchart is beyond the current scope of this paper due to space and focus constraints, we fully recognize its importance. We plan to include this taxonomy and decision framework as part of our future work, where it can be supported with case studies, illustrative design examples and simulations.

 

Reviewer Comment 4: ML/AI discussion remains largely conceptual without presenting specific model architectures, datasets, or training methodologies. Accordingly, I suggest detailing ML/AI models used for spectrum prediction, including feature sets, algorithm selection, and computational requirements.
Response: The discussion is mainly on numerical analysis and from other literatures on how they can enhance coexistence. Simulations are still in development and will be presented in later publications, but field trials applying ML has been extended in the discussion to point out the positives from using ML during field trials.

“””Importantly, the trials did not report significant integration of \ac{ML/AI} techniques in real-world deployments, although presenting a 100\% accuracy in predicting interference zones. Extending this to \ac{DSUE}, \ac{ML/AI} can enable predictive \ac{EZ} management, adaptive \ac{UE} power/beam control, and demand forecasting, highlighting a research opportunity. Future work could build upon these frameworks by introducing ML-driven spectrum management and interference mitigation strategies that dynamically adapt to real-time satellite and mobile network conditions.””

 

 

Reviewer Comment 5: Economic feasibility analysis of implementation (especially for developing regions) is only superficially discussed. Consequently, I suggest expanding the economic analysis to include CAPEX trade-offs and cost-benefit scenarios for operators and regulators.
Response: We have added highlights on CAPEX/OPEX trade-offs and consideration for economic feasibility studies in deciding on commissioning these frameworks and how it benefits the market, by lowering the barriers to market.

Considerations on adding census data and economic activity to enhance spectrum pricing is still in development and to be discussed further in details in another manuscript.

 

Reviewer Comment 6: It is my opinion that the first “layer” word in this sentence should be omitted, “Consequently, the trade-offs on sensing/measurement capabilities due to the complexity of the system can lead to threats at the Medium Access Control layer (MAC) layer, such as the hidden node problem, and sub-optimal false alarm and detection probability issues can affect the Physical Layer (PHY) [42].”
Response: Update the wording  \ac{MAC} in the acronym from an errored acronym \ac{MAC Layer}.

 

Reviewer Comment 7: The following sentence should be modified grammatically: “The FSS/SS, operating with high sensitivity and stringent interference thresholds must be protected from harmful emissions in Table. 3 originating from terrestrial wireless mobile transmissions.”
Response: Updated the sentence to below state as below,

“””The FSS/SS, operating with high sensitivity and stringent interference thresholds must be protected from harmful interference in Table 3 originating from terrestrial wireless mobile transmissions”””

Reviewer Comment 8: ITU-R stands for the Radiocommunication Sector of the International Telecommunication Union. It is one of the three sectors of the ITU, along with ITU-D (Development) and ITU-T (Standardization). ITU-R is primarily responsible for managing the radio-frequency spectrum and satellite orbits, ensuring efficient and interference-free operation of radiocommunication systems worldwide. Therefore “The Spectrum Access System Radiocommunication Sector (ITU-R) Sector and 3rd Generation Partnership Project (3GPP) both play significant roles in the evolution and standardization of DSA, from TVWS to 5G NR, while also considering coexistence with FSS.” should be modified to address a proper acronym.

Response: Thank you for the reminder on the sectors and introduction to the other two. We have updated the ITU-R acronym as per comment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

My comments have been addressed satisfactorily. I suggest to accept the paper, but the authors should proofread carefully. I can spot some typos in the revised mansucript, such as "[30?]..". 

Reviewer 3 Report

Comments and Suggestions for Authors

I confirm that the authors have addressed all concerns satisfactorily. The revisions have improved the clarity, rigor, and overall quality of the manuscript. Given these improvements, I recommend acceptance of the manuscript in its current form for publication in the journal.

Comments for author File: Comments.pdf