Pith. sign in

REVIEW 2 major objections 5 minor 100 references

6G Takes Shape

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read 6G will be an efficiency-first, services-augmented evolution of 5G rather than a leap in communication targets.

desk verdict A coherent, contrarian 6G roadmap with a defensible D2C link-budget analysis; the no-KPI-leap claim is a forecast with thin direct evidence. read the letter →

arxiv 2411.18435 v1 pith:5JOBPHDD submitted 2024-11-27 cs.IT math.IT

Jeffrey G. Andrews , Todd E. Humphreys , Tingfang Ji This is my paper · ORCID
classification cs.ITmath.IT
keywords 6GcellularstandardsspectralefficiencyenergyFR3spectrumOFDMdirect-to-handsetsatelliteO-RAN
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

This paper argues that the sixth-generation cellular standard, expected around 2030, will not chase dramatic new speed, latency, or reliability records. Instead, 6G design will be driven by three efficiencies—spectral (bits/Hz/area), energy (bits/Joule), and cost (bits/dollar)—plus three new service directions: sensing and localization, integrated compute, and global or emergency connectivity. If the authors are right, the 6G air interface will keep OFDM, the new mid-band spectrum FR3 (roughly 7–24 GHz) will be the decisive addition, machine learning will assist rather than replace the physical layer, and satellite direct-to-handset service will offer 3G-like speeds rather than true broadband. The paper gives engineers and researchers a concrete, testable picture of what 6G standardization, beginning around 2025, is likely to produce.

What carries the argument

The argument is carried by two organizing figures: the 6G efficiency triangle (area spectral efficiency in bits/Hz/area, energy efficiency in bits/Joule, cost efficiency in bits/currency) and the 6G services triangle (sensing and awareness, compute, global and emergency connectivity). Fusing these gives the 6G value prism, whose volume—service adoption times efficiency gains times rollout—the authors use as the measure of 6G's added value over 5G. These figures translate the thesis into a design agenda: pursue efficiency at every layer, add sensing and compute as native services, and leave communication KPIs roughly where 5G set them.

What would settle it

If global cellular traffic grows at well under about 8% per year over the decade to 2030, the claimed 10x capacity requirement—and the resulting push for FR3—loses its quantitative basis.

Watch

Extended reading notes

Core claim

The paper's central claim is that 6G will be an evolution of 5G rather than a clean break: it will keep OFDM as the dominant waveform, keep communication KPIs roughly at 5G levels, and derive its distinct value from a relentless drive for spectral, energy, and cost efficiency plus three new service dimensions—integrated sensing and localization, network-provided compute, and true global connectivity including emergency access. On spectrum, the authors predict FR3, especially the lower portion around 7–8 GHz, will be the defining new band, while mmWave remains a hotspot and fixed-wireless-access technology and THz and reflective surfaces remain niche. On the physical layer, they expect upgraded LDPC and Polar coding, constellation shaping, sub-band full-duplex, and a native-MIMO design in which machine learning assists channel estimation, beam alignment, localization, and cell tuning without replacing the model-based pipeline. On networks, they expect disaggregation and O-RAN to become the dominant architecture but with continued vendor concentration, and satellite direct-to-handset links to provide revolutionary but 3G-like connectivity rather than broadband.

Load-bearing premise

The argument assumes the industry's historical rhythm continues: a new G roughly every decade, spectrum released on that cadence, traffic growing near 25% per year, and cost and energy remaining the binding constraints.

Editorial extensions

If this is right

  • 6G standardization will focus on making 5G-grade communication KPIs cheaper and more energy-efficient rather than on raising peak-rate or reliability targets.
  • The air interface will remain OFDM/OFDMA, with new waveforms limited to specialized or embedded niches and new coding and modulation constrained by hardware compatibility with 5G LDPC and Polar codes.
  • FR3 (roughly 7–24 GHz, especially 7–8 GHz) will be the key new spectrum, with C-band continuing as a capacity workhorse and mmWave limited to hotspots and fixed wireless access.
  • Satellite direct-to-handset service will deliver roughly 3–5 Mbps downlink plus voice and text, serving as rural infill and emergency backup rather than a competitor to terrestrial broadband.
  • RAN disaggregation and O-RAN will become the dominant network architecture, but only a few vendors will thrive, and vendor lock-in will persist because of performance, energy efficiency, and security concerns.

Reading between the lines

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

  • If the efficiency-first thesis holds, the industry's competitive metric may shift from headline peak rates to bits-per-Joule and bits-per-dollar, changing how operators market 6G and how regulators value spectrum.
  • The paper's D2C capacity arithmetic implies that rural universal-service policy may need to treat 3G-like satellite service as the realistic target, reserving terrestrial small cells and backhaul for broadband demand; actual D2C throughput reports will test this directly.
  • The FR3 roadmap creates a testable dependency on spectrum-sharing rules: if incumbent protection forces frequent service pre-emption, FR3's commercial value would drop even if the paper's propagation claims are correct.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. This article, written as a 20-question-and-answer perspective for IEEE BITS, predicts the technical shape of 6G. Its central thesis is that 6G will be driven primarily by spectral, energy, and cost efficiencies rather than by a leap in communications KPIs, and that three new service dimensions—sensing/localization, compute, and global connectivity—will define its additional value. The paper predicts that OFDM/OFDMA will remain the core waveform, that no new coding family will displace evolved LDPC and Polar codes, that FR3 (roughly 7–16 GHz) will be the principal new spectrum, that THz and RIS will not play major coverage roles, that machine learning will enhance but not replace the physical layer, and that direct-to-handset satellite service will be revolutionary but 3G-like in speed rather than broadband. The authors explicitly acknowledge that the article is speculative because 6G standardization begins only in late 2025.

Significance. If the forecast is correct, this article provides a valuable antidote to 6G hype and a coherent, expert-informed baseline for researchers and engineers. Its main strengths are the transparency of its reasoning, the concreteness of its predictions, and the inclusion of at least one quantitative case study: the D2C capacity calculation in Sec. V-A is simple, reproducible from the stated assumptions, and yields a clear falsifiable claim. The paper is honest about its speculative status and about the possibility that some predictions will fail. It is not a research paper with new theorems or measurements, but as a synthetic perspective it is well organized and engages seriously with 5G lessons, spectrum economics, physical-layer design, and non-terrestrial networks. The efficiency triangle and the three-new-services framing are useful organizing devices, even if the value-prism figure is largely illustrative.

major comments (2)
  1. [II-B] The claim that '6G communication KPIs will not differ much from 5G's' is the hinge of the efficiency-first thesis, but the manuscript never directly engages the IMT-2030 targets (100 Gbps peak, 1 Gbps user-experienced, 0.1 ms latency) that it dismisses as a 'slightly mismatched descriptor.' The stated rationale—that aggressive URLLC is spectral-inefficient and OTA latency is no longer the bottleneck—addresses reliability and latency, but not the peak and user-experienced data-rate targets. Since the downstream predictions (OFDM retention, no new coding family, no THz/RIS in coverage roles) are motivated by this thesis, the authors should either cite ITU-R or operator contributions indicating that these targets are being relaxed, or explicitly state what 2025–2026 standardization outcome would disconfirm their forecast. Without this, the central claim is a defensible prediction but is not yet distinguished from a bare contrarian position.
  2. [II-B and II-F] The 10x capacity requirement and the 'new G every decade' cadence rest on extrapolations: 25% annual traffic growth, a decade-long spectrum-release cycle, and the continuation of current cost and energy constraints. The paper itself notes that growth has slowed to about half that rate in some mature markets, and the same section acknowledges that unmodeled shocks (e.g., a breakthrough in THz hardware or an application with insatiable latency demands) could alter the picture. The authors should add a short scenario analysis—for example, traffic growth at 10–15% per year, or an earlier or later FR3 allocation—to show which of their predictions are robust and which depend on the extrapolation. This would materially strengthen the efficiency-first thesis and would not require a large expansion of the paper.
minor comments (5)
  1. [IV-E] The phrase 'non-idealities such such as low resolution quantization' should read 'such as low resolution quantization.'
  2. [Table I] The abbreviation 'eMMB' in the table should be 'eMBB' (enhanced Mobile Broadband), matching the text.
  3. [III-C] The unquantified claim of a '70-100% capacity gain' in the FDD coverage bands from baseband physical-layer innovations would benefit from a citation or a footnote spelling out the assumed baseline and the individual contributions.
  4. [V-A] The Introduction lists 'global broadband/emergency connectivity' as a 6G service, while the D2C case study concludes that direct-to-handset service will not meet any standard definition of broadband; the text should reconcile these statements by clarifying that global broadband connectivity is expected from the terrestrial-plus-NTN system as a whole, not from D2C alone.
  5. [II-F] The Pentium analogy is effective, but the statement that 6G will be 'a major step towards ending the entire xG paradigm' is stronger than the evidence presented in the same section; consider softening it to 'a step toward decoupling technical innovation from G-numbering.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's predictions are explicit extrapolations, external calculations, and cited empirical results, not reductions to their own inputs.

full rationale

This is a speculative perspective article, not a mathematical derivation, and I found no step where a prediction is equivalent to an input by construction. The 10x capacity claim is simple arithmetic from the paper's explicitly stated 25% annual traffic growth assumption over a decade, not a fitted parameter. The D2C case study is a transparent link-budget-style calculation using public ASTS satellite specifications (area, wavelength, altitude, spectral efficiency) and stated demographic assumptions; the conclusion that broadband D2C is not viable follows from those numbers rather than being assumed. The efficiency-first thesis is an inductive industry argument based on power consumption, cost, and 5G deployment experience, not a definitional tautology. The self-citations in Sec. IV-E (5-10 dB SINR gain from site-specific learning, faster beam alignment) are supporting evidence from the authors' prior peer-reviewed simulation and measurement studies; they are externally falsifiable and do not assume the paper's central 6G predictions. The dispute with the ITU-2030 Hexagon is a correctness or forecasting risk, not circularity. No uniqueness theorem, ansatz, or fitted parameter is smuggled in via self-citation, and the paper does not rename a known result as a derivation. The manuscript is self-contained in the sense that every quantitative claim is traceable to stated assumptions or cited external evidence.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper's predictions rest on a handful of industry-behavior assumptions and several hand-picked parameters in the D2C case study. No new physical or mathematical entities are introduced.

free parameters (4)
  • D2C spectral efficiency assumption = 3 bps/Hz
    Used in Sec. V-A to get 120 Mbps per 40 MHz beam. The paper attributes it to early testing but gives no public source; the 'not broadband' conclusion depends directly on this value.
  • Peak concurrency assumption = 5%
    Sec. V-A converts 4,035 smartphones in a beam-cell into 200 active at peak. The result that fewer than 5 of 200 can receive 25 Mbps scales linearly with this choice.
  • Smartphone ownership and population density = 50%; 30 per km^2
    Sec. V-A: used to compute 4,035 phones in a 269 km^2 beam. The paper notes U.S. ownership is 90%, so 50% is an explicitly conservative choice.
  • Annual traffic growth rate = 25% per year
    Sec. II-B: the 10x 6G capacity requirement follows from 25% growth over 10 years, though the paper notes mature markets may be near half that rate.
assumptions (5)
  • domain assumption The cellular industry will maintain a new-G-per-decade cadence through 6G and beyond.
    Sec. II intro and Sec. II-F: the article treats a 10-year generation cycle as the industry's operating assumption even while noting it is under scrutiny.
  • domain assumption Network traffic will keep growing at about 25% per year globally for the next decade.
    Sec. II-B: this extrapolation produces the 10x capacity target; the paper cites [11] but acknowledges slower mature-market growth.
  • domain assumption Cost and energy consumption are the primary bottlenecks to cellular network growth.
    Sec. II-B: this premise underlies the 6G efficiency triangle; if communication KPIs again dominate design, the article's central thesis weakens.
  • domain assumption Spectrum will continue to be released on a roughly decadal cadence with increasing sharing requirements.
    Sec. II-F and Sec. III-E: used to predict FR3 availability and the end of clean spectrum auctions.
  • domain assumption Lessons from 5G, especially mmWave disappointment and C-band success, will transfer to 6G planning.
    Throughout Sec. II and III: the predictions assume 5G deployment experience is the correct prior for 6G design choices.

how reviews work

0 comments
Cite this review

Pith. "Pith review of 6G Takes Shape." pith.science (2026). https://pith.science/paper/5JOBPHDD

@misc{pith2026241118435,
  author       = {Pith},
  title        = {Pith review of: 6G Takes Shape},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5JOBPHDD}},
  note         = {Machine review of arXiv:2411.18435}
}
read the original abstract

The contours of 6G -- its key technical components and driving requirements -- are finally coming into focus. Through twenty questions and answers, this article defines the important aspects of 6G across four categories. First, we identify the key themes and forces driving the development of 6G, and what will make 6G unique. We argue that 6G requirements and system design will be driven by (i) the tenacious pursuit of spectral (bits/Hz/area), energy (bits/Joule), and cost (bits/dollar) efficiencies, and (ii) three new service enhancements: sensing/localization/awareness, compute, and global broadband/emergency connectivity. Second, we overview the important role of spectrum in 6G, what new spectrum to expect in 6G, and outline how the different bands will be used to provide 6G services. Third, we focus our attention on the 6G physical layer, including waveforms, MIMO advancements, and the potential use of deep learning. Finally, we explore how global connectivity will be achieved in 6G, through non-terrestrial networks as well as low-cost network expansion via disaggregation and O-RAN. Although 6G standardization activities will not begin until late 2025, meaning this article is by definition speculative, our predictions are informed by several years of intensive research and discussions. Our goal is to provide a grounded perspective that will be helpful to both researchers and engineers as we move into the 6G era.

Figures

Figures reproduced from arXiv: 2411.18435 by the authors.

Figure 1
Figure 1. 6G Efficiency Triangle. 6G system design will not focus so strongly on maximizing spectral [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. 6G New Services Triangle. The 6G network will provide three key new types of value: pervasive [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. 6G Network Value Prism. The 6G network’s newly created value versus 5G can be visualized as [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: 6G Spectrum Pyramid. to include spectrum in the 3.1-3.4 GHz band. High speed data traffic will be primarily carried by this band and the FR3 band (7-16 GHz). Collectively, these bands will provide a continuum of capacity-coverage tradeoffs and will carry the vast major…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

100 extracted references · 73 canonical work pages

  1. [1]

    The tactile internet: Applications and challenges,

    G. P. Fettweis, “The tactile internet: Applications and challenges,” IEEE Vehicular Technology Magazine, vol. 9, pp. 64–70, Mar. 2014

    2014

  2. [2]

    Connected vehicles: Solutions and challenges,

    N. Lu, N. Cheng, N. Zhang, X. Shen, and J. W. Mark, “Connected vehicles: Solutions and challenges,” IEEE internet of things journal , vol. 1, no. 4, pp. 289–299, 2014

    2014

  3. [3]

    Connected-vehicles applications are emerging,

    E. Uhlemann, “Connected-vehicles applications are emerging,” IEEE Vehicular Technology Magazine, vol. 11, no. 1, pp. 25–96, 2016

    2016

  4. [4]

    Millimeter wave mobile communications for 5G cellular: It will work!,

    T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y . Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communications for 5G cellular: It will work!,” IEEE Access, vol. 1, pp. 335–349, 2013

    2013

  5. [5]

    Toward 6G networks: Use cases and technologies,

    M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6G networks: Use cases and technologies,” IEEE communications magazine, vol. 58, no. 3, pp. 55–61, 2020

    2020

  6. [6]

    The road towards 6G: A comprehensive survey,

    W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The road towards 6G: A comprehensive survey,” IEEE Open Journal of the Communications Society, vol. 2, pp. 334–366, 2021

    2021

  7. [7]

    On the road to 6G: Visions, requirements, key technologies, and testbeds,

    C.-X. Wang, X. You, X. Gao, X. Zhu, Z. Li, C. Zhang, H. Wang, Y . Huang, Y . Chen, H. Haas, et al., “On the road to 6G: Visions, requirements, key technologies, and testbeds,” IEEE Communications Surveys & Tutorials , vol. 25, no. 2, pp. 905–974, 2023

    2023

  8. [8]

    Communication over fading channels with delay constraints,

    R. Berry and R. Gallager, “Communication over fading channels with delay constraints,” IEEE Trans. on Info. Theory , vol. 48, pp. 1135–1149, May 2002

    2002

Show all 100 references
  1. [9]

    Channel coding rate in the finite blocklength regime,

    Y . Polyanskiy, H. V . Poor, and S. Verdu, “Channel coding rate in the finite blocklength regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, pp. 2307–2359, 2010

    2010

  2. [10]

    A power efficiency metric for comparing energy consumption in future wireless networks in the millimeter-wave and terahertz bands,

    O. Kanhere, H. Poddar, Y . Xing, D. Shakya, S. Ju, and T. S. Rappaport, “A power efficiency metric for comparing energy consumption in future wireless networks in the millimeter-wave and terahertz bands,” IEEE Wireless Communications , vol. 29, no. 6, pp. 56–63, 2022

    2022

  3. [11]

    Ericsson mobility report,

    Ericsson, “Ericsson mobility report,” June 2024

    2024

  4. [12]

    An operator perspective on 6G,

    E. Hardouin, “An operator perspective on 6G,” in IEEE Communications Theory Workshop , (Banff, Canada), May 2024. 33

    2024

  5. [13]

    Femtocells: Past, present, and future,

    J. G. Andrews, H. Claussen, M. Dohler, S. Rangan, and M. C. Reed, “Femtocells: Past, present, and future,” IEEE Journal on Sel. Areas in Communications , vol. 30, pp. 497–508, Apr. 2012

    2012

  6. [14]

    Waveform design and signal processing aspects for fusion of wireless communications and radar sensing,

    C. Sturm and W. Wiesbeck, “Waveform design and signal processing aspects for fusion of wireless communications and radar sensing,” Proceedings of the IEEE , vol. 99, no. 7, pp. 1236–1259, 2011

    2011

  7. [15]

    Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, and S. Buzzi, “Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,” IEEE journal on selected areas in communications , vol. 40, no. 6, pp. 1728–1767, 2022

    2022

  8. [16]

    Toward an intelligent edge: Wireless communication meets machine learning,

    G. Zhu, D. Liu, Y . Du, C. You, J. Zhang, and K. Huang, “Toward an intelligent edge: Wireless communication meets machine learning,” IEEE Communications Magazine , vol. 58, no. 1, pp. 19–25, 2020

    2020

  9. [17]

    Five takeaways on the future of 6G XR from the 6G@UT Forum,

    E. Wang, M. Dohler, G. Phillips, and P. Karlsson, “Five takeaways on the future of 6G XR from the 6G@UT Forum,” Ericsson Blog: https://www.ericsson.com/en/blog/2023/5/five-research-insights-on-6g-xr, 2023

    2023

  10. [18]

    5G New Radio: Unveiling the essentials of the next generation wireless access technology,

    X. Lin, J. Li, R. Baldemair, J.-F. T. Cheng, S. Parkvall, D. C. Larsson, H. Koorapaty, M. Frenne, S. Falahati, A. Grovlen, and K. Werner, “5G New Radio: Unveiling the essentials of the next generation wireless access technology,”IEEE Communications Standards Magazine, vol. 3, ...

    2019

  11. [19]

    Cellular wireless networks in the upper mid-band,

    S. Kang, M. Mezzavilla, S. Rangan, A. Madanayake, S. B. Venkatakrishnan, G. Hellbourg, M. Ghosh, H. Rahmani, and A. Dhananjay, “Cellular wireless networks in the upper mid-band,” IEEE Open Journal of the Communications Society , vol. 5, pp. 2058–2075, 2024

    2024

  12. [20]

    Integrated access and backhaul: A key enabler for 5G millimeter-wave deployments,

    M. Cudak, A. Ghosh, A. Ghosh, and J. Andrews, “Integrated access and backhaul: A key enabler for 5G millimeter-wave deployments,” IEEE Communications Magazine , vol. 59, no. 4, pp. 88–94, 2021

    2021

  13. [21]

    Extreme massive MIMO for macro cell capacity boost in 5G-Advanced and 6G,

    H. Holma, H. Viswanathan, and P. Mogensen, “Extreme massive MIMO for macro cell capacity boost in 5G-Advanced and 6G,” Nokia Bell Labs White Paper, https://www.bell-labs.com/institute/white-papers/, 2023

    2023

  14. [22]

    Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,

    T. S. Rappaport, Y . Xing, O. Kanhere, S. Ju, A. Madanayake, S. Mandal, A. Alkhateeb, and G. C. Trichopoulos, “Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE Access, vol. 7, pp. 78729– 78757, 2019

    2019

  15. [23]

    Wireless communications through reconfigurable intelligent surfaces,

    E. Basar, M. Di Renzo, J. De Rosny, M. Debbah, M.-S. Alouini, and R. Zhang, “Wireless communications through reconfigurable intelligent surfaces,” IEEE Access, vol. 7, pp. 116753–116773, 2019

    2019

  16. [24]

    Intelligent surfaces empowered wireless network: Recent advances and the road to 6G,

    Q. Wu, B. Zheng, C. You, L. Zhu, K. Shen, X. Shao, W. Mei, B. Di, H. Zhang, E. Basar, L. Song, M. D. Renzo, Z.-Q. Luo, and R. Zhang, “Intelligent surfaces empowered wireless network: Recent advances and the road to 6G,” Proceedings of the IEEE, pp. 1–40, 2024

    2024

  17. [25]

    The national spectrum strategy and implementation plan,

    M. Ghosh, “The national spectrum strategy and implementation plan,” IEEE Wireless Communications, vol. 31, no. 2, pp. 6–7, 2024

    2024

  18. [26]

    Tse and P

    D. Tse and P. Viswanath, Fundamentals of Wireless Communication . Cambridge University Press, 2005

    2005

  19. [27]

    Generalized frequency division multiplexing for 5th generation cellular networks,

    N. Michailow, M. Matth ´e, I. S. Gaspar, A. N. Caldevilla, L. L. Mendes, A. Festag, and G. Fettweis, “Generalized frequency division multiplexing for 5th generation cellular networks,” IEEE Transactions on Communications , vol. 62, no. 9, pp. 3045–3061, 2014

    2014

  20. [28]

    Signal structure of the Starlink Ku-band downlink,

    T. E. Humphreys, P. A. Iannucci, Z. M. Komodromos, and A. M. Graff, “Signal structure of the Starlink Ku-band downlink,” IEEE Transactions on Aerospace and Electronic Systems , pp. 1–16, 2023

    2023

  21. [29]

    Purposeful co-design of OFDM signals for ranging and communications,

    A. M. Graff and T. E. Humphreys, “Purposeful co-design of OFDM signals for ranging and communications,” EURASIP Journal on Advances in Signal Processing , 2024

    2024

  22. [30]

    OFDM-based positioning with unknown data payloads: Bounds and applications to LEO PNT,

    A. M. Graff and T. E. Humphreys, “OFDM-based positioning with unknown data payloads: Bounds and applications to LEO PNT,” IEEE Transactions on Wireless Communications , 2024. Submitted for review

    2024

  23. [31]

    OTFS—a mathematical foundation for communication and radar sensing in the delay-doppler domain,

    S. K. Mohammed, R. Hadani, A. Chockalingam, and R. Calderbank, “OTFS—a mathematical foundation for communication and radar sensing in the delay-doppler domain,” IEEE BITS the Information Theory Magazine , vol. 2, no. 2, pp. 36–55, 2022

    2022

  24. [32]

    Spectral versus energy efficiency in 6G: Impact of the receiver front-end,

    A. Lozano and S. Rangan, “Spectral versus energy efficiency in 6G: Impact of the receiver front-end,” IEEE BITS the Information Theory Magazine, vol. 3, no. 1, pp. 41–53, 2023. 34

    2023

  25. [33]

    Design of low-density parity check codes for 5G new radio,

    T. Richardson and S. Kudekar, “Design of low-density parity check codes for 5G new radio,” IEEE Communications Magazine, vol. 56, no. 3, pp. 28–34, 2018

    2018

  26. [34]

    Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels,

    E. Arikan, “Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels,” IEEE Transactions on Information Theory , vol. 55, no. 7, pp. 3051–3073, 2009

    2009

  27. [35]

    Design of polar codes in 5G New Radio,

    V . Bioglio, C. Condo, and I. Land, “Design of polar codes in 5G New Radio,” IEEE Communications Surveys & Tutorials , vol. 23, no. 1, pp. 29–40, 2021

    2021

  28. [36]

    Bandwidth efficient and rate-matched low-density parity-check coded modulation,

    G. B ¨ocherer, F. Steiner, and P. Schulte, “Bandwidth efficient and rate-matched low-density parity-check coded modulation,” IEEE Transactions on Communications , vol. 63, no. 12, pp. 4651–4665, 2015

    2015

  29. [37]

    Passive self-interference suppression for full-duplex infrastructure nodes,

    E. Everett, A. Sahai, and A. Sabharwal, “Passive self-interference suppression for full-duplex infrastructure nodes,” IEEE Transactions on Wireless Communications, vol. 13, no. 2, pp. 680–694, 2014

    2014

  30. [38]

    Subband full-duplex large-scale deployed network designs and tradeoffs,

    M. Abdelghaffar, T. V . P. Santhappan, Y . Tokgoz, K. Mukkavilli, and T. Ji, “Subband full-duplex large-scale deployed network designs and tradeoffs,” Proceedings of the IEEE , vol. 112, no. 5, pp. 487–510, 2024

    2024

  31. [39]

    Capacity of multi-antenna Gaussian channels,

    E. Telatar, “Capacity of multi-antenna Gaussian channels,” European Trans. Telecommun., vol. 6, pp. 585–95, Nov-Dec. 1999

    1999

  32. [40]

    On limits of wireless communications in a fading environment when using multiple antennas,

    G. Foschini and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” Wireless Personal Communications, vol. 6, pp. 311–335, Mar. 1998

    1998

  33. [41]

    On the achievable throughput of a multi-antenna Gaussian broadcast channel,

    G. Caire and S. Shamai, “On the achievable throughput of a multi-antenna Gaussian broadcast channel,” IEEE Trans. on Info. Theory , vol. 49, pp. 1691–1706, July 2003

    2003

  34. [42]

    Duality, achievable rates and sum rate capacity of the Gaussian MIMO broadcast channel,

    S. Vishwanath, N. Jindal, and A. Goldsmith, “Duality, achievable rates and sum rate capacity of the Gaussian MIMO broadcast channel,” IEEE Trans. on Info. Theory , vol. 49, pp. 2658–2668, Oct. 2003

    2003

  35. [43]

    R. W. Heath and A. Lozano, Foundations of MIMO Communication . Cambridge University Press, 2019

    2019

  36. [44]

    A simple transmit diversity technique for wireless communications,

    S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Sel. Areas in Communications , vol. 16, pp. 1451 – 1458, Oct. 1998

    1998

  37. [45]

    Layered space-time architecture for wireless communication in a fading environment when using multiple antennas,

    G. J. Foschini, “Layered space-time architecture for wireless communication in a fading environment when using multiple antennas,” Bell Labs Technical Journal , vol. 1, no. 2, pp. 41–59, 1996

    1996

  38. [46]

    Grassmannian beamforming for MIMO wireless systems,

    D. J. Love, R. W. Heath, and T. Strohmer, “Grassmannian beamforming for MIMO wireless systems,” IEEE Trans. on Info. Theory , vol. 49, pp. 2735–47, Oct. 2003

    2003

  39. [47]

    Full-dimension MIMO for next generation cellular technology.,

    Y .-H. Nam, B. L. Ng, K. Sayana, Y . Li, J. Zhang, Y . Kim, and J. Lee, “Full-dimension MIMO for next generation cellular technology.,” IEEE Communications Magazine , vol. 51, June 2013

    2013

  40. [48]

    W. Chen, P. Gaal, J. Montojo, and H. Zisimopoulos, Fundamentals of 5G Communications . McGraw-Hill, 2021

    2021

  41. [49]

    Exploiting the depth and angular domains for massive near-field spatial multiplexing,

    P. Ramezani, A. Kosasih, A. Irshad, and E. Bj ¨ornson, “Exploiting the depth and angular domains for massive near-field spatial multiplexing,” IEEE BITS the Information Theory Magazine , vol. 3, no. 1, pp. 14–26, 2023

    2023

  42. [50]

    Holographic MIMO communications: Theoretical foundations, enabling technologies, and future directions,

    T. Gong, P. Gavriilidis, R. Ji, C. Huang, G. C. Alexandropoulos, L. Wei, Z. Zhang, M. Debbah, H. V . Poor, and C. Yuen, “Holographic MIMO communications: Theoretical foundations, enabling technologies, and future directions,” IEEE Communications Surveys and Tutorials, vol. 26,...

    2024

  43. [51]

    Ultra-dense cell-free massive MIMO for 6G: Technical overview and open questions,

    G. I. Hien Quoc Ngo, E. G. Larsson, G. Caire, and J. G. Andrews, “Ultra-dense cell-free massive MIMO for 6G: Technical overview and open questions,” Proceedings of the IEEE, available: https://arxiv.org/abs/2401.03898 , 2024

    2024 arXiv

  44. [52]

    Towards 6G MIMO: Massive spatial multiplexing, dense arrays, and interplay between electromagnetics and processing,

    E. Bjornson and et al, “Towards 6G MIMO: Massive spatial multiplexing, dense arrays, and interplay between electromagnetics and processing,” submitted to Proceedings of the IEEE, https://arxiv.org/abs/2401.02844 , 2024

    2024 arXiv

  45. [53]

    Reincorporating circuit theory into information theory,

    A. Mezghani, M. Akrout, M. R. Castellanos, S. Saab, B. Hochwald, R. W. Heath, and J. A. Nossek, “Reincorporating circuit theory into information theory,” IEEE BITS the Information Theory Magazine , pp. 1–17, 2024

    2024

  46. [54]

    An introduction to deep learning for the physical layer,

    T. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563–575, 2017. 35

    2017

  47. [55]

    Deep learning in physical layer communications,

    Z. Qin, H. Ye, G. Y . Li, and B.-H. F. Juang, “Deep learning in physical layer communications,” IEEE Wireless Communications , vol. 26, no. 2, pp. 93–99, 2019

    2019

  48. [56]

    DeepRx: Fully convolutional deep learning receiver,

    M. Honkala, D. Korpi, and J. M. J. Huttunen, “DeepRx: Fully convolutional deep learning receiver,” IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3925–3940, 2021

    2021

  49. [57]

    DeepWiPHY: Deep learning-based receiver design and dataset for IEEE 802.11ax systems,

    Y . Zhang, A. Doshi, R. Liston, W. tian Tan, X. Zhu, J. G. Andrews, and R. W. Heath, “DeepWiPHY: Deep learning-based receiver design and dataset for IEEE 802.11ax systems,” 2020

    2020

  50. [58]

    Deep learning based communication over the air,

    S. Dorner, S. Cammerer, J. Hoydis, and S. ten Brink, “Deep learning based communication over the air,” IEEE Journal of Selected Topics in Signal Processing , vol. 12, pp. 132–143, February 2018

    2018

  51. [59]

    High dimensional channel estimation using deep generative networks,

    E. Balevi, A. Doshi, A. Jalal, A. Dimakis, , and J. G. Andrews, “High dimensional channel estimation using deep generative networks,” IEEE Journal on Sel. Areas in Communications , vol. 39, pp. 18–30, Jan. 2021

    2021

  52. [60]

    MIMO channel estimation using score-based generative models,

    M. Arvinte and J. I. Tamir, “MIMO channel estimation using score-based generative models,” IEEE Trans. Wireless Commun., vol. 22, pp. 3698–3713, June 2023

    2023

  53. [61]

    An attention-aided deep learning framework for massive MIMO channel estimation,

    J. Gao, M. Hu, C. Zhong, G. Y . Li, and Z. Zhang, “An attention-aided deep learning framework for massive MIMO channel estimation,” IEEE Transactions on Wireless Communications , vol. 21, no. 3, pp. 1823–1835, 2022

    2022

  54. [62]

    Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback: Design, simulation, and analysis,

    J. Guo, C.-K. Wen, S. Jin, and G. Y . Li, “Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback: Design, simulation, and analysis,” IEEE Trans. Wireless Commun. , vol. 19, pp. 2827–2840, Apr. 2020

    2020

  55. [63]

    Distributed deep convolutional compression for Massive MIMO CSI feedback,

    M. B. Mashhadi, Q. Yang, and D. G ¨und¨uz, “Distributed deep convolutional compression for Massive MIMO CSI feedback,” IEEE Transactions on Wireless Communications , vol. 20, no. 4, pp. 2621–2633, 2021

    2021

  56. [64]

    Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO,

    F. Sohrabi, K. M. Attiah, and W. Yu, “Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO,” IEEE Trans. Wireless Commun. , vol. 20, pp. 4044–4057, Jul. 2021

    2021

  57. [65]

    Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems,

    Q. Hu, Y . Cai, Q. Shi, K. Xu, G. Yu, and Z. Ding, “Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems,” IEEE Transactions on Wireless Communications , vol. 20, no. 2, pp. 1394–1410, 2021

    2021

  58. [66]

    End-to-end deep learning for TDD MIMO systems in the 6G upper midbands,

    J. Park, F. Sohrabi, A. Ghosh, and J. G. Andrews, “End-to-end deep learning for TDD MIMO systems in the 6G upper midbands,” https://arxiv.org/abs/2402.01033, 2024

    2024 arXiv

  59. [67]

    Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,

    Y . Shen, Y . Shi, J. Zhang, and K. B. Letaief, “Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 1, pp. 101–115, 2021

    2021

  60. [68]

    Joint user scheduling, base station clustering, and beamforming design based on deep unfolding technique,

    C. Xu, Y . Jia, S. He, Y . Huang, and D. Niyato, “Joint user scheduling, base station clustering, and beamforming design based on deep unfolding technique,” IEEE Transactions on Communications , vol. 71, no. 10, pp. 5831–5845, 2023

    2023

  61. [69]

    One-bit OFDM receivers via deep learning,

    E. Balevi and J. G. Andrews, “One-bit OFDM receivers via deep learning,” IEEE Trans. on Communications , vol. 67, pp. 4326–4336, June 2019

    2019

  62. [70]

    Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations,

    C. Zhang, Y .-L. Ueng, C. Studer, and A. Burg, “Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems , vol. 10, no. 2, pp. 149–163, 2020

    2020

  63. [71]

    5GNSS: Fusion of 5G-NR and GNSS localization for enhanced positioning accuracy and reliability,

    F. Campolo, A. Blaga, M. Rea, A. Lozano, and X. Costa-P ´erez, “5GNSS: Fusion of 5G-NR and GNSS localization for enhanced positioning accuracy and reliability,” IEEE Transactions on Vehicular Technology, 2024

    2024

  64. [72]

    Cooperative deep-learning positioning in mmWave 5G-advanced networks,

    B. C. Tedeschini and M. Nicoli, “Cooperative deep-learning positioning in mmWave 5G-advanced networks,” IEEE Journal on Selected Areas in Communications , 2023

    2023

  65. [73]

    CIPAT: Latent-resilient toolkit for performance impact prediction due to configuration tuning,

    K. Patel, C. Ge, A. Mahimkar, S. Shakkottai, and Y . Shaqalle, “CIPAT: Latent-resilient toolkit for performance impact prediction due to configuration tuning,” in Proceedings of the 1st ACM Workshop on Machine Learning for NextG Networks (ACM MLNextG ’24) , ACM MLNextG ’24, (N...

    2024

  66. [74]

    Joint uplink–downlink capacity and coverage optimization via site-specific learning of antenna settings,

    E. Tekgul, T. Novlan, S. Akoum, and J. G. Andrews, “Joint uplink–downlink capacity and coverage optimization via site-specific learning of antenna settings,” IEEE Transactions on Wireless Communications , vol. 23, no. 5, pp. 4032–4048, 2024

    2024

  67. [75]

    Site-specific beam alignment in 6G via deep learning,

    Y . Heng, Y . Zhang, A. Alkhateeb, and J. G. Andrews, “Site-specific beam alignment in 6G via deep learning,” to appear in IEEE Communications Magazine, 2024. 36

    2024

  68. [76]

    Over-the-air design of GAN training for mmwave mimo channel estimation,

    A. Doshi, M. Gupta, and J. G. Andrews, “Over-the-air design of GAN training for mmwave mimo channel estimation,” IEEE Journal on Selected Areas in Information Theory , vol. 3, pp. 557–73, Sep 2022

    2022

  69. [77]

    More is better: Data augmentation for channel-resilient RF fingerprinting,

    N. Soltani, K. Sankhe, J. Dy, S. Ioannidis, and K. Chowdhury, “More is better: Data augmentation for channel-resilient RF fingerprinting,” IEEE Commun. Mag. , vol. 58, pp. 66–72, Oct. 2020

    2020

  70. [78]

    Generating high dimensional user-specific wireless channels using diffusion models,

    T. Lee, J. Park, H. Kim, and J. G. Andrews, “Generating high dimensional user-specific wireless channels using diffusion models,” Submitted to IEEE Trans. on Wireless Commun., https://arxiv.org/abs/2409.03924 , 2024

    2024

  71. [79]

    Beyond transmitting bits: Context, semantics, and task-oriented communications,

    D. G ¨und¨uz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond transmitting bits: Context, semantics, and task-oriented communications,” IEEE Journal on Selected Areas in Communications , vol. 41, no. 1, pp. 5–41, 2023

    2023

  72. [80]

    Towards semantic MAC protocols for 6G: From protocol learning to language-oriented approaches,

    J. Park and et al, “Towards semantic MAC protocols for 6G: From protocol learning to language-oriented approaches,” to appear, IEEE BITS the Information Theory Magazine , 2025

    2025

  73. [81]

    Toward textual transform coding*,

    T. Weissman, “Toward textual transform coding*,” IEEE BITS the Information Theory Magazine , pp. 1–9, Early Access 2023

    2023

  74. [82]

    Permission to deny of Verizon

    Verizon, “Permission to deny of Verizon.” https://licensing.fcc.gov/myibfs/download.do?attachment key=2770485, Nov. 2020. SAT- PDR-20200413-00034

    2020

  75. [83]

    AST SpaceMobile and Verizon announce plans to target 100 percent geographical coverage of the continental United States from space on premium 850 MHz cellular spectrum

    AST SpaceMobile, Inc., “AST SpaceMobile and Verizon announce plans to target 100 percent geographical coverage of the continental United States from space on premium 850 MHz cellular spectrum.” https://www.businesswire.com/news/home/20240529554741/en/, May 2024

    2024

  76. [84]

    Non-terrestrial networks in the 6G era: Challenges and opportunities,

    M. Giordani and M. Zorzi, “Non-terrestrial networks in the 6G era: Challenges and opportunities,” IEEE network , vol. 35, no. 2, pp. 244–251, 2020

    2020

  77. [85]

    A technical comparison of six satellite systems: Suitability for direct-to-device satellite access,

    S. Boumard, I. Moilanen, M. Lasanen, T. Suihko, and M. H ¨oyhty¨a, “A technical comparison of six satellite systems: Suitability for direct-to-device satellite access,” in 2023 IEEE 9th World Forum on Internet of Things (WF-IoT) , pp. 01–06, IEEE, 2023

    2023

  78. [86]

    Role and evolution of non-terrestrial networks towards 6G systems,

    A. Guidotti, A. Vanelli-Coralli, M. El Jaafari, N. Chuberre, J. Puttonen, V . Schena, G. Rinelli, and S. Cioni, “Role and evolution of non-terrestrial networks towards 6G systems,” IEEE Access, 2024

    2024

  79. [87]

    AST SpaceMobile: The mobile satellite cellular network monopoly

    TheKOOKReport, “AST SpaceMobile: The mobile satellite cellular network monopoly.” https://www.kookreport.com/post/ ast-spacemobile-asts-the-mobile-satellite-cellular-network-monopoly-please-find-my-final-comp, Aug. 2024

    2024

  80. [88]

    AST SpaceMobile secures strategic investment from AT&T, Google and V odafone,

    AST SpaceMobile, “AST SpaceMobile secures strategic investment from AT&T, Google and V odafone,” Jan. 2024. https://www.sec. gov/Archives/edgar/data/1780312/000149315224002914/ex99-1.htm

    2024

  81. [89]

    Distributed approach to satellite direct-to-cell connectivity in 6G non-terrestrial networks,

    D. Tuzi, E. F. Aguilar, T. Delamotte, G. Karabulut-Kurt, and A. Knopp, “Distributed approach to satellite direct-to-cell connectivity in 6G non-terrestrial networks,” IEEE Wireless Communications, vol. 30, no. 6, pp. 28–34, 2023

    2023

  82. [90]

    Federated cell-free MIMO in non-terrestrial networks: Architectures and performance,

    A. Guidotti, A. Vanelli-Coralli, and C. Amatetti, “Federated cell-free MIMO in non-terrestrial networks: Architectures and performance,” IEEE Transactions on Aerospace and Electronic Systems , 2024

    2024

  83. [91]

    NTN economics,

    J. Madden, “NTN economics,” in Brooklyn 6G Summit , (Brooklyn, NY), Oct. 2024

    2024

  84. [92]

    Random access procedure over non-terrestrial networks: From theory to practice,

    O. Kodheli, A. Astro, J. Querol, M. Gholamian, S. Kumar, N. Maturo, and S. Chatzinotas, “Random access procedure over non-terrestrial networks: From theory to practice,” IEEE Access, vol. 9, pp. 109130–109143, 2021

    2021

  85. [93]

    Physical layer enhancements in 5G-NR for direct access via satellite systems,

    S. Cioni, X. Lin, B. Chamaillard, M. El Jaafari, G. Charbit, and L. Raschkowski, “Physical layer enhancements in 5G-NR for direct access via satellite systems,” International Journal of Satellite Communications and Networking , vol. 41, no. 3, pp. 262–275, 2023

    2023

  86. [94]

    Single network future: Supplemental coverage from space,

    Federal Communications Commission, “Single network future: Supplemental coverage from space,” Mar. 2024. https://docs.fcc.gov/ public/attachments/FCC-24-28A1.pdf

    2024

  87. [95]

    Loon Library: Lessons from building Loon’s stratospheric communications service,

    A. Alexander, M. Alvidrez, W. Beg, Z. Benezet-Parsons, et al. , “Loon Library: Lessons from building Loon’s stratospheric communications service,” tech. rep., Project Loon, 2021

    2021

  88. [96]

    Space: The dawn of a new age,

    Citigroup, “Space: The dawn of a new age,” May 2022. https://www.citigroup.com/global/insights/space 20220509

    2022

  89. [97]

    Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges,

    M. Polese, L. Bonati, S. D’oro, S. Basagni, and T. Melodia, “Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges,” IEEE Communications Surveys & Tutorials , vol. 25, no. 2, pp. 1376–1411, 2023. 37

    2023

  90. [98]

    Intelligence and learning in O-RAN for data-driven NextG cellular networks,

    L. Bonati, S. D’Oro, M. Polese, S. Basagni, and T. Melodia, “Intelligence and learning in O-RAN for data-driven NextG cellular networks,” IEEE Communications Magazine , vol. 59, no. 10, pp. 21–27, 2021

    2021

  91. [99]

    Embracing AI in 5G-Advanced toward 6G: A joint 3GPP and O-RAN perspective,

    X. Lin, L. Kundu, C. Dick, and S. Velayutham, “Embracing AI in 5G-Advanced toward 6G: A joint 3GPP and O-RAN perspective,” IEEE Communications Standards Magazine , vol. 7, no. 4, pp. 76–83, 2023

    2023

  92. [100]

    On the criticality of integrity protection in 5G fronthaul networks,

    J. Xing, S. Yoo, X. Foukas, D. Kim, and M. K. Reiter, “On the criticality of integrity protection in 5G fronthaul networks,” in 33rd USENIX Security Symposium (USENIX Security 24) , (Philadelphia, PA), pp. 4463–4479, USENIX Association, Aug. 2024. Jeffrey Andrews (F’13) is the...

    2012

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.