pith. sign in

arxiv: 1907.01865 · v1 · pith:EN5N772Inew · submitted 2019-07-03 · 📡 eess.SP · cs.IT· math.IT

Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions

Manijeh Bashar , Alister G. Burr , Katsuyuki Haneda , Kanapathippillai Cumanan , Mehdi M. Molu , Mohsen Khalily , Pei Xiao This is my paper

Pith reviewed 2026-05-25 10:03 UTC · model grok-4.3

classification 📡 eess.SP cs.ITmath.IT
keywords massive MIMOlow complexitychannel correlationuser schedulingCOST 2100precodingCSITeigenvalue spectrum
0
0 comments X

The pith

Massive MIMO systems achieve near-full throughput by scheduling users and designing precoders from channel correlation matrices alone, cutting complexity sharply under the COST 2100 model.

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

The paper studies low-complexity massive MIMO operation with a geometry-based stochastic channel model. It proposes user scheduling via the discrete-time Fourier transform of the antenna correlation function, trading off occupied eigenvalue bins against spectral overlap. Linear precoding then uses only the channel correlation matrix to form approximate eigenchannels. Analysis shows throughput falls only slightly without instantaneous channel state information at the transmitter while complexity drops substantially.

Core claim

Exploiting angular bins in the eigenvalue spectrum of the channel covariance matrix allows construction of approximate eigenchannels that support both user selection and linear precoding without any CSIT. Under the COST 2100 model this yields a small average throughput reduction accompanied by a large reduction in system complexity.

What carries the argument

Approximate eigenchannels built from the occupied bins of the eigenvalue spectrum of the channel covariance matrix, obtained via discrete-time Fourier transform of the antenna correlation function.

If this is right

  • User scheduling selects sets that minimize spectral overlap among occupied eigenvalue bins.
  • Linear precoders are formed directly from the channel correlation matrix without instantaneous channel realizations.
  • Average system throughput decreases only modestly when CSIT is omitted.
  • Overall transceiver complexity falls significantly because full channel estimation and feedback are avoided.

Where Pith is reading between the lines

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

  • The same bin-partitioning method could be tested on other geometry-based models to check whether correlation-only operation remains viable.
  • Base-station hardware could be simplified by replacing full CSI acquisition hardware with correlation estimation circuits.
  • In dense networks the reduced feedback load might allow more users to be served within the same coherence time.

Load-bearing premise

The eigenvalue spectrum bins extracted from the discrete-time Fourier transform of the antenna correlation function give a sufficient proxy for user separability and workable approximate eigenchannels in the COST 2100 model.

What would settle it

Numerical evaluation under the COST 2100 model comparing throughput and complexity when full CSIT is available versus when only correlation-based approximate eigenchannels are used; the claim fails if the throughput gap exceeds the reported slight decrease or complexity savings do not appear.

Figures

Figures reproduced from arXiv: 1907.01865 by Alister G. Burr, Kanapathippillai Cumanan, Katsuyuki Haneda, Manijeh Bashar, Mehdi M. Molu, Mohsen Khalily, Pei Xiao.

Figure 1
Figure 1. Figure 1: The performance of Algorithm 1 with pk = 10 dBm and R = 500 meters. 0 10 20 30 40 Total transmit power (dBm) 0 20 40 60 80 100 120 140 160 Average sum rate (bps/Hz) GWC Proposed Algorithm (CUSBF) JSDM-based scheduling [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The average sum rate vs. transmit power. Solid (blu [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
read the original abstract

A low complexity massive multiple-input multiple-output (MIMO) technique is studied with a geometry-based stochastic channel model, called COST 2100 model. We propose to exploit the discrete-time Fourier transform of the antenna correlation function to perform user scheduling. The proposed algorithm relies on a trade off between the number of occupied bins of the eigenvalue spectrum of the channel covariance matrix for each user and spectral overlap among the selected users. We next show that linear precoding design can be performed based only on the channel correlation matrix. The proposed scheme exploits the angular bins of the eigenvalue spectrum of the channel covariance matrix to build up an "approximate eigenchannels" for the users. We investigate the reduction of average system throughput with no channel state information at the transmitter (CSIT). Analysis and numerical results show that while the throughput slightly decreases due to the absence of CSIT, the complexity of the system is reduced significantly.

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

0 major / 3 minor

Summary. The paper evaluates low-complexity massive MIMO techniques under the COST 2100 geometry-based stochastic channel model. It proposes using the discrete-time Fourier transform of the antenna correlation function to extract eigenvalue spectrum bins of the channel covariance matrix for user scheduling (trading off occupied bins against spectral overlap), followed by linear precoding based solely on the channel correlation matrix to construct approximate eigenchannels. The central claim is that this statistical approach yields only a slight reduction in average system throughput relative to full-CSIT baselines while significantly lowering complexity.

Significance. If the numerical results hold, the work provides concrete evidence that DFT-based binning of the channel covariance can deliver usable approximate eigenchannels for scheduling and statistical precoding under realistic propagation, achieving most of the performance of instantaneous-CSIT methods at far lower overhead. This is a practically relevant finding for massive MIMO deployment, as it quantifies the throughput-complexity trade-off in a standard channel model rather than idealized i.i.d. assumptions.

minor comments (3)
  1. [Section III-B] Section III-B: the precise criterion used to decide the number of occupied bins per user (and the overlap threshold) is described only in prose; a short algorithmic listing or pseudocode would remove ambiguity about how the scheduling rule is implemented.
  2. [Figure 4] Figure 4 and associated text: the throughput curves are presented without error bars or indication of the number of Monte-Carlo drops; adding this information would strengthen the claim of a 'slight' decrease.
  3. [Section IV] Section IV: the complexity comparison is given in big-O notation but does not include concrete flop counts or runtime measurements on the same hardware; a small table of measured operations per coherence block would make the 'significantly reduced' claim more tangible.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary and significance assessment of our work evaluating low-complexity massive MIMO techniques under the COST 2100 model. The recommendation of minor revision is noted. No specific major comments were provided in the report, so we have no individual points requiring response or revision at this time.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

This is an evaluation paper that applies DFT binning of the channel correlation function to user scheduling and constructs approximate eigenchannels for statistical precoding under the external COST 2100 geometry-based stochastic model. The central results are numerical throughput and complexity comparisons between full-CSIT and correlation-only schemes; these outcomes are obtained by direct simulation rather than any closed-form derivation that reduces to fitted parameters or prior self-citations by construction. No load-bearing step matches any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated premise that the COST 2100 model and its correlation matrices are representative.

pith-pipeline@v0.9.0 · 5715 in / 1102 out tokens · 20195 ms · 2026-05-25T10:03:22.076782+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages · 1 internal anchor

  1. [1]

    A twin-clust er MIMO channel model,

    H. Hofstetter, A. F. Molisch, and N. Czink, “A twin-clust er MIMO channel model,” in Proc. IEEE EuCAP , Nov. 2006

    work page 2006

  2. [2]

    Propagation channel mod els for next- generation wireless communications systems,

    A. F. Molisch and F. Tufvesson, “Propagation channel mod els for next- generation wireless communications systems,” IEEE Trans. Commun. , vol. E97-B, no. 10, pp. 2022–2034, Oct. 2014

    work page 2022

  3. [3]

    A study of d ynamic multipath clusters at 60 GHz in a large indoor environment,

    M. Bashar, K. Haneda, A. Burr, and K. Cumanan, “A study of d ynamic multipath clusters at 60 GHz in a large indoor environment,” in Proc. IEEE Globecom W orkshop, Dec. 2018

    work page 2018

  4. [4]

    Joint spatial division and mu ltiplexing for mm-wave channels,

    A. Adhikary, E. A. Safadi, M. Samimi, R. Wang, G. Caire, T. S. Rappaport, and A. F. Molisch, “Joint spatial division and mu ltiplexing for mm-wave channels,” IEEE J. Sel. Areas Commun. , vol. 32, no. 6, pp. 1239–1255, Jun. 2014

    work page 2014

  5. [5]

    Joint spatial d ivision and multiplexing—the large-scale array regime,

    A. Adhikary, J. Nam, J. Ahn, and G. Caire, “Joint spatial d ivision and multiplexing—the large-scale array regime,” IEEE Trans. Commun. , vol. 59, no. 10, pp. 6441–6463, Oct. 2013

    work page 2013

  6. [6]

    User scheduling in massive mimo,

    H. Y ang, “User scheduling in massive mimo,” in Proc. IEEE SPAWC , Jun. 2018, pp. 1–5

    work page 2018

  7. [7]

    A New Approach to User Scheduling in Massive Multi-User MIMO Broadcast Channels

    G. Lee and Y . Sung, “A new approach to user scheduling in massive multi-user mimo broadcast channels,” [online]. Available: http://arxiv.org/pdf/1403.6931.pdf

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    User selection algorithms f or block diagonalization aided multiuser downlink mm-wave communi cation,

    R. Rajashekar and L. Hanzo, “User selection algorithms f or block diagonalization aided multiuser downlink mm-wave communi cation,” IEEE Access , vol. 5, pp. 5760–5772, 2007

    work page 2007

  9. [9]

    Zero-f orcing methods for downlink spatial multiplexing in multiuser mim o channels,

    Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, “Zero-f orcing methods for downlink spatial multiplexing in multiuser mim o channels,” IEEE Trans. Signal Process. , vol. 52, no. 2, pp. 461–471, Feb. 20-4. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2019 7

    work page 2019

  10. [10]

    Sum rate optimal multi-antenn a downlink beamforming strategy based on clique search,

    T. Y oo and A. Goldsmith, “Sum rate optimal multi-antenn a downlink beamforming strategy based on clique search,” in Proc. IEEE Globecom , Dec. 2005

    work page 2005

  11. [11]

    On the optimality of multiantenna broadcast sched uling using zero-forcing beamforming,

    ——, “On the optimality of multiantenna broadcast sched uling using zero-forcing beamforming,” IEEE J. Sel. Areas Commun. , vol. 24, no. 3, pp. 528–541, Mar. 2003

    work page 2003

  12. [12]

    Robust user scheduling with COST 2100 channel model for massive MIMO net - works,

    M. Bashar, A. G. Burr, K. Haneda, and K. Cumanan, “Robust user scheduling with COST 2100 channel model for massive MIMO net - works,” IET Microwave Antenna and Propag. , vol. 12, no. 11, Apr. 2018

    work page 2018

  13. [13]

    Robust geometry-based user scheduling for large MIMO syst ems under realistic channel conditions,

    M. Bashar, A. G. Burr, D. Maryopi, K. Haneda, and K. Cuman an, “Robust geometry-based user scheduling for large MIMO syst ems under realistic channel conditions,” in Proc. IEEE EW , May 2018, pp. 1–6

    work page 2018

  14. [14]

    Threshold-ba sed CSI feed- back reduction for time-varying multiple-input multiple- output broadcast channels,

    M. Bashar, M. Eslami, and M. J. Dehghani, “Threshold-ba sed CSI feed- back reduction for time-varying multiple-input multiple- output broadcast channels,” IET Microwave Antenna and Propag. , vol. 8, no. 9, pp. 1616– 1625, Jun. 2014

    work page 2014

  15. [15]

    Zero-forcing precoding with partially outdated C SI over time- varying MIMO broadcast channels,

    ——, “Zero-forcing precoding with partially outdated C SI over time- varying MIMO broadcast channels,” in Proc. IEEE VTC , Jun. 2013, pp. 1–5

    work page 2013

  16. [16]

    Capacity bounds and estimates for the finite scatterers MIMO wireless channel,

    A. G. Burr, “Capacity bounds and estimates for the finite scatterers MIMO wireless channel,” IEEE J. Sel. Areas Commun. , vol. 21, no. 5, pp. 812–818, Jun. 2003

    work page 2003

  17. [17]

    Multiplexing gain of multiuser MIMO on finite scatt ering chan- nels,

    ——, “Multiplexing gain of multiuser MIMO on finite scatt ering chan- nels,” in Proc. IEEE ISWCS , May 2010, pp. 1–5

    work page 2010

  18. [18]

    Grenander and G

    U. Grenander and G. Szeg˜ o, Toeplitz forms and their applications . London, UK: Chelsea, 1984

    work page 1984

  19. [19]

    On the role o f transmit cor- relation diversity in multiuser MIMO systems,

    M. Steinbauer, A. F. Molisch, and E. Bonek, “On the role o f transmit cor- relation diversity in multiuser MIMO systems,” IEEE Antennas Propag. Mag, vol. 43, no. 4, pp. 51–63, Aug. 2001

    work page 2001

  20. [20]

    T. V . Chien and E. Bj¨ ornson, 5G Mobile Communications, Chapter: Massive MIMO Communications . Springer International Publishing, 2017

    work page 2017

  21. [21]

    Massiv e MIMO: ten myths and one critical question,

    E. Bj¨ ornson, E. G. Larsson, and T. L. Marzetta, “Massiv e MIMO: ten myths and one critical question,” IEEE Commun. Mag. , vol. 54, no. 2, pp. 114–123, Feb. 2016

    work page 2016

  22. [22]

    V erdone and E

    R. V erdone and E. A. Zanella, Pervasive Mobile and Ambient Wireless Communications: COST Action 2100 . Springer, 2012

    work page 2012

  23. [23]

    Spatial lo ng-term variations in urban, rural and indoor environments,

    I. Viering, H. Hofstetter, and W. Utschick, “Spatial lo ng-term variations in urban, rural and indoor environments,” in Proc. the 5th Meeting of COST273, Lisbon, Portugal , Sep. 2002

    work page 2002

  24. [24]

    Max-min rate of cell-free massive MIMO uplink with optimal uniform quantization,

    M. Bashar, K. Cumanan, A. G. Burr, H. Q. Ngo, M. Debbah, an d P .Xiao, “Max-min rate of cell-free massive MIMO uplink with optimal uniform quantization,” IEEE Trans. Commun. , pp. 1–18, Accepted

    work page

  25. [25]

    On the uplink max-min SINR of cell-free massive MIMO systems,

    M. Bashar, K. Cumanan, A. G. Burr, M. Debbah, and H. Q. Ngo , “On the uplink max-min SINR of cell-free massive MIMO systems,” IEEE Trans. Wireless Commun. , vol. 18, no. 4, pp. 2021–2036, Apr. 2019

    work page 2021

  26. [26]

    Cooperative acce ss networks: Optimum fronthaul quantization in distributed massive MIM O and cloud RAN,

    A. G. Burr, M. Bashar, and D. Maryopi, “Cooperative acce ss networks: Optimum fronthaul quantization in distributed massive MIM O and cloud RAN,” in Proc. IEEE VTC , Jun. 2018, pp. 1–7

    work page 2018

  27. [27]

    Cell- free massive MIMO with limited backhaul,

    M. Bashar, K. Cumanan, A. G. Burr, H. Q. Ngo, and M. Debbah , “Cell- free massive MIMO with limited backhaul,” in Proc. IEEE ICC , May 2018, pp. 1–7

    work page 2018

  28. [28]

    Mixed quality of service in cell-free massive MIMO,

    M. Bashar, K. Cumanan, A. G. Burr, H. Q. Ngo, and H. V . Poor , “Mixed quality of service in cell-free massive MIMO,” IEEE Commun. Lett. , vol. 22, no. 7, pp. 706–709, Jul. 2018

    work page 2018

  29. [29]

    Energy efficiency of the cell-free massive MIMO upl ink with optimal uniform quantization,

    M. Bashar, K. Cumanan, A. G. Burr, H. Q. Ngo, E. G. Larsson , and P . Xiao, “Energy efficiency of the cell-free massive MIMO upl ink with optimal uniform quantization,” IEEE Trans. Green Commun. and Net. , Accepted

    work page

  30. [30]

    Optimal precoding in the relay and the optimality of largest eigenmode relaying with statistical channel state information,

    M. M. Molu and N. Goertz, “Optimal precoding in the relay and the optimality of largest eigenmode relaying with statistical channel state information,” IEEE Trans. Wireless Commun. , vol. 3, no. 4, pp. 2113– 2123, Apr. 2014

    work page 2014

  31. [31]

    A novel equivalent definition of modified bessel functions for performance analysis of multi-hop wireless communicat ion systems,

    M. M. Molu and P . Xiao and M. Khalily and L. Zhang and R. Tafazolli, “A novel equivalent definition of modified bessel functions for performance analysis of multi-hop wireless communicat ion systems,” IEEE Trans. Wireless Commun. , vol. 5, no. 1, pp. 7594–7605, 2017

    work page 2017

  32. [32]

    Statistical analysi s of multiantenna relay systems and power allocation algorithms in a relay wit h partial channel state information,

    M. M. Molu, A. Burr, and N. Goertz, “Statistical analysi s of multiantenna relay systems and power allocation algorithms in a relay wit h partial channel state information,” IEEE Trans. Wireless Commun., vol. 14, no. 9, pp. 5123–5134, Sep. 2015

    work page 2015

  33. [33]

    Low- complexity polynomial channel estimation in large-scale M IMO with arbitrary statistics,

    N. Shariati, E. Bj¨ ornson, M. Bengtsson, and M. Debbah, “Low- complexity polynomial channel estimation in large-scale M IMO with arbitrary statistics,” IEEE J. Sel. Topics Signal Process , vol. 14, no. 5, pp. 2868–2882, Jan. 2015

    work page 2015

  34. [34]

    V . Y . Pan and Z. Q. Chen, The complexity of the matrix eigenproblem . 31st Annual ACM Symp. on Theory of Computing, New Y ork, 1999

    work page 1999

  35. [35]

    Y oo, Sum-capacity, scheduling, and multi-user diversity in MIM O broadcast systems

    T. Y oo, Sum-capacity, scheduling, and multi-user diversity in MIM O broadcast systems. Ph.D. dissertation, Stanford University, United states, 2007

    work page 2007

  36. [36]

    Bashar, Cell-free Massive MIMO and Millimeter W ave Channel Modelling for 5G and Beyond

    M. Bashar, Cell-free Massive MIMO and Millimeter W ave Channel Modelling for 5G and Beyond . Ph.D. dissertation, University of Y ork, United Kingdom, 2019

    work page 2019

  37. [37]

    Energy and s pectral effi- ciency of very large multiuser MIMO systems,

    H. Q. Ngo, E. G. Larsson, and T. L. Marzetta, “Energy and s pectral effi- ciency of very large multiuser MIMO systems,” IEEE Trans. Commun. , vol. 61, no. 4, pp. 1436–1449, Apr. 2013

    work page 2013

  38. [38]

    Scaling up MIMO: Opportunities and chall enges with very large arrays,

    F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marze tta, O. Edfors, and F. Tufvesson, “Scaling up MIMO: Opportunities and chall enges with very large arrays,” IEEE Signal Process. Mag. , vol. 30, no. 5, pp. 40–60, Jan. 2013

    work page 2013

  39. [39]

    Sum rate analysis of multiuser MIMO system with zero-forcing transmit beamforming,

    P . Lu and H. C. Y ang, “Sum rate analysis of multiuser MIMO system with zero-forcing transmit beamforming,” IEEE Trans. on Commun. , vol. 57, no. 9, pp. 2585–2589, Sep. 2009

    work page 2009

  40. [40]

    Max–min fair transmit precoding for multi-group multicas ting in mas- sive MIMO,

    M. Sadeghi, E. Bj¨ ornson, E. G. Larsson, C. Y uen, and T. L . Marzetta, “Max–min fair transmit precoding for multi-group multicas ting in mas- sive MIMO,” IEEE Trans. Wireless Commun. , vol. 17, no. 2, pp. 1358– 1373, Feb. 2017

    work page 2017