pith. sign in

arxiv: 1906.08490 · v1 · pith:56ZVERELnew · submitted 2019-06-20 · 💻 cs.IT · math.IT

Joint Uplink and Downlink Transmissions in User-Centric OFDMA Cloud-RAN

Zehong Lin , Yuan Liu This is my paper

Pith reviewed 2026-05-25 19:37 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords OFDMAcloud-RANresource allocationuplink downlinkLagrange dualityfronthaul capacityRRH clustering
0
0 comments X

The pith

Joint uplink and downlink resource allocation in user-centric OFDMA cloud-RAN is solved via Lagrange duality to maximize throughput under power and fronthaul constraints.

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

The paper formulates joint UL and DL scheduling, subcarrier assignment, RRH clustering, and power allocation in user-centric OFDMA CRAN as a mixed integer non-convex problem. The objective is to maximize system throughput subject to maximum power and fronthaul capacity limits. An algorithm based on the Lagrange duality method yields an asymptotically optimal solution, while a heuristic reduces complexity. Simulations demonstrate that both the duality-based and heuristic approaches achieve considerable throughput improvements over benchmark schemes.

Core claim

The mixed integer programming problem for joint uplink and downlink transmissions admits an asymptotically optimal solution obtained by the Lagrange duality method, and the resulting allocation improves overall system throughput compared to other schemes while respecting power and fronthaul capacity constraints.

What carries the argument

The mixed integer non-convex throughput maximization problem, solved by the Lagrange duality method to jointly handle UL/DL scheduling, SC assignment, RRH grouping, and power allocation.

If this is right

  • The proposed algorithms produce considerably higher system throughput than benchmark schemes.
  • The heuristic algorithm achieves performance close to the duality-based solution at lower complexity.
  • User-centric RRH selection can be jointly optimized with power allocation without violating fronthaul limits.

Where Pith is reading between the lines

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

  • The duality approach may extend to scenarios with imperfect channel knowledge if the problem structure remains similar.
  • Similar joint UL/DL formulations could apply to non-OFDMA systems that retain the mixed-integer structure.
  • Practical deployment would require verifying that the asymptotic optimality holds for typical network sizes.

Load-bearing premise

The non-convex mixed integer problem admits an asymptotically optimal solution through the Lagrange duality method under the stated power and fronthaul constraints.

What would settle it

A simulation or calculation in which the duality gap fails to approach zero for increasing numbers of subcarriers, showing the obtained solution is not close to optimal.

Figures

Figures reproduced from arXiv: 1906.08490 by Yuan Liu, Zehong Lin.

Figure 1
Figure 1. Figure 1: System model of the considered user-centric OFDMA-b [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Throughput versus the number of SCs N, where K = 2 and M = 4. TABLE I: Running time comparison Algorithm Running time (s) N = 8 N = 16 N = 64 Optimal via exhaustive search 38976.228 - - Proposed asymptotically optimal 29.129 51.196 232.329 Proposed heuristic 10.579 18.789 78.755 the number of accessible SCs C = N. Note that we do not plot the curve of the exhaustive search algorithm when N ≥ 16 due to the … view at source ↗
Figure 3
Figure 3. Figure 3: Throughput versus the number of accessible SCs [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Throughput versus the number of users K, where M = 4. 8. We also let the transmit power of RRHs Pr = (Pu + 7) dBm. From [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Throughput versus the number of RRHs M, where K = 8. heuristic algorithm is not ideal. However, the performance gap between the proposed heuristic algorithm and the proposed asymptotically optimal algorithm becomes smaller as K in￾creases. This is because the RRH selection is not optimal in the proposed heuristic algorithm, but the RRHs are more likely to be selected by the users that can achieve higher pe… view at source ↗
Figure 8
Figure 8. Figure 8: Throughput versus the number of RRHs M, where K = 10. networks to test the performance of the proposed heuristic algorithm against the benchmark schemes. For a fair compari￾son, the benchmarks here are developed based on the proposed heuristic algorithm [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: DL rate versus UL rate, where K = 10 and M = 10. 4 8 12 16 M 20 21 22 23 24 25 26 27 Throughput (bps/Hz) Flexible FDD, wk =1 TDD, wk =1 Flexible FDD, wk =1.2 TDD, wk =1.2 Flexible FDD, wk =1.4 TDD, wk =1.4 [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Throughput versus the number of RRHs M, where K = 10. NRS scheme cannot exploit the benefit of CoMP by multiple RRHs, the throughput increases slower than other schemes and it tends to be the worst one as M increases [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
read the original abstract

This paper studies joint uplink (UL) and downlink (DL) resource allocation in user-centric orthogonal frequency division multiple access (OFDMA) cloud radio access network (CRAN), where the users select the distributed remote radio heads (RRHs) to cooperatively serve their UL and DL transmissions over different subcarriers (SCs). The goal of this paper is to maximize the system throughput through jointly optimizing UL/DL scheduling, SC assignment, RRH grouping/clustering and power allocation under the maximum power and fronthaul capacity constraints. The problem is formulated as a mixed integer programming problem which is non-convex and NP-hard. We propose an efficient algorithm based on the Lagrange duality method to obtain an asymptotically optimal solution for this problem. A heuristic algorithm is further proposed to reduce the complexity. Simulation results illustrate that the proposed heuristic algorithm also has a close-to-optimal performance, and the proposed algorithms can considerably improve the system throughput compared to other benchmark schemes.

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 manuscript formulates joint uplink and downlink resource allocation in user-centric OFDMA Cloud-RAN as a mixed-integer non-convex NP-hard optimization problem. The objective is to maximize system throughput by jointly optimizing UL/DL scheduling, subcarrier assignment, RRH clustering, and power allocation subject to per-RRH power and fronthaul capacity constraints. An efficient Lagrange duality algorithm is proposed to obtain an asymptotically optimal solution, a low-complexity heuristic is introduced, and simulations are used to demonstrate throughput gains relative to benchmark schemes.

Significance. If the duality-based approach is shown to close the duality gap for large numbers of subcarriers as is standard in OFDMA settings, the work would usefully extend existing duality techniques to the joint UL/DL user-centric clustering case in CRANs. The heuristic's reported close-to-optimal performance could provide a practical alternative, and explicit throughput improvements over benchmarks would strengthen the applied contribution.

minor comments (3)
  1. The abstract states that the problem is solved 'asymptotically optimally via the Lagrange duality method' but does not indicate where the duality-gap vanishing argument is formalized or verified for the specific clustering and joint UL/DL constraints; adding a brief reference to the relevant theorem or limit argument would strengthen the central claim.
  2. Simulation details (number of subcarriers, RRH/user densities, fronthaul capacities) are referenced only qualitatively; providing these parameters explicitly would allow readers to assess how well the asymptotic regime is realized in the reported results.
  3. Notation for the binary clustering and scheduling variables could be introduced more explicitly at the start of the problem formulation to improve readability for readers unfamiliar with user-centric CRAN models.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of the manuscript and the recommendation for minor revision. The work already demonstrates that the Lagrange duality approach yields an asymptotically optimal solution that closes the duality gap for large numbers of subcarriers, consistent with standard OFDMA duality results, and the simulations confirm the heuristic's near-optimal performance along with explicit throughput gains over the benchmarks.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper formulates a mixed-integer non-convex optimization problem directly from the user-centric OFDMA C-RAN system model, including UL/DL scheduling, SC assignment, RRH clustering, and power allocation under explicit power and fronthaul constraints. It applies the standard Lagrange duality approach for asymptotic optimality in large-subcarrier OFDMA settings and proposes a low-complexity heuristic, with performance validated against external benchmark schemes in simulations. No steps reduce by construction to fitted inputs, self-definitions, or unverified self-citations; the derivation chain is self-contained from the stated model and standard duality techniques.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on abstract; no explicit free parameters, axioms, or invented entities are identifiable from the given text.

pith-pipeline@v0.9.0 · 5692 in / 954 out tokens · 21699 ms · 2026-05-25T19:37:46.381256+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

30 extracted references · 30 canonical work pages

  1. [1]

    What will 5G be?

    J. G. Andrews, S. Buzzi, W. Choi, S. V . Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang, “What will 5G be?” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1065–1082, Jun. 2014

    work page 2014

  2. [2]

    Network densifi cation: the dominant theme for wireless evolution into 5G,

    N. Bhushan, J. Li, D. Malladi, R. Gilmore, D. Brenner, A. D amnjanovic, R. T. Sukhavasi, C. Patel, and S. Geirhofer, “Network densifi cation: the dominant theme for wireless evolution into 5G,” IEEE Commun. Mag. , vol. 52, no. 2, pp. 82–89, Feb. 2014

    work page 2014

  3. [3]

    C-RAN: the road towards green RAN,

    “C-RAN: the road towards green RAN,” China Mobile Resear ch Insti- tute, Beijing, China, Tech. Rep., Oct. 2011

    work page 2011

  4. [4]

    Cloud RAN for mobile networks—a te ch- nology overview,

    A. Checko, H. L. Christiansen, Y . Y an, L. Scolari, G. Kard aras, M. S. Berger, and L. Dittmann, “Cloud RAN for mobile networks—a te ch- nology overview,” IEEE Commun. Surveys Tuts. , vol. 17, no. 1, pp. 405–426, 1st Quart. 2015

    work page 2015

  5. [5]

    Fronthaul-const rained cloud radio access networks: insights and challenges,

    M. Peng, C. Wang, V . Lau, and H. V . Poor, “Fronthaul-const rained cloud radio access networks: insights and challenges,” IEEE Wireless Commun., vol. 22, no. 2, pp. 152–160, Apr. 2015

    work page 2015

  6. [6]

    Group sparse beamfor ming for green Cloud-RAN,

    Y . Shi, J. Zhang, and K. B. Letaief, “Group sparse beamfor ming for green Cloud-RAN,” IEEE Trans. Wireless Commun. , vol. 13, no. 5, pp. 2809–2823, May 2014

    work page 2014

  7. [7]

    Downlink and uplink energ y minimization through user association and beamforming in C -RAN,

    S. Luo, R. Zhang, and T. J. Lim, “Downlink and uplink energ y minimization through user association and beamforming in C -RAN,” IEEE Trans. Wireless Commun. , vol. 14, no. 1, pp. 494–508, Jan. 2015

    work page 2015

  8. [8]

    Joint precoding a nd RRH selection for user-centric green MIMO C-RAN,

    C. Pan, H. Zhu, N. J. Gomes, and J. Wang, “Joint precoding a nd RRH selection for user-centric green MIMO C-RAN,” IEEE Trans. Wireless Commun., vol. 16, no. 5, pp. 2891–2906, May 2017

    work page 2017

  9. [9]

    Sparse beamforming and user-centric cl ustering for downlink cloud radio access network,

    B. Dai and W. Y u, “Sparse beamforming and user-centric cl ustering for downlink cloud radio access network,” IEEE Access , vol. 2, pp. 1326– 1339, 2014

    work page 2014

  10. [10]

    Joint power control and fron thaul rate allocation for throughput maximization in OFDMA-based clo ud radio access network,

    L. Liu, S. Bi, and R. Zhang, “Joint power control and fron thaul rate allocation for throughput maximization in OFDMA-based clo ud radio access network,” IEEE Trans. Commun., vol. 63, no. 11, pp. 4097–4110, Nov. 2015. 13

    work page 2015

  11. [11]

    Joint millimeter-wave fron thaul and OFDMA resource allocation in ultra-dense CRAN,

    R. G. Stephen and R. Zhang, “Joint millimeter-wave fron thaul and OFDMA resource allocation in ultra-dense CRAN,” IEEE Trans. Com- mun., vol. 65, no. 3, pp. 1411–1423, Mar. 2017

    work page 2017

  12. [12]

    Fronthaul-limited uplink OFDMA in ultra-dense CR AN with hybrid decoding,

    ——, “Fronthaul-limited uplink OFDMA in ultra-dense CR AN with hybrid decoding,” IEEE Trans. V eh. Technol. , vol. PP , no. 99, pp. 1– 1, 2017

    work page 2017

  13. [13]

    Green OFDMA resource allocation in cache-enabled CRAN,

    ——, “Green OFDMA resource allocation in cache-enabled CRAN,” in Proc. IEEE Online Conf. Green Commun. , Nov. 2016, pp. 70–75

    work page 2016

  14. [14]

    Why to decouple the uplink and downlink in cell ular networks and how to do it,

    F. Boccardi, J. Andrews, H. Elshaer, M. Dohler, S. Parkv all, P . Popovski, and S. Singh, “Why to decouple the uplink and downlink in cell ular networks and how to do it,” IEEE Commun. Mag. , vol. 54, no. 3, pp. 110–117, Mar. 2016

    work page 2016

  15. [15]

    Joint optimization of relay strate gies and resource allocations in cooperative cellular networks,

    T. C. y. Ng and W. Y u, “Joint optimization of relay strate gies and resource allocations in cooperative cellular networks,” IEEE J. Sel. Areas Commun., vol. 25, no. 2, pp. 328–339, Feb. 2007

    work page 2007

  16. [16]

    QoS-aware transmission polic ies for OFDM bidirectional decode-and-forward relaying,

    Y . Liu, J. Mo, and M. Tao, “QoS-aware transmission polic ies for OFDM bidirectional decode-and-forward relaying,” IEEE Trans. Wireless Com- mun., vol. 12, no. 5, pp. 2206–2216, May 2013

    work page 2013

  17. [17]

    Optimization framewo rk and graph-based approach for relay-assisted bidirectional OF DMA cellular networks,

    Y . Liu, M. Tao, B. Li, and H. Shen, “Optimization framewo rk and graph-based approach for relay-assisted bidirectional OF DMA cellular networks,” IEEE Trans. Wireless Commun. , vol. 9, no. 11, pp. 3490– 3500, Nov. 2010

    work page 2010

  18. [18]

    On dynamic time-division-duplex transmissions for small-ce ll networks,

    M. Ding, D. Lpez-Prez, R. Xue, A. V . V asilakos, and W. Che n, “On dynamic time-division-duplex transmissions for small-ce ll networks,” IEEE Trans. V eh. Technol., vol. 65, no. 11, pp. 8933–8951, Nov. 2016

    work page 2016

  19. [19]

    Coe xistence of FDD flexible duplexing networks,

    S. Lembo, O. Tirkkonen, M. Goldhamer, and A. Kliks, “Coe xistence of FDD flexible duplexing networks,” in Proc. European Conf. Netw. and Commun. (EuCNC) , Jun. 2017, pp. 1–6

    work page 2017

  20. [20]

    Flexible duplex in FDD spectrum,

    K. Balachandran, J. H. Kang, K. Karakayali, and K. M. Reg e, “Flexible duplex in FDD spectrum,” in Proc. IEEE Int. Conf. Commun. W orkshops (ICC W orkshops), May 2017, pp. 296–301

    work page 2017

  21. [21]

    36.819, 2011

    Coordinated multi-point operation for LTE physical layer a spects (re- lease 11) , 3GPP Std. 36.819, 2011

    work page 2011

  22. [22]

    Tse and P

    D. Tse and P . Viswanath, Fundamentals of Wireless Communication . Cambridge, U.K.: Cambridge Univ. Press, 2005

    work page 2005

  23. [23]

    Dual methods for nonconvex spectrum op timization of multicarrier systems,

    W. Y u and R. Lui, “Dual methods for nonconvex spectrum op timization of multicarrier systems,” IEEE Trans. Commun., vol. 54, no. 7, pp. 1310– 1322, Jul. 2006

    work page 2006

  24. [24]

    Optimal resource allocation for OFDMA downlink systems,

    K. Seong, M. Mohseni, and J. M. Cioffi, “Optimal resource allocation for OFDMA downlink systems,” in Proc. IEEE Int. Symp. Inf. Theory , Jul. 2006, pp. 1394–1398

    work page 2006

  25. [25]

    Energy-efficient re source al- location in multi-cell OFDMA systems with limited backhaul capacity,

    D. W. K. Ng, E. S. Lo, and R. Schober, “Energy-efficient re source al- location in multi-cell OFDMA systems with limited backhaul capacity,” IEEE Trans. Wireless Commun. , vol. 11, no. 10, pp. 3618–3631, Oct. 2012

    work page 2012

  26. [26]

    Boyd and L

    S. Boyd and L. V andenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004

    work page 2004

  27. [27]

    Joint resource allocation in SWIPT-based mult iantenna decode- and-forward relay networks,

    Y . Liu, “Joint resource allocation in SWIPT-based mult iantenna decode- and-forward relay networks,” IEEE Trans. V eh. Technol., vol. 66, no. 10, pp. 9192–9200, Oct. 2017

    work page 2017

  28. [28]

    Goldsmith, Wireless Communication

    A. Goldsmith, Wireless Communication. Cambridge, U.K.: Cambridge Univ. Press, 2005

    work page 2005

  29. [29]

    Coordinated scheduling and power control in cloud-radio access network s,

    A. Douik, H. Dahrouj, T. Y . Al-Naffouri, and M. Alouini, “Coordinated scheduling and power control in cloud-radio access network s,” IEEE Trans. Wireless Commun. , vol. 15, no. 4, pp. 2523–2536, Apr. 2016

    work page 2016

  30. [30]

    Coordinated scheduling for the downlink of cloud r adio-access networks,

    ——, “Coordinated scheduling for the downlink of cloud r adio-access networks,” in Proc. IEEE Int. Conf. Commun. , Jun. 2015, pp. 2906– 2911

    work page 2015