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REVIEW 4 major objections 6 minor 2 cited by

Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that an online Lyapunov-based policy for edge-cloud task offloading and bandwidth allocation keeps time-average user-device energy consumption within $C_{\mathrm{opt}} + B/V$ of the offline optimum while maintaining…

desk verdict A coherent Lyapunov-plus-heuristic MEC paper whose central optimality bound rests on an unproven per-slot optimality premise and a missing appendix. read the letter →

arxiv 2501.02952 v1 pith:CMN56IAM submitted 2025-01-06 cs.NI eess.SP

Geng Sun , Minghua Yuan , Zemin Sun , Jiacheng Wang , Hongyang Du , Dusit Niyato , Zhu Han , Dong In Kim This is my paper · ORCID
classification cs.NIeess.SP
keywords multi-accessedgecomputingtaskoffloadingcommunicationresourceallocationLyapunovoptimizationenergyminimizationqueuingdelayedge-cloudcollaborationonline
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

The paper tackles the problem that energy-limited user devices, bandwidth-limited edge servers, and a distant cloud must jointly decide, in real time, where to process each arriving task and how to split wireless bandwidth. It formulates this as an NP-hard mixed-integer nonlinear program and then proposes an online algorithm that converts the time-coupled problem into per-slot decisions using Lyapunov drift-plus-penalty optimization. The central claim is a quantitative guarantee: the time-average user-device energy consumption of the proposed policy stays within $C_{\mathrm{opt}} + B/V$ of the offline optimum, where $C_{\mathrm{opt}}$ is the best possible value with full future knowledge, while the edge and cloud queues remain stable. If correct, this gives a causal, polynomial-time policy with a provable energy-delay tradeoff for a three-layer edge-cloud MEC system, rather than a heuristic with no performance bound.

What carries the argument

The carrying mechanism is the Lyapunov drift-plus-penalty framework combined with a per-slot decomposition. Virtual queues $Z^E_m(t)$ and $Z^C_m(t)$ convert the long-term queuing-delay constraints into queue-stability constraints, and the drift-plus-penalty bound of Theorem 1 turns the time-coupled problem into a real-time per-slot problem $P''$. The per-slot problem is then decoupled by the Tammer decomposition mechanism into a convex bandwidth-allocation subproblem with a closed-form solution (Theorem 3) and an integer task-offloading subproblem solved by bilateral matching, a remove-action refinement, and dependent rounding. The central identity is the bound $\Lambda(\Theta(t)) \leq B + \sum_m (V\,\text{energy} + \text{queue terms})$, whose constant $B$ and parameter $V$ reappear in the final optimality gap $C_{\mathrm{opt}} + B/V$.

What would settle it

For a single randomly chosen time slot with small $U$ and $M$, solve problem $P''$ to global optimality by exhaustive enumeration over all binary offloading choices and compare that optimum with the output of Algorithm 3; if the ratio of the two objective values is not uniformly bounded across random channel states, then the proof of Eq. (47) fails, because the Lyapunov argument assumes per-slot optimality or a bounded gap.

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Extended reading notes

Core claim

The paper's central discovery, stated as Theorem 9, is that the proposed online joint communication resource allocation and task offloading approach (OJCTA) achieves a time-average user-device energy consumption no larger than $C_{\mathrm{opt}} + B/V$, where $C_{\mathrm{opt}}$ is the offline optimal energy consumption, $B$ is a finite constant from the Lyapunov drift bound, and $V$ is a tunable penalty parameter. The same theorem asserts that this holds while keeping the edge computing queues and cloud offloading queues strongly stable, so the long-term queuing-delay constraints are satisfied. The paper also claims that the worst-case per-slot complexity is polynomial, $O(LU^3)$, and that simulations show OJCTA outperforms the benchmark approaches in energy consumption while maintaining moderate and stable queuing delays.

Load-bearing premise

The energy guarantee holds only if the per-slot solver truly finds the best, or near-best, offloading and bandwidth choice at every slot, but the paper does not prove how close its heuristic solver gets to that best choice.

Editorial extensions

If this is right

  • Time-average user-device energy consumption is within $C_{\mathrm{opt}} + B/V$ of the offline optimum, so increasing $V$ pushes energy consumption toward the optimum at the cost of larger queue backlogs.
  • Edge and cloud queues are stable under the policy, so the long-term queuing-delay constraints derived from Little's law are satisfied without requiring future knowledge of task arrivals or channel states.
  • Each time slot only needs current queue backlogs, current task arrivals, and current channel gains, making the policy implementable online in a dynamic MEC environment.
  • The worst-case per-slot complexity is $O(LU^3)$, polynomial in the number of users $U$ and the capacity-sweep length $L$, so the approach scales to dense deployments.
  • The edge-cloud collaborative architecture and the cloud-offloading queue reduce queuing delay and energy consumption compared to edge-only baselines, according to the simulation comparisons.

Reading between the lines

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

  • If the per-slot solver were replaced by an exact solver, or by one with a proven approximation ratio, the same drift-plus-penalty machinery would convert that ratio into an explicit energy-delay tradeoff curve, which the paper leaves implicit.
  • The four-queue Lyapunov construction is general enough to carry other long-term constraints: adding one virtual queue per average power, bandwidth, or latency budget would extend the same proof structure to a broader class of MEC resource-management problems.
  • Because the dependent-rounding step preserves the total amount of data sent to the cloud, the rounding error is mostly a redistribution across users; a natural test is whether per-user fairness is affected when task sizes are highly skewed.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes a hierarchical edge-cloud MEC architecture and formulates EEDAOP, a problem that minimizes time-average UD energy consumption under task-deadline and long-term queuing-delay constraints. The authors apply Lyapunov drift-plus-penalty optimization to convert the time-coupled problem into a per-slot problem P'', then decouple P'' via a claimed Tammer decomposition, solve the communication-resource subproblem in closed form (Eq. (29)), and solve the task-offloading subproblem with a two-stage heuristic that combines swap matching, convex relaxation with dependent rounding, and a capacity sweep (Algorithms 1-3). The main analytical claims are that OJCTA has worst-case complexity O(LU^3) (Theorem 8) and that its time-average energy is bounded by Copt + B/V (Theorem 9, Eq. (47)), with simulation results reported in Section 6.

Significance. If the analytical claims were fully established, OJCTA would be a valuable polynomial-time online policy with a rigorous near-optimality gap for a realistic edge-cloud MEC setting. The paper has several genuine strengths: the Lyapunov transformation is the standard and appropriate tool, the closed-form bandwidth allocation in Eq. (29) is a plausible result for the stated convex subproblem, and the simulation study compares against multiple external baselines rather than being fitted to reproduce known results. However, the central contribution is the optimality bound in Theorem 9, and that bound is not supported by the presented material: every theorem proof is deferred to a nonexistent appendix, and the per-slot solver is a heuristic with no demonstrated optimality or approximation guarantee. The empirical section does not compensate because it does not compare against the claimed bound. The paper would need a substantially different argument, either an approximation guarantee for the per-slot heuristic or a weakened claim, to make the main theorem load-bearing.

major comments (4)
  1. [§5.3.2, Theorem 9, Eq. (47)] The bound in Eq. (47) is the standard Lyapunov drift-plus-penalty result and is valid only if the per-slot action minimizes the conditional drift-plus-penalty expression, or is within a uniform additive constant of the minimum. The actual per-slot solver is Algorithm 3, which combines the swap-matching heuristic of Algorithm 1, the convex-relaxation plus dependent rounding of Algorithm 2, and a capacity sweep. None of these components is shown to solve P'' (Eq. (22)) or to have any suboptimality bound. The appeal to Tammer decomposition in §5.2.1 does not fill this gap, because even if the decomposition were exact, Algorithms 1-3 do not solve the decomposed subproblems to optimality. Consequently, Eq. (47) does not follow from the presented arguments.
  2. [§5.1-§5.3, Theorems 1-9] Every theorem in the paper, including the central Theorem 9 and the complexity claim Theorem 8, is proved only in "Appendix ?? of the supplemental material," which is absent from the manuscript. As submitted, the paper contains no verifiable proof for any of its analytical claims; the proofs are not merely deferred to a real appendix but to a placeholder that does not exist. This alone prevents acceptance in the present form.
  3. [§5.2.3, Theorem 5, Algorithm 1] The paper states that the energy minimization subproblem P'''2.1 is NP-hard, then proposes a matching-based heuristic without an approximation guarantee. Pairwise stability of the swap matching plus the "remove action" refinement does not imply global optimality of the resulting offloading decisions, and Algorithm 3 only evaluates this heuristic at different allowed matching sizes. Since Theorem 9 requires per-slot decisions that are optimal or within a bounded gap, the mismatch between the heuristic solver and the per-slot optimum is load-bearing and is not addressed anywhere in the manuscript.
  4. [§6, Simulation Results] The evaluation reports average energy and queue-delay curves without confidence intervals, standard deviations, or multiple random seeds, and it does not compare the observed time-average energy consumption with the theoretical bound Copt + B/V from Eq. (47). As a result, the simulations cannot substitute for the missing analytical guarantee of Theorem 9, and the empirical claims should be interpreted with caution.
minor comments (6)
  1. [§3.3, Eq. (7)] The coefficient in Eq. (7) is written as ζk, but the model defines it as ζu for UD u; the notation should be consistent.
  2. [Eq. (20), Theorem 1] The summand in the penalty term contains E^{loc}_k(t), which should be E^{loc}_u(t) with the correct index; also "for all all possible" is a typo.
  3. [§3.4 and §5.2.1] There are several notation inconsistencies between sets and indices: Um(t) is sometimes used before being defined, and the objective in Eq. (22) uses k in sums where u is the intended index; the sets U^S_m and U^C_m are defined but some equations later reuse k as an index instead of u.
  4. [§5.2.3, Eq. (41)] The queue stability subproblem includes "xm→c_u ≤ (xm_u)^*" as (41b), but since the domain is already restricted to u ∈ U^O_m (where (xm_u)^* = 1), this constraint is redundant; this should be clarified.
  5. [Table 1 and §6] The table uses parameter names Z and Iu, while the text uses ρ and s_u(t); these should be aligned to avoid confusion.
  6. [Algorithms 2 and 3] Algorithm 2 line 16 writes (xm→c_{u2}) without the prime used elsewhere in the update, and Algorithm 3 does not explicitly define how it obtains the final continuous resource allocation A(t) beyond the closed-form Eq. (29).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: OJCTA's derivation is self-contained in the sense that no fitted parameter or self-citation is renamed as a prediction; the unsupported per-slot optimality gap is a correctness concern, not circularity.

full rationale

The paper's central derivation chain is standard: EEDAOP is transformed via virtual queues and Lyapunov drift-plus-penalty into per-slot problem P'' (Eq. 22); Theorem 1 bounds the drift-plus-penalty by B plus a per-slot expression; the per-slot expression is decomposed by Tammer decomposition into communication-allocation subproblem (Eq. 24) and task-offloading subproblem (Eq. 25); Theorem 3 derives the closed-form bandwidth allocation (Eq. 29) from the convex subproblem; Algorithms 1-3 construct heuristic per-slot decisions; Theorem 8 bounds complexity; and Theorem 9 invokes the standard Lyapunov [O(1/T), B/V] bound. No step fits a parameter to the benchmark outputs and then reports that fit as a prediction. The matching and rounding heuristics are evaluated against external baselines (LC, RO, ECF, SSC, NCC, GJTORA), not against a fitted version of themselves. The cited Tammer decomposition and dependent-rounding results are external prior work, not the authors' own previous results, and they are not used to define the target quantity Copt. The main weaknesses are non-circular: several theorem proofs are deferred to a missing appendix ('Appendix ??'), Theorem 9's Copt+B/V conclusion requires per-slot near-optimality that Algorithm 3 is not shown to satisfy, and swap-matching stability does not imply global optimality of P''. These are unproven-premise/correctness issues, not circular reasoning.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central derivation rests on standard Lyapunov machinery plus two unproved optimality assumptions: that Tammer decomposition preserves exactness for this MINLP and that the heuristic per-slot solver has a small enough suboptimality gap for Theorem 9. V is a hand-tuned tradeoff parameter. No new physical entities are introduced.

free parameters (1)
  • V (Lyapunov penalty factor) = [5, 40]
    Hand-tuned tradeoff parameter in the drift-plus-penalty objective (Eq. 19). It controls the energy versus queue-stability tradeoff, and its value is varied in simulation rather than derived.
assumptions (5)
  • domain assumption System dynamics such as task arrivals, channels, and mobility are such that all queues have finite second moments and the Lyapunov drift bound constant B is finite.
    Invoked implicitly by Theorem 1 and Theorem 9. The paper does not state stationarity or bounded-moment conditions, but the Lyapunov bounds require them.
  • standard math The Lyapunov optimization and virtual queue transformation correctly converts the long-term average constraints (12) and (13) into queue stability constraints.
    Standard machinery from Neely [32], used in Section 5.1 to produce the real-time problem P''. This is accepted background.
  • domain assumption The Tammer decomposition mechanism preserves optimality for the considered per-slot MINLP P'''.
    Stated in Section 5.2.1 with citation [31], but no proof is given for this specific problem. If this assumption fails, the decoupling into independent subproblems introduces an unquantified gap.
  • standard math Dependent rounding yields a feasible integral solution while preserving the total amount of task data sent to the cloud, with near-optimal objective value.
    Theorems 6 and 7 state this property, but their proofs are in the missing appendix. The method is borrowed from [36], where the technique is established.
  • ad hoc to paper The heuristic per-slot algorithm in Algorithm 3 attains a solution close enough to the per-slot optimum for the global bound in Eq. (47) to hold.
    Theorem 9 relies on the standard Lyapunov optimality gap, but Algorithm 3 uses matching, a capacity sweep, and dependent rounding, with no worst-case suboptimality bound. This is the weakest assumption and is unproved.

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Cite this review

Pith. "Pith review of Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing." pith.science (2026). https://pith.science/paper/CMN56IAM

@misc{pith2026250102952,
  author       = {Pith},
  title        = {Pith review of: Online Collaborative Resource Allocation and Task Offloading for Multi-access Edge Computing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CMN56IAM}},
  note         = {Machine review of arXiv:2501.02952}
}
read the original abstract

Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches.

Figures

Figures reproduced from arXiv: 2501.02952 by the authors.

Figure 1
Figure 1. The proposed three-layer collaborative MEC system [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The framework of OJCTA. The EEDAOP is first transformed to a per-time-slot real-time optimization problem, and [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. System performance with time slots. servers alone, limiting its energy-saving potential. Besides, the genetic algorithm adopted by GJTORA often faces slow convergence and high computational demands, which can lead to suboptimal offloading decisions, especially in real￾time scenarios. In addition, while the ECF approach natu￾rally achieves lower energy consumption than the proposed OJCTA by prioritizing energy effici… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: System performance with bandwidth. In conclusion, the simulation results indicate that the proposed OJCTA successfully manages the trade-off be￾tween queuing delay and energy consumption. It achieves a balanced performance by using a dynamic task offloading strategy th…
Figure 5
Figure 5. Figure 5: System performance with the MEC server connection capacity. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]
Figure 6
Figure 6. Figure 6: System performance with the number of UDs. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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  2. A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks

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Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages · cited by 2 Pith papers

  1. [31]

    The application of parametric optimization and imbedding to the foundation and realization of a generalized primal decomposition approach,

    K. Tammer, “The application of parametric optimization and imbedding to the foundation and realization of a generalized primal decomposition approach,” Mathematical research , vol. 35, pp. 376–386, Apr. 1987

    work page 1987

  2. [1]

    Stackelberg game-based pricing and offloading in mobile edge computing,

    M. Tao, K. Ota, M. Dong, and H. Yuan, “Stackelberg game-based pricing and offloading in mobile edge computing,” IEEE Wirel. Commun. Lett., vol. 11, no. 5, pp. 883–887, Dec. 2021

    work page 2021

  3. [2]

    Multi-objective parallel task offloading and content caching in d2d-aided mec networks,

    Z. Xiao, J. Shu, H. Jiang, J. C. Lui, G. Min, J. Liu, and S. Dustdar, “Multi-objective parallel task offloading and content caching in d2d-aided mec networks,” IEEE Trans. Mob. Comput. , vol. 22, no. 11, pp. 6599–6615, Jan. 2023. 17

    work page 2023

  4. [3]

    DDPG-based joint resource management for latency minimization in NOMA- MEC networks,

    J. Wang, Y. Wang, P . Cheng, K. Yu, and W. Xiang, “DDPG-based joint resource management for latency minimization in NOMA- MEC networks,” IEEE Commun. Lett., Jul. 2023

    work page 2023

  5. [4]

    Joint communication, computation, caching, and control in big data multi-access edge computing,

    A. Ndikumana, N. H. Tran, T. M. Ho, Z. Han, W. Saad, D. Niyato, and C. S. Hong, “Joint communication, computation, caching, and control in big data multi-access edge computing,”IEEE Trans. Mob. Comput., vol. 19, no. 6, pp. 1359–1374, Mar. 2020

    work page 2020

  6. [5]

    Workload re-allocation for edge computing with server collaboration: A cooperative queueing game approach,

    C. Yi, J. Cai, T. Zhang, K. Zhu, B. Chen, and Q. Wu, “Workload re-allocation for edge computing with server collaboration: A cooperative queueing game approach,” IEEE Trans. Mob. Comput., vol. 22, no. 5, pp. 3095–3111, Nov. 2023

    work page 2023

  7. [6]

    A multihop task offloading decision model in MEC-enabled internet of vehicles,

    C. Chen, Y. Zeng, H. Li, Y. Liu, and S. Wan, “A multihop task offloading decision model in MEC-enabled internet of vehicles,” IEEE Internet Things J., vol. 10, no. 4, pp. 3215–3230, 2023

    work page 2023

  8. [7]

    Energy-efficient task of- floading for three-tier wireless-powered mobile-edge computing,

    M. Bolourian and H. Shah-Mansouri, “Energy-efficient task of- floading for three-tier wireless-powered mobile-edge computing,” IEEE Internet Things J., vol. 10, no. 12, pp. 10 400–10 412, Jan. 2023

    work page 2023

Show all 41 references
  1. [8]

    Game-combined multi-agent DRL for tasks offloading in wireless powered MEC networks,

    A. Gao, S. Zhang, Y. Hu, W. Liang, and S. X. Ng, “Game-combined multi-agent DRL for tasks offloading in wireless powered MEC networks,” IEEE Trans. Veh. Technol., vol. 72, no. 7, pp. 9131–9144, Jul. 2023

    2023

  2. [9]

    Joint task offloading and radio resource management in stochastic MEC systems,

    T. H. Hoang, C. T. Nguyen, T. N. Do, and G. Kaddoum, “Joint task offloading and radio resource management in stochastic MEC systems,” IEEE Trans. Commun., Jan. 2024

    2024

  3. [10]

    Delay minimization for rate-splitting multiple access-based multi-server MEC offloading,

    M. Diamanti, C. Pelekis, E. E. Tsiropoulou, and S. Papavassil- iou, “Delay minimization for rate-splitting multiple access-based multi-server MEC offloading,” IEEE/ACM Trans. Netw. , vol. 32, no. 2, pp. 1035–1047, Feb. 2024

    2024

  4. [11]

    Throughput-aware dynamic task offloading under resource constant for MEC with energy harvesting devices,

    J. Mei, L. Dai, Z. Tong, X. Deng, and K. Li, “Throughput-aware dynamic task offloading under resource constant for MEC with energy harvesting devices,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 3, pp. 3460–3473, Apr. 2023

    2023

  5. [12]

    Joint task offloading and resource allocation in heterogeneous edge environments,

    Y. Liu, Y. Mao, Z. Liu, F. Ye, and Y. Yang, “Joint task offloading and resource allocation in heterogeneous edge environments,” IEEE Trans. Mob. Comput., vol. 23, no. 6, pp. 7318–7334, Nov. 2024

    2024

  6. [13]

    Energy-saving service offloading for the internet of medical things using deep reinforcement learning,

    J. Jiang, J. Guo, M. Khan, Y. Cui, and W. Lin, “Energy-saving service offloading for the internet of medical things using deep reinforcement learning,” ACM Trans. Sens. Netw., vol. 19, no. 3, pp. 1–20, Jan. 2023

    2023

  7. [14]

    Energy-efficient heuristic computation offloading with delay constraints in mobile edge computing,

    J. Mei, Z. Tong, K. Li, L. Zhang, and K. Li, “Energy-efficient heuristic computation offloading with delay constraints in mobile edge computing,” IEEE Trans. Serv. Comput. , vol. 16, no. 6, pp. 4404–4417, Oct. 2023

    2023

  8. [15]

    Game-based task offloading and resource allocation for vehicular edge computing with edge-edge cooperation,

    W. Fan, M. Hua, Y. Zhang, Y. Su, X. Li, B. Tang, F. Wu, and Y. Liu, “Game-based task offloading and resource allocation for vehicular edge computing with edge-edge cooperation,” IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 7857–7870, Jul. 2023

    2023

  9. [16]

    Efficient multi- task computation offloading game for mobile edge computing,

    S. Chu, C. Gao, M. Xu, K. Ye, Z. Xiao, and C. Xu, “Efficient multi- task computation offloading game for mobile edge computing,” IEEE Trans. Serv. Comput., Nov. 2023

    2023

  10. [17]

    DRL-driven joint task offloading and re- source allocation for energy-efficient content delivery in cloud- edge cooperation networks,

    C. Fang, Z. Hu, X. Meng, S. Tu, Z. Wang, D. Zeng, W. Ni, S. Guo, and Z. Han, “DRL-driven joint task offloading and re- source allocation for energy-efficient content delivery in cloud- edge cooperation networks,” IEEE Trans. Veh. Technol. , vol. 72, no. 12, pp. 16 195–16 207, ...

    2023

  11. [18]

    Cooperative task offloading for mobile edge computing based on multi-agent deep reinforcement learning,

    J. Yang, Q. Yuan, S. Chen, H. He, X. Jiang, and X. Tan, “Cooperative task offloading for mobile edge computing based on multi-agent deep reinforcement learning,” IEEE Trans. Netw. Serv. Manag. , vol. 20, no. 3, pp. 3205–3219, Jun. 2023

    2023

  12. [19]

    Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things,

    X. Dai, Z. Xiao, H. Jiang, M. Alazab, J. C. Lui, S. Dustdar, and J. Liu, “Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things,” IEEE Trans Ind. Informat. , vol. 19, no. 1, pp. 480–490, Mar. 2022

    2022

  13. [20]

    Predictive distance-based mobility man- agement for PCS networks,

    B. Liang and Z. J. Haas, “Predictive distance-based mobility man- agement for PCS networks,” inProc. of IEEE INFOCOM, Mar. 1999, pp. 1377–1384

    1999

  14. [21]

    Online trajectory and resource optimization for stochastic UAV-enabled MEC systems,

    Z. Yang, S. Bi, and Y. A. Zhang, “Online trajectory and resource optimization for stochastic UAV-enabled MEC systems,” IEEE Trans. Wirel. Commun., vol. 21, no. 7, pp. 5629–5643, Aug. 2022

    2022

  15. [22]

    Study on channel model for frequencies from 0.5 to 100 GHz (Release 16),

    G. T. . V16.1.0, “Study on channel model for frequencies from 0.5 to 100 GHz (Release 16),” Jul. 2020

    2020

  16. [23]

    Efficient link scheduling in wireless networks under rayleigh- fading and multiuser interference,

    J. Yu, K. Yu, D. Yu, W. Lv, X. Cheng, H. Chen, and W. Cheng, “Efficient link scheduling in wireless networks under rayleigh- fading and multiuser interference,” IEEE Trans. Wireless Commun., vol. 19, no. 8, pp. 5621–5634, Aug. 2020

    2020

  17. [24]

    Processor design for portable systems,

    T. D. Burd and R. W. Brodersen, “Processor design for portable systems,” J. VLSI Signal Process. Syst. Signal Image Video Technol. , vol. 13, no. 2, pp. 203–221, Feb. 1996

    1996

  18. [25]

    NOMA-enabled computation and communication resource trading for a multi-user MEC sys- tem,

    S. Gupta, D. Rajan, and J. Camp, “NOMA-enabled computation and communication resource trading for a multi-user MEC sys- tem,” IEEE Trans. Veh. Technol., vol. 71, no. 7, pp. 7532–7547, Apr. 2022

    2022

  19. [26]

    Computation offloading in resource-constrained multi-access edge computing,

    K. Li, X. Wang, Q. He, J. Wang, J. Li, S. Zhan, G. Lu, and S. Dustdar, “Computation offloading in resource-constrained multi-access edge computing,” IEEE Trans. Mob. Comput., Mar. 2024

    2024

  20. [27]

    A simple proof of: L = λW,

    W. S. Jewell, “A simple proof of: L = λW,” Oper. Res., vol. 15, no. 6, pp. 1109–1116, Dec. 1967

    1967

  21. [28]

    S. P . Boyd and L. Vandenberghe,Convex Optimization. Cambridge University Press, Mar. 2004

    2004

  22. [29]

    Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing,

    C.-F. Liu, M. Bennis, M. Debbah, and H. V . Poor, “Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing,” IEEE Trans. Commun., vol. 67, no. 6, pp. 4132– 4150, Oct. 2019

    2019

  23. [30]

    A lyapunov optimization approach for long-term task assignment in vehicular crowdsensing,

    X. Guo, X. Wang, W. Pan, W. Wu, and K. Liu, “A lyapunov optimization approach for long-term task assignment in vehicular crowdsensing,” IEEE Trans. Consum. Electron., Apr. 2024

    2024

  24. [32]

    M. J. Neely, Stochastic Network Optimization with Application to Com- munication and Queueing Systems , ser. Synthesis Lect. Commun. Netw. Morgan & Claypool Publishers, Jan. 2010

    2010

  25. [33]

    Task-based network reconfiguration in distributed UAV swarms: A bilateral matching approach,

    D. Liu, Z. Du, X. Liu, H. Luan, Y. Xu, and Y. Xu, “Task-based network reconfiguration in distributed UAV swarms: A bilateral matching approach,” IEEE/ACM Trans. Netw. , vol. 30, no. 6, pp. 2688–2700, Dec. 2022

    2022

  26. [34]

    Coexistence of energy-minimizing urllc and embb in power iot via noma-based collaborative mec heterogeneous network,

    X. Liu, X. Wang, X. Zhao, F. Du, Y. Zhang, S. Geng, Y. Lu, and C. Zhong, “Coexistence of energy-minimizing urllc and embb in power iot via noma-based collaborative mec heterogeneous network,” IEEE Transactions on Vehicular Technology, Oct. 2024

    2024

  27. [35]

    A survey on matching theory for distributed computation offloading in IoT-fog-cloud systems: Per- spectives and open issues,

    H. Tran-Dang and D. Kim, “A survey on matching theory for distributed computation offloading in IoT-fog-cloud systems: Per- spectives and open issues,” IEEE Access , vol. 10, pp. 118 353– 118 369, Nov. 2022

    2022

  28. [36]

    Depen- dent rounding and its applications to approximation algorithms,

    R. Gandhi, S. Khuller, S. Parthasarathy, and A. Srinivasan, “Depen- dent rounding and its applications to approximation algorithms,” J. ACM, vol. 53, no. 3, pp. 324–360, Jul. 2006

    2006

  29. [37]

    Computation peer offloading for energy-constrained mobile edge computing in small-cell net- works,

    L. Chen, S. Zhou, and J. Xu, “Computation peer offloading for energy-constrained mobile edge computing in small-cell net- works,” IEEE/ACM Trans. Netw., vol. 26, no. 4, pp. 1619–1632, Jun. 2018

    2018

  30. [38]

    Multi- timescale multi-dimension resource allocation for NOMA-edge computing-based power IoT with massive connectivity,

    H. Yu, Z. Zhou, Z. Jia, X. Zhao, L. Zhang, and X. Wang, “Multi- timescale multi-dimension resource allocation for NOMA-edge computing-based power IoT with massive connectivity,” IEEE Trans. Green Commun. Netw., vol. 5, no. 3, pp. 1101–1113, Apr. 2021

    2021

  31. [39]

    QoE aware and cell capacity enhanced computation offloading for multi-server mobile edge computing systems with energy harvesting devices,

    H. Zhao, W. Du, W. Liu, T. Lei, and Q. Lei, “QoE aware and cell capacity enhanced computation offloading for multi-server mobile edge computing systems with energy harvesting devices,” in Proc. of IEEE Int. Conf. Ubiquitous Intell. Comput., Dec. 2018, pp. 671–678

    2018

  32. [40]

    Computation rate maxi- mization in UAV-enabled wireless-powered mobile-edge comput- ing systems,

    F. Zhou, Y. Wu, R. Q. Hu, and Y. Qian, “Computation rate maxi- mization in UAV-enabled wireless-powered mobile-edge comput- ing systems,” IEEE J. Sel. Areas Commun. , vol. 36, no. 9, pp. 1927– 1941, Aug. 2018. Geng Sun (S’17-M’19) received the B.S. de- gree in communication en...

    1927

  33. [2024]

    for contributions to game theory and distributed management of autonomous communication networks

    He is the Editor-in-Chief assistant of IEEE Communications Surveys & Tutorials (2022-2024). He is the recipient of the IEEE Daniel E. Noble Fellowship Award from the IEEE Vehicular Technology Society in 2022, the IEEE Signal Processing Society Schol- arship from the IEEE Signa...

    2022

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