pith. sign in

arxiv: 1907.05210 · v1 · pith:QMWCSOVPnew · submitted 2019-06-26 · 💻 cs.NI · eess.SP

Cross-layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

Changyang She , Yifan Duan , Guodong Zhao , Tony Q. S. Quek , Yonghui Li , Branka Vucetic This is my paper

Pith reviewed 2026-05-25 14:43 UTC · model grok-4.3

classification 💻 cs.NI eess.SP
keywords cross-layer designmission-critical IoTmobile edge computingshort packetsprocessor sharingpacket loss probabilitylatency distribution5G new radio
0
0 comments X

The pith

Processor-sharing servers and closed-form latency analysis minimize packet loss for short-packet MC-IoT services under end-to-end delay constraints in MEC systems.

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

The paper presents a cross-layer optimization framework for MC-IoT services that uses short packets in MEC systems while treating eMBB traffic as background. It deploys processor-sharing servers at the edge to avoid long queueing delays for short packets and derives their latency distribution in closed form. The overall packet loss probability is then minimized subject to a strict end-to-end delay requirement through joint decisions on user association, offloading rates, and bandwidth allocation. An iterative algorithm solves the resulting non-convex problem and reaches a near-optimal point when eMBB throughput greatly exceeds MC-IoT throughput; exact optima are also given for the asymptotic regimes where communication or computation forms the reliability bottleneck.

Core claim

By modeling processor-sharing servers that divide service capacity equally among all buffered packets and deriving the closed-form distribution of latency experienced by short packets, the packet loss probability of mission-critical IoT traffic can be minimized subject to an end-to-end delay constraint through optimized user association, packet offloading, and bandwidth allocation; the non-convex problem admits a convergent iterative solution when eMBB throughput dominates and admits closed-form optima when either the radio link or the edge processor is the sole bottleneck.

What carries the argument

Processor-sharing servers that equally allocate service rate to all packets in the buffer, together with the closed-form latency distribution for short packets.

If this is right

  • Processor-sharing servers achieve lower packet loss probability than first-come-first-served servers for the same delay target.
  • Closed-form optimal solutions exist when communication is the sole bottleneck and when computing is the sole bottleneck.
  • The iterative algorithm converges to a near-optimal point whenever eMBB throughput substantially exceeds MC-IoT throughput.
  • Simulation results confirm that the derived latency distribution matches the empirical distribution and that the optimized design satisfies the end-to-end delay requirement.

Where Pith is reading between the lines

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

  • The closed-form latency expression could support online adaptation of offloading rates when channel conditions change rapidly.
  • Extending the framework to scenarios where eMBB and MC-IoT throughputs are comparable would remove the main convergence assumption.
  • The same processor-sharing model might apply to other short-packet traffic classes that share edge resources with long-packet background flows.

Load-bearing premise

The assumption that eMBB throughput is much higher than MC-IoT throughput is required for the proposed algorithm to converge to a near-optimal solution.

What would settle it

A measurement or simulation in which the achieved packet loss probability remains above the predicted minimum after applying the optimized association, offloading, and bandwidth values under the stated delay constraint would falsify the central claim.

Figures

Figures reproduced from arXiv: 1907.05210 by Branka Vucetic, Changyang She, Guodong Zhao, Tony Q. S. Quek, Yifan Duan, Yonghui Li.

Figure 1
Figure 1. Figure 1: System model. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Local and edge servers in our system. We consider the scheduling policies at MEC servers and local servers in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Optimal association scheme when computing is the bot [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Simulation Scenario The simulation scenario is shown in [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: CCDFs of processing delay in the AP, where the service [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Overall packet loss probabilities achieved by the op [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The difference between the association schemes, whe [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Overall packet loss probability v.s. the number of MC [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
read the original abstract

In this work, we propose a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for Mission-Critical Internet-of-Things (MC-IoT) services with short packets in Mobile Edge Computing (MEC) systems, where enhanced Mobile BroadBand (eMBB) services with long packets are considered as background services. To reduce communication delay, the 5th generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, Processor-Sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed-form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the non-convex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. Simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers.

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

1 major / 0 minor

Summary. The manuscript proposes a cross-layer framework for user association, packet offloading, and bandwidth allocation in MEC systems supporting MC-IoT short-packet services alongside eMBB background traffic. It derives a closed-form latency distribution for short packets under processor-sharing (PS) servers at the edge, formulates a non-convex optimization to minimize overall packet loss probability subject to end-to-end delay constraints, and presents an iterative algorithm that converges to a near-optimal solution when eMBB throughput greatly exceeds MC-IoT throughput (with an extension claimed for general cases). Asymptotic closed-form solutions are also derived for the regimes where communication or computing is the bottleneck, and simulations are used to validate the analysis and show PS outperforming FCFS.

Significance. If the central claims hold, the work supplies a concrete cross-layer design for reliable MC-IoT in 5G MEC, with the closed-form latency distribution and the two asymptotic optimal solutions providing analytical tools that could guide system dimensioning. The explicit comparison of PS versus FCFS scheduling for short packets supplies practical evidence of performance gains under the modeled traffic mix.

major comments (1)
  1. [Abstract] Abstract (and the section describing the proposed algorithm): the statement that the algorithm 'converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios' is load-bearing for the claim that the cross-layer solution is reliable. Convergence is established only under the dominance assumption; no proof, bound, or error analysis is supplied for the general-case extension despite the problem being explicitly non-convex. This gap directly limits the scope of the central optimization result.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the insightful comments on our manuscript. We address the major comment regarding the algorithm's convergence below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and the section describing the proposed algorithm): the statement that the algorithm 'converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios' is load-bearing for the claim that the cross-layer solution is reliable. Convergence is established only under the dominance assumption; no proof, bound, or error analysis is supplied for the general-case extension despite the problem being explicitly non-convex. This gap directly limits the scope of the central optimization result.

    Authors: We concur that the rigorous convergence guarantee applies specifically when eMBB throughput dominates MC-IoT throughput. In the manuscript, the extension to general scenarios is proposed as a practical iterative method, with its effectiveness demonstrated through extensive simulations. We recognize that no analytical proof or performance bound is given for the general non-convex case. Accordingly, we will revise the abstract and the algorithm description section to explicitly note that convergence to a near-optimal solution is proven only under the dominance condition, while the general case relies on empirical validation. This change will accurately reflect the scope of our results without overstating the theoretical guarantees. revision: partial

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper derives a closed-form latency distribution for short packets from the processor-sharing queueing model at the MEC server, then uses this distribution to formulate and solve a non-convex optimization minimizing overall packet loss probability subject to end-to-end delay constraints. The algorithm is shown to converge under the external condition that eMBB throughput greatly exceeds MC-IoT throughput, with an extension stated for general cases; this condition is not a fitted parameter or self-referential definition but an assumption invoked to address non-convexity. No equation or claim reduces the derived distribution, optimal solutions, or performance claims to the inputs by construction, self-definition, or load-bearing self-citation. The central results remain grounded in independent queueing analysis and optimization.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on standard queueing and wireless network assumptions rather than new postulates; no free parameters or invented entities are identified from the abstract.

axioms (2)
  • domain assumption Processor-sharing servers equally allocate service rate to all packets in the buffer.
    Invoked to derive closed-form latency distribution for short packets and avoid long queueing delays.
  • domain assumption 5G new radio reduces communication delay in the radio access network.
    Stated as the radio access technology adopted for the system model.

pith-pipeline@v0.9.0 · 5779 in / 1369 out tokens · 33716 ms · 2026-05-25T14:43:16.036083+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

54 extracted references · 3 linked inside Pith

  1. [1]

    Delay analysis and computing offloading of URLLC in mobile edge computing syste ms,

    Y . Duan, C. She, G. Zhao, and T. Q. S. Quek, “Delay analysis and computing offloading of URLLC in mobile edge computing syste ms,” in Proc. WCSP, 2018

    2018

  2. [2]

    Efficient next generat ion emer- gency communications over multi-access edge computing,

    E. K. Markakis, I. Politis, A. Lykourgiotis, Y . Rebahi, G . Mastorakis, C. X. Mavromoustakis, and E. Pallis, “Efficient next generat ion emer- gency communications over multi-access edge computing,” IEEE Com- mun. Mag. , vol. 55, no. 11, pp. 92–97, 2017

    2017

  3. [3]

    Analysis of ultra-reliable and low-l atency 5G communication for a factory automation use case,

    O. N. C. Yilmaz, Y .-P . E. Wang, N. A. Johansson, N. Brahmi, S. A. Ashraf, and J. Sachs, “Analysis of ultra-reliable and low-l atency 5G communication for a factory automation use case,” in IEEE ICC W orkshop, 2015

    2015

  4. [4]

    L atency critical IoT applications in 5G: Perspective on the design o f radio interface and network architecture,

    P . Schulz, M. Matthe, H. Klessig, M. Simsek, G. Fettweis, J. Ansari, S. A. Ashraf, B. Almeroth, J. V oigt, I. Riedel, A. Puschmann, A. Mitschele-Thiel, M. Muller, T. Elste, and M. Windisch, “L atency critical IoT applications in 5G: Perspective on the design o f radio interface and network architecture,” IEEE Commun. Mag., vol. 55, no. 2, pp. 70–78, 2017

    2017

  5. [5]

    Low-latency networking: Where latenc y lurks and how to tame it,

    X. Jiang, et al., “Low-latency networking: Where latenc y lurks and how to tame it,” Proc. IEEE , vol. 107, no. 2, pp. 280–306, Feb. 2019

    2019

  6. [6]

    Study on scenarios and requirements for next generation access technologies

    3GPP TR 38.913, “Study on scenarios and requirements for next generation access technologies.” Tech. rep. Release 14. 3G PP , Jun. 2017

    2017

  7. [7]

    Study on new radio (NR) access technology; physic al layer aspects (release 14)

    3GPP, “Study on new radio (NR) access technology; physic al layer aspects (release 14).” TR 38.802 V2.0.0, Apr. 2017

    2017

  8. [8]

    Channel coding rate in the finite blocklength regime,

    Y . Polyanskiy, H. V . Poor, and S. V erd´ u, “Channel coding rate in the finite blocklength regime,” IEEE Trans. Inf. Theory , vol. 56, no. 5, pp. 2307–2359, May 2010

    2010

  9. [9]

    Enabling low-latency applications in fog-radio access ne tworks,

    Y .-Y . Shih, W.-H. Chung, A.-C. Pang, T.-C. Chiu, and H.-Y . Wei, “Enabling low-latency applications in fog-radio access ne tworks,” IEEE Netw., vol. 31, no. 1, pp. 52–58, Jan./Feb. 2017

    2017

  10. [10]

    A su rvey on mobile edge computing: The communication perspective,

    Y . Mao, C. Y ou, J. Zhang, K. Huang, and K. B. Letaief, “A su rvey on mobile edge computing: The communication perspective,” IEEE Commun. Surveys Tuts. , vol. 19, no. 4, pp. 2322–2358, 2017

    2017

  11. [11]

    Offloading in mobile edge computing: Task allocation and computational frequen cy scaling,

    T. Q. Dinh, J. Tang, Q. D. La, and T. Q. Quek, “Offloading in mobile edge computing: Task allocation and computational frequen cy scaling,” IEEE Trans. on Commun. , vol. 65, no. 8, pp. 3571–3584, Aug. 2017

    2017

  12. [12]

    View on 5G architec ture,

    5GPPP Architecture Working Group, “View on 5G architec ture,” in 5G Architecture White Paper , Dec. 2017

    2017

  13. [13]

    Quasi-s tatic multiple- antenna fading channels at finite blocklength,

    W. Y ang, G. Durisi, T. Koch, and Y . Polyanskiy, “Quasi-s tatic multiple- antenna fading channels at finite blocklength,” IEEE Trans. Inf. Theory , vol. 60, no. 7, pp. 4232–4264, Jul. 2014

    2014

  14. [14]

    Throughput of cognitive radi o systems with finite blocklength codes,

    G. Ozcan and M. C. Gursoy, “Throughput of cognitive radi o systems with finite blocklength codes,” IEEE J. Sel. Areas Commun. , vol. 31, no. 11, pp. 2541–2554, Nov. 2013

    2013

  15. [15]

    Blocklength-limited performance of relaying under quasi-static Rayleigh channels,

    Y . Hu, A. Schmeink, and J. Gross, “Blocklength-limited performance of relaying under quasi-static Rayleigh channels,” IEEE Trans. Wireless Commun., vol. 15, no. 7, pp. 4548–4558, Jul. 2016

    2016

  16. [16]

    Relaying-enabled ultra-reliable low-latency communications in 5G,

    Y . Hu, M. C. Gursoy, and A. Schmeink, “Relaying-enabled ultra-reliable low-latency communications in 5G,” IEEE Network , vol. 32, no. 2, pp. 62–68, Mar.-Apr. 2018

    2018

  17. [17]

    Energ y-efficient packet scheduling with finite blocklength codes: Convexity analysis and efficient algorithms,

    S. Xu, T.-H. Chang, S.-C. Lin, C. Shen, and G. Zhu, “Energ y-efficient packet scheduling with finite blocklength codes: Convexity analysis and efficient algorithms,” IEEE Trans. Wireless Commun. , vol. 15, no. 8, pp. 5527–5540, Aug. 2016

    2016

  18. [18]

    Cross-layer optimiza tion for ultra- reliable and low-latency radio access networks,

    C. She, C. Y ang, and T. Q. S. Quek, “Cross-layer optimiza tion for ultra- reliable and low-latency radio access networks,” IEEE Trans. Wireless Commun., vol. 17, no. 1, pp. 127–141, Jan. 2018

    2018

  19. [19]

    Joint uplink and downlink resource configuration f or ultra- reliable and low-latency communications,

    ——, “Joint uplink and downlink resource configuration f or ultra- reliable and low-latency communications,” IEEE Trans. Commun. , vol. 66, no. 5, pp. 2266–2280, May 2018

    2018

  20. [20]

    Optimal p ower al- location for QoS-constrained downlink multi-user network s in the finite blocklength regime,

    Y . Hu, M. Ozmen, M. C. Gursoy, and A. Schmeink, “Optimal p ower al- location for QoS-constrained downlink multi-user network s in the finite blocklength regime,” IEEE Transactions on Wireless Communications , vol. 17, no. 9, pp. 5827–5840, Sep. 2018

    2018

  21. [21]

    Ho ma: A receiver-driven low-latency transport protocol using ne twork priorities,

    B. Montazeri, Y . Li, M. Alizadeh, and J. Ousterhout, “Ho ma: A receiver-driven low-latency transport protocol using ne twork priorities,” in Proc. ACM SIGCOMM , 2018. [Online]. Available: https://arxiv.org/pdf/1803.09615.pdf

    Pith/arXiv arXiv 2018

  22. [22]

    Harchol-Balter, Performance Modeling and Design of Computer Systems: Queueing Theory in Action

    M. Harchol-Balter, Performance Modeling and Design of Computer Systems: Queueing Theory in Action . Cambridge University Press, 2013

    2013

  23. [23]

    Sojourn time asymptotics in the M/G/1 processor sharing queue,

    A. P . Zwart and O. J. Boxma, “Sojourn time asymptotics in the M/G/1 processor sharing queue,” Queueing Systems, vol. 35, pp. 141–166, 2000

    2000

  24. [24]

    Martingales-based energy- efficient D- ALOHA algorithms for MTC networks with delay-insensitive/ URLLC terminals co-existence,

    L. Zhao, X. Chi, and Y . Zhu, “Martingales-based energy- efficient D- ALOHA algorithms for MTC networks with delay-insensitive/ URLLC terminals co-existence,” IEEE Internet of Things J. , vol. 5, no. 2, pp. 1285–1298, Apr. 2018

    2018

  25. [25]

    Delay analys is for wireless fading channels with finite blocklength channel coding,

    S. Schiessl, J. Gross, and H. Al-Zubaidy, “Delay analys is for wireless fading channels with finite blocklength channel coding,” in Proc. ACM MSWiM, 2015

    2015

  26. [26]

    Industrial internet of things: Challenges, opportunities, and directions,

    E. Sisinni, A. Saifullah, S. Han et al. , “Industrial internet of things: Challenges, opportunities, and directions,” IEEE Trans Ind. Informat. , vol. 14, no. 11, pp. 4724–4734, Nov. 2018

    2018

  27. [27]

    Adaptive transmission optimization in SDN- based industrial internet of things with edge computing,

    X. Li, D. Li, J. Wan et al., “Adaptive transmission optimization in SDN- based industrial internet of things with edge computing,” IEEE Internet of Things J. , vol. 5, no. 3, pp. 1351–1360, Jun. 2018

    2018

  28. [28]

    Energy-aware r eal-time routing for large-scale industrial internet of things,

    N. B. Long, H. Tran-Dang, and D.-S. Kim, “Energy-aware r eal-time routing for large-scale industrial internet of things,” IEEE Internet of Things J. , vol. 5, no. 3, pp. 2190–2199, Jun. 2018

    2018

  29. [29]

    Fog computing f or 5G tactile industrial internet of things: QoE-aware resource allocat ion model,

    M. Aazam, K. A. Harras, and S. Zeadally, “Fog computing f or 5G tactile industrial internet of things: QoE-aware resource allocat ion model,” IEEE Trans Ind. Informat., early access , 2019

    2019

  30. [30]

    Traffic matc hing in 5G ultra-dense networks,

    Y . Zhong, X. Ge, H. H. Y ang, T. Han, and Q. Li, “Traffic matc hing in 5G ultra-dense networks,” IEEE Commun. Mag. , vol. 56, no. 8, pp. 100–105, Aug. 2018

    2018

  31. [31]

    Computation rate maximization fo r wireless powered mobile-edge computing with binary computation offl oading,

    S. Bi and Y . J. Zhang, “Computation rate maximization fo r wireless powered mobile-edge computing with binary computation offl oading,” IEEE Trans. Wireless Commun. , vol. 17, no. 6, pp. 4177–4190, Jun. 2018

    2018

  32. [32]

    Mobile-edge computation offloading for ultradense IoT networks,

    H. Guo, J. Liu, J. Zhang et al. , “Mobile-edge computation offloading for ultradense IoT networks,” IEEE Internet of Things J. , vol. 5, no. 6, pp. 4977–4988, Dec. 2018

    2018

  33. [33]

    Energy-aware com putation offloading and transmit power allocation in ultra-dense IoT networks,

    H. Guo, J. Zhang, J. Liu, and H. Zhang, “Energy-aware com putation offloading and transmit power allocation in ultra-dense IoT networks,” IEEE Internet of Things J., early access , 2019

    2019

  34. [34]

    Cloudlet enhanced fi ber-wireless access networks for mobile-edge computing,

    B. P . Rimal, D. P . V an, and M. Maier, “Cloudlet enhanced fi ber-wireless access networks for mobile-edge computing,” IEEE Trans. Wireless Commun., vol. 16, no. 6, pp. 3601–3618, Jun. 2017

    2017

  35. [35]

    Collaborative computation offloadin g for multiac- cess edge computing over fibercwireless networks,

    H. Guo and J. Liu, “Collaborative computation offloadin g for multiac- cess edge computing over fibercwireless networks,” IEEE Trans. V eh. Technol., vol. 67, no. 5, pp. 4514–4526, May 2018

    2018

  36. [36]

    Delay-optim al computation task scheduling for mobile-edge computing systems,

    J. Liu, Y . Mao, J. Zhang, and K. B. Letaief, “Delay-optim al computation task scheduling for mobile-edge computing systems,” in Proc. IEEE ISIT, 2016

    2016

  37. [37]

    Latency and reliab ility-aware task offloading and resource allocation for mobile edge comp uting,

    C.-F. Liu, M. Bennis, and H. V . Poor, “Latency and reliab ility-aware task offloading and resource allocation for mobile edge comp uting,” in IEEE Globecom W orkshops, 2017

    2017

  38. [38]

    Offloading schemes in mobile edge co mputing for ultra-reliable low latency communications,

    J. Liu and Q. Zhang, “Offloading schemes in mobile edge co mputing for ultra-reliable low latency communications,” IEEE Access, vol. 6, pp. 12 825–12 837, 2018

    2018

  39. [39]

    Wireless networks for mo bile edge computing: Spatial modelling and latency analysis,

    S.-W. Ko, K. Han, and K. Huang, “Wireless networks for mo bile edge computing: Spatial modelling and latency analysis,” IEEE Trans. Wireless Commun., early access , 2018

    2018

  40. [40]

    Performan ce evalu- ation of VeMAC supporting safety applications in vehicular networks,

    H. A. Omar, W. Zhuang, A. Abdrabou, and L. Li, “Performan ce evalu- ation of VeMAC supporting safety applications in vehicular networks,” IEEE Trans. Emerg. Topics Comput. , vol. 1, no. 1, pp. 69–83, Aug. 2013

    2013

  41. [41]

    Ultra-reliable and low-latency communication for wirele ss factory automation: From LTE to 5G,

    S. A. Ashraf, I. Aktas, E. Eriksson, K. W. Helmersson, an d J. Ansari, “Ultra-reliable and low-latency communication for wirele ss factory automation: From LTE to 5G,” in Proc. IEEE Emerg. Tech. and Factory Automation (ETF A), 2016

    2016

  42. [42]

    Burstin ess-aware bandwidth reservation for ultra-reliable and low-latency communications in tactile internet,

    Z. Hou, C. She, Y . Li, T. Q. Quek, and B. Vucetic, “Burstin ess-aware bandwidth reservation for ultra-reliable and low-latency communications in tactile internet,” IEEE J. Select. Areas Commun., , vol. 36, no. 11, pp. 2401–2410, Nov. 2018

    2018

  43. [43]

    Characterizing superposition arrival processes in packet multiplexers for voice and data,

    K. Sriram and W. Whitt, “Characterizing superposition arrival processes in packet multiplexers for voice and data,” IEEE J. Sel. Areas Commun. , vol. 4, no. 6, pp. 833–846, Sep. 1986

    1986

  44. [44]

    Toward massive, ul tra-reliable, and low-latency wireless communication with short packets,

    G. Durisi, T. Koch, and P . Popovski, “Toward massive, ul tra-reliable, and low-latency wireless communication with short packets,” Proc. IEEE , vol. 104, no. 9, pp. 1711–1726, Aug. 2016

    2016

  45. [45]

    Energy-ef ficient joint offloading and wireless resource allocation strategy in multi-mec server systems,

    K. Cheng, Y . Teng, W. Sun, A. Liu, and X. Wang, “Energy-ef ficient joint offloading and wireless resource allocation strategy in multi-mec server systems,” in Proc. IEEE ICC , 2018

    2018

  46. [46]

    Asynchronous mo bile-edge computation offloading: Energy-efficient resource managem ent,

    C. Y ou, Y . Zeng, R. Zhang, and K. Huang, “Asynchronous mo bile-edge computation offloading: Energy-efficient resource managem ent,” 2018. [Online]. Available: https://arxiv.org/pdf/1801.03668.pdf

    Pith/arXiv arXiv 2018

  47. [47]

    Performance optimization in mobile-edge compu ting via deep reinforcement learning,

    X. Chen, H. Zhang, C. Wu, S. Mao, Y . Ji, and M. Bennis, “Performance optimization in mobile-edge compu ting via deep reinforcement learning,” 2018. [Online]. Availab le: https://arxiv.org/pdf/1804.00514v1.pdf

    Pith/arXiv arXiv 2018

  48. [48]

    On the Geo/D/1 a nd Geo/D/1/n queues,

    A. Gravey, J.-R. Louvion, and P . Boyer, “On the Geo/D/1 a nd Geo/D/1/n queues,” Performance Evaluation, vol. 11, no. 2, pp. 117–125, Jul. 1990

    1990

  49. [49]

    Low- latency ultra-reliable 5G communications: Finite-blockl ength bounds and coding schemes,

    J. ¨Ostman, G. Durisi, E. G. Str ¨om, J. Li, H. Sahlin, and G. Liva, “Low- latency ultra-reliable 5G communications: Finite-blockl ength bounds and coding schemes,” in Int. ITG Conf. on Syst., Commun. and Coding , Feb. 2017

    2017

  50. [50]

    Improving network availability of ultra-reliable and low -latency com- munications with multi-connectivity,

    C. She, Z. Chen, C. Y ang, T. Q. S. Quek, Y . Li, and B. Vuceti c, “Improving network availability of ultra-reliable and low -latency com- munications with multi-connectivity,” IEEE Trans. Commun. , vol. 66, no. 11, pp. 5482–5496, Nov. 2018

    2018

  51. [51]

    Toward ultra-reliable low-latency communications: Typical scenarios, possible solutions, a nd open issues,

    D. Feng, C. She, K. Ying et al. , “Toward ultra-reliable low-latency communications: Typical scenarios, possible solutions, a nd open issues,” IEEE V eh. Tech. Mag., early access , 2019

    2019

  52. [52]

    Energy-efficient resource a llocation for MIMO-OFDM systems serving random sources with statistical QoS requirement,

    C. She, C. Y ang, and L. Liu, “Energy-efficient resource a llocation for MIMO-OFDM systems serving random sources with statistical QoS requirement,” IEEE Trans. Commun. , vol. 63, no. 11, pp. 4125–4141, Nov. 2015

    2015

  53. [53]

    Boyd and L

    S. Boyd and L. V andanberghe, Convex Optimization. Cambridge Univ. Press, 2004

    2004

  54. [54]

    ETSI TR 36.931 v9.0.0, May 2011

    3GPP , LTE; Evolved Universal Terrestrial Radio Access . ETSI TR 36.931 v9.0.0, May 2011

    2011