On Multi-User Binary Computation Offloading in the Finite-Block-Length Regime

Salmani, Mahsa; Davidson, T.N.

doi:10.1109/ieeeconf44664.2019.9048917

“…To confront this challenge, a MEC network assisted by short packet transmission is proposed. For which, several remarkable attempts are worth to be introduced [12]- [14]. More specifically, the authors in [12] studied the offloading decision for MEC systems supporting single-carrier time division multiple access.…”

Section: Introductionmentioning

confidence: 99%

“…For which, several remarkable attempts are worth to be introduced [12]- [14]. More specifically, the authors in [12] studied the offloading decision for MEC systems supporting single-carrier time division multiple access. Meanwhile, in [13], [14], the resource allocation for MEC systems supporting OFDMA was analyzed with short packet transmission consideration.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

Fu

¹

,

Yang

²

,

Yang

³

et al. 2020

Preprint

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The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

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“…To meet the stringent requirements of latency-sensitive applications, a MEC network assisted by short packet transmission is proposed. For which, several remarkable attempts are worth to be introduced [14][15][16]. More specifically, the authors in [14] studied the offloading decision for MEC systems supporting single-carrier time division multiple access.…”

mentioning

confidence: 99%

“…For which, several remarkable attempts are worth to be introduced [14][15][16]. More specifically, the authors in [14] studied the offloading decision for MEC systems supporting single-carrier time division multiple access. Meanwhile, in [15,16], the resource allocation for MEC systems supporting OFDMA was analyzed with short packet transmission consideration.…”

mentioning

confidence: 99%

Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

Fu

¹

,

Yang

²

,

Yang

³

et al. 2021

J Wireless Com Network

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The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

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“…To overcome this issue, recent works applied finite blocklength transmission (FBT) [17] for resource allocation algorithm design for URLLC MEC systems. In particular, the authors in [18] studied binary offloading in single-carrier TDMA systems. However, single-carrier systems suffer from poor spectrum utilization and require complex equalization at the receiver.…”

mentioning

confidence: 99%

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Ghanem

¹

,

Jamali

²

,

Sun

³

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we study resource allocation algorithm design for secure multi-user downlink ultra-reliable low latency communication (URLLC). To enhance physical layer security (PLS), the base station (BS) is equipped with multiple antennas and artificial noise (AN) is injected by the BS to impair the eavesdroppers' channels. To meet the stringent delay requirements in secure URLLC systems, short packet transmission (SPT) is adopted and taken into consideration for resource allocation design. The resource allocation algorithm design is formulated as an optimization problem for minimization of the total transmit power, while guaranteeing quality-of-service (QoS) constraints regarding the URLLC users' number of transmitted bits, packet error probability, information leakage, and delay. Due to the nonconvexity of the optimization problem, finding a global solution entails a high computational complexity. Thus, we propose a low-complexity algorithm based successive convex approximation (SCA) to find a sub-optimal solution. Our simulation results show that the proposed resource allocation algorithm design ensures the secrecy of the URLLC users' transmissions, and yields significant power savings compared to a baseline scheme.

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On Multi-User Binary Computation Offloading in the Finite-Block-Length Regime

Cited by 7 publications

References 18 publications

Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

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