Opportunistic Access Point Selection for Mobile Edge Computing Networks

Junjuan Xia, Lisheng Fan*, Nan Yang, Yansha Deng, Trung Q. Duong, George K. Karagiannidis, Arumugam Nallanathan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami- $m$ distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami- $m$ fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

Original languageEnglish
Article number9217972
Pages (from-to)695-709
Number of pages15
Issue number1
Publication statusPublished - Jan 2021


  • energy consumption
  • latency
  • Mobile edge computing
  • opportunistic selection


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