.png){kind=icon}
Return
Rate-Splitting Multiple Access Aided Mobile Edge Computing With Randomly Deployed Users
.png){kind=icon}
PRE
AI
DOI：10.1109/JSAC.2023.3240786.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a rate-splitting multiple access (RSMA) scheme is proposed to aid a mobile edge computing (MEC) system where multiple randomly deployed users offload their computation tasks to a MEC server. Considering that users are divided into the center and edge groups, a cognitive radio (CR)-inspired rate-splitting is designed to enable the paired users to simultaneously offload to MEC server. Under the CR principles, the rate-splitting parameters are jointly designed to attain the maximum achievable rate for the secondary user, meanwhile maintaining the primary user's offloading performance same as in orthogonal multiple access. For the case of the paired users with fixed locations, we derive a closed-form expression for the successful computation probability (SCP) achieved by the RSMA-aided MEC (RSMA-MEC) scheme and formulate a SCP maximization problem to obtain the optimal offloading parameters. To reveal the impact of the user locations on the offloading performance of the RSMA-MEC system, various distance-based user pairing schemes are investigated by invoking stochastic geometry techniques. We provide closed-form expressions for the SCPs achieved by the user pairing schemes to characterize the offloading performance. Pros and cons of the user locations to maximize the SCP are highlighted. Simulation results verify the accuracy of the analytical results and clarify the superior offloading performance achieved by the RSMA-MEC scheme, which attains a higher SCP than the existing schemes.
Keywords:
Uplink
Servers
NOMA
Task analysis
Downlink
Decoding
Complexity theory
Rate-splitting multiple access (RSMA)
cognitive radio (CR)
mobile edge computing (MEC)
stochastic geometry
Journal
IF:
17.2
Papers:
6.4K
Citations:
3.1W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveExpression of p53, bcl-2, and bax as predictors of response to radiotherapy in esophageal cancer
PREAI
ShareSave
ShareSaveShareSave
View more