TY - GEN
T1 - Mobility-Aware Routing and Caching
T2 - 2021 IEEE International Conference on Communications, ICC 2021
AU - Cao, Yuwen
AU - Maghsudi, Setareh
AU - Ohtsuki, Tomoaki
N1 - Funding Information:
The work of Y. Cao and T. Ohtsuki was supported by JSPS KAKENHI Grant Number JP20J12528. The work of S. Maghsudi was supported in part by JSPS Fellowship Grant under ID PE19732 and in part by Grant 16KIS1165 from the German Federal Ministry of Education and Research.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - We develop mobility-aware routing and caching strategies to solve the network cost minimization problem for dense small-cell networks. The challenge mainly stems from the insufficient backhaul capacity of small-cell networks and the limited storing capacity of small-cell base stations (SBSs). The optimization problem is NP-hard since both the mobility patterns of the mobilized users (MUs), as well as the MUs' preference for contents, are unknown. To tackle this problem, we start by dividing the entire geographical area into small sections, each of which containing one SBS and several MUs. Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs, and make caching decision. Notably, FRPL enables the completion of the multi-tasks in one shot, thereby reducing the average processing time per global aggregation.1 Theoretical and numerical analyses show the effectiveness of our proposed approach.
AB - We develop mobility-aware routing and caching strategies to solve the network cost minimization problem for dense small-cell networks. The challenge mainly stems from the insufficient backhaul capacity of small-cell networks and the limited storing capacity of small-cell base stations (SBSs). The optimization problem is NP-hard since both the mobility patterns of the mobilized users (MUs), as well as the MUs' preference for contents, are unknown. To tackle this problem, we start by dividing the entire geographical area into small sections, each of which containing one SBS and several MUs. Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs, and make caching decision. Notably, FRPL enables the completion of the multi-tasks in one shot, thereby reducing the average processing time per global aggregation.1 Theoretical and numerical analyses show the effectiveness of our proposed approach.
KW - Routing
KW - caching
KW - dense small-cell networks
KW - federated learning
KW - one-stop-shop
UR - http://www.scopus.com/inward/record.url?scp=85115703280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115703280&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500804
DO - 10.1109/ICC42927.2021.9500804
M3 - Conference contribution
AN - SCOPUS:85115703280
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 June 2021 through 23 June 2021
ER -