TY - GEN
T1 - Learning Distributed Coded Caching Strategy in a Cellular Network
AU - Doshi, Yash
AU - Bharath, B. N.
AU - Garg, Navneet
AU - Bhatia, Vimal
AU - Ratnarajah, Tharmalingam
N1 - Funding Information:
Yash Doshi is a System R&D engineer at Lekha wireless Pvt. Ltd., Bengaluru, e-mail: [email protected], B. N. Bharath is with Indian Institute of Technology Dharwad, INDIA, e-mail: [email protected], Navneet Garg is with Heriot-Watt University, Edinburgh EH14 4AS, U.K., e-mail: [email protected], Vimal Bhatia is with Indian Institute of Technology Indore, Indore 453552, India, e-mail: [email protected], Tharmalingam Ratnarajah is with the Institute for Digital Communications, The University of Edinburgh, Edinburgh EH8 9YL, U.K, e-mail: [email protected]. This work was supported by the UK-India Education and Research Initiative Thematic Partnerships under grants DST-UKIERI-2016-17-0060,DST/INT/UK/P129/2016, and SPARC/2018-2019/P148/SL.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - The caching of popular contents in a cellular network is known to reduce the data load in the backhaul link, and have been an active area of research. This paper considers the problem of efficient distributed content coded caching in a small-cell Base Station (sBS) wireless network to improve the cache hit performance. The demands at each sBS across time and sBSs is assumed to be correlated, and is unknown. A new weighted (across time and sBS) caching strategy is proposed. A high probability lower bound on the cache hit is derived, which is obtained using the proposed strategy as a function of the cache hit of the optimal caching strategy. The bound is shown to depend on (i) the weighted average of cache hits, (ii) regret, and (iii) the discrepancy across time and sBSs (a measure of correlation of demands across time and sBSs). This provides the following insight on obtaining the caching strategy: (i) find a sequence of caching strategies by running regret minimization across time at each sBS, and (ii) maximize an estimate of the bound to obtain a set of weights. The insight is shown to result in an iterative distributed algorithm to obtain caching strategies at each sBS. The performance of the proposed caching strategy is shown to outperform Least Recently Frequently Used (LRFU) algorithm by a large margin.
AB - The caching of popular contents in a cellular network is known to reduce the data load in the backhaul link, and have been an active area of research. This paper considers the problem of efficient distributed content coded caching in a small-cell Base Station (sBS) wireless network to improve the cache hit performance. The demands at each sBS across time and sBSs is assumed to be correlated, and is unknown. A new weighted (across time and sBS) caching strategy is proposed. A high probability lower bound on the cache hit is derived, which is obtained using the proposed strategy as a function of the cache hit of the optimal caching strategy. The bound is shown to depend on (i) the weighted average of cache hits, (ii) regret, and (iii) the discrepancy across time and sBSs (a measure of correlation of demands across time and sBSs). This provides the following insight on obtaining the caching strategy: (i) find a sequence of caching strategies by running regret minimization across time at each sBS, and (ii) maximize an estimate of the bound to obtain a set of weights. The insight is shown to result in an iterative distributed algorithm to obtain caching strategies at each sBS. The performance of the proposed caching strategy is shown to outperform Least Recently Frequently Used (LRFU) algorithm by a large margin.
KW - Caching
KW - online learning
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85112437365&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Spring51267.2021.9449047
DO - 10.1109/VTC2021-Spring51267.2021.9449047
M3 - Conference contribution
AN - SCOPUS:85112437365
BT - 93rd IEEE Vehicular Technology Conference (VTC2021-Spring)
PB - IEEE
T2 - 93rd IEEE Vehicular Technology Conference 2021
Y2 - 25 April 2021 through 28 April 2021
ER -