Joint Optimization of File Placement and Delivery in Cache-Assisted Wireless Networks With Limited Lifetime and Cache Space

Abstract

In this paper, the scheduling of downlink file transmission in one cell with the assistance of cache nodes with finite cache space is studied. Specifically, requesting users arrive randomly and the base station (BS) reactively multicasts files to the requesting users and selected cache nodes. The latter can offload the traffic in their coverage areas from the BS. We consider the joint optimization of the abovementioned file placement and delivery within a finite lifetime subject to the cache space constraint.Within the lifetime, the allocation of multicast power and symbol number for each file transmission at the BS is formulated as a dynamic programming problem with a random stage number. Note that there are no existing solutions to this problem. We develop an asymptotically optimal solution framework by transforming the original problem to an equivalent finite-horizon Markov decision process (MDP) with a fixed stage number. A novel approximation approach is then proposed to address the curse of dimensionality, where the analytical expressions of approximate value functions are provided. We also derive analytical bounds on the exact value function and approximation error. The approximate value functions depend on some system statistics, e.g., requesting users' distribution. One reinforcement learning algorithm is proposed for the scenario where these statistics are unknown.

Machine Translation of Scheduling Joint Optimization Algorithm in Japanese Passive Statistics

“…It is assumed that, in a scheduling period, the system allocates a dedicated time segment Ts for each different transmission mode, and each transmission mode can only transmit data in its own dedicated time segment [15]. e length of the time slice may be 0.…”

Diversified Demands Integration-Based Proactive Caching Strategy towards Edge Networks

“…. , K do (6) calculate LPP of the kthfile p lp,f k by ( 24); (7) for t � 1 to T do (8) calculate p t lc,f k by ( 27); (9) use LCP prediction model to predictp t+1 lc,f k , and have p lc,f k ≜ p t+1 lc,f k ; (10) end for (11) calculate regional popularity of the kth content, p f k � p lp,f k + p lc,f k by ( 28); (12) end for (13) where the denominator |Z| represents the size of the data set, the numerator 􏽢 r uf represents the ratings predicted by the model, and r uf represents the actual ratings of the users in the data set.…”

“…us, D2D communications in cellular networks can increase network throughput and improve spectrum utilization, reducing backhaul costs and transmission delays [1,8]. Moreover, with the developments of hard disk expansion technologies and personal devices, such as smartphones, larger cache space can be obtained at lower costs [9]. Consequently, it is essential to research how to combine the above technologies to further improve the caching efficiency of the cellular networks.…”

Delay Aware Hybrid Routing for Large Scale Cooperative D2D Networks

“…The authors in [19] develop an asymptotically optimal solution framework by transforming the original problem to an equivalent finite-horizon Markov decision process (MDP) with a fixed stage number. A novel approximation approach was proposed to address the curse of dimensionality, where the analytical expressions of approximate value functions are provided.…”