2 research outputs found
Joint Long-Term Cache Updating and Short-Term Content Delivery in Cloud-Based Small Cell Networks
Explosive growth of mobile data demand may impose a heavy traffic burden on
fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates
users' quality of service (QoS) and requires substantial power consumption.
This paper proposes an efficient maximum distance separable (MDS) coded caching
framework for a cache-enabled C-SCNs, aiming at reducing long-term power
consumption while satisfying users' QoS requirements in short-term
transmissions. To achieve this goal, the cache resource in small-cell base
stations (SBSs) needs to be reasonably updated by taking into account users'
content preferences, SBS collaboration, and characteristics of wireless links.
Specifically, without assuming any prior knowledge of content popularity, we
formulate a mixed timescale problem to jointly optimize cache updating,
multicast beamformers in fronthaul and edge links, and SBS clustering.
Nevertheless, this problem is anti-causal because an optimal cache updating
policy depends on future content requests and channel state information. To
handle it, by properly leveraging historical observations, we propose a
two-stage updating scheme by using Frobenius-Norm penalty and inexact block
coordinate descent method. Furthermore, we derive a learning-based design,
which can obtain effective tradeoff between accuracy and computational
complexity. Simulation results demonstrate the effectiveness of the proposed
two-stage framework.Comment: Accepted by IEEE Trans. Commu
Multi-Agent Reinforcement Learning for Cooperative Coded Caching via Homotopy Optimization
Introducing cooperative coded caching into small cell networks is a promising
approach to reducing traffic loads. By encoding content via maximum distance
separable (MDS) codes, coded fragments can be collectively cached at small-cell
base stations (SBSs) to enhance caching efficiency. However, content popularity
is usually time-varying and unknown in practice. As a result, cache contents
are anticipated to be intelligently updated by taking into account limited
caching storage and interactive impacts among SBSs. In response to these
challenges, we propose a multi-agent deep reinforcement learning (DRL)
framework to intelligently update cache contents in dynamic environments. With
the goal of minimizing long-term expected fronthaul traffic loads, we first
model dynamic coded caching as a cooperative multi-agent Markov decision
process. Owing to MDS coding, the resulting decision-making falls into a class
of constrained reinforcement learning problems with continuous decision
variables. To deal with this difficulty, we custom-build a novel DRL algorithm
by embedding homotopy optimization into a deep deterministic policy gradient
formalism. Next, to empower the caching framework with an effective trade-off
between complexity and performance, we propose centralized, partially and fully
decentralized caching controls by applying the derived DRL approach. Simulation
results demonstrate the superior performance of the proposed multi-agent
framework.Comment: Submitted to IEEE for possible publicatio