5 research outputs found
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 5 pages, 2 figure
Online Reinforcement Learning of X-Haul Content Delivery Mode in Fog Radio Access Networks
We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in
the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The
system aims at delivering contents on demand with minimal average latency from
a time-varying library of popular contents. Information about uncached
requested files can be transferred from the cloud to the eRRHs by following
either backhaul or fronthaul modes. The backhaul mode transfers fractions of
the requested files, while the fronthaul mode transmits quantized baseband
samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the
eRRHs to be updated, which may lower future delivery latencies. In contrast,
the fronthaul mode enables cooperative C-RAN transmissions that may reduce the
current delivery latency. Taking into account the trade-off between current and
future delivery performance, this paper proposes an adaptive selection method
between the two delivery modes to minimize the long-term delivery latency.
Assuming an unknown and time-varying popularity model, the method is based on
model-free Reinforcement Learning (RL). Numerical results confirm the
effectiveness of the proposed RL scheme.Comment: 12 pages, 2 figure
Minimum Rate Maximization for Wireless Powered Cloud Radio Access Networks
This paper studies the optimization of signal processing strategies for downlink and uplink of a cloud radio access network (C-RAN) that serves wireless powered users with non-linear energy harvesting (EH) circuits. On the downlink, a baseband processing unit (BBU) sends radio frequency signals to the users through a set of remote radio heads (RRHs), which is centrally managed by the BBU via finite-fronthaul links. Then, each user splits the received signal for information decoding and EH by utilizing the power splitting circuit. By using the harvested energy, each user communicates with the BBU via the RRHs on the uplink. In this paper, we tackle a problem of maximizing the minimum uplink rate of the users subject to the minimum downlink rate constraint as well as the per-node transmit power and fronthaul capacity constraints. To overcome the non-convexity of the problem, we propose an iterative algorithm based on a successive convex approximation method, which obtains a locally optimal solution. Numerical results confirm the effectiveness of the proposed techniques for C-RAN systems with battery-limited users