Online edge caching and wireless delivery in fog-aided networks

Multimedia content is the significant fraction of transferred data over the wireless medium in the modern cellular and wireless communication networks. To improve the quality of experience perceived by users, one promising solution is to push the most popular contents as close as to users, also known as the edge of network. Storing content at the edge nodes (ENs) or base stations (BSs) is called caching . In Fog Radio Access Network (F-RAN), each EN is equipped with a cache as well as a fronthaul connection to the content server. Among the new design problems raised by the outlined scenarios, two key issues are addressed in this dissertation: 1) How to utilize cache and fronthaul resources while taking into account the wireless channel impairments; 2) How to incorporate the time-variability of popular set in the performance evaluation of F-RAN. These aspects are investigated by using information-theoretic models, obtaining fundamental insights that have been corroborated by various illustrative examples. To address point 1), two scenarios are investigated. First, a single-cell scenario with two transmitters is considered. A fog-aided small-cell BS as one of the transmitters and a cloud-aided macro-cell BS as the second transmitter collaborate with each other to send the requested content over a partially connected wireless channel. The intended and interference channels are modeled by erasure channels. Assuming a static set of popular contents, offline caching maps the library of files to cached contents stored at small-cell BS such that the cache capacity requirement is met. The delivery time per bit (DTB) is adopted as a measure of the coding latency, that is, the duration of the transmission block, required for reliable delivery. It is proved that optimal DTB is a linear decreasing function of cache capacity as well as inversely proportional with capacity of fronthaul link. In the second scenario, the same single-cell model is used with the only caveat that the set of popular files is time-varying. In this case, online caching maps the library of files to cached contents at small-cell BS. Thanks to availability of popular set at macro-BS, the DTB is finite and has upper and lower bounds which are functions of system resources i.e., cache and fronthaul link capacities. As for point 2), the model is comprised of an arbitrary number of ENs and users connected through an interference-limited wireless channel at high-SNR regime. All equally important ENs are benefited from cache capacity as well as fronthaul connection to the content server. The time-variability of popular set necessitates online caching to enable ENs keep track of changes in the popular set. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high-SNR regime. Online edge caching and delivery schemes based on reactive and proactive caching principles are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. The outcome of analytical results provides a controversial view of contemporary research on the edge caching. It is proved that with a time-varying set of popular files, the capacity of fronthaul link between ENs and content server set a fundamental limit on the system performance. This is due to the fact that the original information source is content server and the only way to retrieve information is via fronthaul links. While edge caching can provide some gains in term of reduced latency, the gain diminishes as a result of the fact that the cached content is prone to be outdated with time-varying popularity

On the Interplay Between Edge Caching and HARQ in Fog-RAN

In a Fog Radio Access Network (Fog-RAN), edge caching is combined with cloud-aided transmission in order to compensate for the limited hit probability of the caches at the base stations (BSs). Unlike the typical wired scenarios studied in the networking literature in which entire files are typically cached, recent research has suggested that fractional caching at the BSs of a wireless system can be beneficial. This paper investigates the benefits of fractional caching in a scenario with a cloud processor connected via a wireless fronthaul link to a BS, which serves a number of mobile users on a wireless downlink channel using orthogonal spectral resources. The fronthaul and downlink channels occupy orthogonal frequency bands. The end-to-end delivery latency for given requests of the users depends on the HARQ processes run on the two links to counteract fading-induced outages. An analytical framework based on theory of Markov chains with rewards is provided that enables the optimization of fractional edge caching at the BSs. Numerical results demonstrate meaningful advantages for fractional caching due to the interplay between caching and HARQ transmission. The gains are observed in the typical case in which the performance is limited by the wireless downlink channel and the file popularity distribution is not too skewed

Fundamental Limits of Cloud and Cache-Aided Interference Management with Multi-Antenna Edge Nodes

In fog-aided cellular systems, content delivery latency can be minimized by jointly optimizing edge caching and transmission strategies. In order to account for the cache capacity limitations at the Edge Nodes (ENs), transmission generally involves both fronthaul transfer from a cloud processor with access to the content library to the ENs, as well as wireless delivery from the ENs to the users. In this paper, the resulting problem is studied from an information-theoretic viewpoint by making the following practically relevant assumptions: 1) the ENs have multiple antennas; 2) only uncoded fractional caching is allowed; 3) the fronthaul links are used to send fractions of contents; and 4) the ENs are constrained to use one-shot linear precoding on the wireless channel. Assuming offline proactive caching and focusing on a high signal-to-noise ratio (SNR) latency metric, the optimal information-theoretic performance is investigated under both serial and pipelined fronthaul-edge transmission modes. The analysis characterizes the minimum high-SNR latency in terms of Normalized Delivery Time (NDT) for worst-case users' demands. The characterization is exact for a subset of system parameters, and is generally optimal within a multiplicative factor of 3/2 for the serial case and of 2 for the pipelined case. The results bring insights into the optimal interplay between edge and cloud processing in fog-aided wireless networks as a function of system resources, including the number of antennas at the ENs, the ENs' cache capacity and the fronthaul capacity.Comment: 34 pages, 15 figures, submitte

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

Cloud-Aided Interference Management with Cache-Enabled Edge Nodes and Users

This paper considers a cloud-RAN architecture with cache-enabled multi-antenna Edge Nodes (ENs) that deliver content to cache-enabled end-users. The ENs are connected to a central server via limited-capacity fronthaul links, and, based on the information received from the central server and the cached contents, they transmit on the shared wireless medium to satisfy users' requests. By leveraging cooperative transmission as enabled by ENs' caches and fronthaul links, as well as multicasting opportunities provided by users' caches, a close-to-optimal caching and delivery scheme is proposed. As a result, the minimum Normalized Delivery Time (NDT), a high-SNR measure of delivery latency, is characterized to within a multiplicative constant gap of $3/2$ under the assumption of uncoded caching and fronthaul transmission, and of one-shot linear precoding. This result demonstrates the interplay among fronthaul links capacity, ENs' caches, and end-users' caches in minimizing the content delivery time.Comment: 9 pages, 3 figures, submitte