Distributed Coded Caching with Application to Content Delivery in Wireless Networks
Doktorsavhandling, 2021

The amount of content downloaded to mobile devices, mainly driven by the demand for video content, threatens to completely congest wireless networks and the trend of ever increasing video traffic is expected to continue unabated for many years. A promising solution to this problem is to store popular content closer to end users, effectively trading expensive bandwidth resources for affordable memory, a technique known as caching.

In this thesis, we study the use of erasure correcting codes (ECCs) to increase the amount of data that can be downloaded directly from the caches when content is cached in a distributed fashion across several base stations (BSs) or mobile devices. When content is cached in mobile devices, users may download coded packets directly from caching devices using device-to-device communication and, if necessary, from the BS at a higher communication cost. Devices moving out of range or turning off will cause a loss of cached content. To restore the initial state of reliability in the network, data is transmitted to available mobile devices in a process known as content repair. We compare the amount of data transmitted in the network due to content download and content repair using various ECCs when content is repaired at periodic times. We analyze the performance when mobile devices enter the network with or without usable cached content and show that increasing the time between repairs, so called lazy repairs, can be beneficial. Furthermore, we analyze content caching in mobile devices using maximum distance separable codes for scenarios where the density of devices is high. We optimize the number of mobile devices to involve in the caching of content and demonstrate the significant gains that can be achieved in terms of data downloaded from caching devices.

We proceed to analyze how to optimally manage cached content over time when users request content according to a renewal process, i.e., a process with memory. Specifically, we consider the distributed coded caching of content at small BSs where coded packets may be evicted from the caches at periodic times. We prove that the problem of maximizing the amount of data that users can download from the caches is concave and that our problem formulation is a generalization of the previously studied cases where content is cached in a single cache and where content is not managed over time, so called static caching. We show that optimizing caching policies can offer considerable gains in the amount of data that can be downloaded from the caches, especially when the request process is bursty. Conversely, we prove that static caching is optimal for request processes without memory. Finally, we suggest a multi-agent reinforcement learning approach to learn cache management policies for spatially non-uniform renewal request processes. Our algorithm obtains cache management policies, substantially increasing the amount of data that can be downloaded from the caches.

machine learning

erasure correcting codes

optimization

Caching

device-to-device communication

content delivery networks

time-to-live

Opponent: Prof. Deniz Gündüz, Department of Electrical and Electronic Engineering, Imperial College London, UK

Författare

Jesper Pedersen

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Dynamic Coded Caching in Wireless Networks

IEEE Transactions on Communications,; Vol. 69(2021)p. 2138-2147

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Optimizing MDS Coded Caching in Wireless Networks with Device-to-Device Communication

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Distributed Storage in Mobile Wireless Networks with Device-to-Device Communication

IEEE Transactions on Communications,; Vol. 64(2016)p. 4862-4878

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J. Pedersen, A. Graell i Amat, F. Brännström, and E. Rosnes, “Dynamic Coded Caching in Wireless Networks Using Multi-Agent Reinforcement Learning"

Everywhere around us, e.g., on the bus, at the café, or in the shopping mall, people are using smartphones to watch the latest videos on YouTube, TikTok, or Instagram, or perhaps to listen to the latest podcast. The data is sent to the users over wireless networks, such as the cellular network or WiFi. Naturally, users want to decide for themselves when they consume content, i.e., we have long ago left the age of broadcasting video content, such as the evening news, for everyone to watch at the same time. These asynchronous requests for content, together with the fact that some videos become extremely popular, have increased the amount of data transmitted over wireless networks dramatically and the trend is expected to continue for many years. Supporting this data traffic puts immense pressure on already burdened wireless networks, leading to long delays.

In stark contrast to the trend of ever increasing data traffic, the price of memory is steadily decreasing. A promising idea is hence to deploy hard drives at base stations or use spare memory on smartphones to store frequently requested content closer to end-users, known as caching. If users can download content from the caches, the pressure on the networks is alleviated.

Users in wireless networks can typically access several caches, either due to overlapping base station coverage or device mobility. However, a user will only access some of the caches. This means that we have to add redundancy to the data to allow the requested content to be recovered from the data downloaded from the caches. In this thesis, we study how to add this redundancy optimally, i.e., how to efficiently utilize the distributed caches to minimize the strain on wireless networks.

Distribuerad lagring för datalagring och trådlös leverans av data

Vetenskapsrådet (VR), 2016-01-01 -- 2019-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Data- och informationsvetenskap

Elektroteknik och elektronik

ISBN

978-91-7905-475-5

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4942

Utgivare

Chalmers tekniska högskola

Online

Opponent: Prof. Deniz Gündüz, Department of Electrical and Electronic Engineering, Imperial College London, UK

Mer information

Senast uppdaterat

2021-04-28