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Distributed Resource Management in Downlink Cache-Enabled Multi-Cloud Radio Access Networks

Robert-Jeron Reifert, Alaa Alameer Ahmad, Hayssam Dahrouj, Anas Chaaban, Aydin Sezgin, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

TL;DR

A multi-cloud radio access network (MC-RAN), where each cloud is connected to a distinct set of cache-enabled base stations via limited capacity fronthaul links, is considered, where the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide energy efficiency subject to fr onthaul capacity and transmit power constraints is investigated.

Abstract

In light of the premises of beyond fifth generation (B5G) networks, the need for better exploiting the capabilities of cloud-enabled networks arises, so as to cope with the large-scale interference resulting from the massive increase of data-hungry systems. A compound of several clouds, jointly managing inter-cloud and intra-cloud interference, constitutes a practical solution to account for the requirements of B5G networks. This paper considers a multi-cloud radio access network model (MC-RAN), where each cloud is connected to a distinct set of base stations (BSs) via limited capacity fronthaul links. The BSs are equipped with local cache storage and baseband processing capabilities, as a means to alleviate the fronthaul congestion problem. The paper then investigates the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide energy efficiency (EE) subject to fronthaul capacity and transmit power constraints. This paper solves such a mixed discrete-continuous, non-convex optimization problem using fractional programming (FP) and successive inner-convex approximation (SICA) techniques to deal with the non-convexity of the continuous part of the problem, and $l_0$-norm approximation to account for the binary association part. A highlight of the proposed algorithm is its capability of being implemented in a distributed fashion across the network's multiple clouds through a reasonable amount of information exchange. The numerical simulations illustrate the pronounced role the proposed algorithm plays in alleviating the interference of large-scale MC-RANs, especially in dense networks.

Distributed Resource Management in Downlink Cache-Enabled Multi-Cloud Radio Access Networks

TL;DR

A multi-cloud radio access network (MC-RAN), where each cloud is connected to a distinct set of cache-enabled base stations via limited capacity fronthaul links, is considered, where the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide energy efficiency subject to fr onthaul capacity and transmit power constraints is investigated.

Abstract

In light of the premises of beyond fifth generation (B5G) networks, the need for better exploiting the capabilities of cloud-enabled networks arises, so as to cope with the large-scale interference resulting from the massive increase of data-hungry systems. A compound of several clouds, jointly managing inter-cloud and intra-cloud interference, constitutes a practical solution to account for the requirements of B5G networks. This paper considers a multi-cloud radio access network model (MC-RAN), where each cloud is connected to a distinct set of base stations (BSs) via limited capacity fronthaul links. The BSs are equipped with local cache storage and baseband processing capabilities, as a means to alleviate the fronthaul congestion problem. The paper then investigates the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide energy efficiency (EE) subject to fronthaul capacity and transmit power constraints. This paper solves such a mixed discrete-continuous, non-convex optimization problem using fractional programming (FP) and successive inner-convex approximation (SICA) techniques to deal with the non-convexity of the continuous part of the problem, and -norm approximation to account for the binary association part. A highlight of the proposed algorithm is its capability of being implemented in a distributed fashion across the network's multiple clouds through a reasonable amount of information exchange. The numerical simulations illustrate the pronounced role the proposed algorithm plays in alleviating the interference of large-scale MC-RANs, especially in dense networks.

Paper Structure

This paper contains 38 sections, 3 theorems, 54 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Overview
  3. Related Works
  4. Contributions
  5. Notations
  6. System Model
  7. Received Signal Model
  8. Cache Model
  9. Baseband Processing and Fronthaul Communication Cost Models
  10. Transmit Scheme and Functional Split
  11. Design Transmit Signals at the CP
  12. Design Transmit Signals Locally at BSs
  13. Hybrid Transmit Strategy
  14. Achievable Rates and Fronthaul Constraints
  15. Energy Efficiency at the Cloud
  16. ...and 23 more sections

Key Result

Lemma 1

For all feasible values $\left(\mathbf{t}',\mathbf{r}'\right)$ and all $(c,b)\in(\mathcal{C},\mathcal{B}_c)$, the function $g_1(\mathbf{t},\mathbf{r},\mathbf{t}',\mathbf{r}')$ satisfies

Figures (7)

  • Figure 1: System model of an MC-RAN consisting of three clouds, $5$ BSs and $8$ users. Examples for inter-cloud and intra-cloud interference, as well as generic communication links are provided.
  • Figure 2: EE as a function of fronthaul capacity for different cache sizes.
  • Figure 3: EE as a function of fronthaul capacity, where different processing powers are utilized.
  • Figure 4: Caching gain and EE as functions of different system parameters.
  • Figure 5: EE as a function of fronthaul capacity and number of users for different cache sizes and schemes.
  • ...and 2 more figures

Theorems & Definitions (6)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Theorem 1
  • proof