# Joint Uplink and Downlink Transmissions in User-Centric OFDMA Cloud-RAN

## TL;DR

This paper introduces a joint resource allocation framework for user-centric OFDMA Cloud-RAN that optimizes uplink and downlink transmissions, significantly improving system throughput.

## Contribution

It formulates a complex non-convex optimization problem and proposes an efficient duality-based algorithm along with a heuristic to enhance throughput in user-centric CRAN.

## Key findings

- Proposed algorithms outperform benchmark schemes in throughput.
- Heuristic algorithm achieves near-optimal performance.
- Joint UL/DL optimization significantly boosts system capacity.

## Abstract

This paper studies joint uplink (UL) and downlink (DL) resource allocation in user-centric orthogonal frequency division multiple access (OFDMA) cloud radio access network (CRAN), where the users select the distributed remote radio heads (RRHs) to cooperatively serve their UL and DL transmissions over different subcarriers (SCs). The goal of this paper is to maximize the system throughput through jointly optimizing UL/DL scheduling, SC assignment, RRH grouping/clustering and power allocation under the maximum power and fronthaul capacity constraints. The problem is formulated as a mixed integer programming problem which is non-convex and NP-hard. We propose an efficient algorithm based on the Lagrange duality method to obtain an asymptotically optimal solution for this problem. A heuristic algorithm is further proposed to reduce the complexity. Simulation results illustrate that the proposed heuristic algorithm also has a close-to-optimal performance, and the proposed algorithms can considerably improve the system throughput compared to other benchmark schemes.