# Group Sparse Precoding for Cloud-RAN with Multiple User Antennas

## TL;DR

This paper develops a joint RAP selection and precoding method for multi-antenna users in Cloud-RAN, optimizing the tradeoff between sum rate and active RAPs using a convex approximation inspired by compressive sensing.

## Contribution

It introduces a novel convex optimization approach for group sparse precoding with multiple antennas per user in Cloud-RAN, addressing joint transmit and receive beamforming.

## Key findings

- Achieves near-optimal sum rate compared to exhaustive search.
- Effectively reduces the number of active RAPs.
- Provides an efficient algorithm with proven convergence.

## Abstract

Cloud radio access network (C-RAN) has become a promising network architecture to support the massive data traffic in the next generation cellular networks. In a C-RAN, a massive number of low-cost remote antenna ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed low-latency fronthaul links, which enables efficient resource allocation and interference management. As the RAPs are geographically distributed, the group sparse beamforming schemes attracts extensive studies, where a subset of RAPs is assigned to be active and a high spectral efficiency can be achieved. However, most studies assumes that each user is equipped with a single antenna. How to design the group sparse precoder for the multiple antenna users remains little understood, as it requires the joint optimization of the mutual coupling transmit and receive beamformers. This paper formulates an optimal joint RAP selection and precoding design problem in a C-RAN with multiple antennas at each user. Specifically, we assume a fixed transmit power constraint for each RAP, and investigate the optimal tradeoff between the sum rate and the number of active RAPs. Motivated by the compressive sensing theory, this paper formulates the group sparse precoding problem by inducing the $\ell_0$-norm as a penalty and then uses the reweighted $\ell_1$ heuristic to find a solution. By adopting the idea of block diagonalization precoding, the problem can be formulated as a convex optimization, and an efficient algorithm is proposed based on its Lagrangian dual. Simulation results verify that our proposed algorithm can achieve almost the same sum rate as that obtained from exhaustive search.