2 research outputs found
Hybrid Precoding for Massive MIMO Systems in Cloud RAN Architecture with Capacity-Limited Fronthauls
Cloud RAN (C-RAN) is a promising enabler for distributed massive MIMO
systems, yet is vulnerable to its fronthaul congestion. To cope with the
limited fronthaul capacity, this paper proposes a hybrid analog-digital
precoding design that adaptively adjusts fronthaul compression levels and the
number of active radio-frequency (RF) chains out of the entire RF chains in a
downlink distributed massive MIMO system based on C-RAN architecture. Following
this structure, we propose an analog beamformer design in pursuit of maximizing
multi-user sum average data rate (sum-rate). Each element of the analog
beamformer is constructed based on a weighted sum of spatial channel covariance
matrices, while the size of the analog beamformer, i.e. the number of active RF
chains, is optimized so as to maximize the large-scale approximated sum-rate.
With these analog beamformer and RF chain activation, a regularized zero-
forcing (RZF) digital beamformer is jointly optimized based on the
instantaneous effective channel information observed through the given analog
beamformer. The effectiveness of the proposed hybrid precoding algorithm is
validated by simulation, and its design criterion is clarified by analysis.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems
Hybrid beamforming is known to be a cost-effective and wide-spread solution
for a system with large-scale antenna arrays. This work studies the
optimization of the analog and digital components of the hybrid beamforming
solution for remote radio heads (RRHs) in a downlink cloud radio access network
(C-RAN) architecture. Digital processing is carried out at a baseband
processing unit (BBU) in the "cloud" and the precoded baseband signals are
quantized prior to transmission to the RRHs via finite-capacity fronthaul
links. In this system, we consider two different channel state information
(CSI) scenarios: 1) ideal CSI at the BBU 2) imperfect effective CSI.
Optimization of digital beamforming and fronthaul quantization strategies at
the BBU as well as analog radio frequency (RF) beamforming at the RRHs is a
coupled problem, since the effect of the quantization noise at the receiver
depends on the precoding matrices. The resulting joint optimization problem is
examined with the goal of maximizing the weighted downlink sum-rate and the
network energy efficiency. Fronthaul capacity and per-RRH power constraints are
enforced along with constant modulus constraint on the RF beamforming matrices.
For the case of perfect CSI, a block coordinate descent scheme is proposed
based on the weighted minimum-mean-square-error approach by relaxing the
constant modulus constraint of the analog beamformer. Also, we present the
impact of imperfect CSI on the weighted sum-rate and network energy efficiency
performance, and the algorithm is extended by applying the sample average
approximation. Numerical results confirm the effectiveness of the proposed
scheme and show that the proposed algorithm is robust to estimation errors