Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks

Abstract

This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple multiantenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sumrate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs.

Joint Decoding and Adaptive Compression with QoS Constraint for Uplinks in Cloud Radio Access Networks

“…, and the first constraint in (11) is obtained by using Pr{c →c} in (9). We note that the optimal solution of (11) always satisfies (10), i.e., the optimal objective value of (11) is an upper-bound for that of (10).…”

“…The above mentioned compression techniques, however, are proposed for single base station scenarios, and thus might not exploit correlation among the RRHs. Recently, joint compression techniques have been proposed to optimize quantization noise power [9][10][11]. The compression process is implemented via a test channel in which the quantization noise is modeled as an independent Gaussian random variable.…”

“…The compression process is implemented via a test channel in which the quantization noise is modeled as an independent Gaussian random variable. It is shown that in general the joint design of precoding and quantization noise matrix can significantly improve the system sum rate, compared to the separate design [9]. This improvement comes from the correlation between RRHs to which distributed source coding can be applied [12].…”

“…For example, a text message application usually support higher BLER threshold than a video call. Specifically, we aim to minimize the transmission rate in fronthaul links with an acceptable distortion of the compressed signal so that the BBU can support more RRHs, which is fundamentally different from [9][10][11]. First, we propose a Joint Decompression and Decoding (JDD) algorithm that effectively exploits the correlation among the RRHs, which has been utilized for significant throughput improvement as shown under the theoretical aspect in [12], [14].…”

Capacity Bounds on the Downlink of Symmetric, Multi-Relay, Single-Receiver C-RAN Networks

“…We refer to [1][2][3] and the references therein for an overview of the challenges and coding techniques for C-RANs. Several coding schemes have been proposed in recent years for the downlink of C-RANs including message sharing [4], backhaul compression [5], hybrid schemes [6] and generalized data sharing using Marton's coding [7,8]. The paper [9] gives an upper bound on the sum-rate of two-relay C-RANs with two users and numerically compares the performance of the aforementioned schemes with the upper bound.…”