Abstract
A key problem in the design of cloud radio access networks (CRANs) is to devise effective baseband compression strategies for transmission on the fronthaul links connecting a remote radio head (RRH) to the managing central unit (CU). Most theoretical works on the subject implicitly assume that the RRHs, and hence the CU, are able to perfectly recover time synchronization from the baseband signals received in the uplink, and focus on the compression of the data fields. This paper instead does not assume a priori synchronization of RRHs and CU, and considers the problem of fronthaul compression design at the RRHs with the aim of enhancing the performance of time and phase synchronization at the CU. The problem is tackled by analyzing the impact of the synchronization error on the performance of the link and by adopting information and estimation-theoretic performance metrics such as the rate-distortion function and the Cramer-Rao bound (CRB). The proposed algorithm is based on the Charnes-Cooper transformation and on the Difference of Convex (DC) approach, and is shown via numerical results to outperform conventional solutions.
Highlights
As mobile operators are faced with increasingly demanding requirements in terms of data rates and operational costs, the novel architecture of cloud radio access networks (C-RANs) has emerged as a promising solution [1, 2]
In a C-RAN, the baseband processing and higherlayers operations of the base stations are migrated to a central unit (CU) in the “cloud”, to which the base station, typically referred to a remote radio head (RRH), are connected via fronthaul links, which in turn may be realized via fiber optics, microwave or mmwave technologies
We study the problem of optimal fronthaul compression of the training field with the aim of enhancing the performance of time and phase synchronization at the CU
Summary
As mobile operators are faced with increasingly demanding requirements in terms of data rates and operational costs, the novel architecture of cloud radio access networks (C-RANs) has emerged as a promising solution [1, 2]. We study the problem of optimal fronthaul compression of the training field with the aim of enhancing the performance of time and phase synchronization at the CU. Using the standard additive quantization noise model, the resulting compressed signal for each nth polyphase sequence can be written as ynp[m] = ynp[m] +qpn[m] , m = 0, ..., Np − 1,. We observe that an optimized correlation for the quantization noise on the data phase could be designed, similar to [10], but we leave this aspect to future work in order to concentrate on training for synchronization. Following the discussion above, the fronthaul rate required to convey the compressed data signal yd =[ yd[ 0] , ..., yd[ Nd − 1] ], from the RRH to the CU is given by Rd = supτ,θ I(yd; yd), with vector yd being defined, with.
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