Multivariate Fronthaul Quantization for Downlink CRAN Wonju

The Cloud-Radio Access Network (C-RAN) cellular architect ure relies on the transfer of complex baseband signals to and from a central unit (CU) over digital fronthaul links to enable th virtualization of the baseband processing functionaliti es of distributed radio units (RUs). The standard design of digital fronthaul ing is based on either scalar quantization or on more sophist icated pointto-point compression techniques operating on baseband sig al . Motivated by network-information theoretic results , techniques for fronthaul quantization and compression that improve ov r point-to-point solutions by allowing for joint processing across multiple fronthaul links at the CU have been recently propos ed for both the uplink and the downlink. For the downlink, a fo rm of joint compression, known in network information theory a s multivariate compression, was shown to be advantageous under a non-constructive asymptotic information-theoretic fram ework. In this paper, instead, the design of a practical symb ol-by-symbol fronthaul quantization algorithm that implements the idea of multivariate compression is investigated for the C-RAN d ownlink. As compared to current standards, the proposed multivariat e quantization (MQ) only requires changes in the CU processi ng while no modification is needed at the RUs. The algorithm is ex tended to enable the joint optimization of downlink precodi ng and quantization, reduced-complexity MQ via successive bl ock quantization, and variable-length compression. Numer ical esults, which include performance evaluations over standard cellu lar models, demonstrate the advantages of MQ and the merits o f a joint optimization with precoding. Index Terms C-RAN, downlink, multivariate quantization, Lloyd-Max, f ronthaul, precoding. W. Lee and J. Kang are with the Department of Electrical Engin eeri g, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, South Korea (e-mail: wonjulee@kaist.ac.kr, jhkang@ee.ka ist.ac.kr). O. Simeone is with the Center for Wireless information Proce ssing (CWiP), Department of Electrical and Computer Engine eri g, New Jersey Institute of Technology (NJIT), Newark, NJ 07102, USA (e-mail: osvaldo. simeone@njit.edu). The work of O. Simeone was partially sup ported by U.S. NSF under grant CCF-1525629. S. Shamai (Shitz) is with the Department of Electrical Engin eering, Technion, Haifa, 32000, Israel (e-mail: sshlomo@e e.technion.ac.il). The work of S. Shamai was supported by the Israel Science Foundation and th e S. AND N. grand research fund. JOURNAL OF LTEX CLASS FILES, VOL. X, NO. XX, JULY 2016 2 I. I NTRODUCTION Cloud Radio Access Network (C-RAN) refers to the centraliza tion of base station functionalities by means of cloud computing. It is universally accepted as one of the key techn ologies for 5G systems due to its significant advantages in terms of lower expenses, flexibility and enhanced spectral e fficiency. In particular, C-RAN enables the implementation f coordinated multipoint (CoMP) schemes across all the ratio units (RUs) connected to the central processing unit (CU) in the “cloud” [1]. The main obstacle to the realization of the prom ises of C-RAN resides in the restrictions on the capacity and latency of the so-called fronthaul links that provide conne ctivity between RUs and the CU (see [2] for a review and Fig. 1 f or an illustration). The standard design of digital fronthaul ing, that is, of the transmission of digitized baseband comp lex samples on the fronthaul links, is based on either scalar quantizati on, as in the Common Public Radio Interface (CPRI) standard, or on more sophisticatedpoint-to-point compression techniques operating on baseband signals [3][9]. This means that aseparate quantizer, and possibly compressor, is implemented for eac h fronthaul link and hence for each RU connected to the CU [2]. Motivated by network-information theoretic results, tech niques for fronthaul quantization/compression that impro ve ver point-to-point solutions by allowing for joint processing across multiple fronthaul links at the CU have been studied f or both the uplink and the downlink (see [10] and references the rein). In particular, for the uplink, distributed source co ding, which can be implemented by means of Wyner-Ziv coding, was de monstrated to yield significant performance gains by leveragingjoint decompression at the CU [10][11]. In a dual fashion, for the downlink, a form of joint compression, known in network information theory as multivariate compression, was shown to be advantageous in [12]. In both cases, the desi gn and performance gains of joint decompression or joint compr ession depend on the density of the deployment of RUs in the network and rely on some degree of channel state information (CSI) regarding the uplink or downlink channels at the CU. The results reviewed above concerning the advantages of joi nt fr nthaul processing at the CU were derived under a nonconstructive asymptotic information-theoretic framewor k that assumes long coding blocks and only asserts the existe nce of coding schemes that achieve given performance bounds. In th is paper, instead, we consider the downlink of a C-RAN and investigate the design of a quantization algorithm that imp le ents the idea of multivariate compression put forth in [1 0] and is hence referred to as Multivariate Quantization (MQ). We s p cifically focus on a practical implementation that operat s on a symbol-by-symbol basis. Hence, as compared to conventi onal CPRI, MQ only requires changes in the CU processing, whereby quantization is performed jointly, rather than sep arately, on the baseband signals intended for multiple RUs, while no modification is needed at the RUs, which still perform scalar decompression. Furthermore, following the theoretical in sights of [12] on the benefits of joint optimization of fronthaul compr ession and downlink precoding, we propose an algorithm for t he joint design of MQ and beamforming. Next, to tackle the computatio n l complexity issue of MQ, we propose a reduced-complexity JOURNAL OF LTEX CLASS FILES, VOL. X, NO. XX, JULY 2016 3