Optimal fronthaul compression for synchronization in the uplink of cloud radio access networks
- Eunhye Heo^{1}Email authorView ORCID ID profile,
- Osvaldo Simeone^{2} and
- Hyuncheol Park^{1}
https://doi.org/10.1186/s13638-017-0804-8
© The Author(s) 2017
Received: 6 October 2015
Accepted: 2 January 2017
Published: 28 January 2017
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.
Keywords
1 Introduction
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 higher-layers 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. By simplifying the network edge and by centralizing baseband processing, the C-RAN architecture is expected to provide significant benefits in energy efficiency, load balancing, and interference management capabilities (see review in [2]).
A key issue in C-RANs is to devise effective methods of transporting digitized baseband signals on the fronthaul links with the limited capacity. The Common Public Radio Interface (CPRI) standard [3] defines the communication interface between CU and RRHs on the fronthaul network, including the use of sampling and scalar quantization for the digitization of the baseband signals. However, the basic approach prescribed by CPRI is bound to produce bit rates that are difficult to accommodate within the available fronthaul capacities. This has motivated the design of strategies that reduce the bit rate of the fronthaul data stream while limiting the distortion incurred on the quantized signal. In order to reduce the fronthaul rate by means of compression, there are CPRI techniques based on a number of principles such as filtering and downsampling [4], optimized non-uniform quantization [5], and lossless compression [6]. In addition to the mentioned point-to-point compression algorithms, there are works that tackle the design of fronthaul transmission strategies from a network-aware perspective (see, e.g., [7–10][13]).
Most theoretical works on fronthaul compression for C-RAN implicitly assume perfect time synchronization and channel state information (CSI) at the RRHs and the CU. However, on the one hand, this assumption violates the C-RAN paradigm that minimal baseband processing should be carried out at the RRHs, and, on the other hand, the resulting design neglects the additional requirements on fronthaul processing at the RRHs that are imposed by synchronization and channel estimation. This limitation is alleviated by [10], which considers robust compression in the presence of imperfect CSI and by papers [11, 12], which study the impact of fronthaul compression on channel estimation. To the best of our knowledge, analyses that account for imperfect time synchronization are not available.
To this end, the effect of the synchronization error on the signal to noise ratio (SNR) is analyzed by adopting the Cramer-Rao bound (CRB) as the performance criterion of interest and by accounting for compression via information theoretic tools. The resulting proposed algorithm is based on the Charnes-Cooper transformation [14] and the Difference of Convex (DC) approach [15]. Numerical results show that optimized fronthaul compression that targets enhanced synchronization performance outperforms conventional solution that do not account for the impact of synchronization errors. The rest of the paper is organized as follows. Section 2 introduce system model of uplink C-RAN cellular system. The analytic study of the performance and optimization are presented in Section 3: the CRBs of time and phase offset estimation carried at CU is derived in Section 3.1, and the analysis of impact of the synchronization error on the effective SNR in Section 3.2, and the optimization of fronthaul compression in Section 3.3. Finally, the performance is evaluated through simulations to present benefits of the proposed compression scheme in Section 4.
1.1 Notation
Boldface lowercase letters denotes column vectors and boldface uppercase letters denotes matrices. The superscripts (·)^{ † } denotes conjugate transpose of its argument. (·)^{−1} denotes inverse operation of its argument. The determinant of matrix A is denoted as |A|. The expectation operation with respect to x is denoted as \(\mathbb {E}_{x}[\!\cdot ]\); the correlation matrix of random vector x is defined as \(\mathbf {K}_{\mathbf {x}}=\mathbb {E}[\!\mathbf {x}\mathbf {x}^{\dag }]\).
2 System model
In this paper, we consider training-based synchronization for the uplink of a C-RAN cellular system. We specifically focus on the operation of a single cell, as illustrated in Fig. 1, and assume that, as in current cellular implementations, the MSs transmit over orthogonal time/frequency resources, so that we can focus on a single active MS in a given resource block. The MS transmits a frame consisting of a training and a data field. We further assume that the active MS and the RRH have a single antenna. The RRH is connected to a CU in the cloud via a fronthaul link that can deliver C bits per uplink sample to the CU. It is also assumed that the RRH is synchronized at the frame level so as to be able to distinguish between the training and data fields that compose each transmitted frame.
2.1 Training phase
where A is a positive amplitude that accounts for the attenuation due to fading; θ is the phase offset, which models the effect of the channel and of the phase mismatch between the oscillators at the MS and at the RRH; τ accounts for the residual timing offset between MS and RRH; T is the symbol period; x _{ p }[l] is the lth pilot symbol transmitted by the MS; N _{ p } is the number of pilot symbols; g(t) is the pulse shape, which includes the effect of the transmit and receive filter and is assumed to be supported in the interval [0,(L−1)T] for some integer L>1; and z _{ p }(t) is the complex additive white Gaussian noise with two-sided power spectral density N _{0}. We assume that the RRH is able to estimate the channel amplitude A, for instance, by means of automatic gain control in the presence of constant amplitude symbols. Instead, the time offset τ and phase offset θ need to be estimated from the received signal (1).
The training sequence is generated randomly such that the symbols x _{ p }[ l] for l=0,...,N _{ p }−1 are independent and distributed as \(\mathcal {CN}(0,E_{x_{p}})\). The training sequence is known to the CU and the random generation is assumed here for the sake of simplifying the analysis in the spirit of Shannon’s random coding (see, e.g., [16]). We further assume that the pilot symbols are preceded by a cyclic prefix of duration equal to (L−1)T. This implies that x _{ p }[−l]=x _{ p }[−l+N _{ p }] for 1≤l≤L−1. Alternatively, as it will be discussed, the analysis below holds as long as the number of training symbols N _{ p } is sufficiently larger than the support of the waveform g(t)L.
In order to potentially enhance the performance of phase and time synchronization, we allow the receiver to oversample the received signal at the BS with a sampling period T _{ s }=T/F, where F is the oversampling factor. For simplicity of analysis, we consider a raised cosine pulse g(t) with zero excess bandwidth (i.e., a sinc function) so that the two-sided bandwidth is B=1/T. As a result, setting F=1, i.e., no oversampling, is an acceptable choice that leads to no spectral aliasing. However, as it will be seen in Section 4, the selection F>1 may yield an improved performance. Note that this is true even under the given assumption of zero excessive bandwidth. The reason is that collecting a larger number of samples enables the mitigation of the effect of the additive noise.
where we have defined \({z^{n}_{p}}[m]\triangleq z_{p}(mT+{nT}_{s}), g_{\tau,\theta }^{n}[m]\triangleq e^{j\theta }g(mT+{nT}_{s}-\tau)\), and \(\circledast \) denotes the circular convolution. Assuming that the noise z _{ p }(t) is white over the bandwidth [−1/2T _{ s },1/2T _{ s }], the discrete-time noise sequence \({z^{n}_{p}}[m]\) is an i.i.d. process with zero mean and power N _{0}/T _{ s }.
Remark 1
The presampling filter has a cut-off frequency of 1/2T since it is matched to the signal waveform. As a result, the noise prior to sampling is bandlimited with two-sided bandwidth B=1/T. As such, it is correlated with auto-correlation function proportional to sinc(t/T). Therefore, with oversampling, the discrete-time noise samples, which are taken at times multiple of T/F, are more properly modelled as correlated if F>1. Here, following many related references (see, e.g., [18,19]), we instead make the simplifying assumption that the noise is white. This choice can be seen to lead to lower bounds on the actual system performance. □
2.2 Data phase
where x _{ d }[l] is the lth data symbol transmitted by the MS, which is generated randomly in a constellation set Ω _{ x } with zero mean and power \(E_{x_{d}}\), and N _{ d } is the number of data symbols. The other parameters are defined as in (1).
where the discrete-time noise sequence z _{ d }[m] is an i.i.d. process with zero mean and power N _{0}/T. Note that oversampling could be adopted also for the data field by following the same model used for the training field, but we do not further pursue this here in order to focus on training for synchronization.
2.3 Fronthaul compression
Following the C-RAN principle, compression is performed at the RRH in order to convey the baseband signal over the limited-capacity fronthaul link to the CU. For the training field, we assume the use of block quantizers that compress each nth polyphase sequence y ^{ n }[m], with n=0,...,F−1, separately for transmission over the fronthaul link. Note that, while joint compression of these sequences generally leads to an improved compression efficiency, here we adopt separate compression both for its lower computation complexity and for its analytical tractability. In particular, each polyphase sequence is stationary and can be hence compressed by using standard compression strategies, including universal methods [15, Ch. 10]. Furthermore, the resulting compression rate can be computed using rates distortion theory as discussed next.
where \({q^{n}_{p}}[m]\) indicates the quantization noise and \({q^{n}_{p}}[\!m]\) is assumed to be complex Gaussian and generally correlated across the discrete-time index m. Due to the separate quantization of the polyphase sequences, the quantization noise is independent across the index n. From the covering lemma of rate-distortion theory [16], vector quantization schemes can be designed such that the joint (empirical) distribution of the input and output of the quantizer satisfies (5), as long as the rate is sufficiently large (see, e.g., [16]). Furthermore, the relationship (5) can be in practice approximated by a high-dimensional dithered vector quantizers [20]. The practical relevance of the additive-noise quantization model for system design is further validated in Section 4 by means of numerical results.
where \(X_{p}[k], G^{n}_{\tau,\theta }[k], {Z^{n}_{p}}[k]\), and \({Q^{n}_{p}}[k]\) are obtained by taking the DFT of the sequences \(\left \{x_{p}[m]\}_{m=0}^{N_{p}-1}, \{g^{n}_{\tau,\theta }[m]\}_{m=0}^{N_{p}-1}, \{{z^{n}_{p}}[m]\right \}_{m=0}^{N_{p}-1}\), and \(\{{q^{n}_{p}}[m]\}_{m=0}^{N_{p}-1}\), respectively. Due to the lack of spectral aliasing afforded by the chosen waveform and sampling frequency, we can write \(G^{n}_{\tau,\theta }[k]=G^{n}[k]e^{-j(2\pi \frac {k}{N_{p}T_{s}} \tau -\theta)}\).^{1}
where \(I(\mathbf {y}_{p};\hat {\mathbf {y}}_{p})\) is given in (8).
3 Analysis and optimization
In this section, we analyze the performance of the C-RAN system introduced above by accounting for the impact of imperfect synchronization, with the aim of enabling the optimization of fronthaul quantization. We will first discuss the performance of time and phase synchronization at the CU in Section 3.1. Then, we study the impact of synchronization errors on the SNR in Section 3.2. Finally, we investigate the optimization of fronthaul compression in Section 3.3.
3.1 CRBs for the time and phase offset estimation
The derivation of (13)–(14) is given in the Appendix. Note that the bounds (13) and (14) do not depend on the phase θ and delay τ.
3.2 Impact of the synchronization error on the SNR
where \(\Delta \tau =\hat {\tau }(\hat {\mathbf {y}},\mathbf {x}) -\tau \) and \(\Delta \theta =\hat {\theta }(\hat {\mathbf {y}},\mathbf {x})-\theta \) are the synchronization errors for timing and phase, respectively. We note that compensation of the time offset requires interpolation, which is possible given the lack of spectral aliasing. Moreover, under the mentioned assumption on the zero excess bandwidth waveform g(t), the statistics of the (white Gaussian) noise terms are unchanged by interpolation.
Coefficients in the piecewise linear approximation of the raised cosine pulse
l | m−3 | m−2 | m−1 | m+1 | m+2 | m+3 |
---|---|---|---|---|---|---|
\(a^{+}_{l}\) | 0 | c _{4} | −c _{2} | c _{1} | −c _{3} | c _{5} |
\(a^{-}_{l}\) | −c _{5} | c _{3} | −c _{1} | c _{2} | −c _{4} | 0 |
where \(\bar {a}=\Sigma ^{l=m+3}_{l=m-3,l\neq m}|a_{l}|^{2} \) and \(\bar {\mathbf {x}}_{d}=[x_{d}[m-3]\ x_{d}[m-2] x_{d}[m-1]\ x_{d}[m+1]\ x_{d}[m+2]\ x_{d}[m+3]]^{T}\).
where \(f_{\tau }= 1-\frac {\eta }{2T}\sqrt {12 \textrm {CRB}_{\tau }} \), and for analytical tractability, we made the further approximation f _{ τ }≈1. We observe that the expression (23b) captures the effect of time and phase errors by means of additional noise terms in the denominator of the effective SNR. We remark that the approximations made in deriving (23b) will be validated in the numerical results by evaluating the performance of proposed optimization schemes for fronthaul compression that are based on (23b) and discussed next.
3.3 Optimization of fronthaul compression
where constraints (24b) and (24c) correspond to (9) and (12), respectively.
where \(e_{n,k}^{(i)}=-b_{n,k}/(\ln (2)(\frac {N_{0}}{T_{s}}+b_{n,k}-b_{n,k}v_{n,k}^{(i)})), f_{n,k}^{(i)}=\log _{2}(-b_{n,k}v_{n,k}^{(i)}+b_{n,k}+\frac {N_{0}}{T_{s}})-e_{n,k}^{(i)}v_{n,k}^{(i)}\).
The DC algorithm performs successive optimization of the convex problem obtained by substituting the right-hand side of (30) for the concave part in (29) until convergence. Given the known properties of the DC algorithm [15], the proposed approach, summarized in Algorithm 1, provides a feasible solution at every iteration and converges to a local minimum of problem (25). Moreover, since it only requires the solution of convex problems, the algorithm has a polynomial complexity per iteration.
4 Numerical results
In this section, we present numerical results to give insight into optimal fronthaul compression for synchronization and to validate the analysis presented in the previous sections. Throughout, we set A=0.7 and the SNR during training phase and SNR during data phase are defined as \(\textrm {SNR}_{p} = E_{x_{p}}/(N_{0}/T_{s})\phantom {\dot {i}\!}\) and \(\phantom {\dot {i}\!}\textrm {SNR}_{d} = E_{x_{d}}/(N_{0}/T)\), respectively.
with \(\Phi (\tilde {\theta }, \tilde {\tau })= \sum _{n,k}|{r^{n}_{k}}-{r^{n}_{k}}(\tilde {\theta }, \tilde {\tau })|^{2}\) where \({r^{n}_{k}}=\arg (\hat {Y}^{n}[k]X^{*}[k])/{2\pi }\) and \({r^{n}_{k}}(\tilde {\theta }, \tilde {\tau })=\tilde {\theta }-k/N_{p}(n+\tilde {\tau })\). By applying the estimator (31), we evaluate the performance of optimized compression scheme in terms of MSEs of time and phase offsets as compared to white-PSD compression that is constant across all frequencies. The white-PSD compression scheme is considered as reference since it does not attempt to optimize quantization with the aim of enhancing synchronization.
5 Conclusions
This paper tackles the problem of optimal fronthaul compression with the aim of enhancing the effective SNR in the presence of time and phase synchronization errors at the CU. The proposed algorithm optimizes the PSD of quantization noise at the RRHs by using the Charnes-Cooper transformation and the DC approach, and is shown to outperform the conventional solution that assumes an equal quantizer at all frequencies. Numerical results validate the analysis by evaluating the performance of the proposed design under practical synchronization algorithms and with scalar quantization. An interesting direction for future research is the consideration of frequency-selective channels and of frequency synchronization.
6 Endnote
^{1} The more general case with spectral aliasing could be handled by using the analysis in [17] and is left as an open problem.
7 Appendix
7.1 Proof of the CRBs for time and phase offset estimates
The summation in (35) follows from the fact that the vectors \(\hat {\mathbf {{y}}}^{n}_{p}\) in \(\hat {\mathbf {{y}}}_{p}=[\hat {\mathbf {{y}}}^{0}_{p}, \cdots, \hat {\mathbf {{y}}}_{p}^{F-1}]\) are independent for all n given the pilot signal x _{ p }. Furthermore, in (35), \(\mathbf {K}_{\tilde {\mathbf {z}}^{n}}\) is the covariance matrix of the effective noise \(\tilde {\mathbf {z}}^{n}=\mathbf {z}^{n}_{p}+\mathbf {q}^{n}_{p}\) and we have defined s ^{ n }=[s ^{ n }[0],...,s ^{ n }[N _{ p }−1]]^{ T } with \(s^{n}[m]=Ax_{p}[m]\circledast g^{n}_{\tau,\theta }[m]\). Finally, equality (36) follows from Szeg\(\ddot {\mathrm {o}}\) theorem. By inserting (36) into (34), and noting that \(\mathbb {E}[|X[k]|^{2}]=E_{x_{p}}\), the proof of (13) is concluded. The proof of (14) can be obtained using similar steps and is omitted.
7.2 Derivation of (20), (21), and (22)
where in (37c) we used the assumption \(\Delta \tau \sim {U}[-\frac {\Delta \tau _{\text {max}}}{2},\frac {\Delta \tau _{\text {max}}}{2}]\), which implies \(\mathbb {E}[|\Delta \tau |]=\frac {\Delta \tau _{\text {max}}}{4}\) and \(\mathbb {E}[|\Delta \tau |^{2}]=\frac {\Delta \tau _{\text {max}}^{2}}{12}\); (37d) follows by removing higher-order terms in Δ τ _{max} under the assumption that Δ τ _{max} is small enough; and (37e) is a consequence of the approximation \(\mathbb {E}[\Delta \tau ^{2}] = \frac {\Delta \tau _{\text {max}}^{2}}{12} \approx \textrm {CRB}_{\tau }\).
where (39c) follows from the Taylor series of the sinc function up to the second order, and (39e) is a consequence of the approximation \(\mathbb {E}[\Delta \theta ^{2}] = \frac {\Delta \theta _{\text {max}}^{2}}{12} \approx \textrm {CRB}_{\theta }\).
Declarations
Acknowledgements
The work of O. Simeone was partially supported by U.S. NSF under grant CCF-1525629. This work was supported by ’The Cross-Ministry Giga KOREA Project’ grant from the Ministry of Science, ICT, and Future Planning, Korea.
Competing interests
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
- K Chen, Duan R. C-RAN: The road towards green RAN. China mobile research institute ver. 2, (2011).Google Scholar
- A Checko, HL Christiansen, Y Yan, L Scolari, G Kardaras, MS Berger, L Dittmann, Cloud RAN for mobile networks: A technology overview. IEEE Comm. Surv. Tutorials. 17(1), 405–426 (2012).View ArticleGoogle Scholar
- AB Ericsson. Huawei Technologies, NEC Corporation, Alcatel Lucent and Nokia Siemens Networks, Common public radio interface (CPRI); interface specification, CPRI specification. vol. 5, (2011).Google Scholar
- D Samardzija, J Pastalan, M MacDonald, S Walker, R Valenzuela, Compressed transport of baseband signals in radio access networks. IEEE Trans. Wirel. Comm. 11(9), 3216–3225 (2012).View ArticleGoogle Scholar
- B Guo, W Cao, A Tao, D Samardzija, in Proc. Int. ICST Conf. CHINACOM. CPRI compression transport for LTE and LTE-A signal in C-RAN, (2012), pp. 843–849.Google Scholar
- A Vosoughi, M Wu, JR Cavallaro, in Proc. IEEE Global Communications Conference. Baseband signal compression in wireless base stations, (2012), pp. 4505–4511.Google Scholar
- A Sanderovich, O Somech, HV Poor, S Shamai, Uplink macro diversity of limited backhaul cellular network. IEEE Trans.Inf. Theory. 55(8), 3457–3478 (2009).MathSciNetView ArticleGoogle Scholar
- L Zhou, W Yu, Optimized backhaul compression for uplink Cloud Radio Access Network. IEEE J. Sel. Areas Comm. 32(6), 1295–1307 (2014).View ArticleGoogle Scholar
- A del Coso, S Simoens, Distributed compression for MIMO coordinated networks with a backhaul constraint. IEEE Trans. Wirel. Comm. 8(9), 4698–4709 (2009).View ArticleGoogle Scholar
- S-H Park, O Simeone, O Sahin, S Shamai, Robust and efficient distributed compression for cloud radio access networks. IEEE Trans. Veh. Techn. 62(2), 692–703 (2013).View ArticleGoogle Scholar
- J Hoydis, M Kobayashi, M Debbah, Optimal channel training in uplink network MIMO systems. IEEE Trans. Sig. Proc. 59(6), 2824–2833 (2011).MathSciNetView ArticleGoogle Scholar
- J Kang, O Simeone, J Kang, S Shamai, Joint signal and channel state information compression for the backhaul of uplink network MIMO systems. IEEE Trans. Wirel. Comm. 13(3), 1555–1567 (2014).View ArticleGoogle Scholar
- S-H Park, O Simeone, O Sahin, S Shamai, Fronthaul compression for cloud radio access networks: signal processing advances inspired by network information theory. IEEE Signal Proc. Mag. 31(6), 69–79 (2014).View ArticleGoogle Scholar
- A Charnes, WW Cooper, Programming with linear fractional functionals. Nav. Res. Logist. Q. 9(3–4), 181–186 (1962).MathSciNetView ArticleMATHGoogle Scholar
- R Horst, NV Thoai, DC Programming: Overview. J. Optim. Theory Appl. 103(1), 1–43 (1999).MathSciNetView ArticleMATHGoogle Scholar
- A El Gamal, Y Kim, Network information theory (Cambridge University Press, 2012).Google Scholar
- Y Chen, YC Eldar, AJ Goldsmith, Shannon meets Nyquist: Capacity of sampled Gaussian channels. IEEE Trans. Inf. Theory. 69(8), 4889–4914 (2013).MathSciNetView ArticleGoogle Scholar
- L Tong, G Xu, B Hassibi, T Kailath, Blind channel identification based on second-order statistics: A frequency-domain approach. IEEE Trans. Inf. Theory. 41(1), 329–334 (1995).View ArticleMATHGoogle Scholar
- S Gault, W Hachem, P Ciblat, in Proc. IEEE ICASSP, 4. Cramer-Rao bounds for data-aided sampling clock offset and channel estimation, (2004), pp. iv-1029-32.Google Scholar
- R Zamir, M Feder, On lattice quantization noise. IEEE Trans. Inf. Theory. 42(4), 1152–1159 (1996).View ArticleMATHGoogle Scholar
- RM Gray, Toeplitz and Circulant Matrices: A review (Now Publishers Inc, 2006).Google Scholar
- S Jagannathan, H Aghajan, A Goldsmith, in Proc. IEEE GLOBECOM 2004. The effect of time synchronization errors on the performance of cooperative MISO systems, (2004), pp. 102–107.Google Scholar
- F Gini, R Reggiannini, U Mengali, The modified Cramer-Rao bound in vector parameter estimation. IEEE Trans. Comm. 46(1), 52–60 (1998).View ArticleGoogle Scholar
- SM Kay, Fundamentals of Statistical Signal Processing: Estimation Theory (Prentice-Hall, Englewood Cliffs, NJ, 1993).MATHGoogle Scholar