Uplink Cloud Radio Access Network Research Articles

Quantization-Aware Processing for Massive MIMO Uplink Cloud RAN

As the deployment of a large number of antennas and more dense networks, the degradation brought by the finite fronthaul capacity needs to be taken into account in uplink cloud radio access networks (RANs). This letter proposes dimensionality reduction schemes to mitigate the degradation induced by quantization noise. The key idea is to transform observations at radio heads (RHs) in a reduced size, leading to less distorted quantized signals to be sent to the central processor (CP). By intensively using the quantization resource on these punctured observations, the decoding performance can be enhanced at the CP, especially for low-fronthaul capacity links. In the Gaussian source and Gaussian quantization setup, we prove that our scheme achieves a higher sum rate than conventional schemes. This gain is also confirmed by simulations.

Fronthaul Compression and Beamforming Optimization for Uplink C-RAN With Intelligent Reflecting Surface-Enhanced Wireless Fronthauling

This letter studies a joint design of transmit beamforming and fronthaul compression for the uplink cloud radio access network (C-RAN) with intelligent reflecting surface (IRS) aided wireless fronthauling. In C-RAN, a number of users communicate with baseband unit (BBU) pool through multiple remote radio heads (RRH), wherein RRHs compress the received signals by Wyner-Ziv (WZ) coding and forward the quantization bits to BBU pool through wireless fronthaul link. With the goal of maximizing the uplink sum rate, an alternating algorithm is proposed for jointly optimizing the fronthaul quantization noise covariance matrices, the passive beamformer of IRS and the transmit beamformers of users and RRHs. Via numerical results, the effectiveness of the proposed joint design is verified.

Precoder and Spatial Compression Filter Designs for Uplink Cloud Radio Access Networks

This work considers an uplink cloud radio access network (C-RAN), and proposes a MIMO spatial compression filtering and which compressed symbols to upload at RRH side as well as MIMO precoding at UE (user equipment) side. The error rate performance of this proposed scheme is analyzed and closed-form analytical results are obtained. Based on the analytical results, we propose solutions for the spatial compression filter, compressed symbols and UE precoder. Fronthaul loading can be significantly reduced by the proposed MIMO spatial compression filter and compressed symbols; meanwhile this proposed scheme can greatly outperform conventional schemes under the same settings of fronthaul loading. Moreover, when the UE has multiple antennas, the performance can further be significantly improved by using the proposed UE precoder. Simulation results are provided to show the accuracy of the analytical results as well as the advantages of the proposed solutions in terms of error rate performance and reduced fronthaul loading.

Distributed Learning Assisted Fronthaul Compression for Multi-Antenna C-RAN

This paper investigates the uplink reception in the cloud radio access network (C-RAN), sometimes called centralized-RAN. C-RAN is an architecture for cellular networks which consists of many remote radio units (RRUs) connected to a central processor (CP). Due to the prohibitive complexity of computations, the most efficient uplink C-RAN schemes are challenging to be implemented in practical systems. Using deep neural networks (DNNs), we propose a new and low complex method for uplink C-RAN subject to some quantization rules. This is the first work that uses DNNs to mimic the C-RAN system to the best of our knowledge. Our architecture’s objective, called QDNet, is to jointly optimize the processing done at the RRUs, which considers the quantization constraints and the processing done at the CP side. Inspired by the projected gradient descent algorithm, QDNet is designed as a distributed DNN with sparse connections. The performance of QDNet is compared to current solutions such as the zero-forcing (ZF) equalizer and the sphere decoder (SD). In some scenarios, experiment results show that our scheme achieves 2 dB SNR gain over the linear ZF with the same or lower computational complexity. It also achieves near-optimal performance compared to the SD algorithm, especially for low-to-moderate fronthaul link capacity and many RRUs in the C-RAN system.

Joint Optimization of Power and Fronthaul Compression for Data and Pilot Signals in Uplink C-RANs

For efficient implementation of 5G base stations, cloud-radio access network (C-RAN), where distributed units (DUs) are connected to a central unit (CU) through a fronthaul, has been considered. To support 5G with a wide bandwidth and large number of antennas, the fronthaul capacity becomes one of the limitations. To mitigate this problem, moving more blocks from the CU to the DU has been considered. One of the split options for the uplink is to deliver data and pilot symbols separately from a DU to a CU. For more efficiency in the fronthaul, the compression of data and pilot symbols has been adopted. As an objective of optimization, both the maximization of the uplink user rate and minimization of the fronthaul rate are considered. The optimization of the transmit powers and distortion variances of data and pilot symbols is considered. The closed-form expressions of the optimal variances of the data and pilot signal distortions with fixed data and pilot power are derived. Additionally, the optimal power allocation between the data and pilot signals are obtained when the data and pilot distortion variances caused by compression are fixed. Using an alternating optimization, the convergent solutions to the joint optimization problem can be obtained.

On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views.

This tutorial paper focuses on the variants of the bottleneck problem taking an information theoretic perspective and discusses practical methods to solve it, as well as its connection to coding and learning aspects. The intimate connections of this setting to remote source-coding under logarithmic loss distortion measure, information combining, common reconstruction, the Wyner–Ahlswede–Korner problem, the efficiency of investment information, as well as, generalization, variational inference, representation learning, autoencoders, and others are highlighted. We discuss its extension to the distributed information bottleneck problem with emphasis on the Gaussian model and highlight the basic connections to the uplink Cloud Radio Access Networks (CRAN) with oblivious processing. For this model, the optimal trade-offs between relevance (i.e., information) and complexity (i.e., rates) in the discrete and vector Gaussian frameworks is determined. In the concluding outlook, some interesting problems are mentioned such as the characterization of the optimal inputs (“features”) distributions under power limitations maximizing the “relevance” for the Gaussian information bottleneck, under “complexity” constraints.

Integer-Forcing Architectures for Uplink Cloud Radio Access Networks

Consider an uplink cloud radio access network where users are observed simultaneously by several base stations, each with a rate-limited link to a central processor, which wishes to decode all transmitted messages. Recent efforts have demonstrated the advantages of compression-based strategies that send quantized channel observations to the central processor, rather than attempt local decoding. We study the setting where channel state information is not available at the transmitters, but known fully or partially at the base stations. We propose an end-to-end integer-forcing framework for compression-based uplink cloud radio access, and show that it operates within a constant gap from the optimal outage probability if channel state information is fully available at the base stations. We demonstrate via simulations that our framework is competitive with state-of-the-art Wyner-Ziv-based strategies.

Novel Properties of Successive Minima and Their Applications to 5G Tactile Internet

The lattice $\mathcal {L}(\boldsymbol{A})$ of a full-column rank matrix $\boldsymbol{A}\in \mathbb {R}^{m\times n}$ is defined as the set of all the integer linear combinations of the column vectors of $\boldsymbol{A}$ . The successive minima $\lambda _i(\boldsymbol{A}),\,1\leq i\leq n,$ of lattice $\mathcal {L}(\boldsymbol{A})$ are important quantities since they have close relationships with the following problems: shortest vector problem, shortest independent vector problem, and successive minima problem. These problems arise from many practical applications, such as communications and cryptography. This paper first investigates some properties of $\lambda _i(\boldsymbol{A})$ . Specifically, we develop lower and upper bounds on $\lambda _i(\boldsymbol{A})$ , where $\boldsymbol{A}$ are, respectively, the Cholesky factor of $\boldsymbol{G}_1+\boldsymbol{G}_2$ and $(\boldsymbol{G}_1+\boldsymbol{G}_2)^{-1}$ for two given symmetric positive definitive matrices $\boldsymbol{G}_1$ and $\boldsymbol{G}_2$ . The bounds are, respectively, expressed as the successive minima of $\mathcal {L}(\boldsymbol{A}_1)$ and $\mathcal {L}(\boldsymbol{A}_2)$ , and $\mathcal {L}(\hat{\boldsymbol{A}}_1)$ and $\mathcal {L}(\hat{\boldsymbol{A}}_2)$ , where $\boldsymbol{A}_1, \boldsymbol{A}_2, \hat{\boldsymbol{A}}_1$ and $\hat{\boldsymbol{A}}_2$ are, respectively, the Cholesky factors of $\boldsymbol{G}_1, \boldsymbol{G}_2, \boldsymbol{G}_1^{-1}$ , and $\boldsymbol{G}_2^{-1}$ . Then, we show how some properties of $\lambda _i(\boldsymbol{A})$ are used to design a suboptimal integer-forcing strategy for cloud radio access network. Our approach provides much higher time efficiency while keeping the same achievable rate as the algorithm reported by Bakoury and Nazer (I. E. Bakoury and B. Nazer, “Integer-forcing architectures for uplink cloud radio access networks,” in Proc. 55th Annu. Allerton Conf. Commun. Control Comput. , Oct. 2007, pp. 67–75). Simulation tests are performed to illustrate our main results.

Distributed Uplink Reception in Cloud Radio Access Networks: A Linear Coding Approach

This paper considers an uplink cloud radio access network (C-RAN), in which L antenna terminals (ATs) are connected to a central process (CP) via digital error-free links of a finite-capacity R 0 , and serves K user terminals. In this network, novel low-complexity detection methods at the CP are proposed by incorporating a lattice-quantize-and-forward (LQF) framework, which converts the C-RAN into an equivalent finite-field multipleinput-multiple-output (FF-MIMO) channel. In particular, under this equivalent FF single-input-multiple-output channel, an optimal receive combining method is presented by using a simple repetition code. In addition, using linear block codes, a low-complexity detection method is presented for the equivalent FF-MIMO channel. Finally, by simulations, it is demonstrated that the proposed detection method combined with the LQF framework provides high achievable sum rates for uplink C-RANs especially when a lot of low-cost ATs are deployed.

End-to-End Network Slicing in Virtualized OFDMA-Based Cloud Radio Access Networks

We consider the resource allocation for the virtualized OFDMA uplink cloud radio access network (C-RAN), where multiple wireless operators (OPs) share the C-RAN infrastructure and resources owned by an infrastructure provider (InP). The resource allocation is designed through studying tightly coupled problems at two different levels. The upper-level problem aims at slicing the fronthaul capacity and cloud computing resources for all OPs to maximize the weighted profits of OPs and InP considering practical constraints on the fronthaul capacity and cloud computation resources. Moreover, the lower-level problems maximize individual OPs’ sum rates by optimizing users’ transmission rates and quantization bit allocation for the compressed I/Q baseband signals. We develop a two-stage algorithmic framework to address this two-level resource allocation design. In the first stage, we transform both upper-level and lower-level problems into corresponding problems by relaxing underlying discrete variables to the continuous ones. We show that these relaxed problems are convex and we develop fast algorithms to attain their optimal solutions. In the second stage, we propose two methods to round the optimal solution of the relaxed problems and achieve a final feasible solution for the original problem. Numerical studies confirm that the proposed algorithms outperform two greedy resource allocation algorithms and their achieved sum rates are very close to sum rate upper-bound obtained by solving relaxed problems. Moreover, we study the impacts of different parameters on the system sum rate, performance tradeoffs, and illustrate insights on a potential system operating point and resource provisioning issues.

SDN Based Optimal User Cooperation and Energy Efficient Resource Allocation in Cloud Assisted Heterogeneous Networks

Cloud radio access network (C-RAN) is considered to be a promising architecture for the future network due to its competitive advantage in both spectral efficiency and energy efficiency (EE). However, the tremendous increase in the mobile data traffic lead resource allocation in C-RANs to be less flexible and efficient. To solve this problem, an energy efficient resource allocation scheme for uplink C-RAN is investigated, and its software-based architecture, which provides the proposed framework in a software-defined network fashion is designed. The proposed framework analyzes the information from the data plane, and completes the resource allocation process in the control plane. In the control plane, a relay region selection algorithm is designed to reduce the computational complexity after the user classification module. Then an optimal power allocation, relay selection and network selection scheme with total power constraint, quality of service requirements, and radio resource constraints are proposed to maximize EE. Based on the dual decomposition method and the Dinkelbach method, optimal power allocation, relay selection, and network selection can be obtained from the reformulated convex problem. Numerical results demonstrate the effectiveness of the proposed scheme.

On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base-stations (BSs) are connected to a cloud-computing based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users' messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Further, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.

Semi-blind pilot-aided channel estimation in uplink cloud radio access networks

In this paper, a quasi-Newton method for semi-blind estimation is derived for channel estimation in uplink cloud radio access networks (C-RANs). Different from traditional pilot-aided estimation, semi-blind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel. An initial channel state information (CSI) obtained by least-squared (LS) estimation is needed in semi-blind estimation. BFGS (Brayben, Fletcher, Goldfarb and Shanno) algorithm, which employs data as well as pilot symbols, estimates the CSI though solving the problem provided by maximum-likelihood (ML) principle. In addition, mean-square-error (MSE) used to evaluate the estimation performance can be further minimized with an optimal pilot design. Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols, which demonstrates the estimation accuracy and spectral efficiency are both improved by semi-blind estimation for C-RANs.

Training Design for Channel Estimation in Uplink Cloud Radio Access Networks

In this paper, a training design and channel estimation scheme is considered for uplink cloud radio access networks (C-RANs) consisting of multiple user equipments (UEs), remote radio heads (RRHs), and a centralized baseband unit (BBU) pool. Since most signal processing functions in C-RANs are moved from RRHs to the BBU pool, the individual channels over the links between UEs and RRHs and the links between RRHs and the BBU pool cannot be estimated directly. To address this issue, segment training based individual channel estimation for C-RANs is proposed in this paper, in which channel state information acquisition is performed through two consecutive segments. By using the Kalman filter, the sequential minimum mean-square-error (SMMSE) estimator is developed to efficiently estimate the individual channel states through prior knowledge of long-term channel correlation statistics and the latest radio channel state. A training structure design subject to a power constraint is obtained by minimizing the mean-square-error (MSE) of the SMMSE estimator. Since the MSE is insufficient to fully evaluate the overall performance of C-RANs, the uplink ergodic capacity is derived to exploit the impact of channel estimation on the data transmission by taking the estimation errors into consideration, and the tradeoff between the lengths of two segment training sequences is optimized by maximizing the corresponding spectral efficiency. Furthermore, the Cramér-Rao bound is used to evaluate the proposed SMMSE estimator's performance. Simulation results show that the SMMSE estimator and the corresponding training design can effectively decrease MSE and significantly increase the quality and efficiency of data transmission in C-RANs.

Fronthaul Compression and Transmit Beamforming Optimization for Multi-Antenna Uplink C-RAN

This paper considers the joint fronthaul compression and transmit beamforming design for the uplink cloud radio access network (C-RAN), in which multi-antenna user terminals communicate with a cloud-computing based centralized processor (CP) through multi-antenna base-stations (BSs) serving as relay nodes. A compress-and-forward relaying strategy, named the VMAC scheme, is employed, in which the BSs can either perform single-user compression or Wyner-Ziv coding to quantize the received signals and send the quantization bits to the CP via capacity-limited fronthaul links; the CP performs successive decoding with either successive interference cancellation (SIC) receiver or linear minimum-mean-square-error (MMSE) receiver. Under this setup, this paper investigates the joint optimization of the transmit beamformers at the users and the quantization noise covariance matrices at the BSs for maximizing the network utility. A novel weighted minimum-mean-square-error successive convex approximation (WMMSE-SCA) algorithm is first proposed for maximizing the weighted sum rate under the user transmit power and fronthaul capacity constraints with single-user compression. Assuming a heuristic decompression order, the proposed algorithm is then adapted for optimizing the transmit beamforming and fronthaul compression under Wyner-Ziv coding. This paper also proposes a low-complexity separate design consisting of optimizing transmit beamformers for the Gaussian vector multiple-access channel along with per-antenna quantizers with uniform quantization noise levels across the antennas at each BS. Numerical results show that with optimized beamforming and fronthaul compression, C-RAN can significantly outperform conventional cellular networks. Furthermore, the low complexity separate design already performs very close to the optimized joint design in regime of practical interest.

Training Design and Channel Estimation in Uplink Cloud Radio Access Networks

To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio head prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed. Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the average effective signal-to-noise ratio (AESNR) in C-RANs.

Distributed Fronthaul Compression and Joint Signal Recovery in Cloud-RAN

The cloud radio access network (C-RAN) is a promising network architecture for future mobile communications, and one practical hurdle for its large scale implementation is the stringent requirement of high capacity and low latency fronthaul connecting the distributed remote radio heads (RRH) to the centralized baseband pools (BBUs) in the C-RAN. To improve the scalability of C-RAN networks, it is very important to take the fronthaul loading into consideration in the signal detection, and it is very desirable to reduce the fronthaul loading in C-RAN systems. In this paper, we consider uplink C-RAN systems and we propose a distributed fronthaul compression scheme at the distributed RRHs and a joint recovery algorithm at the BBUs by deploying the techniques of distributed compressive sensing (CS). Different from conventional distributed CS, the CS problem in C-RAN system needs to incorporate the underlying effect of multi-access fading for the end-to-end recovery of the transmitted signals from the users. We analyze the performance of the proposed end-to-end signal recovery algorithm and we show that the aggregate measurement matrix in C-RAN systems, which contains both the distributed fronthaul compression and multiaccess fading, can still satisfy the restricted isometry property with high probability. Based on these results, we derive tradeoff results between the uplink capacity and the fronthaul loading in C-RAN systems.

Investigation of cell association techniques in uplink cloud radio access networks

AbstractCloud radio access networks (C‐RANs) have been proposed as promising solutions to increase both the spectrum and energy‐efficiency performances in the fifth generation wireless communication systems. Many published works have discussed the concrete implementation technology to justify the huge development potential of C‐RAN. However, few litterateurs focused on characterising the cell association in the uplink. In this paper, we study the cell association techniques in uplink C‐RAN systems. Three received power based cell association techniques, namely the single best, N‐best and N‐th best, are presented and analysed. To characterise the random deployment of remote radio heads (RRHs) with multiple antenna configuration, stochastic geometry is used to evaluate the proposals' ergodic capacity, and the corresponding closed‐form expressions are derived. For the received power based cell association techniques, both analytical and simulation results demonstrate that the ergodic capacity gain from an increase in RRH density is larger than that of increasing the number of antennas per RRH. Copyright © 2014 John Wiley & Sons, Ltd.

Joint Coding and Stochastic Data Transmission for Uplink Cloud Radio Access Networks

We study the uplink cloud radio access networks using quantize and forward as an underlying relaying scheme. In all previous works, each radio unit (RU) is assumed to choose a compression rate equal to an instantaneous backhaul capacity. In this letter, we consider a more general network model for which each RU is equipped with a queue whose arrival rate is equal to a compression rate and departure rate is equal to a current backhaul capacity. For each time slot, it can choose a higher compression rate than the given backhaul capacity as long as the queue is stable. Using the principle of stochastic network optimization, we present a distributed algorithm to find the compression rate of each RU such that a time-averaged sum-rate is maximized subject to the queue stability. Under the optimal compression policy, some of RUs are active with a higher compression rate and the others are silent, for a given time slot, which is completely different from the conventional approach that all RUs are always active.

Optimized Backhaul Compression for Uplink Cloud Radio Access Network

This paper studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base-stations(BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP decodes the quantization codewords first, then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly, establish that setting the quantization noise levels to be proportional to the background noise levels is near optimal for sum-rate maximization when the signal-to-quantization-noise ratio (SQNR) is high. In addition, with Wyner-Ziv coding, the approximate quantization noise level is shown to achieve the sum-capacity of the uplink C-RAN model to within a constant gap. With single-user compression, a similar constant-gap result is obtained under a diagonal dominant channel condition. These results lead to an efficient algorithm for allocating the backhaul capacities in C-RAN. The performance of the proposed scheme is evaluated for practical multicell and heterogeneous networks. It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.