Multivariate Fronthaul Quantization for Downlink C-RAN

Cloud radio access networks (C-RANs) provide a novel architecture for next-generation wireless cellular systems whereby the baseband processing is migrated from the base stations (BSs) to a control unit (CU) in the ?cloud.? The BSs, which operate as radio units (RUs), are connected via fronthaul links to the managing CU. The fronthaul links carry information about the baseband signals?in the uplink from the RUs to the CU and vice versa in the downlink?in the form of quantized in-phase and quadrature (IQ) samples. Due to the large bit rate produced by the quantized IQ signals, compression prior to transmission on the fronthaul links is deemed to be of critical importance and is receiving considerable attention. This article provides a survey of the work in this area with emphasis on advanced signal processing solutions based on network information theoretic concepts. Analysis and numerical results illustrate the considerable performance gains to be expected for standard cellular models.

This study addresses the achievable sum-rate for the uplink of a cloud radio access network (C-RAN) operating in a linear Wyner-type topology, i.e., with partial channel connectivity. In the system, the radio units (RUs) communicate with a central, or cloud, unit (CU) by means of digital finite-capacity fronthaul links. The messages sent by the user equipments (UEs) are jointly decoded by the CU based on the compressed baseband signals received on the fronthaul links. Unlike prior works, each RU is assumed to be also connected to its neighboring RUs via finite-capacity fronthaul links. Under the standard assumption that the RUs do not perform channel decoding (i.e., oblivious RUs), each RU performs in-network processing of the uplink received signal and of the compressed baseband signal received from the adjacent RU, with the CU carrying out channel decoding. A closed-form expression of the achievable sum-rate is derived assuming point-to-point compression, and then analytical expressions are provided for more advanced fronthaul compression schemes that leverage side information. Numerical examples provide insights into the advantages of inter-RU communications and into the performance gap to existing sum-rate upper bounds.

Massive connectivity for massive machine-type communications (mMTC) is key enabler for many 5G and Intternet-of-Things (IoT) applications. In mMTC scenarios, a large number of low-complexity, low-rate devices transmit sporadically over shared scarce communication resources. Reliability and low-latency with short packet transmissions are major design criteria that require integration of novel network architecture solutions and multiple access methods. This study addresses the challenge of massive connectivity in the context of a Cloud Radio Access Network (C-RAN), characterized by a hierarchical structure in which part of the processing functionalities of the Radio Units (RUs) are migrated to a centralized cloud processor or Central Unit (CU). A major architectural constraint of C-RANs is imposed by capacity-limited fronthaul links connecting the RUs with the CU. In this study, the performance of a transmission scheme for massive connectivity is investigated under this architectural constraint. In particular, a non-coherent, grant-free transmission scheme is proposed where the encoding is performed based on a Gabor frame structure. The fronthaul processing is a particular instance of Detect-and-Forward (DtF), where local detection at the RUs is performed using a one-step thresholding procedure, and the local estimates are then merged at the central processor via the capacity-limited fronthaul links. Numerical results illustrate the potential of the proposed scheme to support massive, reliable random access with short messages.

The present paper investigates the trade‐offs between the energy efficiency (EE) and spectral efficiency (SE) in the full‐duplex (FD) multiuser multi‐input multioutput (MU‐MIMO) cloud radio access networks (CRANs) with simultaneous wireless information and power transfer (SWIPT). In the considered network, the central unit (CU) intends to concurrently not only transfer both energy and information toward downlink (DL) users using power splitting structures but also receive signals from uplink (UL) users. This communication is executed via FD radio units (RUs) which are distributed nearby users and connected to the CU through limited capacity fronthaul (FH) links. In order to unveil interesting trade‐offs between the EE and SE metrics, we first introduce three conventional single‐objective optimization problems (SOOPs) including (i) system sum rate maximization, (ii) total power minimization, and (iii) fractional energy efficiency maximization. Then, by making use of the multiobjective optimization (MOO) framework, the MOO problem (MOOP) with the objective vector of the achievable rate and power consumption is addressed. All considered problems are nonconvex with respect to designing variables comprising precoding matrices, compression matrices, and DL power splitting factors; thus, it is extremely intractable to solve these problems directly. To overcome these issues, we develop iterative algorithms by utilizing the sequential convex approximation (SCA) approach for the first two SOO problems and the SCA‐based Dinkelbach method for the fractional EE problem. Regarding the MOOP, we first rewrite it as an SOOP by applying the modified weighted Tchebycheff method and, then, propose the iterative algorithm‐based SCA to find its optimal Pareto set. Various numerical simulations are conducted to study the system performance and appealing EE‐SE trade‐offs in the considered system.

An end-to-end DPDK-integrated open-source 5G standalone Radio Access Network: A proof of concept

The compression step of the Cloud Radio Access Networks (C-RAN) is studied in this paper. In order to maximize the achievable sum rate in the uplink of C-RAN, constrained by the finite-capacity fronthaul links to the Central Processor (CP) in the cloud, an optimization algorithm of quantizers used for compression is proposed. In C-RAN, there are multiple Radio Units (RUs) compressing and forwarding the correlated received signals simultaneously. Due to the correlations, Wyner-Ziv coding is used. Hence a joint optimization of the quantizers is required at the CP. In this paper we extend the Information Bottleneck (IB) method for this joint optimization. It is a combination of the Alternating Information Bottleneck method (AIB) and the Alternating Bi-Section method, which are both proposed in this paper. Then we use the proposed algorithms to study some key features of C-RAN.

This work considers the downlink of a cloud radio access network (C-RAN) in which a baseband processing unit (BBU) sends independent messages to multiple user equipments (UEs) by controlling a set of remote radio heads (RRHs) which are connected to the BBU via finite-capacity fronthaul links. In standard C-RAN systems, it is prescribed that the fronthaul links are used in a soft-transfer mode whereby quantized signals of the baseband signals, that encode the UEs' messages, are transferred on the fronthaul links. Although this approach is advantageous in a sense that the RRHs do not need to have baseband signal processing functionalities, it has been reported that one can achieve better performance with hard-transfer fronthauling scheme, whereby the fronthaul links are used to transfer hard information of the UEs' messages, particularly when the fronthaul links have relatively large capacity. This work proposes a unified scheme that leverages hybrid soft-transfer and hard-transfer fronthauling strategies. This is enabled by a superposition coding where each UE message is split into two submessages that are delivered to the UEs by means of soft-transfer or hard-transfer schemes. Some numerical results are provided to confirm the advantages of the proposed hybrid scheme.

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.

Cloud radio-access network (C-RAN) has the most promising network architecture to support the new fifth generation (5G) mobile network. The C-RAN witnesses a revolutionary base station functionality split, where it is composed of simplified remote radio heads (RRH) and several baseband units (BBUs) pools in network backbone. The C-RAN assists in employing several functionalities more efficiently. It has privileges over the existing radio-access networks (RANs) in case of global resource management, interference management, and radio-access technology (RAT) selection to guarantee higher system throughput and lower energy consumption. In this paper, we demonstrate a C-RAN system-level simulator that performs centralized user scheduling, edge-user joint transmission (JT), and global per-antenna carrier aggregation (CA). In addition, we demonstrate the capability of our system-level simulator to investigate the bottleneck effect of the fronthaul-link capacity as the system expands or C-RAN coordinated facilities increase. Our analysis shows throughput enhancement compared to the traditional RAN; however, with high fronthaul links utilization rates.

In this paper we consider a cloud radio access network (C-RAN) where the remote radio heads (RRHs) are separated from the baseband signal processing servers, named baseband units (BBUs). The latter forms a centralized pool of high-performance data center resources. To benefit from network function virtualization, we consider virtualized BBU (V-BBU) resources where the BBU functionality and services have been virtualized in the form of virtual network functions. All RRHs in the C-RAN form a single cluster. Each RRH of such a cluster may accommodate random or quasi-random traffic. That means that new calls in a RRH can be generated by an infinite number of mobile users (random traffic) or by a finite number of mobile users (quasi-random traffic). An arriving call requires a radio resource unit from the serving RRH and a computational resource unit from the V-BBU. If these resource units are available, then the call is accepted and remains in the system for a generally distributed service time. Otherwise, the call is blocked and lost. In order to analyze this C-RAN we model it as a loss system and study two cases: (i) all RRHs accommodate quasi-random traffic and (ii) some RRHs accommodate random traffic and the rest accommodate quasi-random traffic. In both cases, we show that the steady state probabilities have a product form solution and propose convolution algorithms for the accurate determination of the main teletraffic performance measure which is congestion probability. The accuracy of these algorithms is verified via simulation.

This paper considers a downlink transmission of cloud radio access network (C-RAN) in which precoded baseband signals at a common baseband unit are compressed before being forwarded to radio units (RUs) through limited fronthaul capacity links. We investigate the joint design of precoding, multivariate compression and RU-user selection which maximizes the energy efficiency of downlink C-RAN networks. The considered problem is inherently a rank-constrained mixed Boolean nonconvex program for which a globally optimal solution is difficult and computationally expensive to find. In order to derive practically appealing solutions, we invoke some useful relaxation and transformation techniques to arrive at a more tractable (but still nonconvex) continuous program. To solve the relaxation problem, we propose an iterative procedure based on DC algorithms which is provably convergent. Numerical results demonstrate the superior of the proposed solution in terms of achievable energy efficiency compared to existing schemes.

In 5G networks, mitigating blockage effects in millimeter-wave (mmWave) signals is a major challenge to overcome. Coordinated multipoint (CoMP) transmission is one of the inference mitigation schemes that can be used in a densified cellular network. CoMP transmission is usually implemented within a cloud radio access network (C-RAN) via fronthaul links which is connected to a control unit (CU). In this paper, a radio unit clustering scheme (RCS) was proposed based on CoMP transmission to mitigate blockage effect of mmWave links. Applying a clustering scheme in C-RAN can increase the network capacity by avoiding data transmission via non-line-of-sight (NLOS) links. Based on stochastic geometry, the coverage probability of the proposed RCS is derived into a closed-form expression by modeling the position of base stations as a Poisson point process. In addition, an enhanced cooperative handover scheme is proposed to increase mobility robustness of the proposed RCS. Simulation results show that the coverage probability is enhanced as the cluster size increases in mmWave C-RANs and a radio link failure (RLF) probability during handover procedure is reduced.

Cloud radio access network (C-RAN) is a key technology for the new-generation mobile network (5G). Multi-wavelength passive optical networks (PONs) such as wavelength division multiplexing (WDM) and time wavelength division multiplexing (TWDM) PONs are outstanding solutions for providing a sufficient bandwidth to support mobile fronthaul in C-RAN-based 5G architecture. In this paper, a joint allocation framework for multi-wavelength PONs fronthaul uplink resources and C-RAN radio interface uplink resources is presented. From the principle that the uplink resource allocation in mobile networks is an NP-hard optimization problem, this paper proposes a novel uplink scheduling algorithm based on reinforcement learning (RL). The performance of the algorithm is evaluated with numerical simulations and compared with two other algorithms from the literature; namely genetic algorithm (GA) and tabu search (TS) algorithm. The simulation results show that the new algorithm achieves higher system throughput, faster convergence and lower scheduling latency compared to the two other algorithms. Also, show that RL-based adaptive allocation of fronthaul transport block size based on actual radio resource block capacity can significantly reduce the capacity required on fronthaul link and decrease the total end to end uplink scheduling latency compared to the static allocation method.

Due to the unique spatial and temporal focusing effects, time-reversal (TR) communication can be utilized in the cloud radio access network (C-RAN), where it creates “tunneling effects” such that the traffic load in the front-haul links can be alleviated in both downlink and uplink. Although the basic TR waveforms are simple to use, and they cannot provide the optimal performance in some cases. Since the C-RAN is usually expected to serve massive wireless devices, the severe inter-user interference will limit the performance of the system, especially in the high signal-to-noise ratio region where the interference power dominates the noise power. In this paper, we propose to optimize both downlink and uplink transmissions in the TR-based C-RAN so as to alleviate the interference. In the downlink transmission, an optimal content-aware waveform design is proposed, so that the baseband units (BBUs) are able to combine both the channel information and the content information to suppress the interference. In the uplink transmission, an optimal receiver design algorithm is proposed, such that the BBUs can detect the symbols transmitted by the terminal devices more accurately by leveraging the channel information. We study the bit error rate performance of the proposed algorithms based on extensive measurements of the wireless channel in a real-world environment. Numerical results demonstrate the significant performance improvement over the basic TR transmission techniques and the traditional waveform design techniques.

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can solve lots of challenges in the next generation mobile communication system, including the demand for higher energy efficiency(EE). In C-RAN EE research field, most of recent work focuses on systematic energy saving and ignores the needs of users. To improve the performance of C-RAN under fronthaul capacity constraint, signal quantization techniques have been developed. But how to introduce ‘quantization’ into C-RAN EE field is still an open issue. Motivated by this, in this paper, based on informational-optimal Gaussian quantization, we intend to design the suitable algorithms to maximize user-centric EE in the uplink communication of an orthogonal frequency division multiple access (OFDMA) based C-RAN. In the special case of single user and single RRH, we propose a joint optimization algorithm to maximize the uplink user-centric EE by optimizing power and fronthaul rate allocation. In the extended general case of multi-user and multi-RRH, we propose a Modified Particle Swarm Optimization(M-PSO) algorithm to solve the non-linear and non-convex issue for simplicity. Our simulation results show the proposed algorithms can improve the user-centric EE obviously compared with other optimal algorithms.