Remote Radio Head Clustering Research Articles

C-RAN Zero-Forcing With Imperfect CSI: Analysis and Precode & Quantize Feedback

Downlink joint transmission by a cluster of remote radio-heads (RRHs) is essential for enhancing throughput in future cellular networks. This method requires global channel state information (CSI) at the processing unit that designs the joint precoder. To this end, extensive CSI must be shared between the RRHs and that unit. This paper proposes two contributions. The first is a new upper bound on the rate loss, which implies a lower bound on the achievable rate for an RRHs-cluster employing joint zero-forcing (ZF) with incomplete CSI with single-user decoding (SUD) at the mobile stations (MSs). The second contribution, which follows insights from the bound, is a new CSI sharing scheme that drastically reduces the significant overhead associated with acquiring global CSI for joint transmission. In a nutshell, each RRH applies a local precoding matrix that creates low-dimensional effective channels that can be quantized more accurately with fewer bits, thereby reducing the overhead of sharing CSI. In addition to the CSI sharing-overhead, this scheme reduces the data rate delivered to each RRH in the cluster.

Analytical Modeling of RSMA-Enabled User-Centric RRH Clustering in C-RAN Over Generalized Fading Channels

Network densification and coordinated multi-point (CoMP) have been shown to be promising and practical approaches to meet the dramatic increase in mobile traffic demand. Motivated by the ever-increasing popularity of rate splitting multiple access (RSMA) in wireless communications, this letter proposes an RSMA-enabled user-centric remote radio heads (RRH) clustering in cloud radio access networks (C-RAN) and then develops a statistical framework to evaluate the average sum-rate and the area spectral efficiency of this mechanism under generalized fading conditions by stochastic geometry. In addition to this framework, new insights into the tradeoff between the average sum-rate and area spectral efficiency in terms of RRH clustering radius are presented. Further, two widely utilized fading models, namely Rayleigh and Nakagami-m, are discussed to characterize the fading behavior in C-RAN. Finally, simulations are demonstrated to verify the effectiveness of the proposed analytical framework.

A fully distributed approach for joint user association and RRH clustering in cloud radio access networks

Cloud radio access network (C-RAN) is a new centralized architecture to meet the exponential growing of demand of mobile traffic in 5G cellular wireless networks. However, C-RAN requires an efficient mechanism for the joint user association and the Remote Radio Head (RRH) clustering to improve network performance. In this paper, we investigate the problem of joint user association (UA) and RRH clustering (RC) in C-RAN. Our objective is to maximize the network utility function incurred by both network power consumption and total user throughput for both streaming and elastic traffic. The formulation of the joint optimization problem is a mixed-integer non-linear programming problem (MINLP), which is NP-hard and usually has no feasible solution. To solve it, we propose to decouple the joint problem into two sub-optimization problems: the user association (UA) sub-problem and the RRH clustering (RC) sub-problem. These two sub-problems are sequentially and iteratively solved until convergence is reached. Leveraging on the information delivered by the Call Detail Records (CDR), simulation results reveal the effectiveness of our heuristic solution for the RC sub-problem in enhancing network utility and adapting to the traffic load variation for both elastic and streaming traffic. It outperforms the performance of the state-of-the-art algorithms for RRH clustering solutions, including no-clustering and grand coalition methods. Moreover, the results show that our approach for the UA sub-problem provides close performance to the optimal UA sub-problem.

Energy-Aware Joint Clustering and Scheduling for Multicast Beamforming in Cloud-RAN Downlink

In cloud radio access network (Cloud-RAN), dense remote radio heads (RRHs) connect to baseband unit (BBU) pool through optical transport link, causing significant interference and power consumption in multicast downlinks. Coordinated beamforming with RRHs clustering is expected to be a key element towards improving network performance with more consideration of energy efficiency and interference management. This letter proposes an energy-aware joint clustering and scheduling (EA-JCS) scheme for multicast beamforming in Cloud-RAN downlink, which is formulated as a network utility maximization problem with fractional formulation and low-rank constraints. To efficiently solve this problem, we first develop a heuristic algorithm to provide sub-optimal performance by Dinkelbach’s transformation. To handle non-convex formulation, we propose an iterative approximation algorithm with linear approximation and constraints relaxation. The effectiveness of the proposed scheme is confirmed by simulations.

Novel Cloud-RRH Architecture With Radio Resource Management and QoS Strategies for 5G HetNets

With the increase of data traffic in the global mobile network, the limitation in resources of data computing close to the edge is becoming an important issue to resolve. This article addresses the cloud radio access network (CRAN) in 5G HetNets architecture and proposes to take benefit of extra computing and storage resources in the edge to enable the offloading of a set of mobile user services from the remote cloud computing servers to an edge cloud computing infrastructure deployed next to remote radio heads (RRHs) to better serve mobile users and improve energy efficiency. However, this architecture poses many challenges. The first one is related to the clustering of the various deployed RRH to better serve end-users. For that, we propose a two-stage RRH clustering mechanism in order to fully exploit the benefits of C-RAN architecture. The second challenge is related to the scheduling of the offloading. Therefore, we propose a cost-based scheduling scheme (CBSS) that aims to minimize the scheduling cost while considering resource availability in the infrastructure, resource requirements from users' applications, services execution deadlines, and load balancing. The proposed solution permits us to make better offloading decisions and to improve the users' experiences. The solution was implemented in a simulator to highlight its performances and compare them with other existing approaches.

Programmable Hierarchical C-RAN: From Task Scheduling to Resource Allocation

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider a cloud radio access network architecture with a hierarchical structure of virtual controllers and multiple clusters of remote radio heads (RRHs). A high-level controller coordinates control plane decisions among local controllers and each local controller is in charge of a cluster of RRHs. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute the users’ baseband processing tasks. Then, under the considered architecture, we aim to minimize the average delay consisting of task execution delay and signal transmission delay under total power constraint, by joint optimization of task scheduling and resource allocation, including VM allocation and RRH assignment. Due to the non-deterministic polynomial-time hardness (NP-hardness) of the joint optimization problem, we translate it into a matroid constrained submodular maximization problem and propose heuristic algorithms to find solutions with 0.5-approximation. Besides, both centralized and distributed control schemes are considered. In the centralized control scheme, all decisions about task scheduling, VM allocation, and RRH assignment are made in the high-level controller. But in the distributed control scheme, the high-level controller is only in charge of task scheduling based on graph theory and the local controllers are responsible for their respective VM allocation and RRH assignment. The simulation results show that the proposed algorithms can achieve better performance than the separate optimization of VM allocation and RRH assignment.

Delay-Optimal Joint Processing in Computation-Constrained Fog Radio Access Networks

With fog radio access networks (F-RANs), the computation capability is provided in the physical proximity of the users, which can significantly lower the delay and mitigate the heavy traffic over backhaul links. As the number of remote radio head (RRH) increases, the computational complexity becomes a severe issue and it is necessary to group the RRHs into multiple clusters. In this paper, we optimize the joint processing strategy, including the RRH clustering and the RRH-server matching to minimize the delay for F-RANs. We model the delay-optimal joint processing problem in computation-constrained F-RANs as a Markov decision process (MDP) problem. By deriving the optimality condition of this MDP, we obtain a per-slot weighted sum rate maximization problem, in which the RRH clustering and the RRH-server matching are solved jointly. Specifically, we transform the weighted sum rate maximization problem into a combinatorial auction problem (CAP). Since the CAP is NP-hard, we greedily obtain an initial solution of CAP and gradually improve the performance by adopting local α-good improvement algorithm based on the weighted independent set problem (WISP). Furthermore, we theoretically derive the bound of the performance of the proposed algorithm.

Rateless Coded Multi-User Downlink Transmission in Cloud Radio Access Network

In this paper, we propose a rateless coded transmission scheme for the multi-user downlink of Cloud Radio Access Network (C-RAN). In the network, multiple users are served by a cluster of remote radio heads (RRH), which are connected to the building baseband units (BBU) pool through the fronthaul links with limited capacity. In the proposed transmission scheme, the precoder and compressor at the BBU pool, and the decoding algorithm at the users are designed. To further improve the performance of the proposed transmission scheme, we investigate the joint optimization of the precoder and the degree profiles of the rateless codes implemented at the BBU pool to maximize the sum throughput of the network. Explicitly, the optimization problem is formulated according to the extrinsic information transfer (EXIT) function analysis on the decoding process at the users. We give simulation results on the BER and throughput performance achieved by the proposed rateless coded transmission scheme, which verify the effectiveness of the joint optimization on the degree profiles and the precoder.

User-centric Clustering and Beamforming for Energy Efficiency Optimization in Cloud-RAN

User-centric and energy efficient are becoming two foremost design principles in the cloud radio access networks (Cloud-RAN). In this paper, we thus consider the problem of how to assign each user to several preferred remote radio heads (RRHs) and design the corresponding beamforming coefficients in a user-centric and energy efficient manner. We formulate this problem as a joint clustering and beamforming optimization problem, with the objective to maximize the energy efficiency (EE) while satisfying the users' quality of service (QoS) requirement and respecting the RRHs' transmit power limits. We first transform it into an equivalent parametric subtractive problem using the approach in fractional programming, and then it is cast into a tractable convex optimization problem by introducing a lower bound of the objective function. Finally, the structure of the optimal solution is derived and a two-tier iterative scheme is developed to find the clustering pattern and beamforming coefficients that maximize EE. Specially, we derive a RRH-user association threshold, based on which the RRH clustering pattern and the corresponding beamforming coefficients can be simultaneously determined. Through simulations, we show the superior performance of the proposed user-centric clustering and beamforming scheme in Cloud-RAN.

Multi-Layer Downlink Precoding for Cloud-RAN Systems Using Full-Dimensional Massive MIMO

Full-dimensional (FD) massive multiple-input multiple-output (MIMO) has recently emerged as one of the physical layer enablers of 5G systems. Grounded on prior work on layered precoding, a novel framework for low-complexity multi-layer downlink precoding in multi-cluster cloud radio access network (C-RAN) systems using FD massive MIMO is introduced in this paper. The precoding matrix used at the cluster of remote radio heads (RRHs) associated to a given central control unit (CCU) of the C-RAN is decoupled as a multiplication of three precoding sub-matrices (or layers), a precoding architecture that leverages the special characteristics of the elevation component of the channel correlation matrix to manage both the C-RAN inter-cluster interference and the massive MIMO pilot contamination based on the availability of statistical channel state information. The proposed multi-layer approach is then adapted to the compress-after-precoding-based CCU-RRH functional split where the first and second precoding layers are locally applied at each of the RRHs in a cluster, whereas only the third precoding layer is implemented at the CCU. Optimal and suboptimal solutions are provided, which take into account both the per-RRH transmit power constraints and the capacity constraints of the fronthaul links between the CCU and the associated RRHs. The suboptimal approach, which is rooted on the vector normalization techniques used in massive MIMO precoding schemes with uniform power allocation, is shown to provide minor spectral efficiency losses when compared with the optimal solution. Numerical simulations are used to illustrate the potential of the proposed C-RAN-based framework when benchmarked against the classical FD massive MIMO scheme.

Two-Timescale User-Centric RRH Clustering and Precoding Optimization for Cloud RAN via Local Stochastic Cutting Plane

In a cloud radio access network (C-RAN), many distributed remote radio heads (RRHs) are connected to a centralized baseband unit pool via high-speed fronthaul links. Such an architecture improves the spectral efficiency but suffers from huge implementation costs. We propose a mixed timescale radio interference processing framework to optimize the tradeoff between the average weighted sum rate and the implementation cost in the C-RAN downlink. The radio interference processing is decomposed into short-term precoding and long-term user-centric RRH clustering (UCRC) subproblems. The short-term precoding subproblem can be solved using a modified weighted minimum mean squared error approach. To solve the challenging UCRC subproblem, we first propose a novel approximate stochastic cutting plane algorithm . Then, we bound the optimality gap of the proposed overall solution, and establish its asymptotic optimality in the weak interference and high SNR regimes. Simulations show that the proposed two-timescale solution achieves a better tradeoff performance than the baselines.

Hierarchical content caching in fog radio access networks: ergodic rate and transmit latency

In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks (F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.

Exploiting Hybrid Clustering and Computation Provisioning for Green C-RAN

By migrating baseband processing functionalities into a centralized cloud-based baseband unit (BBU) pool, cloud radio access network (C-RAN) facilitates cooperative transmission among remote radio heads (RRHs) and enables flexible computation provisioning in the BBU pool. In C-RAN, due to the high amount of data transfer from the BBU pool to RRHs through fronthauls, limited fronthaul capacity becomes a key factor when designing cooperative transmission schemes among RRHs. Meanwhile, as computational resources are provisioned to mobile users (MUs) for baseband processing in the form of virtual machines (VMs) in the BBU pool, an effective VM assignment strategy is also with great significance. In this paper, we propose a holistic framework for green C-RAN under the constraint of limited fronthaul capacity, where we jointly optimize hybrid clustering and computation provisioning to appropriately provide a cluster of RRHs and a VM to each MU for cooperative transmission and baseband processing, aiming at minimizing the system power consumption. The system power minimization problem is formulated as an integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem to an equivalent hybrid clustering problem embedded with a series of VM assignment problems. On this basis, we first achieve the optimal solution for system power minimization with high computational complexity, and then, a greedy algorithm is proposed to solve the hybrid clustering problem for practical implementation. Finally, the simulation results demonstrate that the proposed joint optimization of hybrid clustering and computation provisioning can significantly reduce the system power consumption.