Baseband Unit Pool Research Articles

Joint FSO Fronthaul and Millimeter-Wave Access Link Optimization in Cloud Small Cell Networks: A Statistical-QoS Aware Approach

We investigate resource optimization for the downlink cloud small cell network, where the baseband unit pool communicates with the buffer-aided small remote radio heads (SRRHs) through free space optical fronthaul, and SRRHs transmit to the user equipments (UEs) by using time division multiplexing-based millimeter wave access links. Our objective is to maximize the supportable aggregate data arrival rate in the network by exploiting the inter-dependence of fronthaul and access links. Toward this objective, we consider maximum acceptable end-to-end queue-length bound violation probability constraints, load-balancing constraints in the access link, fronthaul link selection constraints, and transmit power budget constraints of fronthaul and access links. Since the joint fronthaul and access link optimization is a non-convex and combinatorial problem, we develop an iterative solution by decomposing the original optimization problem into two sub-problems. The first sub-problem optimally obtains fronthaul and access link power allocation and fronthaul link selection by using Lagrangian dual decomposition and canonical one-to-one matching techniques. By employing the Lagrangian dual decomposition and alternating optimization techniques, the second sub-problem obtains near optimal data arrival rate for each UE, UE-SRRH associations, fronthaul rate allocation among the transmitted data for the UEs, and the transmission duration scheduling in millimeter wave access link. An algorithm of polynomial complexity is developed in order to determine the supportable aggregate data arrival rate by considering the statistical quality-of-service requirements, and its convergence is proved. The simulation results depict that the proposed scheme significantly improves the aggregate data arrival rate over several benchmark schemes.

Elastic-RAN: An adaptable multi-level elasticity model for Cloud Radio Access Networks

Cellular mobile networks in 2020 will increase ten times their coverage area, with more than 50 billion connected devices. We will lead to a massive increase in data traffic, also fostering the development of 5G networks. Therefore industry and scientific initiatives have a crucial role in proposing related projects to meet such demand. Cloud Radio Access Networks (C-RANs) are gaining more and more attention in this context by adopting an architecture in which baseband units (BBUs) run into cloud computing resources, therefore taking advantage of distributed systems flexibility and cloud elasticity. One of the significant challenges in C-RANs lies in the high complexity of orchestrating computational resources to process incoming requests with both high performance and low infrastructure cost. In this regard, this article presents the Elastic-RAN model, which proposes a multi-level and adaptable elasticity for C-RANs. First, we explore the multi-level feature as follows: (i) one level for the BBU pools (i.e., physical machines), given the high volume of traffic to particular BBU pools; (ii) another level for BBUs themselves (virtual machines) due to the high CPU and memory demands to process the incoming requests. Second, the adaptive feature refers to the moldable elasticity grain which resources in both previous levels are provisioned as close as possible to the current processing needs. We evaluated Elastic-RAN through experiments that simulated different load profiles, considering both CPU and network demands. We observed that Elastic-RAN might achieve gains up to 64% in the execution time when compared to a traditional C-RAN. Cellular network operators using the proposed technique will spend less energy and will have a solution that dynamically adjusts the baseband signal processing accordingly to the demand in their access networks.

Resource-efficiency improvement based on BBU/RRH associated scheduling for C-RAN

Cloud radio access network (C-RAN) is an attractive technology to improve power efficiency through providing a novel architecture of baseband unit (BBU) pool to centrally process mass of transmitte...

Energy-Efficient Resource Allocation in Heterogeneous Cloud Radio Access Networks via BBU Offloading

Energy efficient resource allocation in heterogeneous cloud radio access networks (H-CRANs) is considered in this paper where a set of users within a specific region is served by femto-cell access points (FAPs) and remote radio heads (RRHs) connected to the base-band unit (BBU) pool through front-haul links. For orthogonal frequency division multiple access (OFDMA) based uplink H-CRANs, we formulate an energy efficient optimization problem with the novel utility function in order to jointly assign the access point (RRH/FAP), sub-carrier, transmit power, RRH, front-haul link and BBU. Due to highly complex relation between diverse optimization variables and their effects on each other, the formulated optimization problem is non-convex and NP-hard, suffering from high computational complexity. To tackle this issue, we develop an efficient two-step iterative algorithm with low computational complexity to solve the proposed problem based on the successive convex approximation (SCA) and complementary geometric programming (CGP). The simulation results reveal that our proposed approach can effectively offload the traffic from C-RAN to low-power femto-cells, and reduce the energy consumption cost by switching off the under-utilized BBUs.

Resilient BBU placement in 5G C-RAN over optical aggregation networks

The huge data demand envisioned for the 5G requires radical changes in the mobile network architecture and technology. Centralized radio access network (C-RAN) is introduced as a novel mobile network architecture, designed to effectively support the challenging requirements of the future 5G mobile networks. In C-RAN, BaseBand Units (BBUs) are physically separated from their corresponding radio remote heads (RRHs) and located in a central single physical location called BBU pool. The RRHs are connected to the BBU pool via the so-called fronthaul network. The “centralization” demonstrates remarkable benefits in terms of computational resources as well as power savings. Following this centralization, designing a survivable C-RAN becomes crucial as BBU pool and link failures might cause service outage for large number of users. In this paper, we propose three different approaches for the survivable BBU pool placement problem and traffic routing in C-RAN deployment over a 5G optical aggregation network. Namely, we define the following protection scenarios: (1) dedicated path protection, (2) dedicated BBU protection and (3) dedicated BBU and path protection. The three approaches are formalized as integer linear programming (ILP) problems. The ILPs objectives are to minimize the number of BBU pools, the number of used wavelengths and the baseband processing computational resources, in terms of giga operations per second. We provide numerical results to compare the aforementioned protection strategies considering different network topologies. The results show the effect of the latency and the transport–network capacity on the BBU placement. We show the trade-off between the centralization degree and the tight latency requirements. Moreover, we discuss important insights about considering the different objective functions for each protection approach.

Mini-Base Station: A Novel Smart Virtual eNB for 5G and Beyond Mobile Networks

This paper presents a proposal for an innovative base station for 5G and beyond for a mobile network system based on the evolved packet system (EPS). Functions of EPS elements can be achieved by the SVeNB, according to the type of function or service and the device that is used to achieve that function or service. The current system, even if these functions and services have been virtualized by the system, it may put them away from the users. Consequently, this causes a delay in completing the establishment of a connection. Since these functions and services can be virtualized and programmed in virtual machines (VMs), we have been simulated the kernel of those functions and services executed by the SVeNB through its VMs. Moreover, The SVeNB position is located close to the users, which helps to reduce the End-to-End delay. Our proposed approach gives the SVeNB the ability to share and contribute in making decisions with the core network (CN) and BaseBand Unit (BBU) pool to initiate a connection, further in making determinations within its vision regarding its users. This procedure leads to a reduction in the load on the BBU pool and the CN, thus lessening the latency, and to increase the data rate that is delivered to the users. That means the SVeNB can regionally decide for all local users that are covered by the SVeNB to connect to each other or the Internet. The MATLAB platform has been used to evaluate the SVeNB performance, with the results demonstrating that it surpasses. The obtained results point that the SVeNB passes the C-RAN system by 68% in terms of user profiles processing, whilst also and it reducing the End-to-End delay by 62%.

Joint Computation Offloading and Resource Allocation in C-RAN With MEC Based on Spectrum Efficiency

The cloud radio access network (C-RAN) with mobile edge computing (MEC) structure which consists of a baseband unit (BBU) pool integrating with an MEC server and several remote radio heads (RRHs) beside the mobile terminals can help users with computational resource-intensive tasks and bring extra profits to network operator at the same time. This paper presents a novel task-aware C-RAN with MEC structure and formulates a profit maximization problem by jointly optimizing offloading strategy, radio and computational resources allocation under the constraints of offloading latency, fronthaul capacity along with limited bandwidth and computational resource. To solve this NP-hard optimization problem in a distributed and efficient way, we propose a spectrum efficiency (SE)-based joint optimization for offloading and resource allocation (SJOORA) scheme which decomposes the original problem into two sub-problems. A SE-based offloading strategy is proposed with confirmed resource allocation, and on the other hand, bandwidth and computational resource allocation problem is solved by using a Lagrangian multiplier method with predetermined offloading strategy. Finally, by solving these two sub-problems iteratively, a suboptimal solution is obtained for the original problem. The simulation results show that the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.

Cross-Layer Optimization & VGALA Structure for 5G Grid

Heterogeneous cloud radio access network (HCRAN) promises higher energy efficiency (EE) than the conservative cellular networks by centralizing the baseband signal processing into the baseband unit (BBU) pool hosted by cloud computing platforms. In existing energy-efficient networking mechanisms designed for conservative cellular networks cannot fully leverage the parameters like latency, load balancing, power consumption and the network energy consumption. In this work ,Cross-layer based VGALA optimization structure is proposed which endeavours a balance between network utilities. The average traffic delivery latency, green energy utilization are techniques used to optimize the packet dropping policy, power allocation policy and bandwidth allocation policy to minimize the transmit power under the QoS constraint. The optimal solution is obtained, depends upon both channel and queue state information. The final result show that recommended framework empowers an adaptable trade off among the on grid power consumption and the normal traffic latency. And finally saves a significant amount of on-grid power and the optimal solution obtained depends on both channel and queue state information. It also shows that setting the three packet loss probabilities as equal causes marginal power loss caused in H-CRAN design.

Improving Energy-Efficiency for Resource Allocation by Relay-Aided In-Band D2D Communications in C-RAN-Based Systems

In device-to-device (D2D) communications underlaying cellular systems based on the cloud radio access network, including distributed remote radio heads (RRHs) and centralized BaseBand Unit (BBU) pool, due to dense deployment and spectrum reuse, there are strong intra-cell interference and inter-cell interference, which will severely degrade both the energy efficiency (EE) and the quality of service. To address this problem, we propose an energy-efficient resource allocation scheme to determine channel selection and power allocation by introducing the relay-aided in-band D2D communication mode and the non-cooperative game theory. We explore the above resource allocation problem in a hybrid manner and model it as a non-cooperative game, where each player optimizes its EE individually with the aid of RRHs. Then, we alleviate the inter-cell interference from the cellular UEs near the cell edge by allowing these UEs to select in-band D2D relay links to shorten the long transmission distances between them and RRHs. Moreover, the interfering signal processing load of the BBU is lowered due to the reduction of the interfering signal strength, and the EE is improved due to the reduction of transmission powers of these cellular UEs. The achievable performance of the proposed scheme is analyzed through simulations.

Research on Resource Migration Based on Novel RRH-BBU Mapping in Cloud Radio Access Network for HSR Scenarios

Cloud radio access network (C-RAN) is considered as a promising architecture for 5G with advantages of green energy, convenient resources allocation. In this paper, we explore the feasibility of C-RAN for high-speed railway (HSR) scenarios. A novel phenomenon of group handover is defined in the extensively and densely distributed railway network and we present a resource migration cost with a closed-form expression to depict the group handover. To reduce the cost, we propose a novel connection relationship between the remote radio head (RRH) and the baseband unit (BBU) pool. Based on this, we establish a flexible network so as to allocate the resource dynamically and formulate a graph by abstracting the RRH-BBU and BBU-BBU mapping relationship. Then the minimization of resource migration cost along the high-speed train (HST) routine is converted into the shortest path problem (SPP). By using the modified Floyd-Warshall algorithm, the SPP can be solved with high efficiency compared with the conventional algorithm. Finally, the simulation result shows that the proposed mechanism can decrease the resources migration cost significantly.

Supporting QoS requirements provisions on 5G network slices using an efficient priority-based polling technique

Mobile operators aim to enhance the Quality of Service (QoS) in each new mobile network generation. The upcoming 5G mobile network is expected to make a revolutionary enhancement in the provided service level. Serving diverse requirements from different mobile network users in a flexible and a dynamic way is a key demand. Thus, the 5G network-slicing concept provides an optimal and an efficient way to deal with the diversity of end users requirements and demands. In 5G, three different network slices share the network resources in a virtually isolated slicing manner. These slices will be in different priority classes. Indeed, priority scheduling algorithms are needed to guarantee the service level for each slice among all other lesser priority slices. This paper proposes a priority-based polling scheme with multiple scheduling techniques for the data traffic coming from multiple 5G network slices. Cyclic and random polling with/without priority are analyzed and discussed for multiple 5G use cases. The analytical model of the proposed priority-based polling scheme is presented and the performance measures in terms of the average waiting time and utilization are derived. The results prove that the proposed polling scheme shows a powerful ability to control the 5G virtual base-band units pool and guard the QoS of the 5G slices, which is what 5G networks need.

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.

A Distributed Approach to Improving Spectral Efficiency in Uplink Device-to-Device-Enabled Cloud Radio Access Networks

Device-to-device (D2D)-enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two users. However, due to the need to acquire global channel state information (CSI) and to execute centralized algorithms, heavy burdens are placed on the fronthaul and the baseband unit (BBU) pool. To alleviate these burdens, a distributed approach to mode selection and resource allocation for potential D2D pairs under pre-determined resource allocation of C-RAN users is proposed, in which pairs of users are endowed with decision-making capabilities. The proposed procedure is divided into three stages: communication mode and subchannel selection, utility value determination, and reinforcement-learning-based strategy update. The core idea is that the D2D pairs self-optimize the mode selection and resource allocation without global CSI under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the impacts of the fronthaul capacity, the centralized signal processing capability of the BBU pool, and the distance between the D2D transmitter and the remote radio head are demonstrated and analyzed.

QoS-Aware Joint BBU-RRH Mapping and User Association in Cloud-RANs

Cloud radio access network (C-RAN) is a promising wireless network architecture that can reduce the energy consumption by the centralized cloud architecture and subsequently decrease the number of required traditional base station sites and the site support equipments. C-RAN consists of the baseband units (BBUs) and the remote radio heads (RRHs). BBUs are pooled in a central cloud, i.e., the BBU pool is to provide powerful computation and storage resources while RRHs are distributed across multiple sites to provide coverage and interact with user equipments. In order to exploit the benefits of C-RAN, each BBU can be actualized by a virtual machine, i.e., virtual BBU (VB). VBs can be initiated and shut down as needed to serve clusters of RRHs (i.e., many-to-one mapping between RRHs and BBUs). RRHs can be turned into the sleep mode to reduce the energy consumption. In this paper, we jointly optimize BBU-RRH mapping and user association with the objective to minimize the system cost incurred by the energy bill from RRHs and VB rentals under the constraint of user quality of service, which is formulated as an integer linear programming problem. Furthermore, we decompose the joint problem into two subproblems and design a time-efficient algorithm to solve the problem. Simulation results demonstrate that our proposed algorithm performs close to the optimal solutions obtained from CPLEX.

Maximizing the Energy Efficiency of Virtualized C-RAN via Optimizing the Number of Virtual Machines

In cloud radio access networks (C-RAN), more accurate prediction of the number of virtual machines (VMs) one server can support would improve network capacity and energy efficiency (EE). In this paper, the problem of allocating an optimal number of VMs to the cloud server is introduced. Monte Carlo-based evolutionary algorithm [particle swarm optimization (PSO), quantum PSO, or genetic algorithm] are used to find the suboptimal number of VMs that optimizes the EE of C-RAN. To enable such evaluation, a power model is proposed to evaluate the power consumption of each unit within a virtualized server. This evaluation occurs under the circumstances of increased number of hosted VMs, and processed resource blocks (RBs) at each VM. Moreover, power allocation methods are proposed to transmit the power from base band unit pool to the remote radio heads (RRHs), and from RRHs to the users (UEs). This allocation is based on the combination of one or more of RRH distance, RRH channel gain, UE distance, UE channel gain, and UE path loss. The EE problem was constrained to crucial quality of service indicators, including minimum UE data rate, number of allocated RBs, and latency imposed due to virtualization.

Power- and Rate-Adaptation Improves the Effective Capacity of C-RAN for Nakagami-$m$ Fading Channels

We propose a power-and rate-adaptation scheme for cloud radio access networks (C-RANs), where each radio remote head (RRH) is connected to the baseband unit pool through optical links. The RRHs jointly support the users by efficiently exploiting the enhanced spatial degrees of freedom. Our proposed scheme aims at maximizing the effective capacity (EC) of the user subject to both per-RRH average- and peak-power constraints, where the EC is defined as the maximum arrival rate that can be supported by the C-RAN under the statistical delay requirement. We first transform the EC maximization problem into an equivalent convex optimization problem. By using the Lagrange dual decomposition method and solving the Karush–Kuhn–Tucker equations, the optimal transmission power of each RRH can be obtained in the closed form. Furthermore, an online tracking method is provided for approximating the average power of each RRH. For the special case of two RRHs, the expression of the average power of each RRH can be calculated in the explicit form. Hence, the Lagrange dual variables can be computed in advance in this special case. Furthermore, we derive the power allocation for two important extreme cases: first, no delay constraint and, second, extremely stringent delay requirements. Our simulation results show that the proposed scheme significantly outperforms the conventional algorithm without considering the delay requirements. Furthermore, when appropriately tuning the value of the delay exponent, our proposed algorithm is capable of guaranteeing a delay outage probability below $10^{-9}$ when the maximum tolerable delay is 1 ms. This is suitable for the future ultra-reliable low-latency communications.

Bandwidth and Energy-Aware Resource Allocation for Cloud Radio Access Networks

Cloud radio access network (C-RAN) is emerging as a transformative paradigmatic architecture for the next generation of cellular networks. In this paper, a novel resource allocation solution that optimizes the energy consumption of a C-RAN is proposed. First, an energy consumption model that characterizes the computation energy of the base band unit (BBU) pool is introduced based on the empirical results collected from a programmable C-RAN testbed. Then, the resource allocation problem is split into two subproblems—namely the bandwidth power allocation (BPA) and the BBU energy-aware resource allocation (EARA). The BPA, which is first cast via mixed-integer nonlinear programming and then reformulated as a convex problem, aims at assigning a feasible bandwidth and power to serve all users while meeting their quality of service (QoS) requirements. The second subproblem, i.e., the BBU EARA, is defined as a bin-packing problem that aims at minimizing the number of active virtual machines in the BBU pool to save energy. Simulation results coupled with the real-time experiments on a small-scale C-RAN testbed show that the proposed resource allocation solution optimizes the energy consumption of the network while meeting practical constraints and QoS requirements, and outperforms competing algorithms, such as best fit decreasing, RRH-clustering, and SINR-based.

On Designing Energy-Efficient Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access network (H-CRAN) promises higher energy efficiency (EE) than the conventional cellular networks by centralizing the baseband signal processing into the baseband unit (BBU) pool hosted by cloud computing platforms. Because of the difference between H-CRAN and conventional cellular networks, existing energy-efficient networking mechanisms designed for conventional cellular networks cannot fully leverage H-CRAN in terms of reducing the network energy consumption. In this paper, we bridge this gap by proposing a radio resource management scheme to optimize the network EE (NEE) of H-CRAN. We develop a network energy consumption model that characterizes the energy consumption of radio access points, fronthaul, and the BBU pool in H-CRAN. Based on the network energy consumption model, we formulate the NEE optimization problem with the consideration of the capacity constrained fronthaul. The NEE optimization problem is a mixed integer non-linear programming problem. We propose the H-CRAN energy-efficient radio resource management (HERM) algorithm to solve the NEE optimization problem efficiently. Various properties of the proposed solution are derived and extensive simulations are conducted. The simulation results show that the HERM algorithm significantly improves the NEE of H-CRAN. As compared with a baseline algorithm in which the radio resource management is not optimized, HERM boosts the NEE by 59% under the dynamic network traffic. As compared to an energy-efficient radio resource allocation (ERA) algorithm which does not optimize the energy consumption of the BBU pool, the NEE of H-CRAN achieved by the HERM algorithm is up to 51% better than that by the ERA algorithm with network traffic dynamics.

Compressive Channel Estimation and User Activity Detection in Distributed-Input Distributed-Output Systems

We address the cloud radio access network with wireless fronthaul links for massive machine-type communication as a distributed-input distributed-output (DIDO) system for simplicity. In this letter, the channel estimation and user activity detection problems in the DIDO system are studied. We notice that there are two types of sparsity in DIDO systems: The first is the sparsity of user equipment (UE) activities, and the second is the spatial sparsity of UE signals. In response, a two-stage compressed sensing process is proposed in which UE activities and the overall channel states from active UEs to the baseband unit pool are identified at the first stage, and channel states from active UEs to remote radio heads are estimated at the second stage. A low-complexity method is proposed to accelerate the process in the first stage. Simulation results are also presented to show the performance of the proposed approach.

Joint remote radio heads and baseband units pool resource scheduling for delay-aware traffic in cloud radio access networks

Due to the centralized signal processing and powerful computational ability, cloud radio access networks (C-RAN) is considered as a promising technique to meet the increasing demand of high-data-rate services of the fifth generation wireless communications. This paper investigates the stochastic optimization problem of C-RAN by joint remote radio heads and baseband units pool resource scheduling to achieve the throughput utility maximization. We formulate a problem to maximize the average throughput utility while stabilizing all processing and transmission queues under the power, subcarrier and computational resources constraints. By utilizing Lyapunov optimization technique, the primal problem can be decomposed into four subproblems. Throughput utility maximization, admission control and computational resource allocation subproblems can be solved easily. For the joint power and subcarrier allocation subproblem, we utilize time-sharing and alternating approaches to obtain a feasible solution. By utilizing the results of four subproblems, a joint admission control and resource allocation (JACRA) algorithm is proposed. The simulation results are provided to show that the total average throughput of JACRA algorithm is increased by 4.02% compared with joint Hungarian and iterative waterfilling (JHIW) algorithm, but the total average backlogs of JACRA algorithm are decreased by 7.04% when the control parameter grows from 1 to 10.