Baseband Unit Pool Research Articles

A bandwidth adaptation mechanism for Cloud Radio Access Networks

The Cloud Radio Access Network (C-RAN) is regarded as the possible implementation approach of the next-generation mobile networks. C-RAN splits a traditional base station into remote radio heads (RRHs) and the base band unit (BBU). Many BBUs are co-located into a centralized BBU pool. This enables the baseband resources to be managed in a centralized BBU pool and thus improves the efficiency. In this paper, a new bandwidth adaptation (BA) mechanism is designed for the C-RAN with the dual connectivity capability. The dual connectivity allows the bearer to be split so as to access more resources from RRHs simultaneously. The new BA mechanism is developed so as to dynamically allocate the resources to establish a new or a handoff bearer. The new BA mechanism is designed with a new downgrading index, which aims to fairly release the proportional resource from more RRHs and then aggregates the released resource to a new or a handoff bearer. Simulation and analysis results illustrate that the proposed BA mechanism reduces both the handoff bearer dropping and the bearer blocking probabilities.

Game-Assisted Distributed Decision Making to Build Virtual TDM-PONs in C-RANs Adaptively

Although a promising solution for next-generation radio access networks (RANs), cloud RAN (C-RAN) still faces technical challenges due to the separate locations of the cloud-based baseband unit (BBU) pool and remote radio heads (RRHs). Fortunately, time- and wavelength-division multiplexing passive optical networks (TWDM-PONs) can efficiently bridge the communications between the BBU pool and RRHs. In this work, we address the problem of formulating virtual time division multiplexing PONs (vTDM-PONs) adaptively in a TWDM-PON-based C-RAN. However, instead of relying on a centralized approach, we develop algorithms that can work in a distributed manner. Specifically, we consider a scenario in which the optical network unit (ONU) of each RRH has the initiative to choose a proper vTDM-PON to register to based on its knowledge on the C-RAN. We formulate game theoretic models for the vTDM-PON formation for both static and dynamic operation of a C-RAN, then transform the games into weighted potential games and therefore prove the existence of Nash equilibrium (NE) points in the games. Moreover, we propose a distributed algorithm that can converge to the NE point(s) to enable each ONU to choose its vTDM-PON intelligently. As a comparison, we formulate a centralized mixed-integer linear programming model to provide the optimum vTDM-PON formation solution. Simulation results confirm the effectiveness of the design of the proposed game models and distributed algorithm.

System Cost Minimization in Cloud RAN With Limited Fronthaul Capacity

Cloud radio access network (C-RAN) is emerging as a potential alternative for the next generation RAN by merging RAN and cloud computing together. In this paper, we consider the baseband unit (BBU) pool of C-RAN as a collection of virtual machines (VMs). We allow each user equipment (UE) to associate with multiple VMs in the BBU pool, and each remote radio head (RRH) can only serve a limited number of UEs. Under this model, we jointly optimize the VM activation in the BBU pool and sparse beamforming in the coordinated RRH cluster, which is constrained by limited fronthaul capacity, to minimize the system cost of C-RAN. We formulate this problem as a mixed-integer nonlinear programming problem, and then propose efficient methods to optimize the number of active VMs, as well as the sparse beamforming vectors. Moreover, we derive a closed-form solution for the beamforming vectors. Simulation results suggest that our proposed algorithms have better performance than the benchmark algorithms in terms of both system cost and robustness.

Meeting the Requirements to Deploy Cloud RAN Over Optical Networks

Radio access network (RAN) cost savings are expected in future cloud RAN (C-RAN). In contrast to traditional distributed RAN architectures, in C-RAN, remote radio heads (RRHs) from different sites can share baseband processing resources from virtualized baseband unit pools placed in a few central locations (COs). Due to the stringent requirements of the several interfaces needed in C-RAN, optical networks have been proposed to support C-RAN. One of the key elements that needs to be considered are optical transponders. Specifically, sliceable bandwidthvariable transponders (SBVTs) have recently shown many advantages for core optical transport networks. In this paper, we study the connectivity requirements of C-RAN applications and conclude that dynamicity, fine granularity, and elasticity are needed. However, there is no SBVT implementation that supports those requirements, and thus, we propose and assess an SBVTarchitecture based on dynamic optical arbitrary generation/measurement. We consider different long-term evolution-advanced configurations and study the impact of the centralization level in terms of the capital expense and operating expense. An optimization problem is modeled to decide which COs should be equipped and which equipment, including transponders, needs to be installed. The results show noticeable cost savings from installing the proposed SBVTs compared to installing fixed transponders. Finally, compared to the maximum centralization level, remarkable cost savings are shown when a lower level of centralization is considered.

Social C-RAN: Novel Futuristic Paradigm for Next-Generation Cellular Networks

ABSTRACTProgressive growth of smartphones and number of users in the network demands for increase in network capacity. Proliferation in network capacity in turn triggers the problems like increase in Capital and Operational Expenditures (CAPEX/OPEX), decrease in Quality of Services (QoS), inflation in power consumption and the like. To cater the aforementioned network challenges, Cloud Radio Access Network (C-RAN) is introduced with the capability of centralization, virtualization and co-ordination. The commercialization of C-RAN further faces problems like dynamic resource allocation environment, co-operative transmission and reception techniques, interconnection and management of base band unit (BBU) instances in a BBU pool, etc. Therefore, the social paradigm when blended with C-RAN, the evolved C-RAN, i.e. Social C-RAN, promises to overcome the challenges of C-RAN. We contribute a fresh novel proposal of Social C-RAN in which network elements will be connected with the help of social relationships and properties they exhibit. Our quantitative results show that the vision of social layer introduced in the C-RAN architecture automates several functions like handover, frequency assignment, dynamic resource allocation and results in reducing communication delays in various applications. We select the potential applications that play a major role in evolving cellular networks and discuss the possibilities to optimize these applications further using our proposed Social C-RAN architecture.

Massive MIMO operation in partially centralized cloud radio access networks

Massive multiple-input multiple-output (MIMO) is considered as one of the key technologies in next generation cellular systems due to its higher multiplexing gain and energy efficiency. However, in a cloud radio access network (C-RAN) with massive MIMO, operation of many antennas incurs a huge amount of digital sampled data to be transported over the fronthaul link between a baseband unit (BBU) pool and each remote radio head (RRH). Accordingly, the bandwidth requirement for the fronthaul link increases in proportion to the number of antennas at the RRH, and it incurs enormous fronthaul link cost to operators. In this paper, we propose a new C-RAN architecture called massive MIMO partially-centralized C-RAN (MPC-RAN), which is designed for a more efficient use of fronthaul resources in massive MIMO operation. Differently from a conventional fully-centralized C-RAN (FC-RAN) where most L1/L2/L3 functions are performed at the BBU pool, some beamforming-related functions in the proposed MPC-RAN are performed at RRHs. We further provide an MPC-RAN operation strategy that aims to efficiently exploit the proposed C-RAN structure in massive MIMO systems. Specifically, we formulate a wireless sum-rate maximization problem for constrained fronthaul capacity and provide a heuristic algorithm that targets at obtaining optimal beamforming configuration and bandwidth allocation. The simulation results confirm that our proposed architecture requires less fronthaul link capacity compared to FC-RAN for the same transmission configuration, and our operation strategy allows for flexible utilization of capacity-limited fronthaul resources.

Baseband Unit Pool Planning for Cloud Radio Access Networks: An Approximation Algorithm

Cloud radio access networks (C-RANs) are proposed as promising architecture to improve the capacity and enhance the coverage of mobile communication systems. In this letter, we study the baseband unit (BBU) pools planning problem in the C-RAN, where we try to minimize the total deployment cost while satisfying the traffic demands of remote radio heads connected to the BBU pools, the processing capacity of each BBU pool, and the latency requirements of the C-RAN. Our problem formulation leads a mixed integer linear programming problem that is NP-hard. We introduce an approximation algorithm to address it efficiently. Numerical results show that our proposed algorithm performs much better than other heuristic ones that are popular to deal with such kind of optimization tasks. Moreover, our proposal also yields a performance guaranteed scheme for the BBU pools planning problem in the C-RAN.

Social-Aware Edge Caching in Fog Radio Access Networks

Fog radio access networks (F-RANs) are becoming an emerging and promising paradigm for fifth generation cellular communication systems. In F-RANs, distributed edge caching techniques among remote radio heads (RRHs) and user equipment (UE) can effectively alleviate the burdens on the fronthaul toward the base band unit pool and the bandwidth of the RANs. However, it is still not clear as to how social relationships affect the performance of edge caching schemes. This paper attempts to analyze the impact of mobile social networks (MSNs) on the performance of edge caching in F-RANs. We propose a Markov-chain-based model to analyze edge caching among edge nodes (i.e., RRHs and MSNs), as well as data sharing among the potential MSNs from the viewpoint of content diffusion in the F-RANs. Moreover, we analyze the edge caching schemes among UE to minimize the bandwidth consumption in the RANs. Finally, the optimal edge caching strategies among RRHs in terms of caching locations and time are introduced to minimize the bandwidth consumption of fronthaul and storage costs in the F-RANs. Simulation results show that the proposed edge caching schemes among UE and RRHs are able to reduce the bandwidth consumption of RANs and fronthaul effectively.

How Much Computing Capability Is Enough to Run a Cloud Radio Access Network?

Cloud radio access network (C-RAN) has emerged as a promising solution to support exponentially increasing demand in data rate. The attractive capacity enhancement mainly comes from centralized and coordinated processing, which poses great challenges on computing capability in the baseband unit pool. This requires the efficient allocation of computing resources to minimize the hardware and energy costs of C-RANs. Therefore, in this letter, we first model the computing resource consumption of joint downlink transmissions from remote radio heads (RRHs) to users. Then, we investigate the computing resource minimization problem on how much computing capability is needed given certain number of RRHs and user density. Numerical results show that the computing resource consumption increases non-linearly with the user density. In particular, the required computing resource drastically increases when densely populated hotspots are present in the system.

Multi-Layer Cloud-RAN With Cooperative Resource Allocations for Low-Latency Computing and Communication Services

To improve low-latency computing and communication services, a new type of mobile edge computing architecture named multi-layer cloud radio access network (Multi-layer CRAN) is designed in this paper. In Multi-layer CRAN, a high-level edge cloud is deployed next to base band unit pool to handle the computing tasks of user equipment (UE) in centralized way. Meanwhile, a low-level edge cloud is deployed in each remote radio head (RRH) to locally handle UEs’ computing tasks in a distributed way. Based upon Multi-layer CRAN, a cooperative communication and computation resource allocation (3C-RA) algorithm is further designed for lower service latency and energy cost, and higher network throughput in this paper. 3C-RA utilizes a distributed RRH cell coloring algorithm to enable each RRH to work out the resource allocation in an efficient and distributed way. 3C-RA employs a proportional fairness-based approach to allocate communication and computation resource in each RRH cell. A series of simulations on Multi-layer CRAN with 3C-RA were carried out. The simulation results validate that Multi-layer CRAN is more capable of providing low-latency computing and communication services, and 3C-RA enables Multi-layer CRAN to have lower service latency and energy cost and higher network throughput.

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.

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.

Traffic Density-Based RRH Selection for Power Saving in C-RAN

Cloud radio access network (C-RAN) is deemed as a promising architecture to meet the exponentially increasing traffic demand in a mobile network. However, it needs a huge amount of transport capacity between the remote radio heads (RRHs) and the baseband unit pool. Though an optical transport network can be employed to support such a massive capacity, it always leads to enormous power consumption that is comparable with the transmission powers of the RRHs, which can significantly decrease the performance of the C-RAN if not well addressed. In this paper, we investigate the power saving issue in the C-RAN, where we attempt to get a power consumption tradeoff between the optical transport network and the RRHs. An RRH selection problem is formulated to achieve this goal, which is based on the traffic density of the service area. Our optimization task is to select a subset of the RRHs to minimize the total power consumption of the C-RAN while satisfying a series of network constraints, including the power and bandwidth budgets of the RRHs, the traffic demands of users, and the spectral efficiency. We develop an efficient local search algorithm, which includes three types of local improvement operations: “add,” “open,” and “close,” to address the intractable optimization task. Numerical results indicate that our proposal can significantly reduce the power consumption of the C-RAN. Moreover, our proposed algorithm converges stably and quickly, indicating that it is promising for applications.

Energy Efficient Baseband Unit Aggregation in Cloud Radio and Optical Access Networks

A cloud radio access network enabled by optical transport technology has been recently proposed as a next-generation mobile access network. It separates the legacy base station into remote radio heads (RRHs) and centralized baseband unit (BBU) pools. “Any-to-any” connections between RRHs and BBUs can be realized through a flexible optical fronthaul network. In this paper, we focus on the energy consumption of a BBU pool under a tidal traffic scenario, and an energy-efficient BBU aggregation is proposed by dynamically sleeping and awaking BBU cards. We discuss a BBU capacity model and transform BBU aggregation into an evolved 2D bin-packing problem. An integer linear programming formulation and two heuristic algorithms are developed to minimize the number of active BBU cards. Simulation comparisons are performed among different cell sites (i.e., macro cell, micro cell, pico cell, and heterogeneous cell) and network coverage. Numerical evaluations show that BBU aggregation can achieve significant energy savings, especially for a small cell scenario.

Hybrid precoding for heterogeneous cloud radio access network based on nested array

Future wireless system meets the requirements of high network capacity but is suffering from the problems of high interference and resource shortage. In this paper, we develop a hybrid precoding solution by applying the heterogeneous cloud radio access network (H-CRAN) with remote radio heads (RRHs) equipped with nested arrays for a better system control and higher efficiency. We first summarize the related work and highlight our contribution. Then we propose the hybrid precoding scheme after introducing the system model based on the H-CRAN architecture and the nested array. For the proposed hybrid precoding scheme, the RRHs first identify the interference sources around them, then report it to the baseband unit (BBU) pool. The BBU pool identifies the sources of interference and searches for available resource for the RRH. As the frequency may be reused in specific area, the eNB processes the hybrid precoding considering the null-space of the victim users so that the interference can be avoided. Finally, we evaluate the scheme and prove its performance with numerical results.

Cost effective wavelength reused MDM system for bidirectional mobile fronthaul.

In this paper, we propose a cost-effective wavelength-reused mode-division-multiplexing (MDM) system for high speed symmetrical bidirectional mobile fronthaul application. At the base band unit (BBU) pool, one of the spatial modes is used to transmit signal carrier while the others are used for downstream (DS) signal channels. At the remote radio unit (RRU) side, the signal carrier is split and reused as modulation carrier for all the upstream (US) signal channels after mode demultiplexing. Thanks to the low mode crosstalk characteristic of the mode multiplexer/demultiplexer (MUX/DEMUX) and few-mode fiber (FMF), the signal carrier and each signal channel can be effectively separated. The spectral efficiency (SE) is significantly enhanced when multiple spatial channels are used. Compared with other wavelength reused scheme in which the downstream and upstream be modulated in orthogonal dimension, the modulation format of both directions are independent in the proposed wavelength reused MDM system. Therefore, it can easily achieve symmetrical bidirectional transmission without residual re-modulation crosstalk. The proposed scheme is scalable to multi-wavelength application when wavelength MUX/DEMUX is utilized. With the proposed scheme, we demonstrate a proof of concept intensity modulated 4 × 25-Gb/s 16-QAM orthogonal frequency division multiplexing (OFDM) transmission over 10-km FMF using low modal-crosstalk two-mode FMF and MUX/DEMUX with error free operation. The downstream receiver sensitivity is -21 dBm while the upstream receiver sensitivity is -18 dBm for bidirectional transmission. Due to the Rayleigh backscattering and other spurious reflections, the upstream suffers 2 dB power penalty compared with unidirectional transmission without downstream. To mitigate bidirectional transmission impairments, we propose a simple and effective method to suppress Rayleigh backscattering by shifting the downstream subcarrier frequency. A receiver sensitivity improvement of up to 2.5 dB is achieved for upstream with different downstream power.

FBMC in Next-Generation Mobile Fronthaul Networks With Centralized Pre-Equalization

In this letter, we proposed a centralized pre-equalization mechanism in a fiber-wireless integrated mobile fronthaul system to compensate the channel degradation among multiple component carriers (CCs) with reduced complexity and cost at user terminals. Orthogonal frequency division multiplexing and filter-bank multicarrier (FBMC) with and without frequency-domain pre-equalization are compared in a 50-GHz radio-over-fiber testbed. In comparison, FBMC with the proposed pre-equalization performs better because asynchronous up-link CCs can be quasi-seamlessly aggregated without interference, which enables baseband-unit (BBU) pool to precisely estimate the channel quality and perform pre-equalization for multiple bands with high spectral efficiency. Bidirectional transmission experiments are conducted with two independent asynchronous user terminals controlled by one BBU pool. An error vector magnitude of <6% is obtained with 1.1-GHz 5-CC carrier aggregation over 25-km standard single-mode fiber and 1.2-m wireless channel.

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.

Energy-Efficient Resource Allocation for D2D Communications Underlaying Cloud-RAN-Based LTE-A Networks

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.

Time-Reversal Tunneling Effects for Cloud Radio Access Network

The explosion of today’s wireless traffic requires operators to deploy more access points (APs) and design efficient collaboration mechanism to alleviate the interference among them. However, the collaborative techniques cannot work efficiently due to the high latency and low bandwidth interface between the APs in traditional networks. To address this challenge, cloud radio access network (C-RAN) is proposed, where a pool of base band units (BBUs) are connected to the distributed remote radio heads (RRHs) via high bandwidth and low latency links (i.e., the front-haul) and are responsible for all the baseband processing. But the limited front-haul link capacity may prevent the C-RAN from fully utilizing the benefits made possible by the centralized baseband processing. As a result, the front-haul link capacity becomes a bottleneck. To address this challenge, in this work, we propose using the time-reversal (TR)-based communication as the air interface in C-RAN. Due to the unique spatial and temporal focusing effects of TR-based communications, multiple terminal devices (TDs) are naturally separated by their location-specific signatures. Such a property allows signals to be combined to deliver without demanding more bandwidth. Therefore, the TR-based communication in essence creates a “tunneling” effect such that the baseband signals for all the TDs can be efficiently combined and transmitted in the front-haul. We study the performance of the proposed C-RAN architecture in terms of spectral efficiency and front-haul rate, based on extensive measurements of the wireless channel in a real-world environment. It is shown that with nearly the same amount of traffic load in the front-haul, more information can be transmitted when there are more TDs. The proposed TR tunneling effect can help deliver more information in the C-RAN and alleviate the burden of the front-haul caused by network densification.