Optimizing functional split of baseband processing on TWDM-PON based fronthaul network

Currently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient connections between a large number of remote radio heads (RRHs) in the cell sites and the baseband unit (BBU) pool in the central location, known as the fronthaul, has emerged as a new challenge. Many wired and wireless solutions have been proposed to address this bottleneck. Specifically, optical technologies presented by passive optical networks (PONs) are introduced as the best suitable solution for 5G and beyond network fronthaul due to their properties of providing high capacity and low latency connections. We considered time and wavelength division multiplexed passive optical networks (TWDM-PONs) as a fronthaul for 5G and beyond. Taking that into consideration, in this paper, we propose an integer linear program (ILP) that results in the optimal optical fronthaul deployment while minimizing the total cost of 5G and beyond instances. However, for larger network instances, solving the ILP problem becomes unscalable and time-consuming. To address that, we developed two heuristic-based algorithms (the K-means clustering algorithm and the one based on the genetic algorithm-GA). We evaluated the suitability of our proposed ILP and heuristic algorithms in simulations by utilizing them to plan different network instances (dense and sparse).

Time- and wavelength- division multiplexed passive optical network (TWDM-PON) has attracted considerable attention for the next generation optical access systems. Among potential applications of TWDM-PON, a major application is the support of mobile fronthaul streams between radio units (RUs) and distributed units (DUs) in the centralized radio access network (C-RAN) architecture, which consists of central units (CUs), DUs, and RUs. The upstream fronthaul traffic that an optical line terminal (OLT) receives is expected to become highly bursty due to the variable data rate generated by employing new functional split options and the synchronization of data transmission between neighboring RUs caused by time-division duplex (TDD). However, there has been no wavelength and bandwidth allocation scheme for TWDM-PON that is designed to efficiently accommodate fronthaul streams satisfying the strict delay requirement. Therefore, in this paper we propose a novel wavelength and bandwidth allocation algorithm that can minimize the number of active wavelength channels considering the high burstiness and delay requirement of fronthaul data transmission. Through computer simulations it was confirmed that the number of active wavelength channels can be reduced by 50% with the proposed algorithm, and thus more RUs can be efficiently accommodated using TWDM-PON.

5G is coming with a promise to provide ubiquitous coverage with high data rate availability. To do so, densification of access points to enhance the system capacity is anticipated. For managing such densely populated network, 5G will be employing Centralized Radio Access Network (CRAN), where most of the Radio Access Network (RAN) functionalities are centralized in a central processing unit. This centralization reduces operational costs and eases implementation of advanced technologies, such as, Cooperative multipoint (CoMP) and enhanced inter-cell interference coordination (eICIC), in a cost efficient way. However, CRAN imposes stringent requirements on the fronthaul, i.e. the link connecting access points to the central unit, in terms of capacity and latency. Furthermore, future fronthaul networks are expected to rely on wireless technologies, since wired options are costly, not scalable and not always suitable for all scenarios. Therefore, meeting the expected requirements of fronthaul network utilizing capacity-limited wireless technologies may become an inescapable bottleneck. In this paper, we study different functional splits at the PHY layer in terms of data rate requirements and operational cost, and discuss the combination of different splits aimed at minimizing the overall cost and maximizing the centralization gains, while keeping the capacity requirements below the limit of the fronthaul.

To meet the 5G requirements in terms of high capacity and enhanced quality of service, the Cloud Radio Access Network (C-RAN) relies on the network densification and coordination. The main idea behind the C- RAN is to split the base station into two parts: the first component is the Remote Radio Head (RRH) which will be responsible for all Radio transmissions and the second one is the Base Band Unit (BBU) which will be responsible for the processing and control. Only the RRH will be densified to offer more capacity. The processing resources will be pooled and clustered so that many RRHs will be managed by one BBU pool which will reduce the related CAPEX and OPEX of the network's deployment, operation and maintenance. The main goal of this paper is to propose an optimized clustering algorithm able to improve interference management between BBU clusters and decrease the network mobility signaling cost. In this work, we will be interested by the implementation and optimization of local search algorithm (Tabu Search) and evolution algorithm (Genetic Algorithm) as well as the combination of both methods in order to enhance the convergent solution.

Cloud radio access networks (C-RAN) is a potential solution to meet the traffic demand of the future network, which migrates the baseband units (BBU) to the BBU pool in the cloud. The connection between BBU and remote radio head (RRH) can be dynamically changed and the resources can be allocated more flexible according to some factor, e.g., traffic demand of RRHs. Therefore, the resource allocation scheme in C-RAN is redefined, which regards the BBUs as a sharing resource. Existing studies execute the mapping scheme with the consideration of the single BBU but not the whole network, e.g., the BBU with high utilization switches the supported RRH to other BBU to reduce the resource usage. To achieve this goal, this paper makes use of the concept of cloud resources and centralized management to fully utilize the resources and allocate the resources more centralized. The main contribution of this paper is to propose a dynamic BBU–RRH mapping scheme using a borrow-and-lend approach to dynamically allocate the resources to RRHs in a clever and efficient way. With the centralized management, the max busy group borrows the resource for RRH switching, whereas the min helper group lends the resource to handle the switching RRHs. Finally, simulation results illustrate the proposed mechanism in C-RAN significantly reduces the waste of the resource usage and improves the throughput.

New innovation approach called -centralized radio access network (Cloud-RAN or C-RAN)- architecture is developed to meet the explosive growth in mobile traffic in 5G mobile networks especially in the need of high -speed, high capacity, real-time data, and high-reliability. In C-RAN architecture, fronthaul networks connect between the radio units called -Remote Radio Heads (RRHs)- and the processing unit called -Base Band Processing Unit (BBU)-. Recently, hybrid free space optics (FSO) and millimeter waves (mmW) network is considered to be a promising solution that can match with the requirements of 5G fronthaul networks. In this paper, hybrid FSO/mmW links at each RRH is proposed to enhance the performance of C-RAN fronthaul network at various weather conditions. The performance of hybrid FSO/mmW technology as a fronthaul network is evaluated at different weather conditions and compared to other technologies. The numerical results reveal that improvements are obtained in network reliability using hybrid FS O/mm W fronthaul network, especially at severe weather conditions.

Fronthaul networks are leveraging communication links between the remote radio heads and baseband units in cloud radio access networks, and common public radio interface (CPRI) is an interface to carry time-sensitive traffic over the fronthaul. For the fronthaul networks which impose stringent bandwidth, latency, and jitter requirements, Ethernet can be a cost-effective solution to carry the CPRI traffic. However, Ethernet networks have limitations to transport the multiple CPRI streams simultaneously and satisfy the strict service requirements due to the accumulation of unavoidable intrinsic delays at switches. To address these issues, this paper evaluates the impact of intrinsic delay on the performance of CPRI over Ethernet (CoE) networks with experimental results and proposes a virtual local area network-based CoE network architecture along with two transport algorithms to carry the multiple CPRI streams over Ethernet despite the intrinsic delays. We confirmed with computer simulations that with the proposed techniques, the end-to-end delays and jitter for a number of CPRI streams within the acceptable range of fronthaul requirements can be achieved.

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.

In 5th generation mobile communications systems (5G), the number of small cells with centralized-radio access network (C-RAN) architecture is expected to increase greatly. Time division multiplexing-passive optical networks (TDM-PON) are promising transmission systems for networks between a baseband unit (BBU) and a remote radio head (RRH). However, there is a challenge as regards the ultra-low latency requirements of 5G including less than 1 ms for end-to-end communication, because a TDM-PON generally transmits using time division multiple access (TDMA) as a basis. In this paper, we derive the achievable latency and the number of RRHs accommodable by TDM-PON, and analyze the applicable range of a TDM-PON for mobile use. Our analysis shows that it is possible to design a PON application in accordance with the latency requirements of mobile systems.

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

As the number of mobile users and the variety of contents increase, future access networks need to improve their capacity and latency. The concept of Cloud Radio Access Network (C-RAN), which densely deploys a large number of Remote Radio Heads (RRHs), presents a solution to guarantee such communication quality. However, the network still suffers cost issues related to power line supply to a large number of RRHs. Therefore, in this paper, we describe our envisioned C-RAN based on Passive Optical Network (PON) exploiting Power over Fiber (PoF), which is able to provide communication services without external power supply for RRHs. We also describe the conventional network design and operation schemes. Then, power-effective network design and operation approaches to equalize the transmission power of all Optical Line Terminals (OLTs) in the assumed network are presented. Our proposed power-effective network design demonstrates how to deploy the RRHs. Also, based on the proposed design approach, we present a joint control algorithm of the sleep schedule of RRHs and the transmission power of OLTs by considering the distribution of users. Furthermore, the effectiveness of our proposed approach is evaluated through numerical calculation.

A learning algorithm for TWDM-PON’ DWBA with 5G fronthaul networks

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 demo presents an end-to-end hierarchical control of a multi-domain multi-technology integrated fronthaul and backhaul network where a parent controller coordinates the operation of a wireless and optical child controllers managing real wireless and optical data plane resources. The services offered by this hierarchical control infrastructure are accessed through the offered REST-based northbound API by the resource management application (RMA), which is able to determine the appropriate network resource allocation based on the requested traffic profiles and the position of the different mobile entities deployed in the network. Interestingly, the same REST-based API is used between the parent and child controllers inside the control infrastructure, allowing recursive and scalable deployments. The RMA manages fronthaul and backhaul traffic generated by a flexible mobile network deployment featuring user equipments, a remote radio head, a baseband unit, an eNodeB, and an EPC.

In recent years, the Cloud Radio Access Network (CRAN) has become a promising solution for increasing network capacity in terms of high data rates and low latencies for fifth-generation (5G) cellular networks. In CRAN, the traditional base stations (BSs) are decoupled into remote radio heads (RRHs) and base band units (BBUs) that are respectively responsible for radio and baseband functionalities. The RRHs are geographically proximated whereas the the BBUs are pooled in a centralized cloud named BBU pool. This virtualized architecture facilitates the system to offer high computation and communication loads from the impetuous rise of mobile devices and applications. Heterogeneous service requests from the devices to different RRHs are now sent to the BBUs to process centrally. Meeting the baseband processing of heterogeneous requests while keeping their Quality-of-Service (QoS) requirements with the limited computational resources as well as enhancing service provider profit is a challenging multi-constraint problem. In this work, a multi-objective non-linear programming solution to the Quality-of-Experience (QoE) and Profit-aware Resource Allocation problem is developed which makes a trade-off in between the two. Two computationally viable scheduling algorithms, named First Fit Satisfaction and First Fit Profit algorithms, are developed to focus on maximization of user QoE and profit, respectively, while keeping the minimum requirement level for the other one. The simulation environment is built on a relevant simulation toolkit. The experimental results demonstrate that the proposed system outperforms state-of-the-art works well across the requests QoS, average waiting time, user QoE, and service provider profit.