Centralized/cloud Radio Access Network Research Articles

A deep learning based dynamic bandwidth allocation method for XG-PON based mobile fronthaul for CRAN

In the context of 5G and beyond era, the use of Time Division Multiplexed Passive Optical Network (TDM-PON) for mobile fronthaul (MFH) in centralized/cloud radio access network (CRAN) has proven to be an optimal solution for addressing low-latency requirements. While existing literature has predominantly focused on optimizing mobile fronthaul latency rather than end-to-end latency, this paper introduces an end-to-end latency model for TDM-PON, specifically within a 10-gigabit Passive Optical Network (XG-PON) based MFH for CRAN. The paper then proposes an Intelligent Dynamic Bandwidth Allocation (DBA) scheme to minimize end-to-end latency of the network. The proposed scheme predicts the buffer occupancy reports using deep learning techniques at optical network units (ONUs). Thereafter, optical line terminal (OLT) schedules the available bandwidth using conventional DBA schemes (Group-GIANT i.e., Ggiant, Optimized Round Robin, Dynamic Service Interval). Primarily, the proposed DBA scheme transforms conventional DBA schemes (Ggiant, Optimized Round Robin, Dynamic Service Interval) into Intelligent versions (Intelligent Ggiant, Intelligent ORR, Intelligent DSI), showcasing a reduction of 28.63 %, 45.86 %, and 48.60 % in end-to-end latency in the XG-PON-based MFH for CRAN. Further, the analysis of obtained results has confirmed the supremacy of the Intelligent DSI DBA scheme over the Intelligent Ggiant and the Intelligent ORR DBA scheme.

Traffic-Aware Coordinated Beamforming for mmWave Backhauling of 5G Dense Networks

We study the problem of downlink Coordinated Beamforming for dense fixed-wireless millimeter wave (mmWave) networks under a Centralized/Cloud Radio Access Network (C-RAN) setting. To compensate for the increased mmWave path loss, we consider directional transmissions via antenna array analog beamforming, which leads to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">discrete</i> set of available beams. We apply the Lyapunov optimization framework to propose a throughput-optimal policy which explicitly accounts for stochastic traffic and channel fluctuations and dynamically performs joint Base Station-to-user association and analog beam selection. Our model makes minimal assumptions and considers realistic mmWave antenna radiation patterns, while it can be easily extended to include additional MAC and PHY layer controls. Since this flexibility comes at the cost of high computational complexity, we also propose two heuristic policies offering reduced complexity. Their performance is compared against a baseline Round-Robin (RR)-based policy, while a theoretical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worst-case</i> performance bound is derived. Extensive simulations show the optimal Lyapunov policy achieving a stable throughput increase of more than 2X <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">per user</i> compared to the RR policy, while the proposed heuristics incur only a 20-30% performance penalty with respect to the optimal Lyapunov policy with 60X-900X computational time savings.

GROVE: A Cost-Efficient Green Radio Over Ethernet Architecture for Next Generation Radio Access Networks

Centralized/Cloud Radio Access Network (C-RAN) comes into prominence to reduce the rising energy consumptions and maintenance difficulties of the next-generation networks. However, C-RAN has strict delay requirements, and it needs large fronthaul bandwidth. Function splitting and Radio over Ethernet are two promising approaches to reduce these drawbacks of the C-RAN architecture. Meanwhile, the usage of renewable energy sources (RESs) in a C-RAN boosts the energy-efficiency potential of this network. In this paper, we propose a novel model, which is called Green Radio OVer Ethernet (GROVE), that merges these three approaches to maximize the benefits of C-RAN while maintaining the economic feasibility of this architecture. We briefly explain this model and formulate an operational expenditure minimization problem by considering the several restrictions due to the network design and the service provisioning. Then we linearize the quadratic routing decision constraints in the problem to solve it with a mixed-integer linear programming (MILP) solver. Results show that it is cost-effective to choose routing, function splitting, and RES decisions together. Our solution surpasses classical disjoint approaches for all studied cases. Besides, we provide a network scalability analysis to determine the MILP solver's limits for larger network topologies.

Ultra-Low-Latency, High-Fidelity Analog-to-Digital-Compression Radio-Over-Fiber (ADX-RoF) for MIMO Fronthaul in 5G and Beyond

Wired-wireless convergence technology such as radio-over-fiber (RoF) is regarded as a promising candidate to deliver broadband wireless data from/to edge cloud to/from antenna sites, a notable example being the fronthaul (FH) in centralized/cloud radio access network (C-RAN). In 5G and beyond, the latency, bandwidth and fidelity requirements on FH pose great challenges to the RoF schemes. In this article, we focus on analog-to-digital-compression RoF (ADX-RoF) scheme based on low-latency MIMO data compression, which has the potential to reduce FH data rate by ~90% compared with traditional digital RoF (CPRI) while still maintaining signal fidelity for high-order radio modulation. To verify the practicality of ADX-RoF in the latency-sensitive FH scenario, real-time hardware demonstration with latency evaluation is indispensable. We propose and design a real-time ADX, which achieves low-latency and high-throughput together with high-fidelity. Enabled by the ADX prototyped on a single-chip field-programmable radio platform (i.e., Xilinx RFSoC), we experimentally demonstrate 16-channel MIMO radio reception, real-time ADX processing and RoF transport. With <; 500 ns one-way ADX latency, less than 1.5% average EVM is achieved for 1024QAM, 61.44 MHz NR-class signals at a compression ratio of 13.3%. Negligible fiber-induced performance penalty is observed after 40 km transport. The results highlight the attractiveness of ADX-RoF for future FH.

An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios

The next evolution in cellular communications will not only improve upon the performance of previous generations, but also represent an unparalleled expansion in the number of services and use cases. One of the foundations for this evolution is the design of highly flexible, versatile, and resource-/power-efficient hardware components. This paper proposes and evaluates an FPGA-oriented baseband processing architecture suitable for communication scenarios such as non-contiguous carrier aggregation, centralized Cloud Radio Access Network (C-RAN) processing, and 4G/5G waveform coexistence. Our system is upgradeable, resource-efficient, cost-effective, and provides support for three 5G waveform candidates. Exploring Dynamic Partial Reconfiguration (DPR), the proposed architecture expands the design space exploration beyond the available hardware resources on the Zynq xc7z020 through hardware virtualization. Additionally, Dynamic Frequency Scaling (DFS) allows for run-time adjustment of processing throughput and reduces power consumption up to 88%. The resource overhead for DPR and DFS is residual, and the reconfiguration latency is two orders of magnitude below the control plane latency requirements proposed for 5G communications.

Wireless performance and mobile fronthaul bandwidth of uplink joint reception with LLR combining in split-PHY processing

In this paper, we evaluate the wireless uplink performance and the mobile fronthaul (MFH) bandwidth of split-physical layer (PHY) processing (SPP), which is a base station (BS) functional split scheme used to link baseband units (BBUs) and remote radio heads (RRHs) in a centralized/cloud — radio access network (C-RAN). SPP enables MFH bandwidth reduction by splitting the BS functions between channel coding/decoding and modulation/demodulation within the PHY layer functions. SPP also enables high coordinated multipoint (CoMP) transmission and reception performance by employing uplink joint reception (JR) with log likelihood ratio (LLR) combining. Link level simulation results show that SPP with LLR combining achieves a block error rate (BLER) performance of 10% with a signal to noise ratio (SNR) penalty of less than 1 dB compared with that of conventional CRAN, which centralizes PHY layer functions in BBU. In addition, SPP has an SNR gain of more than 3 dB over medium access control (MAC)-PHY split, which splits the BS functions between the MAC and PHY layer functions. System level simulation results show that SPP with LLR combining improves the cell-edge user throughput by more than 33% over MAC-PHY split with selection combining and reduces the MFH bandwidth by 91% when compared with conventional C-RAN. Moreover, link and system level simulation results above are also observed when the channel estimation is non-ideal.

Adaptive space-time compression for efficient massive MIMO fronthauling.

One of the key features of the forthcoming fifth-generation (5G) communications is the deployment of massive multiple-input-multiple-output (MIMO) antennas to support ultra-high mobile traffic density. This scenario will pose a serious challenge on the capacity of mobile fronthaul in the centralized/cloud radio access network (C-RAN) since the required fronthaul bandwidth would linearly increase with number of antennas if conventional fronthaul interfaces (e.g., CPRI) are used. In this paper, we propose an adaptive space-time compression technique to significantly improve bandwidth efficiency of fronthaul. The technique incorporates an adaptive spatial filter to track the signal subspace and reduce the number of spatial channels, followed by adaptive quantizers to compress bandwidth of each channel in time domain. Enabled by the technique, the required fronthaul bandwidth becomes only dependent on the number of users, which is no longer proportional to the number of antennas. Moreover, compared with traditional fronthaul compression schemes in only the time domain, the flexibility of the compressor increases, and joint space-time optimization becomes feasible. On the other hand, optical fronthaul bandwidth is usually limited by cost-effective optical and electronic components. Moreover, increased reach would limit the bandwidth of IM-DD-based fronthaul (due to chromatic dispersion) as well as the received optical power. We experimentally investigate the combined optimization of a proposed space-time compressor with an optical fronthaul link. Experimental results of uplink 256-antenna fronthaul (259.5-Gb/s CPRI-equivalent rate) show that 32 users with 20MHz (30.72MSa/s) OFDM signal with lower-than-1% EVM are supported by 10GBd PAM4 optical interface.

Performance Evaluation of Ethernet-Based Mobile Fronthaul and Wireless CoMP in Split-PHY Processing

We experimentally evaluate the mobile fronthaul (MFH) optical bandwidth and wireless coordinated multipoint (CoMP) transmission and reception performance in Ethernet-based MFH with our proposed split-physical (PHY) processing (SPP). To reduce the MFH optical bandwidth, SPP redefines the base station (BS) functional split between the baseband unit (BBU) and the remote radio head (RRH) in a centralized/cloud radio access network (C-RAN) with a common public radio interface. The functional split point of SPP is between wireless channel coding and wireless modulation within the PHY layer functions. SPP employs its own CoMP joint transmission and reception schemes to achieve high wireless CoMP performance comparable to that in a conventional C-RAN while reducing the MFH optical bandwidth. Experimental results in the uplink and downlink show that SPP reduces the MFH optical bandwidth by over 90% compared to the conventional C-RAN. At the same time, SPP achieves wireless performance with a signal-to-noise ratio penalty of less than 2 dB compared to the conventional C-RAN in the uplink and outputs wireless signals identical to the wireless signal output in the conventional C-RAN in the downlink.

Invited paper: The audacity of fiber-wireless (FiWi) networks: revisited for clouds and cloudlets

There is a growing awareness among industry players of reaping the benefits of mobile-cloud convergence by extending today's unmodified cloud to a decentralized two-level cloud-cloudlet architecture based on emerging mobile-edge computing (MEC) capabilities. In light of future 5G mobile networks moving toward decentralization based on cloudlets, intelligent base stations, and MEC, the inherent distributed processing and storage capabilities of radio-and-fiber (R&F) networks may be exploited for new applications, e.g., cognitive assistance, augmented reality, or cloud robotics. In this paper, we first revisit fiber-wireless (FiWi) networks in the context of conventional clouds and emerging cloudlets, thereby highlighting the limitations of conventional radio-overfiber (RoF) networks such as China Mobile's centralized cloud radio access network (C-RAN) to meet the aforementioned trends. Furthermore, we pay close attention to the specific design challenges of data center networks and revisit our switchless arrayed waveguide grating (AWG) based network with efficient support of east-west flows and enhanced scalability.