Nuberu: Reliable RAN Virtualization in Shared Platforms

Vol.17 No.1 September 2023 Special Issue on Open Network Technologies — Network Technologies and Advanced Solutions at the Heart of an Open and Green Society

RAN virtualization will become a key technology for next-generation mobile networks. However, due to the computing fluctuations inherent to wireless dynamics and resource contention in shared computing infrastructure, the price to migrate from dedicated to shared platforms may be too high. We present Nuberu, a novel pipeline architecture for 4G/5G DUs specifically engineered for shared platforms. Nuberu has one objective to attain reliability: to guarantee a minimum set of signals that preserve synchronization between the DU and its users during computing capacity shortages and, provided this, maximize network throughput. To this end, we use techniques such as tight deadline control, jitter-absorbing buffers, predictive HARQ, and congestion control.

1. Introduction

The virtualization of radio access networks (RANs), based hitherto on hardwired ASICs, will become the spearhead of next-generation mobile systems beyond 5G. Initiatives such as the carrier-led O-RAN ALLIANCE have spurred the market and the research community to find novel solutions that import the flexibility and cost-efficiency of network function virtualization (NFV) into the very far edge of mobile networks.

Fig. 1 shows the architecture of a vRAN, with base stations (BSs) split into a central unit (CU), which hosts the highest layers of the stack; a distributed unit (DU), which hosts the physical layer (PHY); and a radio unit (RU), which hosts basic radio functions such as amplification or sampling. To reduce costs, vRANs may rely on cloud platforms comprised of pools of shared computing resources (mostly CPUs, but also hardware accelerators brokered by an abstraction layer), to host virtualized functions such as the PHY.

Fig. 1 vRAN architecture.

However, shared computing platforms provide a harsh environment for DUs because they trade off the predictability supplied by dedicated platforms for higher flexibility and cost-efficiency. While CUs are amenable to virtualization in regional clouds, virtualized DUs (vDUs) — namely, the vPHY therein — require fast and predictable computation in the edge.

To ease the explanation, we focus on frequency division duplex where uplink (UL) and downlink (DL) transmissions occur concurrently in different frequency bands, and on 5G’s baseline numerology (μ = 0 in 3GPP TS 38.211), which yields one transmission time interval (TTI) per subframe (SF), and a SF has a duration of 1 ms. Fig. 2 illustrates the basic operation of a typical 4G/5G DU processor. Every TTI n, a worker initiates a DU job comprised of a pipeline of tasks (hereafter referred to as DU tasks).

  • (1)
    Process data
  • (2)
    Control channels carried by UL SF n
  • (3)
    Schedule UL/DL radio grants to be transported by DL SF n + M
  • (4)
    Process data
  • (5)
    Control channels for DL SF n + M
Fig. 2 Every TTI (=1 ms), a worker must execute a DU job, comprised of a pipeline of interdependent DU tasks to process UL SF n and DL SF n + M, within M-1 ms.

A worker executes a DU job in a thread, using computing resources allocated by a task scheduler; and multiple workers perform DU jobs in parallel to handle one DL SF and one UL SF every TTI, as shown in Fig. 2. 3GPP establishes a 4-ms one-way latency budget between UEs and CUs for eMBB traffic1). Consequently, there is a hard constraint on M that imposes a computing time budget of roughly M-1 ms to process each DU job (usually, M = 4).

Indeed, completing a DU job every TTI is vital to preserve synchronization between the BS and its users and thus attain reliability. However, this is challenged by some compute-intensive operations within DU tasks such as forward error correction (FEC). These operations require substantial processing time and the solutions applied today on the market, namely, dedicated hardware acceleration, diminish the very reasons that make virtualization appealing for the RAN in the first place: flexibility and cost-efficiency.

2. Nuberu design

We propose Nuberu, a novel pipeline architecture for 4G/5G DUs that is suitable for shared computing platforms. Our design follows one objective: to build a minimum viable subframe (MVSF) with critical signals for synchronization and control every TTI first to provide reliability first during moments of computing capacity shortage and, provided this, maximize network throughput.

To this end, we set up a deadline within every DU job to begin building an MVSF even if data processing tasks are unfinished. This deadline, depicted in black in Fig. 3, is set such that there is enough time to process an MVSF before the final job completion deadline (in red in the figure). This is viable because, different from data processing tasks, the tasks involved in building an MVSF require little and roughly deterministic time. To do this efficiently, we need to decouple data processing tasks such that the information required to build an MVSF is ready on time and network throughput is maximized during computing capacity fluctuations. Consequently, we apply the following techniques.

Fig. 3 To provide reliability, Nuberu decouples UL/DL data processing tasks from the rest of the pipeline by integrating a 2-stage radio scheduler, a novel E-HARQ mechanism, and a congestion controller.
  • (1)
    To process DL data channel tasks
    • 1)
      We adopt a two-stage DL radio scheduling approach
      • We issue temporary DL grants as early as possible in the DU pipeline, as shown in Fig. 3. Dedicated workers process (encode, modulate, etc.) these grants in separated threads and store the resulting data in a buffer.
      • Upon the MVSF deadline, final DL data grants are computed based on those already processed successfully that are available in the buffer by that time. Grants generated in a job n that are not processed on time are hence delayed for a later job.
    • 2)
      To mitigate the number of delayed DL data grants, the amount of DL data granted by the temporary scheduler is regulated by a congestion controller that adapts the flow of DL data grants to the availability of computing resources. To this end, Nuberu’s radio schedulers use a DL congestion window (mathematical formula image) that regulate the flow of DL grants. We adopt an additive-increase / multiplicative-decrease (AIMD) algorithm where mathematical formula image increases by α PRBs every DU job mathematical formula image as long as congestion is not detected or the maximum PRB capacity is reached, and multiplicatively decreases by β≤1 mathematical formula image if congestion is detected. Nuberu infers congestion if the buffer of encoded TBs contains λ > 0 times the vDU’s PRB capacity or more.
  • (2)
    To process UL data channel tasks
    • 1)
      Dedicated workers process (demodulate, decode, etc.) UL data carried by each UL SF in separated threads.
    • 2)
      Upon the MVSF deadline, an early HARQ (E-HARQ) mechanism infers the decodability of UL data based on feedback from the workers, as shown in Fig. 3. This enables us to estimate the radio information that is required to build an MVSF even if UL data processing tasks have not finished on time. The key idea behind our E-HARQ approach rests upon the concept of extrinsic information, which spawns organically by belief propagation algorithms used by both turbo and LDPC codes. We refer the reader to the literature2) for detailed information about these coding techniques. In a nutshell, belief information is encoded into log-likelihood ratios (LLRs), mathematical formula image, where “input” refers to all the inputs of each decoding node i in a decoder, and b represents the information symbol (bit). The key to iterative decoding is the sequence of a posteriori LLRs of the information symbols, which is exchanged every iteration k between the decoding nodes of the decoder so each node takes advantage of the information computed by the others. To improve the bit estimations every iteration, the different nodes need to exchange belief information that do not originate from themselves. The original concept of extrinsic information was in fact conceived to identify the information components that depend on redundant information introduced by the incumbent code. Such extrinsic LLRs are used to transform a posteriori LLRs into a priori LLRs used as an input in the next iteration. The evolution of the average magnitude of extrinsic information over decoding iterations is easily distinguishable for decodable data. Hence, by observing the pattern of extrinsic information we build a simple predictor that classifies the input data into three types: UNKNOWN, if the uncertainty is too high; DECODABLE, if the predictor is certain the data will be decoded successfully; and UNDECODABLE, if the predictor is certain the data will not be decoded successfully.
    • 3)
      To maximize the predictive performance of E-HARQ, which depends on the amount of work done (iterations) before the MVSF deadline, another congestion controller adapts the allocation of UL radio resources to the available computing capacity. Like its downlink counterpart, we define a congestion window mathematical formula image, which bounds the number of UL PRBs that can be allocated to the DU’s users. Again, we adopt a simple AIMD algorithm that increases mathematical formula image additively mathematical formula image when congestion is not inferred, and decreases multiplicatively , mathematical formula image with β<1, when congestion is inferred. The obvious approach to estimate congestion from UL workload is to signal so every time a UL-Data worker does not finish before the MVSF deadline. However, this method does not fully exploit the predictive capability of our E-HARQ mechanism, which can infer the decodability of a TB well before explicit CRC confirmation. Conversely, our approach infers congestion every time E-HARQ cannot provide a prediction with certainty, i.e., outputs UNKNOWN, which occurs every time a UL-Data worker is unable to run sufficient decoding iterations before its deadline, and hence the uncertainty over the prediction is too large.

3. Evaluation

We next evaluate an experimental prototype of Nuberu.

We first set up an experiment with two DUs implemented with vanilla srsRAN (M = 4), an open-source implementation of a fully-fledged 3GPP-compliant base station. We associate each DU with one user implemented with srsUE and virtualized over Linux containers sharing 5 Intel Xeon x86 cores @ 1.9GHz.

In this experiment, one DU (vDU 1) transmits and receives as much data as possible. Conversely, the second DU (vDU 2) transmits and receives traffic following a random process with different parameters, which generate normally-distributed computing workload with the mean (line) and variance (shaded area) shown at the bottom of Fig. 4: the higher the load variance of vDU 2, the larger the fluctuations of the computing capacity available for vDU 1.

Fig. 4 Two vDUs competing for computing resources.

Fig. 4 (top) depicts vDU 1’s relative network throughput in yellow (“Baseline”) as a function of the workload produced by vDU 2. The figure shows that the performance of vDU 1 quickly deteriorates. The reason is that, because both vDUs share the same CPU pool, vDU 1 occasionally suffers from CPU resource deficit when vDU 2 produces a peak in demand. As a result, vDU 1 workers executing DU jobs violate their deadline to send out the corresponding DL SF, as illustrated at the bottom of Fig. 2, which causes the user to lose synchronization and throughput to drop.

Conversely, as shown by the purple line in Fig. 4, Nuberu can sustain maximum throughput despite severe fluctuations in computing capacity.


  • 1)
    3rd Generation Partnership Project (3GPP): 3GPP TR 38.913; Technical Specification Group Radio Access Network; Study on Scenarios and Requirements for Next Generation Access Technologies; (Release 17). Technical Report, 2022
  • 2)
    Yang Sun and Joseph R Cavallaro: A flexible LDPC/turbo decoder architecture, Journal of Signal Processing Systems 64, 1–16, 2011

Authors’ Profiles

Principal Research Scientist
6G Networks
NEC Laboratories Europe
6G Networks
NEC Laboratories Europe