Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this article we show that mixing scheduled and random access incurs and inherent throughput/delay cost, the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.Comment: 14 pages, 8 figures, journa

Thwarting Selfish Behavior in 802.11 WLANs

The 802.11e standard enables user configuration of several MAC parameters, making WLANs vulnerable to users that selfishly configure these parameters to gain throughput. In this paper we propose a novel distributed algorithm to thwart such selfish behavior. The key idea of the algorithm is for honest stations to react, upon detecting a selfish station, by using a more aggressive configuration that penalizes this station. We show that the proposed algorithm guarantees global stability while providing good response times. By conducting a game theoretic analysis of the algorithm based on repeated games, we also show its effectiveness against selfish stations. Simulation results confirm that the proposed algorithm optimizes throughput performance while discouraging selfish behavior. We also present an experimental prototype of the proposed algorithm demonstrating that it can be implemented on commodity hardware.Comment: 14 pages, 7 figures, journa

Rigorous and Practical Proportional-fair Allocation for Multi-rate Wi-Fi

Recent experimental studies confirm the prevalence of the widely known performance anomaly problem in current Wi-Fi networks, and report on the severe network utility degradation caused by this phenomenon. Although a large body of work addressed this issue, we attribute the refusal of prior solutions to their poor implementation feasibility with off-the-shelf hardware and their impre- cise modelling of the 802.11 protocol. Their applicability is further challenged today by very high throughput enhancements (802.11n/ac) whereby link speeds can vary by two orders of magnitude. Unlike earlier approaches, in this paper we introduce the first rigorous analytical model of 802.11 stations’ throughput and airtime in multi-rate settings, without sacrificing accuracy for tractability. We use the proportional-fair allocation criterion to formulate network utility maximisation as a con- vex optimisation problem for which we give a closed-form solution. We present a fully functional light-weight implementation of our scheme on commodity access points and evaluate this extensively via experiments in a real deployment, over a broad range of network conditions. Results demonstrate that our proposal achieves up to 100% utility gains, can double video streaming goodput and reduces TCP download times by 8x

On the Optimization of Multi-Cloud Virtualized Radio Access Networks

We study the important and challenging problem of virtualized radio access network (vRAN) design in its most general form. We develop an optimization framework that decides the number and deployment locations of central/cloud units (CUs); which distributed units (DUs) each of them will serve; the functional split that each BS will implement; and the network paths for routing the traffic to CUs and the network core. Our design criterion is to minimize the operator's expenditures while serving the expected traffic. To this end, we combine a linearization technique with a cutting-planes method in order to expedite the exact solution of the formulated problem. We evaluate our framework using real operational networks and system measurements, and follow an exhaustive parameter-sensitivity analysis. We find that the benefits when departing from single-CU deployments can be as high as 30% for our networks, but these gains diminish with the further addition of CUs. Our work sheds light on the vRAN design from a new angle, highlights the importance of deploying multiple CUs, and offers a rigorous framework for optimizing the costs of Multi-CUs vRAN.Comment: This preprint is to be published in Proc. of IEEE International Conference on Communications (ICC) 202

WizHaul: An Automated Solution for vRAN Deployments Optimization

Future 5G deployments will support a flexible split of Base Station (BS) functions, i.e., it will be possible to decide which atomic operations will be co-located on the edge and which ones will be processed on a Central Unit (CU). Thus, network owners will be able to decide how much centralization they would like to retain in different deployments. However, deciding which BS components should be offloaded to a CU becomes a challenge because routing and BS function placement choices are coupled. We present WizHaul, a software framework enabling the implementation of a centralized functional split decision- making engine for future 5G networks. The purpose of WizHaul is twofold. First, it may be used in a network planning phase to settle the optimal amount of centralization. Second, it may also be used to support network automation/adaptation scenarios where network failures or congestion in the cloud may draw the current configuration infeasible.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 761536 (5G-Transformer project)

WizHaul: On the Centralization Degree of Cloud RAN Next Generation Fronthaul

Cloud Radio Access Network (C-RAN) will become a main building block for 5G. However, the stringent requirements of current fronthaul solutions hinder its large-scale deployment. In order to introduce C-RAN widely in 5G, the next generation fronthaul \agsrev{interface} (NGFI) will be based on a cost-efficient packet-based network with higher path diversity. In addition, NGFI shall support a flexible functional split of the RAN to adapt the amount of centralization to the capabilities of the transport network. In this paper we question the ability of standard techniques to route NGFI traffic while maximizing the centralization degree---the goal of C-RAN. We propose two solutions jointly addressing both challenges: (i) a nearly-optimal backtracking scheme, and (ii) a low-complex greedy approach. We first validate the feasibility of our approach in an experimental proof-of-concept, and then evaluate both algorithms via simulations in large-scale (real and synthetic) topologies. Our results show that state-of-the-art techniques fail at maximizing the centralization degree and that the achievable C-RAN centralization highly depends on the underlying topology structure.This work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 671598 (5G-Crosshaul project) and 761536 (5G-Transformer project)

Bayesian online learning for energy-aware resource orchestration in virtualized RANs

Proceedings of: IEEE International Conference on Computer Communications, 10-13 May 2021, Vancouver, BC, Canada.Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We perform an in-depth experimental analysis of the energy consumption of virtualized Base Stations (vBSs) and render two conclusions: (i) characterizing performance and power consumption is intricate as it depends on human behavior such as network load or user mobility; and (ii) there are many control policies and some of them have non-linear and monotonic relations with power and throughput. Driven by our experimental insights, we argue that machine learning holds the key for vBS control. We formulate two problems and two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the convergence and flexibility of our approach and assess its performance using an experimental prototype.This work was supported by the European Commission through Grant No. 856709 (5Growth) and Grant No. 101017109 (DAEMON); and by SFI through Grant No. SFI 17/CDA/4760

Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks