297 research outputs found
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
Indoor Millimeter Wave Localization using Multiple Self-Supervised Tiny Neural Networks
We consider the localization of a mobile millimeter-wave client in a large
indoor environment using multilayer perceptron neural networks (NNs). Instead
of training and deploying a single deep model, we proceed by choosing among
multiple tiny NNs trained in a self-supervised manner. The main challenge then
becomes to determine and switch to the best NN among the available ones, as an
incorrect NN will fail to localize the client. In order to upkeep the
localization accuracy, we propose two switching schemes: one based on a Kalman
filter, and one based on the statistical distribution of the training data. We
analyze the proposed schemes via simulations, showing that our approach
outperforms both geometric localization schemes and the use of a single NN.Comment: 5 pages, 7 figures. Under Revie
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
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)
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: 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
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