1,721,079 research outputs found
Performance of mMIMO FD Relay Networks with Limited Relay State Knowledge
Full text linkMassive MIMO (mMIMO) is a key technology for improving propagation conditions and extending geographical coverage of wireless communications. We here address a mMIMO full-duplex relay network for machine-type-communications where channel state information availability at the transmitter is impractical. In this scenario, we argue that high end-to-end data rates can be achieved even if no precoding is performed at the transmitting nodes. We first formulate an optimization problem aiming at maximizing the achievable rate, considering the source transmit power to depend on the transmit power distribution at the relay node. We then solve this problem by letting the number of antennas grow large, and derive closed-form expressions for the transmit power at the source and relay, as well as for the system data rate. Our results, show that the rate obtained when no precoding is implemented at the relay, or at any of the transmitters, closely matches that of SVD precoding under the optimum receiver, and still achieves very high values in the case of the ZF and the MMSE receiver
Quality of service in ad hoc and sensor networks
No full text10.1016/j.peva.2006.08.003Performance Evaluation645377-378PEEV
Asymptotics of multi-fold vandermonde matrices with applications to communications and radar problems
No full textWe study the performance of signal estimation and reconstruction systems, that exploit the linear minimum mean square error (LMMSE) technique. This model often occurs in signal processing and wireless communications; some examples are radar applications, MIMO communications, or sensor networks sampling a physical field. Our performance analysis implies the characterization of a random matrix product, involving a multifold Vandermonde matrix with complex exponential entries. We therefore derive the LMMSE by computing the η-transform of this matrix product, which can be evaluated either by implicit as well as by explicit expression, using the matrix asymptotic moments. Finally, we show how our results can be applied in some cases of practical interest. ©2009 IEEE
Towards D2D-Enhanced Heterogeneous Networks
Full text linkIn this paper, we examine upcoming 5G networks where the support of device-to-device (D2D) communication is expected to be a key asset for operators and users alike. Firstly, we argue the need to functionally integrate D2D and infrastructure-to-device (I2D) modes. Next, we address practical issues such as integrated resource scheduling of D2D communication within heterogeneous networks, proposing an extension of the proportional fairness algorithm, which we call multi-modal proportional fairness (MMPF). We evaluate the impact of D2D in a two-tier scenario combining macro- and micro- coverage, finding that, although I2D retains a clear edge for general-purpose downloading, D2D is an appealing solution for localized transfers as well as for viral conten
Efficient Distributed DNNs in the Mobile-edge-cloud Continuum
Full text linkIn the mobile-edge-cloud continuum, a plethora of heterogeneous data sources
and computation-capable nodes are available. Such nodes can cooperate to
perform a distributed learning task, aided by a learning controller (often
located at the network edge). The controller is required to make decisions
concerning (i) data selection, i.e., which data sources to use; (ii) model
selection, i.e., which machine learning model to adopt, and (iii) matching
between the layers of the model and the available physical nodes. All these
decisions influence each other, to a significant extent and often in
counter-intuitive ways. In this paper, we formulate a problem addressing all of
the above aspects and present a solution concept called RightTrain, aiming at
making the aforementioned decisions in a joint manner, minimizing energy
consumption subject to learning quality and latency constraints. RightTrain
leverages an expanded-graph representation of the system and a delay-aware
Steiner tree to obtain a provably near-optimal solution while keeping the time
complexity low. Specifically, it runs in polynomial time and its decisions
exhibit a competitive ratio of , outperforming state-of-the-art
solutions by over 50%. Our approach is also validated through a real-world
implementation
Active Learning-based Classification in Automated Connected Vehicles
Full text linkMachine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. A key challenge, however, is to collect and select real-time and reliable information for the correct classification of unexpected, and often uncommon, events that may happen on the road. Indeed, the data generated by vehicles, or received from neighboring vehicles, may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. In particular, given the available information, our solution selects the data to add to the training set by trading off between two essential features: quality and diversity. The results, obtained using realworld data sets, show that our method significantly outperforms state-of-the-art solutions, providing high classification accuracy at the cost of a limited bandwidth requirement for the data exchange between vehicles
Virtualization-based Evaluation of Backhaul Performance in Vehicular Applications
Full text linkNext-generation networks, based on SDN and NFV, are expected to support a wide array of services, including vehicular safety applications. These services come with strict delay constraints, and our goal in this paper is to ascertain to which extent SDN/NFV-based networks are able to meet them. To this end, we build and emulate a vehicular collision detection system, using the popular Mininet and Docker tools, on a real-world topology with mobility information. Using different core network topologies and open-source SDN controllers, we measure (i) the delay with which vehicle beacons are processed and (ii) the as- sociated overhead and energy consumption. We find that we can indeed meet the latency constraints associated with vehicular safety applications, and that SDN controllers represent a moderate contribution to the overall energy con- sumption but a significant source of additional delay
Modelling User Radio Access in Dense Heterogeneous Networks
Full text linkOne of the distinctive features of today’s mobile networks is the densification of the access nodes and their heterogeneity, which lead to complex, multi-tier, multi-radio access systems. Unlike previous work, which has focussed on optimal techniques for user assignment and technology selection schemes, in this paper we present a flexible analytical model for the performance evaluation and the efficient design of the above complex systems. Leveraging a Markovian agent formalism, the model captures sev- eral essential elements, including the spatial and temporal dynamics of the user traffic demand and the availability of radio resources. Importantly, the model exhibits low complexity and an excellent match with simulation results; furthermore, it is general enough to accommodate various network architecture and radio technologies. Through an innovative mean-field solution, we derive a number of relevant performance metrics and show the ability of our framework to represent the system behavior in large-scale, real-world scenarios, with time-varying user traffic
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