The Experts below are selected from a list of 8586 Experts worldwide ranked by ideXlab platform

Abdellatif Zaidi - One of the best experts on this subject based on the ideXlab platform.

  • on achievability for downlink cloud radio access networks with base station cooperation
    Wireless Communications and Networking Conference, 2017
    Co-Authors: Chienyi Wang, Michele Wigger, Abdellatif Zaidi
    Abstract:

    This work investigates the downlink of cloud radio access networks (C-RANs), assuming digital cooperation links among the base stations (BSs). A generalization of the data-sharing scheme is proposed for the case of two BSs and two mobile users. The generalized data-sharing scheme includes a common part and allows full exploitation of correlation among auxiliary codewords. The cooperation links between the BSs are used to exchange and to redirect indices precomputed at the Central Processor. On the other hand, by simplifying the achievable rate region of the distributed decode-forward (DDF) scheme, it is shown that the DDF scheme for broadcast achieves the capacity region of a downlink $N$-BS $L$-user C- RAN with BS cooperation under the memoryless Gaussian model to within a gap of $\frac{L}{2}#x002B;\frac{\min\ {N,L\log_2 N\}}{2}$ bits per dimension. Numerical evaluations for the memoryless Gaussian model indicate that the generalized data-sharing scheme 1) outperforms the DDF scheme in the low-power regime and when the channel gain matrix is ill-conditioned and 2) benefits more from BS cooperation.

Ayfer Ozgur - One of the best experts on this subject based on the ideXlab platform.

  • lower bounds for learning distributions under communication constraints via fisher information
    Journal of Machine Learning Research, 2020
    Co-Authors: Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur
    Abstract:

    We consider the problem of learning high-dimensional, nonparametric and structured (e.g. Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use $k$ bits to communicate its sample to a Central Processor. We consider three different models for communication. Under the independent model, each node communicates its sample to a Central Processor by independently encoding it into $k$ bits. Under the more general sequential or blackboard communication models, nodes can share information interactively but each node is restricted to write at most $k$ bits on the final transcript. We characterize the impact of the communication constraint $k$ on the minimax risk of estimating the underlying distribution under $\ell^2$ loss. We develop minimax lower bounds that apply in a unified way to many common statistical models and reveal that the impact of the communication constraint can be qualitatively different depending on the tail behavior of the score function associated with each model. A key ingredient in our proofs is a geometric characterization of Fisher information from quantized samples.

  • fisher information for distributed estimation under a blackboard communication protocol
    International Symposium on Information Theory, 2019
    Co-Authors: Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur
    Abstract:

    We consider the problem of learning high-dimensional discrete distributions and structured (e.g. Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use k bits to communicate its sample to a Central Processor. We consider a blackboard communication model, where nodes can share information interactively through a public blackboard but each node is restricted to write at most k bits on the final transcript. We characterize the impact of the communication constraint k on the minimax risk of estimating the underlying distribution under l2 loss, and develop minimax lower bounds that apply in a unified way to many common statistical models. This is achieved by explicitly characterizing the Fisher information from the blackboard transcript.

  • distributed statistical estimation of high dimensional and nonparametric distributions
    International Symposium on Information Theory, 2018
    Co-Authors: Yanjun Han, Ayfer Ozgur, Pritam Mukherjee, Tsachy Weissman
    Abstract:

    We consider the problem of estimating high-dimensional and nonparametric distributions in distributed networks, where each sensor in the network observes an independent sample from the underlying distribution and can communicate it to a Central Processor by writing at most $k$ bits on a public blackboard. We obtain matching upper and lower bounds for the minimax risk of estimating the underlying distribution under $L$ 1 loss. Our results reveal that the minimax risk reduces exponentially in k. Instead of relying on strong data processing inequalities for the converse as commonly done in the literature, we build on a new representation of the communication constraint, which leads to a tight characterization of the problem.

Chienyi Wang - One of the best experts on this subject based on the ideXlab platform.

  • on achievability for downlink cloud radio access networks with base station cooperation
    Wireless Communications and Networking Conference, 2017
    Co-Authors: Chienyi Wang, Michele Wigger, Abdellatif Zaidi
    Abstract:

    This work investigates the downlink of cloud radio access networks (C-RANs), assuming digital cooperation links among the base stations (BSs). A generalization of the data-sharing scheme is proposed for the case of two BSs and two mobile users. The generalized data-sharing scheme includes a common part and allows full exploitation of correlation among auxiliary codewords. The cooperation links between the BSs are used to exchange and to redirect indices precomputed at the Central Processor. On the other hand, by simplifying the achievable rate region of the distributed decode-forward (DDF) scheme, it is shown that the DDF scheme for broadcast achieves the capacity region of a downlink $N$-BS $L$-user C- RAN with BS cooperation under the memoryless Gaussian model to within a gap of $\frac{L}{2}#x002B;\frac{\min\ {N,L\log_2 N\}}{2}$ bits per dimension. Numerical evaluations for the memoryless Gaussian model indicate that the generalized data-sharing scheme 1) outperforms the DDF scheme in the low-power regime and when the channel gain matrix is ill-conditioned and 2) benefits more from BS cooperation.

Volker Kuehn - One of the best experts on this subject based on the ideXlab platform.

  • backhaul traffic balancing and dynamic content centric clustering based on beamforming in the downlink of fog radio access network
    2016
    Co-Authors: Di Chen, Stephan Schedler, Volker Kuehn
    Abstract:

    Recently, an evolution of the Cloud Radio Access Network (C-RAN) has been proposed, named as Fog Radio Access Network (F-RAN). Compared to C-RAN, the Radio Units (RUs) in F-CAN are equipped with local caches, which can store some frequently requested files. In the downlink, users requesting the same file form a multicast group, and are cooperatively served by a cluster of RUs. The requested file is either available locally in the cache of this cluster or fetched from the Central Processor (CP) via backhauls. Thus caching some frequently requested files can greatly reduce the burden on backhaul links. Whether a specific RU should be involved in a cluster to serve a multicast group depends on its backhaul capacity, requested files, cached files and the channel. Therefore it is subject to optimization. In this paper we investigate the joint design of multicast beamforming, dynamic clustering and backhaul traffic balancing. Beamforming and clustering are jointly optimized in order to minimize the power consumed, while QoS of each user is to be met and the traffic on each backhaul link is balanced according to its capacity.

  • Backhaul traffic balancing and dynamic content-centric clustering for the downlink of Fog Radio Access Network
    IEEE Workshop on Signal Processing Advances in Wireless Communications SPAWC, 2016
    Co-Authors: Di Chen, Stephan Schedler, Volker Kuehn
    Abstract:

    Recently, an evolution of the Cloud Radio Access Network (C-RAN) has been proposed, named as Fog Radio Access Network (F-RAN). Compared to C-RAN, the Radio Units (RUs) in F-CAN are equipped with local caches, which can store some frequently requested files. In the downlink, users requesting the same file form a multicast group, and are cooperatively served by a cluster of RUs. The requested file is either available locally in the cache of this cluster or fetched from the Central Processor (CP) via backhauls. Thus caching some frequently requested files can greatly reduce the burden on backhaul links. Whether a specific RU should be involved in a cluster to serve a multicast group depends on its backhaul capacity, requested files, cached files and the channel. Therefore it is subject to optimization. In this paper we investigate the joint design of multicast beamforming, dynamic clustering and backhaul traffic balancing. Beamforming and clustering are jointly optimized in order to minimize the power consumed, while QoS of each user is to be met and the traffic on each backhaul link is balanced according to its capacity.

Yanjun Han - One of the best experts on this subject based on the ideXlab platform.

  • lower bounds for learning distributions under communication constraints via fisher information
    Journal of Machine Learning Research, 2020
    Co-Authors: Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur
    Abstract:

    We consider the problem of learning high-dimensional, nonparametric and structured (e.g. Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use $k$ bits to communicate its sample to a Central Processor. We consider three different models for communication. Under the independent model, each node communicates its sample to a Central Processor by independently encoding it into $k$ bits. Under the more general sequential or blackboard communication models, nodes can share information interactively but each node is restricted to write at most $k$ bits on the final transcript. We characterize the impact of the communication constraint $k$ on the minimax risk of estimating the underlying distribution under $\ell^2$ loss. We develop minimax lower bounds that apply in a unified way to many common statistical models and reveal that the impact of the communication constraint can be qualitatively different depending on the tail behavior of the score function associated with each model. A key ingredient in our proofs is a geometric characterization of Fisher information from quantized samples.

  • fisher information for distributed estimation under a blackboard communication protocol
    International Symposium on Information Theory, 2019
    Co-Authors: Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur
    Abstract:

    We consider the problem of learning high-dimensional discrete distributions and structured (e.g. Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use k bits to communicate its sample to a Central Processor. We consider a blackboard communication model, where nodes can share information interactively through a public blackboard but each node is restricted to write at most k bits on the final transcript. We characterize the impact of the communication constraint k on the minimax risk of estimating the underlying distribution under l2 loss, and develop minimax lower bounds that apply in a unified way to many common statistical models. This is achieved by explicitly characterizing the Fisher information from the blackboard transcript.

  • distributed statistical estimation of high dimensional and nonparametric distributions
    International Symposium on Information Theory, 2018
    Co-Authors: Yanjun Han, Ayfer Ozgur, Pritam Mukherjee, Tsachy Weissman
    Abstract:

    We consider the problem of estimating high-dimensional and nonparametric distributions in distributed networks, where each sensor in the network observes an independent sample from the underlying distribution and can communicate it to a Central Processor by writing at most $k$ bits on a public blackboard. We obtain matching upper and lower bounds for the minimax risk of estimating the underlying distribution under $L$ 1 loss. Our results reveal that the minimax risk reduces exponentially in k. Instead of relying on strong data processing inequalities for the converse as commonly done in the literature, we build on a new representation of the communication constraint, which leads to a tight characterization of the problem.