Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Wireless Communications

Alhussein Abouzeid Rensselaer Polytechnic Institute Raviraj Adve University of Toronto Dharma Agrawal University of Cincinnati Walid Ahmed Tyco M/A-COM Sonia Aissa University of Quebec, INRSEMT Huseyin Arslan University of South Florida Nallanathan Arumugam National University of Singapore Saewoong Bahk Seoul National University Claus Bauer Dolby Laboratories Brahim Bensaou Hong Kong University of Science and Technology Rick Blum Lehigh University Michael Buehrer Virginia Tech Antonio Capone Politecnico di Milano Javier Gómez Castellanos National University of Mexico Claude Castelluccia INRIA Henry Chan The Hong Kong Polytechnic University Ajit Chaturvedi Indian Institute of Technology Kanpur Jyh-Cheng Chen National Tsing Hua University Yong Huat Chew Institute for Infocomm Research Tricia Chigan Michigan Tech Dong-Ho Cho Korea Advanced Institute of Science and Tech. Jinho Choi University of New South Wales Carlos Cordeiro Philips Research USA Laurie Cuthbert Queen Mary University of London Arek Dadej University of South Australia Sajal Das University of Texas at Arlington Franco Davoli DIST University of Genoa Xiaodai Dong, University of Alberta Hassan El-sallabi Helsinki University of Technology Ozgur Ercetin Sabanci University Elza Erkip Polytechnic University Romano Fantacci University of Florence Frank Fitzek Aalborg University Mario Freire University of Beira Interior Vincent Gaudet University of Alberta Jairo Gutierrez University of Auckland Michael Hadjitheodosiou University of Maryland Zhu Han University of Maryland College Park Christian Hartmann Technische Universitat Munchen Hossam Hassanein Queen's University Soong Boon Hee Nanyang Technological University Paul Ho Simon Fraser University Antonio Iera University "Mediterranea" of Reggio Calabria Markku Juntti University of Oulu Stefan Kaiser DoCoMo Euro-Labs Nei Kato Tohoku University Dongkyun Kim Kyungpook National University Ryuji Kohno Yokohama National University Bhaskar Krishnamachari University of Southern California Giridhar Krishnamurthy Indian Institute of Technology Madras Lutz Lampe University of British Columbia Bjorn Landfeldt The University of Sydney Peter Langendoerfer IHP Microelectronics Technologies Eddie Law Ryerson University in Toronto

Wireless communications

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

What Will 5G Be

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

This paper formulates a distributed computation problem, where a master asks $N$ distributed workers to compute a linearly separable function. The task function can be expressed as $K_c$ linear combinations to $K$ messages, where each message is a function of one dataset. Our objective is to find the optimal tradeoff between the computation cost (number of datasets assigned to each worker) and the communication cost (number of symbols the master should download), such that from the answers of any $N_r$ out of $N$ workers the master can recover the task function. The formulated problem can be seen as the generalized version of some existing problems, such as distributed gradient descent and distributed linear transform. 
In this paper, we consider the specific case where the computation cost is minimum, and propose novel converse and achievable bounds on the optimal communication cost. The proposed bounds coincide for some system parameters; when they do not match, we prove that the achievable distributed computing scheme is optimal under the constraint of a widely used `cyclic assignment' on the datasets. Our results also show that when $K = N$, with the same communication cost as the optimal distributed gradient descent coding scheme propose by Tandon et al. from which the master recovers one linear combination of $K$ messages, our proposed scheme can let the master recover any additional $N_r -1$ independent linear combinations of messages with high probability.

Distributed Linearly Separable Computation

We consider schemes for joint scheduling training and downlink MU-MIMO in multi-cell deployments that can yield high cell and cell-edge throughputs with tens of antennas per base-station and single antenna terminals TDD schemes are considered as they are able to support MU-MIMO over large antenna arrays despite the inherent training dimensionality bottleneck The proposed schemes operate by dividing users into bins based on their relative location (pathloss) to different stations MU-MIMO is then performed by splitting transmission resources among such bins, and optimizing the MU-MIMO design on a per-bin basis We demonstrate the viability of these schemes in the context of a one-dimensional uniform cellular topology and show that high cell and cell-edge rates can be expected, provided the palette of per-bin signaling options is sufficiently large

Achieving large spectral efficiencies from MU-MIMO with tens of antennas: Location-adaptive TDD MU-MIMO design and user scheduling

This paper considers an extended wireless network with multi-antenna nodes in line-of-sight (LoS) propagation environment. Assuming that the number of antennas at each node can be scaled as some arbitrary function of the number of nodes, we study the scalability of this network, i.e., its ability to deliver non-zero rate to each source-destination pair. Since the rank of the LoS multiple-input multiple-output (MIMO) channel starts collapsing with the increasing separation between the transmitter and the receiver, we consider two competing transmission strategies: (i) long hop: each source-destination pair minimizes the number of hops by sacrificing multiplexing gain and ideally achieving full power gain over each hop, and (ii) short hop: each source-destination pair communicates through a series of short hops each achieving full multiplexing gain. By characterizing the number of antennas required to achieve scalability in both the cases, we show that the antenna requirement is significantly less for the short hop case. These results have key applications in the design of wireless backhaul for cellular networks, where the possibility of having massive MIMO links is becoming a reality due to the increasing maturity of higher transmission frequencies, e.g., 28 and 38 GHz.

/pdf/scalability-of-line-of-sight-massive-mimo-mesh-networks-for-11wivoza3d.pdf

Scalability of line-of-sight massive MIMO mesh networks for wireless backhaul

We examine a multiple-access AWGN channel with synchronous DS-CDMA in which the channel amplitude and noise variance parameters are unknown a priori. We derive an iterative joint multiuser decoder and parameter estimator based on soft interference cancellation and on soft decision-driven least-squares estimation. Our derivation is obtained by applying the sum-product algorithm to the factor graph of the joint a posteriori probability measure of the information bits and of the unknown channel parameters.

Iterative multiuser joint detection and parameter estimation: a factor-graph approach

The capacity of caching networks has received considerable attention in the past few years. A particularly studied setting is the shared link caching network, in which a single source with access to a file library communicates with multiple users, each having the capability to store segments (packets) of the library files, over a shared multicast link. Each user requests one file from the library according to a common demand distribution and the server sends a coded multicast message to satisfy all users at once. The problem consists of finding the smallest possible average codeword length to satisfy such requests. In this paper, we consider the generalization to the case where each user places L >= 1 independent requests according to the same common demand distribution. We propose an achievable scheme based on random vector (packetized) caching placement and multiple groupcast index coding, shown to be order-optimal in the asymptotic regime in which the number of packets per file B goes to infinity. We then show that the scalar (B = 1) version of the proposed scheme can still preserve order-optimality when the number of per-user requests L is large enough. Our results provide the first order-optimal characterization of the shared link caching network with multiple random requests, revealing the key effects of L on the performance of caching-aided coded multicast schemes.

Giuseppe Caire

Papers

Distributed Linearly Separable Computation

Achieving large spectral efficiencies from MU-MIMO with tens of antennas: Location-adaptive TDD MU-MIMO design and user scheduling

Scalability of line-of-sight massive MIMO mesh networks for wireless backhaul

Iterative multiuser joint detection and parameter estimation: a factor-graph approach

Caching-Aided Coded Multicasting with Multiple Random Requests