Sign-Compute-Resolve for Random Access

We present an approach to random access that is based on three elements: physical-layer network coding, signature codes and tree splitting. Upon occurrence of a collision, physical-layer network coding enables the receiver to decode the sum of the information that was transmitted by the individual users. For each user this information consists of the data that the user wants to communicate as well as the user's signature. As long as no more than $K$ users collide, their identities can be recovered from the sum of their signatures. A splitting protocol is used to deal with the case that more than $K$ users collide. We measure the performance of the proposed method in terms of user resolution rate as well as overall throughput of the system. The results show that our approach significantly increases the performance of the system even compared to coded random access, where collisions are not wasted, but are reused in successive interference cancellation.Comment: Accepted for presentation at 52nd Annual Allerton Conference on Communication, Control, and Computin

Sign-Compute-Resolve for Tree Splitting Random Access

We present a framework for random access that is based on three elements: physical-layer network coding (PLNC), signature codes and tree splitting. In presence of a collision, physical-layer network coding enables the receiver to decode, i.e. compute, the sum of the packets that were transmitted by the individual users. For each user, the packet consists of the user's signature, as well as the data that the user wants to communicate. As long as no more than K users collide, their identities can be recovered from the sum of their signatures. This framework for creating and transmitting packets can be used as a fundamental building block in random access algorithms, since it helps to deal efficiently with the uncertainty of the set of contending terminals. In this paper we show how to apply the framework in conjunction with a tree-splitting algorithm, which is required to deal with the case that more than K users collide. We demonstrate that our approach achieves throughput that tends to 1 rapidly as K increases. We also present results on net data-rate of the system, showing the impact of the overheads of the constituent elements of the proposed protocol. We compare the performance of our scheme with an upper bound that is obtained under the assumption that the active users are a priori known. Also, we consider an upper bound on the net data-rate for any PLNC based strategy in which one linear equation per slot is decoded. We show that already at modest packet lengths, the net data-rate of our scheme becomes close to the second upper bound, i.e. the overhead of the contention resolution algorithm and the signature codes vanishes.Comment: This is an extended version of arXiv:1409.6902. Accepted for publication in the IEEE Transactions on Information Theor

Perfect tag identification protocol in RFID networks

Radio Frequency IDentification (RFID) systems are becoming more and more popular in the field of ubiquitous computing, in particular for objects identification. An RFID system is composed by one or more readers and a number of tags. One of the main issues in an RFID network is the fast and reliable identification of all tags in the reader range. The reader issues some queries, and tags properly answer. Then, the reader must identify the tags from such answers. This is crucial for most applications. Since the transmission medium is shared, the typical problem to be faced is a MAC-like one, i.e. to avoid or limit the number of tags transmission collisions. We propose a protocol which, under some assumptions about transmission techniques, always achieves a 100% perfomance. It is based on a proper recursive splitting of the concurrent tags sets, until all tags have been identified. The other approaches present in literature have performances of about 42% in the average at most. The counterpart is a more sophisticated hardware to be deployed in the manufacture of low cost tags.Comment: 12 pages, 1 figur

A Bayesian regression tree approach to identify the effect of nanoparticles' properties on toxicity profiles

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose- and time-response surface smoothing. The resulting posterior distribution is sampled by Markov Chain Monte Carlo. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physico-chemical properties and their marginal effect on toxicity. We illustrate the application of our method to the analysis of a library of 24 nano metal oxides.Comment: Published at http://dx.doi.org/10.1214/14-AOAS797 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Network Coding Tree Algorithm for Multiple Access System

Network coding is famous for significantly improving the throughput of networks. The successful decoding of the network coded data relies on some side information of the original data. In that framework, independent data flows are usually first decoded and then network coded by relay nodes. If appropriate signal design is adopted, physical layer network coding is a natural way in wireless networks. In this work, a network coding tree algorithm which enhances the efficiency of the multiple access system (MAS) is presented. For MAS, existing works tried to avoid the collisions while collisions happen frequently under heavy load. By introducing network coding to MAS, our proposed algorithm achieves a better performance of throughput and delay. When multiple users transmit signal in a time slot, the mexed signals are saved and used to jointly decode the collided frames after some component frames of the network coded frame are received. Splitting tree structure is extended to the new algorithm for collision solving. The throughput of the system and average delay of frames are presented in a recursive way. Besides, extensive simulations show that network coding tree algorithm enhances the system throughput and decreases the average frame delay compared with other algorithms. Hence, it improves the system performance

Leveraging Coding Techniques for Speeding up Distributed Computing

Large scale clusters leveraging distributed computing frameworks such as MapReduce routinely process data that are on the orders of petabytes or more. The sheer size of the data precludes the processing of the data on a single computer. The philosophy in these methods is to partition the overall job into smaller tasks that are executed on different servers; this is called the map phase. This is followed by a data shuffling phase where appropriate data is exchanged between the servers. The final so-called reduce phase, completes the computation. One potential approach, explored in prior work for reducing the overall execution time is to operate on a natural tradeoff between computation and communication. Specifically, the idea is to run redundant copies of map tasks that are placed on judiciously chosen servers. The shuffle phase exploits the location of the nodes and utilizes coded transmission. The main drawback of this approach is that it requires the original job to be split into a number of map tasks that grows exponentially in the system parameters. This is problematic, as we demonstrate that splitting jobs too finely can in fact adversely affect the overall execution time. In this work we show that one can simultaneously obtain low communication loads while ensuring that jobs do not need to be split too finely. Our approach uncovers a deep relationship between this problem and a class of combinatorial structures called resolvable designs. Appropriate interpretation of resolvable designs can allow for the development of coded distributed computing schemes where the splitting levels are exponentially lower than prior work. We present experimental results obtained on Amazon EC2 clusters for a widely known distributed algorithm, namely TeraSort. We obtain over 4.69$\times$ improvement in speedup over the baseline approach and more than 2.6$\times$ over current state of the art

Goodbye, ALOHA!

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The vision of the Internet of Things (IoT) to interconnect and Internet-connect everyday people, objects, and machines poses new challenges in the design of wireless communication networks. The design of medium access control (MAC) protocols has been traditionally an intense area of research due to their high impact on the overall performance of wireless communications. The majority of research activities in this field deal with different variations of protocols somehow based on ALOHA, either with or without listen before talk, i.e., carrier sensing multiple access. These protocols operate well under low traffic loads and low number of simultaneous devices. However, they suffer from congestion as the traffic load and the number of devices increase. For this reason, unless revisited, the MAC layer can become a bottleneck for the success of the IoT. In this paper, we provide an overview of the existing MAC solutions for the IoT, describing current limitations and envisioned challenges for the near future. Motivated by those, we identify a family of simple algorithms based on distributed queueing (DQ), which can operate for an infinite number of devices generating any traffic load and pattern. A description of the DQ mechanism is provided and most relevant existing studies of DQ applied in different scenarios are described in this paper. In addition, we provide a novel performance evaluation of DQ when applied for the IoT. Finally, a description of the very first demo of DQ for its use in the IoT is also included in this paper.Peer ReviewedPostprint (author's final draft

A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication

This paper has two contributions. First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matrix multiplication under storage and communication constraints. This technique uses "garbage alignment," i.e., aligning computations in coded computing that are not a part of the desired output. Generalized PolyDot codes bridge between Polynomial codes and MatDot codes, trading off between recovery threshold and communication costs. Second, we demonstrate that Generalized PolyDot can be used for training large Deep Neural Networks (DNNs) on unreliable nodes prone to soft-errors. This requires us to address three additional challenges: (i) prohibitively large overhead of coding the weight matrices in each layer of the DNN at each iteration; (ii) nonlinear operations during training, which are incompatible with linear coding; and (iii) not assuming presence of an error-free master node, requiring us to architect a fully decentralized implementation without any "single point of failure." We allow all primary DNN training steps, namely, matrix multiplication, nonlinear activation, Hadamard product, and update steps as well as the encoding/decoding to be error-prone. We consider the case of mini-batch size $B=1$, as well as $B>1$, leveraging coded matrix-vector products, and matrix-matrix products respectively. The problem of DNN training under soft-errors also motivates an interesting, probabilistic error model under which a real number $(P,Q)$ MDS code is shown to correct $P-Q-1$ errors with probability $1$ as compared to $\lfloor \frac{P-Q}{2} \rfloor$ for the more conventional, adversarial error model. We also demonstrate that our proposed strategy can provide unbounded gains in error tolerance over a competing replication strategy and a preliminary MDS-code-based strategy for both these error models.Comment: Presented in part at the IEEE International Symposium on Information Theory 2018 (Submission Date: Jan 12 2018); Currently under review at the IEEE Transactions on Information Theor

Interactive Visual Analysis of Networked Systems: Workflows for Two Industrial Domains

We report on a first study of interactive visual analysis of networked systems. Working with ABB Corporate Research and Ericsson Research, we have created workflows which demonstrate the potential of visualization in the domains of industrial automation and telecommunications. By a workflow in this context, we mean a sequence of visualizations and the actions for generating them. Visualizations can be any images that represent properties of the data sets analyzed, and actions typically either change the selection of data visualized or change the visualization by choice of technique or change of parameters