Abstract

Reed-Solomon codes are polynomial evaluation codes and it has been shown that these can be efficiently repaired in the case of single node failures by considering the code symbols as vectors over a subfield and the helper nodes transfer multiple symbols over the subfield for repair. Tamo-Barg codes are a class of optimal LRCs which are also polynomial evaluation codes. These codes have Reed-Solomon codes as their local codes. In the case of single node failures, the repair takes place only within the local groups. In this paper, we address the question of whether the repair bandwidth within the local group can be further reduced by using the technique of Reed-Solomon repair. We give a construction based on cosets where the scheme requires lesser repair bandwidth than naive Reed-Solomon repair. We also give another construction based on optimal Reed-Solomon codes which achieve the cutset bound, where the Tamo-Barg codes are designed such that the local Reed-Solomon codes can be optimally repaired. We make the connection between these class of codes and the codes with local regeneration.

Abstract

Radars with mmWave frequency modulated continuous wave (FMCW) technology accu- rately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple- input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW Radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classifi- cation technique extends the potential of mmWave FMCW Radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW Radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

Abstract

In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77 - 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logistic Regression. The proposed technique will be highly useful for several applications in cost-effective and reliable ground station traffic management systems for autonomous systems. The end-to-end framework presented here, expands the capabilities of mmWave Radar beyond range detection to classification. The implications of this added functionalities will facilitate utilization of mmWave Radars in computer vision, object recognition, and towards fully autonomous traffic control and management systems.

Abstract

Codes with locality are a class of codes which minimize the number of nodes accessed to repair a failed node. These codes can be used to handle single and multiple erasures. In an extension, codes with hierarchical locality have been proposed, which offer multiple levels of locality as the number of erasure increase. Given the constraints imposed by locality, maximally recoverable codes (MRC) are a class of codes which allow for recoverability from all information theoretically recoverable erasure patterns. In this work, we consider MRC for the case of codes with hierarchical locality. We derive a field size lower bound on MRC with hierarchical locality. We also give constructions of MRC with hierarchical locality for some parameters, whose field size is smaller than that of earlier known constructions.

Abstract

The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange between the nodes with the optimal (minimum) amount of communication load. In this work, we present a converse to such a general data exchange problem. The expression of the converse depends only on the number of bits to be moved between different subsets of nodes, and does not assume anything further specific about the parameters in the problem. Specific problem formulations, such as those in Coded Caching, Coded Data Shuffling, and Coded Distributed Computing, can be seen as instances of this generic data exchange problem. Applying our generic converse, we can efficiently recover known important converses in these formulations. Further, for a generic coded caching problem with heterogeneous cache sizes at the clients with or without a central server, we obtain a new general converse, which subsumes some existing results. Finally we relate a “centralized” version of our bound to the known generalized independence number bound in index coding and discuss our bound’s tightness in this context.

Abstract

Trellis coded modulation (TCM) is a technique combining modulation with coding using trellises designed with heuristic techniques that maximize the minimum Euclidean distance of a codebook. We propose a neural networks based decoder for decoding TCM. We show experiments with our decoder that suggest the use of Convolutional Neural Network (CNN) with Recurrent Neural Network (RNN) can improve decoding performance and provide justification for the same. We show the generalization capability of the decoder by training it with small block length and testing for larger block length. We also test our decoder for its performance on noise model unseen in the training. Index Terms—Trellis Coded Modulation, Neural Networks, Decoding

Abstract

In a blockchain network, full nodes aid in decentralization by validating incoming transactions and also by assisting new nodes who want to join the network. However, due to the high storage cost incurred by full nodes, the total number of full nodes in a system is falling. In order to curb this problem and maintain decentralization, a secure fountain code (SeF) approach has been proposed earlier, where a class of fountain codes known as LT codes have been used. In this paper, we present Secure Raptor Encoder and Decoder (S-RED) built on Raptor Codes, which in addition to reducing storage costs, also has constant decoding time with respect to k (number of input blocks). We also present error-resilient double-peeling decoder that can recover original blocks with a high probability, even under the presence of adversaries

Abstract

The problem of data exchange between multiple nodes with (not necessarily uniform) storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange between the nodes with the optimal (minimum) amount of communication load. In this work, we present a converse to such a general data exchange problem between multiple nodes. The expression of the converse depends only on the number of bits to be moved between different subsets of nodes, and does not assume anything further specific about the parameters in the problem. Specific problem formulations, such as those in Coded Caching, Coded Data Shuffling, Coded Distributed Computing, and some of their variants, naturally can be seen as instances of this generic data exchange problem. Applying our generic converse to such problems, we recover known important converses for these settings and some of their variants in a simpler way. Further, for a generic coded caching problem with multiple transmitters, receivers and cache sizes, we show a new general converse which subsumes many existing results. We also employ our bound to obtain a new tight converse bound for the multi-node removal case in the Coded Data Rebalancing problem, in which nodes must exchange information to ‘rebalance’ a storage cluster after some node failures occur.Due to space restrictions, the full version of this paper, containing proofs and additional results, is made available in [1].

Abstract

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle; and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality. Index Terms— Broadcast rate, directed cycle, index coding, linear codes, locally decodable codes, minrank.

Abstract

—Blockchain is a distributed ledger with wide applications. Due to the increasing storage requirement for blockchains, the computation can be afforded by only a few miners. Sharding has been proposed to scale blockchains so that storage and transaction efficiency of the blockchain improves at the cost of security guarantee. This paper aims to consider a new protocol, Secure-Repair-Blockchain (SRB), which aims to decrease the storage cost at the miners. In addition, SRB also decreases the bootstrapping cost, which allows for new miners to easily join a sharded blockchain. In order to reduce storage, coding-theoretic techniques are used in SRB. In order to decrease the amount of data that is transferred to the new node joining a shard, the concept of exact repair secure regenerating codes is used. The proposed blockchain protocol achieves lower storage than those that do not use coding, and achieves lower bootstrapping cost as compared to the different baselines