Anoosheh Heidarzadeh

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Network coding is known to improve the throughput and the resilience to losses in most network scenarios. In a practical network scenario, however, the accurate modeling of the traffic is often too complex and/or infeasible. The goal is thus to design codes that perform close to the capacity of any network (with arbitrary traffic) efficiently. In this(More)
In this paper, we present a new approach for the analysis of iterative node-based verification-based (NB-VB) recovery algorithms in the context of compressed sensing. These algorithms are particularly interesting due to their low complexity (linear in the signal dimension <i>n</i>). The asymptotic analysis predicts the fraction of unverified signal elements(More)
In this paper, the problem of designing network codes that are both communicationally and computationally efficient over packet line networks with worst-case schedules is considered. In this context, random linear network codes (dense codes) are asymptotically capacity-achieving, but require highly complex coding operations. To reduce the coding complexity,(More)
To lower the complexity of network codes over packet line networks with arbitrary schedules, chunked codes (CC) and overlapped chunked codes (OCC) were proposed in earlier works. These codes have been previously analyzed for relatively large chunks. In this paper, we prove that for smaller chunks, CC and OCC asymptotically approach the capacity with an(More)
Consider a set of clients in a broadcast network, each of which holds a subset of packets in the ground set X. In the (coded) cooperative data exchange problem, the clients need to recover all packets in X by exchanging coded packets over a lossless broadcast channel. Several previous works analyzed this problem under the assumption that each client(More)
This paper considers the problem of cooperative data exchange with different client priority classes. In this problem, each client initially knows a subset of packets in the ground set X of size K, and all clients wish to learn all packets in X. The clients exchange packets by broadcasting coded combinations of their packets. The primary objective is to(More)
In this paper, we consider distributed source coding (DSC) problem over actual noisy channel addressed as distributed joint source-channel coding (DJSCC) while targeting the important applications of real-world transmission. Our objective is to design robust syndrome-based Slepian-Wolf decoding schemes guaranteeing both efficient distributed source(More)
In this paper, we study the coding delay and the average coding delay of random linear network codes (dense codes) over line networks with deterministic regular and Poisson transmission schedules. We consider both lossless networks and networks with Bernoulli losses. The upper bounds derived in this paper, which are in some cases more general, and in some(More)
In the cooperative data exchange problem, a set of clients share a lossless broadcast channel. Each client initially has a subset of packets in the ground set X, and wishes to learn all packets in X. The clients exchange their packets with each other by broadcasting coded or uncoded packets. In this paper, we consider a generalization of this problem for(More)
—The problem of designing efficient feedback-based scheduling policies for chunked codes (CC) over packet networks with delay and loss is considered. For networks with feedback, two scheduling policies, referred to as random push (RP) and local-rarest-first (LRF), already exist. We propose a new scheduling policy, referred to as minimum-distance-first(More)