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Register a new userOn the Performance Analysis of Streaming Codes over the Gilbert-Elliott Channel
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Added by
Myna Vajha
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The Gilbert-Elliot (GE) channel is a commonly-accepted model for packet erasures in networks. Streaming codes are a class of packet-level erasure codes designed to provide reliable communication over the GE channel. The design of a streaming code may be viewed as a two-step process. In the first, a more tractable, delay-constrained sliding window (DCSW) channel model is considered as a proxy to the GE channel. The streaming code is then designed to reliably recover from all erasures introduced by the DCSW channel model. Simulation is typically used to evaluate the performance of the streaming code over the original GE channel, as analytic performance evaluation is challenging. In the present paper, we take an important first step towards analytical performance evaluation. Recognizing that most, efficient constructions of a streaming code are based on the diagonal embedding or horizontal embedding of scalar block codes within a packet stream, this paper provides upper and lower bounds on the block-erasure probability of the underlying scalar block code when operated over the GE channel.
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Applications where multiple users communicate with a common server and desire low latency are common and increasing. This paper studies a network with two source nodes, one relay node and a destination node, where each source nodes wishes to transmit a sequence of messages, through the relay, to the destination, who is required to decode the messages with a strict delay constraint $T$. The network with a single source node has been studied in cite{Silas2019}. We start by introducing two important tools: the delay spectrum, which generalizes delay-constrained point-to-point transmission, and concatenation, which, similar to time sharing, allows combinations of different codes in order to achieve a desired regime of operation. Using these tools, we are able to generalize the two schemes previously presented in cite{Silas2019}, and propose a novel scheme which allows us to achieve optimal rates under a set of well-defined conditions. Such novel scheme is further optimized in order to improve the achievable rates in the scenarios where the conditions for optimality are not met.
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