RANDOM LINEAR NETWORK CODING FOR TIME DIVISION DUPLEXING

Patent №

US 8,279,781

Granted

2012-10-02

Filed 2009

Owner

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

AI components

1

evo

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

12549725

A new random linear network coding scheme for reliable communications for time division duplexing channels is proposed. The setup assumes a packet erasure channel and that nodes cannot transmit and receive information simultaneously. The sender transmits coded data packets back-to-back before stopping to wait for the receiver to acknowledge (ACK) the number of degrees of freedom, if any, that are required to decode correctly the information. Provided herein is an analysis of this problem to show that there is an optimal number of coded data packets, in terms of mean completion time, to be sent before stopping to listen. This number depends on the latency, probabilities of packet erasure and ACK erasure, and the number of degrees of freedom that the receiver requires to decode the data. This scheme is optimal in terms of the mean time to complete the transmission of a fixed number of data packets. It is shown that its performance is very close to that of a full-duplex system, while transmitting a different number of coded packets can cause large degradation in performance, especially if latency is high. Also described herein is the throughput performance of the novel system and technique along with a comparison to existing half-duplex Go-back-N and Selective Repeat ARQ schemes. Numerical results, obtained for different latencies, show that the novel system and technique described herein has similar performance to the Selective Repeat in most cases and considerable performance gain when latency and packet error probability is high.

Evolutionary computationH04L 1/1671H04L 1/0041H04L 1/0052H04L 2001/0097

AI classification

Evolutionary computation0.56
Knowledge representation0.02
Vision0.01
Speech0.00
Natural language0.00
Planning0.00
AI hardware0.00
Machine learning0.00

Ownership

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

assignment · 233500445

Assignors

LUCANI, DANIEL E., STOJANOVIC, MILICA, MEDARD, MURIEL

On an employer assignment, the assignors are typically the inventors.

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