On the capacity of wireless erasure networks

We determine the capacity of a certain class of wireless erasure relay networks. We first find a suitable definition for the "cut-capacity" of erasure networks with broadcast at transmission and no interference at reception. With this definition, a maxflow mincut capacity result holds for the capacity of these networks

Capacity of wireless erasure networks

In this paper, a special class of wireless networks, called wireless erasure networks, is considered. In these networks, each node is connected to a set of nodes by possibly correlated erasure channels. The network model incorporates the broadcast nature of the wireless environment by requiring each node to send the same signal on all outgoing channels. However, we assume there is no interference in reception. Such models are therefore appropriate for wireless networks where all information transmission is packetized and where some mechanism for interference avoidance is already built in. This paper looks at multicast problems over these networks. The capacity under the assumption that erasure locations on all the links of the network are provided to the destinations is obtained. It turns out that the capacity region has a nice max-flow min-cut interpretation. The definition of cut-capacity in these networks incorporates the broadcast property of the wireless medium. It is further shown that linear coding at nodes in the network suffices to achieve the capacity region. Finally, the performance of different coding schemes in these networks when no side information is available to the destinations is analyzed

Poset belief propagation-experimental results

Poset belief propagation, or PBP, is a flexible generalization of ordinary belief propagation which can be used to (approximately) solve many probabilistic inference problems. In this paper, we summarize some experimental results comparing the performance of PBP to conventional BP techniques

Poset belief propagation-experimental results

Poset belief propagation, or PBP, is a flexible generalization of ordinary belief propagation which can be used to (approximately) solve many probabilistic inference problems. In this paper, we summarize some experimental results comparing the performance of PBP to conventional BP techniques

Iterative Decoding for Wireless Networks

The invention of turbo codes and low density parity check (LDPC) codes has made it possible for us for design error correcting codes with low decoding complexity and rates close to channel capacity. However, such codes have been studied in detail only for the most basic communication system, in which a single transmitter sends data to a single receiver over a channel whose statistics are known to both the transmitter and the receiver. Such a simplistic model is not valid in the case of a wireless network, where multiple transmitters might want to communicate with multiple receivers at the same time over a channel which can vary rapidly. While the design of efficient error correction codes for a general wireless network is an extremely hard problem, it should be possible to design such codes for several important special cases. This thesis takes a few steps in that direction. We analyze the performance of low density parity check codes under iterative decoding in certain simple networks and prove Shannon-theoretic results for more complex networks. More specifically, we analyze the iterative decoding algorithm in two very important special cases: (a) when the transmitter and receiver have no prior knowledge of the channel and (b) when the channel is a multiple access channel. We also apply iterative decoding to some non-LDPC codes on the binary symmetric channel and the additive white Gaussian noise channel. Finally, we derive capacity results for a class of wireless multicast networks and a class of fading channels.</p

To

my parent

Mitsubishi Electric Research Laboratories

We study the performance of the newly invented rateless codes (LT and Raptor codes) on noisy channels such as the BSC and the AWGNC. We find that Raptor codes outperform LT codes, and have good performance on a wide variety of noisy channels