Compression and Combining Based on Channel Shortening and Rank Reduction Techniques for Cooperative Wireless Sensor Networks

TL;DR

This paper proposes a rank-reduction preprocessing technique for cooperative wireless sensor networks that reduces computational complexity and improves bit error rate performance compared to traditional channel-shortening methods.

Contribution

It introduces a novel preprocessing scheme using rank reduction instead of channel-shortening, enhancing efficiency and performance in sensor network communications.

Findings

The proposed scheme reduces computational complexity.

It outperforms channel-shortening in BER for fixed gain sensors.

Tradeoff exists in BER performance for variable gain sensors.

Abstract

This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of $L$ sensors. As the destination has the capacity of processing only $U$ out of these $L$ signals, the strongest $U$ signals are selected while the remaining $(L-U)$ signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.