Blind separation of mutually correlated sources using precoders
IEEE Transactions on Neural Networks
| Hi-index | 35.68 |
In densely deployed wireless sensor networks, signals of adjacent sensors can be highly cross-correlated. This paper proposes to utilize such a property to develop efficient and robust blind channel identification and equalization algorithms. Blind equalization can be performed with complexity as low as O(N˜), where N˜ is the length of equalizers. Transmissions can be more power and bandwidth efficient in multipath propagation environment, which is especially important for wideband sensor networks such as those for acoustic location or video surveillance. The cross-correlation property of sensor signals and the finite sample effect are analyzed quantitatively to guide the design of low duty-cycle sensor networks. Simulations demonstrate the superior performance of the proposed method.