Distributed estimation in wireless sensor networks under a semi-orthogonal multiple access technique

Abstract

This thesis is concerned with distributed estimation in a wireless sensor network (WSN) with analog transmission. For a scenario in which a large number of sensors are deployed under a limited bandwidth constraint, a semi-orthogonal multiple-access channelization (MAC) approach is proposed to provide transmission of observations from K sensors to a fusion center (FC) via N orthogonal channels, where K≥N. The proposed semi-orthogonal MAC can be implemented with either fixed sensor grouping or adaptive sensor grouping. The mean squared error (MSE) is adopted as the performance criterion and it is first studied under equal power allocation. The MSE can be expressed in terms of two indicators: the channel noise suppression capability and the observation noise suppression capability. The fixed version of the semi-orthogonal MAC is shown to have the same channel noise suppression capability and two times the observation noise suppression capability when compared to the orthogonal MAC under the same bandwidth resource. For the adaptive version, the performance improvement of the semi-orthogonal MAC over the orthogonal MAC is even more significant. In fact, the semi-orthogonal MAC with adaptive sensor grouping is shown to perform very close to that of the hybrid MAC, while requiring a much smaller amount of feedback. Another contribution of this thesis is an analysis of the behavior of the average MSE in terms of the number of sensors, namely the scaling law, under equal power allocation. It is shown that the proposed semi-orthogonal MAC with adaptive sensor grouping can achieve the optimal scaling law of the analog WSN studied in this thesis. Finally, improved power allocations for the proposed semi-orthogonal MAC are investigated. First, the improved power allocations in each sensor group for different scenarios are provided. Then an optimal solution of power allocation among sensor groups is obtained by the convex optimization theory, and shown to outperform equal power allocation. The issue of balancing between the performance improvement and extra feedback required by the improved power allocation is also thoroughly discussed.