Steiner, SteffenSteffenSteinerKuehn, VolkerVolkerKuehnStark, MaximilianMaximilianStarkBauch, GerhardGerhardBauch2021-09-072021-09-072021-07-11IEEE Statistical Signal Processing Workshop (SSP 2021)http://hdl.handle.net/11420/10277This paper addresses a distributed sensing scenario, widely known as the Chief Executive Officer (CEO) problem. Considering the logarithmic loss distortion measure, the distributed scalar compression can be optimized using an information bottleneck (IB) approach. The recently proposed Greedy Distributed IB (GDIB) algorithm optimizes all quantizers successively exploiting the statistics of previously designed quantizers as side-information. It was shown, that jointly optimizing the scalar quantizers results in a significant performance improvement compared to individual scalar optimization without side-information. However, processing the side-information becomes a major bottleneck as the memory complexity grows exponentially with the network size. This paper proposes a sequential compression scheme in order to compress this side-information to ensure feasibility even for larger networks. The compression is performed again by means of the information bottleneck method. Presented simulation results show that despite the compression of side-information the overall loss in relevant information can be made sufficiently small.enConference Paper10.1109/SSP49050.2021.9513805Other