Distributed Channel-Aware Quantization Based on Maximum Mutual Information

Abstract

In distributed sensing systems with constrained communication capabilities, sensors' noisy measurements must be quantized locally before transmitting to the fusion centre. When the same parameter is observed by a number of sensors, the local quantization rules must be jointly designed to optimize a global objective function. In this work we jointly design the local quantizers by maximizing the mutual information as the optimization criterion, so that the quantized measurements carry the most information about the unknown parameter. A low-complexity iterative approach is suggested for finding the local quantization rules. Using the mutual information as the design criterion, we can easily integrate the effect of communication channels in the design and consequently design channel-aware quantization rules. We observe that the optimal design depends on both the measurement and channel noises. Moreover, our algorithm can be used to design quantizers that can be deployed in different applications. We demonstrate the success of our technique through simulating estimation and detection applications, where our method achieves estimation and detection errors as low as when designing for those special purposes.

Forward-Aware Information Bottleneck-Based Vector Quantization: Multiterminal Extensions for Parallel and Successive Retrieval

“…In practice, it occurs rather frequently that the compressed signals at the quantizers' outputs have to be transmitted over second error-prone hops to be fed into a (distant) processing unit. Several instances for such scenarios incorporate the distributed inference sensor networks with imperfect connections to the fusion center [28], [29], the Cloud-based Radio Access Networks (Cloud-RANs) with noisy fronthaul links [30], [31], the cooperative relaying setups with the Quantize-and-Forward strategy [32], [33] and also devices with unreliable memories [34], [35]. The aforementioned assorted applications can be subsumed under a more general framework, the Joint Source-Channel Coding (JSCC) [36], [37], wherein the impacts of the imperfect forwarding of the quantizers' outputs are taken into account within the quantization design formulation.…”

“…output signals [38], [39] or, concentrating on the squared-error distortion, specific modifications have been suggested [40], [41] for adapting the conventional Lloyd algorithm [42] to the JSCC setup. Furthermore, in [28], [43], [44], as the fidelity criterion, the MI has been considered to acquire quantization schemes, maximizing the end-to-end transmission rate. An extensive review of different approaches has been provided in [45], [46].…”