LAUSR

To address the communication resource limitations the uplink data in some distributed networks suffers from, quantization enables these graph signals to realize compression. However, the compression process is accompanied by quantization errors, which pose threat to the communication quality. In addition, in real-world scenarios, graph signals tend to evolve with time, where the information loss would be larger without adaption to the evolvement. To tackle the above problems, we first propose an adaptive rate allocation scheme, which allocates rate to each quantizer under a total rate constraint, for time-varying graph signals. Along with the smoothness of graph signals at the same time instant and the rate-distortion features of scalar quantization, the smoothly evolving characteristics of time-varying graph signals are leveraged to adaptively adjust the allocated rate with time to reduce quantization distortion of uplink data incurred by the time-varying feature. Simulation results demonstrate the superiority of distortion performance of the proposed rate allocation scheme on both synthetic and real-world graphs.