Frequency-domain diffusion adaptation with quantized communication

In energy- and bandwidth-limited networks, frequency-domain diffusion algorithms based on periodic communication are attractive candidates because of their low computational complexity and communication overhead. Nevertheless, these algorithms assume that intermediate estimates are transmitted exactly in each communication round, which is not realistic in practice. To this end, this paper proposes novel frequency-domain diffusion algorithms with quantized communication. Using the randomized quantizer to compress the shared information, we first develop the quantized frequency-domain diffusion least mean square (Q-FDLMS) algorithm. To mitigate the performance degradation incurred by quantization errors, the improved Q-FDLMS (IQ-FDLMS) algorithm is also designed. This enhanced version modifies the combination step and incorporates the moving-average scheme. Additionally, the mean-square stability conditions and steady-state error bound of the IQ-FDLMS algorithm are derived. Experiment results indicate that our proposed algorithms are superior to other competing algorithms.