Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices
Authors: Soroosh Miri, Sepehr Abolhasani, Shahrokh Farahmand, S. Mohammad Razavizadeh, Jiguang He
Organizations: School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran · Great Bay University, Dongguan 523000, China
Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions. Utilization of deep neural networks (DNNs) combined with semantic communication has emerged as a promising paradigm to address these limitations. Deep joint source-channel coding (DJSCC) has recently been proposed to enable semantic communication of images. Building upon the original DJSCC formulation, low-complexity attention-style architectures has been added to the DNNs for further performance enhancement. As a main hurdle, training these DNNs separately for various signal-to-noise ratios (SNRs) will amount to excessive storage or communication overhead, which can not be maintained by small IoT devices. SNR Adaptive DJSCC (ADJSCC), has been proposed to train the DNNs once but feed the current SNR as part of the data to the channel-wise attention mechanism. We improve upon ADJSCC by a simultaneous utilization of doubly adaptive channel-wise and spatial attention modules at both transmitter and receiver. These modules dynamically adjust to varying channel conditions and spatial feature importance, enabling robust and efficient feature extraction and semantic information recovery. Simulation results corroborate that our proposed doubly adaptive DJSCC (DA-DJSCC) significantly improves upon ADJSCC in several performance criteria, while incurring a mild increase in complexity. These facts render DA-DJSCC a desirable choice for semantic communication in performance demanding but low-complexity IoT networks.
Figures & tables
Fig. 1 : System model of the proposed DA-DJSCC approach.
Fig. 2 : Overall architecture of the proposed DA-DJSCC model.
Fig. 3 : Channel-wise attention module.
Fig. 4 : Spatial attention module.
Fig. 5 : PSNR performance for k/n=1/12 .
Fig. 6 : SSIM performance for k/n=1/12 .
Fig. 7 : Classification accuracy for k/n=1/12 .
Model
Complexity Metrics
Parameters
Memory (MB)
Latency (ms)
DJSCC
0.143M
0.547
0.5
ADJSCC
0.157M
0.601
2
Proposed DA-DJSCC
0.159M
0.607
3
a All values are measured under the same hardware and batch size conditions.
TABLE I : Comparison of model complexity for proposed model and baseline DJSCC models.
Polytechnic Institute, Zhejiang University, Hangzhou, China · College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China