Background The emergent Internet-of-Things (IoT) data explosion will inevitably exert a tremendous data transmission burden onto the wireless edge networks, which are often low-power wide-area networks (LPWAN) with very limited bandwidth. Thus, improving the bandwidth efficiency of wireless edge networks will be of great importance. In addition, the...

Background Compressive sensing (CS) is a transformative sampling technique that allows sparse signals to be sampled in compressed form at a rate much lower than the Nyquist rate. In particular, end-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of speed and...

Background Compressive sensing (CS) is a transformative sampling technique that allows sparse signals to be sampled in compressed form at a rate much lower than the Nyquist rate. However, conventional CS reconstruction techniques, based on either convex optimization or iterative methods, come with three drawbacks in imaging applications: (1) High complexity...