Compressed Sensing (CS) is a promising method that recovers the sparse and compressible signals from severely under-sampled measurements. CS can be applied to wireless communication to enhance its capabilities. As this technology is proliferating, it is possible to explore its need and benefits for emerging applications

Compressive Sensing for Wireless Communication provides:

• A clear insight into the basics of compressed sensing
• A thorough exploration of applying CS to audio, image and computer vision
• Different dimensions of applying CS in Cognitive radio networks
• CS in wireless sensor network for spatial compression and projection
• Real world problems/projects that can be implemented and tested
• Efficient methods to sample and reconstruct the images in resource constrained WMSN environment

Wireless sensor networks, compressed sensing, sparsity, signal recovery, image processing, audio, video, computer vision, wireless networks, cognitive radio