Compressive Sensing for Wireless Networks
Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits and limitations, and the skills needed to take advantage of compressive sensing in wireless networks.
Why Read This Book
You should read this book if you want a communications- and networks-oriented view of compressive sensing rather than a purely mathematical one. It shows how CS techniques are adapted to practical wireless problems (channel estimation, spectrum sensing, sensor-network sampling), presents algorithms and performance tradeoffs, and connects signal processing, optimization and information-theoretic viewpoints for engineering use.
Who Will Benefit
Graduate students, researchers, and practicing engineers working on wireless communications, sensor networks, or spectrum sensing who need to apply sparse-signal methods to real network problems.
Level: Advanced — Prerequisites: Linear algebra (matrix decompositions), probability/statistics, basic DSP and communications (channel models, OFDM, MIMO), and familiarity with convex optimization concepts and basic sparse representations.
Key Takeaways
- Implement and compare common CS recovery algorithms (l1 minimization, OMP, CoSaMP, AMP) and know their complexity/performance tradeoffs.
- Apply compressive sensing to wireless tasks such as channel estimation, spectrum sensing for cognitive radio, and sparse MIMO/OFDM parameter recovery.
- Design and evaluate distributed/compressive sensing strategies for sensor networks including data fusion and cooperative sampling.
- Analyze performance using CS theory (RIP, mutual coherence) and understand achievable bounds and robustness to noise and model mismatch.
- Integrate CS with practical wireless system blocks and tools (measurement matrix design, quantization, hardware considerations, and solver toolboxes).
Topics Covered
- 1. Introduction to Compressive Sensing and Sparse Representations
- 2. Mathematical Foundations: Sparsity, RIP, and Coherence
- 3. Convex Relaxation and l1-based Reconstruction Methods
- 4. Greedy and Iterative Algorithms: OMP, CoSaMP, AMP
- 5. CS in Sensor and Wireless Network Architectures
- 6. Compressive Channel Estimation for MIMO and OFDM
- 7. Spectrum Sensing and Cognitive Radio Applications
- 8. Distributed and Joint Sparse Recovery for Cooperative Networks
- 9. Optimization Methods and Practical Solver Implementations (CVX, SPGL1)
- 10. Performance Analysis, Noise Robustness and Quantization Effects
- 11. Hardware, Measurement Matrices and Implementation Issues
- 12. Case Studies, Simulations and Future Directions
Languages, Platforms & Tools
How It Compares
More application- and communications-focused than the mathematically rigorous 'Compressed Sensing' (Eldar & Kutyniok); complements theoretical texts by showing how CS is adapted to wireless problems and network constraints.
