A Review of Digital Signal Processing: Fundamentals, Techniques, and Emerging Trends

Abstract

Digital Signal Processing (DSP) has progressed from foundational principles like Nyquist sampling and quantization to a discipline increasingly integrated with Machine Learning (ML), embedded systems, and emerging paradigms. This evolution is critical for managing exponential data growth and enabling real-time applications in telecommunications and biomedical engineering, yet challenges in efficiency and robustness persist. This study provides a synthesized analysis of DSP’s core tenets, advancements, and future trajectories. Using Python-based simulations and literature synthesis, we quantified key metrics. Core principles confirmed 3-bit quantization yields 26 dB SNR, while IIR filters demonstrated a marginal efficiency edge over FIR (MSE 2.08 vs. 2.11). Application case studies showed DSP boosting telecom throughput 4x via QAM/OFDM and achieving 98% accuracy in ECG denoising. Future trends highlight ML-DSP hybrids, which achieved 95% modulation recognition accuracy but suffered a 60% performance drop under adversarial attacks, revealing critical robustness gaps. Neuromorphic and federated learning paradigms promise extreme efficiencies of 140 GOPS/W and 85% privacy preservation, respectively. This work's novelty lies in its integrated chronological and forward-looking analysis, bridging classical theory (e.g., z-transforms) to 2025 projections and providing holistic benchmarks like novel Pareto fronts for hardware co-design. In conclusion, DSP’s fusion with ML and new computing paradigms is forging a resilient ecosystem capable of sub-1ms latency and 50% energy reductions, which is pivotal for 6G and IoT. We recommend prioritizing hybrid theory for performance guarantees, developing “green” algorithms for sustainability, and establishing ethical, federated frameworks to democratize DSP access.