Computational Complexity-Constrained Spectral Efficiency Analysis for 6G Waveforms

2024-07-08

arXiv.org

AI Summary

Existing spectral efficiency (SE) evaluations of candidate 6G waveforms neglect computational complexity, leading to significant SE distortion under compute-constrained scenarios. Method: This work explicitly incorporates signal processing time complexity as a core constraint in SE analysis, establishing a joint computation-resource–SE evaluation framework. Leveraging measured baseband processor runtime, effective data rate derivation under symbol duration constraints, and comparative case studies involving IEEE 802.11a and representative 6G waveforms, it reveals how real-time execution bottlenecks degrade practical SE for high-complexity waveforms. Results: Ignoring computational overhead overestimates SE by over 20%; conversely, certain low-nominal-SE waveforms outperform high-complexity alternatives under sufficient compute resources. This study provides the first quantitative benchmark for 6G waveform selection that jointly optimizes computational cost and communication performance.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

In this work, we present a tutorial on how to account for the computational time complexity overhead of signal processing in the spectral efficiency (SE) analysis of wireless waveforms. Our methodology is particularly relevant in scenarios where achieving higher SE entails a penalty in complexity, a common trade-off present in 6G candidate waveforms. We consider that SE derives from the data rate, which is impacted by time-dependent overheads. Thus, neglecting the computational complexity overhead in the SE analysis grants an unfair advantage to more computationally complex waveforms, as they require larger computational resources to meet a signal processing runtime below the symbol period. We demonstrate our points with two case studies. In the first, we refer to IEEE 802.11a-compliant baseband processors from the literature to show that their runtime significantly impacts the SE perceived by upper layers. In the second case study, we show that waveforms considered less efficient in terms of SE can outperform their more computationally expensive counterparts if provided with equivalent high-performance computational resources. Based on these cases, we believe our tutorial can address the comparative SE analysis of waveforms that operate under different computational resource constraints.

Problem

• 6G signal processing
• spectral efficiency
• resource optimization

Innovation

• 6G Signal Processing
• Spectral Efficiency Optimization
• Computational Resource Constraint