Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey
Bibliographic record
Abstract
Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of various vertical applications that require higher traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, the deployment of these technologies poses several challenges, including the lack of network transparency, management decentralization, and reliability. In addition, the heterogeneity of these networks raises security concerns, particularly, confidentiality, privacy, and trustworthiness. Typically, networks rely on encryption techniques at higher layers to ensure security and privacy. However, due to the emergence of novel computing paradigms, such as quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Therefore, 5G/xG networks must consider smarter and more efficient security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of wireless communications, while blockchain can guarantee the decentralization, integrity, and trustworthiness of networks. Motivated by these technological advancements, this paper provides an in-depth review of the existing literature on PLS and blockchain. Then, for the first time, we present a framework for the integration of PLS with blockchain in 5G/xG systems. We begin by providing a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Next, we present our vision of a cross-layer architecture that leverages both PLS and blockchain in 5G/xG networks. Finally, we identify related challenges and open issues and shed light on future research directions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".