Coverage, Capacity, and Error Rate Analysis of Multi-Hop Millimeter-Wave Decode and Forward Relaying
Bibliographic record
Abstract
In this paper, we analyze the end-to-end (e2e) performance of a millimeter-wave (mmWave) multi-hop relay network. The relays in it are decode-and-forward (DF) type. As appropriate for mmWave bands, we incorporate path loss and blockages considering the links to be either line of sight (LOS) or non line of sight (NLOS). The links also experience Nakagami-m fading with different m-parameters for the LOS and NLOS states. We consider two scenarios, namely sparse and dense deployments. In the sparse case, the nodes (relays and the destination) are limited by additive noise only. We derive closed-form expressions for the distribution of equivalent e2e signal-to-noise-ratio (SNR), coverage probability, ergodic capacity, and symbol error rate (SER) for the three classes of digital modulation schemes, namely, binary phase shift keying (BPSK), differential BPSK (DBPSK), and square-quadrature amplitude modulation (QAM). In the dense case, the nodes are limited by interference only. Here, we consider two situations: 1) interference powers are independent and identically distributed (i.i.d.) and 2) they are independent but not identically distributed (i.n.i.d.). For the latter situation, closed-form analysis is exceedingly difficult. Therefore, we use the Welch-Satterthwaite Approximation for the sum of Gamma variables to derive the distribution of the total interference. For both situations, we derive the distribution of signal-to-interference ratio (SIR), coverage probability, ergodic capacity, and SERs for the DBPSK and BPSK. We study how these measures are affected by the number of hops. The accuracy of the analytical results is verified via Monte-Carlo simulation. We show that multi-hop relaying provides significant coverage improvements in blockage-prone mmWave networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".