5G Modulation in Impulsive Noise Environment
Bibliographic record
Abstract
This paper presents a study on a 5G communication system using Quadrature Phase Shift Keying (QPSK) modulation, focusing on the effects of impulsive noise based on the Bernoulli distribution, and the impact of applying a Finite Impulse Response (FIR) Low-Pass Filter (LPF) on the system's performance. The main goal is to analyze the system's resilience to impulsive noise and assess the effectiveness of the FIR LPF in reducing its negative effects while maintaining overall communication quality. The LPF used in this study is a complex Finite Impulse Response (FIR) filter with a predetermined order ($\mathrm{N}=100$) and cutoff frequency. The LPF considers various factors to effectively reduce the impact of impulsive noise on the communication system. The proposed approach is evaluated under Additive White Gaussian Noise (AWGN) with and without impulsive noise, as well as Rayleigh fading channel conditions, using key performance metrics such as Bit Error Rate (BER), Symbol Error Rate (SER), and Error Vector Magnitude (EVM). The results show that the QPSK modulation is reliable in the presence of impulsive noise and that the LPF effectively recovers its harmful effects. The performance with and without impulsive noise is evaluated to demonstrate the robustness of the proposed LPF approach.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".