Massive MIMO relaying with imperfect RF chains and coarse ADC/DAC in beyond 5G networks
Bibliographic record
Abstract
Abstract Massive connectivity, low cost, and energy saving are key requirements in providing Internet of Things (IoT) services in the beyond 5G (B5G) communication networks. Motivated by these requirements, we investigate a massive multiple‐input multiple‐output (MIMO) relaying system with imperfect radio frequency (RF) chains and coarse analog‐to‐digital converters/digital‐to‐analog converters (ADCs/DACs), where IoT user pairs communicate through the assistance of a relay equipped with transceiver antennas in quantity. First, the accurate and the approximate achievable rate expressions are derived in closed form. Then, we evaluate the impacts of critical design parameters on the rate performance. Moreover, scaling laws for transmit powers and RF hardware impairments are established when the number of antennas, M, at the relay grows infinity. It is revealed that, as M increases, the system can yield a non‐vanishing rate while cutting down the transmit powers of the IoT devices and relay, and/or scaling up the RF impairments of the relay. The power allocation scheme for maximizing the sum rate is proposed. Numerical results are conducted to demonstrate the analysis and show that, in the large scale antennas regime, employing high‐quality RF hardware at the IoT users and high‐resolution DACs at the transmit end of the relay can significantly improve the system's sum rate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".