Impact of residual transceiver impairments on MMSE filtering performance of Rayleigh-product MIMO channels
Bibliographic record
Abstract
Recent studies have demonstrated the presence of residual transceiver hardware impairments even after employing calibration and compensation techniques in different wireless systems. The effect of these impairments becomes more severe in the systems involving a large number of inexpensive Radio Frequency (RF) chains such as massive Multiple Input Multiple Output (MIMO) systems due to the requirement of cost-efficient implementation. However, most of the existing studies consider ideal transceivers without incorporating the effect of residual hardware impairments. In this regard, this paper studies the impact of additive residual transceiver hardware impairments on the Minimum Mean Square Error (MMSE) filtering performance of Rayleigh-Product (RP) MIMO channels. Using principles from Random Matrix Theory (RMT), the MMSE filtering performance of the RP channels is analyzed and a tight lower bound is derived by taking the effects of residual additive transceiver impairments into account. Moreover, some useful insights on the performance of the considered system with respect to various parameters such as the transmit Signal to Noise Ratio (SNR), the number of scatterers and the severity of impairments on both the transmit and receive sides are provided.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".