Asymptotic BER Analysis of MMSE Receivers in Multicell MU-MIMO Systems
Bibliographic record
Abstract
For large-scale antenna array multiple-input multiple-output (MIMO) systems, it has become a common assumption to use low-complexity linear receivers such as zero-forcing and minimum mean-square error (MMSE) combiners. Typical formulations for such combiners imply that only the desired signals within the cell of interest are explicitly taken into account for interference-rejection combining. In the context of a multi-cell multiuser MIMO system, we study the performance of uplink MMSE detection with the presence of the interfering signals from outside the cell. An asymptotic signal-to-interference-plus-noise ratio (SINR) lower bound is proposed, which becomes exact as the input signal-to-noise ratio (SNR) tends to infinity. The SINR lower bound is in simple form and helps to understand the impact of the interfering signals. From this, a closed-form bit-error rate (BER) upper bound is derived, which can be applied for users with arbitrary power and an arbitrary number of antennas at the base station. The BER bound is validated through simulations, and is found to be very tight for the whole range of the input SNR.
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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 | 0.001 |
| 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".