Interference Cancellation Aided Hybrid Beamforming for mmWave Multi-User Massive MIMO Systems
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Bibliographic record
Abstract
In large scale multiple-input multiple-output (MIMO) systems (or massive MIMO systems), hybrid beamforming is a promising technique due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. In this paper, we investigate the downlink millimeter wave (mmWave) multi-user massive MIMO system and propose an interference cancellation (IC) framework on hybrid beamforming design. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Specifically, for the first proposed algorithm, we use zero-forcing (ZF) to cancel inter-user interference and use SIC to cancel intra-user interference. For the second one, SIC is used to cancel inter-user interference and ZF is used to cancel intra-user interference. Both inter-user interference and intra-user interference are suppressed by SIC in the third algorithm. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed SIC-aided hybrid beamforming algorithms outperforms the existing approaches in terms of spectral efficiency (SE) at the cost of computational complexity for the SIC procedure. Moreover, the results indicate that the proposed algorithms can achieve good SE performance with 2-bit finite resolution phase shifters and channel estimation error.
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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 it