Large-scale MIMO versus network MIMO for multicell interference mitigation
Bibliographic record
Abstract
This paper compares two distinct downlink multicell interference mitigation techniques for wireless cellular networks: large-scale (LS) multiple-input multiple-output (MIMO) and network MIMO. The considered cellular network operates in a time-division duplex (TDD) fashion and includes non-overlapping cooperating clusters, where each cluster comprises B base-stations (BSs), each equipped with multiple antennas, and schedules multiple single-antenna users. In the LS-MIMO system, each BS is equipped with BM antennas, serving its K scheduled users using zero-forcing (ZF) beamforming, while sacrificing its excess number of spatial degrees of freedom (DoF) using interference coordination to prevent causing interference to the other K (B - 1) users within the cooperating cluster. In the network MIMO system, although each BS is equipped with M antennas, the intra-cluster interference cancellation is enabled by data and channel state information sharing across the cooperating BSs and joint downlink transmission to BK users via ZF beamforming. Accounting for uplink-downlink channel reciprocity provided by TDD and invoking the orthogonality principle of ZF beamforming, respectively, the channel acquisition overhead in each cluster and the number of spatial DoF per user are identical in both systems. Therefore, it is not obvious whether one system is superior to the other from the performance point of view. Building upon the channel distribution functions in the two systems and adopting tools from stochastic orders, this paper shows that in fact an LS-MIMO system provides considerably better performance than a network MIMO system. Thus, given the likely lower cost of adding excess number of antennas, LS-MIMO could be a preferred multicell coordination approach for interference mitigation.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".