Holographic MIMO: How Many Antennas Do We Need for Energy Efficient Communication?
Bibliographic record
Abstract
Holographic multiple-input multiple-output (HMIMO) communication systems utilize spatially-constrained arrays equipped with large number of antennas (NoA) to benefit from increased spatial multiplexing and spatial resolution gains. We consider a multi-user HMIMO system under an electromagnetic wave compliant channel model, and study its energy-efficiency (EE). We first derive closed-form expressions of the ergodic achievable rates under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding in the downlink, and maximum ratio combining (MRC) and ZF combining in the uplink, implemented at the base station (BS) with reduced complexity dictated by the number of degrees-of-freedom (DoF) offered by the channel. Using these expressions, we formulate an EE maximization problem with respect to the power allocation (PA) and the NoA arranged within spatially-constrained HMIMO surfaces at the BS and users, and solve it using an alternating optimization algorithm. For fixed PA, the optimal NoA is derived as the solution of two analytical equations, while for fixed NoA we use sequential fractional programming to obtain the optimal PA. Numerical results yield useful insights into the EE performance in different operating regimes and under different side-lengths of HMIMO surfaces. The presented results show that under MRT and MRC in the downlink and uplink respectively, deploying more antennas in excess of the number of DoF increases the EE in low power budget (noise-limited) regime, whereas fixing the NoA to the number of DoF maximizes the EE in higher power budget (interference-limited) regime. On the other hand, under ZF precoders and combiners, the NoA that achieve the optimal EE is larger than the DoF under all power budgets, with the number of additional antennas decreasing with increasing power budget.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".