New Efficient Transmission Technique for HetNets With Massive MIMO Wireless Backhaul
Bibliographic record
Abstract
In order to cope with the rapid increase in power consumption of heterogeneous cellular networks, this article proposes a new efficient transmission technique for heterogeneous networks with massive MIMO wireless backhaul with the objective of minimizing power consumption cost. We assume that transmissions on the backhaul link and the access link occur simultaneously on the same frequency band (in-band) thanks to MIMO spatial multiplexing. On the other hand, we consider that uplink and downlink transmissions are separated in time. In order to prevent multi-user and inter-tier interference, block diagonalization beamforming is considered at the macro base station (MBS) and the signal from the small base station (SBS) to the MBS is transmitted orthogonally to the channel of the SBS's users. The problem of minimizing the transmit power of base stations under users' minimum-rate constraints is formulated. We first derive analytically the optimal time splitting parameter and the allocated transmit power considering that the inter-SBS interference is generated by fixed power. Next, we solve the power allocation problem when the generated inter-SBS interference is no longer considered fixed by proposing an efficient iterative power allocation algorithm. A heuristic user scheduling algorithm is devised in order to deal with the feasibility problem. Finally, simulations validate our analysis and show that the proposed transmission technique outperforms the conventional reverse time division duplex with bandwidth splitting (out-band) in terms of total power consumption.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".