Energy‐Efficient Uplink Transmission in RIS‐Aided M‐MIMO IoT Systems*
Bibliographic record
Abstract
Internet of things (IoT)[1] applications are wide spreading, recently empowered by the dissemination of technologies like smart cities, autonomous vehicles, smart grids, industry 4.0, home automation, wearables, etc. On the other hand, the global warming and climate crisis demand urgent, bold actions toward energy-efficient (EE) technologies for telecommunication systems, which are responsible for a significant part of global energy consumption. To this end, reconfigurable intelligent surfaces (RISs) appear as an important technology to improve the propagation channel gain at the expense of very little power expenditures, recognized as a key element in achieving green telecommunication systems. In this chapter, we focus on the EE uplink (UL) transmission of massive multiple-input multiple-output (M-MIMO) IoT systems aided by an RIS. We propose and evaluate different schemes to minimize the total UL transmit power by optimizing the transmit power of IoT devices, the RIS phase-shift element, and the combining matrix at the base station (BS). Particularly, we give special attention to manifold optimization techniques, which are well suited to the RIS phase-shifts optimization problem. Herein, we treat jointly via iterative alternating optimization (i-AO) approach the three optimization variables: RIS phase-shift vector; BS combining matrix, and unit terminal (UT) power allocation vector. Extensive numerical results are provided and discussed, revealing that the proposed conjugate gradient (CG) method based on Riemannian manifold (RM) with the zero-forcing (ZF) combining achieves the highest power savings, being able to reduce the UL transmit power by up to 89% under typical operation conditions scenarios in comparison with conventional systems without RIS.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".