Generalized Framework for Hybrid Analog/Digital Signal Processing in Massive and Ultra-Massive-MIMO Systems
Bibliographic record
Abstract
The conventional fully-digital implementation of massive-MIMO systems is not efficient due to the large required number of radio-frequency (RF) chains. To address this issue, hybrid analog/digital (A/D) beamforming was proposed and to date remains a topic of ongoing research. In this paper, we explore the hybrid A/D structure as a general framework for signal processing in massive and ultra-massive-MIMO systems. To exploit the full potential of the analog domain, we first focus on the analog signal processing (ASP) network. We investigate a mathematical representation suitable for any arbitrarily connected feed-forward ASP network comprised of the common RF hardware elements in the context of hybrid A/D systems, i.e., phase-shifter and power-divider/combiner. A novel ASP structure is then proposed which is not bound to the unit modulus constraint, thereby facilitating the hybrid A/D systems design. We then study MIMO transmitter and receiver designs to exploit the full potential of digital processing as well. It is shown that replacing the linear transformation in the digital domain with a generic mapping can improve the system performance. In some cases, the performance of optimal fully-digital MIMO systems can be achieved without extra calculations compared to sub-optimal hybrid A/D techniques. An optimization model based on the proposed structure is presented that can be used for hybrid A/D system design. Specifically, precoding and combining designs under different conditions are discussed as examples. Finally, simulation results are presented which illustrate the superiority of the proposed architecture to the conventional hybrid designs for massive-MIMO systems.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".