Optimal transceiver design for non-regenerative MIMO relay systems
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) relaying can increase system throughput, overcome shadowing and expand network coverage more efficiently than its single-antenna counterpart.Non-regenerative (amplify-and-forward) strategies, in which the relays apply linear transformation matrices to their received baseband signals before retransmitting them, are favored in many applications due to low processing delays and implementation complexity.In this regard, transceiver design is crucial to fulfilling the great potential of MIMO relay communication systems.In this thesis, we explore this general problem from two different perspectives: coherent combining and adaptation.Within the first perspective, we design linear transceivers for a one-source-multiplerelays-one-destination system in which the source sends information to the destination through multiple parallel relay stations, such that the signals from these relays are coherently combined at the destination to benefit from distributed array gain.Two approaches are proposed: a low-complexity structured hybrid framework and a minimum mean square error (MSE) optimization approach.In the first approach, the non-regenerative MIMO relaying matrix at each relay is generated by cascading two substructures, akin to an equalizer for the backward channel and a precoder for the forward channel.For each of them, we introduced one-dimensional parametric families of candidate matrix transformations.This hybrid framework allows for the classification and comparison of all possible combinations of these substructures, including several previously investigated methods and their generalizations.The design parameters can further be optimized based on individual channel realizations or on channel statistics; in the latter case, the optimum parameters can be well approximated by linear functions of the signal-to-noise ratios (SNRs).This hybrid framework achieves a good balance between performance and complexity.In the second approach, the relaying matrices are designed to minimize the MSE between the transmitted and received signal symbols.Two types of constraints on the transmit power of the relays are considered separately: weighted sum and per-relay power constraints.Under the weighted sum power constraint, we are able to derive a closed-form expression for the optimal solution, by introducing a complex scaling factor at the destination and using Lagrangian duality.Under the per-relay power constraints, we propose a power balancing algorithm that converts the problem into an equivalent one with a weighted sum power constraint.In addition, we investigate the joint design of the MIMO equalizer at the
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".