Precoding designs in multiuser multicell wireless systems: competition and coordination
Bibliographic record
Abstract
In a multicell system, universal frequency reuse can be employed in a network of neighboring cells for higher spectral efficiency. However, universal frequency reuse comes at the price of severe inter-cell interference (ICI), especially at cell- edge mobile stations (MS), which may effectively impair the overall system performance. To actively deal with the ICI, the emerging wireless communication standard advocates the concept of interference-aware multicell coordination. Known as coordinated multipoint transmission/reception (CoMP), the new paradigm allows the multicell system to actively control and even take advantage of the ICI. The objective of this research is to study the precoding perspectives in the multiuser multicell system with CoMP. Specifically, this research examines various precoding techniques and develops low-complexity and distributed algorithms in designing optimal precoders that either minimize the transmit power or maximize the sum-rate of the CoMP systems. In addition, this research brings new perspectives and understanding to the CoMP system where the interactions among the coordinated BSs are characterized under two operating modes: interference aware (IA) and interference coordination (IC).Under the IA mode, the multicell system is said to be in competition where each BS selfishly adapts its precoding strategies accordingly to the ICI. Naturally, the IA mode represents a strategic noncooperative game (SNG) with the BSs being the rational players, who try to maximize a certain utility for their connected MSs. This work characterizes the SNG played among the BSs by examining the existence and uniqueness of a stable operating point of the system, which is corresponding to a Nash equilibrium (NE) of the multicell game. The convergence to the NE and its efficiency are then thoroughly analyzed.Under the IC mode, the multicell systems are said to be in coordination where the transmissions from the BSs are coordinated to jointly maximize the performance gain of CoMP. Optimality and distributed implementation are key considerations for the precoding designs under the IC mode. In this work, we propose multicell precoding designs that jointly maximize the weighted sum-rate (WSR) of the coordinated multicell system. Due to the nonconvexity of the WSR optimization problems, we focus on the development of low-complexity convex approximation techniques to decompose them into a sequence of simpler convex problems, which can be solved distributively with local processing at each coordinated cell. Simulation results show significant performance improvements in terms of transmit power and achievable sum-rate by the IC mode over the IA mode.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".