MétaCan
Menu
Back to cohort
Record W2129746337 · doi:10.1109/isit.2010.5513342

Layered interference alignment: Achieving the total DOF of MIMO X-channels

2010· article· en· W2129746337 on OpenAlexaff
Seyyed Hassan Mah, Seyed Abolfazl Motahari, Amir K. Khandani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMIMOInterference alignmentInterference (communication)Computer scienceAntenna (radio)MultiplexingChannel (broadcasting)Spatial multiplexingJoint (building)Electronic engineeringTopology (electrical circuits)TelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The K × 2 multiple input multiple output (MIMO) X-channel with constant channel coefficients available at all transmitters and receivers is considered. A new alignment scheme, named layered interference alignment, is proposed in which both vector and real interference alignment techniques are exploited together with joint processing at receiver sides. Data streams, having fractional multiplexing gains, in the desired directions are sent by transmitters to align the interfering signals at receivers efficiently. To decode the intended messages at receivers, a new number theoretic joint processing technique which exploits the availability of several received antennas, is proposed. This processing is backed up by a recent result in the field of Simultaneous Diophantine Approximation, which is introduced in this paper for the firs time. It is shown that incorporating the layered interference alignment is essential to characterize the total DOF of 2 km/ k+1, in the k × 2 m-antenna X-channel.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.241
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Wireless Communication TechniquesFrench-language works237,207