Semi blind channel estimation for IRS-assisted MIMO communication system
Bibliographic record
Abstract
Intelligent Reflective Surface (IRS) has emerged as a promising approach in our pursuit of continuous enhancements in terms of speed, connectivity, security, latency, and other aspects of wireless communications.IRS is a controllable planar meta-surface that is made of multiple passive reflective elements, each possessing the ability to modify the properties of incident waves by manipulating their phase.This remarkable technology allows for controlling the wireless propagation environment and has a wide range of applications, including passive beamforming, sensing, localization, and security among others.To fully exploit the potential of IRS, accurate knowledge of the channel state information is required.Therefore, in this thesis, we focus on the channel estimation component of an IRS-aided Multiple Input-Mulitple Output (MIMO) communication system.Specifically, we adopt the semi-blind approach and develop a channel estimation algorithm based on the Expectation Maximization (EM) framework.In contrast to other research works, our algorithm is applicable not only to Single Input Multiple Output (SIMO) systems but to general MIMO systems, and assumes that the transmitted data is sourced from a discrete constellation, e.g., 4-QAM modulation scheme.Further, we consider two different channel estimation protocols: non-superimposed and superimposed.The non-superimposed protocol utilizes a traditional pilot-data structure, where the pilots are transmitted first followed by data symbols.On the other hand, in the superimposed protocol both the data and pilot symbols are transmitted simultaneously.To deal with the complexity involved in Abstract ii computing Expectation step (E-step) of the EM algorithm for discrete data sets, a detection-based E-step is introduced where with the help of the Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Soft Decision Fixed Complexity (SDFC) detectors, we estimate the conditional probability of the unknown data symbols given the received signal.Simulation results show that the proposed algorithm performs better than a EM-based method from the recent literature, while the use of detectors for estimating the data vectors, allows us to trade off accuracy and time complexity.Finally, it is shown that the non-superimposed protocol performs comparatively better than the superimposed protocol as we increase the number of pilot symbols.thankful to thank my roommates and friends Vishal, Manoj, Nithila, Barfi, Rhythm, Akriti, Shubham, Nancy, Nehal, Aishwarya, Sandeep, Aashika and all others in Canada for unflagging support throughout the difficult research environment brought on by the COVID-19 epidemic.I also want to deeply appreciate my childhood friend Adharsh for helping me with my academics and never-ending support during challenging times.A special thanks to Prakriti for sharing
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".