INTERSYMBOL INTERFERENCE (ISI) MITIGATION SCHEMES IN IR-UWB SYSTEMS EMPLOYING ENERGY DETECTION RECEIVER
Bibliographic record
Abstract
Ultra-Wideband (UWB) is an emerging wireless technology that has attracted many applications in modern day communications. Its ability to provide high data rates at very low complexity makes the system attractive for many indoor high-speed wireless communications. UWB signal can be transmitted by either impulse radio (IR) or multicarrier techniques. Impulse radio technique in particular, is a carrier less technology using pulses in the range of nanoseconds or less providing a low complexity, low power and low interference susceptible wireless system. These features motivate the usage of energy detection based receiver structures that operates at very low power. With the recent developments in UWB technology, a promising feature of this system is to provide high data rate with transceivers operating at very low power. High data rate on the other hand can be achieved only by using a complex modulation schemes that requires more transmitted power. As a limitation in the spectral emission associated with UWB, only low-level modulation technology can be used in UWB systems. Hence, in order to achieve high data rates using low-level modulation schemes, the Inter-symbol interference (ISI) becomes unavoidable. Decision feedback equalization (DFE) is one of the signal process techniques that can be used to mitigate the effects of ISI. This thesis proposes an energy subtraction algorithm combining with the principles of DFE to mitigate the effects of ISI in an impulse radio UWB system employing energy detection receiver. Computer simulations have been performed to verify the operation of the new proposed algorithm under UWB channel characteristics and relevant comparisons have been made with the basic energy detection receiver. Simulation results show that the ISI can be effectively mitigated with low system complexity.
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 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 | 0.000 |
| 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) | 0.001 | 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".