arXiv subjects

Preety Priya

Publications and source records attributed to Preety Priya.

3 recordsLinked to original sources

arXiv2026

Channel Estimation for OTFS Systems With Overspread Doppler Shifts

In this paper, we consider an orthogonal time frequency space (OTFS) system in time-varying channels with overspread Doppler shifts, typically found in non-terrestrial multi-satellite links. The overspread Doppler shifts with magnitude greater than half of the subcarrier spacing, result in aliased Doppler shifts in the delay-Doppler (DD) domain due to the OTFS modulo operation. This makes channel estimation very challenging and the traditional channel estimation methods become ineffective. To address this challenge, we propose a DD training frame and a two-stage channel estimation method. The training frame comprises a cosine pilot signal and a pilot symbol. In the first stage of the channel estimation, the pilot symbol in the DD domain is utilized to estimate the delays, aliased Doppler shifts, and channel gains of the propagation paths. In the second stage, the received time domain signal is converted into the frequency domain to detect the peaks of all the Doppler shifts using the cosine pilot signal. Then, we present a threshold-based method to pair the estimated actual Doppler shifts with their corresponding delays and channel gains. The complexity of the proposed channel estimation is also discussed. Finally, the performance of the proposed channel estimation is validated in terms of the normalized mean square error (NMSE) and bit error rate (BER) in various scenarios.

cs.IT↗

arXiv2024

OTFS Channel Estimation and Detection for Channels with Very Large Delay Spread

In low latency applications and in general, for overspread channels, channel delay spread is a large percentage of the transmission frame duration. In this paper, we consider OTFS in an overspread channel exhibiting a delay spread that exceeds the block duration in a frame, where traditional channel estimation (CE) fails. We propose a two-stage CE method based on a delay-Doppler (DD) training frame, consisting of a dual chirp converted from time domain and a higher power pilot. The first stage employs a DD domain embedded pilot CE to estimate the aliased delays (due to modulo operation) and Doppler shifts, followed by identifying all the underspread paths not coinciding with any overspread path. The second stage utilizes time domain dual chirp correlation to estimate the actual delays and Doppler shifts of the remaining paths. This stage also resolves ambiguity in estimating delays and Doppler shifts for paths sharing same aliased delay. Furthermore, we present a modified low-complexity maximum ratio combining (MRC) detection algorithm for OTFS in overspread channels. Finally, we evaluate performance of OTFS using the proposed CE and the modified MRC detection in terms of normalized mean square error (NMSE) and bit error rate (BER).

cs.IT↗

arXiv2015

A Cancer Biotherapy Resource

Cancer Biotherapy (CB), as opposed to cancer chemotherapy, is the use of macromolecular, biological agents instead of organic chemicals or drugs to treat cancer. Biological agents usually have higher selectivity and have less toxic side effects than chemical agents. The I.S.B.T.C., being the only major information database for CB, seems lacking in some crucial information on various cancer biotherapy regimens. It is thus necessary to have a comprehensive curated CB database. The database accessible to cancer patients and also should be a sounding board for scientific ideas by cancer researchers. The database/web server has information about main families of cancer biotherapy regimens to date, namely, Protein Kinase Inhibitors, Ras Pathway Inhibitors, Cell-Cycle Active Agents, MAbs (monoclonal antibodies), ADEPT (Antibody-Directed Enzyme Pro-Drug Therapy), Cytokines, Anti-Angiogenesis Agents, Cancer Vaccines, Cell-based Immunotherapeutics, Gene Therapy, Hematopoietic Growth Factors, Retinoids, and CAAT. For each biotherapy regimen, we will extract the following attributes in populating the database: Cancer type, Gene/s and gene product/s involved, Gene sequence, Organs affected, Reference papers, Clinical phase/stage, Survival rate, Clinical test center locations, Cost, Patient blogs, Researcher blogs, and Future work. The database will be accessible to public through a website and had FAQs for making it understandable to the laymen and discussion page for researchers to express their views and ideas. In addition to information about the biotherapy regimens, the website will link to other biologically significant databases like structural proteomics, metabolomics, glycomics, and lipidomics databases, as well as to news around the world regarding cancer therapy results. The database attributes would be regularly updated for novel attributes as discoveries are made.

q-bio.BM↗