The ECE Corporate Affiliates Day was recently on March 29th. I was involved in two research posters.
1st was the neural network based DPD that I am working on with our VIP team. The undergraduates won the “Best Undergraduate Research Award.” Their poster is available here. This is unpublished work so far that is promising. I have worked on DPD for my entire time at Rice, and it is still an interesting problem to me.
By implementing reinforcement learning-aided listen-before-talk (LBT) schemes over a citizens broadband radio service (CBRS) network, we increase the spatial reuse at secondary nodes while minimizing the interference footprint on higher-tier nodes. …
The 2018 DySPAN paper was invited for submission to IEEE Transactions on Cognitive Communications and Networking (TCCN) for a special issue featuring the top papers from this year’s conference. We accepted the invitation and have made improvements to the paper including additional simulations focusing on new topologies. We submitted the updated manuscript to the journal yesterday, and it is targeted for publication in June 2019 pending the review process.
I just got notified that our submission to DySPAN 2018, Opportunistic Channel Access Using Reinforcement Learning in Tiered CBRS Networks, was accepted. Matthew Tonnemacher from SMU and Samsung Research America led this paper which focuses on using machine learning to help overcome the hidden terminal problem in the emerging CBRS band.
Machine learning has been getting extensive attention throughout the world over the last few years. Much of the buzz surrounds the achievements in classification tasks such as image recognition or the ability to outperform humans in complicated games such Go.
The upcoming deployments of devices on the new 3.5 GHz, Citizens Broadband Radio Service (CBRS) is expected to enable innovation by lowering the barrier to entry into LTE and other technologies. With a three-tiered spectrum-sharing solution, the CBRS …