Abhinav Kumar

TL;DR: In this article, a recurrent neural network-based QoE prediction model using an LSTM network is proposed, which is a network of cascaded long short-term memory (LSTM) blocks to capture the nonlinearities and the complex temporal dependencies involved in the time-varying QoEs.

TL;DR: This paper presents a database consisting of videos at full high definition and ultrahigh definition resolutions, and presents a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering, and performs an objective evaluation of popular video quality assessment and continuous timeQoE metrics over the constructed database.

TL;DR: The proposed adaptive user pairing (A-UP) algorithm results in better performance than state-of-the-art NOMA pairing algorithms in the presence of SIC imperfections and is proposed for achieving better user rates.

TL;DR: A set of BSS patterns, at a global system-level, that have the potential to provide full coverage if the appropriate schedulers are used and can be used to quantify, offline, the energy-performance trade-offs under different operating scenarios.

TL;DR: Through simulation results, it is shown that there exists a trade off in selection of the detection threshold such that high sensing threshold results in improved overall network throughput at the cost of WLAN's performance; whereas, low thresholdresults in improved W LAN's performance at thecost of the overall network performance.