This paper proposes a multivariate and online prediction of stock prices via the paradigm of kernel adaptive filtering (KAF). The prediction of stock prices in traditional classification and regression problems needs independent and batch-oriented nature of training. In this article, we challenge this existing notion of the literature and propose an online kernel adaptive filtering-based approach to predict stock prices. We experiment with ten different KAF algorithms to analyze stocks’ performance and show the efficacy of the work presented here. In addition to this, and in contrast to the current literature, we look at granular level data. The experiments are performed with quotes gathered at the window of one minute, five minutes, ten minutes, fifteen minutes, twenty minutes, thirty minutes, one hour, and one day. These time windows represent some of the common windows frequently used by traders. The proposed framework is tested on 50 different stocks making up the Indian stock index: Nifty-50. The experimental results show that online learning and KAF is not only a good option, but practically speaking, they can be deployed in high-frequency trading as well.

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OFDM is a broadband wireless technology which supports data rates in excess of 100 Mbps. In addition to this, OFDM has some limitations as it requires strict frequency synchronization and low peak to average power ratio. So considering DFT-OFDMA, which performance in terms of PAPR, is equivalent to SC-FDMA. This paper represents the suitability of using OFDMA in the up-link for high-data-rate scenarios in local area by considering target performance metrics PAPR. The proposed design shows PAPR performance varies depending on the subcarrier allocation method. It has been also shown that performance of the PAPR by DFT-spreading technique with IFDMA and LFDMA, varying with roll-off factor α of the RC (Raised-Cosine) filter. Effect of the number of subcarriers on PAPR performance is also discussed in this paper.