Channel Protection Using Random Modulation.


Channel Protection Using Random Modulation

This paper shows that modulation protects a bandlimited signal against convolutive interference. A signal s(t), bandlimited to BHz, is modulated (pointwise multiplied) with a known random sign sequence r(t), alternating at a rate Q, and the resultant spread spectrum signal s(t) \odot r(t) is convolved against an M-tap channel impulse response h(t) to yield the observed signal y(t) = (s(t) \odot r(t)) \circledast h(t), where \odot and \circledast denote pointwise multiplication, and circular convolution, respectively.We show that both s(t), and h(t) can be provably recovered using a simple gradient descent scheme by alternating the binary waveform r(t) at a rate Q \geq B + M(to within log factors and a signal coherences) and sampling y(t) at a rate Q. We also present a comprehensive set of phase transitions to depict the trade-off between Q, M, and B for successful recovery. Moreover, we show stable recovery results under noise.


AI Applications

One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.

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In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.