Publications
Here you can find a list of my publications and patents.
Deep Joint Source-Channel Coding for Semantic Communications
In this article, we present an adaptive deep learning based JSCC (DeepJSCC) architecture for semantic communications, introduce its design principles, highlight its benefits, and outline future research challenges that lie ahead.
Generative Joint Source-Channel Coding for Semantic Image Transmission
In this work, we propose two novel JSCC schemes that leverage the perceptual quality of deep generative models (DGMs) for wireless image transmission.
U.K. Patent
Encoder, decoder and communication system and method for conveying sequences of correlated data items from an information source across a communication channel using joint source and channel coding, and method of training an encoder neural network and decoder neural network for use in a communication system.
Deep joint source-channel and encryption coding
In this paper, we propose the first DeepJSCC scheme for wireless image transmission that is secure against eavesdroppers, called DeepJSCEC. DeepJSCEC not only preserves the favorable properties of DeepJSCC, it also provides security against chosen-plaintext attacks from the eavesdropper, without the need to make assumptions about the eavesdropper’s channel condition, or its intended use of the intercepted signal.
DeepWiVe
We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform.
DeepJSCC-Q
We propose DeepJSCC-Q, an end-to-end optimized joint source-channel coding scheme for wireless image transmission, which is able to operate with a fixed channel input alphabet. We show that DeepJSCC-Q can achieve similar performance to models that use continuous-valued channel input.
Federated mmWave Beam Selection Utilizing LIDAR Data
In this letter, we propose a federated LIDAR aided beam selection method for V2I mmWave communication systems. In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system.
Effective Communications
We propose a novel formulation of the effectiveness problem in communications, put forth by Shannon and Weaver in their seminal work The Mathematical Theory of Communication, by considering multiple agents communicating over a noisy channel in order to achieve better coordination and cooperation in a multi-agent reinforcement learning (MARL) framework.