6 accepted papers
Future wireless technologies will require accurate localization of multiple users in the radiative near-field. A leading approach employs subspace decomposition of the input covariance and localizes by peak-finding over the MUltiple SIgnal Classification (MUSIC) spectrum, which is suitable for non-c…
Gain-phase impairments (GPIs) affect both communication and sensing in 6G integrated sensing and communication (ISAC). We study the effect of GPIs in a single-input, multiple-output orthogonal frequency-division multiplexing ISAC system and develop a model-based unsupervised learning approach to sim…
Modern communication systems rely on accurate channel estimation to achieve efficient and reliable transmission of information. As the communication channel response is highly related to the user's location, one can use a neural network to map the user's spatial coordinates to the channel coefficien…
Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in or-der to determine appropriate precoders. CSI acquisition requires sending pilot symbols which induce an important overhead. In this paper, a method whose objective is to…
Signal processing on graphs is a recent research domain that seeks to extend classical signal processing tools such as the Fourier transform to irregular domains given by a graph. In such a graph setting, a way to rapidly apply the Fourier transform, i.e. a Fast Fourier Transform (FFT), is lacking.…
Dictionary learning aims at finding a frame (called dictionary) in which some training data admits a sparse representation. Traditional dictionary learning is limited to relatively small-scale problems, because high-dimensional dense dictionaries can be costly to manipulate, both at the learning sta…