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Integrating coded caching (CC) into multi-input multi-output (MIMO) setups significantly enhances the achievable degrees of freedom (DoF). We consider a cache-aided MIMO configuration with a CC gain t, where a server with L Tx-antennas communicates with K users, each equipped with G Rx-antennas. Similar to existing works, we also extend a core CC approach, designed initially for multi-input single-output (MISO) scenarios, to the MIMO setup.


In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed concept in which a VAE is trained on channel state information (CSI) data and used to parameterize an approximation to the mean squared error (MSE)-optimal estimator. The contributions in this work extend the existing framework from fully-determined (FD) to UD systems, which are of high practical relevance.


In this paper, we propose a robust algorithm for designing unstructured Grassmannian constellations for noncoherent MIMO communications that accounts for the effect of hardware impairments (HWIs) such as I/Q imbalance (IQI) and carrier frequency offset (CFO). The algorithm uses the minimum diversity product as a cost function to ensure full-diversity constellations. The constellation points in the Grassmannian are optimized to be robust against any value of the HWIs belonging to a given uncertainty set, the values of which are determined by the characteristics of the hardware used.


In the past two decades convex optimization gained increasing popularity in signal processing and communications, as many fundamental problems in this area can be modelled, analyzed and solved using convex optimization theory and algorithms. In emerging large scale applications such as compressed sensing, massive MIMO and machine learning, the underlying optimization problems often exhibit convexity, however, the classic interior point methods do not scale well with the problem dimensions.


There exists a large variety of applications, for instance in estimation and detection as well as network optimization, that involve both integer (discrete) decision making andthe optimization of continuous parameters. The integer decision making requirementsusually stem from the nature of the problem. In many applications, the physical quantitiesto be optimized are naturally undividable. Think for example of a cellular networkin which a subset of users needs to be selected for transmission. For a given user aconnection is either established or not.


This paper considers the one-bit precoding problem for the multiuser downlink massive multiple-input multiple-output (MIMO) system with phase shift keying (PSK) modulation and focuses on the celebrated constructive interference (CI)-based problem formulation. The existence of the discrete one-bit constraint makes the problem generally hard to solve. In this paper, we propose an efficient negative ℓ 1 penalty approach for finding a high-quality solution of the considered problem.