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Binary Chirps (BCs) are 2^m dimensional complex vectors employed in deterministic compressed sensing and in random/unsourced multiple access in wireless networks. The vectors are obtained by exponentiating codewords from a 2nd order Reed-Muller code defined over Z4, the ring of integers modulo 4. We doubled the size of the BC codebook, without compromising performance in wireless multiple access. 

We consider autocorrelation-based low-complexity decoders for identifying Binary Chirp codewords from noisy signals in N = 2^m dimensions. The underlying algebraic structure enables dimensionality reduction from N complex to m binary dimensions, which can be used to reduce decoding complexity, when decoding is successively performed in the m binary dimensions. Existing low-complexity decoders suffer from poor performance in scenarios with strong noise. This is problematic especially in a vector quantization scenario, where quantization noise power cannot be controlled in the system. We construct two improvements to existing algorithms; a geometrically inspired algorithm based on successive projections, and an algorithm based on adaptive decoding order selection. When combined with a breadth-first list decoder, these algorithms make it possible to approach the performance of exhaustive search with low complexity. 

Abstract We consider a multipoint channel charting (MPCC) algorithm for radio resource management (RRM) in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems. A massive MIMO (mMIMO) infrastructure network performs logical localization of vehicles to a MPCC, based on V2I communication signals. Combining logical distances given by channel charting with V2V measurements, the network trains a function to predict the quality of a direct V2V communication link from observed V2I communication signals. In MPCC, the network uses machine learning techniques to learn a logical radio map from V2I channel state information (CSI) samples transmitted from unknown locations. The network extracts CSI features, constructs a dissimilarity matrix between CSI samples, and performs dimensional reduction of the CSI feature space. Here, we use Laplacian Eigenmaps (LE) for dimensional reduction. The resulting MPCC is a two-dimensional map where the spatial distance between a pair of vehicles is closely approximated by the distance in the MPCC. In addition to V2I CSI, the network acquires V2V channel quality information for vehicles in the training set and develops a link quality predictor. MPCC provides a mapping for any vehicle location in the training set. To use MPCC for cognitive RRM of V2I and V2V communications, network management has to find logical MPCC locations for vehicles not in the training set, based on newly acquired V2I CSI measurements. For this, we develop an extension of LE-based MPCC to out-of-sample CSI samples. We evaluate the performance of link quality prediction for V2V communications in a mMIMO millimeter-wave scenario, in terms of the relative error of the predicted outage probability. 

We propose a joint channel estimation and data detection (JED) algorithm for cell-free massive multi-user (MU) multiple-input multiple-output (MIMO) systems. Our algorithm yields improved reliability and reduced latency while minimizing the pilot overhead of coherent uplink transmission. The proposed JED method builds upon a novel non-convex optimization problem that we solve approximately and efficiently using forward- backward splitting. We use simulation results to demonstrate that our algorithm achieves robust data transmission with more than 3x reduced pilot overhead compared to orthogonal training in a 128 antenna cell-free massive MU-MIMO system in which 128 users transmit data over 128 time slots. 

Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.