Abstract
Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable spectral efficiency and mean square error compared to the unconstrained maximum likelihood estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal fully digital systems.
Highlights
R ECENT advances in millimeter-wave hardware [1] and the potential availability of spectrum has encouraged the wireless industry to consider mmW, for the Fifth Generation of cellular systems (5G) [2] and, in particular, for Vehicle-to-Everything (V2X) applications [3], [4]
To demonstrate the effectiveness of the proposed channel estimation methods, we present the results obtained through numerical simulations using ray-tracing channel data and a set of realistic vehicle trajectories
Low-Rank (LR) channel estimation to hybrid systems, and we propose two LR methods, namely Joint-Space LowRank (JS-LR) and Disjoint-Space Low-Rank (DS-LR), for deriving the hybrid channel eigenmodes
Summary
R ECENT advances in millimeter-wave (mmW) hardware [1] and the potential availability of spectrum has encouraged the wireless industry to consider mmW, for the Fifth Generation of cellular systems (5G) [2] and, in particular, for Vehicle-to-Everything (V2X) applications [3], [4]. From the analytical point of view, the channel decompression can be achieved by applying the hybrid echoing method proposed in [22], which consists of consecutively transmitting and receiving training sequences, while using all possible analog precoders/combiners (obtained, for example, from a subset of a Fourier basis) and decompressing the channel after the concatenation of the received signals for each subset This approach turns out to be infeasible for practical systems due to (i) mobility of the terminals and (ii) the low SNR resulting from mismatched Tx-Rx beams. From [34], we adapt this concept to Multi-Vehicular (MV) LR and we specialize the channel estimation to high-mobility hybrid massive MIMO systems in mmW/sub-THz bands, e.g., Vehicle-to-Infrastructure (V2I), considering both FC-HBF and SC-HBF architectures. E[·] is the expectation operator, while R and C stand for the set of real and complex numbers, respectively. δn is the Kronecker delta
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