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6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz)bands to meet the ever-increasing demand for wireless access. Sub-THzcommunication comes with many existing challenges of mmWave communication andadds new challenges associated with the wider bandwidths, more antennas, andharsher propagations. Notably, the frequency- and spatial-wideband(dual-wideband) effects are significant at sub-THz. This paper presents acompressed training framework to estimate the time-varying sub-THz MIMO-OFDMchannels. A set of frequency-dependent array response matrices are constructed,enabling channel recovery from multiple observations across subcarriers viamultiple measurement vectors (MMV). Using the temporal correlation, MMV leastsquares (LS) is designed to estimate the channel based on the previous beamsupport, and MMV compressed sensing (CS) is applied to the residual signal. Werefer to this as the MMV-LS-CS framework. Two-stage (TS) and MMV FISTA-based(M-FISTA) algorithms are proposed for the MMV-LS-CS framework. Leveraging thespreading loss structure, a channel refinement algorithm is proposed toestimate the path coefficients and time delays of the dominant paths. To reducethe computational complexity and enhance the beam resolution, a sequentialsearch method using hierarchical codebooks is developed. Numerical resultsdemonstrate the improved channel estimation accuracy of MMV-LS-CS overstate-of-the-art techniques.

Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO