Bridging the complexity gap in Tbps-achieving THz-band baseband processing

Recent advances in electronic and photonic technologies have allowed efficient signal generation and transmission at terahertz (THz) frequencies. However, as the gap in THz-operating devices narrows, the demand for terabit-per-second (Tbps)-achieving circuits is increasing. Translating the available hundreds of gigahertz (GHz) of bandwidth into a Tbps data rate requires processing thousands of information bits per clock cycle at state-of-the-art clock frequencies of digital baseband processing circuitry of a few GHz. This paper addresses these constraints and emphasizes the importance of parallelization in signal processing, particularly for channel code decoding. By leveraging structured sub-spaces of THz channels, we propose mapping bits to transmission resources using shorter code words, extending parallelizability across all baseband processing blocks. THz channels exhibit quasi-deterministic frequency, time, and space structures that enable efficient parallel bit mapping at the source and provide pseudo-soft bit reliability information for efficient detection and decoding at the receiver.

Cross-Field Channel Estimation for Ultra Massive-MIMO THz Systems

The large bandwidth combined with ultra-massive multiple-input multiple-output (UM-MIMO) arrays enables terahertz (THz) systems to achieve terabits-per-second throughput. The THz systems are expected to operate in the near, intermediate, as well as the far-field. As such, channel estimation strategies suitable for the near, intermediate, or far-field have been introduced in the literature. In this work, we propose a cross-field, i.e., able to operate in near, intermediate, and far-field, compressive channel estimation strategy. For an array-of-subarrays (AoSA) architecture, the proposed method compares the received signals across the arrays to determine whether a near, intermediate, or far-field channel estimation approach will be appropriate. Subsequently, compressed estimation is performed in which the proximity of multiple subarrays (SAs) at the transmitter and receiver is exploited to reduce computational complexity and increase estimation accuracy. Numerical results show that the proposed method can enhance channel estimation accuracy and complexity at all distances of interest.