The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. Phase noise (PN) is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the modest output power of the power amplifier demands limits on peak to average power ratio (PAPR) signal design. In this work, we design a PN-robust, low PAPR single-carrier (SC) waveform by geometrically shaping the constellation and adapting the pulse shaping filter pair under practical PN modelling and adjacent channel leakage ratio (ACLR) constraints for a given excess bandwidth. We optimize the waveforms under conventional and state-of-the-art PN-aware demappers. Moreover, we introduce a neural-network (NN) demapper enhancing transceiver adaptability.We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a design that is numerically robust to PN, while adhering to PAPR and ACLR constraints. The results substantiate the efficacy of the method, yielding up to 2.5 dB in the requiredEb/N0under stronger PN along with a PAPR reduction of 0.5 dB. Moreover, PAPR reductions up to 1.2 dB are possible with competitive BLER and SE performance in both low and high PN conditions.

The large untapped spectrum in the sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. One of the challenges in radio communications in sub-THz is the hardware impairments. Specifically, phase noise is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the moderate output power of the sub-THz power amplifier demands limits on peak to average power ratio (PAPR) signal design. Single carrier frequency domain equalization (SC-FDE) has been identified as a suitable candidate for sub-THz, although some challenges such as phase noise and PAPR still remain to be tackled. In this work, we design a phase noise robust, modest PAPR SC waveform by geometrically shaping the constellation under practical conditions. We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a constellation design that is numerically robust to phase noise, while maintaining a relatively low PAPR compared to the conventional waveforms.

Sub-Terahertz communication is a key enabling technology to deliver ultra-high rates up to Terabit/s - one of 6G envisioned goals. However, hardware limitation and impairments set practical challenges in realizing real-world networks operating at such high frequency range. In this paper, based on simulation findings, we analyze the performance of different single- and multi-carrier transmission concepts in the sub-Terahertz range under hardware impairments and limitations, both from a today’s New Radio (NR) up-scaling perspective, and whether it is best to deviate from the straightforward up-scaling into a new design. We therefore focus on the following sub-Terahertz network determinants: the signal peak amplitude characteristics, the phase noise of the local oscillators and the quantization effect of the analog-to-digital-converter. Insights drawn from the analysis are considered to make proposals on the signaling concepts for a practical access network design in the sub-Terahertz range. We also demonstrate how further improvement can be harvested considering signal amplitude variation optimized constellation modulation.

The interference limitation of cellular systems can be addressed by coordinated multi-point (CoMP) transmission and reception, where different base stations act together as a distributed antenna system, sharing data. CoMP has been shown to be sensitive to accuracy of channel knowledge. This work deals with practical channel estimation in an uplink joint reception scenario for multiple users across multiple cells. In most academic work, the parameter knowledge, like second order statistics of the channel and noise, is assumed to be perfectly known. In contrast to that, in this paper, we fully estimate all parameters using pilots. Practical algorithms are designed here to achieve fast convergence of parameter estimation, avoiding the need for too many samples, with manageable complexity. This is done in a multi-stage way, where the outputs of simpler estimators provide the parameters for the more advanced stages. Even for large coordination set sizes, like 7 cells, we show that our multi-stage approach is roughly just 1 dB below perfect channel knowledge in terms of post-combining SINR.