Orthogonal time-frequency space (OTFS) is a promising waveform for future Sixth Generation (6G) networks and beyond, offering robustness to high-mobility scenarios and enabling seamless integration of sensing and communication services by operating in the delay-Doppler (DD) domain. Reliable channel estimation is essential for both functions; however, conventional embedded-pilot (EP) schemes sustain high pilot overhead and an increased peak-to-average power ratio (PAPR). Superimposed training (ST) methods have been proposed as an alternative to reduce these limitations, since pilots and data symbols share the same DD resources. Nevertheless, existing ST-based techniques typically depend on computationally expensive algorithms or exhibit degraded communication and sensing performance. To overcome these restrictions, this work introduces a novel low-power superimposed pilot design, called sunscape pattern, and a corresponding ST-based channel estimation algorithm specifically tailored to this structure. The core design principle of the sunscape pattern is to distribute pilot energy following a structured layout in the DD domain that resembles a seascape with a sun over the sea. The sun enables accurate delay and Doppler detection (both integer and fractional), while the sea inherently supports interference averaging between pilot and data components, thereby mitigating self-interference and noise without requiring computationally expensive iterative interference cancellation. Simulation results validate the proposed scheme. They show that it achieves accurate channel estimation and reliable DD-domain detection with low computational complexity and manageable PAPR, outperforming existing approaches in both communication and sensing performance.

Integrated sensing and communications (ISAC) is a new feature to be deployed in the evolution of the Sixth Generation (6G) of mobile networks. Currently, this service is planned to use the recently introduced positioning reference signal (PRS) at the expense of further sacrificing the data rate. In this work, we propose the use of a superimposed training (ST)-based simultaneous channel estimation and sensing via the superposition of a pilot Zadoff-Chu (ZC) sequence on the orthogonal frequency division multiplexing (OFDM). The proper design of the parameters of the ZC sequence and the use of matched filtering at the receiver significantly improve the quality of the channel estimates and enable accurate detection of the targets. The ZC pilot signal shares all time-frequency-space resources, thereby achieving zero pilot overhead and eliminating the need for any resources specifically dedicated to reference signals. The analysis and numerical results confirm that the proposed design achieves low-complex and accurate channel estimation and sensing, simplifying and enhancing the implementation of ISAC for 6G applications.

This paper introduces a novel Reference Signal and a channel estimation and equalization technique for Zero-Padded waveforms, like ZP-OFDM and the recently proposed FM-OFDM, under doubly-dispersive channels with Doppler and phase noise. We describe a two-stage pilot structure aimed to separately capture the long-term and short-term channel variations and a piecewise estimation and equalization technique, based on approximation of the time-varying channel impulse response by a set of time-invariant channel responses, which are independently equalized and further combined to compensate the channel’s dispersion. Design criteria for the proposed Reference Signal are also given. Numerical results under high phase noise show that piecewise-equalized ZP-OFDM can outperform MMSE-equalized CP-OFDM with CPE compensation, thus avoiding the need of an additional Reference Signal for phase noise mitigation. Results under high mobility demonstrate the superiority of piecewise-equalized FM-OFDM and ZP-OFDM waveforms over MMSE-equalized OTFS and CP-OFDM, outperforming also the highly complex DFE-equalized OTFS for certain modulation orders, without the need to estimate any Doppler components. The degradation incurred by the proposed realistic piecewise estimation technique with respect to ideal estimation depends on the richness of the channel’s multipath profile rather than its Doppler spread.

This paper contributes by providing a comprehensive set of block error rate (BLER) vs. signal-to-noise ratio (SNR) curves under additive white Gaussian noise (AWGN) channel conditions for 5G new radio (NR) modulation and coding schemes (MCS) belonging to the 3GPP 5G NR TS 38.214 standard, with low-density parity check (LDPC) coded scenario according to TS 38.212. To enhance practical relevance in the context of O-RAN networks, this paper also introduces the effect of optimum quantization and compares the results without quantization, showing that despite system degradation, the performance remains very close to the unquantized case. By providing this comprehensive dataset, the paper offers valuable insights to support the selection of the most appropriate MCS depending on the required BLER-SNR scenario, serving as a guide in the design of 5G communication systems, for the scheduler, and as lookup tables for the physical layer (PHY) abstraction in link-level simulators (LLS).

The increasing demand for bandwidth has led fifth generation (5G) mobile communication systems to adopt the use of millimeter waves (mmWaves), to enable higher data rates. However, operating at such high frequencies poses technological challenges, one of the most prominent being the phenomenon known as phase noise (PN), which degrades the system, especially when combined with the orthogonal frequency-division multiplexing (OFDM) waveform. Traditionally, this effect is corrected by using exclusively dedicated pilots for PN tracking, which severely degrade the data rate. This paper presents pilotless machine learning (ML)-based methods for estimating and compensating PN with the goal of eliminating the need for pilots, leading to a substantial increase of the data rates. In addition, this paper demonstrates the feasibility of these solutions for practical mmWaves deployments through tests with real measurements in an experimental setup using an mmWave 5G-developed platform.