Integrated sensing and communication (ISAC) in wireless systems has emerged as a promising paradigm, offering the potential for improved performance, efficient resource utilization, and mutually beneficial interactions between radar sensing and wireless communications, thereby shaping the future of wireless technologies. In this work, we present two novel methods to address the joint angle of arrival and angle of departure estimation problem for bistatic ISAC systems. Our proposed methods consist of a deep learning (DL) solution leveraging complex neural networks, in addition to a parameterized algorithm. By exploiting the estimated channel matrix and incorporating a preprocessing step consisting of a coarse timing estimation, we are able to notably reduce the input size and improve the computational efficiency. In our findings, we emphasize the remarkable potential of our DL-based approach, which demonstrates comparable performance to the parameterized method that explicitly exploits the multiple-input multiple-output (MIMO) model, while exhibiting significantly lower computational complexity.

In this paper, we investigate three different concepts for robust link-level performance under doubly-dispersive wireless channels, namely, i) channel estimation, ii) cyclic prefix (CP)-free transmission, and iii) waveform design. We employ a unique word-based channel estimation, where we decouple the channel related errors into channel estimation error (CEE) and Doppler error (DE). Then, we show that a trade-off between CEE and DE emerges in the frame design, where the system can be optimized to achieve the minimum composite channel error. Another strategy to improve the link-level performance is to suppress the CP of the sub-blocks. This allows for better channel estimation due to the reduced transmission time, with the penalty of requiring the CP-restoration processing at the receiver. Furthermore, we propose the waveform design based on the equal-reliability criterion (ERC), leading to the block multiplexing-orthogonal chirp division multiplexing (BM-OCDM). This waveform is advantageous in the CP-free transmission mode, where the data symbols have equally distributed interference from adjacent sub-blocks. Our framework is a generalization of the recently proposed orthogonal time frequency space (OTFS), which fails to achieve the ERC. The link-level simulations show that at high modulation and coding scheme, the proposed BM-OCDM provides superior link-level performance than OTFS.

A highly relevant case of wireless transmission over time and/or frequency selective channels is when the channel state information is only known at the receiver. Under these scenarios, this paper aims to derive the necessary conditions for an unitary transmitter matrix to maximize the coded modulation (CM) capacity. We approach the problem by considering the CM capacity upper bound (UB) instead of the general one due to simplification of the problem and tightness at high signal-tonoise ratio (SNR) regions. The theoretical analysis reveals that the optimal waveforms spread the symbols over the domain of selectivity the channel. To demonstrate this result, we evaluate OFDM, single-carrier (SC) and the Orthogonal Chirp Division Multiplexing (OCDM) where we have that i) OCDM and SC are optimal for frequency-selective channels and ii) OCDM and OFDM are optimal for time-selective channels. Finally, we show that the UB has the same performance order as the real CM capacity, providing evidence that the UB criteria is meaningful for the optimization of a communication system.

In this letter, we present a generic modulation framework for robust wireless communications over doubly dispersive channels. This robustness is achieved by spreading the symbols over time and frequency such that they experience the same channel gain. Interestingly, we show that our modulation framework is a generalization of the recently proposed orthogonal time frequency space modulation (OTFS). By exploiting an additional degree of freedom enabled by the generic framework, we propose in this paper a low-complex waveform based on the sparse Walsh-Hadamard transform (WHT). We show that the proposed sparse WHT modulation exhibits the same frame error rate performance as OTFS, while significantly reducing the implementation complexity due to the multiplication-free small size WHT compared with the discrete Fourier transform used by symplectic finite Fourier transform (SFFT) of OTFS.

This paper considers an integrated sensing and communication (ISAC) system with monostatic radar functionality using a zero-padding orthogonal frequency division multiplexing (ZP-OFDM) downlink transmission. We focus on ISAC’s sensing aspect, employing an energy-detection (ED) method. The ZP-OFDM transmission is motivated by the fact that sensing can be performed during the silent periods of the transmitter, thereby avoiding self-interference (SI) cancellation processing of the in-band full duplex operation, which is needed for the cyclic prefix (CP)-OFDM. Additionally, we also show that ZP-OFDM can reject nearby clutter interference. We derive the probability of detection (PD) for the ZP and CP-OFDM systems, allowing useful performance analyses. In particular, we show that the PD expressions lead to an upper bound for the ZP-OFDM transmission, which is useful for selecting the best ZP size for a given system configuration. We also provide an expression that allows range comparison between ZP and CP-OFDM, where we consider a general case of imperfect SI cancellation for the CP-OFDM system. The results show that when the ZP size is 25% of the fast Fourier transform size, the range loss of the ZP system range is only 17% larger than the CP transmission.

Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum, i.e. \(7.125\)-\(24.25\)\(GHz\), by delving into its key enablers and addressing the multifaceted challenges that lie ahead for these new frequency bands. Here we highlight the physical properties of this never-before-used spectrum by reviewing recent channel measurements for outdoor and indoor environments, including path loss, delay and angular spreads, and material penetration loss, all of which offer insights that underpin future 5G/6G wireless communication designs. Building on the fundamental knowledge of the channel properties, we explore FR3 spectrum agility strategies that balance coverage and capacity (e.g., data rate) tradeoffs, while also examining coexistence with incumbent systems, such as satellites, radio astronomy, and earth exploration. Moreover, we discuss the potential of massive multiple-input multiple-output, compact and digital architectures, and evaluate the potential of multiband sensing for FR3 integrated sensing and communications. Finally, we outline 6G standardization features that are likely to emerge from 3GPP radio frame innovations and open radio access network developments.