Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals

Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an ``iceberg hidden in the sea", where the ``iceberg'' represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the ``sea level'' characterizes the corresponding variance, caused by the randomness of the data payload. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance.

Unveiling the Potential of NOMA: A Journey to Next Generation Multiple Access

Revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, and healthcare 5.0, are driving next-generation wireless networks (NGWNs). These technologies generate massive data, requiring swift transmission and trillions of device connections, fueling the need for sophisticated next-generation multiple access (NGMA) schemes. NGMA enables massive connectivity in the 6G era, optimizing NGWN operations beyond current multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as NGMA's frontrunner, exploring What has NOMA delivered?, What is NOMA providing?, and What lies ahead?. We present NOMA variants, fundamental operations, and applicability in multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, and unmanned aerial vehicles (UAVs). Additionally, we explore NOMA's interplay with state-of-the-art wireless technologies, highlighting its advantages and technical challenges. Finally, we unveil NOMA research trends in the 6G era and provide design recommendations and future perspectives for NOMA as the leading NGMA solution for NGWNs.

Deep Learning-Enabled ISAC-OTFS Pre-equalization Design for Aerial-Terrestrial Networks

Orthogonal time frequency space (OTFS) modulation has been viewed as a promising technique for integrated sensing and communication (ISAC) systems and aerial-terrestrial networks, due to its delay-Doppler domain transmission property and strong Doppler-resistance capability. However, it also suffers from high processing complexity at the receiver. In this work, we propose a novel pre-equalization based ISAC-OTFS transmission framework, where the terrestrial base station (BS) executes pre-equalization based on its estimated channel state information (CSI). In particular, the mean square error of OTFS symbol demodulation and Cramer-Rao lower bound of sensing parameter estimation are derived, and their weighted sum is utilized as the metric for optimizing the pre-equalization matrix. To address the formulated problem while taking the time-varying CSI into consideration, a deep learning enabled channel prediction-based pre-equalization framework is proposed, where a parameter-level channel prediction module is utilized to decouple OTFS channel parameters, and a low-dimensional prediction network is leveraged to correct outdated CSI. A CSI processing module is then used to initialize the input of the pre-equalization module. Finally, a residual-structured deep neural network is cascaded to execute pre-equalization. Simulation results show that under the proposed framework, the demodulation complexity at the receiver as well as the pilot overhead for channel estimation, are significantly reduced, while the symbol detection performance approaches those of conventional minimum mean square error equalization and perfect CSI.

Low-Complexity Minimum BER Precoder Design for ISAC Systems: A Delay-Doppler Perspective

Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization problem takes into account the maximum available transmission power budget and the required sensing performance. Different from prior studies that considered ISAC in the time-frequency (TF) domain, we devise the precoder from the perspective of the delay-Doppler (DD) domain by exploiting the equivalent DD domain channel due to the fact that the DD domain channel generally tends to be sparse and quasi-static, which can facilitate a low-overhead ISAC system design. To address the non-convex optimization design problem, we resort to optimizing the lower bound of the derived average BER by adopting Jensen's inequality. Subsequently, the formulated problem is decoupled into two independent sub-problems via singular value decomposition (SVD) methodology. We then theoretically analyze the feasibility conditions of the proposed problem and present a low-complexity iterative solution via leveraging the Lagrangian duality approach. Simulation results verify the effectiveness of our proposed precoder compared to the benchmark schemes and reveal the interplay between sensing and communication for dual-functional precoder design, indicating a trade-off where transmission efficiency is sacrificed for increasing transmission reliability and sensing accuracy.

E-Healthcare Systems: Integrated Sensing, Computing, and Semantic Communication with Physical Layer Security

This paper introduces an integrated sensing, computing, and semantic communication (ISCSC) framework tailored for smart healthcare systems. The framework is evaluated in the context of smart healthcare, optimising the transmit beamforming matrix and semantic extraction ratio for improved data rates, sensing accuracy, and general data protection regulation (GDPR) compliance, while considering IoRT device computing capabilities. Semantic metrics such as semantic transmission rate and semantic secrecy rate are derived to evaluate data rate performance and GDPR risk, respectively, while the Cram\'er-Rao Bound (CRB) assesses sensing performance. Simulation results demonstrate the framework's effectiveness in ensuring reliable sensing, high data rates, and secure communication.

Symbiotic Sensing and Communication: Framework and Beamforming Design

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramer-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

Pulse Shaping for Random ISAC Signals: The Ambiguity Function Between Symbols Matters

Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems.

OFDM Achieves the Lowest Ranging Sidelobe Under Random ISAC Signaling

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

Near-field Beamforming for Extremely Large-scale MIMO Based on Unsupervised Deep Learning

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems' rate and spectral efficiency. However, as ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region where the spherical wavefront propagates. This inevitably leads to a significant increase in the overhead of beam training, requiring complex two-dimensional beam searching in both the angle domain and the distance domain. To address this problem, we propose a near-field beamforming method based on unsupervised deep learning. Our convolutional neural network efficiently extracts complex channel state information features by strategically selecting padding and kernel size. We optimize the beamformers to maximize achievable rates in a multi-user network without relying on predefined custom codebooks. Upon deployment, the model requires solely the input of pre-estimated channel state information to derive the optimal beamforming vector. Simulation results show that our proposed scheme can obtain stable beamforming gain compared with the baseline scheme. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the beam training costs in near-field regions.

Improving the Ranging Performance of Random ISAC Signals Through Pulse Shaping Design

In this paper, we propose a novel pulse shaping design for single-carrier integrated sensing and communication (ISAC) transmission. Due to the communication information embedded in the ISAC signal, the resulting auto-correlation function (ACF) is determined by both the information-conveying random symbol sequence and the signaling pulse, where the former leads to random fluctuations in the sidelobes of the ACF, impairing the range estimation performance. To overcome this challenge, we first analyze the statistical characteristics of the random ACF under the symbol-wise pulse shaping (SWPS) regime. As a step further, we formulate an optimization problem to design ISAC pulse shaping filters, which minimizes the average integrated sidelobe level ratio (ISLR) while meeting the Nyquist criterion, subject to power and bandwidth constraints. We then show that the problem can be recast as a convex quadratic program by expressing it in the frequency domain, which can be readily solved through standard tools. Numerical results demonstrate that the proposed pulse shaping design achieves substantial ranging sidelobe reduction compared to the celebrated root-raised cosine (RRC) pulse shaping, given that the communication throughput is unchanged.