An HFM-Inspired Random Access Preamble Design for NTN under High Doppler

Non-terrestrial networks (NTNs) are a key enabler of ubiquitous 6G connectivity, but the high orbital velocity and long propagation distances in low-Earth orbit (LEO) NTN operation introduce large Doppler shifts and substantial delay uncertainty that challenge New Radio (NR) physical random access channel (PRACH) design. Conventional Zadoff Chu (ZC) and linear frequency modulated (LFM) preambles are particularly vulnerable, as Doppler induced ambiguity and delay Doppler coupling degrade timing estimation and preamble identification. This paper proposes a hyperbolic frequency modulation (HFM) inspired PRACH preamble for robust synchronization and reliable identification under uncompensated or unknown Doppler, detected with a conventional matched filter receiver so that the gains reflect the preamble design. A unified delay Doppler ambiguity function framework characterizes the self and cross ambiguity behavior of ZC, LFM, and HFM inspired preambles, and a scaling-factor based codebook ensures multi-user separability. Simulation results under NTN channel conditions confirm higher detection probability, lower timing root mean square error (RMSE), and improved peak to sidelobe and integrated sidelobe levels compared with ZC and frequency-domain superposed LFM baselines.

PHY Authentication for Ambient IoTs in Near-Field XL-MIMO Backscatter Communication Systems

This letter exploits near-field (NF) spherical wavefronts to derive geometry-dependent physical signatures for backscatter device (BD) authentication in extremely large-scale multiple-input multiple-output (XL-MIMO) backscatter communication systems. We propose a binary element-wise delay shift keying (EW-DSK) modulation scheme based on a multi-antenna BD architecture. The proposed scheme introduces a deterministic, bit-dependent delay that shifts the power delay profile (PDP) while preserving the aperture-dependent curvature induced by NF propagation. Using the dominant PDP component extracted across the receive antennas, we develop a compact delay-curvature signature extractor and a corresponding authentication test to mitigate BD impersonation attacks. Simulation results evaluate the proposed scheme in terms of curvature extraction accuracy, authentication accuracy, and spoofing success probability under different spoofer distances, signal-to-noise ratios, and effective bandwidths.

Temporal Broadening-Aware Multiplexing for Joint Sensing and Communication in THz Band

High-resolution wireless sensing has become an integral component of futuristic 6G networks alongside high-rate communication. Terahertz (THz) band enables both functions through its extremely large bandwidth, providing sub-centimeter level sensing precision and multi-gigabit data rates. However, THz propagation suffers from severe channel impairments such as molecular absorption (MoA) and the resulting temporal broadening effect (TBE). For sensing, TBE causes temporal spreading of received echoes, leading to degraded range resolution and necessitating long guard intervals between consecutive sensing pulses to avoid overlap. These guards, while necessary for far sensing receiver (Rxsens), cause latency and inefficient temporal use. To overcome this limitation, this paper proposes a TBE-aware multiplexing framework that exploits the distance-dependent nature of TBE to enable interference-free coexistence of sensing and communication (S&C) pulses. A guard interval preallocated for the worst-case TBE at far Rxsens is opportunistically reused to embed a low-power single-carrier communication pulse for a nearby user experiencing minimal broadening. Limited TBE confines S&C pulses within their designated slots at short distances, while the broadened and attenuated communication pulse at the distant Rxsens becomes negligible, eliminating the need for successive interference cancellation (SIC). Simulation results reveal that compared with power-domain non-orthogonal multiple access (PD-NOMA) and fixed-guard alternative, the proposed scheme achieves superior bit-error rate, sensing accuracy, and latency performance, with up to 66.5% latency reduction under heavy traffic.

Energy-Efficient Target-Aware Hybrid Beamforming for THz Near-Field ISAC with Sparse Connectivity

Integrated sensing and communication (ISAC) at terahertz (THz) frequencies enables ultra-high-resolution perception while facing a key limitation: highly directional THz beams cannot illuminate extended targets within a single beam. Conventional solutions rely on sequential beam scanning, reducing sensing accuracy and increasing energy consumption. Moreover, in conventional sparse-array, grating lobes are generally treated as undesirable artifacts that should be suppressed to avoid ambiguity and interference. In contrast, this paper adopts a reverse design philosophy by intentionally engineering sparsity-induced grating lobes as controllable auxiliary illumination beams for extended-target sensing. This paper exploits grating lobes and proposes a sparse-connected hybrid beamforming architecture that intentionally engineers and exploits grating lobes to enable single-shot, full-aperture illumination of extended targets while supporting multi-user downlink communication. A switch-controlled sparse RF network preserves the array aperture and generates a dominant main lobe with structured secondary lobes covering the entire target extent. A covariance-driven alternating-minimization framework jointly optimizes digital precoders, quantized phase shifters, and antenna-RF switching. Simulations at 140 GHz demonstrate near fully-digital Cramer-Rao sensing accuracy, competitive communication performance in low-rank THz channels, rapid convergence, and significant hardware and energy savings, establishing structured sparse connectivity as a scalable and energy-efficient solution for extended-target THz ISAC.

Posterior-Confidence Driven Beamforming for Energy-Efficient Integrated Sensing and Communication

Energy efficiency will pose an essential limitation for sixth-generation (6G) integrated sensing and communication (ISAC) systems, given the high sensing power consumption associated with persistent sensing, despite stable communication requirements. This paper proposes an energy-efficient multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) beamforming framework that minimizes transmit power while guaranteeing per-user signal-to-interference-plus-noise ratio (SINR) and reliable multi-target tracking. The key innovation is a tracking-aware, skip-enabled sensing policy that departs from the conventional always-on probing paradigm. Instead of enforcing sensing at every epoch, sensing is selectively triggered according to two complementary statistics derived from an extended Kalman filter (EKF): a posterior confidence metric and the normalized innovation squared (NIS). While the former ensures accurate estimation, the latter guarantees reliable measurements, and thus sensing can only be activated when additional information is required. To ensure robustness under intermittent sensing, sector-based beampattern constraints are combined with a nonzero safety illumination floor imposed to guarantee reliable target tracking when skipping occurs. Numerical results show that the proposed framework achieves a significant reduction in transmit power compared to other baselines, without any deterioration in the communication system's performance or excessive impact on the sensing process.

Memory-Native Non-Terrestrial Networks for Embodied Intelligence

Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in wilderness to leverage cloud resources or report critical information to remote centers. However, the synergy is nontrivial due to the highly-dynamic, resource-constrained, topology-varying, and task-oriented environment. Existing memoryless NTN protocols become inefficient, since the decisions are driven by local channel conditions and instantaneous service demands. To address these limitations, this paper proposes the memory-native NTN (MemNTN) paradigm that leverages long-horizon contexts for memory augmented system optimization. To realize this paradigm shift, we establish a dual-memory architecture that distinguishes between physical memory representing the state of the world and digital memory encoding historical network experience. We develop memory acquisition, compression, valuation, update, and utilization mechanisms that facilitate cross-layer, memory-native decision-making, spanning from the physical and access layers up to the network and application layers. Experiments in satellite embodied question answering (SEQA) demonstrate that the proposed MemNTN significantly outperforms conventional stateless NTN and terrestrial approaches.

Memory Centric Power Allocation for Multi-Agent Embodied Question Answering

This paper considers multi-agent embodied question answering (MA-EQA), which aims to query robot teams on what they have seen over a long horizon. In contrast to existing edge resource management methods that emphasize sensing, communication, or computation performance metrics, MA-EQA emphasizes the memory qualities. To cope with this paradigm shift, we propose a quality of memory (QoM) model based on generative adversarial exam (GAE), which leverages forward simulation to assess memory retrieval and uses the resulting exam scores to compute QoM values. Then we propose memory centric power allocation (MCPA), which maximizes the QoM function under communication resource constraints. Through asymptotic analysis, it is found that the transmit powers are proportional to the GAE error probability, thus prioritizing towards high-QoM robots. Extensive experiments demonstrate that MCPA achieves significant improvements over extensive benchmarks in terms of diverse metrics in various scenarios.

Optimized Non-Uniform Pilot Pattern for OFDM Sensing

Standard periodic pilot patterns in orthogonal frequency division multiplexing (OFDM) systems induce severe delay-domain grating lobes, compromising radar sensing. This paper proposes a two-stage framework to design non-periodic pilot patterns that minimize the peak sidelobe level (PSL) while strictly enforcing communication anchor constraints. We black solve this combinatorial problem using a low-complexity hybrid greedy-stochastic cyclic coordinate descent (SCCD) algorithm. This approach shatters cyclic periodicities to suppress deterministic grating lobes beneath the impassable data-to-pilot interference (DPI) noise floor. System-level evaluations demonstrate the performance of the proposed design in resolving the sensing-communication trade-off, showing improved range root mean square error (RMSE) without degrading the primary communication bit error rate (BER).

ISAC with Affine Frequency Division Multiplexing: An FMCW-Based Signal Processing Perspective

This paper investigates the sensing potential of affine frequency division multiplexing (AFDM) in high-mobility integrated sensing and communication (ISAC) from the perspective of radar waveforms. We introduce an innovative parameter selection criterion that establishes a precise mathematical equivalence between AFDM subcarriers and Nyquist-sampled frequency-modulated continuous-wave (FMCW). This connection not only provides a clear physical insight into AFDM's sensing mechanism but also enables a direct mapping from the DAFT index to delay-Doppler (DD) parameters of wireless channels. Building on this, we develop a novel input-output model in a DD-parameterized DAFT (DD-DAFT) domain for AFDM, which explicitly reveals the inherent DD coupling effect arising from the chirp-channel interaction. Subsequently, we design two matched-filtering sensing algorithms. The first is performed in the time-frequency domain with low complexity, while the second is operated in the DD-DAFT domain to precisely resolve the DD coupling. Simulations show that our algorithms achieve effective pilot-free sensing and demonstrate a fundamental trade-off between sensing performance, communication overhead, and computational complexity. The proposed AFDM outperforms classical AFDM and other variants in most scenarios.

Waveform-domain NOMA: An Enabler for ISAC in Uplink Transmission

According to the recent 3GPP decisions on 6G air interface, orthogonal frequency-division multiplexing (OFDM)-based waveforms are the primary candidates for future integrated sensing and communication (ISAC) systems. In this paper, we consider a monostatic sensing scenario in which OFDM is used for the downlink and its reflected echo signal is used for sensing. OFDM and discrete Fourier transform-spread OFDM (DFT-s-OFDM) are the options for uplink transmission. When OFDM is used in the uplink, the power difference between this signal and the echo signal leads to a power-domain non-orthogonal multiple access (PD-NOMA) scenario. In contrast, adopting DFT-s-OFDM as uplink signal enables a waveform-domain NOMA(WD-NOMA). Affine frequency-division multiplexing (AFDM) and orthogonal time frequency space (OTFS) have been proven to be DFT-s-OFDM based waveforms. This work focuses on such a WD-NOMA system, where AFDM or OTFS is used as uplink waveform and OFDM is employed for downlink transmission and sensing. We show that the OFDM signal exhibits additive white Gaussian noise (AWGN)-like behavior in the affine domain, allowing it to be modeled as white noise in uplink symbol detection. To enable accurate data detection performance, an AFDM frame design and a noise power estimation (NPE) method are developed. Furthermore, a two-dimensional orthogonal matching pursuit (2D-OMP) algorithm is applied for sensing by iteratively identifying delay-Doppler components of each target. Simulation results demonstrate that the WD-NOMA ISAC system, employing either AFDM or OTFS, outperforms the PD-NOMA ISAC system that uses only the OFDM waveform in terms of bit error rate (BER) performance. Furthermore, the proposed NPE method yields additional improvements in BER.