In this study, we perform statistical radar cross section (RCS) analysis for various test targets in an indoor factory at 25-28 GHz, with the goal of determining the best-fit parametric distributions that characterize the target scattering properties to be used in integrated sensing and communication channel modeling standardization. The analysis is conducted based on measurements in quasi-monostatic and bistatic configurations with bistatic angles of 20°, 40°, and 60°. The test targets include unmanned aerial vehicles, an autonomous mobile robot, and a robotic arm. Goodness-of-fit tests validate that the RCS of these targets is best modeled by lognormal and gamma distributions with high statistical confidence. Additionally, we provide a framework for evaluating the near-field (NF), specular-dominant effective bistatic RCS of a rectangular sheet under controlled bistatic geometries. Novel deterministic RCS models are evaluated, incorporating dependencies on the bistatic angle, transmitter-target separation ( 2 m to 10 m ). The results demonstrate that some proposed deterministic RCS models accurately fit the measured data, highlighting their applicability in deterministic RCS characterization in NF bistatic configurations.

This paper proposes a comprehensive framework for a geometry-based statistical model for integrated sensing and communication (ISAC) tailored for bistatic systems. Our dualcomponent model decomposes the ISAC channel into a target channel encompassing all multipath components produced by a sensing target parameterized by the target’s radar cross-section and scattering points, and a background channel comprising all other propagation paths that do not interact with the sensing target. The framework extends TR38.901 via a hybrid clustering approach, integrating spatiotemporally consistent deterministic clusters with stochastic clusters to preserve channel reciprocity and absolute delay alignment for sensing parameter estimation. Extensive simulations across urban macro, urban micro, and indoor factory scenarios demonstrate that the model maintains communication performance parity with the standard TR38.901, validated through bit-error rate analysis obtained via simulated and measured ISAC channels and channel capacity assessment, while enabling sensing performance evaluation, such as target ranging error for localization and receiver operating characteristic curves for detection probability.

The following paper presents a systematic 3rd Generation Partnership Project (3GPP)-compliant characterization of radar cross section (RCS) for indoor factory (InF) objects, including small and mid-sized unmanned aerial vehicles (UAVs), robotic arms, and automated guided vehicles (AGVs). Through measurements in the 25GHz to 28GHz range, we validate the 3GPP standardized log-normal distribution model for RCS for above-mentioned target objects. The 3GPP-complaint RCS parameters obtained for the small-sized UAV are in close agreement (< 1 dB deviation) with 3GPP agreed values. The mid-sized UAVs exhibit higher reflectivity compared to the small-sized UAV due to enhanced specular components attributed to material and lithium-ion battery packs. On the other hand, the robotic arm exhibits dynamic RCS behavior due to mechanical articulation, whereas AGVs show height and motion-dependent reflectivity patterns. Our findings provide empirical validation for RCS characterization for integrated sensing and communication channel modeling in InF environments.

The following paper presents a framework for DC-biased optical OFDM (DCO-OFDM) in visible light communication (VLC) systems, addressing critical implementation challenges in timing synchronization and channel estimation. We propose a novel preamble design based on modified Zadoff-Chu (ZC) sequences that preserve constant amplitude zero autocorrelation (CAZAC) properties in the frequency-domain while satisfying intensity modulation/direct detection (IM/DD) constraints. The frequency-domain cross-correlation of the modified ZC sequence enables robust timing offset estimation, particularly in low-SNR scenarios. We evaluate the probability of correct timing offset estimation and the normalized mean square error (NMSE) for the proposed channel estimation scheme. Furthermore, we characterize the system-level trade-offs between clipping distortion, quantization resolution, and DC biasing, deriving optimal operating points for power efficiency under the proposed framework. Simulation results demonstrate significant improvements in synchronization accuracy compared to other alternatives, with the proposed method achieving a correct timing estimation probability of ≥ 0.99 even at low SNR values. The channel estimation NMSE performance confirms the scheme’s efficiency, outperforming conventional approaches. The approach presented hereby establishes practical design guidelines for robust timing synchronization and channel estimation for IM/DD-based DCO-OFDM implementations in VLC systems.

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about $\mathbf{1 7. 1 8 \%}$ in indoor environments and $\mathbf{9. 7 9 \%}$ in outdoor environments.

This article examines two chirp spread spectrum techniques specifically devised for low-power wide-area networks (LPWANs) to optimize energy and spectral efficiency (SE). These methods referred to as layered CSS (LCSS) and layered dual-mode CSS (LDMCSS), involves utilizing multiple layers for multiplexing symbols with varying chirp rates. These waveform designs exemplify a high degree of SE compared to existing schemes. Additionally, LDMCSS necessitates a lesser number of layers than LCSS to attain comparable SE, thereby reducing computational complexity. These proposed techniques can employ coherent and non-coherent detection and can be adjusted to achieve various spectral efficiencies by altering the number of multiplexed layers. Unlike our proposed LCSS and LDMCSS, other CSS alternatives for LPWANs cannot provide the same level of flexibility and SE. The performance of these techniques is evaluated in terms of bit error rate under different channel conditions, as well as with phase and frequency offsets.

In this article, we present proof-of-concept of M-ary asymmetrically clipped (AC)-frequency-shift keying (FSK) for energy-efficient/low data rate visible light communications (VLC). M-ary AC-FSK uses the odd frequency waveforms from 2M-ary FSK; which exhibit half-wave symmetry. Owing to this half-wave symmetry, the negative amplitude excursions are clipped without loss of information. Hereby, we investigate the performance of three different receivers for M-ary AC-FSK exhibiting different complexities. These receivers are: (i) an optimal time-domain maximum likelihood (ML) receiver; (ii) 1-tap discrete cosine transform (DCT) receiver; and (iii) a 2-tap harmonic receiver which exploits the spectral properties of AC-FSK waveforms. We shall exemplify that the 2-tap harmonic receiver overcomes the energy efficiency versus complexity trade-off that exists for optimal ML and 1-tap DCT receivers. Simulation results shall confirm that the performance of 2-tap harmonic receiver reaches the performance of the previously proposed unipolar (U)-FSK with the optimal receiver, but with a drastically reduced receiver complexity.

Multimodulus algorithms (MMA) based adaptive blind equalizers mitigate inter-symbol interference in a digital communication system by minimizing dispersion in the quadrature components of the equalized sequence in a decoupled manner, i.e., the in-phase and quadrature components of the equalized sequence are used to minimize dispersion in the respective components of the received signal. These unsupervised equalizers are mostly incorporated in bandwidth-efficient digital receivers (wired, wireless or optical) which rely on quadrature amplitude modulation based signaling. These equalizers are equipped with nonlinear error-functions in their update expressions which makes it a challenging task to evaluate analytically their steady-state performance. However, exploiting variance relation theorem, researchers have recently been able to report approximate expressions for steady-state excess mean square error (EMSE) of such equalizers for noiseless but interfering environment. In this work, in contrast to existing results, we present exact steady-state tracking analysis of two multimodulus equalizers in a non-stationary environment. Specifically, we evaluate expressions for steady-state EMSE of two equalizers, namely the MMA2-2 and the βMMA. The accuracy of the derived analytical results is validated using different set experiments and found in close agreement.