This paper studies the effect of beam-squint on the coverage performance in wideband sub-Terahertz (sub-THz) multi-carrier systems from a system-level analysis perspective. Beam-squint, a frequency-dependent beam misalignment, intensifies in wideband systems, reducing beamforming accuracy and overall network performance. To address this issue, we use tools from stochastic geometry and provide an analytical framework to investigate the coverage probability performance of sub-THz networks under the effect of beam-squint. Our framework integrates important system parameters, such as the spatial deployment of base stations (BSs), system transmission bandwidth, transmit and receive antenna array sizes, channel propagation conditions, and blockage impacts. Using numerical and Monte Carlo simulations, we validate our framework's accuracy and highlight the critical impact of beam-squint in constraining the performance of wideband sub-THz networks. The findings reveal that in wideband multi-carrier systems, coverage performance declines as subcarrier frequencies diverge further from the center frequency due to the beam-squint effect. Additionally, the results highlight that although larger antenna arrays improve the coverage performance, their benefits diminish at higher subcarrier frequencies. This is due to reduced beamwidth, which makes the communication link more susceptible to beam-squint effects, ultimately degrading system performance. These insights are valuable for optimizing sub- THz network parameters to mitigate beam-squint's adverse effects and enhance overall network performance.

The proliferation of space debris in low Earth orbit (LEO) presents critical challenges for orbital safety, particularly for satellite constellations. Integrated sensing and communication (ISAC) systems provide a promising dual-function solution by enabling both environmental sensing and data communication. This study explores the use of space shift keying (SSK) modulation within ISAC frameworks, evaluating its performance when combined with sinusoidal and chirp radar waveforms. SSK is particularly attractive due to its low hardware complexity and robust communication performance. Our results demonstrate that both waveforms achieve comparable bit error rate (BER) performance under SSK, validating its effectiveness for ISAC applications. However, waveform selection significantly affects sensing capability: while the sinusoidal waveform supports simpler implementation, its high ambiguity limits range detection. In contrast, the chirp waveform enables range estimation and provides a modest improvement in velocity detection accuracy. These findings highlight the strength of SSK as a modulation scheme for ISAC and emphasize the importance of selecting appropriate waveforms to optimize sensing accuracy without compromising communication performance. This insight supports the design of efficient and scalable ISAC systems for space applications, particularly in the context of orbital debris monitoring.

This study investigates the anti-jamming space-air-ground integrated network (SAGIN) scenario wherein a reconfigurable intelligent surface (RIS) is deployed on a fixed Unmanned Aerial Vehicle (UAV) to counteract malevolent jamming attacks. In contrast to existing research, in this paper, we consider that a Low Earth Orbit (LEO) satellite is sending the signal to the user on the ground in the presence of jamming from a Geostationary Equatorial Orbit (GEO) satellite side. We aim to maximize the signal-to-jamming plus noise ratio (SJNR) by optimizing the RIS beamforming and transmit power of the LEO satellite. Assuming the availability of global channel state information (CSI) at the RIS, we propose alternating optimization (AO) and semidefinite relaxation (SDR) techniques to address the complexity. Simulation results show that the optimization schemes lead to considerable performance improvements. The results also indicate that, given the high jamming power and the relatively small number of RIS elements, deploying the RIS on UAVs near the user is more effective in mitigating the impact of jamming interferers.

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

This paper investigates a user-centric clustering model for a hybrid network comprising both millimeter-wave (mmWave) and terahertz (THz) base stations (BSs). Based on the proposed model, a user can choose to be cooperatively served by multiple mmWave or multiple THz BSs depending on their link quality. Besides, to maximize the cooperation gains, the serving clusters are dynamically adjusted to user's channel conditions pertaining to the different properties of the mmWave and THz networks. Finally, we evaluate the coverage probability of the hybrid network by using stochastic geometric tools and validate the analysis through numerical simulations.

The user-centric base station cooperation is a new approach that allows a mobile user to be connected to a set (cluster) of base stations instead of being associated with a single one. This approach is highly valuable in millimeter wave networks where the base stations are expected to be densely deployed. In this paper, we evaluate the performance, in terms of coverage probability, of user-centric millimeter wave networks with dynamic clustering. First, we propose a dynamic clustering model for base stations that will cooperate to serve a given user. Then, based on the proposed model, we investigate analytically the coverage probability performance of the considered user-centric network using stochastic geometry tools. Finally, numerical and simulation results are provided, showing that the proposed dynamic clustering model always outperforms static clustering and single base station selection schemes for given network parameters.

In millimeter wave (mmWave) networks, base stations (BSs) are expected to be densely deployed in order to meet the demands of mobile users. A major challenge in dense mmWave networks is the interference experienced by the user from the neighboring BSs which limits the density of deployed BSs. A promising solution to this challenge is to adopt user-centric BS cooperation that allows a user to be connected to a set (cluster) of BSs instead of being associated with a single one. In this paper, we investigate the performance of user-centric mmWave networks with dynamic clustering. First, we propose a dynamic clustering model to enable the user-centric BS cooperation. Then, based on the proposed model, we analyze the coverage probability and average spectral efficiency (ASE) performances using stochastic geometry tools. We also propose a BS clustering optimization framework to achieve maximum ASE performance for given network configuration. Finally, numerical and simulation results are provided, showing that the proposed dynamic clustering schemes always outperform static clustering and single-BS selection schemes and yield an optimum system performance for given network parameters.

In cellular networks that exploit the millimeter wave bands, access points are expected to be densely deployed and to share limited radio resources. In such systems, the geometric distribution of the downlink signal-to-interference-plus-noise ratio (SINR) is affected by several random variables such as path loss, blockage, small scale channel fading, interferer density, antenna array orientation, and thermal noise. Therefore, using fixed modulation in such varying environments is inefficient as it degrades the spectral efficiency and/or increases the outage probability. To tackle this issue, the transmitter needs to adapt the modulation order to the link condition. In this paper, we introduce an adaptive modulation technique, which depends on the geometric distribution of the SINR, for millimeter wave cellular systems in order to enhance their performances in terms of average spectral efficiency. In this case, the transmitter adjusts the modulation type of the transmitted signal based on the receiver geometry and the link condition. First, we present an analytical model using geometry tools to compute the statistical distributions of the downlink SINR. Then, the performance of the adaptive modulation scheme is studied and evaluated in terms of the average spectral efficiency. Numerical results show that, while keeping the outage probability as minimum as possible, this geometry-based adaptive modulation can efficiently enhance the millimeter wave system average spectral efficiency.