Multi-access Edge Computing (MEC) enables users to handle resource-intensive and latency-sensitive tasks. However, the offloading behaviors, which are closely correlated with wireless channel conditions, can inadvertently reveal users' location information to untrustworthy MEC servers. Existing location privacy-aware task offloading (LPTO) mechanisms have not fully considered and comprehensively analyzed personalized location privacy protection requirements. To address this gap, this paper proposes a differential privacy (DP)-based personalized LPTO mechanism for MEC environments that jointly optimizes the perturbation region, privacy budget, and offloading rate while maximizing the offloading utility. We quantify personalized privacy requirements by incorporating task sensitivity, user privacy preference, and task priority. Then, we propose a two-timescale (2Ts) optimization framework to solve the complex personalized location privacy-aware task offloading optimization problem. Specifically, we optimize the perturbation region on a long timescale to align with long-term privacy requirements. In contrast, the offloading ratio and privacy budget are dynamically optimized on a short timescale based on instantaneous channel states and offloading workloads. Furthermore, we model the privacy-aware offloading problem as a Markov decision process (MDP) and develop a dual-agent deep reinforcement learning (DRL)-based personalized LPTO mechanism (DDPLM) to optimize strategies under dynamic MEC systems. Simulation results validate that the proposed DDPLM achieves personalized location privacy protection while reducing computational costs.

The extremely short wavelength of terahertz (THz) communications leads to an extended radiative near-field region, in which some canonical far-field assumptions fail. Existing near-field boundary formulations (Fraunhofer distance) for uniform linear/planar array (ULA/UPA) configurations assume ideal alignment between transceivers, overlooking practical misalignments caused by mobility or mechanical imperfections. This paper addresses this critical gap by analyzing the impact of spatial misalignment on near-field distance calculations in THz systems. We derive exact analytical expressions and simplified approximations for the near-field boundary in both ULA–ULA and UPA–UPA configurations under arbitrary misalignment off-sets. Through numerical simulations, we validate our theoretical models and quantify how misalignment reshapes the near-field region. These findings provide essential guidelines for optimizing THz system deployment in realistic scenarios.

Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support the ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typically employed in THz systems. To address these challenges, we propose a novel low-complexity two-phase channel estimation scheme for RIS-assisted THz systems with beam split effect. In the proposed scheme, we first estimate the full CSI over a small subset of subcarriers (SCs), then extract angular information at both the base station and RIS. Subsequently, we recover the full CSI across remaining SCs by determining the corresponding spatial directions and angle-excluded coefficients. Theoretical analysis and simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean-square error while significantly reducing computational complexity compared to existing algorithms.

Reconfigurable intelligent surface (RIS)-aided tera-hertz (THz) communication is considered as a promising technique for the sixth-generation network. However, employment of hybrid beamforming in THz systems introduces beam split effect, resulting in severe achievable data rate loss. To realize the full potential of RIS-aided THz systems, it becomes crucial to acquire accurate channel state information. Therefore, in this paper, we propose a novel cyclic beam split generalized approximating message passing (CBS-GAMP) channel estimation scheme without requiring the knowledge of number of propagation paths. Specifically, we expand cascaded channels into sparse representations by designing a CBS dictionary, and then we propose the CBS-GAMP algorithm based on statistical inference framework. Numerical simulations demonstrate effectiveness of the proposed CBS-GAMP scheme against the existing solutions.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) cell-free massive multiple-input-multiple-output (mMIMO) networks have been envisioned as a prospective technology for future 6G networks. However, due to the ultrawide bandwidth and the frequency-dependent characteristics of RISs, beam-split effect has become an unavoidable obstacle. To compensate the severe performance degradation caused by beam split effect, we introduce additional time delay (TD) layers at both access points (APs) and RISs. Accordingly, we propose a joint precoding framework at APs and RISs to fully unleash the potential of the considered network. Specifically, we first formulate the joint precoding as a nonconvex optimization problem. Then, given the location of unchanged RISs, we adjust the TDs of APs to align the generated beams toward RISs. After that, with knowledge of the optimal TDs of APs, we decouple the optimization problem into three subproblems of optimizing the baseband beamformers, RISs and TDs of RISs, respectively. Exploiting multidimensional complex quadratic transform, we transform the subproblems into convex forms and solve them under alternate optimizing framework. Numerical results verify that the proposed method can effectively mitigate beam split effect and significantly improve the achievable rate compared with conventional cell-free mMIMO networks.

To provide high data rate for high-speed trains (HSTs), it is required to use emerging wireless communication systems, such as the fifth generation (5G). An asymmetric 5G mobile relay system is investigated for HSTs, where the mobile relay is deployed at the HST to avoid high penetration loss of the direct link between the base station (BS) and the users (TIE) inside carriages. The sub-6GHz frequency is utilized for the BS-relay link while the relay-TIE link adopts the millimeter wave frequency. Therefore, the BS-relay link experiences κ-μ fading and the relay-TIE link experiences static fluctuating two-ray fading. Moreover, the channel aging effect is considered due to the mobility of HST. For the considered system, we first study the exact statistical characterizations of the end-to-end signalto-noise ratios. Then, we derive exact closed-form expressions for key performance metrics, such as outage probability, average bit-error rate, and average achievable rate per unit bandwidth. The significant effects of channel aging, system and channel parameters on the mobile relay system are revealed from theoretical analysis and are further illustrated by simulation results. Our investigation reveals that the mobile relay system is a promising network architecture for HST communications and can provide steady and high-speed data provisioning to HST passengers against the significant bottleneck of channel aging.

In this paper, we present a comprehensive framework for the performance analysis of dual-hop relaying communications with variable gain amplify-and-forward (AF) relays and operating in the presence of both multipath fading and shadowing, modeled by the Fisher-Snedecor F-distribution. Novel closed-form expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) of the considered system subject to hardware impairments are first derived. Single-integral expressions for the numerical evaluation of the n-th moment of the end-to-end SNR, the outage probability (OP), the ergodic capacity under different adaptive transmission schemes, the effective capacity and the average bit error rate (ABER) of M-ary modulation schemes are further presented. The proposed analytical expressions are valid for most of the well-known fading distributions, provided that the moment generating function (MGF) of the inverse SNR of each hop is readily available. For the special case of ideal hardware, it is shown that the above performance metrics can be expressed in closed-form. It is worth pointing out that the proposed analysis is valid even when the destination node is equipped with multiple antennas and maximal ratio combining (MRC) is employed. The correctness of the proposed mathematical analysis is validated through extensive numerically evaluated results accompanied with Monte-Carlo simulations.

In this paper, we investigate the performance of unmanned aerial vehicle (UAV) relaying systems with simultaneous wireless information and power transfer over the recently proposed Fisher-Snedecoer composite F fading channels. Closed form exact expressions for the outage probability (OP) and the average capacity (AC) of both decode-and-forward (DF) and amplify-and-forward (AF) protocols are derived. In the high signal-to-noise ratio region, we obtain asymptotic OP expressions to provide valuable insights into the impact of system and fading parameters on the UAV relaying performance. Finally, the accuracy of our derived expressions is validated via Monte Carlo simulations.