In the future generation of mobile communication systems, many scenarios will require high-resolution range-based positioning. However, the accuracy of time-of-arrival (TOA) estimation algorithms for single wideband systems is generally limited due to the constraints of available bandwidth. Currently, leveraging multiple available frequency bands and carrier frequency switching to obtain multi-band channel state information (CSI) for TOA estimation is gaining popularity, as it effectively constructs an equivalent wideband signal. In this paper, we proposed a subspace-based TOA estimation algorithm using multi-band CSI by constructing a correlation matrix and then exploiting its mathematical properties. The proposed algorithm eliminates the need for grid search and thus has lower computational complexity. We analyze the Cramér-Rao Bound (CRB) for the multi-band data model and derive a tighter lower bound for our algorithm. Simulation results show that our algorithm converges to the CRB at high SNR and closely follows the proposed lower bound across all SNR levels. Additionally, our algorithm demonstrates superior performance compared to single-band algorithms and offers advantages in either accuracy or complexity when compared to other multi-band algorithms.

The extremely large-scale array is considered to be one of the key technologies for 6G, which can significantly improve spectral efficiency. However, the extremely-large number of antennas results in a larger range of the near field (e.g., hundreds of meters), leading to the electromagnetic wave propagation modeling changing from plane wave to spherical wave. This makes conventional channel estimation methods suffer from inevitable performance degradation due to the additional distance information in the spherical wavefront. To address this problem, this paper proposes a deep reinforcement learning based near-field channel estimation, in which the multi-agent deep deterministic policy gradient (MADDPG) is employed. Specifically, the near-field channel estimation task is first formulated as a compressed sensing problem by using a sparse spatial grid-based dictionary. Then, the Actor Critic (AC) network based MADDPG algorithm is employed to jointly optimize the angular and distance information by an approximate global search. In addition, an advantage Actor Critic network-based channel estimation algorithm is proposed, which improves both the stability and efficiency of the AC-based algorithm. Finally, the numerical results show that the proposed algorithms outperform the benchmarks in terms of normalized mean square error.

This paper introduces a novel system architecture that integrates an active simultaneous transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) with rate-splitting multiple access (RSMA) to achieve wireless covert communication in the presence of a multi-antenna warden. Departing from conventional approaches that deploy STAR-RIS at fixed positions, we treat the aSTAR-RIS location as a design variable to be optimized for maximizing the covert transmission rate. Our objective is to jointly optimize the aSTAR-RIS placement, transmit beamforming, transmission/reflection coefficients (TRCs), and common rate allocation so as to maximize both the common and private rates for the covert user. The resulting multi-variable optimization problem is addressed via an alternating optimization (AO) framework, which decomposes the problem into four tractable subproblems. For the multi-ratio fractional programming subproblem in the context of transmit beamforming and TRCs optimization, we adopt a Lagrangian dual formulation and quadratic transformation to reformulate the objective into a difference of convex (DC) form. The rank-one constrained beamforming design is then resolved using a penalized successive convex approximation (SCA) method combined with Gaussian randomization. A closed-form solution is derived for the common rate allocation subproblem, while the aSTAR-RIS location optimization is efficiently tackled via the SCA technique. Simulation results validate that the proposed scheme substantially enhances covert transmission rates while preserving communication covertness and guaranteeing reliable common signal reception. Importantly, the results highlight that optimizing the aSTAR-RIS location plays a critical role in improving the overall covert performance of the system.

In this paper, we redesign the hybrid analog/digital precoding scheme to form focused beams at specific spatial locations for near-fieldintegrated sensing and communications(ISAC) systems. Specifically, we derive the near-fieldCramér-Rao bound(CRB) for joint distance and angle sensing with arbitrary signal coherence matrices. Unlike previous optimization strategies that either maximize the communication rate or maximize the sensing performance, we aim to maximize the achievable communication rate under unit sensing error. The optimization problem is modeled to maximize the ratio of the sum rate to the CRB by jointly optimizing the analog and digital precoders. Since it is difficult to solve the problem with complicated non-convex fractional program, we first perform an equivalent reformulation to remove the fractional constraint. Then, the fully-digital precoder is obtained bysuccessive convex approximation(SCA). Finally, a low-complexity hybrid precoding algorithm based on alternate optimization is proposed to divide the fullydigital precoder into analog and digital precoders. Simulation results show the effectiveness of the proposed algorithm and the performance of the proposed beam focusing is superior to beam steering in the near-field.

Precoding technology is very promising in edge computing-assisted cell-free massive multiple-input multiple-output (ECF-mMIMO) systems because it can eliminate interference and thus improve performance. However, it is challenging to design a computationally efficient hybrid precoding scheme to maximize the achievable rate when only the local channel state information (CSI) is known. To maximize the achievable rate and minimize the computational energy consumption within the tolerable computational latency, we propose a novel optimization framework for joint design of distributed hybrid precoding and computational offloading decision in ECF-mMIMO systems. The joint optimization problem is modeled as maximizing the sum of the ratio of the achievable rate to the computational energy consumption. Since it is difficult to directly solve the joint optimization problem with non-convex and fractional constraints, we first use the quadratic transform method to remove the fractional constraint and perform an equivalent transformation of the original problem. Next, we decompose the equivalent optimization problem into three subproblems and propose an alternate optimization algorithm. Specifically, we successively adopt a local block diagonalization hybrid precoding scheme and a game-based power allocation algorithm to maximize the total rate, and a computational resource allocation scheme to minimize computational offloading energy consumption under the constraint of computational latency. Numerical simulation results illustrate that the performance obtained by our proposed scheme with only local CSI is 93% of that as compared to the full CSI case. Besides, we also prove the convergence of the alternate optimization algorithm theoretically.