Carrier Aggregation (CA) is investigated to address spectrum scarcity in 6G Integrated Sensing and Communication (ISAC) systems. However, inter-band phase offsets impose high complexity on achieving coherent gain across multiple bands. Furthermore, typical CA focuses on multi-frequency-band expansion, while multi-time-duration expansion has received limited attention. To address the above issues, this paper proposes a novel high-accuracy Multi-Band Joint Carrier Phase Sensing (MBJCPS) framework that utilizes carrier phase information from multiple time-frequency bands to improve range and velocity sensing accuracy. A carrier phase extraction method is introduced based on oversampling Range-Doppler phase spectrum. The impact of non-ideal factors-including time, frequency, and phase offsets, as well as phase noise-on carrier phase is modelled, analyzed, and eliminated via an inter-path difference method. A Real-valued Multi-Carrier Ambiguity Resolution (RMCAR) algorithm is designed to resolve integer ambiguities in the range and velocity carrier phase models. The time-frequency selection problem is formulated as a constrained Rayleigh-quotient optimization problem and solved via successive convex approximation. The Cramer-Rao lower bound (CRLB) for the range and velocity estimation under phase offsets is derived, demonstrating the performance gain of CA in both frequency and time domains. Numerical simulations show that the proposed method achieves higher sensing accuracy with lower complexity than benchmark algorithms in 3GPP Uma-AV environments.

The 6G Computing Power Network (CPN) is envisioned to orchestrate vast, distributed computing resources for future intelligent applications. However, achieving efficient, trusted, and privacy-preserving computing resource sharing in this decentralized environment poses significant challenges. To address these intertwined issues, this article proposes a holistic blockchain and evolutionary algorithm-based computing resource sharing (BECS) mechanism. BECS is designed to dynamically and adaptively balance task offloading among computing resources within the 6G CPN, thereby enhancing resource utilization. We model computing resource sharing as a multi-objective optimization problem, aiming to navigate these trade-offs. To tackle this NP-hard problem, we devise a kernel-distance-based dominance relation and incorporate it into the Non-dominated Sorting Genetic Algorithm III (NSGA-III), thereby significantly enhancing population diversity. In addition, we propose a pseudonym scheme based on zero-knowledge proofs to protect user privacy during computing resource sharing. Finally, security analysis and simulation results demonstrate that BECS can effectively leverage all computing resources in the 6G CPN, thereby significantly improving resource utilization while preserving user privacy.

This paper studies a Cramér–Rao bound (CRB) optimization problem based on partially-connected hybrid precoding design in integrated sensing and communication (ISAC) systems under practical RF hardware constraints. We adopt the CRB for direction-of-arrival (DOA) estimation as the sensing metric, where both target angle and complex amplitude (including path-loss and RCS) are treated as unknown parameters, and formulate a CRB minimization problem subject to users’ SINR constraints, a total power constraint, and constant-modulus constraints on the analog precoder. An efficient alternating optimization framework is developed: the digital precoder is designed via Schur-complement-based semidefinite relaxation (SDR), and the analog precoder is optimized by the majorization–minimization (MM) method based on either convex relaxation or Riemannian manifold optimization. Simulation results show that the proposed schemes achieve excellent DOA estimation performance while guaranteeing communication quality-of-service (QoS), outperform a baseline that only optimizes the digital precoder with channel-gain-based analog precoder initialization, and are more practical than angle-only CRB designs that ignore amplituderelated effects.

Integrated sensing and communications (ISAC) is an essential 6G capability for joint data transmission and environmental sensing. To support 6G scenarios with stringent ISAC performance requirements, existing massive-MIMO-based systems are expected to scale toward ultra-massive MIMO. However, this scaling incurs prohibitive cost and power consumption when realized using widely adopted phased arrays with complex phase shifters and feeding networks. Recently, holographic integrated sensing and communications (HISAC) has emerged as a promising paradigm to address this issue. It employs reconfigurable holographic surfaces (RHSs), a type of leaky-wave antenna, as a cost- and energy-efficient implementation of ultra-massive MIMO-based ISAC, and offers enhanced flexibility for ISAC beam synthesis through holographic beamforming. In this paper, we provide a comprehensive tutorial on HISAC, focusing on how RHS-enabled holographic beamforming can be exploited to jointly support communication and sensing under practical hardware constraints. We first introduce the fundamentals of RHSs and discuss the unique leakage power constraint of holographic beamforming. We then present a general optimization framework for HISAC and show how HISAC enhances joint communication and sensing, sensing-assisted communication, and communication-assisted sensing. We further present HISAC system implementations and experimental results. Finally, we outline promising research directions for HISAC, highlighting the potential of HISAC in advancing efficient, flexible, and high-performance ISAC networks.

The ongoing evolution of network technology and increasing service requirements have positioned spectrum resource management and exploration as a central focus in current and future network research. To accommodate the diverse services anticipated in future 6G networks, dynamic spectrum sharing (DSS) must be implemented across multiple factors to optimize the utilization of existing resources, in addition to exploring new frequency bands. This article proposes BEE, a two-stage, sharding blockchain-based DSS mechanism designed for service-centric 6G networks operating across various frequency bands. In the first stage, BEE utilizes an improved evolutionary algorithm to establish a fine-grained spectrum allocation scheme, facilitating dynamic spectrum management between providers and requesters. In the second stage, BEE offers price-guided spectrum trading for operators and users, utilizing evolutionary game theory to maximize the number of served users. The security analysis demonstrates that BEE provides a secure and reliable platform for DSS. Furthermore, simulation results demonstrate that BEE effectively improves spectrum utilization across various factors and offers operators effective guidance for price adjustments, thereby meeting the personalized spectrum management needs of 6G networks.

Recently, reconfigurable intelligent surfaces (RIS) technology has been researched to realize holographic communications, by utilizing its property of ultra-dense element spacing. However, the position-fixed RIS can only take advantage of phase-shift domain and reconfigurable holographic surfaces (RHS) can only take advantage of the amplitude domain. Furthermore, most of the previous researches only realize the holographic beamforming, which is only part of the holography theory. Therefore, to leverage the full potential of holography theory, a Fluid-RIS-enabled system is proposed to utilize holographic parameter reconstruction process to circumvent the challenging RIS channel estimation process. Furthermore, ideal and practical communication scenarios are considered, in which the two types of interference are different from the optical holography scenarios. The first type of interference stems from the background noise in the communication environment, and the second type of interference arises from the hardware impairment or phase shift error of the RIS system. To address these challenges, algorithms are proposed to eliminate the influence of second type of interference, even in the simultaneous presence of both interference sources. Consequently, we can perfectly reconstruct the channels’ information as well as the interferences’ information. Finally, simulation results verify the effectiveness and necessity of the proposed algorithms.

Wireless communication technology is one of the key technologies to achieve the key requirements of high reliability, low latency and mass connectivity in the Internet of Things (IoT). To meet these requirements, we apply the reconfigurable intelligent surface (RIS) in the wireless IoT networks. Specifically, we study a scheme that combines the joint beamforming design with RIS selection strategy for multi-RIS aided multicell wireless networks. In the joint beamforming design, we propose the controlling mode of each base station (BS) controlling its local RISs for nonfree propagation scenarios in wireless IoT networks, such as smart city and intelligent transportation system. Specifically, we give the details of algorithm for this control mode, and prove that it can achieve the performance comparable to the in the nonfree propagation scenario, with lower algorithm complexity and control signaling overhead. Moreover, based on the joint beamforming design, we further propose a genetic algorithm-based RIS selection strategy to maximize the sum-rate under the constraint of limited RIS deployment. Finally, simulation results show that the proposed RIS selection strategy is effective and outperforms the existing schemes.

In future communication systems, users require the ability to receive signals from multiple satellites while mitigating interference and improving communication quality. This necessitates the application of antenna arrays to achieve high receiving gain. To address this challenge, our study presents a low-resolution analog receiving beamforming algorithm. This algorithm successfully overcomes two problems: first, uncertainty in satellite positions, and second, the local optimality limitation of traditional approaches, ensuring global optimality. Initially, a robust optimization objective function is formulated based on the uncertainty of position. Subsequently, a customized branch-and-bound algorithm is designed to find the beamforming weights that maximize this objective function, considering the constraints of low resolution. Simulation results demonstrate the effectiveness of our approach.

The relentless advancement of wireless mobile communication technology, evolving from 1G to 5G and now venturing into 6G, has prompted an exploration of massive random-access (MRA) techniques to accommodate the anticipated massive communication demands. This paper delves into the domain of MRA technology, garnering increasing attention in 6G Internet of Things (IoT) networks. Based on extensive prior research on MRA, this paper analyzes the impact of repetitions and post-encoding processing on MRA performance. Two scenarios are considered: the ideal post-encoding processing scenario with the maximum processing gain, where each repetition is treated as independent; and the identical post-encoding processing scenario with the most basic processing gain, where all repetitions undergo the same processing steps. Through theoretical derivations, performance bounds for MRA with joint decoding across all actual IoT devices and all repetitions are established for both scenarios. The special scenario concerning performance bounds, where the repetition number is 1, aligns with existing results. Numerical evaluations have confirmed the accuracy of the theoretical derivations and further demonstrated that, as the number of repetitions increases, the performance disparity between the ideal post-encoding processing scenario and the identical post-encoding processing scenario widens. The performance bounds of other post-encoding processing schemes lie between the ideal and the identical post-encoding processing. These findings emphasize optimizing post-encoding processing schemes to bring actual performance closer to the ideal post-encoding processing scenario.

Benefited from its high spectral efficiency, extremely large-scale multiple-input multiple-output (XL-MIMO) has emerged as a leading technology, captivating the attention of both academia and industry in the 6G era, particularly in the millimeter wave (mm-Wave) band due to its wide bandwidth. Despite its potential, mm-Wave XL-MIMO faces significant challenges due to prohibitively high hardware costs and power consumption of large-scale antenna arrays. In this article, we propose a cost-efficient and power-efficient way to enable XL-MIMO using reconfigurable holographic surface (RHS) which is a novel antenna fabricated with numerous programmable metamaterial elements instead of phase shifters. Specifically, we design a 256-element RHS array prototype operating in mm-Wave band. Utilizing the orthogonal polarizations of multiple RHS arrays, a highly-integrated transmitter is developed to support 2-stream transmission via multiple RHS arrays. Given that, we implement an RHS-enabled XL-MIMO communication prototype for multi-user multi-stream communication. Based on the standards of 3GPP, two test cases are presented to evaluate the performance of our prototype with respect to single-user beam tracking and multi-user transmission. Experimental results show that our prototype boasts both a high data rate and lower power consumption compared to traditional antenna arrays. Future research directions and challenges for RHS-enabled XL-MIMO communications are also discussed.