The multi-base-station cooperative (MBC) ISAC mechanism is promising to satisfy the higher-quality sensing requirements of accuracy and coverage in 6G. STAR-RIS can help ISAC BSs provide cooperative sensing and communication (S&C) across full-space radio signal coverage. Short-packet transmission (SPT) is expected to be performed by ISAC BSs in 6G vertical applications to provide enhanced ultra-reliable low-latency S&C services. This paper proposes a STAR-RIS-assisted heterogeneous (SRH) MBC ISAC mechanism within the finite blocklength (FBL) regime. Multiple ISAC BSs and a communication-only (Comm-only) BS collaborate to provide S&C services to users and the target using STAR-RISs and cooperative detection results. A composite SPT (C-SPT) time frame structure is proposed by dynamically designing the S&C block lengths from the perspectives of the sub-frame and the entire transmission block. The systemic sum achievable capacity (SAC) over the entire transmission block is maximized by jointly optimizing the pilot and data block lengths and powers, as well as the active and passive beamforming of ISAC BSs and STAR-RISs under constraints of temporal, spatial, and power resources. The non-convex optimization problem is solved by combining the methods of alternating optimization (AO), quadratic transform (QT), sequential rotation (SR), and particle swarm optimization (PSO). Simulation results demonstrate the superiority of the proposed SRH-MBC ISAC mechanism with the C-SPT frame structure in achieving higher systemic communication performance with the assistance of STAR-RIS.

Integrated sensing and communication (ISAC) has been identified as a potential key 6G technology that empowers cellular systems with wireless sensing capabilities. It is challenging for an individual ISAC node to meet the increased sensing demands of 6G Internet of Everything (IoE) applications, such as larger coverage and higher accuracy. To address this issue, multinode cooperative ISAC (MNC-ISAC) schemes have recently been explored to improve sensing performances for 6G IoE applications. This article innovatively proposes a distributed MNC-ISAC (DMNC-ISAC) scheme, which differs from the conventional centralized MNC-ISAC (CMNC-ISAC) scheme where all cooperative ISAC nodes form a single sensing coalition. In the proposed DMNC-ISAC scheme, multiple ISAC nodes form a satisfied number of cooperative groups by coordinating their sensing abilities and resources in a distributed way. The interactions between multiple ISAC nodes, characterized by collaboration in sensing and competition for resources, are modeled as a local cooperation-based game. Then, to maximize the overall sensing and communication (S&C) performance of the proposed DMNC-ISAC scheme, the cooperation grouping and transmission beamforming of multiple ISAC nodes are jointly optimized. The stochastic learning automata (SLA) and particle swarm optimization algorithms are utilized to obtain the equilibrium grouping results and design the beamforming of multiple ISAC nodes. Finally, simulation results show that the distributed cooperative ISAC scheme enhances sensing performance by at least 5%, leading to improved systemic performance in most ISAC scenarios. This improvement is particularly notable in challenging sensing environments and when the possible status of the target is ambiguous.

For fifth-generation wireless networks to transition to sixth-generation wireless networks, the integration of coordinated multipoint (CoMP) and non-orthogonal multiple access (NOMA) techniques is expected to overcome new challenges and enhance performance compared to the CoMP or NOMA scheme. The joint-transmission CoMP (JT-CoMP) technique is a typical technical implementation of the CoMP scheme. In this study, we investigate a downlink network with a joint JT-CoMP-NOMA scheme. Based on the generalized Stienen model from stochastic geometry, we divide far and near NOMA user equipment (UE) and develop a theoretical framework to analyze the system performance. Expressions for the coverage probabilities and average achievable rates of two types of UEs (named CoMP and non-CoMP UEs) are derived. By comparing analytical results with Monte Carlo simulations, we show that the approximations in the analytical derivations are tight. The impact of certain network parameters, such as the power allocation coefficient, on the system performance is also studied. Notably, the developed transmission scheme is shown to outperform the NOMA-only and the JT-CoMP-only schemes.

Integrated sensing and communication (ISAC) has the potential to facilitate coordination gains from mutual assistance between sensing and communication (S&C), especially sensing-aided communication enhancement (SACE). Reconfigurable intelligent surface (RIS) is another potential technique for achieving resource-efficient communication enhancement. Therefore, this paper proposes an innovative RIS-assisted SACE (R-SACE) mechanism with the goal of improving the systemic communication performance of the ISAC system in practical scenarios where the channel status information (CSI) is imperfectly known. In the proposed R-SACE mechanism, a dual-functional base station (BS) provides downlink communication services to both the communication user and the dynamically changing target that is detected using the communication signals. RIS assists in both sensing and communications of the BS. A typical scenario is investigated in which either or both the direct and RIS-assisted reflected communication links are available depending on sensing results. The average systemic throughput (AST) over the entire timeline of the R-SACE mechanism is maximized by jointly optimizing both temporal and spatial resources under the probabilistic constraint and the sensing performance, transmission power, and communication interference constraints. The non-convex probabilistic mixed optimization problem is transformed and then solved by the proposed fixed-point iterative (FPI) algorithm. Simulation results demonstrate that the proposed FPI algorithm and R-SACE mechanism outperform the baseline algorithms and communication enhancement mechanisms in achieving higher systemic communication performance.

Integrated sensing and communication (ISAC) and reconfigurable intelligent surface (RIS) are two critical enablers for sixth-generation (6G) communication systems. Sensing-aided communication enhancement (SACE) is one of the foreseeable scenarios of ISAC. This paper focuses on a RIS-assisted SACE (R-SACE) mechanism where the base station (BS) acts as a sensor and communicator. In particular, the RIS and the sensing at the BS assist in suppressing harmful direct interference and meanwhile reconstructing another feasible communication link. The average achievable capacity maximization problem of the RIS-assisted communication link is investigated by jointly optimizing the time resource allocation within a coherent ISAC time frame and the reflecting recoding of the RIS under the constraints of sensing performance and interference. Alternating algorithm (AO) and particle swarm optimization (PSO) methods are utilized to solve the above optimization problem. Simulation results validate the performance gains of the proposed R-SACE mechanism in suppressing interference and enhancing average achievable capacity.

Abstract A first‐order finite‐state Markov chain (FSMC) typically models the Rayleigh fading channel in the open literature because the first‐order FSMC is analytically tractable and can derive closed‐form results. Non‐orthogonal multiple access (NOMA) has been recognised as a novel wireless technology that addresses challenges in the next generation of mobile communications. According to the power‐domain NOMA protocol, channels in the NOMA wireless network are sorted by the channel gain. Then considering NOMA, there is insufficient information on how to further form a suitable model for ordered Rayleigh fading channels based on the first‐order FSMC. Given the mathematical statement on how to model the order statistics of multidimensional Markov chains for ordered Rayleigh fading channels, the authors consider these order statistics as a Markov chain, and propose specific processes of representing the state space and constructing the transition probability matrix accordingly. Numerical and simulation results validate the mathematical correctness and accuracy of these novel processes. In addition, for ordered Rayleigh fading channels, the performances of various methods of partitioning the entire signal‐to‐noise ratio range are compared. The performance comparison results are the same as those obtained for the individual unordered Rayleigh fading channel.