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, multi-node cooperative ISAC (MNC-ISAC) schemes have recently been explored to improve sensing performances for 6G IoE applications. This paper innovatively proposes a distributed multinode cooperative ISAC (DMNC-ISAC) scheme, which differs from the conventional centralized multi-node cooperative 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.

Sensing begins to rise as a fundamental functionality in integrated sensing and communication (ISAC), especially for sensing-aided communication enhancement (SACE), where base stations (BSs) are promising to perform ubiquitous sensing for communication enhancement. Resource allocation between sensing and communication is a crucial issue in SACE. Therefore, this paper investigates a RIS-assisted SACE (R-SACE) mechanism that jointly designs the time resource allocation between sensing and communication at the BS and the reflecting precoding at the RIS. The performance-guaranteed sensing result obtained at the BS serves as the basis for the proposed R-SACE mechanism, which aims to maximize the average achievable capacity of the RIS-assisted communication link and suppress caused interference. The closed-form expressions of sensing performance metrics are derived. Alternating algorithm (AO) is used to iteratively solve a proved convex optimization subproblem and a transformed quadratic constraint quadratic programming (QCQP) subproblem. Simulation results show priorities of the proposed R-SACE mechanism in enhancing communication capacity while suppressing interference with assists of sensing and RIS.

Efficient radio resource utilization is crucially important for future wireless communication systems. The advanced reconfigurable intelligent surface (RIS) has recently been incorporated into the spectrum sharing mechanism, allowing for both spectrum efficiency (SE) and energy efficiency (EE). Spectrum sensing is the cornerstone of spectrum sharing. Nevertheless, due to the doubling fading effect, the path loss experienced from the reflection link is thousands of times that from the direct link. Consequently, the detection performance gains obtained through passive RIS are negligible, especially in typical communication scenarios where the direct link is not weak. To address this issue, this paper proposes an active RIS assisted spectrum sensing mechanism that can notably improve the detection performance gains. Multiple active RISs are introduced and the closed-form expressions of the detection performance metrics are derived in this paper. More importantly, due to the extra energy consumption in amplifying reflected signals, it's necessary to investigate the EE maximization problem for the active RIS assisted mechanism by jointly optimizing the detection parameter and the reflecting precoding. Alternating optimization (AO) algorithm and quadratic transform (QT) method are utilized to decouple the multidimensional fractional programming (FP) problem. Then, the optimal detection parameter is obtained with a closed-form solution based on the convex optimization theory. The phase shift and amplification coefficient of each active element are optimized through the sequential rotation (SR) method and an improved differential evolutionary (DE) algorithm, respectively. Simulation results show that the proposed active-RIS mechanism could outperform the conventional passive-RIS mechanism in achieving notably enhanced detection performance gains and higher EE by optimizing the reflecting precoding.

Device-to-device (D2D) communications have significant advantages such as short range and low energy consumption. Caching at mobile user devices by leveraging D2D communications relieves the heavy load on backhaul links and enhances users’ quality of service. Therefore, in a cache-enabled clustered D2D network, we analyze the probability of successful transmission and propose a jointly optimizing bandwidth partitioning and caching policy strategy to solve the average energy consumption minimization problem. To simultaneously have the clustered and bounded characteristics of D2D communication in real hotspots areas, we use Matérn cluster process (MCP) to model the cache-enabled clustered D2D network. For a given D2D receiver (DR), we derive the successful transmission probability of getting its requested content. Moreover, leveraging the analysis of successful transmission probability, a joint bandwidth partitioning and caching policy optimization problem is formulated to reduce average energy consumption. Considering the complexity of directly solving the optimization problem, two sub-problems are decoupled and solved iteratively. The optimized solutions of bandwidth partitioning and caching policy sub-problems are obtained by taking the derivative and adaptive mutation particle swarm optimization (AMPSO) algorithm, respectively. The simulations show the influence of the related parameters on system performance and illustrate that the strategy to jointly optimize bandwidth partitioning and caching policy can significantly reduce energy consumption.

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.

Non-orthogonal multiple access (NOMA) is an enabling and key wireless technology for the next generation of mobile communications. Generally, previous works focus on the performance in NOMA networks, based on some assumptions. For instance, the number of users in a NOMA group, is two-category: 1) only two users; 2) any number of users without restriction. And just a certain factor of channel gain, e.g., the propagation path loss or the small scale fading, is subjectively considered to be dominant. Besides, there is lack of the closed-form expression for the probability density function (PDF) of ordered channel gain. In this paper, we investigate a downlink cellular NOMA network with the directional beamforming and the upper limit for user number (ULFUN) in a NOMA group. Meanwhile, the path loss and the Rayleigh fading are taken into account as a whole for ordering channel gains. Based on the derived closed-form PDF of ordered channel gain, we analyze the performance for an arbitrary user in the network and derive closed-form expressions for the outage probability, the approximate meta distribution of conditional success probability (CSP) and the ergodic data rate. The provided simulations demonstrate that the proposed analytical results are accurate and reasonable. Moreover, it illustrates that the existence of ULFUN can have a positive impact on the performance gain in low SINR environments, while in terms of the outage probability, ULFUN would become a non-negligible and negative factor in the improvement of performance for high SINR environments.