ABSTRACT Non‐orthogonal multiple access (NOMA) combined with integrated sensing and communication (ISAC) presents a promising solution to 6G spectrum scarcity. However, non‐line‐of‐sight channels caused by obstacles significantly degrade performance. Unmanned aerial vehicles (UAVs) can establish line‐of‐sight links, thereby improving communication rates. Existing research primarily focuses on stationary targets, while studies on moving targets rely on single‐UAV sensing, leading to substantial estimation errors in dynamic environments. To address this, we propose a UAV‐enabled NOMA‐ISAC network that employs an extended Kalman filter for accurate target tracking. We aim to jointly optimize the UAV trajectory and beamforming vector using a block coordinate descent framework, where the subproblems are efficiently solved via successive convex approximation and semidefinite programming. Numerical results demonstrate that the proposed scheme achieves an 18.47% higher sum rate compared to benchmark schemes.

ABSTRACT Relay‐assisted symbiotic radio (SR) has been recently proposed to overcome the blocking of the direct link from the primary transmitter (PT) or backscatter node (BN) to the destination node (DN). However, the energy efficiency (EE), which is an important performance metric for SR networks, has been largely ignored in existing studies of the relay‐assisted SR. To fill the gap, this work maximizes the EE of a relay‐assisted parasitic SR network, which comprises a PT, a BN, a relay node (RN), and a DN. More specifically, we formulate a mixed‐integer programming optimization problem that maximizes the system EE by jointly optimizing the transmit power of the PT, the power reflection coefficient of the BN, the transmit power and the power allocation ratio at the RN as well as the successive interference cancellation (SIC) decoding order at the DN. We decompose the formulated non‐convex problem into two subproblems corresponding to the two different SIC decoding orders, respectively. For each subproblem, we convert its objective function from a fractional form into a subtractive form by using a Dinkelbach‐based method, and then utilize the block coordinate descent (BCD) method to further decouple it into two subsubproblems that are proved to be convex. Based on the obtained solutions, we devise an iterative algorithm to solve each subproblem by solving its two subsubproblems alternately. The optimal solution to the subproblem with a higher system EE returns a near‐optimal solution to the original problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and validate the significant advantages of our proposed algorithms over the baseline schemes.

ABSTRACT Integrated sensing and communication (ISAC) is regarded as a promising paradigm for future sixth‐generation (6G) networks, which effectively improves spectral efficiency and reduces hardware costs by simultaneously performing communication and sensing. Due to the limited coverage of a single base station (BS), the single‐BS ISAC system struggles to meet the demands of various emerging intelligent applications for high‐quality communication and high‐precision sensing. In this paper, we investigate a cooperative multi‐BS ISAC system with multi‐target and multi‐user. In particular, communication and sensing are performed by multiple BSs with a cooperative manner. We formulate a problem for the purpose of maximizing the sensing mutual information (MI) via jointly designing the transmit beamforming of multiple BSs for communication and sensing, while guaranteeing the achievable communication rate requirements. To address this non‐convex problem, an iterative optimization algorithm is developed based on the Lagrangian transform and the quadratic fractional transform. Simulation results validate the advancement of the proposed cooperative beamforming scheme in enhancing the sensing performance of multi‐BS ISAC system.

The age of information (AoI) as a new metric in vehicular communication system has begun to attract public attention. This paper considers a muti-user vehicular communication network, where the base station (BS) transmits with vehicle users by adaptively selecting orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA) schemes. The cognitive radio (CR) technology has been performed in NOMA system to improve the spectrum resources utilization. The adaptive CR-NOMA/OMA scheduling scheme is proposed based on user matching algorithm to reduce the user interference of inter-groups and intra-group. And the scheduling schemes for primary user and secondary user of CR-NOMA system using the generate-at-will (GAW) and the generate-at-request (GAW) data generation models are proposed. Then the nonlinear average AoI of CR-NOMA and OMA systems are formulized. Based on GAW and GAR models, the minimizing average AoI are derived. The quality of service (QoS) as to average AoI and achievable rate with different vehicle speeds, signal to interference plus noise ratio (SINR), transmit power and outage probability are analyzed. The simulation results are compared with validated analysis and better performances of vehicular systems can be achieved.

In this paper, we propose a novel multi-functional reconfigurable intelligent surface (MF-RIS) that supports signal reflection, refraction, amplification, and target sensing simultaneously. Our MF-RIS aims to enhance integrated communication and sensing (ISAC) systems, particularly in multi-user and multi-target scenarios. Equipped with reflection and refraction components (i.e., amplifiers and phase shifters), MF-RIS is able to adjust the amplitude and phase shift of both communication and sensing signals on demand. Additionally, with the assistance of sensing elements, MF-RIS is capable of capturing the echo signals from multiple targets, thereby mitigating the signal attenuation typically associated with multi-hop links. We propose a MF-RIS-enabled multi-user and multi-target ISAC system, and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of sensing targets. This problem involves jointly optimizing the transmit beamforming and MF-RIS configurations, subject to constraints on the communication rate, total power budget, and MF-RIS coefficients. We decompose the formulated non-convex problem into three sub-problems, and then solve them via an efficient iterative algorithm. Simulation results demonstrate that: 1) The performance of MF-RIS varies under different operating protocols, and energy splitting (ES) exhibits the best performance in the considered MF-RIS-enabled multi-user multi-target ISAC system; 2) Under the same total power budget, the proposed MF-RIS with ES protocol attains$\rm {52.2}\%$,$\rm {73.5}\%$, and$\rm {60.86}\%$sensing SINR gains over active RIS, passive RIS, and simultaneously transmitting and reflecting RIS (STAR-RIS), respectively; 3) The number of sensing elements will no longer improve sensing performance after exceeding a certain number.

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences. The multi-user interference (MUI) may critically degrade the communication performance. To eliminate the MUI, we employ the constructive interference mechanism into the ISAC system, which saves the power budget for communication. However, due to the conflict between C&S metrics, it is intractable for the ISAC system to achieve the optimal performance of C&S objective simultaneously. Therefore, it is important to strike a trade-off between C&S objectives. By virtue of the multi-objective optimization theory, we propose a weighted Tchebycheff-based transformation method to re-frame the C&S trade-off problem as a Pareto-optimal problem, thus effectively tackling the constraints in ISAC systems. Finally, simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under different C&S performance preferences in MIMO-ISAC systems.

Power line and wireless hybrid communication address the problem that wireless and power line cannot communicate with each other in the Internet of Things (IoT). However, the theoretical performance of amplify-and-forward (AF) relay communication based on hybrid fading condition has no closed expression. This article takes a relay system model of wireless and power line hybrid media as a representative and investigates a unified performance analysis method for AF relay systems with hybrid fading. The mixture lognormal (LogN) approximate algorithm based on the moment-generating function (MGF) is applied to approximate a wireless fading coefficient to mixture LogN distribution. Moreover, we propose a joint optimization algorithm for determining the key parameters given that the adjustable variables in the MGF equations evidently affect the approximation accuracy. Importantly, this article is then extended to other fading models, taking general gamma (GG) as an example. The effectiveness and reliability of the algorithm are verified by the Monte Carlo simulation. Finally, closed analytic expressions of the system performance, such as bit error rate (BER), outage probability, and average channel capacity, are derived. The effects of hybrid fading factor, impulse noise parameter, and power allocation on system performance are analyzed using the theoretical formulas.

The mobile fog computing (MFC) network that integrates unmanned aerial vehicles (UAV) fully exerts its advantages of flexible deployment, load balance, and rapid response. Under complex network environment, proposing a reasonable offloading model and according resource optimization of the MFC network is important to satisfy high-requirement offloading standard. In this paper, a multilevel MFC offloading model where UAV and fog nod undertake relay nodes and offloading computing nodes are established for computation-intensive and latency-critical tasks, considering heterogeneous network selection , dynamic channel quality and central cloud access . With the total system utility optimality function including reward function maximization as the goal, the MDP algorithm is applied to solve the best offloading decision of the computing task and the balanced load mode of the MFC network. Finally, the simulation section verifies the excellent performance of the proposed multilevel MFC offloading model in network resource utilization. Simulation results show that the model can optimize the relative position of service nodes in MFC network and ensure the offloading reliability of terminal equipment.

Massive multiple-input multiple-output techniques have attracted wide attention as one of the key technologies of 5G. Pilot reuse based on the same pilot sequence is necessary among different users due to limited pilot resources. In this study, a pilot reuse mode based on continuous pilot reuse factors is proposed to pursue a flexible pilot reuse mode with high spectral efficiency (SE). In this mode, users in every cell are grouped, and different groups use different pilot reuse factors. Therefore, any overall pilot reuse factor is achieved to increase the flexibility of pilot reuse considerably. A theoretical analysis proves that the proposed pilot reuse based on continuous pilot reuse factors is superior to the traditional pilot reuse based on single pilot reuse factor to some extent in terms of SE. A new method to search the optimal pilot reuse based on continuous pilot reuse factors is also introduced. Simulation results demonstrate that, in most cases, the optimal pilot reuse mode based on continuous pilot reuse factors is better than the traditional mode. Such superiority still exists under a limited number of antennas.

Mobile edge computing (MEC) nodes are deployed at positions close to users to address excessive latency and converging flows. Nevertheless, the distributed deployment of MEC nodes and offload of computational tasks among several nodes consume additional energy. Accordingly, how to reduce the energy consumption of edge computing networks while satisfying latency and quality of service (QoS) demands has become an important challenge that hinders the application of MEC. This paper built a local-edge-cloud edge computing network and proposes a multinode collaborative computing offloading algorithm. It can be applied to smart homes, realize the development of green channels, and support local users of Internet of Things (IoT) to decompose computational tasks and offload them to multiple MEC or cloud nodes. The simulation analysis reveals that the new local-edge-cloud edge computing offload method not only reduces network energy consumption more effectively compared with traditional computing offload methods but also ensures the implementation of more data samples.

A network access selection algorithm based on the intuitionistic fuzzy analytic hierarchy process (AHP) and bilateral profit drive is proposed in this study for addressing problems regarding user–network bilateral profits. User preference, business demands, and network parameter changes are comprehensively considered in the algorithm. First, the initial weights centered at users are gained by intuitionistic fuzzy AHP. Second, the network participates in network access selection as a subject with competitive consciousness, and the entire selection process is transformed into a multiobjective optimization problem by the construction of a competitive model, thereby obtaining dynamic competitive weights. Third, the initial weights centered at users and the dynamic competitive weights are combined to obtain comprehensive weights. In this way, the dynamic adjustment of comprehensive weights is realized. Finally, candidate networks are ordered according to a comprehensive performance evaluation, and the optimal one is selected. The proposed algorithm is validated by simulation results to be valid in reducing the blocking rate of networks and optimizing network resource allocation. Therefore, it not only protects user–network bilateral profits but also maximizes comprehensive profits.

Deploying dense network is a promising technique of fifth‐generation communication to meet the challenge of soaring demand from explosive growth of mobile users. However, this technique obviously increases the power consumption, thereby indirectly causing heavy financial burden on mobile network operators and CO2 emission, which is considered as a major threat to the environment. In consideration of energy storage device, self‐discharge effect, and preventing repeated switch (PRS) mechanism, a comprehensive power management model for wireless communication system in smart grid is investigated in this study from the power consumption and economic cost aspects. Two base sleep mechanisms, namely, energy cost first (ECF) algorithm and power consumption first (PCF) algorithm, are proposed. The ECF algorithm focuses on the minimum cost of system operation by selecting the low‐cost energy scheme. Meanwhile, the PCF algorithm considers the minimum power consumption first and then optimizes the cost. Compared with conventional scheme, simulation results show that the two proposed algorithms can decrease the energy cost of communication base system significantly. Setting an energy storage device can further stabilize the energy cost, while the switching frequency can be reduced to a large extent when combined with the PRS mechanism.