In internet of things (IoT) networks, resource-constrained devices can offload data to mobile edge computing (MEC) servers for processing. Nevertheless, offloading incurs additional energy consumption, making energy-efficient offloading a critical issue. Furthermore, the mismatch of transmission and computation traffic rates further exacerbates energy inefficiency. Additionally, ensuring quality of service (QoS) requires not only meeting the per-slot processed data volume thresholds of devices but also satisfying their long-term energy consumption limits imposed by limited battery capacity. The coexistence of these heterogeneous constraints renders algorithm design more challenging. In this paper, we propose a safe deep reinforcement learning (Safe-DRL)-based resource allocation strategy for traffic matching of transmission and computation in QoS-guaranteed MEC-IoT networks, aiming to maximize energy efficiency. Specifically, we formulate an energy efficiency maximization problem that captures the interdependence among transmission, buffering, and computation traffic, subject to both data processing and energy consumption constraints. To reduce complexity, closed-form solutions for a subset of variables are derived via mathematical analysis. Subsequently, we develop a Safe-DRL algorithm, termed action projection augmented Lagrangian soft actor-critic (APAL-SAC), which integrates a penalty-based action projection mechanism to enforce per-slot constraints and a Lagrangian dual method to ensure long-term constraint satisfaction. Simulation results demonstrate the effectiveness of APAL-SAC.

Computing tasks offloaded from user devices (UDs) can be carried out by one or more mobile edge computing (MEC) servers to alleviate the computing burden of UDs. The incentive is needed to encourage MEC servers to provide their computing services to other network nodes. In this article, inspired by the fact that bargaining games have the available features of incentive, self-enforcement, and satisfaction for all participants, we propose a two-level bargaining-based incentive mechanism for task offloading and collaborative computing in MEC-enabled networks. In the first-level bargaining between UDs and local MEC server (LMECS), both UDs and LMECS try to maximize their respective offloading utilities, which are all defined as a saved-cost function considering the time and energy consumption of task execution, and computing service fees. The task offloading decision, uplink transmitting power of UDs, computing resource allocation of LMECS, and the fees paid by UDs to LMECS are jointly optimized. When large computing tasks are offloaded to LMECS, which results in LMECS overload, the second-level bargaining is proposed to achieve a computing load balance of LMECS and maximize the respective collaboration utilities of LMECS and collaborative MEC server group (CMECG), in which the optimized normalized fees paid by LMECS to CMECG for additional computing resources are obtained. The first-level and the second-level bargainings are proved to be quasi-concave and concave, respectively, and each has a unique Nash bargaining solution (NBS). The simulation results show that the proposed method gets better performance than benchmark methods.

Unlicensed cognitive-radio (CR)-assisted Internet of Vehicles (IoV) users can access licensed providers’ radio spectrum and concurrently utilize the dedicated channel for data transmission in vehicular communication. Optimizing channel access in cognitive IoV networks can help maximize available spectrum resources. This article proposes a novel sensing and communication integrated framework, dubbed as the CR-assisted IoV network (CRAV-Net), using a cluster-based hybrid optimization approach with adaptive congestion-aware modeling for dynamic high-mobility vehicular networks in an urban city context. In CRAV-Net, intelligent hybrid learning spectrum agents are introduced, which perform spectrum sensing (SS) using a deep learning (DL) model. It dynamically learns the multilevel spatial and temporal graphical features from input spectrograms through layer-by-layer propagation. It efficiently predicts the spectrum occupancy in the primary spectrum, without a priori knowledge of the radio environment. Then, to assign the vacant channels to the secondary vehicles, a support vector machine classifier is trained based on several learning features, including the vehicle stay time, vehicle density, and network capacity, to select the optimal resource route. The proposed framework achieves an overall accuracy of 99.74% in SS using the custom data set, outperforming state of the art by 12.60% at −25-dB signal-to-noise ratio. In addition, it brings a performance gain of 0.81% in SS accuracy when evaluated on real-world signals. Furthermore, in optimal network node allocation, the proposed framework achieves a mean accuracy of 98.45%, outperforming the existing methods by 0.63% and 18.32% in terms of accuracy and allocation time, respectively.

Mobile crowdsensing (MCS) is an effective perception paradigm for large-scale tasks, driven by the proliferation of mobile devices with more powerful sensing and computing capabilities. An effective incentive mechanism is critical to the operation of an MCS system in promoting public engagement. However, the great majority of works discuss fixed task pricing, while the inherent inequality of the supply–demand relationship of the tasks exists. Therefore, it is essential to study the dynamic task pricing problem in the peer-to-peer data sharing MCS system. In this article, we formulate the interactions between the requester and the sensors as a two-stage Stackelberg differential game model, while considering the average behavior of sensors to solve the dynamic task pricing problem. Specifically, in the game model, the requester is the leader who first announces the issued task rate and provides decisive state-changing task pricing dynamics to the sensors. Then, the sensors are the followers who decide the rate of tasks completed noncooperatively based on requesters’ observed strategy, using the level of effort as the state dynamics. The requester and the sensors interact through a mean-field term included in the dynamic state functions, which catches the average behavior of all users. By solving the model, the optimal strategies for the users and the optimal tasks pricing trends in the dynamic environment are obtained. Furthermore, the effectiveness and feasibility of the scheme are verified by a series of numerical simulation experiments.

The device-to-device (D2D) communication has become one of the most promising methods for alleviating the deficiency of wireless spectrum. In spite of the potential gains brought by activating D2D communications, the problem of severe interference between cellular and D2D users cannot be ignored. In this paper, we introduce the power-control scheme based on interference limited area (ILA) for alleviating the abovementioned interference. In a single cell where there are M cellular user equipments (CUEs) supporting both the conventional cellular mode and the D2D mode, the controlling thresholds λD and λC are defined for the D2D transmitter (DT) and the base station (BS), respectively; we forbid all the DT from transmission within ILA-S1 but activating DT only within the inner section of ILA-S2, where ILA-S1 and ILA-S2 are defined as the areas in which the signal-to- interference ratio (SIR) is higher than λC and λD, respectively. Furthermore, we propose a novel resource-allocation scheme relying on interference control mechanism based on the criteria of "maximum/minimum power of DT". Numerical results show that the proposed scheme is capable of achieving the best system performance in terms of capacity, where the optimal ILA size is attainable.

Device‐to‐device (D2D) communication is an excellent technology for improving the system capacity through sharing the spectrum resources of cellular networks. Multicast service is considered an effective transmission mode for the future mobile social contact services. Therefore, multicast with D2D technology can improve the resource efficiency. In this paper, a resource allocation scheme based on cognitive radio (CR) for D2D underlay multicast communication (CR‐DUM) is proposed to improve system performance. In each D2D multicast group of the cognitive cellular system, the secondary users reuse the different orthogonal cellular resources to accomplish a multicast transmission. To maximise the total system capacity under the condition of interference and noise impairment, the authors formulate an optimal transmitting power allocation for the cellular and D2D multicast communications jointly. The proposed scheme includes two steps. First, two channel allocation rules are proposed to reduce the interference from cellular networks to receivers in D2D multicast group. Second, the optimal power allocation is formulated as a non‐linear programming problem and the optimal solution is achieved by searching from a finite set for the allocated channel. The simulation results show that the proposed method can ensure the quality of service (QoS) and improve the system capacity.

Link evaluation is very important to system level simulation. Current algorithms for link evaluation show pretty good accuracy for wireless system with linear detections. However, when it comes to MIMO-OFDM system with ML Detection, problems still exists. This paper proposes an Extended Received Block Information Rate algorithm for link evaluation, which is deduced from the view of information and detecting theory. Accuracy and universality of this algorithm for MIMO-OFDM system with ML detection are highlighted. Simulation results show that, comparing to current algorithms for link evaluation, the proposed ERBIR algorithm obtains better accuracy and universality.