In this paper, we consider the network-welfare optimization for a Cognitive Radio Network (CRN) consisting of multiple Primary-Users (PUs) and multiple Secondary-Users (SUs). For each pair of PU and SU, the interaction between them is modeled as a Stackelberg game where the PU shares its licensed channel with the SU by charging the interference, and the SU is allowed to access the PU channel by paying the interference fee. The objective of the CRN thus is to maximize the network-welfare of all PUs and SUs by forming optimal pairs among them, which can be considered as a Maximum Weight Matching (MWM) problem on a bipartite graph. We first propose an efficient algorithm to quantify the optimal social welfare associated with each pair of PU and SU. Then, by exploiting the special structure of the MWM problem, we propose two algorithms, namely, a modified Belief Propagation (BP) algorithm and a heuristic algorithm based on Simulated Annealing (SA) to determine the optimal matching.
This letter studies a joint design of transmit beamforming and fronthaul compression for the uplink cloud radio access network (C-RAN) with intelligent reflecting surface (IRS) aided wireless fronthauling. In C-RAN, a number of users communicate with baseband unit (BBU) pool through multiple remote radio heads (RRH), wherein RRHs compress the received signals by Wyner-Ziv (WZ) coding and forward the quantization bits to BBU pool through wireless fronthaul link. With the goal of maximizing the uplink sum rate, an alternating algorithm is proposed for jointly optimizing the fronthaul quantization noise covariance matrices, the passive beamformer of IRS and the transmit beamformers of users and RRHs. Via numerical results, the effectiveness of the proposed joint design is verified.
Wireless sensor networks (WSN) have recently emerged as a hot research topic. One of the most important concerns for WSN is energy. To obtain long lifetime, one potential method is to use clustering algorithm. Moreover, WSN should meet various requirements for quality of service (Qos). Accordingly, this paper presents an energy-aware Qos routing algorithm for WSN, which can also run efficiently with best-effort traffic. Furthermore, our work differs from existing algorithms in two ways: (1) improve the first order energy consumption model with dynamic clustering; (2) use clustering to build the multi-objectives programming model to support Qos. Simulations and comparisons with some typical route algorithms show that our algorithm is robust and effective. Keywords-wireless sensor network; dynamic clustering; multi-objectives programming; quality of service
Next-generation mobile communication networks promise the support for vast number of IoT devices with strong demand for spectrum access. The cater for the continuous growth of IoT applications, one of the challenges to mobile communication network providers is to allow dynamic spectrum access to a gigantic number of densely distributed and low-power IoT networks. In this paper, a novel dynamic spectrum access method is proposed based on spectrum trading for distributed IoT devices. The notion of access benefit is introduced which is based on the purchased band number and modulation mode provided to the mobile user. In the proposed solution, IoT users aim achieve optimization of their spectrum access by ensuring that the spectrum price will not exceed the access benefits from the spectrum trading by solving the spectrum optimization function. Due to the complexity of optimization objective function, pattern search algorithm is utilized to complete the final spectrum allocation optimization. Numerical results are also provided to testify the performances of the proposed spectrum optimization method.
When the server cluster is processing concurrent task requests, if the performance difference among servers is not fully considered, task allocation will be unreasonable, which will lead to an increase in task making span and a decrease in cluster resource utilization. As one of the core technologies of server cluster, load balancing is used to balance the load of each server by allocating tasks to each server through an algorithm before the task processing. Therefore, this paper proposes a dynamic load balancing algorithm based on optimal matching of weighted bipartite graph. First, we constructed a bipartite graph with servers and tasks as vertices. The management server collects the load indicators of each server in the cluster in real time, using the real-time processing rate of each server as the load indicator. Each edge of the bipartite graph is determined by comparing the expected completion time of the tasks with the load of each server. The degree of matching between each task amount and each server load capacity is defined as the weight matrix of the edges, and the bipartite graph is weighted to construct a weighted bipartite graph. The Kuhn-Munkres algorithm was used to solve the optimal matching of the weighted bipartite graph, and the optimal assignment of tasks to servers was achieved based on the result of the optimal matching. The proposed algorithm fully considers the differentiation of each task amount and each server load capacity. By building a server cluster example and conducting comparison experiments, it is demonstrated that the algorithm can achieve load balancing of the server cluster and improve the resource utilization efficiency of the cluster, while offsetting the extra time overhead caused by the algorithm.
A novel iterative Doppler shift estimator is proposed in very low signal-noise-ratio (SNR) environments in single carrier mobile systems. The proposed method exploits the logarithm envelope (LE) of channel estimates, and iteratively finds the minimum mean square error (MMSE) estimation of Doppler shift. Extensive simulations conducted under a wide range of noise corruption clearly show that the proposed estimator substantially outperforms several existing estimators in terms of accuracy.
Current research work focuses on the tribological and thermal properties of epoxy resin matrix composites, which were modified by polyaryletherketone (PAEK-C). The results of the infrared spectra and morphologies of fracture surfaces experiments corroborate the successful synthesis of the materials. From the tribological experiments, it can be known that when the mass fraction of PAEK-C was 10 phr., the corresponding composite exhibited the outstanding wear performances, which could be ascribed to the higher H/E ratio. Based on the results of tribological experiments, it could be obtained that the main wear mechanism is governed by combination of the plastic deformation, creation of vertical cracks in the sliding track, separation of debris, and material waves due to adhesions. In addition, the glass transition temperatures ( T g ) and heat-resistance index ( T HRI ) of the PAEK-C/epoxy resin higher than those of pure epoxy resin matrix, respectively. Furthermore, when the mass fraction of PAEK-C increased, the heat resistance index ( T HRI ) of the corresponding composite is 196.3°C, which is higher than that of neat epoxy resin (180.9°C). Also, according to the results of thermogravimetric analysis experiments, it could conclude that the activation energy of the curing process is situated in the range of 150–160 kJ mol −1 depending on the mass fraction of epoxy resins.
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