Next generation heterogeneous wireless networks (HWNs) will integrate various wireless access technologies, such as cellular networks, wireless local area network (WLAN), and Worldwide Interoperability for Microwave Access (WiMAX), in order to support quality of service (QoS) requirements of various services. To connect mobile users to the best wireless network continuously, network selection has become a hotspot for research in HWNs. This paper designs a network selection algorithm based on service characteristics and user preferences. First, utility functions are used to calculate the utility value of each network attribute for different services. Next, the entropy method and the fuzzy-analytic hierarchy process (FAHP) are used to calculate the objective weight and subjective weight of network attributes respectively, with FAHP specifically being used to calculate the user preference values of services for candidate networks. Finally, simple additive weighting (SAW), multiplicative exponent weighting (MEW), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used according to network attribute utility values and weights to calculate the score of each candidate network. These scores are converted into a comprehensive score for the candidate network based on such user preferences, thus obtaining the ranking of candidate networks. Simulation results show that the proposed algorithm can allow users to choose the most suitable network to access according to different service characteristics while reducing the number of network handovers.

The next generation of heterogeneous wireless networks (HWNs) will integrate various radio access technologies, which will make how to connect mobile users based on the performance parameters of each wireless network and the quality of service (QoS) requirements (as to enable mobile users to be connected to the most suitable wireless network) a hot topic for HWNs. This paper designs an algorithm for joint access selection and bandwidth allocation in HWNs. Taking into account the environment in which World Wide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), and Wireless Local Area Network (WLAN) may co-exist, the algorithm uses received signal strength (RSS), network load (NL), and user rate requirements as input decision parameters, and adjusts the parameters of the membership function in the five-layer fuzzy neural network structure through supervised learning to obtain the score and bandwidth allocation value for each candidate network. Simulation results show that the proposed algorithm can enable users to choose the most suitable network to access, and may modify the fuzzy rules and adjust the resource utilization of different networks based on user preferences.

An important feature of the wireless network scenario is that there are multi- radio access technologies in the same area, and the signal coverage of these networks overlaps each other, forming the heterogeneous wireless network area. Network selection algorithm is the key technology of heterogeneous wireless network. The common network selection algorithms are based on accurate network attribute values. However, due to the mobility of users, the interference of wireless signals and the fluctuation of network state, the network attributes obtained by the algorithms are often uncertain. To solve this problem, this paper designs a multi-attribute access selection approach based on the fuzzy network attributes. This approach calculates the network attribute values by interval hesitant fuzzy theory at first. Then, it calculates the subjective weights of network attribute values by the analytic hierarchy process and the objective weights of network attribute values by the entropy method. The integrated weights of subjective weights and objective weights are obtained by the method based on the longest geometric distance to the negative ideal solution. In the end, we calculate the scores of candidate networks by grey relational analysis based on the intuitionistic fuzzy decision matrix. The simulation shows that the algorithm proposed by this paper can select the most suitable network and reduce the number of handoffs under the environment of uncertain network attribute values.

A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.

In this letter, we consider the multiantenna spectrum sensing for correlated signal in a cognitive radio network, where uncalibrated multiple antennas are employed at the secondary user to detect the presence of the primary user (PU). Under such a scenario, we demonstrate that the correlation function matrices of the received signals differ between the null hypothesis and the alternative hypothesis, which can be leveraged to probe the state of PU. Based on this, a correlation-based local average variance (CLAV) detection method is proposed to exploit the correlated property of the primary signals. Also, its asymptotic distribution under the null hypothesis is derived with the aid of the central limit theorem, which enables us to theoretically obtain the decision threshold of the CLAV method. Finally, we carry out the simulation results to illustrate the superior performance of proposed method compared to the conventional methods.