When solving real‐world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity‐comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self‐organizing map (SOM) to enhance population diversity and uniformity. We track the IGD value and use K‐means and CNN refinement with crossover and mutation techniques during evolutionary stages. Our algorithm’s uniformity and objective function balance superiority were verified through comparative analysis with 13 other algorithms, including eight traditional multiobjective optimization algorithms, three machine learning‐based enhanced multiobjective optimization algorithms, and two algorithms with objective initialization improvements. Based on these comprehensive experiments, it has been proven that our algorithm outperforms other existing algorithms in these areas.

The heterogeneous network structure is a promising paradigm to improve the quality of service across the entire network. Nevertheless, such a structure is challenging due to the presence of multiple‐tier secondary users (SUs). In this study, the authors investigated the effect of spectrum allocation in heterogeneous cognitive radio networks with a primary network and two‐tier secondary networks, and proposed a two‐tier spectrum trading strategy which includes two trading processes. In Process One, they model the spectrum trading as a monopoly market, where the primary spectrum owner (PO) acts as the monopolist and the first‐tier secondary users (FSUs) act as the buyers. They design an optimal quality‐price contract to maximise the utility of PO, and the FSUs will choose the spectrum with appropriate quality and price to enhance their satisfaction. In Process Two, spectrum trading is modelled as a multi‐seller, multi‐buyer market. The dynamic behaviour of second‐tier SUs is studied using the theory of evolution game, while the competition among FSUs is analysed via a non‐cooperative game where the Nash equilibrium is considered as the solution. The existences of the optimal contract, evolutionary equilibrium and Nash equilibrium are demonstrated in the performance evaluation.

The indoor positioning method based on received signal strength indication (RSSI) ranging is a lack of systematic quantitative research on the factors affecting the characteristics of indoor RSSI, which affect the accuracy of positioning. This paper quantitatively analyzes the characteristics of the 2.4 GHz RSSI collected in indoor scenes from the following four aspects: antenna orientation of the receiver, type of wireless network interface card (NIC) of the receiver, time period of the data measurement and height difference between the transmitting and receiving antenna. The experimental results show that: (i) the RSSI value measured is the strongest when the antenna of the receiver is vertically oriented to the antenna of the transmitter, while the weakest when the antenna is vertically back‐facing and the difference between the strongest signal and the weakest signal is 20–25% at the same test point; (ii) the wider the measurement range of NIC, the more conducive to data collection; (iii) the distribution of RSSI signals generated by an access point at a fixed position is inconsistent with time; (iv) when the height of receiving antenna is 0.5 m different from each other, the path loss index of ranging model produces a deviation of about 10%.

In 2008, the Federal Communications Commission issued a ruling permitting the unlicensed usage of TV white spaces (TVWS), i.e. locally vacant TV channels. Due to its low‐frequency range (50–698 MHz), the TV spectrum has much better propagation characteristic and higher‐spectral efficiency, resulting in a wide range of potentially important applications. However, unlike typical cellular and industrial scientific medical bands, TVWS are subjected to high‐spatial variation, temporal variation, and fragmentation, resulting in new challenges in TVWS identification and in implementing a wireless network in this band. Identification and network design are the two key issues required to be addressed while investigating TVWS. These two problems have been widely discussed in several existing literature. Applications in TVWS are also an important topic, which has not been adequately explored. This study provides an up‐to‐date survey of TVWS and its applications in future wireless networks and communication. Various problems and challenges associated with each use case as well as the possible enabling methods to address these challenges are also presented.