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Long-term evolution (LTE) deployment in the unlicensed band has been a promising solution to handle the ever-increasing data traffic growth. However, spectrum sharing on unlicensed band poses a significant challenge regarding the interaction between LTE licensed assisted access (LAA) and Wi-Fi. In this paper, a radio access technology (RAT) heterogeneous network that consists of an LAA tier and a Wi-Fi tier is constructed, to achieve the coexistence and alleviate the intra-RAT and inter-RAT interference, a listen-and-talk (LAT) scheme is utilized in Wi-Fi while LAA adopts the listen-before-talk (LBT). By leveraging the full-duplex (FD) techniques in Wi-Fi, the collision can be avoided and the utilization of unlicensed spectrum can be improved. Furthermore, the sensing errors are derived based on the FD strategy and an enhanced Markov model is presented to analyze the performance of the heterogeneous network with consideration of the residual self-interference (RSI). The fairness between LAA and Wi-Fi is also investigated. At last, to ensure the performance of Wi-Fi when serious RSI exists, a switched MAC (S-MAC) that can adaptively switch between the FD mode and half-duplex (HD) mode is presented.

Nowadays, an Internet-of-Things (IoT) connected system brings a tremendous paradigm shift into the medium access control (MAC) design. In this paper, we present a distributed MAC framework assisted by machine learning for the Heterogeneous IoT system, where the IoT devices coexist with the WiFi users in the unlicensed industrial, scientific, and medical (ISM) spectrum. Specifically, the superframe is divided into two phases: a rendezvous phase and a transmission phase. During the rendezvous phase, the gateway that is capable of machine learning predicts the number of WiFi users and the IoT devices by performing a triangular handshake on the primary channel. The prediction takes advantage of the deep neural network (DNN) model which is pretrained on our universal software radio peripheral (USRP2) testbed offline. The gateway allocates the frequency channels to the WiFi and IoT systems based on the inference results. Then, the IoT devices and WiFi users initiate data transmissions during the transmission phase. Furthermore, system throughput is analyzed and optimized in two typical scenarios, respectively. An optimized MAC framework is proposed to maximize the total system throughput by finding the key design parameters. The analytical and simulation results that are conducted using the ns-2 demonstrate the effectiveness of the proposed MAC framework.

Cognitive radio is one of the enabling technologies considered for the next generation communication systems for many mission-critical applications. In cognitive radio systems, cooperative spectrum sensing is one of the key techniques that can improve reliability and agility. In this paper, a framework that integrates quickest detection and belief propagation is applied to the cooperative spectrum sensing, where the primary user activities are heterogeneous in the space and dynamic in the time. The performance of the proposed scheme is analyzed mathematically. Using numerical simulations, detection performance measured by false alarm rate and average detection delay is obtained for different setups. The results show that the proposed scheme achieves better receiver operational curves than traditional detection method.

In this paper, energy efficient spectrum access is considered for a wireless cognitive radio ad hoc network, where each node is equipped with cognitive radio, has limited energy, and the network is an OFDMA system operating on time slots. In each slot, the users with new traffic demand will sense the spectrum and locate the available subcarrier set. Given the data rate requirement and maximal power limit, a constrained optimization problem is formulated for each individual user to minimize the energy consumption per bit over all selected subcarriers, while avoid introducing harmful interference to the existing users. Because of the multi-dimensional and non-convex nature of the problem, a fully distributed subcarrier selection and power allocation algorithm is proposed by combining an unconstrained optimization method with a constrained partitioning procedure. Due to the non-cooperative behavior among new users, they will execute distributed power control to manage the co-channel interference when needed. Simulation results demonstrate that the proposed scheme performs tightly to the global optimal solution. In addition, the comparison between the proposed energy efficient allocation scheme and the well established rate or power efficient allocation algorithms is carried out to demonstrate the advantage of the proposed scheme in terms of network lifetime.