In this letter, we propose a Throughput-aimed MAC Protocol with Quality of Service (QoS) provision (T-MAC) for cognitive Ad Hoc networks. This protocol operates based on the Time Division Multiple Access (TDMA) slot assignments and the power control mechanism, which can improve the QoS provision and network throughput. Our simulation results show that the T-MAC protocol can efficiently increase the network throughput and reduce the access delay.

Utilizing available channels to improve the network performance is one of the most important targets for the cognitive MAC protocol design. Using antenna technologies is an efficient way to reach this target. Therefore, in this paper, we propose a novel cognitive MAC protocol, called Polarization-based Long-range Communication Directional MAC Protocol (PLRC-DMAC), for Cognitive Ad Hoc Networks (CAHNs). The proposed protocol uses directional antennas to acquire better spatial reuse and establish long-range communication links, which can support more nodes to access the same channel simultaneously. Moreover, the PLRC-DMAC also uses polarization diversity to allow nodes in the CAHN to share the same channel with Primary Users (PUs). Furthermore, we also propose a Long-range Orientation (LRO) algorithm to orient the long-range nodes. Simulation results show that the LRO algorithm can accurately orient the long-range nodes, and the PLRC-DMAC can significantly increase the network throughput as well as reduce the end-to-end delay.

In this letter, we propose a Partially Observable Markov Decision Process (POMDP) based Distributed Adaptive Opportunistic Spectrum Access (DA-OSA) Strategy for Cognitive Ad Hoc Networks (CAHNs). In each slot, the source and destination choose a set of channels to sense and then decide the transmission channels based on the sensing results. In order to maximize the throughput for each link, we use the theories of sequential decision and optimal stopping to determine the optimal sensing channel set. Moreover, we also establish the myopic policy and exploit the monotonicity of the reward function that we use, which can be used to reduce the complexity of the sequential decision.