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The energy detection technology is recommended in the licensed assisted access (LAA) scheme by 3GPP because of its simplicity and low cost. However, due to its inherent limitation, there may exist imperfect channel detection, which can lead to the decrease of the channel utilization efficiency and the deterioration of fairness. The imperfect detection can generally be represented by the detection probability and false alarm probability, which depend on detection time, signal to noise ration (SNR), sampling rate and energy threshold. However, among the parameters, only the energy threshold can be dominated by LAA small base stations (SBSs) in the LAA scheme. Therefore, the energy threshold should be dynamically adjusted in the changeable channel environment such that the detection accuracy can improved as high as possible. Consider the fact that the optimization theory cannot be used to optimize the energy threshold since the expressions of performance indexes about the energy threshold are extremely complex, a Q-learning based energy threshold optimization algorithm (QLET) is thus proposed in the paper, where LAA SBSs act as the agent, the energy threshold is defined as the agent action, the different combinations of fairness and throughput are defined as the agent states, and the fairness and the reward function are also redefined. In order to ensure the smooth implementation of the proposed QLET algorithm, the information exchange mechanism, where the sending-confirmation mechanism and 1- persistent CSMA are used, is also proposed. Based on the proposed QL framework, the agent can learn the optimal energy threshold by repeatedly interacting with the environment, which enables the coexistence system to obtain the best coexistence performance. A large number of simulation results show that the proposed QLET is superior to the traditional fixed energy threshold scheme (FET) in terms of the fairness, WiFi collision probability and transmission delay, and that QLET is almost the same as FET in term of throughput. [ABSTRACT FROM AUTHOR]

In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

Flexible multimedia broadcast/multicast services such as mobile auction and advertisements are vital services for future mobile ad hoc networks. A great number of applications rely on a reliable and efficient medium access control (MAC) layer broadcast. The current IEEE 802.11 broadcast protocol based on carrier sense multiple access with collision avoidance does not offer any MAC layer recovery on broadcast frames. Consequently, unexpected lost frames may deteriorate the quality of broadcast/multicast services. Previous protocols extended request-to-send, clear-to-send, and acknowledgement to enhance the broadcast reliability. However, they still suffered from the hid- den terminal problem and the excessive retransmission problem at the same time. In this paper, we first formulate the excessive retransmission problem as an optimization problem and show that it is NP-hard even if the upper layer service is periodical beacon exchange. An approximation algorithm with a guaranteed approximation ratio is also suggested. Then, a reliable and efficient MAC layer broadcast protocol, which is named broadcast protocol with busy tone (BPBT), is proposed. BPBT applies a busy tone to solve the hidden terminal problem and applies the proposed approximation algorithm to solve the excessive retransmission problem. Finally, BPBT is compared with previous protocols for performance evaluation by simulation. [ABSTRACT FROM AUTHOR]