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In this paper, we study efficient rate control schemes for delay sensitive communications over wireless fading channels based on reinforcement learning. Our objective is to find a rate control scheme that optimizes the link layer performance, specifically, maximizes the system throughput subject to a fixed bit error rate (BER) constraint and longterm average power constraint. We assume the buffer...
In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage...
Most media access control (MAC) protocols can be classified as contention based or controlled based according to their transmission mechanisms. To classify contention based or control based MAC protocols in an unknown primary network, we choose received power mean and variance as two features for support vector machines (SVMs) in a machine learning based algorithm. The data consisting of these two...
In this paper, a quantum reinforcement learning method is proposed for repeated game theory. First, the quantum reinforcement learning algorithm is introduced based on quantum state superposition principle and its superiority is analyzed. Then, it is applied to repeated games and the experiments show its effectiveness. Related issues are also discussed before the conclusion is given
An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous...
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