There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

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Abstract-We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.Index Terms-Cognitive radio networks, opportunistic spectrum access, distributed channel selection, exact potential game, stochastic learning automata.

Abstract-In cognitive radio networks, spectrum sensing is a critical to both protecting the primary users and creating spectrum access opportunities of secondary users. Channel sensing itself, including active probing and passive listening, often incurs cost, in terms of time overhead, energy consumption, or intrusion to primary users. It is thus not desirable to sense the channel arbitrarily. In this paper, we are motivated to consider the following problem. A secondary user, equipped with spectrum sensors, dynamically accesses a channel. If it transmits without/with colliding with primary users, a certain reward/penalty is obtained. If it senses the channel, accurate channel information is obtained, but a given channel sensing cost incurs. The third option for the user is to turn off the sensor/transmitter and go to sleep mode, where no cost/gain incurs. So when should the secondary user transmit, sense, or sleep, to maximize the total gain? We derive the optimal transmitting, sensing, and sleeping structure, which is a threshold-based policy. Our work sheds light on designing sensing and transmitting scheduling protocols for cognitive radio networks, especially the in-band sensing mechanism in 802.22 networks.

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