Decentralized Learning for Multiplayer Multiarmed Bandits

In this paper, we propose a new multiple-level power allocation strategy for the secondary user (SU) in cognitive radio (CR) networks. Different from the conventional strategies, where SU either stays silent or transmit with a constant/binary power depending on the busy/idle status of the primary user (PU), the proposed strategy allows SU to choose different power levels according to a carefully designed function of the receiving energy. The way of the power level selection at SU side is optimized to maximize the achievable rate of SU under the constraints of average transmit power at SU and average interference temperature to PU. Simulation results demonstrate that the proposed strategy can significantly improve the throughput of SU compared to the conventional strategies.

In cognitive radio networks, primary users (PUs) can leverage secondary users (SUs) as cooperative relays to increase their transmission rates, and SUs will in turn obtain more spectrum access opportunities. While most existing works assume that SUs are passively selected by PUs regardless of SUs' willingness, in this paper, we propose a cooperative strategy for SUs to actively decide whether to cooperate or not. Basically, due to PUs' time-varying traffic demands, it is essential for SUs to firstly observe the channels and then select a specific PU to cooperate with in order to save the energy. In our paper, this decision related problem is formulated based on optimal stopping theory where SUs observe PUs in time sequence and then make decisions whether to stop observation and cooperate right away or wait till next time slot to repeat the same process. We address this problem by using backward induction and derive the energy-efficient strategy for SUs. To validate the feasibility of our proposed scheme, extensive simulations are conducted to show the impact of PUs' traffic demands on SUs' decisions. The results also reveal that the proposed optimal rule outperforms the greedy selection strategy and is thus more energy- efficient to be applied to the cooperative cognitive radio networks.

We consider a multichannel cognitive radio network where multiple secondary users (SUs) share a single channel and where multiple channels are simultaneously used by a single SU to satisfy their rate requirements. We attempt to evaluate the optimal power and rate distribution choices that each SU has to make to maintain their quality of service (QoS). Our measures for QoS include bit error rate (BER) and minimum rate requirement. We design a QoS-constrained bi-objective optimization problem with the objective of minimizing transmit power and maximizing rate. Unlike prior efforts, we transform the BER constraint into a convex constraint to guarantee optimality of the resulting solution. Our interest in this paper is to develop a user-based distributed approach to solve the optimization problem and compare the solution with the centralized approach. We employ dual decomposition theory to derive three different formulations of the distributed problem. Simulation results demonstrate that optimal transmit power follows the "reverse water filling" process and that rate allocation follows signal-to-interference-plus-noise ratio (SINR). We show that the solutions from the distributed formulations follow the centralized solution.

In a cognitive radio network (CRN), a primary (licensed) user (PU) can share its frequency spectrum by renting this spectrum to a secondary (unlicensed) user (SU). In this paper, we propose an auction-based CRN model whose novelty is considering multiple PUs along with multiple SUs. These PUs compete with each other so that they can earn more profit while the SUs pay less and earn higher payoffs. This situation is formulated as an oligopoly market. For the SUs, we adopt Hackner utility function to obtain the demands for the frequency spectrum and according to their payoff functions the best scenario in each stage of the dynamic spectrum sharing game is selected in the case in which there is a limit on PUs to supply SUs' demands. Simulation results show that our proposed model satisfies both groups of PUs and SUs.

Opportunistic channel access in cognitive radio networks (CRNs) under an unknown environment is gradually receiving a great deal of attention. This article studies the basic problem of decentralized secondary users (SUs) performing multiple channel sensing and access in CRN, when sensing is not perfect. The channel availability information is unknown and must be estimated and learned through big-data samples from the wireless channels by SUs. Both the independent identical distribution (i.i.d.) channel model and the Markov channel model are considered. In the i.i.d. model, the availability of each channel is modeled as an i.i.d. process, while in the Markov model, the availability of each channel is set as a Markov chain with an unknown probability transition matrix. If multiple SUs access to the identical channel, collision will occur and none of SUs gets a reward. Learning loss, which is also referred to as regret, is thus inevitable. To handle with the sampling data on large scale, we formulate the channel sensing and access process as a multi-armed bandit problem (MABP), based on which big-data driven online algorithms are proposed. The theoretical analysis and simulations prove that the regret of our algorithms is both logarithmic in finite time and asymptotically.

In cognitive radio network, multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users and obtain the economic benefits. According to the problem of spectrum pricing for primary users in cognitive radio network, a novel spectrum pricing algorithm based on static Bertrand game theory is proposed in this paper. Not only does this algorithm extremely improve the spectral efficiency, but also ensures the maximum economic benefits of primary users. Comparing with the related algorithms proposed currently, this algorithm synthetically considers the interference from secondary users and the new spectrum demands from primary users themselves during the lease period of licensed spectrum. Then, designs a novel utility function for primary users and gets the Nash equilibrium strategies of spectrum pricing of primary users by using the Bertrand game theory. Finally, the results of simulation and analysis show that the algorithm proposed in this paper achieves the stable spectrum pricing strategies and enhances the spectral efficiency. Furthermore, the model built in this paper is more reasonable and complete.

In this paper, we investigate localization techniques based on direction-of-arrival (DoA) measurements to estimate the position of primary users in cognitive radio networks. In the proposed approach, different multi-antenna sensors estimate the DoA of a primary signal by subspace-based techniques and the target position is estimated from the available DoA measurements using maximum-likelihood, least-squares, or Stansfield estimators. The resulting localization performance is evaluated numerically and compared to the Cramr-Rao bound derived under a considered problem setting. The impact of several system parameters (in particular, number of sensors and number of antennas per sensors) is thoroughly analyzed and discussed.

In a Cognitive Radio (CR) network, secondary users search idle spectrum and use the channel intelligently to increase spectral efficiency. In a CR network with energy-harvesting secondary users, differentiated contention strategy is desirable. Since the energy is finite for users, efficient energy use is needed in a energy-harvesting system to increase lifetime of user devices. In this paper, an efficient sensing and contention algorithm for energy-harvesting CR users is considered. We propose an algorithm to give priority to low energy users to ensure the transmission opportunity before going into the sleep mode. An energy efficient sensing strategy is also presented. High energy users are controlled to go to the sleep mode more than low energy users to reduce the number of contending nodes and save energy consumption for channel sensing.

In this paper, the performance of adaptive modulation in multi-user cognitive radio networks over fading channels is analyzed. Multi-user diversity is considered for opportunistic user selection among multiple secondary users. The analysis is obtained for Nakagami-m fading channels. Both adaptive continuous rate and adaptive discrete rate schemes are analysed in opportunistic spectrum access and spectrum sharing. Numerical results are obtained and depicted to quantify the effects of multiuser fading environments on adaptive modulation operating in cognitive radio networks. 2013 ICST - The Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Cognitive Radio Networks (CRNs) provide a solution for the spectrum scarcity problem facing the wireless communications community. To be able to utilize CRNs in practical applications, a certain level of quality-of-service (QoS) should be guaranteed to the secondary users (SUs) in such networks. In this paper, we propose a packet scheduling scheme that orders the SUs' transmissions according to the packet dropping rates and the number of packets queued waiting for transmission. A medium access control (MAC) protocol, based on the mentioned scheduling scheme, is proposed for a centralized CRN. In addition, the scheduling scheme is adapted for a distributed CRN, by introducing a feature that allows SUs to organize access to the available spectrum without the need for a central unit. Extensive simulation results are presented to evaluate the proposed protocols, in comparison with other MAC protocols designed for CRNs. The results demonstrate the effectiveness of our proposed protocols to guarantee the required QoS for voice packet transmission, while maintaining fairness among SUs.

Due to the deficiency of radio spectrum resources caused by the progress in technology, cognitive radio networks (CRNs) have made significant progress. CRNs have two types of users, namely, primary users (PUs) and secondary users (SUs). Considering that PUs have a higher priority and diversified data transmission requirements, this study divides PUs into two levels, namely, PU1s with a higher priority and PU2s with a lower priority. On the other hand, the occurrence of failures is inevitable in CRNs, which affects the data transmission of users. In this paper, combined with an adjustable PU packets transmission rate mechanism, a communication failure and repair mechanism with classified PUs based on the single-channel CRNs is proposed, and different preemption principles are set according to different system states. A queueing model is established and analyzed with a Markov chain, the performance index expressions that need targeted research are listed, numerical experiments are conducted, and the system performance change trends are obtained. The comparison experiment shows that the proposed communication failure and repair mechanism with classified PUs can improve the throughput of PU1 packets and reduce the blocking rate of PU1 packets compared with the conventional communication failure and repair mechanisms with unclassified PUs.

In cognitive radio networks, security issues arise when intelligent malicious users can attack and decrease the permitted channel for unlicensed users (secondary users). In this paper, we propose a novel scheme, based on friend or foe (FoF) detection technique with physical layer network coding, to enable discrimination between secondary users and intelligent malicious users. Theoretical expressions are derived for probabilities of detection of secondary user, miss detection, and false alarm. In addition, the effectiveness of the proposed approach is evaluated by theoretical analysis and Monte Carlo simulation. Furthermore, an algorithm is proposed for distinguishing between secondary user and intelligent malicious user. Finally, based on the outcome of simulation of probabilities and normalised cross-correlation, it is determined that the proposed scheme outperforms in terms of orthogonal frequency-division multiplexing (OFDM) signal compared with quadrature phase-shift keying (QPSK) signal over cognitive radio network.

Cognitive Radio (CR) is an intelligent radio that can dynamically access the radio spectrum. Secondary users in Cognitive Radio Network (CRN) can access the licensed spectrum without causing harmful interference to primary users. The performance of cognitive radio networking functionalities depends on the properties of the spectrum band in use. This necessitates a crosslayer design in the entire CR networking protocol stack. Current researches are investigating different techniques of using cognitive radio to reuse more locally unused spectrums to increase the total system capacity. This paper provides a comprehensive survey of cross-layer design in cognitive radio network. The cross-layer design approach jointly considers the functions of the layers to maximize the performance of CR networks.

Opportunistic Spectrum Access (OSA) in distributed cognitive radio networks (CRNs) has been well studied in the literature from a theoretical perspective. However, such theoretically-optimized distributed OSA approaches are challenged by several practical implementation issues. In this paper, we design a custom cross-layer framework that enables the: (i) clean-slate implementation of a wide variety of OSA mechanisms; (ii) experimental evaluation of the individual practical OSA components of the Rate-Adaptive Probabilistic (RAP) framework; and (iii) detailed comparison of the performance of such a practical OSA approach against theoretical OSA approaches developed for fully-capable CRNs. Our evaluation reveals the multi-fold goodput improvement and remarkable fairness characteristics of the practical RAP OSA approach compared to the OSA approaches that overlook the OSA and CR practical limitations. However, the superior performance of practical OSA comes at the expense of more outages to the primary licensed networks but within the permissible bounds. Another key finding is that the wide family of existing theoretically-optimized OSA protocols can benefit from the gains available to the individual components of the practical RAP approach, namely, the random spectrum sensing and the probabilistic non-greedy access.

All users in Cognitive radio networks (CRN) try to occupy the vacant channels sharing with the primary users and other users in CRN under limited channel utilization. Each selfish user can independently choose his strategy to maximize his own payoff at the Nash equilibrium (NE) state. In this work, we analyze the channel allocation problem of multiple CR users in the presence of interference of Primary transmitter (PT) and other Secondary users (SUs) over Rayleigh fading channel in overlay and underlay mode. Many policies for channel allocation are utilized to analyze the performances of CRN at NE state in term of normalized cumulative total throughput while the analysis results and comparisons are confirmed by our simulations.