Xiaoshuang Xing

Spectrum prediction in cognitive radio networks

Spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility are four major functions of cognitive radio systems. Spectrum sensing is utilized to observe the spectrum occupancy status and recognize the channel availability, while CR users dynamically access the available channels through the regulation processes of spectrum decision, spectrum sharing, and spectrum mobility. To alleviate the processing delays involved in these four functions and to improve the efficiency of spectrum utilization, spectrum prediction for cognitive radio networks has been extensively studied in the literature. This article surveys the state of the art of spectrum prediction in cognitive radio networks. We summarize the major spectrum prediction techniques, illustrate their applications, and present the relevant open research challenges.

Cooperative multi-hop relaying via network formation games in cognitive radio networks

The cooperation between the primary and the secondary users has attracted a lot of attention in cognitive radio networks. However, most existing research mainly focuses on the single-hop relay selection for a primary transmitter-receiver pair, which might not be able to fully explore the benefit brought by cooperative transmissions. In this paper, we study the problem of multi-hop relay selection by applying the network formation game. In order to mitigate interference and reduce delay, we propose a cooperation framework FTCO by considering the spectrum sharing in both the time and the frequency domain. Then we formulate the multi-hop relay selection problem as a network formation game, in which the multi-hop relay path is computed via performing the primary players strategies in the form of link operations. We also devise a distributed dynamic algorithm PRADA to obtain a global-path stable network. Finally, we conduct extensive numerical experiments and our results indicate that cooperative multi-hop relaying can significantly benefit both the primary and the secondary network, and that the network graph resulted from our PRADA algorithm can achieve the global-path stability.

Channel quality prediction based on Bayesian inference in cognitive radio networks

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.

Cooperative Relay Selection in Cognitive Radio Networks

The benefits of cognitive radio networking (CRN) have been well recognized with the emerging wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interferences to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the PUs. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem is how to select a relay efficiently. Due to the potentially large number of SUs, it is infeasible for a PU to first scan all the SUs and then pick the best. Basically, the PU transmitter intends to observe the SUs sequentially. After observing an SU, the PU needs to make a decision regarding whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory and derive the optimal stopping rule. We also discuss the optimal observation order of the SUs and analyze the collision probability. To evaluate the performance of our proposed scheme, we compare our optimal stopping policy with the random selection policy through simulation study, and the results demonstrate the superiority of our policy. Extensive simulation study is conducted to investigate the impact of different parameters on the system performance, and the results indicate that our algorithm can satisfy different system requirements by carefully tuning the corresponding system parameters.

Utility-based cooperative spectrum sensing scheduling in cognitive radio networks

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network (CRN) when there exist multiple primary channels. Our work focuses on a scenario in which each secondary user (SU) has the freedom to decide whether to participate in cooperative spectrum sensing; if not, the SU becomes a free rider. Such a mechanism can conserve the energy for spectrum sensing at a risk of sacrificing the spectrum sensing performance. To overcome this problem, we address the following two questions: “Which action (contributing to spectrum sensing or not) should be taken?” and “which channel should be sensed?” We model our framework as an evolutionary game in which each SU makes its decision based on its utility history and takes an action more frequently if it brings a relatively higher utility. We also develop a coalition formation algorithm based on the channel status, where each SU always chooses the coalition that brings the most information regarding the status of the corresponding channel. The simulation results demonstrate that the proposed scheme can guarantee the detection probability at a low false alarm rate. The results also indicate that our algorithm can satisfy different requirements by carefully tuning the system parameters.

Optimal Spectrum Sensing Interval in Cognitive Radio Networks

Traditional spectrum sensing methods require that a secondary user (SU) senses the spectrum at the beginning of each time slot. A closer look at the network activities of a cognitive radio network reveals that the access pattern of a primary user (PU) typically consists of a succession of transmission periods, alternating with idle periods, each of which lasts a number of time slots. Based on this observation, it becomes clear that forcing the SU to sense the channel at the beginning of each time slot is unnecessary and may lead to considerable waste of energy. The main objective of this paper is to investigate new approaches for spectrum sensing by exploring the tradeoffs between energy consumption and secondary network throughput. To this end, we propose a stochastic, energy-aware model to derive the optimal spectrum sensing interval an SU can use to dynamically determine when the next spectrum sensing should be performed. The proposed model allows an SU to adaptively derive the sensing interval based on its required quality of service and current network state, including the PUs network activities and traffic load. Extensive simulation study is performed to assess the effectiveness of our proposed approach in achieving high accuracy with reduced energy consumption. The analysis of the results show that careful tuning of key parameters leads to improved energy efficiency and increased secondary network throughput.

A multi-unit truthful double auction framework for secondary market

As one of the most powerful tools in game theory, double auction is widely utilized to tackle the spectrum allocation problem in a secondary market. In this paper, we propose a multi-unit double auction framework in which the conflict graph-based bidder group formation, the winner determination strategy, and the spectrum pricing are elaborately designed. Through an in-depth theoretical analysis, we prove that our auction scheme can achieve three critical properties including the individual rationality, the ex-post budget balance, and the truthfulness. Extensive simulation results validate that the proposed auction framework can significantly improve the user satisfaction degree.

Cooperative spectrum prediction in multi-PU multi-SU cognitive radio networks

Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU’s return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs.

Utility-Based Cooperative Spectrum Sensing Scheduling in Cognitive Radio Networks

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network (CRN) when there exist multiple primary channels. Our work focuses on a scenario in which each secondary user (SU) has the freedom to decide whether to participate in cooperative spectrum sensing; if not, the SU becomes a free rider. Such a mechanism can conserve the energy for spectrum sensing at a risk of sacrificing the spectrum sensing performance. To overcome this problem, we address the following two questions: “Which action (contributing to spectrum sensing or not) should be taken?” and “which channel should be sensed?” We model our framework as an evolutionary game in which each SU makes its decision based on its utility history and takes an action more frequently if it brings a relatively higher utility. We also develop a coalition formation algorithm based on the channel status, where each SU always chooses the coalition that brings the most information regarding the status of the corresponding channel. The simulation results demonstrate that the proposed scheme can guarantee the detection probability at a low false alarm rate. The results also indicate that our algorithm can satisfy different requirements by carefully tuning the system parameters.

Optimal Stopping Theory Based Jammer Selection for Securing Cooperative Cognitive Radio Networks

In this paper, we investigate the problem of jammer selection for securing Cooperative Cognitive Radio Networks (CCRNs) with the Multiple-Input Multiple- Output (MIMO) capability. In the CCRN under our consideration, there exist a pair of Primary Users (PUs), a relay node, a number of Secondary User (SU) pairs, and an eavesdropper. The PUs need to select a pair of SUs as jammers to interfere with the eavesdropper so as to preserve the secrecy of their wireless communications. To address this problem, we propose an Optimal Stopping based Jammer Selection (OSJS) scheme. Specifically, OSJS examines the primary secrecy capacity for each candidate SU pair in a sequential order. The first SU pair that makes the primary secrecy capacity higher than an optimal threshold is selected as the jammers. The optimal threshold is calculated based on the distribution function of the primary secrecy capacity. We derive the distribution function from the chi-square distribution function of the Signal-to-Noise Ratio (SNR) under the MIMO channel conditions. Since our OSJS scheme does not have to check all the candidate SU pairs, much time can be saved for data transmissions. Our rigorous analysis and simulation results demonstrate that our proposed scheme can achieve secure communications with improved network throughput.