Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literature calls for better understanding of the characteristics of current spectrum utilization as well as better tools for analysis. A number of measurement studies have been conducted recently, revealing previously unknown features. On the other hand, analytical studies largely continues to rely on standard models like the two-state Markov (Gilbert-Elliot) model. In this paper, we present an alternative, stochastic differential equation (SDE) based spectrum utilization model that captures dynamic changes in channel conditions induced by primary users' activities. The SDE model is in closed form, can generate spectrum dynamics as a temporal process, and is shown to provides very good fit for real spectrum measurement data. We show how synthetic spectrum data can be generated in a straightforward manner using this model to enable realistic simulation studies. Moreover, we show that the SDE model can be viewed as a more general modeling framework (continuous in time and continuous in value) than commonly used discrete Markovian models: it is defined by only a few parameters but can be used to obtain the transition matrix of any N-state Markov model. This is verified by comparing the two-state GE model generated by the SDE model and that trained directly from the data. We show that the GE model is a good fit for the (quantized) data, thereby a fine choice when binary descriptions of the channel condition is sufficient. However, when highly resolution (in channel condition) is needed, the SDE model is much more accurate than an N-state model, and is much easier to train and store.

We revisit the jamming defense problem in a multi-channel wireless system, using a general formulation of online learning against an adversary via repeated game-playing. We provide the explicit form of the worst-case optimal channel-hopping strategy of a legitimate user in a multi-stage interaction with a resource-replenishing jamming attacker. Interestingly, we show that the worst imaginary enemy can be given as an adversary who behaves in an i.i.d. manner in this multi-stage interaction, and the optimal strategy of the user is determined by the induced random walk of the adversarial behavior. In addition to the jamming defense, our framework is also applicable to other competitive game problems with finite action spaces.

In this paper, we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users/buyers. It needs to determine how to optimally price the excess spectrum so as to maximize its profit, knowing that this excess capacity is stochastic in nature, does not come with exclusive access, and cannot provide deterministic service guarantees to a buyer. At the same time, buyers are of different types, characterized by different communication needs, tolerance for the channel uncertainty, and so on, all of which are a buyer's private information. The license holder must then try to design different contracts catered to different types of buyers in order to maximize its profit. We address this problem by adopting as a reference a traditional spectrum market where the buyer can purchase exclusive access with fixed/deterministic guarantees. We fully characterize the optimal solution in the cases where there is a single buyer type, and when multiple types of buyers share the same known channel condition as a result of the primary user activity. In the most general case, we construct an algorithm that generates a set of contracts in a computationally efficient manner and show that this set is optimal when the buyer types satisfy a monotonicity condition.

In this paper we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users/buyers. It needs to determine how to optimally price the excess spectrum so as to maximize its profit, knowing that this excess capacity is stochastic in nature, does not come with exclusive access, and cannot provide deterministic service guarantees to a buyer. At the same time, buyers are of different types, characterized by different communication needs, tolerance for the channel uncertainty, and so on, all of which a buyer's private information. The license holder must then try to design different contracts catered to different types of buyers in order to maximize its profit. We address this problem by adopting as a reference a traditional spectrum market where the buyer can purchase exclusive access with fixed/deterministic guarantees. We fully characterize the optimal solution in the cases where there is a single buyer type, and when multiple types of buyers share the same, known channel condition as a result of the primary user activity. In the most general case we construct an algorithm that generates a set of contracts in a computationally efficient manner, and show that this set is optimal when the buyer types satisfy a monotonicity condition.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literature calls for better understanding of the characteristics of current spectrum utilization as well as better tools for analysis. A number of measurement studies have been conducted recently, revealing previously unknown features. On the other hand, analytical studies largely continues to rely on standard models like the two-state Markov (Gilbert-Elliot) model. In this paper we present an alternative, stochastic differential equation (SDE) based spectrum utilization model that captures dynamic changes in channel conditions induced by primary users' activities. It is in closed form and verified using real spectrum measurement data. We also present a straightforward procedure to generate synthetic spectrum data that can be used for realistic simulation. We then compare this model (in its time-discretized and value-quantized form) with the standard Gilbert-Elliot model, and in particularly examine their performance when the same channel access policy is applied. Finally we introduce a 3-state Markov model that captures certain key features of the SDE model.

Vehicular ad hoc network protocol with hybrid relay architecture is proposed for improving the success ratio. Access gateway estimation and a probability table based on the routing information are developed and applied in the backhaul-connected infrastructure network in order to estimate the access gateway region where the destination node locates and reduce the transmission flooding in wireless and wired network. The proposed Access Gateway Discovery mechanisms and Access Gateway Selection scheme have been shown effective by the significant improvement of success ratio in NS-2 simulation based on realistic vehicular mobility models.